University of Southern California Dissertations and Theses

Development of a multi-mode optical imaging system for preclinical applications in vivo

(USC Thesis Other)

Development of a multi-mode optical imaging system for preclinical applications in vivo

PDF

Download

Open document

Flip pages

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content DEVELOPMENT OF A MULTI-MODE OPTICAL IMAGING SYSTEM FOR PRECLINICAL APPLICATIONS IN VIVO by Jae Youn Hwang A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (BIOMEDICAL ENGINEERING) August 2009 Copyright 2009 Jae Youn Hwang ii DEDICATION I dedicate this thesis to my lovely wife, Hayong Kim, my parents, Moon Sung Hwang and Myung Lim Jeong, my sisters, Jeeyoung Hwang and Joohyun Hwang, and my parents-in-law, Inkil Kim and Eunja Lee. Without their patience, understanding, support, and most of all love in many difficulties, the completion of this work would not have been possible. iii ACKNOWLEDGEMENTS During these graduate years at the University of Southern California, I have been tremendously blessed to be accompanied and supported by many people directly or indirectly. The completion of this dissertation would not have been possible without them. It is a great pleasure that I have a brilliant opportunity to thank all of them. First, I would like to gratefully and sincerely thank to my advisor, Dr. Daniel L. Farkas for his guidance, understanding, and patience during my graduate studies. His mentorship was paramount in providing a well-rounded experience consistent my long-term career goals. He encouraged me to not only grow as a scientist but also as an independent thinker. Also, he has kept consistent interest in the progress of my thesis and was available when I needed his expertise and advice. Special thanks also go to Drs. Lali Medina-Kauwe, Harry B. Gray and Zeev Gross for much of their support for my research. Drs. Kevin Burton and Erik Lindsley provided helpful and perceptive comments. In addition, I would like to thank all my colleagues at the Minimally Invasive Surgical Technologies Institute in Cedars-Sinai Medical Center for their kindness and friendship. Among them, V. Krishnan Ramanujan deserves special thanks, as does Dr. Sebastian Wachsmann-Hogiu, now at University of California at Davis. Finally, I would like to thank my lovely family, my wife, my parents, and parents-in-law. Thank you for all your understanding, patience, and support, Love you all. iv TABLE OF CONTENTS DEDICATION.................................................................................................................... ii ACKNOWLEDGEMENTS...............................................................................................iii LIST OF TABLES............................................................................................................. vi LIST OF FIGURES .......................................................................................................... vii ABBREVIATIONS ........................................................................................................... xi ABSTRACT.....................................................................................................................xiii CHAPTER 1: INTRODUCTION....................................................................................... 1 1.1. Small animal imaging ...........................................................................................1 1.2. Imaging modalities for small animals...................................................................2 1.3. Necessity of a multimode optical imaging system ...............................................3 1.4. Limitations of current commercial systems..........................................................5 1.5. Significance and Hypothesis.................................................................................6 1.6. Dissertation Organization .....................................................................................7 CHAPTER 2: BACKGROUND....................................................................................... 10 2.1. Optical properties of fluorophores ......................................................................10 2.2. Optical imaging techniques for small animal imaging .......................................15 2.3. Effects of the tissue window on both excitation and emission photons..............23 2.4. Chemotherapy molecules for cancer treatments .................................................24 CHAPTER 3: METHODS AND MATERIALS .............................................................. 27 3.1. Multimode optical imaging system.....................................................................27 3.2. Imaging modes in a multimode optical imaging system ....................................34 3.3. Wide-field two-photon excitation for multimode optical imaging in vivo .........42 3.4. Experimental setup and materials for investigation of optical characteristics of S2Ga ...............................................................................................................46 3.5. Fluorescence lifetime imaging of HerGa and S2Ga on human cancer cells.......49 3.6. Measurement of mitochondria membrane potential variations of the breast cancer cells induced by HerGa ...........................................................................50 3.7. Spectral imaging with a ratiometric analysis method for dynamic monitoring of HerGa accumulation and clearance ................................................................51 v CHAPTER 4: RESULTS.................................................................................................. 54 4.1. Single modality experiments...............................................................................54 4.2. The feasibility of multimode optical imaging in vivo for simultaneous complementary/different information.................................................................75 4.3. Multimode optical imaging in chemotherapy assessment and cancer detection..............................................................................................................77 CHAPTER 5: CONCLUSIONS AND DISCUSSION..................................................... 99 BIBLIOGRAPHY........................................................................................................... 110 APPENDIX A: System control programs....................................................................... 122 A.1. Main system control program ..........................................................................122 A.2. Scanning/wide-field two-photon control program...........................................124 A.3. Joystick/mFLIM program ................................................................................125 A.4. 3D fluorescence imaging program...................................................................126 A.4. Source code ......................................................................................................126 APPENDIX B: Fluorescence lifetime imaging and analysis.......................................... 127 B.1. First-order exponential fitting method for fluorescence lifetimes....................127 B.2. Source code ......................................................................................................127 vi LIST OF TABLES Table 1-1. Selected modalities for small animal imaging....................................................3 Table 2-1. Peak excitation and emission wavelength of fluorophores. .............................12 Table 8-1. Application and capabilities of each imaging mode in the multimode optical imaging system. ....................................................................................99 Table 8-2. Multimode optical imaging in assessment of nanoconstructs and HerGa chemotherapy..................................................................................................100 vii LIST OF FIGURES Figure 1-1. Functionality of multimode optical imaging system.........................................6 Figure 2-1. Jablonski diagram of excitation and emission, and the corresponding excitation and emission spectra.......................................................................12 Figure 2-2. Fluorescence intensity image ..........................................................................15 Figure 2-3. Spectral imaging..............................................................................................17 Figure 2-4. Fluorescence lifetime imaging ........................................................................19 Figure 2-5. Jablonski diagram and geometry of two-photon excitation. ...........................21 Figure 2-6. Scanning method through a fiber bundle and the applications of the intra- vital confocal mode (from Mauna Kea Technologies, Paris, France).............22 Figure 2-7. Chemical reaction process of luciferase enzymes and enzyme-specific substrates (luciferin)........................................................................................23 Figure 2-8. Optical window for tissue imaging. ................................................................24 Figure 2-9. Schematic of Nanoconstructs (Polycefin) (Ljubimova et al., 2008)...............24 Figure 2-10. Chemical structure of sulfonated gallium corroles (from Harry B. Gray)....26 Figure 3-1. Multimode optical imaging system.................................................................28 Figure 3-2. Laser delivery system......................................................................................30 Figure 3-3. Relay lens system for filter installation...........................................................32 Figure 3-4. System control program ..................................................................................33 Figure 3-5. Overall system control scheme. ......................................................................33 Figure 3-6. Fluorescence imaging mode............................................................................34 Figure 3-7. Spectral imaging mode....................................................................................35 Figure 3-8. Mosaic fluorescence lifetime imaging using fs pulsed laser...........................38 viii Figure 3-9. Intra-vital confocal imaging mode..................................................................40 Figure 3-10. Bioluminescence imaging mode ...................................................................40 Figure 3-11. Scanning/wide-field two-photon imaging modes .........................................42 Figure 3-12. Schematics of the experimental set-up for wide-field two-photon excitation .......................................................................................................45 Figure 3-13. Experimental setup for corrole fluorescence lifetime imaging.....................47 Figure 3-14. Composite spectra with varying ratios of spectra of autofluorescence and HerGa fluorescence.......................................................................................52 Figure 4-1. Fluorescence intensity images of a mouse injected with drug molecules tagged by rhodamine B and Alexafluor 680 ...................................................55 Figure 4-2. Contrast comparison between images recorded with a commercial system and our multimode optical system ..................................................................56 Figure 4-3. Spectral imaging of a nude mouse with injection of fluorescein into implanted tumors.............................................................................................57 Figure 4-4. Large area image obtained by mosaic fluorescence lifetime imaging ............59 Figure 4-5. Fluorescence lifetime image of a nude mouse with an implanted breast tumor ...............................................................................................................60 Figure 4-6. Intra-vital confocal imaging of the rat spine...................................................61 Figure 4-7. Bioluminescence image from engineered mice (normalized by highest value)...............................................................................................................61 Figure 4-8. Two-photon excited fluorescence image of a mouse liver stained with fluorescein (Nikon 20x, NA: 0.50) .................................................................62 Figure 4-9. Optical characteristics of wide-field two-photon excited fluorescence imaging............................................................................................................65 Figure 4-10. Comparisons of depth dependence in one-photon, scanning two-photon, and wide-field two-photon excited fluorescence imaging of a tissue phantom.........................................................................................................66 Figure 4-11 Wide-field two-photon excited fluorescence images of a mouse intestine....67 ix Figure 4-12. One-photon and wide-field two-photon excited fluorescence imaging of an eye specimen of an alzheimer’s mouse ....................................................68 Figure 4-13. Wide-field two-photon excited fluorescence imaging and multimode optical imaging with wide-field two-photon excitation of a nude mouse with implanted tumors after subcutaneous injection of corroles and miscrosphere solution....................................................................................70 Figure 4-14. Multimode optical images of a mouse with corroles injected into an implanted tumor region and the middle of back ...........................................76 Figure 4-15. Fluorescence intensity changes within tumor region and background .........79 Figure 4-16. Clearance examination of the drug nanoconstruct using spectral imaging...80 Figure 4-17. Quantitative images of organs.......................................................................81 Figure 4-18. Concentration dependence of fluorescence lifetime of 50 μM S2Ga in pH 7.5 PBS (room temperature)..........................................................................83 Figure 4-19. pH dependence of fluorescence lifetime of S2Ga at different temperatures ..................................................................................................83 Figure 4-20. Fluorescence lifetime changes of S2Ga due to temperature changes ...........84 Figure 4-21. Fluorescence of corrole (50uM) versus excitation wavelength (power : 300mW, emission : 620nm) ..........................................................................85 Figure 4-22. Fluorescence lifetime changes of 50 μM S2Ga (at pH 7.5 in PBS) on breast cancer cells (MDA-MB-435) at 36.5ºC..............................................86 Figure 4-23. Fluorescence lifetime changes of HerGa due to glioma cancer cell endocytosis....................................................................................................87 Figure 4-24. Measurement of mitochondrial membrane potential of the breast cancer cells using TMRM fluorescence imaging .....................................................88 Figure 4-25. HerGa distribution in nude mice...................................................................90 Figure 4-26. Quantitative examination of HerGa accumulation kinetics and clearance from a mouse at different time points using spectral imaging with ratiometric and standard spectral classification ............................................92 Figure 4-27. Fluorescence lifetime image and a lifetime histogram of the mouse at 4 day .................................................................................................................94 x Figure 4-28. Specific organs and tumor images obtained using multi-mode optical imaging..........................................................................................................95 Figure 4-29. Multimode optical imaging for cancer detection and delineation of HerGa ............................................................................................................97 Figure A-1. Port number panel ........................................................................................122 Figure A-2. Main system control panel ...........................................................................123 Figure A-3. Server connection panel ...............................................................................124 Figure A-4. Scanning two-photon excited fluorescence imaging panel..........................125 Figure A-5. Joystick/FLIM panel ....................................................................................125 Figure A-6. 3D fluorescence imaging panel....................................................................126 xi ABBREVIATIONS AMP : Adenosine monosphate. AON : Antisense oligonucleotides. ATP : Adenosine triphosphate. BBO: Barium-borate. CCD : Charge-coupled device. CT : Computed tomography. DIC : Differential interference contrast. FLIM : Fluorescence lifetime imaging. FRET : Fluorescence resonance energy transfer fs : Femotosecond. H&E : Hematoxylin and eosin (predominant stain in histopathology) HerPBK10 : Breast cancer-targeted carrier protein. HerGa : Targeted sulfonated gallium corrole, HerPBK10 + S2Ga. LCTF : Liquid crystal tunable filter. LED : Light-emitting diode. MRI : Magnetic resonance imaging. Nanoconstruct : Novel platform for tumor treatment (Polecefin). PBS : Phosphate buffered saline. PEG : Polyethylene glycol. PET : Positron emission tomography. PMT : Photomultiplier tube. xii PMLA : Poly β-L-malic acid. ps : Picosecond. RSSE : Root sum of square error method. SPECT : Single photon emission computed tomography. S2Ga : Sulfonated gallium corrole. TMRM : Tetramethyl rhodamine methyl ester. TMRE : Tetramethyl rhodamine ethyl ester. US : Ultrasound. xiii ABSTRACT This thesis reports advanced optical imaging technology in a stand-alone system that enables functional mesoscopic imaging (whole-body or endoscopic imaging with microscopic resolution) of small animals in vivo, and provides for quantitative, dynamic, and functional monitoring of chemo- and nanoconstruct therapy. Many currently available imaging approaches have been applied to preclinical studies of cancer, stem cells, and pharmaceutical outcomes. Moreover, useful in vivo imaging may require several, preferably combined, and advanced imaging modalities to examine different but complementary characteristics of molecules, cells, or tissues. Although commercial systems perform well for standard imaging of small animals, they have limitations stemming from being single-modality instruments. Thus, a new multimode optical imaging system that is designed to be application-optimizable, with higher sensitivity and specificity has been developed here in order to overcome these limitations. The instrument combines various in vivo imaging modes, including fluorescence intensity, spectral, lifetime, intra-vital confocal, two-photon excited fluorescence, and bioluminescence imaging. Also this system is a unique and comprehensive imaging platform for analyzing kinetic, quantitative, environmental, and other highly-relevant information with macro- to micro-scopic resolution. This system can be optimized for various applications, and the combination of multiple imaging modes for increased contrast and complementary/synergetic information in chemotherapy assessment and cancer detection is emphasized here. 1 CHAPTER 1 INTRODUCTION 1.1. Small animal imaging Biomedical research has translational aims, consisting in moving laboratory-derived methods, experiences and understanding into the clinic. This has shifted emphasis from cells alone to higher levels of biological organization, and enhanced the use of animal models with human disease phenotypes such as cancer and Alzheimer’s (Spires and Hyman, 2005). Such models play a very significant role in preclinical studies since these allow the detection of primary and metastatic tumors, as well as monitoring the effects of repeated pharmacological intervention (Gleysteen et al., 2008; McElroy et al., 2008; Takeda et al., 2008). In the past, it was necessary to sacrifice an animal in order to detect the tumors or monitor the drug molecules inside the tissues. The excised tissues are not functional, and thus the normal physiology such as blood flow, cell-cell interactions, and metabolic activity are suspended, altered, or degraded. This makes it impossible to continuously monitor biochemical, genetic, and pharmacological processes in vivo and in the same animal, and in a repeated fashion. Thus, better methods to track disease development and to monitor tissues and molecules within living animals are needed. It is for this purpose that in vivo small animal imaging methods are being developed. The small animal imaging allows us to measure and analyze anatomical, functional, and molecular processes noninvasively and repeatedly in entire animals. Thus, these new 2 methods go beyond the study of thin tissue sections or cell cultures to technologies that can operate in vivo with high sensitivity and specificity (Contag, 2007; Graves et al., 2003; Rice et al., 2001; Weissleder, 2002). 1.2. Imaging modalities for small animals Currently, many non-invasive technologies such as ultrasound (US), X-ray, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), single photon emission computed tomography (SPECT), and optical imaging are available for small animal imaging. These were originally developed for clinical applications, but have been redesigned for small animal imaging. US, CT and MRI are capable of resolving the anatomy and physiology through the energy-tissue interaction. On the other hand, PET and SPECT require reporter probes or contrast agents in order to monitor metabolism of organisms (Contag, 2007; Diamandis, 1991; Mahmood and Weissleder, 2003; Rice et al., 2001; Weissleder, 2002). In particular, various optical imaging technologies which range from 2D imaging to 3D tomography of internal organs and tissues have been developed for small animal imaging (Davis et al., 2008; Deniset- Besseau et al., 2007; Ryu et al., 2008; Soubret and Ntziachristos, 2006; Zacharakis et al., 2005). These methods have several advantages over the other modalities previously mentioned (Frostig, 2002). They can offer simultaneous monitoring of multiple targets or molecular pathways, and are relatively cheaper and less harmful than the non-optical modalities (Contag, 2007; Diamandis, 1991; Rice et al., 2001; Weissleder, 2002). 3 Technique Resolution Primary use MRI ~100 μm US ~50 μm X-ray ~50 μm Anatomy/ Physiology PET ~2000 μm SPECT ~2000 μm Metabolism/ Molecules Optical Imaging ~1 μm Molecules Table 1-1. Selected modalities for small animal imaging. 1.3. Necessity of a multimode optical imaging system Preclinical research including cancer, chemotherapy, stem cell, and toxicological study may require single or multiple optical imaging techniques, depending on their application (Brinkmann et al., 2008; Kaijzel et al., 2009; Medarova et al., 2006). The optical imaging techniques, including single-photon, single wavelength fluorescence intensity, spectral, fluorescence lifetime, intra-vital confocal, two-photon, and bioluminescence imaging, have the inherent capability to provide different information; fluorescence intensity imaging can provide either kinetic or dynamic information through monitoring the changes of fluorescence intensity of molecules stained with fluorophores (Almutairi et al., 2008; Levitt et al., 2006); spectral imaging allow us to obtain better quantitative information than the fluorescence intensity imaging (Li et al., 2008; Stamatas and Kollias, 2007; Thaler and Vogel, 2006; Wells et al., 2007; Burton et al., 2009); fluorescence lifetime imaging can provide functional and environmental information around fluorophores since the fluorescence lifetime of fluorophores is extremely sensitive to 4 their surroundings (Bloch et al., 2005; Elson et al., 2004; Jo et al., 2005; Nothdurft et al., 2009; Ramanujan et al., 2008); the two-photon excited fluorescence and intravital confocal imaging can provide high resolution information of cellular and molecular events from intact tissues or small animals in vivo (Contag, 2007; Tirlapur and Konig, 2001). In particular, in chemotherapy research, a variety of assessments of the chemotherapy drug molecules are required such as drug targeting capability (fluorescence intensity imaging) (Agadjanian et al., 2009; Ljubimova et al., 2008), in vivo clearance of the drugs (spectral imaging), and the effects on tissues and organs (spectral, lifetime, and two- photon excited fluorescence imaging) (Han et al., 1995). Here, it is necessary to utilize appropriate optical imaging techniques for accurate examinations. Furthermore, a combination of multiple optical imaging techniques can provide better assessment of drugs (Bullen, 2008; Cai et al., 2008; Ljubimova et al., 2008; Medarova et al., 2009; Wang et al., 2007; Zhang et al., 2008). Finally, a multimode optical imaging platform, combining novel techniques for clinical applications may enhance cancer detection, diagnosis, decision-making, and treatment outcomes during surgical procedures (Balas, 2001; de Ryk et al., 2008; Feifel and Hildebrandt, 1992). 5 1.4. Limitations of current commercial systems A number of companies have developed optical imaging systems for small animal imaging. Although the commercial systems perform well for the tasks they have been designed for, they have limitations that stem from being single modality instruments (fluorescence or luminescence intensity, or intravital confocal imaging). However, a variety of biomedical research may require the combination of several advanced imaging modalities to examine different but complementary characteristics of molecules, cells, and tissues. Currently, there is no such multimodal optical imaging system commercially available. Although it is possible to carry out small animal imaging with each commercial instrument separately, the different times, locations, and animal positioning (in addition to costs and logistics) may hinder efficacy. These limitations, and the need we see for more versatile and advanced in vivo imaging, prompted our development of a multimode optical imaging system, based on the multimode concept originally introduced for microscopy in the early nineties (Farkas et al., 1993). In this dissertation, a new multimode optical imaging system designed to be application-optimizable, with higher sensitivity and specificity, is developed, and the application to chemotherapy research is described. This instrument combines various in vivo imaging modes, including one or two-photon excited fluorescence, spectral, fluorescence lifetime, intra-vital confocal, and bioluminescence imaging. 6 1.5. Significance and Hypothesis This multimode optical imaging system employs a synergetic combination of multiple imaging modes that exploit the combined advantages of fluorescence, spectral, high sensitivity lifetime, two-photon excited fluorescence, and intra-vital confocal detection of small animals in vivo. Figure 1-1 shows the functionality and capability of each imaging technique. A successful completion of this system implies the following outcomes: (i) designing an innovative imaging platform, (ii) establishing multimode imaging as a new pre-clinical research tool, (iii) monitoring chemotherapy drug dynamics and targeting efficacy, and (iv) building a stand-alone system for various applications and a common work flow of small animal studies with all optical, hardware, and software components needed. Figure 1-1. Functionality of multimode optical imaging system. Most importantly, in chemotherapy research, the combination of the imaging modes can be established as a powerful mesoscopic imaging method for dynamic, quantitative, and functional monitoring of drug molecules and their discrimination with macro- and micro- Kinetic/dynamic Functional Endoscopic high resolution Multimode optical imaging system Spectral Intra-vital confocal Bioluminescence Fluorescence lifetime (FLIM) Two-photon excited fluorescence High sensitivity High resolution and penetration Quantitative Single-wavelength, single-photon intensity 7 scopic resolution. In addition, these capabilities will enhance the imaging discrimination ability of normal and diseased tissues in vivo. Furthermore, when the multimode optical imaging is adapted to endoscope techniques, it may provide better detection, diagnosis, navigation, decision-making, and treatment during surgical procedures, more closely linking – spatially and temporally – diagnosis and intervention. 1.6. Dissertation Organization In Chapter 2, background for a multimode optical imaging system will be introduced. A variety optical imaging techniques is described in detail. Also, nanoconstucts and targeted sulfonated gallium corroles among chemotherapy molecules will be described because we use these molecules in this thesis. In Chapter 3, methods and materials are described. The development of the multimode optical imaging system is introduced. A schematic of the multimode optical imaging system is demonstrated, as well as functionalities (laser delivery, fluorescence detection and possible imaging modes) and operation strategy (operation procedures and program) of the system is described in detail. Also, each imaging mode in the multimode optical imaging system is described. In Chapter 4, results are described. Here, the evaluation of each imaging mode, including fluorescence intensity, spectral, lifetime, intra-vital confocal, bioluminescence, and scanning/wide-field two-photon excited fluorescence imaging, is performed with in vivo and in vitro specimens, and the feasibility of the multimode optical imaging is examined 8 in chemotherapy study. Additionally, the wide-field two-photon excitation method for live small animal and multimode optical imaging in vivo using non-scanning imaging devices is demonstrated because of it’s novelty and the usefulness in the multimode optical imaging system. As one of applications of multimodality, multimode optical imaging for the assessment of nanoconstucts in breast cancer treatments is introduced. Here, the targeting capability and effects of the nanoconstruct are evaluated using fluorescence and spectral imaging mode with spectral unmixing. As another application of our system, the power of combining advanced optical imaging technologies (fluorescence intensity, spectral, lifetime, and two-photon excited fluorescence imaging) is brought to cancer detection and treatment assessment of targeted gallium corrole (HerGa). Here, basic optical characteristics (fluorescence lifetime and two-photon excitation wavelength) of the sulfonated gallium corrole (S2Ga) are investigated. Also, in order to investigate cell mechanism of the corroles, the fluorescence lifetime changes of HerGa and S2Ga due to the endocytosis of cancer cells are monitored in real-time as well as mitochondrial membrane potential variations of the cancer cells, which indicate a degree of health status of cells, induced by the corroles are examined. In addition, multimode optical imaging is performed for the assessment of HerGa in breast cancer chemotherapy. Furthermore, the feasibility of cancer detection and delineation by using multimode optical imaging of HerGa is examined. Finally, spectral imaging with ratiometric analysis method is introduced for monitoring dynamics and kinetics of chemotherapy drug in vivo. Particularly, the examination of HerGa accumulation kinetics in nude mice following intravenous injection of HerGa is described using the spectral imaging with ratiometric analysis method. 9 In Chapter 5, concluding comments and discussion of ideas for future research are presented. 10 CHAPTER 2 BACKGROUND 2.1. Optical properties of fluorophores Fluorophores have been used as key molecules in optical imaging. The fluorophores can emit light with their inherent wavelength. If fluorophores are exposed to light, the light is absorbed by the fluorophores. And then, an electron in the ground state of the fluorophore is promoted to an electronically excited state. Return to the ground state is accompanied by emission of light. This process is referred to as fluorescence. 2.1.1. Principle of fluorescence Fluorescence is light emitted from a molecule. The release of a photon into the environment is a response of a molecule to an excited state. There are four common methods by which the excitation energy is delivered to the molecule: (1) Impinging photons, which are a major source for excitation of a molecule. If photons impinge a molecule such as a fluorophore, it can cause the excitation of the molecule; (2) Change shift (chemical energy). Chemical reaction of molecules can produce chemical energy [adenosine triphosphate (ATP)]. If the chemical energy is absorbed by a molecule, the molecule can be excited; (3) Thermal photons, which can be absorbed by molecules for excitation. If the thermal photons have enough energy to excite molecules, the molecules 11 are excited; (4) Resonance effects. If two different molecules are located close enough to transfer energy each other, fluorescence light from one molecule can excite the other molecule. It is called fluorescence resonance energy transfer (more commonly referred to by the acronym FRET) (Herman, 1998; Jameson et al., 1989; Mason, 1993; Pavesi and Fauchet, 2008; Ploem and Tanke, 1987; Rost, 1992). For absorption of the excitation light to be efficient, the wavelength of light should correspond to the energy difference between the ground state and an excited state. Figure 2-1 shows this process schematically. In Figure 2-1, heavy and thin horizontal lines represent electronic and vibrational energy levels respectively, and upward and downward arrows represent absorption and emission of a photon of light respectively. Here, the vertical axis represents the energy of various levels. Also, in the figure, the fluorescence excitation and emission spectra of the fluorophore are shown. The magnitude of the absorbance and emission is determined by the probability of the corresponding transition. The wavelength corresponds to the energy of the absorption and emission transitions. The wavelength ( λ) is related to energy (E) and frequency ( ν) by the equations: where h is Plank’s constant and c is the speed of light (Guilbault, 1973; Morrison, 2008). The absorption and emission spectra of fluorescence is determined by the energy distance between the ground state and the electronically excited state. Here, when the emitted photon has less energy than the absorbed photon, the energy difference between the / Ehv hc λ == 12 longest wavelength absorbance maximum and the shortest wavelength emission maximum is called the Stoke’s shift. If the emitted photon has more energy, the energy difference is called an anti-Stokes shift (Lakowicz, 2006). S 0 S 1 S 2 S 1 S 0 Wavelength (nm) Intensity Energy 0 1 2 3 0 1 2 3 1 2 3 0 0 1 2 3 1 2 3 0 Excitation Emission S 0 S 1 S 2 S 1 S 0 Wavelength (nm) Intensity Energy 0 1 2 3 0 1 2 3 1 2 3 0 0 1 2 3 1 2 3 0 Excitation Emission Figure 2-1. Jablonski diagram of excitation and emission, and the corresponding excitation and emission spectra: It shows electronic and vibrational energy levels and absorbance and emission transitions between energy levels for fluorophores. In Table 2-1, the peak wavelengths of absorbance and emission of a variety of fluorophores are shown. Dye Absorbance Wavelength(nm) Emission Wavelength(nm) Visible color DAPI 345 455 Blue Alexa fluor 488 494 517 green (light) Fluorescein FITC 495 518 green (light) Cy3 550 570 yellow Rhodamine 123 560 580 yellow Alexa fluor 568 578 603 red Cy5 650 670 red Alexa fluor 680 679 702 red Cy7 743 770 red Table 2-1. Peak excitation and emission wavelength of fluorophores. 13 Among the fluorophores, those with near infrared excitation and emission wavelength within optical window (600nm-1100nm) are more useful for small animal imaging than other fluorophores with blue or green excitation and emission wavelengths since the infrared light can penetrate deeper through tissues than green or blue light (light with longer wavelength produces less scattering in tissue and the absorption coefficients of tissues within the wavelengths is lower). 2.1.2. Intrinsic parameters of fluorescence Fluorescence of fluorophores has several intrinsic parameters: fluorescence intensity, excitation spectra, emission spectra, and lifetime. The parameters are widely used in optical imaging. The fluorescence intensity depends on quantum yield ( Φ) and the concentration of fluorophores. The quantum yield displays the efficiency of the fluorescence process. The definition of it is the ratio of the number of photons emitted (N pe ) to the number of photons absorbed (N pa ) in fluorophores. / pe pa NN Φ= Here, maximum quantum yield is 1.0. The quantum yield of 0.1 is still considered quiet bright(Morrison, 2008). Fluorescence lifetime is also a key parameter of fluorescence that is considerably sensitive to their environment. The fluorescence lifetime can indicate the average time that the molecule stays in its excited state before emission. An excited fluororphore will return to the ground state with a certain probability based on the decay rates through a 14 number of radiative and/or nonradiative decay processes. It follows the first-order rate equation: / ** * 00 F F kt t FFe Fe τ −− == Where *F is the number of electronically excited fluorophores at certain time t and *F 0 is initial fluorescence at t=0, k F is the rate constant by both radiative and nonradiative processes and τ F is the lifetime of the excited state, equal to 1/ k F , and therefore equal to the fluorescence lifetime (Valeur, 2002). The fluorescence lifetime and intensity can be affected by photon quenching. There are collisional quenching and static quenching. Collisional quenching involves collisions with other molecules that result in the loss of excitation energy as heat instead of as emitted light. This process is always present to some extent in solution samples; species that are particularly efficient in inducing the process are referred to as collisional quenchers (e.g. iodide ions, molecular oxygen, nitroxide radical). Static quenching is caused by interaction of the fluorophore with the quencher, which forms a stable non- fluorescent complex. Since this complex typically has a different absorption spectrum from the fluorophore, presence of an absorption change is diagnostic of this type of quenching (by comparison, collisional quenching is a transient excited state interaction and so does not affect the absorption spectrum). A special case of static quenching is self- quenching, where fluorophore and quencher are the same species. Self-quenching is particularly evident in concentrated solutions of tracer dyes (Komiyama and Miwa, 1980; Lakowicz, 1999; Merkle et al., 1987; Prasad et al., 1983). 15 2.2. Optical imaging techniques for small animal imaging 2.2.1. Single-wavelength, single-photon excited fluorescence intensity imaging Traditional fluorescence imaging has been a simple method to continuously track movements and concentrations of labeled molecules in vivo, based on the spatial distribution of intensity at a single emission wavelength. This technology allows monitoring the progression of diseases, the effects of drug candidates on the target pathology, the pharmacokinetic behavior of drug candidates, and the treatment outcomes (Bouvet et al., 2002; Chen et al., 2004; Contag, 2007; Contag and Bachmann, 2002; Farkas and Becker, 2001; Hoffman, 2004, 2005; Paulmurugan et al., 2004; Rice et al., 2001; Vooijs et al., 2002; Yamauchi et al., 2005; Yang et al., 2000). It is based on the linear dependence of fluorescence intensity on the accumulated concentration of labeled molecules. Figure 2-2. Fluorescence intensity image: (A) Fluorescence intensity image of a mouse with a brain tumor. After the injection of tumor-targeted drug molecules stained with Alexafluor 680, the image was obtained with 680nm excitation light and 710nm emission filter. (B) Absorption and emission spectra of Alexafluor 680. (A) (B) Brain tumor Excitation Emission 16 Figure 2-2 shows the fluorescence intensity image of a nude mouse. In the figure, we can see drug molecules stained with Alexafluor 680 are more accumulated in brain tumor regions than other regions. However, it is very difficult to discriminate and quantify the fluorescence signal of interest in the presence of confounding signals (noise) from entities such as endogenous fluorophores. Thus, for more accurate quantitative imaging in the presence of such “noise”, it is necessary to use more advanced imaging methods (Contag, 2007; Farkas and Becker, 2001). 2.2.2. Spectral detection Spectral imaging provides a high-resolution spectral signature at every pixel of an image. The spectral signatures are the specific combination of reflected or absorbed light at varying wavelengths which can uniquely identify an object or a biological molecule (Burton et al, 2009). This capability provides useful information compared to standard fluorescence intensity imaging with a single excitation/emission channel. In spectral imaging, a series of images of a sample through a liquid crystal tunable filter (LCTF) or an acousto-optical tunable filter (AOTF), which only allow passage of light of a narrow, chosen bandpass, are acquired in order to create a spectral data “cube” (x, y, and wavelength) (Gebhart et al., 2007; George and Patonay, 1997; Gupta, 2005, 2009; Lee et al., 2007; Zuzak et al., 2008; Zuzak et al., 2007). In this cube, spectral signatures can be obtained at every pixel of an image. The spectral signatures are analyzed by various spectral classification techniques such as Euclidean distance measure, correlation measure, spectral angle measure, and spectral unmixing. Figure 2-3 shows the band 17 sequential image acquisition, spectral data cube, selected spectral signatures from the cube, and the classified image for spectral imaging. In the classified image, red blood cells (red pseudocolor) are quantitatively discriminated from other cellular matrix (green color). Ultimately, the spectral “signatures” allow a very delicate, specific and reproducible separation of signals of interest from noise, in these particular circumstances. Also, additional information may be obtained by comparing spectra obtained at different excitation wavelengths (Periasamy, 2001). The entire process can be completed in several seconds (Burton et al., 2009; Carano et al., 2004; Chung et al., 2006; Farkas and Becker, 2001; Levenson et al., 2008; Levenson and Mansfield, 2006; MacKinnon et al., 2005; Macville et al., 2001). Figure 2-3. Spectral imaging: (A) A band sequential image acquisition using AOTF or LCTF. (B) Spectral data cube. (C) Spectral signatures from selected pixels. (D) Spectral classification: a spectral data cube is constructed through a (band-sequential) series of images acquisition along a wavelength axis, with a narrow bandwidth. Each pixel contains its own spectral signature. Spectral classification was performed using linear discriminant analysis methods. (B) (C) (D) (A) Object Collecting Lens AOTF, LCTF Detector Wavelength 500 700 600 800 Intensity (A.U.) 0 1 18 2.2.3. Fluorescence lifetime detection Fluorescence lifetime detection is a powerful tool to provide an image based on a fluorophore’s fluorescence lifetime rather than intensity. The fluorescence lifetime is less dependent on transient changes in the local concentration of the fluorophore but can be highly sensitive to physiological environment such as intra/extracellular, pH distribution, blood flow, tissue oxygen, and temperature (Bloch et al., 2005; Cubeddu et al., 1997; Cubeddu et al., 1993; Esposito et al., 2006; Feng and Huang, 2007; Gratton et al., 2003; Hanson et al., 2002; Lin et al., 2003; Lin et al., 1999; Murata et al., 2000; Niesner et al., 2008; Spriet et al., 2008). Thus, unlike intensity or spectral measurements, fluorescence lifetime imaging can show the energy transfer rate from the excited state of fluorophores to its surrounding environment. Also, it can be ued for localizing diseased tissues such as tumors (Bloch et al., 2005; Pelet et al., 2006). Fluorescence lifetime detection can be performed either in the frequency-domain or time- domain. Frequency-domain fluorescence lifetime detection is simpler and easier to implement as well as have more light efficiency than time-domain fluorescence lifetime detection. However, it requires more computational resources and has a limitation in temporal dynamic range (Pelet et al., 2006). On the other hand, time-domain fluorescence lifetime detection requires ultra-short pulsed laser but can provide better analysis of complex exponential decays, particularly in the presence of several lifetime components (Figure 2-4). 19 Figure 2-4. Fluorescence lifetime imaging: (A) Fluorescence intensity decay. After ultra-short pulsed light excitation, fluorescence decays over time. (B) Fluorescence images over time. They are obtained by ultra-fast time-gated camera. From these consecutive images, the fluorescence intensity decay curve at every pixel can be generated. (C) Pseudo-color mapped image of fluorescence lifetime. Each fluorophore has its inherent fluorescence lifetime. In time-domain fluorescence lifetime detection, consecutive images are collected with certain delay steps over time. Then, the decay function (intensity vs. delay time) is measured at every pixel of the images and the measured decay curve is fitted to the first order exponential decay curve in order to acquire time constants (lifetime) of the decay curve. Finally, the lifetime obtained at every pixel is displayed with a color map (Cubeddu et al., 1993). Figure 2-4 shows the fluorescence lifetime imaging of H&E stained breast tissue. In this dissertation, time-domain fluorescence lifetime detection was mainly used for the experiments. 2.2.4. Two-photon excitation Two-photon fluorescence excitation is generated by simultaneous absorption of two photons with half the energy (of an equivalent single photon) for excitation. This 0.3ns 0.5ns 3ns 2ns 1.5ns 1ns 0.3ns 0.5ns 3ns 2ns 1.5ns 1ns (A) (B) (C) Time (C) Lifetime (ns) 2.0 0 Time Excitation Pulsed laser 2 / t e τ − 1 / t e τ − 20 phenomenon requires high fluxes of photons within an ultra-short pulse width. As a result, a confocal-type effect (i.e. 3-D spatial selection/slicing) is generated because excitation occurs only in the confined volume around the plane of focus when using a microscope objective with a high numerical aperture. Figure 2-5 shows the typical Jablonski diagram and geometry of two-photon excitation. Figure 2-5(A) shows the two-photon absorption in a molecule. Figure 2-5(C) shows the 3-D point spread function of two-photon excitation (Diaspro and Robello, 2000; Dong et al., 2003; Ibanez-Lopez et al., 2005). In addition, since near infrared (IR) light is used for two-photon excitation, less photo- bleaching, less photo-toxicity, and a deeper penetration can be obtained. Because of these advantages, scanning two-photon fluorescence microscopy has been widely used as a noninvasive method in tissue and animal imaging (Chirico et al., 2003; Denk et al., 1990; Patterson and Piston, 2000). To name only a few examples, it enables measurement of calcium dynamics deep in brain slices of live animals (Mainen et al., 1999). In cancer research, this technique has been used for in vivo studies of angiogenesis and metastasis (Kirkpatrick et al., 2007; Konig et al., 1996; Wessels et al., 2007). Also, video-rate multiphoton microscopy has been recently developed and used for studies of lymphocyte motility and antigen response in intact lymph nodes (Miller et al., 2002). Recent advances in multiphoton in vivo microscopy include the development of multiphoton endoscopes based on GRIN lenses (Jung and Schnitzer, 2003). 21 Figure 2-5. Jablonski diagram and geometry of two-photon excitation: (A) Jablonski diagram of two-photon absorption/florescence: two photons are simultaneously absorbed in fluorophores for excitation. (B) Geometry of two-photon excitation. the two photon excitation is generated by ultrashort pulsed light, and it has intrinsic sectioning ability and provides less photobleaching. (C) Point spread function of two photon excitation. As previously mentioned, this two-photon excited fluorescence imaging is very useful for in vivo tissue and live animal imaging. Thus, it will allow immediate high quality pathologic assessment of intact unstained tissues or regions of interest in a live animal with high spatial resolution. In this dissertation, either the scanning two-photon excited or wide-field two-photon excited fluorescence imaging were used for the acquisition of high resolution information from intact unstained tissues and small animals. 2.2.5. Intra-vital confocal Intra-vital confocal imaging facilitates high-resolution studies of cellular and molecular events in vivo (Falati et al., 2002). In particular, intra-vital confocal imaging is based on laser scanning confocal technology through an optical fiber bundle (diameter: ~1.5mm) [Figure 2-6(A)]. The optical fiber bundle, which is a high resolution endoscope, can be inserted in a small animal. Thus, it can enable us to observe physiological processes as well as various tissues of interest inside a small animal at the cellular and sub-cellular levels. (A) (B) (C) 22 Figure 2-6. Scanning method through a fiber bundle and the applications of the intra-vital confocal mode (from Mauna Kea Technologies, Paris, France): (A) scanning schematic. (B) Blood vessels. (C) a mouse subcutaneous tumor. Currently, this technology has been utilized for various applications in vivo such as the observation of a subcutaneous tumor, peripheral nerve imaging, and angiogenesis. However, it has some limitations; the specificity is relatively low due to a fixed filter, and artifacts can be easily introduced to an image as the detector is not steady during data acquisition (Laemmel et al., 2004; St Croix et al., 2006). 2.2.6. Bioluminescence Bioluminescence imaging enables the noninvasive study of biological processes without excitation light sources in small animals. For in vivo bioluminescence imaging, a reporter gene, which encodes light-generating enzymes (luciferase), labels biological entities such as bacteria, tumor cells, immune cells, or genes. Luciferase enables generation of visible light by oxidation of an enzyme-specific substrate (luciferin) in the presence of oxygen and ATP, without any excitation. (A) (B) (C) 23 Luciferyl adenylate Oxyluciferin + + O 2 Luciferase Light,AMP Luciferin + ATP Luciferyl adenylate PP i + Luciferyl adenylate Oxyluciferin + + O 2 Luciferase Light,AMP Luciferin + ATP Luciferyl adenylate PP i + Figure 2-7. Chemical reaction process of luciferase enzymes and enzyme-specific substrates (luciferin): the luciferin becomes luciferyl adenylate in the presence of ATP as an energy source, and then, light is generated by that luciferase catalyzes the oxidation of the luciferyl adenylate. Right side of figure shows a crystal structure of firefly luciferase complexed with oxyluciferin and adenosine monosphate (AMP). Figure 2-7 shows the chemical reaction process of luciferase enzymes and enzyme- specific substrate. Bioluminescence imaging thus has several advantages compared to fluorescence imaging: (i) It provides higher sensitivity; (ii) It has the capability to observe the emitted visible light from an enzymatic reaction at few centimeters depth; (iii) Imaging background from excitation or animal autofluorescence can be dramatically reduced because no excitation light source is needed after the injection of a luciferin to initiate the reaction. Thus, the signal to noise ratio is increased; (iv) It can offer similar capabilities to SPECT and PET, besides optical imaging (Bhaumik et al., 2004; Contag, 2007; Contag and Bachmann, 2002; Graves et al., 2003; Patsenker et al., 2008; Paulmurugan et al., 2004). 2.3. Effects of the tissue window on both excitation and emission photons Figure 2-8 shows optical characteristics of tissues. In the figure, the light absorption of the hemoglobin significantly decreased over 600nm as well as the light absorption of water is reduced below 1100nm. Thus, the wavelength range, approximately 600nm- 1100nm, is called an “optical window” (Parrish, 1981). 24 Figure 2-8. Optical window for tissue imaging. 2.4. Chemotherapy molecules for cancer treatments 2.4.1. Nanoconstructs In chemotherapy for tumor treatment, targeted delivery enhances efficiency and tumor specificity, and thus (by allowing lower doses) reduces systemic toxicity. A nanoconstruct (Polycefin) was developed as a novel platform for tumor treatment. The nanoconstruct can carry more than one drug, allowing synergistic effects on tumor cells. Figure 2-9 shows a schematic of the nanoconstruct. Figure 2-9. Schematic of Nanoconstructs (Polycefin) (Ljubimova et al., 2008). Drug releasing unit Inhibitor 1 Inhibitor 2 Tumor cell targeting antibody to transferrin receptor Protector (PEG) Fluorescent dye for drug tracking Endosomal disruption unit for drug release inside the cell Optical window Hemoglobin Water Wavelength (nm) Absorption 1000 1200 1400 800 600 0 0.001 0.01 0.1 1 10 25 It consists of several modules chemically conjugated to poly β-L-malic acid (PMLA). Here, for the targeted delivery of antisense oligonucleotides (AON, targeting the mRNAs of laminin-8 α4 and β1 chains) into certain tumors, antibodies (mouse anti-human TfR mAb) are conjugated to the PMLA. In addition, several functional units are chemically conjugated to the carboxyl groups of the PMLA platform. Polyethylene glycol (PEG) protects against enzymatic degradation. A dye (such as Alexafluor 680) is conjugated for in vivo fluorescence imaging. L-Leucine ethylester moieties function in endosomal escape. This nanoconstruct is biodegradable, non-immunogenic, and non-toxic (Lee et al., 2006; Ljubimova et al., 2008). 2.4.2. Targeted sulfonated gallium corroles Recent inventions of chemotherapy molecules for cancer prompt the development of new multifunctional technologies aimed at improved detection, diagnosis, and/or therapeutic intervention of cancer (Kawasaki and Player, 2005). The 2,17-bis-sulfonated corrole and its metal complexes have similar chemical structure with porphyrins and associated macrocycles that have been explored for cancer therapy, but may have distinct advantages over the latter compounds for physiological application. Specifically, sulfonated corroles are water soluble, thus it enables facile use in physiological fluids. Also, they are unable to enter cells without a carrier molecule, thus it enhances the specificity of delivery. Furthermore, any excitation is not required in order to elicit cytotoxicity (thus expanding the potential tissue depth and distance at which corrole- mediated therapy may be administered). 26 Figure 2-10. Chemical structure of sulfonated gallium corroles (from Harry B. Gray). Here, for breast cancer treatment and detection, a breast cancer-targeted carrier protein (HerPBK10) that promotes uptake by the cell and a sulfonated gallium-metallated corrole (S2Ga) are synthesized. The S2Ga forms a tight assembly with the carrier protein that resists high speed centrifugation and transfer to albumin, brightly fluorescence, and induces toxicity to target cells after delivery and uptake by the carrier. Importantly, corrole cytotoxicity is best supported by a membrane penetrating function, as non- penetrating carriers such as albumin did not enable sufficient cytotoxicity. This suggests that sulfonated corroles entering cells via receptor-mediated endocytosis must somehow escape the endosomal vesicle to induce cytotoxicity. The protein used in these studies provides both the targeting and penetration required for effective corrole delivery (Agadjanian et al., 2009; Agadjanian et al., 2006). 27 CHAPTER 3 METHODS AND MATERIALS 3.1. Multimode optical imaging system A new multimode optical imaging system is designed to be application-optimizable, with higher sensitivity and specificity. This instrument combines various in vivo imaging modes, including one or two photon-excited fluorescence, spectral, fluorescence lifetime, intra-vital confocal, and bioluminescence imaging. 3.1.1. Design A schematic diagram of the instrument we designed and built is shown in Figure 3-1(A). It consists of a light-tight enclosure within which various optical imaging modes are implemented as follows: fluorescence intensity, spectral, lifetime, intravital confocal, scanning/wide-field two-photon excited fluorescence, which has been developed outside but will be incorporated into the system, and bioluminescence imaging. Excitation of the molecules of interest is made with laser light at the appropriate wavelengths. A wide selection of light sources is available, including a tunable femtosecond pulsed laser (MaiTai, SpectraPhysics), a picosecond pulsed laser (Tsunami, SpectraPhysics), continuous wave (CW) lasers, and a broadband ps pulsed light source covering the whole visible and near infrared range (450-1900 nm, Fianium). The light sources are delivered 28 via free space, or through a fiber-based laser delivery system. The fiber-based laser delivery system allows flexible delivery of various lasers inside a light-tight enclosure. Figure 3-1. Multimode optical imaging system: (A) System schematic. This system is capable of several imaging modes, including fluorescence intensity, spectral, life-time, intra-vital confocal, and bioluminescence imaging. Also, for 3D fluorescence imaging, optical components and a control program installed. Furthermore, scanning/WTEF imaging mode will be incorporated with the system for deep tissue imaging at high resolution. (B) Photographic image of the multimode optical imaging system. (B) Intra-vital confocal imaging unit Anesthesia machine AOTF FLIM Scanning/widefield two-photon excited fluorescence imaging unit Laser delivery (A) CCD Moving Stage Controller Joystick Time-gated anesthesia machine Motorized filter wheel 3D Fluorescence Imaging Fiber-based delivery system/small opening AOTF FLIM Diffuser Output Wavelength - 405nm Laser : 405nm - PS Laser : 700~1080nm - HeCd Laser : 442nm - FS Laser : 710~990nm - Ar-Kr+ Laser : 488nm, 514nm, 647nm - HeNe Laser : 633nm Laser Telecentric Heating pad (X, Y) (R) Femtosecond pulsed Argon-Krypton HeNe 405nm Picosecond pulsed Mirror HeCd Optical probe for intravital confocal imaging 29 The light source, which is delivered into the light tight enclosure, is divided to two directions by a 50:50 dichroic mirror. And then, they are delivered onto specimens through diffusers and mirrors. Here, uniform excitation of the specimen is realized by scattering the laser light onto the 90% transmission broadband diffusers (THORLABS, 1” round 20 0 circle tophat diffuser). Light from the specimen is collected by a telecentric lens (MELLES GRIOT, Invaritar TM 59LGL428 and 59LGG950, NA: 0.24). The collected light passes through either (i) standard interference filters (Chroma) installed in a motorized filter wheel, (ii) an AOTF or (iii) a FLIM module (LAVISION, PicoStar HR) before arriving onto a cooled charge-coupled device (CCD) camera (Hamamatsu, ER- ORCA or Princeton Instruments, PIXIS 400) located on top of the light-tight imaging chamber. Every component is modular, and switching between modes is fast (~seconds, if necessary). The AOTF and FLIM modules can be flexibly exchanged by using a sliding rail. In addition, an optical probe (Manua Kea Technologies) has been incorporated for intravital confocal imaging in this system. Also, for 3D fluorescence imaging, the telecentric lens and CCD camera can be attachable onto the enclosure’s side wall. The synchronization between a rotational stage and CCD camera for image acquisition with different angle views is controlled by a program [Figure 3-4(E)] we developed. Furthermore, a two-photon excitation imaging module is developed for high resolution deep tissue imaging. The animal (usually a mouse or rat) is placed inside the light-tight enclosure on a moving stage that has a spatial resolution of 5µm in x and y-axis, 100µm in z-axis, and 2.16 arcsec in rotational axis. The sample stage can move along x-, y-, z-axis, and rotate 30 clockwise and counterclockwise to vary the field of view and a sample position. The stages are actuated by a stepper and a servo motor using software we developed (using CVI/National Instruments). In order to prevent animals from moving during image acquisition, a gated anesthesia system we developed (capable of stopping breathing for the duration of image acquisition alone) that uses a mixture of oxygen and isoflurane is attached to the imaging box. Figure 3-2. Laser delivery system: (A) Laser beam steering schematic. There are two light paths. While the first light path is for pulsed laser light, the second light path is for continuous laser lights. For the prevention of damage of pulsed laser from undesired back propagation, a Faraday rotator and a retarder are installed. (B) Spectrum of the laser light in the system. 405nm diode laser (405nm, 25mW) HeCd laser (442nm, 18mW) Argon-Krypton laser (488nm, 514nm, and 647nm HeNe laser (632.8nm,25mW) Picosecond pulsed laser (700~1080nm, 1.6W) Femtosecond pulsed laser (710~990nm, 1.8W) 670nm diode laser (670nm, 150mW) (inside) (A) 405 405nm diode laser 441.6 Helium Cadmium 457.9 476.5 488 501.7 514 Argon ion laser 530.9 568.2 Krypton ion laser 632.8 He-Ne laser Krypton ion laser 670 647.1 670nm diode laser Retarder a light-tight enclosure Flip mirror Fixed Mirror 10.5" HeCd laser Fs Pulsed Laser Ps Pulsed Laser BBO 405nm Laser Faraday rotator HeNe Laser Argon Krypton Laser External trigger Wheel filter Lens 95:5 Mirror Retarder a light-tight enclosure Flip mirror Fixed Mirror 10.5" HeCd laser Fs Pulsed Laser Ps Pulsed Laser BBO 405nm Laser Faraday rotator HeNe Laser Argon Krypton Laser External trigger Wheel filter Lens 95:5 Mirror Pico-, Femto-second pulsed laser (B) 31 3.1.2. Laser delivery For excitation of molecules of interest, various laser lines (deep blue diode, 670nm diode, picosecond pulsed, femtosecond pulsed, HeCd, Argon-Krypton, and HeNe laser) can be used like as in Figure 3-2 (with their respective excitation wavelengths). Each laser light source is selected by flipping mirrors and is delivered onto a specimen through mirrors. In particular, a Faraday rotator are used as an optical isolator in order to prevent undesired back propagation of light from disrupting or damaging a femtosecond and a picosecond pulsed laser. Also, a Barium-borate crystal (BBO) is used for a second harmonic generation of near-infrared lights in fluorescence lifetime imaging. 3.1.3. Fluorescence light detection A telecentric lens (Navitar) is utilized in order to collect fluorescence emitted from a specimen. The telecentric lens provides no magnification distortions due to an object's distance or position in the field of view. The collected light passes through a selected filter installed in a rotational filter wheel, and then are recorded in a CCD camera (Pixis 400 or Hammastu ORCA-ER) after traveling through a relay lens. The relay lens allows introduction of a filter between a telecentric lens and a CCD camera (Figure 3-3). The CCD camera can be replaced by a spectral or a fluorescence lifetime imaging module depending on applications. The detection characteristics of combination of a Pixis 400 32 camera and a telecentric lens have been examined. The resolution of image is 0.222mm, the field of view 111mm x 88.8mm, the working distance 180mm, and a depth of focus 40mm. Figure 3-3. Relay lens system for filter installation: we installed a relay lens between them in order to introduce a rotational filter wheel, which includes emission filters, between the lens and CCD. The relay lens has 19.2mm image/objective focal distance. 3.1.5. Control software The stage control board is under the control of a program that we developed using CVI/National Instruments. The program panel is shown in Figure 3-4. It allows us to control every stage in the multi-mode optical system as well as a motorized filter wheel. Also, the program can be connected with client programs for control of the system from other locations (Joystick/FLIM), 3D fluorescence/bioluminescence, and scanning/wide- field two-photon excited fluorescence imaging through an internet connection. Figure 3-5 describes overall system control schemes. A system control program is connected with a stage, a filter wheel control board, and a data acquisition board through universal serial bus (USB) port in a computer as well as it is connected with Joystick/FLIM, 3D fluorescence/bioluminescence imaging, and scanning/Wide-field two-photon excited f1=17.526mm Rotational filter wheel 6.35mm Telecentric lens f2=19.2mm f3=19.2mm Relay lens (19.2:image/objective distance) CCD f1=17.526mm Rotational filter wheel 6.35mm Telecentric lens f2=19.2mm f3=19.2mm Relay lens (19.2:image/objective distance) CCD Rotational filter wheel Relay lens (Image/objective distance: 19.2mm) f3=19.2mm f2=19.2mm f1=17.526mm Telecentric lens 33 fluorescence imaging program through internet. Also, the stage and filter wheel control board is connected with a x, y, z-axis translation, rotation stage, and a motorized filter wheel. A data acquisition board (National Instruments) receives data from a scanning/wide-field 2-photon imaging module, and transfers control signals to light- emitting diode (LED). Figure 3-4. System control program: (A) Positioning buttons for controlling x-, y-, z-axis transition, and rotation stage. (B) Emission filter selection. It allows us to exchange emission filters installed in the motorized filter wheel. (C) Scanning FLIM control panel. In this panel, scanning area and scanning step size can be selected. (D) Scanning/wide-field two-photon excited fluorescence imaging panel. (E) Connection panel for joystick and scanning FLIM. (F) Connection panel for 3D fluorescence imaging. Stage and filter wheel control board System control program x, y, z, and rotational stage Motorized filter wheel Joystick/FLIM 3D fluorescence/ bioluminescence imaging USB Internet Scanning/wide-field 2-photon imaging D-sub15 Data acquisition board Stage and filter wheel control board System control program x, y, z, and rotational stage Motorized filter wheel Joystick/FLIM 3D fluorescence/ bioluminescence imaging USB Internet Scanning/wide-field 2-photon imaging D-sub15 Data acquisition board Figure 3-5. Overall system control scheme. (E) (D) (F) (A) (B) (C) 34 3.2. Imaging modes in a multimode optical imaging system 3.2.1. Single-wavelength, single-photon excitation fluorescence intensity imaging In this mode, a laser source can be selected by flipping mirrors for the excitation of a variety of fluorophores. The selected light source is delivered onto a specimen through mirrors, filters, and diffusers. Before a fluorescence image from the specimen is acquired, a background image including a spatial profile of an excitation light source is recorded for flat-field correction. Then, fluorescence collected by a telecentric lens is recorded in a high sensitive cooled CCD camera (Pixis 400), after being selected by an emission filter. Finally, the fluorescence image is corrected by the background image in order to reduce the artifacts due to the profile of an excitation light source. Sometimes, a photographic- equivalent image is taken with LED illumination for overlaying a fluorescence image. Figure 3-6 shows the schematic of the fluorescence imaging mode. Figure 3-6. Fluorescence imaging mode: as mentioned above, six CW and two pulsed laser lines can be selected for excitation of fluorophores. The selected light sources divided by a 50:50 dichroic mirror in a light-tight enclosure are delivered onto a specimen through two diffusers. Then, fluorescence collected by a telecentric lens from a specimen, passed through selected interference filters, is recorded on a CCD. Lasers Filter (bandpass filter) Mirror Diffuser Scanning CCD Computer Filter LED Stage controller 35 3.2.2. Spectral imaging mode In this spectral imaging mode, a laser delivery for excitation of specimen is same with that in fluorescence intensity imaging. In detection of fluorescence, emitted fluorescence from a specimen is collected by a telecentric lens and passes through a long-pass filter which rejects the excitation light source (Figure 3-7). Figure 3-7. Spectral imaging mode: the excitation procedure is the same as the procedure of single-wavelength, single-photon fluorescence intensity imaging. An acousto-optical tunable filter is employed between a filter and CCD for band-sequential spectral selection. In the AOTF, wavelength selection is realized by a high-frequency acoustical compression wave applied to a crystal of tellurium dioxide or quartz in order to locally alter the refractive index of the crystal in a periodic pattern. This leads, in effect, to the generation of a (virtual) diffraction grating so that an orthogonal beam of polarized and collimated light incident at the Bragg angle is diffracted into the first order beam, with selection of a particular wavelength, under computer control, by the RF frequency applied. Then, the light is delivered onto a high sensitivity, low noise CCD camera cooled to - 70 0 C (Princeton Instruments, PIXIS 400) for imaging through an imaging AOTF system we developed (ChromoDynamics, Inc.) which can rapidly and sequentially select a narrow bandwidth (1.5-4.0nm). After the sequential images within the certain range of wavelength are acquired, the spectral signatures on each pixel are generated by our developed program (Chung et al., 2006). Then, the image is classified based on the Lasers Filter (bandpass filter) Mirror Diffuser Scanning CCD Computer AOTF Filter Stage controller 36 selected spectral signatures. In addition, spectral unmixing is performed in order to reject autofluorescence from fluorescence signals of interest using Image J (Gammon et al., 2006). 3.2.2.1. Spectral computation methods In the spectral classification techniques, the Euclidean distance measure is the most simple spectral similarity measure method. In this dissertation, a root sum of square error method (RSSE) in the Euclidean distance measuring methods is usually used for the spectral classification: 2 1 (( ) )/( ) N ii i RSSE p r m M m = =−− − ∑ Where N represents number of spectra, pi and ri refer to the intensity of i th spectrum of the sample pixel signature and reference signature respectively, and m and M are the minimum and maximum of RSSE. Also, in the Euclidean distance measuring method, there is the sum of area difference method. The sum of area difference method considers the possibilities of the different steps in the spectrum axis (Grahn and Geladi, 2007). On the other hand, the correlation measure indicates the strength and direction of a linear relationship between two signatures. For spectral classification, there are the spectral correlation similarity and spectral similarity value method based on the correlation measure. While the spectral correlation similarity method based on the correlation measure uses the Pearson correlation coefficient as a similarity measure, the spectral 37 similarity value method is a combined measure of correlation similarity and the Euclidian distance. The spectral angle measure method, which calculates the angle between two spectra in order to measure spectral similarity between substance signatures for material identification, has been widely used in spectral image analysis. Also, the linear spectral unmixing method is very useful to analyze mixed signals to a pixel of images obtained in spectral imaging. The linear spectral unmixing method is based on the assumption that the recorded signal T at every channel λ can be expressed as a linear combination of the contributing fluorophores. In the consecutive channels obtained through spectral imaging, the distribution of emission signal represents directly the fluorophore emission spectrum and creates a spectral signature. With linear unmixing using these spectral signatures as reference, even combined and mixed signals can be clearly separated into the fluorophores that contribute to the total signal (Farkas et al., 1998; Zimmermann, 2005). Spectral imaging with ratiometric analysis methods have been reported for dynamic monitoring of other cellular processes and kinetics such as pH oscillations, photobleaching, and quenching kinetics. The method was based on a variant of the spectral waveform cross-correlation analysis. In the method, the master reference spectral library is constructed by composite spectra with varying ratios of component spectra (Ramanujan et al., 2006). 38 3.2.3. Fluorescence lifetime imaging mode For fluorescence lifetime imaging in the multi-mode optical imaging system, we developed a mosaic fluorescence lifetime imaging system with a femtosecond (fs) pulsed laser, for a large field of view. In this method, fs pulsed laser light is tuned to 400~480nm, a repetition rate of 80MHz, generated by the second harmonic of fs tunable pulsed laser with 800~960nm (Mai-Tai Ti-Sapphire laser, Spectra-Physics) in a BBO crystal. It was used for the excitation of molecules such as corroles or fluorescein inside a specimen. An ultra-fast time-gated camera (LaVision, PicoStar HR) was utilized for fluorescence lifetime imaging. Figure 3-8 shows the schematic of the mosaic fluorescence lifetime imaging set-up. The fs pulsed laser can be delivered through a small opening in a light- tight enclosure. Figure 3-8. Mosaic fluorescence lifetime imaging using fs pulsed laser: fs pulsed light was used for the excitation of the molecules of interest. The light was generated by the second harmonic of infrared fs pulsed light in BBO crystal following a Faraday rotator. It is delivered onto a specimen through the small opening on the light-tight enclosure via a built-in diffuser. A specimen was scanned by excitation light while an x-y translation motorized stage, which is controlled by the custom software, is moving. Fluorescence life time imaging was done by an ultra-fast time-gated camera. The camera was synchronized with the laser light by an external trigger and a delay unit. The image acquisition was performed by using a FLIM control program which is connected with the actuation motor control program through a TCP/IP internet connection. Motor control program (Server) Femto-second laser (100fs,80Mhz) BBO Filter (<500nm) Mirror Diffuser Scanning Trigger Delay unit Computer External Time Gated Intensifier (LaVison Picostar HR) CCD Computer Internet Connection Motor control program (Client) 39 And then, the light passes through two mirrors and a diffuser in order to excite a specimen. The beam size of the light is controlled by distance between a specimen and the diffuser which makes a 20 degree divergence of light. The fluorescence light from a specimen is collected by the telecentric lens. The fluorescence light passes through an emission filter, and then is delivered onto the CCD connected with time gated intensifier. The CCD and time-gated intensifier are synchronized with the pulsed light via a delay unit which connects with the external trigger. Scanning for a large field of view is performed sequentially after each image acquisition by x-y translation motorized moving stage under the control of the program which has been developed using National Instruments/CVI. It can control scanning width, height, and step size. Also, the program can be connected with a Joystick program which allows us to control the motorized stages and scanning at different locations through TCP/IP connection. Figure 3-8 shows the mosaic fluorescence lifetime imaging system. 3.2.4. Intra-vital confocal imaging Intra-vital confocal imaging enables in vivo endoscopic imaging with high resolution. In this mode, a holder for positioning a fiber bundle probe has been constructed in order to reduce the artifacts due to operators’ movements. The fiber bundle probe is directly connected with a confocal scanner including a scanning unit, an excitation laser source, an emission filter, and a detection module (an avalanche photodiode). Figure 3-9 shows the schematic of intra-vital confocal imaging mode. 40 Figure 3-9. Intra-vital confocal imaging mode: S-probe with a diameter, 1.5mm is utilized for intra-vital confocal imaging. The probe has 0 μm working distance, 5 μm lateral resolution, and 15 μm axial resolution. The excitation and emission lights are delivered and detected through the distal end of the probe. Then the confocal scanner generates a confocal image. The image obtained using the probe has a 599x500 μm field of view and 450x384pixels. 3.2.5. Bioluminescence imaging mode A bioluminescence imaging mode was developed in order to detect ATP and enzymatic activity of engineered nude mice. The experimental setup is shown in the Figure 3-10. A photographic image of the mouse was recorded, using two diodes. Bioluminescence was then collected by a telecentric lens (Melles-Griot, Invaritar TM ) and imaged onto a cooled CCD (Princeton Instruments, PIXIS 400). The bioluminescence image was threshold and overlapped with the photographic image. Figure 3-10. Bioluminescence imaging mode: After injecting enzyme-specific substrates (luciferin) into an animal, the bioluminescence signal is collected by a telecentric lens, and then recorded onto a cooled CCD. After bioluminescence imaging is completed, the photographic image is acquired with LED illumination for overlaying the bioluminescence image. LED light intensity is controlled by a program we developed. Scannin Fiber bundle Confocal scanner Probe positioning holder Stage controller Computer Telecentric lens Scanning CCD Computer Filter LED Bioluminescence Stage controller 41 3.2.6. Scanning/wide-field two-photon excited fluorescence imaging mode Scanning/wide-field two-photon excited fluorescence imaging mode was developed in order to examine the details of molecules or tissues at deeper locations in multimode optical imaging in vivo, outside the system. For wide-field non-linear excitation, a polarized fs pulsed laser (MaiTai – Spectra Physics) with 780~990nm, 50-300mW average power, and 80MHz repetition rate has been utilized. The fs pulsed laser beam passes through a Faraday rotator, several mirrors, and Galvo mirrors. Then, it is finally focused on the back focal plane of an objective (Nikon 40x, 1.3 NA, oil, Nikon 40x, 0.75 NA, air, or Nikon 60x, 0.75 NA) through a doublet lens (L1) (Melles Griot, FL200) in order to obtain a more-or-less parallel beam which makes a larger spot size at the sample. The fluorescence from a specimen is collected by the same objective, and then passes through short/band-pass filters and a tube lens (L2) before being recorded on a cooled CCD. In addition, in order to compensate a non-uniform excitation generated by the Gaussian profile of the beam, flat-field correction of a wide-field two-photon excited fluorescence image was performed by a normalized and inversed Gaussian mask. The Gaussian mask has been constructed through the convolution of the original image with a Gaussian function (radius: over 40) at a focal plane using ImageJ. The corrected image, R, is obtained by the entry-by-entry product of the original image O and the Gaussian mask M (R=O.× M). Moreover, for two-photon excited fluorescence imaging of in-vitro specimens, a scanning unit is incorporated into the system. In the scanning two-photon imaging mode, the CCD is replaced with a photomultiplier tube (PMT) (Hamamatsu, H6780-20), and the doublet 42 lens (L1) is removed from an optical beam path. Here, Galvo mirror and PMT output ignals are synchronized by the program we developed. Figure 3-11 shows the experimental setup. L1 Objective Specimen Dichroic mirror CCD L2 PMT Galvomirror fs laser F1 F2 FR L1 Objective Specimen Dichroic mirror CCD L2 PMT Galvomirror fs laser F1 F2 FR Figure 3-11. Scanning/wide-field two-photon imaging modes: L1 is a doublet lens which enables wider excitation and is removed in scanning two-photon imaging mode. F1 and F2 is a shortpass filter (<700nm) and a bandpass interference filter, respectively. L2 is a tube lens. 3.3. Wide-field two-photon excitation for multimode optical imaging in vivo The two-photon excitation can be combined with fluorescence spectral or lifetime imaging for various applications. Currently, spectral or lifetime imaging has been utilized in order to distinguish and characterize fluorophores targeted to different molecules in intact cells and tissues as an analytical tool with the power of object visualization in biomedical imaging and research (Deniset-Besseau et al., 2007; Macville et al., 2001). In particular, the spectral imaging has been used for multicolor spectral karyotyping of human chromosomes, spectral pathology and diagnosis (Schrock et al., 1996), functional imaging of brain oxygenation (Shonat et al., 1998), and evaluation of melanoma in vivo (Tomatis et al., 2005). On the other hand, fluorescence lifetime imaging has also been 43 used to discriminate fluorophores as well as identify the functional status around the fluorophores since fluorescence lifetime is sensitive to the surroundings around fluorophores such as pH distribution, blood flow, tissue oxygen, and temperature (Hanson et al., 2002; Ramanujan et al., 2005). Moreover, in in vivo imaging, the combination of two-photon excitation with the spectral or fluorescence lifetime imaging can enhance capabilities to discriminate between multiple targeted molecules at the deeper location compared to one-photon spectral and fluorescence lifetime imaging (Deniset-Besseau et al., 2007; Lakowicz, 1996). However, when a scanning two-photon excitation method is utilized with non-scanning imaging device, such as acousto-optical tunable filter and ultrafast time-gated CCD camera for spectral/fluorescence lifetime detection, it is difficult to use scanning and non- scanning element together in good fashion. It may require complex and sophisticated synchronization devices in combining them. Thus, this approach is too burdensome. In addition, scanning two-photon excited fluorescence imaging is not fit for monitoring fast processes with high magnification in vivo, since it is a relatively slow method despite the numerous theoretical advantages in small animal imaging. Moreover, a motion artifact can be easily introduced during scanning two-photon excited fluorescence imaging. Thus, these limitations of scanning two-photon excited fluorescence imaging prompted us to develop another approach, which allows the combination of two-photon excitation with other non-scanning imaging methods in multi-mode optical imaging for more versatile and fast in vivo imaging. 44 Recently, two-photon excitation as a video rate imaging method has been reported. This report shows that the Gaussian excitation profile in the lateral plane and the lack of inherent sectioning ability of a scanning multiphoton microscope does not allow three- dimensional rendering of sectioned data (Fittinghoff et al., 2000). However, despite the disadvantages of wide-field two-photon excited fluorescence imaging compared to scanning two-photon excited fluorescence imaging, it might nonetheless be very useful in small animal imaging in vivo and, furthermore, in multi-mode optical imaging using non- scanning devices, which has the capability to provide quantitative and functional information simultaneously. 3.3.1. Sample preparation In order to examine inherent optical characteristics of wide-field two-photon excited fluorescence imaging, a 16 μm cryostat section of mouse intestine (specifically, the filamentous actin prevalent in the brush border) stained with Alexa Fluor 568 phalloidin (FluoCells prepared slide #4 (F-24631), Invitrogen) and 50 μM gallium corrole solution (Agadjanian et al., 2006) were prepared. Also, an eye specimen of an alzheimer’s mouse was prepared for in vitro test of wide-field two-photon excited fluorescence imaging. In addition, a nude mouse with implanted breast tumors was used for wide-field two-photon excited fluorescence imaging in vivo as well as for spectral and fluorescence life-time imaging with wide-field two-photon excitation. Furthermore, 100 μM gallium corroles and microspheres (Constellation fluorescent microsphere, Invitreogen) were diluted with phosphate buffer saline solution (PH 7.4) as fluorophores for the in vivo experiments. 45 3.3.2. Experimental Setup for wide-field two-photon excitation The experiment setup is almost similar to the setup we described in Figure 3-11. Here, the fluorescence from a specimen, collected by the same objective, passes through a dichroic mirror and either band-pass filters (Chroma Technology, 560nm ±8nm or 630nm±60nm), a band-sequential spectral selection device (acousto-optical tunable filter, ChromoDynamics Inc.), or a time-gated intensifier (LaVision Picostar) before being recorded on a cooled CCD for the combination of wide-field two-photon excitation and multimode instrument. Figure 3-12. Schematics of the experimental set-up for wide-field two-photon excitation: (A) Multimode optical imaging with wide-field two-photon excitation: For spectral/lifetime imaging, we attached an imaging AOTF and LAVISON fluorescence lifetime module onto a NIKON microscope with multiple ports. Also, fs pulsed light was delivered onto a specimen through several mirrors, a lens (L1), and an objective. (B) wide-field two-photon excitation set-up in a scanning confocal imaging microscope: For comparisons among optical characteristics of one- photon, scanning two-photon, and wide-field two-photon excited fluorescence imaging, this experimental set-up was constructed in a scanning confocal microscope. For scanning two- photon excited fluorescence imaging, fs pulsed light source was delivered by Galvo mirrors onto the back focal plane of the objective (PlanApo, 40x/0.75) through a scanning confocal imaging unit. For one-photon excitation, Hg lamps incorporated with a microscopy (Eclipse TE2000, NIKON) was utilized through excitation filters. Figure 3-12(A) shows the experimental (A) (B) fs laser FR L1 Objective Specimen Dichroic mirror CCD Confocal scanning Filter fs laser FR L Objective Specimen Dichroic mirror Mirror AOTF CCD TGI CCD 46 set-up for wide-field two-photon excitation. Also, the set-up of wide-field two-photon excited fluorescence imaging previously mentioned was combined with a scanning confocal imaging system [Figure 3-12(B)] (Olympus IX70, Fluoview) in order to compare the optical characteristics of one-photon, scanning two-photon, and wide-field two-photon excited fluorescence imaging. 3.3.3. Flat-field correction and spectral/fluorescence lifetime Analysis Flat-field correction of wide-field two-photon excited fluorescence images was described previously. All flat-field correction images have been obtained through using the method. In spectral imaging, spectral classification algorithms based on Euclidean distance measure (RSSE) was utilized for spectral analysis which is performed by our own custom software (Chung et al., 2006). Also, for fluorescence lifetime analysis, a single exponential decay fitting method was used to fit the data using our developed analysis program, which provides the time-constant of a single exponential curve fitted to data on same pixel in a series of images and generates a fluorescence lifetime image mapped by the time-constants on each pixel (Foss, 1970). 3.4. Experimental setup and materials for investigation of optical characteristics of S2Ga 3.4.1. Sample preparation 50 μM 125 μM, 250 μM, and 500 μM S2Ga solutions diluted with phosphate buffered saline (PBS) (pH 7.5) were prepared in order to investigate concentration (in)dependence 47 of fluorescence lifetime of the corroles. In addition, 50 μM S2Ga with different pH values (5.0, 5.5, 6.0, 6.5, 7.0, and 7.5) were prepared for examination of pH dependence of corrole fluorescence lifetime. 3.4.2. Experimental setup For fluorescence lifetime imaging, fs pulsed laser light tuned to 424nm, a repetition rate of 80MHz, generated by the second harmonic of fs pulsed laser at 848nm (Mai-Tai Ti- Sapphire laser, Spectra-Physics) in a BBO crystal, was used for the excitation of the sulfonated gallium corroles. An ultra-fast time-gated camera (LaVision, PicoStar HR) was utilized for fluorescence lifetime imaging. Figure 3-13 shows the schematic of the experiment set-up. Figure 3-13. Experimental setup for corrole fluorescence lifetime imaging. The 424nm fs pulsed laser is delivered to Nikon Microscope through a Faraday rotator, macro lenses, the Barium-Borate crystal, a band-pass filter (425±5nm), and several mirrors. The light then passes through a diffuser and is reflected to the back focal plane fs pulsed laser Mirror Faraday rotator Lens BBO Filter Diffuser Dichroic mirror Objective ∆T chamber Filters TGI CCD 48 of 40x objective (Nikon 40x plan fluor, NA: 0.75) by a dichroic mirror inside a filter cube for the excitation of the corroles inside a ∆T microscopic imaging chamber. The fluorescence from the corroles is collected by the objective and delivered onto the CCD connected with time gated intensifier (TGI) through emission filters (625±250nm and 620±60nm). The CCD and time-gated intensifier are synchronized with the pulsed light via a delay unit that connects with the external trigger. In related experiments, we used the ∆T culture dish system (Bioptechs) in order to control the temperature of solution inside the chamber to within 0.1 degrees, for corrole and live-cell imaging. 3.4.3. Fluorescence lifetime of S2Ga For examination of the effect of concentration on fluorescence lifetime of the sulfonated gallium corroles, 100 μl corrole solutions with different concentrations were added into the ∆T chamber, respectively. Then, fluorescence lifetime of these corroles at the respective concentrations (50 μM 125 μM, 250 μM, and 500 μM) was measured at room temperature (24.5ºC). In addition, fluorescence lifetime of the 50 μM corrole solutions with different pH (5.0, 5.5, 6.0, 6.5, 7.0, and 7.5) was measured at room temperature and 36.5ºC respectively. While the measurements, the temperature was strictly (~0.1ºC) controlled by a ∆T culture dish system. Also, before adding the corroles into the ∆T chamber, the pH of the corroles was confirmed with a pH meter. 49 Finally, the effect of temperature on fluorescence lifetimes of corroles has been investigated. Each corrole solution with different pH was added into the ∆T chamber, and then the fluorescence lifetime of the corroles was measured at a room temperature (24.5ºC), 36.5ºC, and again room temperature (24.5ºC) sequentially. Before measurement of fluorescence lifetimes at the room temperature following the fluorescence lifetime imaging at 36.5ºC, we waited for 20 minutes in order to cool down the temperature of corrole. 3.5. Fluorescence lifetime imaging of HerGa and S2Ga on human cancer cells Human breast cancer cells (MDA-MB-435) and U251 glioma cells were cultured in a Bioptechs ∆T chamber for 24 hours in order to examine the fluorescence lifetime changes of HerGa and S2Ga due to the cancer cell endocytosis. Also, 150 μM HerGa solutions were prepared (Agadjanian et al., 2009). After culturing the cancer cells in a ∆T chamber for 24 hours, fluorescence lifetime of the corroles bound to the cells was measured at different time points over 60 minutes. Before adding 200 μl of 50 μM corroles with pH 7.5 into the ∆T chamber, the medium inside the chamber was removed, and then the chamber was washed with PBS (pH 7.4). During 60 minutes, temperature of corroles was maintained around 36.5ºC. 50 3.5.1. Data acquisition and Analysis For the fluorescence lifetime analysis, a total of 25 images were acquired within 4800ps with a 200ps time step, and a gate width of 600ps after excitation of corroles. The images were analyzed using the first order exponential decay fitting method incorporated into the DaVis software (LaVision) and our program. (We have more advanced methods available, but these were not needed). 3.6. Measurement of mitochondria membrane potential variations of the breast cancer cells induced by HerGa Mitochondria in cell physiology and pathology plays a variety of roles, including production of ATP, regulation of the cell redox state, participation in ion homeostasis, transport of metabolites, lipid and amino acid metabolism, and cell death. These important functions highly depend on mitochondrial membrane potential. Thus, the simultaneous measurement of the mitochondrial membrane potential may offer insight into both the basic energy metabolism and its dysfunction in pathologic cells (Solaini et al., 2007). Measurement of mitochondrial membrane potential utilizes fluorescent potentiometric probes. The mitochondrial membrane potential in situ is mainly monitored by measuring the accumulation of cationic fluorescent probes in response to the mitochondrial membrane potential in living cells. Cationic fluorescent probes distribution across the membrane is governed primarily by the Nernst equation (Farkas et al., 1989). Currently, 51 Rhodamine-123, Tetramethyl rhodamine methyl ester (TMRM), and tetramethyl rhodamine ethyl ester (TMRE) have been used as cationic fluorescent probes for the measurement of mitochondrial membrane potential. Among them, TMRM is best since TMRM reduces mitochondrial respiration to a much lesser extent than rhodamine 123 or TMRE (Gottlieb and Granville, 2002). In this thesis, mitochondrial membrane potential variations of breast cancer cells are monitored in real time using TMRM in order to investigate cell mechanism of HerGa. For the experiment, 20nM TMRM solution was diluted with PBS 7.4. MDA-MB-435 breast cancer cells were cultured onto a 20mm cover slip for 24 hours. A NIKON TE2000E microscope was to image cells. TMRM and HerGa fluorescence emission was selected with a Cy3 filter (exitation: 545±30nm and emission: 610±75nm) and HerGa filter set (excitation: 420nm±40nm and emission: 620nm±60nm). Also, a highly sensitive CCD camera (Photometrics, Cool snap HQ2) was utilized to record an image. 3.7. Spectral imaging with a ratiometric analysis method for dynamic monitoring of HerGa accumulation and clearance 3.7.1. Experimental setup For the examination of HerGa accumulation and clearance, we utilized the spectral imaging mode in our multimode optical imaging system shown in Figure 3-7. For the excitation of HerGa injected into the nude mouse, fs pulsed laser light tuned to 424nm at a repetition rate of 80MHz, was generated by the second harmonic of a fs pulsed laser at 52 848nm (Mai-Tai Ti-Sapphire laser, Spectra-Physics) and a 500nm longpass filter was used to reject the excitation light. 3.7.2. Construction of spectral signatures For the spectral classification, a total of 10 sequential images were recorded within the spectral range of 500nm to 680nm with a step size of 20nm and a bandwidth of 10nm. Spectral signatures on each pixel were generated by our custom software. Four different reference spectral signatures with varying ratios of autofluorescence and HerGa fluorescence spectra were constructed. First of all, we acquired the spectral signatures of autofluorescence and HerGa fluorescence from sequential images of a mouse before and after HerGa intra-tumor injection. While the spectral signature of autofluorescence (A) has a peak at 500nm and decreases as wavelength increases, the spectral signature of HerGa (H) has a peak at 620nm. Figure 3-14. Composite spectra with varying ratios of spectra of autofluorescence and HerGa fluorescence. 500 550 600 650 700 -100 0 100 200 300 400 500 600 700 Wavelength(nm ) Fl. Intensity (A.U) 500 550 600 650 700 -100 0 100 200 300 400 500 600 700 Wavelength(nm ) Fl. Intensity (A.U) 500 550 600 650 700 -100 0 100 200 300 400 500 600 700 Wavelength(nm ) Fl. Intensity (A.U) 500 550 600 650 700 -100 0 100 200 300 400 500 600 700 Wavelength(nm ) Fl. Intensity (A.U) Autofluorescence HerGa (1 : 2) (0 : 1) (1 : 1) (1 : 0) 53 The fluorescence from a nude mouse after IV injection of HerGa can be represented by the linear superposition of the two spectral signatures (A and H) as: S=a1A+ a2H Where S is the composite spectral signature and an is the relative concentration of molecules. We constructed four spectral signatures with 1:0, 1:1, 1:2, and 0:1 ratios of A and H for spectral classification. The ratio indicates relative HerGa contribution to autofluorescence. While the 1:0 ratio of intensities represents pure autofluorescence, the 0:1 ratio represents high relative contribution of HerGa to autofluorescence. And, the 1:1 and 1:2 ratios represent the transition between the pure autofluorescence and the large amount of HerGa accumulation, respectively (Figure 3-14). Thus, this approach can give an accurate quantitative estimate of the relative concentration of two set of fluorophores and their variation in space and time in vivo (farkas et al., 1998; Burton et al., 2009). 54 CHAPTER 4 RESULTS As mentioned above, our multimode optical imaging system can be operated in at least six different image acquisition modes, all shown to acquire complementary information. While excessive acquisition times prevent the use of all imaging modes concurrently, it is envisioned that a specific number of modes and image collection sequences can be developed that maximizes the useful information extraction for all application. In this chapter, the evaluation of each imaging mode in our system is performed with in vivo and in vitro specimens. The feasibility of multimode optical imaging in vivo from our system is evaluated. Finally, Multimode optical imaging in chemotherapy assessment and cancer detection are performed. 4.1. Single modality experiments 4.1.1. Single-wavelength, single-photon excitation fluorescence intensity imaging for discrimination between fluorophores conjugated with a drug molecule Single-wavelength, single-photon fluorescence intensity imaging (it will called fluorescence intensity imaging later) has been performed using two laser light-sources and two emission filters in order to discriminate rhodamine B labeled liposomes containing Alexafluor 680 labeled proteins. 514nm and 647nm light sources from 55 Argon/Krypton laser have been utilized for the excitation of rhodamine B and Alexafluor 680, respectively. Also, 590nm±60nm and 700±15nm emission filters have been utilized for the detection of fluorescence from the molecules. After the molecule solutions with different concentrations (20 μM and 100 μM) were injected into two implanted tumor regions respectively, fluorescence from rhodamine B and Alexafluor 680 was detected, sequentially. Figure 4-1 shows the fluorescence intensity images. In the figure, although 514nm light source for the excitation of rhodamin B generates more autofluorescence than 614nm light excitation for Alexafluor 680, the fluorescence of rhodamine B can be easily distinguished from autofluorescence from the mouse. The average light intensity around the regions also has been measured in order to examine a concentration dependency. The average intensity over tumor regions at the right side is five times higher than at the left side. This ratio is almost identical to the concentration ratio of injection molecules. Figure 4-1. Fluorescence intensity images of a mouse injected with drug molecules tagged by rhodamine B and Alexafluor 680: (A) Fluorescence image by rhodamine B (B) Fluorescence image of Alexa680 (C) The overlaid image of a rhodamine B, an Alexafluor 680 and a photographic mouse image. (A) (B) (C) 56 In order to quantitatively evaluate the quality of the fluorescence images recorded, we compared the contrast of tumor areas in the images of a mouse obtained with a commercial system and our system. Here, we injected tumor targeting drug molecules into intra-vein of the mouse, and then the images were acquired after 24 hours. Figure 4-2 shows the comparison of the contrasts as calculated with average intensities within the two circles shown. The contrast values calculated based on Michelson contrast (Michelson, 1927) for the commercial system and our system are 0.12 and 0.26 respectively, with our system showing a roughly two-fold improvement over the best commercially available preclinical imaging system (Figure 4-2). This is by no means the highest ratio obtained, only a representative one. Figure 4-2. Contrast comparison between images recorded with a commercial system and our multimode optical system: (A) Intensity image obtained by the commercial system. (B) Intensity image obtained by our multimode optical system. The contrast values were calculated based on [I t (Tumor)-I b (background)]/(I b +I b ) . 4.1.2. Spectral imaging with spectral unmixing for quantification of fluorescein We have extensive experience in preclinical and clinical spectral imaging (Farkas et al., 1998; Burton et al., 2009; Frykman et al., 2008). The feasibility of spectral imaging in our new multimode system has been investigated after 10 μM fluorescein, diluted with (A) (B) 57 PBS (pH 7.4), was injected into breast tumors implanted in the back of a nude mouse. 488nm laser light was utilized for the excitation of fluorescein inside the mouse, and then a total of 12 images were recorded within the spectral range of 500nm to 720nm with a step size of 20nm. The images were analyzed using software we developed. Spectral classification based on a least mean square error method and spectral unmixing was performed for separating fluorescein fluorescence from autofluorescence respectively. Figure 4-3 shows the classified images obtained by spectral classification and spectral unmixing. In Figure 4-3(A), although fluorescence of fluorescein around tumor regions is shown, the right side of brightest regions (selected region by a red mark) displays similar intensity with autofluorescence around the selected region (by a green mark). However, the fluorescence of fluorescein is clearly discriminated from autofluorescence in the spectrally classified and the spectral unmixed image shown in Figures 4-3(C) and (D). Moreover, Figure 4-3(B) displays the difference of the spectral signatures of fluorescence and autofluorescence. Figure 4-3. Spectral imaging of a nude mouse with injection of fluorescein into implanted tumors: (A) Fluorescence intensity image at 540nm. (B) Spectral signatures within the selected regions. While the spectral signature of fluorescence has a peak at 540nm, the spectral signature of autofluorescence has multiple peaks and shows relatively broader spread of wavelength. (C) Spectral classification image by the selected signatures. While red color shows fluorescence of fluorescein, green color represents autofluorescence. (D) Spectrally unmixed image. After spectral unmixing of the image, the result is overlaid with an intensity image. (A) (B) (C) (D) 480 540 600 660 720 0 1 2 3 4 5 6 7 Fluorescein Autofluorescence Wavelength(nm) Fl. Intensity (A.U) 480 540 600 660 720 0 1 2 3 4 5 6 7 Fluorescein Autofluorescence Wavelength(nm) Fl. Intensity (A.U) 58 4.1.3. Fluorescence lifetime imaging We developed a mosaic fluorescence lifetime imaging system that provides a large field of view, using a femtosecond (fs) pulsed laser, for multi-mode optical imaging of small animals. In order to evaluate the feasibility of this method, a model system study was performed. Additionally, mosaic fluorescence lifetime imaging of the same mouse which was used for a spectral imaging evaluation has been performed. 4.1.3.1. Ex vivo test of mosaic fluorescence lifetime imaging system For the evaluation of the mosaic fluorescence lifetime imaging system, after the letters “MISTI” were written on a white paper with 1mM fluorescein solution diluted by PBS (pH 7.4), the region of interest on the paper was scanned by our mosaic fluorescence lifetime imaging system (2cm x 3cm). fs pulsed laser light at 437.5nm with a beam diameter of 1.8cm, generated through the same setup shown in Figure 3-8, was used for the excitation of the fluorescein on the paper. Also, a 500nm longpass filter rejects the excitation light from emission light in front of the time-gated intensifier CCD camera. A total of six fluorescence lifetime images were obtained by mosaic fluorescence lifetime imaging. And then, the images were merged into a single image for large field of view, and 2x2 Gaussian filter was applied into the merged image in order to reduce the high frequency at the boundary of the tiled images using ImageJ. Figure 4-4 shows the tiled images and the merged image with a size of 2.7x3.8cm. In the figure, the lifetimes of fluorescein are from 3.5ns to 4.5ns. 59 Figure 4-4. Large area image obtained by mosaic fluorescence lifetime imaging: (A) Fluorescence lifetime images obtained for spatially combining image. After the letter was written on a white paper with 1mM fluorescein solution (pH 7.4), the area (2x3cm) of interest in the paper was scanned by mosaic fluorescence lifetime imaging with a step size of 1cm. (B) Image reconstructed from the six images obtained by mosaic FLIM. 4.1.3.2. In vivo test of mosaic fluorescence lifetime imaging with a nude mouse Additionally, mosaic fluorescence lifetime imaging of the same mouse which was used for a spectral imaging evaluation has been performed. Figure 4-5 shows the fluorescence lifetime image displayed in pseudocolor. While the fluorescence lifetimes from a mouse skin display around 3~3.8ns, the fluorescence lifetimes of fluorescein injected into tumor regions show approximately 3.8~4.5ns. In the image, the tumor regions with fluorescein are clearly distinguished from other regions by the fluorescence lifetime difference. Also, the fluorescence lifetime values of fluorescein are in good agreement with values in a literature (Gratton et al., 2003). (B) (A) 1 6 Scan direction 1.8cm White paper 3.8cm 2.7cm Lifetime(ns) 5.0 4.0 3.0 2.0 1.0 0.0 60 Figure 4-5. Fluorescence lifetime image of a nude mouse with an implanted breast tumor: For excitation of the injected dye (fluorescein), a 437.5nm fs pulsed laser with a 3.5cm beam diameter (3.5cm), generated by the second harmonic of 875nm, was used. The mouse was scanned by the system step by step for a large field of view. Two fluorescence lifetime images were acquired sequentially. The images were merged into a single image. For each fluorescence lifetime image, a total of 39 images have been acquired within 0 to 7800ps with a time step of 200ps. 4.1.4. Intra-vital confocal imaging Before intra-vital confocal imaging, the optical fiber bundle probe is calibrated with a calibration tool kits (Mauna Kea Tech.). And then, after a optical fiber bundle probe is positioned inside a small animal by the holder, the intra-vital confocal images are obtained. Figure 3-9 shows the schematic of the intra-vital confocal imaging mode. The feasibility of intravital confocal imaging of a rat spine has been evaluated. After euthanizing a rat, the rat spine was exposed. Then, autofluorescence images of the rat spine were recorded for 8min by using an MKT S-probe (0 µm working distance). The typical frame rate is 12 frames/sec. Figure 4-6 shows the experimental setup and results obtained by using it. The images in Figure 4-6(B) clearly show convolutions of neuromuscular junction (the first image at the first row), skeletal muscle fibers (the first and second image at the second row), and vessels (the second and third image at the second row). Lifetime(ns) 0 4.0 2.0 61 Figure 4-6. Intra-vital confocal imaging of the rat spine: (A) Experiment setup. The arrow indicates the optical fiber bundle probe which is positioned by the holder. (B) Intra-vital confocal images of the rat spine. Several positions have been investigated during image acquisition. 4.1.5. Bioluminescence imaging Using bioluminescence mode in this system, we imaged the presence of ATP and enzymatic activity in nude mice. After luciferin was injected into the abdominal cavity of the nude mice, we took a photographic image and then recorded the bioluminescence signal for 2 minutes. Figure 4-7 shows the overlay image of the photographic and bioluminescence image. These results demonstrate the capability to record bioluminescence signals with good signal-to-noise and versatility, opening up the possibility for further studies using this modality. Figure 4-7. Bioluminescence image from engineered mice (normalized by highest value). (A) (B) 1 0 62 4.1.6. Scanning/wide-field two-photon excited fluorescence imaging mode Scanning/wide-field two-photon excited fluorescence imaging mode was developed in order to examine the details of molecules or tissues at deeper locations in multimode optical imaging in vivo, outside the system. In this section, the evaluation of scanning two-photon excited fluorescence imaging is performed. In addition, wide-field two- photon excitation for multimode optical imaging in vivo is described in detail. 4.1.6.1. Scanning two-photon excited fluorescence imaging of a mouse liver Two-photon excited fluorescence image of a mouse liver stained nonspecifically with fluorescein is obtained by using our scanning two-photon excited fluorescence imaging mode. In Figure 4-8, liver cells stained with fluorescein are shown. Femtosecond pulsed laser light at 780nm was here used for the two-photon excitation of the fluorescein (500 μMol), and a shorpass filter (<700nm) and a bandpass filter (540±40nm) were utilized for the emission light selection. Figure 4-8. Two-photon excited fluorescence image of a mouse liver stained with fluorescein (Nikon 20x, NA: 0.50). 100 μm 63 4.1.6.2. Wide-field two-photon excitation in multimode optical imaging in vivo using non-scanning imaging devices In this section, the optical characteristics of wide-field two-photon excited fluorescence imaging and contrast enhancement by wide-field two-photon excitation compared to one- photon excited fluorescence imaging are demonstrated. In addition, the usefulness of wide-field two-photon excited fluorescence imaging and the feasibility of multimode optical imaging with two-photon excitation for small animals in vivo are examined. In the investigation of the optical characteristics of wide-field two-photon excitation, power, wavelength, numerical aperture, and depth dependence of wide-field two-photon excitation with gallium corrole solution, sectioned mouse intestine, and a tissue phantom are examined. Also, in vitro measurement of wide-field two-photon excited fluorescence imaging is performed on an eye specimen taken from an alzheimer’s-disease mouse for comparison between one-photon and wide-field two-photon excited fluorescence imaging. Here, a flat-field correction method is applied in order to reduce artifacts due to Gaussian beam profile of excitation laser light. In addition, vascularization around implanted tumor regions of a nude mouse is examined by using wide-field two-photon and one-photon excited fluorescence imaging, and then we compare these results in order to evaluate the usefulness of wide-field two-photon excited fluorescence imaging for small animals in vivo. Finally, we examine the feasibility of combination of wide-field two-photon excitation and a multimode instrument using non-scanning in small animal imaging in vivo. 64 4.1.6.2.1. Optical characteristics of wide-field two-photon excited fluorescence imaging The dependence of fluorescence intensity on laser power has been examined with 50 μM sulfonated gallium corrole solution in order to evaluate the wide-field two-photon excitation. A total of six wide-field two-photon excited fluorescence images were acquired with the power range (200 ~ 400mW) of excitation laser at 795nm with a step size of 50mW, and then fluorescence intensity of each image has been measured. After that, curve fitting to the measured intensity along as a function of excitation power has been performed. Figure 4-9(A) shows the measured intensity and fitted curve (I=0.01xP 2.01 ). In the figure, the fluorescence intensity increases nonlinearly with the excitation power, suggesting a nonlinear process of fluorescence generation. We have examined wavelength dependence of wide-field two-photon excited fluorescence imaging with the mouse intestine sample. A wavelength region (780 to 910 nm) from the pulsed laser light was used for two-photon excitation of the sample. Fluorescence intensity of the images obtained by wide-field two-photon excited fluorescence imaging was measured at each wavelength. Figure 4-9(B) shows fluorescence intensity versus wavelengths. In the figure, fluorescence intensity has a peak at 790nm (Dickinson et al., 2003). In addition, photobleaching has been examined before and after wide-field two-photon excited fluorescence imaging to confirm the accuracy of the result. As a result, 0.03% photobleaching occurred during the experiment [intensity (Arbitrary Unit): from 1541 to 1497]. 65 Figure 4-9. Optical characteristics of wide-field two-photon excited fluorescence imaging: (A) Power dependence of wide-field two-photon excited fluorescence of sulfonated gallium corroles. (B) Wide-field two-photon excited fluorescence versus wavelength. We have examined the wavelength dependence of wide-field two-photon excited fluorescence imaging of the mouse intestine stained with Alexafluor 568 phalloidin. The excitation power at each wavelength was 250mW. The exposure time was 568ms. A total of 14 images have been acquired at different wavelengths (780-910nm, step: 10nm). Here, an emission filter (620±60nm) and a 40x air objective were utilized. (C) Wide-field two-photon excitation versus a numerical aperture. Wide- field two-photon excited fluorescence has been investigated with a different numerical aperture of objectives. Here, a fs pulsed laser source (780nm, 200mW) was used for the excitation of the specimen, and 10x (NA: 0.45), 40x (NA:0.75), 40x (NA:1.30), and 60x (NA:1.45) objective were used for collection of fluorescence. The rectangle represents the size of the field of view, 256 μm x 244 μm, 64 μm x 61 μm, and 43 μm x 41 μm, respectively. Characteristics of wide-field two-photon excitation due to numerical aperture were investigated with the mouse intestine specimen. Figure 4-9(C) shows that the fitting curve clearly shows nonlinearity (4 th order) of the wide-field two-photon excitation to a numerical aperture. The field of view does not depend significantly on numerical aperture but is changed in proportion to the magnification of the objectives. Here, the fluorescence intensity obtained with the 40x (NA: 1.30) objective is four times higher than that with the 40x (NA: 0.75) objective. A tissue phantom was constructed as a mimic for comparison of contrast and confocal characteristics of one-photon, scanning two-photon, and wide-field two-photon excited fluorescence imaging (Flock et al., 1992). The phantom is composed of a nude mouse skin, a mixed gel (125 μM fluorescein, 5% intralipid, and 1% microspere), and paraffin. 4 1.602 I NA = 100 μm (A) (B) (C) 0.4 0.6 0.8 1.0 1.2 1.4 1.6 0 10 20 30 40 50 60 70 80 FL. Intensity (A.U) Numerical Aperture 780 800 820 840 860 880 900 920 200 300 400 500 600 700 800 Wavelength (nm) Fl. Intensity (A.U) 200 250 300 350 400 500 1000 1500 2000 Power(mW) Fl. Intensity (A.U) 2.01 0.01 I P =× 66 After a thin sample of nude mouse skin is put on a slide glass in a chamber, the mixed gel is loaded on the mouse skin. Finally, paraffin was filled in the chamber in order to prevent evaporation from the skin [Figure 4-10(D)]. With the constructed tissue phantom, a total of 52 images within 0~153 μm with a step size of 3 μm have been obtained by one-photon, scanning two-photon, and wide-field two-photon excited fluorescence imaging respectively. Figure 4-10. Comparisons of depth dependence in one-photon, scanning two-photon, and wide- field two-photon excited fluorescence imaging of a tissue phantom: (A) Scanning two-photon excited fluorescence image: fs pulsed laser light source at 780nm was used for excitation. The average laser power is 50mW. (B) Wide-field two-photon excited fluorescence image. Excitation wavelength was 780nm. The average laser power was 200mW. (C) One-photon excited fluorescence image: Excitation at 488±30nm and power of 140 μW. (D) Constructed phantom. (E) Intensity profiles over the selected regions along a depth. Figure 4-10(A), (B), and (C) shows the scanning two-photon, widefield two-photon, and one-photon excited fluorescence image at the same focal plane, respectively. In the figure, -20 0 20 40 60 80 100 120 140 160 0.0 0.2 0.4 0.6 0.8 1.0 Scanning two-photon Wide-field two-photon One-photon depth (um) Fl. Intensity (A.U) (A) (B) (C) Mouse skin Glass slide 0 depth mark paraffin 125µM Fluorescein+ 5% intralipid+1%microsperes+agarose gel (D) (E) 50 μm 50 μm 50 μm 67 the intensity profiles of the microsphere selected by a solid rectangle have been examined along a depth for contrast comparison. Figure 4-10(E) shows the profiles along the depth (z-axis). According to the profiles, a full width at half maximum of the intensity profile in the scanning two-photon excited fluorescence imaging is narrowest (approximately 10 μm), while the full width at half maximum in wide-field two-photon excited fluorescence imaging shows approximately 60 μm, which is significantly less than that (over 160 μm) in one-photon excited fluorescence imaging. 4.1.6.2.2. In vitro Measurements Figure 4-11(A) and (B) shows the results obtained by one-photon and wide-field two photon excited fluorescence imaging of a mouse intestine section stained with Alexafluor 568 phalloidin. Figure 4-11 Wide-field two-photon excited fluorescence images of a mouse intestine: (A) One- photon excited fluorescence image of the flamentous actin of the mouse intestine stained with Alexafluor 568 phalloidin with Texas red filter set (Ex: 560nm±40nm, Em: 630nm±60nm) and 838ms exposure time. (B) A wide-field two-photon exited fluorescence image (up and right) of the regions selected by the white dotted rectangle; the image (bottom and right) has been corrected by a Gaussian mask (down and left). The power of the excitation laser light at 800nm was 300mW with an exposure time of 458ms. (C) Profile of cross-sections of the original and the corrected image along a line (A’). (A) (B) (C) 50 μm 50 μm Original Corrected Pixel F.L intensity 0 50 100 150 200 250 1.0 1.5 2.0 2.5 3.0 3.5 4.0 68 In the wide-field two-photon excited fluorescence images, filamentous actin is clearly shown as in one-photon excited fluorescence image. In addition, in order to reduce artifacts by the Gaussian profile of the excitation beam, the image is corrected by the mask generated by the previously described method. In Figure 4-11(B), the mask, the original, and the corrected image are displayed together. The profiles across the original and the corrected image are shown in Figure 4-11(C). The profile of the wide-field two- photon excited fluorescence image corrected by the constructed mask shows uniform baseline compared to that of the uncorrected image. Wide-field two-photon excited fluorescence imaging of an eye specimen of an alzheimer’s mouse was also performed. Figure 4-12. One-photon and wide-field two-photon excited fluorescence imaging of an eye specimen of an alzheimer’s mouse: (A) One photon excited fluorescence image (ex: 560nm, em: 630nm). (B) Wide-field two-photon excited fluorescence image (ex: 830nm, em: 630nm, power: 300mW) and the Gaussian corrected image (white rectangle). Figure 4-12 shows one- and two-photon excited fluorescence image of the eye specimen at the same depth. First of all, plaques on the eye were found using one-photon excited fluorescence imaging, and then wide-field two-photon excited fluorescence image of the same region was acquired. In addition, flat-field correction of the wide-field two-photon excited fluorescence image was performed in order to enhance contrast. Figure 4-12(B) shows the wide-field two-photon excited fluorescence image. Here, more plaques in the 50 μm (A) (B) 50 μm 69 wide-field two-photon excited fluorescence image are clearly observed than in one- photon excited fluorescence image. 4.1.6.2.3. Multimode optical imaging with wide-field two-photon excited fluorescence imaging in vivo Wide-field two-photon excited fluorescence imaging of a small animal in vivo has been performed. After 100µM gallium corrole solution was injected into tumor regions of a euthanized mouse [Figure 4-13(A)], the regions were investigated using one-photon excited fluorescence imaging. Thin vessels are observed in one-photon excited fluorescence image as shown in Figure 4-13(B). In addition, the thin vessels have been examined by wide-field two-photon excited fluorescence imaging, and then the image was flat-field corrected [Figure 4-13(C)]. In the Figure 4-13(C), the thin vessels are more clearly shown than in the one-photon excited fluorescence image [Figure 4-13(B)]. Finally, after the injection of microsphere into the subcutaneous regions indicated by the green dotted circle, a total of 40 one- photon and wide-field two-photon excited fluorescence images with different depths were obtained. Figure 4-13(D) and 4-13(E) show images at four focal planes (10 μm, 10.5 μm, 13.5 μm, and 17 μm) beneath the skin. In these figures, while several microspheres are only seen in wide-field two-photon excited fluorescence images at 13.5 μm and 17 μm [Figure 4-13(E)], they are not easily seen in the one-photon excited fluorescence images at those focal planes [Figure 4-13(D)]. 70 Figure 4-13. Wide-field two-photon excited fluorescence imaging and multimode optical imaging with wide-field two-photon excitation of a nude mouse with implanted tumors after subcutaneous injection of corroles and miscrosphere solution: (A) Euthanized nude mouse. Corroles and miscrosphere solution were injected into breast tumor regions selected by a red solid circle and the subcutaneous of regions selected by a dotted green circle, respectively. (B) One-photon excited fluorescence image of the red circled regions. Light (wavelength: 425nm ± 50nm) filtered from a Hg lamp have been used for one-photon excitation. The bandpass of the emission filter was 620nm± 40nm. (C) Wide-field two-photon excited fluorescence image corrected by a Gaussian mask. 800nm fs laser light (250mW) was been utilized for two-photon excitation (Em: 620nm± 40nm). The field of view is approximately 75 μm. (D) One-photon excited fluorescence images at different depths. 10 μm, 10.5 μm, 13.5 μm, and 17 μm beneath the skin around the regions selected by the green dotted circle (E) Two-photon excited fluorescence image at different depths: 10 μm 10.5 μm, 13.5 μm, and 17 μm. (F) Spectral and fluorescence lifetime images with wide-field two-photon excitation. A total of 13 spectral images within a spectral range from 500nm to 610nm, with 10nm bandwidth and 10nm step size, were obtained. The images were analyzed using our developed software. Spectral signatures of fluorescence from microspheres and autofluorescence are shown in the graph at the bottom and left side. Also, a total of 22 images within 0~4.2ns with step size 0.2ns, have been obtained. The fluorescence lifetime image has been constructed using the first order exponential decay fitting method. The graph at the bottom and right side shows the time-decay curves of fluorescence and autofluorescence within the selected regions. (F) (A) (B) (C) (D) (E) Intensity (A.U) Wavelength (nm) Time (ns) Intensity (A.U) 0 1 1 0 0 2 4 500 620 560 Lifetime(ns) 0 3 2 1 Microsphere Autofluorescence Microsphere Autofluorescence 71 In addition, spectral and fluorescence lifetime imaging of the subcutaneous regions with wide-field two-photon excitation have been performed in order to separate the microspheres from autofluorescence. In Figure 4-13(F), the pseudo-color mapped images represent a spectral classification (top-left) and a fluorescence lifetime image (top-right), respectively. For the spectral classification, we selected two spectral signatures shown in the figure (bottom-left). In the spectral signatures, while the spectral signature of fluorescence from the microspheres has a peak at 560nm, the autofluorescence has a broad spectral signature. Also, Figure 4-13(F) (upper-left) shows the classified image based on the selected signatures. Here, a red color represents fluorescence of microspheres and a green color represents autofluorescence. In the fluorescence lifetime image, while fluorescence lifetime of microspheres is approximately 1.8~2.3ns, the fluorescence lifetime of autofluorescence is below 1.2ns. The graph (bottom-right) in Figure 4-13(F) shows that the intensity of fluorescence from microspheres and autofluorescence decays over a period of 0~4.2ns. The exponential decay coefficients of the fluorescence and autofluorescence display 2.15ns and 0.78ns. 4.1.6.2.4. Partial Summary In this section, we have shown the optical characteristics of wide-field two-photon excitation, usefulness of wide-field two-photon excitation in small animal imaging in vivo, and feasibility of non-scanning multimode instruments with the wide-field two-photon excitation. This excitation method can provide better contrast and more penetration depth than one-photon excited fluorescence imaging, and it is more resistant to artifacts due to animal movement than scanning two-photon excited fluorescence imaging, although the 72 confocal capability and contrast in wide-field two-photon excited fluorescence imaging is less than those in scanning two-photon excited fluorescence imaging. Figure 4-9(A), (B), and (C) show the optical characteristics of wide-field two-photon excitation. The plot of the fluorescence intensity versus wide-field two-photon excitation power increased with a slope of 2.01. This is in good agreement with typical power dependence of two-photon excitation. In addition, the excitation spectra of wide-field two-photon excitation of Alexafluor 568 are similar to those in the literature (Dickinson et al., 2003). These results demonstrate that wide-field two-photon excitation of fluorescence behaves like that using laser light focused to a diffraction-limited spot. Also, in the tissue phantom study for comparison of images acquired by wide-field two- photon, one-photon, and scanning two-photon excited fluorescence imaging, the full width at half maximum in the intensity profiles along a depth can indicate axial resolution and contrast. In Figure 4-10(E), wide-field two-photon excited fluorescence imaging provides better axial resolution and contrast than one-photon excited fluorescence imaging, though less than scanning two-photon excited fluorescence imaging. In addition, the field of view of wide-field two-photon excited fluorescence imaging was approximately 75 μm (40x). The size of the field of view can be enough to resolve several cell-level structures in tissues, simultaneously. Here, the field of view and sectioning capability depends on the L1 lens. The shorter focal length of L1 provides the larger field of view and the less sectioning capability. 73 Furthermore, in in vitro measurement of wide-field two-photon excited fluorescence imaging, the images obtained by wide-field two-photon excited fluorescence imaging show better contrast than one-photon excited fluorescence imaging. In addition, flat-field correction by Gaussian mask enhances the contrast in wide-field two-photon excited fluorescence imaging (Figure 4-11). Here, the Gaussian mask was generated from the original image rather than using a mathematical equation of a Gaussian beam as previously reported (Fittinghoff et al., 2000). This method is more adaptive and compensative than the mathematical method since it contains unknown distortions created inherently by an excitation beam and lenses. In Figure 4-12, several plaques are more clearly shown in wide-field two-photon excited fluorescence image than one- photon excited fluorescence image. Also, it is seen that the Gaussian correction enhances contrast in detection of plaques on the eye. Finally, wide-field two-photon excited fluorescence imaging could be very useful for small animal imaging in vivo compared to one-photon excited fluorescence imaging and scanning two-photon excited fluorescence imaging since this method can provide better contrast than one-photon excited fluorescence imaging as shown in Figure 4-13(C) as well as it is inherently faster than scanning two-photon excited fluorescence imaging and thus more robust to artifacts due to the movement of animals during in vivo experiments. Moreover, the combination of wide-field two-photon excitation and non-scanning spectral and fluorescence lifetime detection devices allows us to discriminate different molecules and enables acquisition of different and complementary information simultaneously at greater depths (Hanson et al., 2002; Rajwa et al., 2007). 74 However, wide-field two-photon excitation dose not preserve all the advantages of scanning two-photon excited fluorescence imaging. For example, the confocal capability will be reduced since the out of focus light does not decrease significantly as illumination with a more or less parallel geometry (Fittinghoff et al., 2000). Also, while the penetration depth of near infrared light is still greater than of visible light, image degradation due to photon migration (of emitted photons) may play a significant role when imaging at greater depths is performed. In addition, phototoxicity may become an issue due to longer exposures to an excitation laser. However, in spite of the disadvantages, the use of wide-field two-photon excited fluorescence imaging overcomes the inherent shortcomings of slow two-photon excited fluorescence imaging resulting from scanning to acquire images. In addition it allows us to investigate the molecules of interest in vivo in multi-mode optical imaging with non-scanning instruments. Here, the loss in image quality and penetration depth is a minor sacrifice compared to the gains made in the versatile use of wide-field two-photon excited fluorescence imaging for small animals in vivo, as this simple method can be employed in situations such as real-time imaging in vivo of small animals where two-photon microscopy was previously unavailable without complications. Also, the confocal capability can be improved via spinning disks or deconvolution methods. 75 4.2. The feasibility of multimode optical imaging in vivo for simultaneous complementary/different information The feasibility of multimode optical imaging for the acquisition of complementary/different information was here examined. For that, multimode optical imaging of a nude mouse has been performed after injecting 100 μM and 500 μM sulfonated gallium corroles, which are novel candidate chemotherapy molecules, into around the region of the tumor implanted into the back of the mouse and non-tumor region at the middle of the back respectively. And then, single-wavelength/single photon fluorescence intensity, spectral, lifetime, intra-vital confocal, one-, and two-photon excited fluorescence imaging have been performed sequentially. In the spectral imaging, a total of 18 images were obtained within a spectral range from 550nm to 710nm with a step size of 10nm and a bandwidth of 8nm. The images were analyzed with spectral signatures of the sulfonated gallium corrole fluorescence and autofluorescence by using our developed program. Figure 4-14(B) shows the spectral classification image. In the image, the corrole fluorescence is clearly distinguished from autofluorescence. Also, in the spectral signature of the corrole, the emission peak wavelength is approximately 620nm. On the other hand, the spectral signature of the regions classified as autofluorescence has a peak at 550nm, and it decreases along the wavelengths. In the mosaic fluorescence lifetime imaging of the nude mouse, fs pulsed laser light at 437.5nm with a beam diameter 3cm has been used for the excitation of the corroles inside the mouse. A 500nm longpass filter was used for the rejection of the excitation light from the emitted light. For large field of view, 3x4 scan has been performed with a scan step 76 size of 1cm. Also, a total of 39 images per each scan were acquired from 200ps to 7800ps with a time step, 200ps, and an exposure time, 640ms. And then, the decay of fluorescence in each pixel of the image was analyzed using a linear regression method to calculate fluorescence lifetime, and then the analyzed images were merged into a single image. Figure 4-14(C) shows the pseudocolor mapped image of the fluorescence lifetime. Figure 4-14. Multimode optical images of a mouse with corroles injected into an implanted tumor region and the middle of back: (A) Single-wavelength/photon fluorescence intensity image (620nm) (B) Spectral classification image. While Green represents corrole, red and blue represents autofluorescence. Each spectral signature of the selected regions is generated. (C) Mosaic fluorescence lifetime image. After the 12 images were cropped appropriately, the cropped images were reconstructed into a single image using ImageJ. The fluorescence lifetime image was then obtained by applying Gaussian filtering to the boundary between adjacent images. (D) One-photon excited fluorescence image. This image obtained using a Nikon microscope objectives (40x) dose not provide any information. (E) Two-photon excited fluorescence image. The laser source at 830nm was used for the excitation of corrole. The image was recorded in CCD through a bandpass filter (620nm). The top image is the Gaussian corrected image, and the bottom image is the original image. This figure shows cell type structures. (F) Intra-vital confocal image. The figure shows irregular patterns of subcutaneous tumors. ` (B) Multimode (C) 50 μm (D) 50 μm (E) (A) (F) 100 μm 2.0 2.5 3.0 3.5 Lifetime(ns) 77 In Figure 4-14(C), the fluorescence lifetime of corrole was calculated to be 2.5~2.8ns, but that of autofluorescence over 3.5ns. The value was little higher than in the ex vivo test. In addition, after the mouse was euthanized, intra-vital confocal, one- [Figure 4-14(D)], and two-photon excited fluorescence imaging was performed for more details around tumor regions. The intra-vital confocal image [Figure 4-14(F)] obtained through the imaging probe, which is inserted inside a skin of the mouse, shows a subcutaneous tumor under the skin. Also, the wide-field two-photon excited fluorescence image of the tumor region is shown in Figure 4-14(E). More detailed structures of cells are shown in the two-photon image (compared to one-photon image). 4.3. Multimode optical imaging in chemotherapy assessment and cancer detection Previously we evaluated each imaging mode and described the functionalities of each mode. In this section, a synergetic combination of multiple imaging modes is applied to chemotherapy assessment and cancer detection in order to exploit the combined advantages of fluorescence, spectral, high sensitivity lifetime, two-photon excited fluorescence, and intra-vital confocal detection of small animals in vivo. Here, multimode optical imaging is performed for dynamic monitoring of nanocnstruct distribution in vivo and the drug effects on specific organs. In addition, as another application of our multimode optical imaging system, the power of combining advanced optical imaging technologies (fluorescence intensity, spectral, lifetime, and two-photon excited fluorescence imaging) is brought to cancer detection and treatment assessment of the targeted gallium corrole (HerGa), a novel single self- 78 assembled complex comprised of a targeted cell penetration protein and a sulfonated corrole. 4.3.1. Multimode optical imaging in nanoconstruct therapy research for dynamic and quantitative monitoring in vivo In this section, as one application of our novel multimode optical imaging system, we evaluated the targeting capability and effect of the nanoconstruct using fluorescence intensity and spectral imaging mode with spectral unmixing in the system. Here, nude mice received subcutaneous injections of MDA-MB 468 human breast cancer cells into the right posterior mid were prepared. The nanoconstruct at concentration of 2.5 mg/kg was injected via the tail vein. Then, using fluorescence intensity imaging mode, we monitored the nanoconstruct distributions at different time points, 0–360 min up to 24 h. We also performed spectral imaging of the nude mouse in order to quantitatively examine the clearance of the nanoconstructs from the mice simultaneously. Finally, we evaluated the extent and topology of nanoconstruct distribution into specific organs and the tumor using spectral unmixing and spectral image analysis. 4.3.1.1. Fluorescence imaging for dynamic monitoring of nanoconstruct accumulation in vivo We monitored a nanoconstruct distribution in a nude mouse with breast tumors after injection of the nanoconstructs conjugated with Alexa680 into a tail vein. Here, we utilized a 670nm diode laser which is installed inside the system, and a longpass and a 79 bandpass filter (>700nm and 719nm ±7nm) were used for light selection. Figure 4-15 shows the nanoconstruct accumulation kinetics in the mouse at different time points. In the figure, the fluorescence intensity in the tumor region seems to increase significantly soon after nanoconstruct injection (within 30 minutes). This increase is followed by a slow decrease in the overall fluorescence, due to elimination of the nanoconstructs from the organism, and apparent preferential accumulation within the tumor region at later time periods, as seen in Figure 4-15. Figure 4-15. Fluorescence intensity changes within tumor region and background: Changes of drug nanoconstruct accumulation over the mouse were monitored at different time points. 4.3.1.2. Spectral imaging for clearance examination of the drug nanoconstruct In addition, we have obtained spectral images at different time points for quantitative examination of clearance of the nanoconstructs from the whole mouse. For the spectral classification, the spectral signatures of autofluorescence and Alexafluor 680 fluorescence were acquired before and after nanoconstruct injection respectively. With 300 Tumor 0h 10min 30min 1h 3h 6h 0 150 Counts 24h After perfusion 80 the spectral signatures, the images were analyzed at 24 hours after injection and after perfusion. Figure 4-16(B) and (C) show the spectral classification of the images obtained at 24 hour and after perfusion. In the images, the red pseudocolor represents nanoconstruct. The figures display that the nanoconstructs are still accumulated over whole area of the mouse at those time points. Figure 4-16. Clearance examination of the drug nanoconstruct using spectral imaging: (A) Spectral signatures of autofluorescence and fluorescence of Alexafluor 680. (B) Spectral classification image (after 24 hour). (C) Spectral classification image (after perfusion). Red pseudocolor represents Alexafluor 680 labeled with nanoconstruct. (B) (A) 700 720 740 760 780 800 -10 0 10 20 30 40 50 60 70 AF Alexa 680 Wavelength(nm) Fl. Intensity (A.U) Before injection (B) (C) 1h 81 4.3.1.3. Spectral imaging with spectral unmixing for quantitative analysis of nanoconstruct distribution into specific organs Finally, we evaluated the extent and topology of nanoconstruct distribution into specific organs and the tumor itself using spectral unmixing and spectral image analysis. Figure 4- 17 shows that the tumor, normal breast, liver, kidney and spleen have been classified and identified from other organs by spectral unmixing and spectral image analysis. Figure 4- 17(A) represents fluorescence images at wavelength 733nm, and Figure 4-17(B) and (C) shows the classified image by spectral unmixing and spectral image analysis (root sum of square error method), respectively. Figure 4-17. Quantitative images of organs: (A) Fluorescence of excised organs. (B) Image obtained by spectral subtraction. Red color represents the distribution of nanoconstruct (Alexafluor 680). (C) Image obtained by spectral classification. While red color represents the distribution of nanoconstruct (Alexafluor 680), green color represents autofluorescence. 4.3.2. Multi-mode optical imaging for chemotherapy assessment of HerGa In this section, as another application of our multimode optical imaging system, the power of combining advanced optical imaging technologies (fluorescence intensity, spectral, lifetime, and two-photon excited fluorescence imaging) is brought to cancer Normal breast Liver Kidney Spleen Lung Heart Brain Tumor (A) (B) (C) 82 detection and treatment assessment of HerGa, a novel single self-assembled complex comprised of a targeted cell penetration protein and a sulfonated corrole. Basic optical characteristics (fluorescence lifetime and two-photon excitation wavelength) of S2Ga are here investigated. Particularly, the characteristics of fluorescence lifetime of the corroles are underlined. Also, the fluorescence lifetime changes of HerGa and S2Ga due to the cancer cell endocytosis are monitored in real-time. In addition, after adding corroles to breast cancer cells cultured in a temperature controlled ΔT chamber (Bioptechs), mitochondrial membrane potential variations in the breast cancer cells, which indicate the health status of the cells, are monitored in order to investigate the mechanism of action of the corroles in cells. In addition, multimode optical imaging is performed to assess the capabilities of HerGa in breast cancer chemotherapy in vivo. Finally, the feasibility of multimode optical imaging of HerGa in cancer detection and delineation is examined. 4.3.2.1. Optical characteristics of S2Ga 4.3.2.1.1. Concentration independence of fluorescence lifetime of S2Ga We investigated the effect of concentration on fluorescence lifetime of the S2Ga. After adding corrole solutions (pH 7.5) with different concentrations (50 μM 125 μM, 250μM, and 500 μM) into the ∆T chamber, the fluorescence lifetime of the corrole solutions was measured five times, respectively. Figure 4-18 shows fluorescence lifetimes of the corrole solutions with different concentrations. In the figure, the fluorescence lifetimes of the corrole solutions display from 1825 to 1846ps at room temperature. There is no significant change of fluorescence lifetime due to the solution concentrations. 83 0 100 200 300 400 500 1700 1750 1800 1850 1900 Tau (ps) Concentration(uM) Figure 4-18. Concentration dependence of fluorescence lifetime of 50 μM S2Ga in pH 7.5 PBS (room temperature). 4.3.2.1.2. pH dependence of fluorescence lifetime of S2Ga Fluorescence lifetime of corroles over a range of pH was measured (Figure 4-19). Each measurement was repeated five times. The fluorescence lifetime of corroles decreased as pH increased. The fluorescence lifetime of the corroles was 2.82~2.83ns at pH 5.0, and decreased to 1.83~1.85ns at pH 7.5, at room temperature (24.5°C). Conversely, while the trend is the same, at 36.5°C the fluorescence lifetime of the corroles at pH 5.0 was 2.69~2.72ns, and at pH 7.5 it was 1.82ns. Figure 4-19. pH dependence of fluorescence lifetime of S2Ga at different temperatures 7.0 7.5 0 5.0 5.5 6.0 6.5 Lifetime (ns) 3.0 2.0 1.0 5.05.5 6.06.5 7.07.5 1800 2000 2200 2400 2600 2800 3000 24.5 0 36.5 0 Tau (ps) pH 84 4.3.2.1.3. Temperature dependence of fluorescence lifetime of S2Ga The effect of temperature on fluorescence lifetime of the corrole was further examined. Temperature was maintained by a ∆T culture dish system. After adding 50 μM corrole with different pH into the ∆T chamber, fluorescence lifetimes of the corroles were measured five times at 24.5ºC, 36.5ºC, and again 24.5ºC, sequentially. Figure 4-20 shows fluorescence lifetimes of corroles with different pH at the respective temperatures. In the figure, the fluorescence lifetimes of corroles become somewhat decreased at 36.5ºC. However, while these changes of lifetime are greater than 100ps at pH 6.0 and below, their changes are less than 50ps at pH greater than 6.5. 1500 2000 2500 3000 RT : room temperature(24.5 0 C) PT : physiological temperature(36.5 0 C) pH 5.0 pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 RT RT Tau (ps) PT Figure 4-20. Fluorescence lifetime changes of S2Ga due to temperature changes. 4.3.2.1.4. Wide-field two-photon excitation wavelength of S2Ga Wide-field two-photon excitation wavelength of the S2Ga was examined. 50 μMol S2Ga solution was prepared for investigation of the wide-field two-photon excitation wavelength. 85 780 800 820 840 860 880 500 1000 1500 2000 Wavelength (nm) Fl. Intensity (A.U) Figure 4-21. Fluorescence of corrole (50uM) versus excitation wavelength (power : 300mW, emission : 620nm) The experimental setup was identical with the setup we described in Figure 3-12(A). A 40x objective was used for fluorescence detection. A 700nm shortpass and 620±60nm bandpass filter were utilized for light selection. The excitation wavelength was controlled from 770nm to 870nm with a step size of 5nm. The light power was 300mW. Figure 4-21 shows fluorescence emitted from corrole versus excitation wavelength. The fluorescence intensity has two peaks, 815nm and 845nm. 4.3.2.2. Fluorescence lifetime imaging of HerGa and S2Ga on human cancer cells 4.3.2.2.1. Fluorescence lifetime change of S2Ga by breast cancer cell endocytosis Fluorescence lifetimes of S2Ga in the presence of human breast cancer cells (MDA-MB- 435) were monitored for 60 minutes. Figure 4-22 shows the fluorescence lifetime changes of corrole on the breast cancer cells. In the more detailed fluorescence image from the selected regions, corroles seem to be accumulated onto cell membranes. After 2 minutes, the average fluorescence lifetime of corroles on the cells is approximately 86 1.82ns. However, the fluorescence lifetimes on the cells go down to approximately 0.75ns in 15 minutes. And then, the fluorescence lifetimes on the cells increase somewhat after 20 minutes, and the values reach approximately 0.85ns after 60 minutes. Figure 4-22. Fluorescence lifetime changes of 50 μM S2Ga (at pH 7.5 in PBS) on breast cancer cells (MDA-MB-435) at 36.5ºC. 4.3.2.2.2. Fluorescence lifetime change of HerGa by glioma cell endocytosis Fluorescence lifetime of HerGa by U251 glioma cell endocytosis was monitored at different time points. A ∆T chamber was here used for maintaining temperature of the cells. After adding 150 μM HerGa into the chamber, the fluorescence lifetime image was acquired at 0 minutes. After the temperature in the chamber was controlled to 37ºC, fluorescence lifetime images were acquired continuously every 10 minutes. Figure 4-23 shows the fluorescence lifetime changes of HerGa on the glioma cells. At 0 minutes, the average fluorescence lifetime of corroles on the cells was approximately 1.5ns. However, the fluorescence lifetime increased to approximately 1.9ns after 60 minutes. Lifetime (ns) 3.0 2.0 1.0 0 2min 5min 7min 10min 10min 15min 20min 30min 40min 50min 60min 87 Figure 4-23. Fluorescence lifetime changes of HerGa due to glioma cancer cell endocytosis. 4.3.2.3. Mitochondrial membrane potential variations of a breast cancer cells induced by HerGa For this experiment, the breast cancer cells cultured on the cover slip were placed into a temperature control chamber. Then, after 200ml of 20nM TMRM solution was added into the chamber, fluorescence intensity images of breast cancer cells were recorded every two minutes for 50 minutes using the Cy3 filter set for TMRM. Figure 4-24(A) shows the TMRM distributions onto the cancer cells at different time points. Here, only representative images are shown. In the figure, TMRM fluorescence intensity on the cells increased over time and reached the equilibrium distribution after about 45 minutes. However, the fluorescence intensity of TMRM was somewhat reduced at 50 minutes. Then 200ml of 2 μM HerGa was added into the chamber and the TMRM fluorescence variations (mitochondrial membrane potential variations) were monitored in the breast 0min 10min 20min 30min 40min 50min Lifetime (ns) 60min 0.5 1.0 1.5 2.0 2.5 - ∆T chamber (temperature control) - 150μM Her-corrole - 40x air objective 88 cancer cells due to HerGa. Figure 4-24(B) shows the TMRM fluorescence variations at different time points after adding HerGa. The fluorescence intensity of TMRM was dramatically decreased soon after adding HerGa. After 10min, TMRM fluorescence from the cancer cells almost disappeared. However, after 50minutes, the accumulation of HerGa onto the cancer cells was clearly shown instead of TMRM. In order to confirm the HerGa accumulation, a HerGa fluorescence image was obtained using the HerGa filter set, then this image was overlaid with the image obtained using the filter set for TMRM at 50 minutes. Figure 4-24(D) shows the overlaid image. In the overlaid image, the two channels are almost matched. Figure 4-24(C) shows a differential interference contrast (DIC) image. Figure 4-24. Measurement of mitochondrial membrane potential of the breast cancer cells using TMRM fluorescence imaging: (A) TMRM distribution on the cancer cells at different time points. (B) TMRM fluorescence intensity variation induced by HerGa and HerGa accumulation on the cells. (C) DIC image. (D) Overlaid of HerGa channel image and TMRM channel image at 50min. 0 min 30 min 50 min 30s 2 min 5 min 10 min 50 min 25 min (A) (B) (C) (D) 89 In this section, the mechanism of the effect of HerGa on breast cancel cells was investigated by monitoring TMRM fluorescence. Also, the kinetics and effects of HerGa on the cancer cells were monitored. HerGa causes dysfunction of mitochondria in breast cancer cells, and thus results in an abrupt decrease in mitochondrial membrane potential [Figure 4-24(A)]. This dysfunction of mitochondria may cause the breast cancer apoptosis. Cancer cells treated by HerGa exhibited membrane disruptions after 24 hours (Agadjanian et al., 2009; Agadjanian et al., 2006). In Figure 4-24(B), even though the TMRM filter set was used for selection of the fluorescence emission, the HerGa accumulations were visible since the absorption wavelength of HerGa overlaps the excitation wavelength for TMRM. This HerGa fluorescence leakage may introduce error into measurements of TMRM fluorescence. Therefore, it is necessary to carry out additional imaging analysis in order to discriminate between TMRM and HerGa fluorescence. Spectral imaging with spectral unmixing is a powerful method for distinguishing the molecules. This approach may offer more accurate measurement of TMRM fluorescence in the presence of HerGa. 4.3.2.4. Fluorescence intensity imaging for dynamic monitoring of corrole distribution in vivo The capability to detect corrole fluorescence in vivo provides not only the potential to detect tumors, but also to track tumor targeted therapy in a live specimen. Here we examine HerGa targeting capability in female nude mice bearing human HER2+ tumors. After a single injection of either HerGa or S2Ga alone into the tail vein of the mice, the 90 mice were monitored in real time using fluorescence intensity imaging. In Figure 4-25(A), whereas S2Ga fluorescence exhibited a broad systemic distribution throughout most of the mouse and appeared to be excluded from the tumors, HerGa showed a preferential accumulation in the tumors and a much lower distribution to extratumoral areas compared to free S2Ga. A high fluorescence signal in the tail region of both mice resulted from some material inadvertently becoming deposited in the tail muscle flanking the site of injection. Figure 4-25. HerGa distribution in nude mice: (A) Live animal imaging of corrole fluorescence after IV delivery of either free corrole or targeted complex. Nude mice bearing human HER2+ tumors (~300 cubic mm) received a single IV injection of either S2Ga or HerGa (15 nmoles with respect to corrole dose) and were imaged at 2.5h post-injection using fluorescence intensity imaging. Schematic to the left indicates the whole body and tumor orientation of the mice in the fluorescent images. (B) Fluorescence intensity images of the mouse at different time points after receiving HerGa as described. Arrows in both A and B point to tumors. Corrole fluorescence is indicated by blue-red pseudocoloring with fluorescence intensity represented according to the color bar on the right (Agadjanian et al., 2009). (A) Minutes after injection 2 4 8 10 15 30 60 120 S2Ga HerGa 100 Color Bar Min=0 Max=100 0 (B) 91 Images acquired at sequential time points in real time after tail vein injection show that HerGa accumulates rapidly at tumor sites within minutes of administration [Figure 4- 25(B)]. 4.3.2.5. Dynamic monitoring of HerGa accumulation kinetics using spectral imaging with a ratiometric analysis method In this section, the spectral imaging with a ratiometric analysis method is adopted to the dynamic and kinetic monitoring of chemotherapy drug molecules in vivo. Particularly, HerGa accumulation kinetics in the nude mouse received IV injection of HerGa was investigated by using spectral imaging with a ratiometric analysis method at different time points. Four reference spectral signatures for ratiometric spectral classification were constructed by composite spectra with varying ratios of spectra of autofluorescence and HerGa fluorescence. Spectral classification method based on Euclidean distance measure was here utilized rather than the spectral waveform cross-correlation analysis. After intravenous injection of HerGa (46nM), consecutive images of the mouse within spectral ranges from 500nm to 680nm with a step size of 20nm were acquired at different time points. Then, the images were analyzed using ratiometric spectral classification method. In addition, the images were classified using typical spectral analysis method for comparison between the ratiometric and the typical analysis method. Figure 4-26(B) shows the reference spectral signatures for ratiometric spectral classification. In the spectral signatures, the ratios represent the relative concentrations of 92 aufluorescence and HerGa fluorescence. The green pseudocolor (ratio: 1:0) represents pure autofluoresence (A.F), which has a peak at 500nm. The spectral signature displayed with a yellow color shows that the ratio of intensities at 500nm and 620nm is 1 to 1. Also, the blue pseudocolor spectral signature has the 0.5:1 ratio of intensities at those wavelengths. The fluorescence spectral signature of highly concentrated HerGa in the mouse is represented by a red pseudocolor and has 0:1 ratio at those wavelengths. Figure 4-26. Quantitative examination of HerGa accumulation kinetics and clearance from a mouse at different time points using spectral imaging with ratiometric and standard spectral classification: (A) Ratiometric spectral classification. (B) Spectral structures with varying ratios of spectra of autofluorescence and HerGa fluorescence. (C) Typical spectral classification. (D) Spectral signatures of Her-Ga corroles and autofluorescence. These spectral signatures are extracted from a pre-constructed signature library which was created from the mouse with IT injection. Figure 4-26(A) shows the spectral classification images obtained using the ratiometric analysis method at the different time points. At 3 minutes, tumor regions (a red pseudocolor) are clearly distinguished from the non-tumor regions due to higher 3min 10min 40min 1 st day th 500 550 600 650 700 -100 0 100 200 300 400 500 600 700 Autofluorescence Her-corrole Wavelength(nm) Fl. Intensity (I) 4 th 3min 10min 40min 1 st day (A) (C) (B) 500 550 600 650 700 -100 0 100 200 300 400 500 600 700 A.F HerGa 1 0 1 1 0.5 1 0 1 Wavelength(nm) Fl. Intensity (A.U) (D) 93 concentrated accumulation of HerGa in tumors than other regions. After 3 minutes, while the regions classified as the blue pseudocolor (A.F: 0.5 and HerGa: 1) continue to shrink, the regions classified as the red pseudocolor (A.F: 0 and HerGa: 1) is expanded over the mouse. After 40 minutes, almost entire regions of the mouse are classified as the red pseudocolor (A.F: 0 and HerGa: 1). Also, the entire regions of the mouse are still classified as the red pseudocolor (A.F: 0 and HerGa: 1) after 1 day. Figure 4-26(C) and (D) shows the spectral classification images obtained using a typical spectral analysis method at the different time points and the reference spectral signatures for the spectral classification respectively. In the images [Figure 4-26(C)], while the green pseudocolor represents autofluorescence, the red pseudocolor represents HerGa. At 3 minutes, the regions classified as HerGa are considerably distributed over the mouse. Thus, the tumor regions are not discriminated from the non-tumor regions due to the HerGa accumulation. The almost entire regions of the mouse are classified as the red pseudocolor (HerGa) after 3 minutes. 4.3.2.6. Fluorescence lifetime imaging (FLIM) of tumors and surrounding tissue In addition, FLIM of the same mouse used in spectral imaging at 4 days has been performed in order to monitor acidity in tissues adjacent to tumors. Figure 4-27(A) shows the fluorescence lifetime image of a mouse. In the figure, fluorescence lifetimes around tumor regions are somewhat higher than non-tumor regions. Also, the lifetime histograms [Figure 4-27(B)] show that fluorescence lifetime of non- tumor regions has a peak around 1800ps, while fluorescence lifetime of tumor regions 94 have multiple peaks (1800ps and 2000ps) with a greater proportion over 2000ps than in the non-tumor tissue. Figure 4-27. Fluorescence lifetime image and a lifetime histogram of the mouse at 4 day: (A) Fluorescence lifetime image. For fluorescence lifetime imaging, a total of 25 images have been acquired within 0 to 4800ps with a time step, 200ps. And then, fluorescence lifetime image has been constructed through a first-order exponential decay fit. (B) Lifetime histograms within the tumor and non-tumor regions selected by a solid and a dotted circle. 4.3.2.7. Multimode optical imaging for HerGa distribution into specific organs Finally, we evaluated the extent and topology of HerGa distribution into specific organs and the tumor itself using fluorescence intensity, spectral, lifetime, and two-photon excited fluorescence imaging. Figure 4-28(A) shows the fluorescence intensity image at 620nm. In the figure, HerGa are preferentially accumulated in tumors compared to other organs, and the fluorescence signal from tumors is significantly higher than that from liver. Figure 4-28(B) shows the spectral classification image by the same spectral signatures we used previously. In the image, while tumor and liver are clearly classified Tumor Lifetime (ns) 0 1.0 2.0 3.0 Lifetime (ns) 2.8 2.4 2.0 1.6 1.2 15 30 0 20 40 0 Tumor Normal (A) (B) 95 as HerGa fluorescence, most areas of other organs are classified as autofluorescence. Figure 4-28(C) shows fluorescence lifetime images of the tumors and the liver. Here, other organs except for the tumors and the liver are not considered for analysis since the signal from them is too low. In Figure 4-28(C), fluorescence lifetime values of HerGa accumulated in tumors are significantly higher than that in liver. In addition, the histograms of fluorescence lifetime in tumors and liver were constructed for detailed examination [Figure 4-28(C)]. The fluorescence lifetimes of HerGa in tumor are populated at higher lifetimes than those in liver. Figure 4-28. Specific organs and tumor images obtained using multi-mode optical imaging: (A) Fluorescence intensity image of organs and tumors. The fluorescence intensity image at 620nm was overlaid with a photographic image. (B) Spectral classified image. A total of 10 images were recorded within the spectral range of 500nm to 680nm with a step size of 20nm. And then, the images were analyzed using software we developed. While the red color represents Her-Ga corrole fluorescence, the green color represents autofluorescence. (C) Fluorescence lifetime images of tumors and liver and the fluorescence lifetime histograms. (D) Two-photon excited fluorescence images of specific organs of interest. These two-photon excited fluorescence images of liver, tumor, and muscle were acquired using excitation pulsed laser at 848nm, an emission filter with 620nm±60nm, Nikon 60x objective. Muscle Tumor Liver Heart Lung Spleen Kidney Tumor Liver Kidney Spleen Heart Muscle Lung Tumor Liver 2.5 2.0 1.5 1.0 0 15 30 0 20 40 Lifetime (ns) Lifetime (ns) Tumor Liver 0 1.0 2.0 3.0 Liver Tumor Muscle 50μm (A) (B) (C) (D) 50μm 50μm 96 Finally, we performed two-photon excited fluorescence imaging of tumor, liver, and muscle in order to examine them in detail with high resolution. Figure 4-28(D) shows the two-photon excited fluorescence images of each organ. In the figure, we can clearly see the bright cellular structures in liver and tumor due to HerGa accumulations compared to the muscle. 4.3.3. Feasibility of multimode optical imaging of HerGa for cancer detection and delineation In Figure 4-28, the fluorescence lifetimes of HerGa are different in the breast tumor and liver. The result opens the possibility of using HerGa in caner detection and delineation. Thus we performed multimode optical imaging of a nude mouse for cancer detection and delineation, after injection of HerGa into tumors and non-tumor regions. Before doing multimode optical imaging, the injected regions were exposed after euthanizing the mouse. Fluorescence intensity, spectral, lifetime, and two-photon excited fluorescence imaging were performed sequentially. Figure 4-29(A) shows a white-light photographic image of a mouse which indicates injected regions (1. tumor, 2. tumor, and 3. muscle). In Figure 4-29(B), fluorescence intensities around injected regions are much higher in other regions. However, a small quantity of HerGa fluorescence is shown around the non-injected regions (particularly between injected areas) because of the circulation of HerGa [Figure 4-29(B)]. Figure 4- 29(C) shows the spectral classification image with the spectral signatures [Figure 4- 29(E)]. In the figure, HerGa injection regions are delineated clearly and quantitatively. 97 Here, injected regions are classified as a red pseudocolor which indicates highly accumulated concentrated accumulation of HerGa. Figure 4-29. Multimode optical imaging for cancer detection and delineation of HerGa: (A) HerGa injection area, 1. tumor, 2. muscle, and 3. tumor. (B) Fluorescence intensity image. (D) Spectral classified image. (D) Fluorescence lifetime image. (F) Spectral signatures for spectral classification. (E) Two-photon excited fluorescence images of tumor regions (1). In Figure 4-29(D), the fluorescence lifetime image of the same mouse is shown. In the figure, the fluorescence lifetimes of HerGa are different in between tumor and non-tumor regions (muscles). The fluorescence lifetimes (1.9-2.2ns) of HerGa in tumors (region 1 and 2) are higher than those (1.2-1.5ns) in the muscles (region 3). Here, it implies that the surroundings around tumors may be more acidic than those in muscles. Furthermore, tumor regions indicated by a white dotted circle were examined in detail using scanning two-photon excited fluorescence imaging. Two-photon excited fluorescence images have 1 2 3 500 550 600 650 700 -100 0 100 200 300 400 500 600 700 A.F HerGa 1 0 1 1 0.5 1 0 1 Wavelength(nm) Fl. Intensity (A.U) 50μm 50μm Lifetime (ns) 0 1.0 2.0 3.0 Tumor Normal 1 2 3 (D) (C) (B) (A) (E) (F) 98 been acquired at different depths (0-200 μm, step size: 20 μm). The images at 20 μm and 40 μm are only shown in Figure 4-29(F). In the figures, the irregular HerGa accumulations onto the tumor are shown with high magnification and resolution. 99 CHAPTER 5 CONCLUSIONS AND DISCUSSION The thesis research described above validates the design and functionality of our multimode imaging system, and shows that the system can be optimized for various applications, needing the performance of each individual imaging mode (Table 8-1) and the synergetic possibilities enabled by the multimode approach (Table 8-2), as illustrated by some of the results above. Imaging mode Application capability *Fluorescence intensity Identification of the relative accumulated concentration and discrimination of two fluorophores (Alexafluor 680 and Rhodamine 123) conjugated to drug molecules in a nude mouse Kinetics/ Dynamics Spectral imaging Quantitative discrimination of fluorophores (fluorescein and corroles) from autofluorescence in the nude mouse Quantization Fluorescence lifetime Monitoring of functional status in the vicinity of fluorophores and quantitative discrimination of fluorophores (fluorescein or corroles) Environment/ quantization Intravital confocal Observation of micro-structures such as skeletal muscle fibers or vessels without a biopsy or staining High resolution /magnification Scanning/Wide- field two-photon excited High magnification and resolution observations of intact tissues (tumor regions) inside the small animals and ex vivo specimens (tumors, livers, and an eyeballs of Alzheimer’s disease mouse) High resolution /magnification Bioluminescence Detection of ATP and enzymatic activity in engineered nude mice High sensitivity Table 8-1. Application and capabilities of each imaging mode in the multimode optical imaging system. 100 Application Combined Imaging mode Information Nanoconstruct therapy *Fluorescence intensity/spectral imaging 1. Dynamic monitoring of nanoconstruct distributions and examination of tumor targeting capability 2. Examination of nanoconstruct clearance 3. Nanconstruct effects onto specific organs HerGa chemotherapy *Fluorescence intensity/spectral/lifet ime/scanning two- photon excited fluorescence imaging 1. Dynamic monitoring of HerGa and S2Ga distributions and examination of tumor targeting capability 2. Examination of HerGa accumulation kinetics 3. Mechanism of HerGa and environmental information of tumors 4. HerGa effects onto specific organs 5. Usefulness of HerGa in tumor detection during Surgery operation * Fluorescence intensity imaging: single wavelength, time averaged, wide-field, single photon fluorescence imaging. Table 8-2. Multimode optical imaging in assessment of nanoconstructs and HerGa chemotherapy. In Figure 4-1, two fluoropores, conjugated with the drug molecules, were clearly distinguished, and the relative accumulated concentrations could be measured from the fluorescence intensity image. The drug molecules were originally constructed to examine whether proteins target tumors and whether liposomes and proteins play a specific role. In the examinations, fluorescence intensity imaging for the discrimination between Rhodamine B labeled liposomes and Alexafluor 680 labeled proteins showed the relative accumulated concentration and the distributions of each molecule in vivo. Also, narrow and versatile bandwidth filter selection (by AOTF) and highly sensitive CCD detection provided better contrast than the leading company’s imaging systems (Figure 4-2). 101 In our multimode imaging system, spectral capability enhances quantitation ability in small animal imaging, as shown in Figure 4-3. In the image, the regions where fluorescein is accumulated are clearly delineated by the spectral classification. This imaging capability can correctly measure compensated fluorescence intensity under severe background noise conditions (autofluorescence or other fluorophores). In addition, we have incorporated FLIM capability into our system. Using this mode, we could also discriminate fluorescein from autofluorescence in the nude mouse, thus were able to confirm the usefulness of this mode for investigating tissues quantitatively (Figure 4-5). Furthermore, this imaging mode can be used for monitoring functional status in the vicinity of fluorophores (Hanson et al., 2002; Kocisova et al., 2004; Lin et al., 1999). Intra-vital confocal imaging has demonstrated its usefulness for detecting structural and functional (including pathological) information in multimode optical imaging (Figure 4- 6). Micro-structures such as skeletal muscle fibers or vessels were observed using this imaging mode without a biopsy. Thus, the convergence of modes allows us to monitor intact tissues inside small animals with high magnification and resolution. Scanning/Wide-field two-photon excited fluorescence imaging mode also enables high magnification and resolution observations of intact tissues inside the small animals. In particular, the feasibility of wide-field (non-scanned) two-photon excited fluorescence imaging was underlined as another potential microscopic imaging mode in multimode optical imaging in vivo. This excitation method can provide better contrast and more penetration depth than one-photon excited fluorescence imaging. When compared to 102 scanned two photon excitation, WTEF imaging has the advantage of reducing the artifacts due to the movement of animals during in vivo image acquisition since it has a capability to image rapidly, in real time. Thus, it is highly useful for in vivo imaging even though its resolution is less than that of confocal or scanning two-photon excited fluorescence imaging. Furthermore, the combination of this method and non-scanning spectral or fluorescence lifetime detection allows us to discriminate different chromophores at deeper locations in multimode optical imaging. In addition, in bioluminescence imaging mode, we obtained the results similar to those obtained with commercially available systems. This modality, combined with fluorescence measurements, could allow bioluminescence resonance transfer (BRET) studies. The usefulness and feasibility of the multimode optical imaging were validated with corroles as another chemotherapy molecule of interest and promise. In Figure 4-14 (A), the fluorescence intensity image shows the relative concentration of corroles in regions of interest. However, the boundary of fluorescence regions of corroles is not clear since autofluorescence intensity from the regions around the right leg is similar to that around corrole injected regions. However, in the mosaic fluorescence lifetime image and the spectral image [Figure 4-14(B) and (C)], corrole regions are clearly and quantitatively distinguished. Here, the mosaic fluorescence lifetime image and the spectral image are complementary each other. Although the regions classified as fluorescence of corrole in the fluorescence lifetime image were almost identical with the regions classified by spectral imaging and analysis, the regions classified as autofluorescence (blue) in a 103 spectral imaging are not shown in the fluorescence lifetime image, and yellow pseudo colored regions in the fluorescence lifetime image are not shown in the spectral image. Thus, this result suggests that the combination of fluorescence lifetime/spectral multiple imaging can complementarily provide better image contrast than single mode imaging. Moreover, the intra-vital confocal or two-photon excited fluorescence imaging has the capabilities to resolve micro structures of intact tissue without any movements, a time- consuming, specimen-altering pathological staining, and sectioning processes. These abilities of multimode optical imaging can be used for monitoring kinetics of dynamic processes around the regions of interest in small animals with high resolution, in real time. In the nanoconstruct study, we could monitor the dynamic accumulation of nanoconstructs over the whole mouse in real time. The nanoconstructs were preferentially accumulated in the tumor region compared to other regions (Figure 4-15). In addition, spectral imaging provided more accurate nanoconstruct clearance information than fluorescence intensity imaging at one wavelength (Figure 4-16). Furthermore, we evaluated the accumulation of the nanoconstructs into different organs excised from the mouse using spectral image analysis and spectral unmixing method (this analysis has been done with significant help from Erik Lindsley at CSMC). This analysis confirmed the fact that the nanoconstructs accumulate specifically in the tumor region and excreting organs like spleen, liver, and kidney, but not in the brain, lung, and heart. In Figure 4-17, the image analyzed by a spectral unmixing method is similar to that using spectral image analysis. However, in the spleen, the spectral unmixing method provides the larger area classification of fluorescence than the spectral imaging analysis. The reason for this may 104 be either the characteristics that the spectral unmixing is based on the ratio subtraction of intensity (thus it relies on the intensity) or the possibility that the autofluorescence spectra from organs can be different from the reference spectra of autofluorescence. Here, the fluorescence intensity and spectral imaging allows monitoring of drug kinetic/dynamic accumulation in the nude mouse in real time and to quantitatively evaluate drug effects on organs. Also, multimode optical imaging (fluorescence intensity, spectral, fluorescence lifetime, and scanning two-photon imaging) is useful for the assessment of HerGa, chemotherapy molecules of promise and interest. The sulfonated gallium corroles are intensely fluorescent macrocyclic compounds that spontaneously assemble with carrier proteins to undergo cell entry. They have a variety of advantages in optical characteristics as chemotherapy molecules. They emit very bright fluorescence with near-infrared wavelength. The fluorescence lifetimes of the corroles depend on pH (Figure 4-19) and are less dependent on their concentration (Figure 4-18). Here, the fluorescence lifetime imaging of the corroles could be utilized for the acquisition of functional/environmental information. Particularly, in Figure 4-22 and 4-23, the temporal and spatial variations of the fluorescence lifetime of S2Ga and HerGa on the cancer cells by endocytosis/drug effects are clearly shown. Here, we could confirm that S2Ga can not be easily internalized inside the cells, but HerGa can be internalized through receptor-mediated endocytosis. Also, the results may suggest that the internal environment of cancer cells is more acidic than cell membranes since fluorescence lifetime of corroles is higher at low pH. 105 In multimode optical imaging in vivo for the assessment of HerGa, fluorescence intensity imaging allowed us to monitor dynamic accumulation of HerGa onto the nude mouse in real-time. In particular, we observed that the HerGa was preferentially accumulated in breast tumors compared to S2Ga as judged by fluorescence intensity imaging (Agadjanian et al., 2009). Also, we monitored the accumulation kinetics of HerGa from the nude mouse at sequential time points using spectral imaging with ratiometric analysis method. In the ratiometric spectral classification images [Figure 4-26(A)], we could clearly distinguish between tumor and non-tumor regions due to HerGa accumulation at the earier time points. The results show that HerGa was earlier and more accumulated in the tumors than in other regions after IV injection of HerGa. In the tumor regions, tumor vasculature actively arises through a process known as angiogenesis in order to provide a blood to tumor tissues (Ergun et al., 2001; Holash et al., 1999; Lyden et al., 2001). Thus, it allows that a larger amount of blood can be easier and faster supplied to tumor tissues. However, in the typical spectral classification images [Figure 4-26(B)], the discrimination between the tumor and non-tumor regions was not clearly shown at the early time points. Here the spectral imaging with ratiometric analysis methods enables monitoring of the accumulation kinetics of HerGa more quantitatively and accurately than with typical spectral analysis methods. In addition, we could see that the HerGa was still present over whole area 1 day after the injection. This spectral imaging with ratiometric analysis method can be useful for more accurate monitoring of the clearance of HerGa at later time points. 106 In addition, FLIM has been performed in order to acquire surrounding information (acidity) around tumor regions at the 4 th day since fluorescence lifetimes of HerGa depends on pH. The fluorescence lifetimes of HerGa at low pH are higher than that at high pH. In Figure 4-27, the fluorescence lifetimes of HerGa accumulated in tumor regions are a little higher than in non-tumor regions. Also, the histogram of fluorescence lifetimes in tumor regions has a greater population at 2.0ns than in non-tumor regions. However, the difference between them is not very great. Here, the lifetime values may be dominated by skin since the fluorescence lifetime values we obtained are the average values from skin, tumor, and muscle. If the skin is removed, the difference of the fluorescence lifetime values of HerGa from tumor and non-tumor regions may become larger. Finally, the multimode optical imaging in the examination of the HerGa accumulations into specific organs and tumors, which were extracted from the same mouse after 4 days, also enables the acquisition of different but complementary information, simultaneously. In the fluorescence intensity image of the organs, we could confirm the preferential accumulation of HerGa into the tumors compared with other organs. Also, in spectral classified images, HerGa was quantitatively discriminated from autofluorescence. While HerGa was clearly shown in whole tumors and in some parts of the liver, HerGa was very sparse in other organs (kidney, lung, heart, spleen, and muscle) as shown in Figure 4-28(B). However, the accumulation of HerGa shown in other organs (kidney, lung, heart, spleen, and muscle) was very low. In Figure 4-28(C), fluorescence lifetimes of HerGa in tumor regions are clearly higher than those of a liver. From this result, we can infer that tumors have more acidic environment than liver and HerGa cannot be internalized in the liver. However, in the figure, fluorescence lifetimes of other 107 organs except for liver and tumors are not shown since the signal was threshold over 1500. Fluorescence lifetime obtained from low signals can easily be affected by shot noise. Finally, scanning two-photon excited fluorescence imaging of tumors, liver, and muscle provides high magnification and high resolution information. In Figure 4-28(D), we can clearly see micro structures in the tumors and liver, but not muscle. Also, the fluorescence lifetime difference of HerGa in tumors and liver opens the possibility of HerGa being used for cancer detection and delineation. Figure 4-29 shows the fluorescence lifetime difference of HerGa in tumors and normal tissues is shown. Furthermore, the combination of fluorescence intensity, spectral, and lifetime imaging enhances contrast in cancer detection and delineation since spectral imaging provides the quantitative delineation of HerGa injection area and intensity imaging offers appropriate injection concentration of HerGa for more accurate measurement. Also, two-photon excited fluorescence imaging of tumors provides the highly resolved micro-structural detail. It may help physician’s decision-making in cancer surgical intervention and detection without the need for rapid histopathological analysis of tissues biopsy during surgery. In this thesis, we developed an advanced optical imaging system which allows functional mesoscopic imaging (whole-body or endoscopic imaging with microscopic resolution) of small animals in vivo. The multimode optical imaging system can be utilized and optimized for various applications. Furthermore, the combination of fluorescence intensity, spectral, lifetime, and two-photon excited fluorescence imaging can provide complementary, dynamic, quantitative, functional, and high resolved information for an 108 assessment of chemotherapy and cancer detection. Theses capabilities also enhance contrast in detecting diseased tissues and in evaluation of drug effects in chemotherapy. As future work, other imaging modes such as 3D volumetric fluorescence imaging, elastic scattering imaging, spinning disk confocal imaging, and Raman or time-gated photon propagation can be included for more versatility and applications. Particularly, in this system, we already constructed optical components and developed a program to enable acquisition of fluorescence images at different angles for the 3D volumetric fluorescence imaging (data not shown). However, highly sophisticated simulations will be needed for 3D fluorescence volumetric imaging (Lin et al., 2007; Soubret and Ntziachristos, 2006; Turchin et al., 2008; Zacharakis et al., 2005). This work will allow monitoring of volumetric tumor growths in vivo. Also, scanning/Wide-field two-photon excited fluorescence imaging unit needs to be incorporated into the system in order to minimize animal disturbance during the experiment. For better understanding of HerGa as a chemotherapy molecule, we need to do investigate the mechanism of action of the chemotherapy agent by multimode cellular imaging (microscopy) including fluorescence intensity, spectral, lifetime, and spinning confocal imaging mode (Arnoult, 2007; Heath-Engel and Shore, 2006). Finally, the multimode optical imaging of HerGa suggests the possibility as a novel method for cancer detection and treatments. Thus, for real clinical studies, optimization for sensitivity and specificity, better decision-making in cancer management is needed. Also, a multimode optical imaging system for a human (clinical) use should be designed. 109 For minimally invasive detection and intervention, the multimode approaches should be combined with an endoscope or surgical microscope (Anandasabapathy, 2008; Andersson et al., 1987; Evans and Nishioka, 2005; Ikeda et al., 1999; Takehana et al., 1999; Xie et al., 2003). 110 BIBLIOGRAPHY Agadjanian, H., Ma, J., Rentsendorj, A., Valluripalli, V., Hwang, J.Y., Mahammed, A., Farkas, D.L., Gray, H.B., Gross, Z., and Medina-Kauwe, L.K. (2009). Tumor detection and elimination by a targeted gallium corrole. Proc Natl Acad Sci U S A 106, 6105-6110. Agadjanian, H., Weaver, J.J., Mahammed, A., Rentsendorj, A., Bass, S., Kim, J., Dmochowski, I.J., Margalit, R., Gray, H.B., Gross, Z., et al. (2006). Specific delivery of corroles to cells via noncovalent conjugates with viral proteins. Pharm Res 23, 367-377. Almutairi, A., Guillaudeu, S.J., Berezin, M.Y., Achilefu, S., and Frechet, J.M. (2008). Biodegradable pH-sensing dendritic nanoprobes for near-infrared fluorescence lifetime and intensity imaging. Journal of the American Chemical Society 130, 444-445. Anandasabapathy, S. (2008). Endoscopic imaging: emerging optical techniques for the detection of colorectal neoplasia. Current opinion in gastroenterology 24, 64-69. Andersson, P.S., Montan, S., Persson, T., Svanberg, S., Tapper, S., and Karlsson, S.E. (1987). Fluorescence endoscopy instrumentation for improved tissue characterization. Med Phys 14, 633-636. Arnoult, D. (2007). Mitochondrial fragmentation in apoptosis. Trends in cell biology 17, 6-12. Balas, C. (2001). A novel optical imaging method for the early detection, quantitative grading, and mapping of cancerous and precancerous lesions of cervix. IEEE transactions on bio-medical engineering 48, 96-104. Bhaumik, S., Lewis, X.Z., and Gambhir, S.S. (2004). Optical imaging of Renilla luciferase, synthetic Renilla luciferase, and firefly luciferase reporter gene expression in living mice. J Biomed Opt 9, 578-586. Bloch, S., Lesage, F., McIntosh, L., Gandjbakhche, A., Liang, K., and Achilefu, S. (2005). Whole-body fluorescence lifetime imaging of a tumor-targeted near-infrared molecular probe in mice. J Biomed Opt 10, 054003. Bouvet, M., Wang, J., Nardin, S.R., Nassirpour, R., Yang, M., Baranov, E., Jiang, P., Moossa, A.R., and Hoffman, R.M. (2002). Real-time optical imaging of primary tumor growth and multiple metastatic events in a pancreatic cancer orthotopic model. Cancer Res 62, 1534-1540. 111 Brinkmann, C.K., Wolf, S., and Wolf-Schnurrbusch, U.E. (2008). Multimodal imaging in macular diagnostics: combined OCT-SLO improves therapeutical monitoring. Graefe's archive for clinical and experimental ophthalmology 246, 9-16. Bullen, A. (2008). Microscopic imaging techniques for drug discovery. Nat Rev Drug Discov 7, 54-67. Cai, W., Niu, G., and Chen, X. (2008). Multimodality imaging of the HER-kinase axis in cancer. European journal of nuclear medicine and molecular imaging 35, 186-208. Carano, R.A., Ross, A.L., Ross, J., Williams, S.P., Koeppen, H., Schwall, R.H., and Van Bruggen, N. (2004). Quantification of tumor tissue populations by multispectral analysis. Magn Reson Med 51, 542-551. Chen, X., Conti, P.S., and Moats, R.A. (2004). In vivo near-infrared fluorescence imaging of integrin alphavbeta3 in brain tumor xenografts. Cancer Res 64, 8009-8014. Chirico, G., Cannone, F., Baldini, G., and Diaspro, A. (2003). Two-photon thermal bleaching of single fluorescent molecules. Biophys J 84, 588-598. Chung, A., Karlan, S., Lindsley, E., Wachsmann-Hogiu, S., and Farkas, D.L. (2006). In vivo cytometry: a spectrum of possibilities. Cytometry A 69, 142-146. Contag, C.H. (2007). In vivo pathology: seeing with molecular specificity and cellular resolution in the living body. Annu Rev Pathol 2, 277-305. Contag, C.H., and Bachmann, M.H. (2002). Advances in in vivo bioluminescence imaging of gene expression. Annu Rev Biomed Eng 4, 235-260. Cubeddu, R., Canti, G., Pifferi, A., Taroni, P., and Valentini, G. (1997). Fluorescence lifetime imaging of experimental tumors in hematoporphyrin derivative-sensitized mice. Photochem Photobiol 66, 229-236. Cubeddu, R., Canti, G., Taroni, P., and Valentini, G. (1993). Time-gated fluorescence imaging for the diagnosis of tumors in a murine model. Photochem Photobiol 57, 480- 485. Davis, A.M., Rothenberg, F.G., Shepherd, N., and Izatt, J.A. (2008). In vivo spectral domain optical coherence tomography volumetric imaging and spectral Doppler velocimetry of early stage embryonic chicken heart development. Journal of the Optical Society of America 25, 3134-3143. de Ryk, J., Namati, E., Weydert, J., Thiesse, J., and McLennan, G. (2008). New imaging approaches for understanding lung cancer response to treatment. Clinical pharmacology and therapeutics 84, 517-522. 112 Deniset-Besseau, A., Leveque-Fort, S., Fontaine-Aupart, M.P., Roger, G., and Georges, P. (2007). Three-dimensional time-resolved fluorescence imaging by multifocal multiphoton microscopy for a photosensitizer study in living cells. Appl Opt 46, 8045- 8051. Denk, W., Strickler, J.H., and Webb, W.W. (1990). Two-photon laser scanning fluorescence microscopy. Science 248, 73-76. Diamandis, E.P. (1991). Multiple labeling and time-resolvable fluorophores. Clin Chem 37, 1486-1491. Diaspro, A., and Robello, M. (2000). Two-photon excitation of fluorescence for three- dimensional optical imaging of biological structures. Journal of photochemistry and photobiology 55, 1-8. Dickinson, M.E., Simbuerger, E., Zimmermann, B., Waters, C.W., and Fraser, S.E. (2003). Multiphoton excitation spectra in biological samples. Journal of biomedical optics 8, 329-338. Dong, C.Y., Koenig, K., and So, P. (2003). Characterizing point spread functions of two- photon fluorescence microscopy in turbid medium. J Biomed Opt 8, 450-459. Elson, D., Requejo-Isidro, J., Munro, I., Reavell, F., Siegel, J., Suhling, K., Tadrous, P., Benninger, R., Lanigan, P., McGinty, J., et al. (2004). Time-domain fluorescence lifetime imaging applied to biological tissue. Photochem Photobiol Sci 3, 795-801. Ergun, S., Kilic, N., Wurmbach, J.H., Ebrahimnejad, A., Fernando, M., Sevinc, S., Kilic, E., Chalajour, F., Fiedler, W., Lauke, H., et al. (2001). Endostatin inhibits angiogenesis by stabilization of newly formed endothelial tubes. Angiogenesis 4, 193-206. Esposito, A., Gerritsen, H.C., Oggier, T., Lustenberger, F., and Wouters, F.S. (2006). Innovating lifetime microscopy: a compact and simple tool for life sciences, screening, and diagnostics. J Biomed Opt 11, 34016. Evans, J.A., and Nishioka, N.S. (2005). Endoscopic confocal microscopy. Current opinion in gastroenterology 21, 578-584. Falati, S., Gross, P., Merrill-Skoloff, G., Furie, B.C., and Furie, B. (2002). Real-time in vivo imaging of platelets, tissue factor and fibrin during arterial thrombus formation in the mouse. Nat Med 8, 1175-1181. Farkas, D.L., and Becker, D. (2001). Applications of spectral imaging: detection and analysis of human melanoma and its precursors. Pigment Cell Res 14, 2-8. 113 Farkas, D.L., Baxter, G., DeBiasio, R., Gough, A., Nederlof, M.A., Pane, D., Pane, J., Patek, D.R., Ryan, K.W., Taylor, D.L. (1993) Multimode light microscopy and the dynamics of molecules, cells and tissues. Annual Review of Physiology, 55, 785-817. Farkas, D. L., C. Du, Fisher, G. W., Lau, C., Niu, W., Wachman, E. S. and Levenson, R. M. (1998) Non-invasive image acquisition and advanced processing in optical bioimaging, Computerized Medical Imaging and Graphics: 22, 89-102. Farkas, D.L., Wei, M.D., Febbroriello, P., Carson, J.H., and Loew, L.M. (1989). Simultaneous imaging of cell and mitochondrial membrane potentials. Biophys J 56, 1053-1069. Feifel, G., and Hildebrandt, U. (1992). New diagnostic imaging in rectal cancer: endosonography and immunoscintigraphy. World journal of surgery 16, 841-847. Feng, Y., and Huang, S.H. (2007). The analysis of sinusoidal modulated method used for measuring fluorescence lifetime. Guang Pu Xue Yu Guang Pu Fen Xi 27, 2523-2526. Fittinghoff, D.N., Wiseman, P.W., and Squier, J.A. (2000). Widefield multiphoton and temporally decorrelated multifocal multiphoton microscopy. Opt Express 7, 273-279. Flock, S.T., Jacques, S.L., Wilson, B.C., Star, W.M., and van Gemert, M.J. (1992). Optical properties of Intralipid: a phantom medium for light propagation studies. Lasers in surgery and medicine 12, 510-519. Foss, S.D. (1970). A method of exponential curve fitting by numerical integration. Biometr 26, 815-821. Frostig, R.D. (2002). In vivo optical imaging of brain function (Boca Raton, CRC Press). Frykman, P.K., Lindsley, E.H., Gaon, M., Farkas, D.L. (2008) Spectral imaging for precise surgical intervention in Hirschsprung’s disease Journal of Biophotonics, 1: 97- 103. Fujimoto, J.G., and Farkas, D.L. (2009). Biomedical Optical Imaging (Oxford, Oxford University Press). Gammon, S.T., Leevy, W.M., Gross, S., Gokel, G.W., and Piwnica-Worms, D. (2006). Spectral unmixing of multicolored bioluminescence emitted from heterogeneous biological sources. Anal Chem 78, 1520-1527. Gebhart, S.C., Thompson, R.C., and Mahadevan-Jansen, A. (2007). Liquid-crystal tunable filter spectral imaging for brain tumor demarcation. Appl Opt 46, 1896-1910. 114 George, A., and Patonay, G. (1997). Fluorescence studies of carbocyanines using AOTF. Talanta 45, 285-289. Gleysteen, J.P., Newman, J.R., Chhieng, D., Frost, A., Zinn, K.R., and Rosenthal, E.L. (2008). Fluorescent labeled anti-EGFR antibody for identification of regional and distant metastasis in a preclinical xenograft model. Head & neck 30, 782-789. Gottlieb, R.A., and Granville, D.J. (2002). Analyzing mitochondrial changes during apoptosis. Methods 26, 341-347. Grahn, H., and Geladi, P. (2007). Techniques and applications of hyperspectral image analysis (England, J. Wiley). Gratton, E., Breusegem, S., Sutin, J., Ruan, Q., and Barry, N. (2003). Fluorescence lifetime imaging for the two-photon microscope: time-domain and frequency-domain methods. J Biomed Opt 8, 381-390. Graves, E.E., Ripoll, J., Weissleder, R., and Ntziachristos, V. (2003). A submillimeter resolution fluorescence molecular imaging system for small animal imaging. Med Phys 30, 901-911. Guilbault, G.G. (1973). Practical fluorescence; theory, methods, and techniques (New York,, M. Dekker). Gupta, N. (2005). Acousto-optic-tunable-filter-based spectropolarimetric imagers for medical diagnostic applications--instrument design point of view. J Biomed Opt 10, 051802. Gupta, N. (2009). Biosensors technologies: acousto-optic tunable filter-based hyperspectral and polarization imagers for fluorescence and spectroscopic imaging. Methods Mol Biol 503, 293-305. Han, X., Kasahara, N., and Kan, Y.W. (1995). Ligand-directed retroviral targeting of human breast cancer cells. Proc Natl Acad Sci U S A 92, 9747-9751. Hanson, K.M., Behne, M.J., Barry, N.P., Mauro, T.M., Gratton, E., and Clegg, R.M. (2002). Two-photon fluorescence lifetime imaging of the skin stratum corneum pH gradient. Biophysical journal 83, 1682-1690. Heath-Engel, H.M., and Shore, G.C. (2006). Mitochondrial membrane dynamics, cristae remodelling and apoptosis. Biochimica et biophysica acta 1763, 549-560. Herman, B. (1998). Fluorescence microscopy, 2nd edn (Oxford, Springer in Association with the Royal Microscopical Society). 115 Hoffman, R.M. (2004). Imaging tumor angiogenesis with fluorescent proteins. Apmis 112, 441-449. Hoffman, R.M. (2005). In vivo cell biology of cancer cells visualized with fluorescent proteins. Curr Top Dev Biol 70, 121-144. Holash, J., Maisonpierre, P.C., Compton, D., Boland, P., Alexander, C.R., Zagzag, D., Yancopoulos, G.D., and Wiegand, S.J. (1999). Vessel cooption, regression, and growth in tumors mediated by angiopoietins and VEGF. Science 284, 1994-1998. Ibanez-Lopez, C., Saavedra, G., Boyer, G., and Martinez-Corral, M. (2005). Quasi- isotropic 3-D resolution in two-photon scanning microscopy. Opt Express 13, 6168-6174. Ikeda, N., Honda, H., Katsumi, T., Okunaka, T., Furukawa, K., Tsuchida, T., Tanaka, K., Onoda, T., Hirano, T., Saito, M., et al. (1999). Early detection of bronchial lesions using lung imaging fluorescence endoscope. Diagnostic and therapeutic endoscopy 5, 85-90. Jameson, D.M., Reinhart, G.D., and Weber, G. (1989). Fluorescent biomolecules : methodologies and applications (New York, Plenum Press). Jo, J.A., Fang, Q., and Marcu, L. (2005). Ultrafast Method for the Analysis of Fluorescence Lifetime Imaging Microscopy Data Based on the Laguerre Expansion Technique. IEEE journal of quantum electronics 11, 835-845. Jung, J.C., and Schnitzer, M.J. (2003). Multiphoton endoscopy. Opt Lett 28, 902-904. Kaijzel, E.L., Snoeks, T.J., Buijs, J.T., van der Pluijm, G., and Lowik, C.W. (2009). Multimodal imaging and treatment of bone metastasis. Clinical & experimental metastasis 26, 371-379. Kawasaki, E.S., and Player, A. (2005). Nanotechnology, nanomedicine, and the development of new, effective therapies for cancer. Nanomedicine 1, 101-109. Kirkpatrick, N.D., Andreou, S., Hoying, J.B., and Utzinger, U. (2007). Live imaging of collagen remodeling during angiogenesis. Am J Physiol Heart Circ Physiol 292, H3198- 3206. Kocisova, E., Praus, P., Rosenberg, I., Seksek, O., Sureau, F., Stepanek, J., and Turpin, P.Y. (2004). Intracellular uptake of modified oligonucleotide studied by two fluorescence techniques. Biopolymers 74, 110-114. Komiyama, T., and Miwa, M. (1980). Fluorescence quenching as an indicator for the exposure of tryptophyl residues in Streptomyces subtilisin inhibitor. Journal of biochemistry 87, 1029-1036. 116 Konig, K., So, P.T., Mantulin, W.W., Tromberg, B.J., and Gratton, E. (1996). Two- photon excited lifetime imaging of autofluorescence in cells during UVA and NIR photostress. J Microsc 183, 197-204. Laemmel, E., Genet, M., Le Goualher, G., Perchant, A., Le Gargasson, J.F., and Vicaut, E. (2004). Fibered confocal fluorescence microscopy (Cell-viZio) facilitates extended imaging in the field of microcirculation. A comparison with intravital microscopy. J Vasc Res 41, 400-411. Lakowicz, J.R. (1996). Emerging applications of fluorescence spectroscopy to cellular imaging: lifetime imaging, metal-ligand probes, multi-photon excitation and light quenching. Scanning microscopy 10, 213-224. Lakowicz, J.R. (2006). Principles of fluorescence spectroscopy, 3rd edn (New York, Springer). Lee, B.S., Fujita, M., Khazenzon, N.M., Wawrowsky, K.A., Wachsmann-Hogiu, S., Farkas, D.L., Black, K.L., Ljubimova, J.Y., and Holler, E. (2006). Polycefin, a new prototype of a multifunctional nanoconjugate based on poly(beta-L-malic acid) for drug delivery. Bioconjug Chem 17, 317-326. Lee, K.J., Yeom, D.I., and Kim, B.Y. (2007). Narrowband, polarization insensitive all- fiber acousto-optic tunable bandpass filter. Opt Express 15, 2987-2992. Levenson, R.M., Lynch, D.T., Kobayashi, H., Backer, J.M., and Backer, M.V. (2008). Multiplexing with multispectral imaging: from mice to microscopy. Ilar J 49, 78-88. Levenson, R.M., and Mansfield, J.R. (2006). Multispectral imaging in biology and medicine: slices of life. Cytometry A 69, 748-758. Levitt, J.M., Baldwin, A., Papadakis, A., Puri, S., Xylas, J., Munger, K., and Georgakoudi, I. (2006). Intrinsic fluorescence and redox changes associated with apoptosis of primary human epithelial cells. J Biomed Opt 11, 064012. Li, Q., Xue, Y., and Liu, Z. (2008). [A novel system for tongue inspection based on hyperspectral imaging system]. Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 25, 368-371. Lin, H.J., Herman, P., and Lakowicz, J.R. (2003). Fluorescence lifetime-resolved pH imaging of living cells. Cytometry A 52, 77-89. Lin, H.J., Szmacinski, H., and Lakowicz, J.R. (1999). Lifetime-based pH sensors: indicators for acidic environments. Anal Biochem 269, 162-167. 117 Lin, Y., Gao, H., Nalcioglu, O., and Gulsen, G. (2007). Fluorescence diffuse optical tomography with functional and anatomical a priori information: feasibility study. Phys Med Biol 52, 5569-5585. Ljubimova, J.Y., Fujita, M., Khazenzon, N.M., Lee, B.S., Wachsmann-Hogiu, S., Farkas, D.L., Black, K.L., and Holler, E. (2008). Nanoconjugate based on polymalic acid for tumor targeting. Chem Biol Interact 171, 195-203. Lyden, D., Hattori, K., Dias, S., Costa, C., Blaikie, P., Butros, L., Chadburn, A., Heissig, B., Marks, W., Witte, L., et al. (2001). Impaired recruitment of bone-marrow-derived endothelial and hematopoietic precursor cells blocks tumor angiogenesis and growth. Nat Med 7, 1194-1201. MacKinnon, N., Stange, U., Lane, P., MacAulay, C., and Quatrevalet, M. (2005). Spectrally programmable light engine for in vitro or in vivo molecular imaging and spectroscopy. Appl Opt 44, 2033-2040. Macville, M.V., Van der Laak, J.A., Speel, E.J., Katzir, N., Garini, Y., Soenksen, D., McNamara, G., de Wilde, P.C., Hanselaar, A.G., Hopman, A.H., et al. (2001). Spectral imaging of multi-color chromogenic dyes in pathological specimens. Anal Cell Pathol 22, 133-142. Mahmood, U., and Weissleder, R. (2003). Near-infrared optical imaging of proteases in cancer. Molecular cancer therapeutics 2, 489-496. Mainen, Z.F., Maletic-Savatic, M., Shi, S.H., Hayashi, Y., Malinow, R., and Svoboda, K. (1999). Two-photon imaging in living brain slices. Methods 18, 231-239, 181. Mason, W.T. (1993). Fluorescent and luminescent probes for biological activity : a practical guide to technology for quantitative real-time analysis (London, Academic Press). McElroy, M., Kaushal, S., Luiken, G.A., Talamini, M.A., Moossa, A.R., Hoffman, R.M., and Bouvet, M. (2008). Imaging of primary and metastatic pancreatic cancer using a fluorophore-conjugated anti-CA19-9 antibody for surgical navigation. World journal of surgery 32, 1057-1066. Medarova, Z., Pham, W., Kim, Y., Dai, G., and Moore, A. (2006). In vivo imaging of tumor response to therapy using a dual-modality imaging strategy. International journal of cancer 118, 2796-2802. Medarova, Z., Rashkovetsky, L., Pantazopoulos, P., and Moore, A. (2009). Multiparametric monitoring of tumor response to chemotherapy by noninvasive imaging. Cancer Res 69, 1182-1189. 118 Merkle, H., Subczynski, W.K., and Kusumi, A. (1987). Dynamic fluorescence quenching studies on lipid mobilities in phosphatidylcholine-cholesterol membranes. Biochimica et biophysica acta 897, 238-248. Michelson, A.A. (1927). Studies in optics (Chicago, University of Chicago Press). Miller, M.J., Wei, S.H., Parker, I., and Cahalan, M.D. (2002). Two-photon imaging of lymphocyte motility and antigen response in intact lymph node. Science 296, 1869-1873. Morrison, L.E. (2008). Basic principles of fluorescence and energy transfer. Methods Mol Biol 429, 3-19. Murata, S., Herman, P., Lin, H.J., and Lakowicz, J.R. (2000). Fluorescence lifetime imaging of nuclear DNA: effect of fluorescence resonance energy transfer. Cytometry 41, 178-185. Niesner, R.A., Andresen, V., and Gunzer, M. (2008). Intravital two-photon microscopy: focus on speed and time resolved imaging modalities. Immunol Rev 221, 7-25. Nothdurft, R.E., Patwardhan, S.V., Akers, W., Ye, Y., Achilefu, S., and Culver, J.P. (2009). In vivo fluorescence lifetime tomography. J Biomed Opt 14, 024004. Parrish, J.A. (1981). New concepts in therapeutic photomedicine: photochemistry, optical targeting and the therapeutic window. The Journal of investigative dermatology 77, 45-50. Patsenker, L., Tatarets, A., Kolosova, O., Obukhova, O., Povrozin, Y., Fedyunyayeva, I., Yermolenko, I., and Terpetschnig, E. (2008). Fluorescent probes and labels for biomedical applications. Ann N Y Acad Sci 1130, 179-187. Patterson, G.H., and Piston, D.W. (2000). Photobleaching in two-photon excitation microscopy. Biophys J 78, 2159-2162. Paulmurugan, R., Massoud, T.F., Huang, J., and Gambhir, S.S. (2004). Molecular imaging of drug-modulated protein-protein interactions in living subjects. Cancer Res 64, 2113-2119. Pavesi, L., and Fauchet, P.M. (2008). Biophotonics (Berlin, Springer). Pelet, S., Previte, M.J., Kim, D., Kim, K.H., Su, T.T., and So, P.T. (2006). Frequency domain lifetime and spectral imaging microscopy. Microsc Res Tech 69, 861-874. Periasamy, A. (2001). Methods in cellular imaging (Oxford, Oxford University Press). Ploem, J.S., and Tanke, H.J. (1987). Introduction to fluorescence microscopy (Oxford, Oxford University Press). 119 Prasad, A.R., Nishimura, J.S., and Horowitz, P.M. (1983). A study of the quenching of the intrinsic fluorescence of succinyl-CoA synthetase from Escherichia coli by acrylamide, iodide, and coenzyme A. Biochemistry 22, 4272-4275. Rajwa, B., Bernas, T., Acker, H., Dobrucki, J., and Robinson, J.P. (2007). Single- and two-photon spectral imaging of intrinsic fluorescence of transformed human hepatocytes. Microscopy research and technique 70, 869-879. Ramanujan, V.K., Biener-Ramanujan, E., Armmer, K., Centonze, V.E., and Herman, B.A. (2006). Spectral kinetics ratiometry: a simple approach for real-time monitoring of fluorophore distributions in living cells. Cytometry A 69, 912-919. Ramanujan, V.K., Jo, J.A., Cantu, G., and Herman, B.A. (2008). Spatially resolved fluorescence lifetime mapping of enzyme kinetics in living cells. J Microsc 230, 329-338. Ramanujan, V.K., Zhang, J.H., Biener, E., and Herman, B. (2005). Multiphoton fluorescence lifetime contrast in deep tissue imaging: prospects in redox imaging and disease diagnosis. Journal of biomedical optics 10, 051407. Rice, B.W., Cable, M.D., and Nelson, M.B. (2001). In vivo imaging of light-emitting probes. J Biomed Opt 6, 432-440. Rost, F.W.D. (1992). Fluorescence microscopy (Cambridge, Cambridge University Press). Ryu, S.Y., Choi, H.Y., Na, J., Choi, E.S., and Lee, B.H. (2008). Combined system of optical coherence tomography and fluorescence spectroscopy based on double-cladding fiber. Opt Lett 33, 2347-2349. Schrock, E., du Manoir, S., Veldman, T., Schoell, B., Wienberg, J., Ferguson-Smith, M.A., Ning, Y., Ledbetter, D.H., Bar-Am, I., Soenksen, D., et al. (1996). Multicolor spectral karyotyping of human chromosomes. Science (New York, NY 273, 494-497. Shonat, R.D., Wachman, E.S., Niu, W., Koretsky, A.P., and Farkas, D.L. (1998). Near- simultaneous hemoglobin saturation and oxygen tension maps in the mouse cortex during amphetamine stimulation. Advances in experimental medicine and biology 454, 149-158. Solaini, G., Sgarbi, G., Lenaz, G., and Baracca, A. (2007). Evaluating mitochondrial membrane potential in cells. Biosci Rep 27, 11-21. Soubret, A., and Ntziachristos, V. (2006). Fluorescence molecular tomography in the presence of background fluorescence. Phys Med Biol 51, 3983-4001. Spires, T.L., and Hyman, B.T. (2005). Transgenic models of Alzheimer's disease: learning from animals. NeuroRx 2, 423-437. 120 Spriet, C., Trinel, D., Riquet, F., Vandenbunder, B., Usson, Y., and Heliot, L. (2008). Enhanced FRET contrast in lifetime imaging. Cytometry A 73, 745-753. St Croix, C.M., Leelavanichkul, K., and Watkins, S.C. (2006). Intravital fluorescence microscopy in pulmonary research. Adv Drug Deliv Rev 58, 834-840. Stamatas, G.N., and Kollias, N. (2007). In vivo documentation of cutaneous inflammation using spectral imaging. J Biomed Opt 12, 051603. Takeda, M., Gonda, K., Higuchi, H., and Ohuchi, N. (2008). In vivo single molecular fluorescence imaging for analysis of pharmacokinetics. Gan to kagaku ryoho 35, 1277- 1280. Takehana, S., Kaneko, M., and Mizuno, H. (1999). Endoscopic diagnostic system using autofluorescence. Diagnostic and therapeutic endoscopy 5, 59-63. Thaler, C., and Vogel, S.S. (2006). Quantitative linear unmixing of CFP and YFP from spectral images acquired with two-photon excitation. Cytometry A 69, 904-911. Tirlapur, U.K., and Konig, K. (2001). Femtosecond near-infrared lasers as a novel tool for non-invasive real-time high-resolution time-lapse imaging of chloroplast division in living bundle sheath cells of Arabidopsis. Planta 214, 1-10. Tomatis, S., Carrara, M., Bono, A., Bartoli, C., Lualdi, M., Tragni, G., Colombo, A., and Marchesini, R. (2005). Automated melanoma detection with a novel multispectral imaging system: results of a prospective study. Physics in medicine and biology 50, 1675-1687. Turchin, I.V., Kamensky, V.A., Plehanov, V.I., Orlova, A.G., Kleshnin, M.S., Fiks, II, Shirmanova, M.V., Meerovich, I.G., Arslanbaeva, L.R., Jerdeva, V.V., et al. (2008). Fluorescence diffuse tomography for detection of red fluorescent protein expressed tumors in small animals. J Biomed Opt 13, 041310. Valeur, B. (2002). Molecular fluorescence : principles and applications (Weinheim, Wiley-VCH). Vooijs, M., Jonkers, J., Lyons, S., and Berns, A. (2002). Noninvasive imaging of spontaneous retinoblastoma pathway-dependent tumors in mice. Cancer Res 62, 1862- 1867. Wang, M.D., Shin, D.M., Simons, J.W., and Nie, S. (2007). Nanotechnology for targeted cancer therapy. Expert review of anticancer therapy 7, 833-837. Weissleder, R. (2002). Scaling down imaging: molecular mapping of cancer in mice. Nat Rev Cancer 2, 11-18. 121 Wells, W.A., Barker, P.E., MacAulay, C., Novelli, M., Levenson, R.M., and Crawford, J.M. (2007). Validation of novel optical imaging technologies: the pathologists' view. J Biomed Opt 12, 051801. Wessels, J.T., Busse, A.C., Mahrt, J., Dullin, C., Grabbe, E., and Mueller, G.A. (2007). In vivo imaging in experimental preclinical tumor research--a review. Cytometry A 71, 542- 549. Xie, T., Xie, H., Fedder, G.K., and Pan, Y. (2003). Endoscopic optical coherence tomography with a modified microelectromechanical systems mirror for detection of bladder cancers. Appl Opt 42, 6422-6426. Yamauchi, K., Yang, M., Jiang, P., Yamamoto, N., Xu, M., Amoh, Y., Tsuji, K., Bouvet, M., Tsuchiya, H., Tomita, K., et al. (2005). Real-time in vivo dual-color imaging of intracapillary cancer cell and nucleus deformation and migration. Cancer Res 65, 4246- 4252. Yang, M., Baranov, E., Jiang, P., Sun, F.X., Li, X.M., Li, L., Hasegawa, S., Bouvet, M., Al-Tuwaijri, M., Chishima, T., et al. (2000). Whole-body optical imaging of green fluorescent protein-expressing tumors and metastases. Proc Natl Acad Sci U S A 97, 1206-1211. Zacharakis, G., Ripoll, J., Weissleder, R., and Ntziachristos, V. (2005). Fluorescent protein tomography scanner for small animal imaging. IEEE Trans Med Imaging 24, 878- 885. Zhang, H., Yee, D., and Wang, C. (2008). Quantum dots for cancer diagnosis and therapy: biological and clinical perspectives. Nanomedicine 3, 83-91. Zimmermann, T. (2005). Spectral imaging and linear unmixing in light microscopy. Advances in biochemical engineering/biotechnology 95, 245-265. Zuzak, K.J., Naik, S.C., Alexandrakis, G., Hawkins, D., Behbehani, K., and Livingston, E. (2008). Intraoperative bile duct visualization using near-infrared hyperspectral video imaging. American journal of surgery 195, 491-497. Zuzak, K.J., Naik, S.C., Alexandrakis, G., Hawkins, D., Behbehani, K., and Livingston, E.H. (2007). Characterization of a near-infrared laparoscopic hyperspectral imaging system for minimally invasive surgery. Analytical chemistry 79, 4709-4715. 122 APPENDIX A System control programs A.1. Main system control program A main system control program is developed using CVI/National Instrument. This program plays a role as a main sever in multi-mode optical imaging system. It can be connected with other client programs (3D fluorescence imaging, Joystick/FLIM, and scanning two-photon excited fluorescence imaging). In order to execute this program, Main server.exe file should be opened. Then, a Port Number panel is shown. Here, any number can be input. The number becomes a main password for connection with other client programs. After inserting numbers, OK button should be clicked. Figure A-1. Port number panel. After that, main system control panel is opened as shown in Figure A-2. For the operation of this control panel, system initialization is needed. In order to do that, after selection of COMPORT, the Initialize motor button should be clicked. If it works properly, the diode above a Shut Down button displays green color. 123 Figure A-2. Main system control panel. Fine adjustment of x, y, z, and rotational stage is realized by the control box A. Also, the coarse adjustment is realized by clicking the buttons in the control box B. Control box C is for filter selection. In control Box D, mosaic FLIM can be initialized by clicking the FLIM button. Also, field of view and step size can be inserted. In addition, LED in the multimode optical imaging system can be turned on or off by using ON and OFF button in control box E. Also, light intensity of LED can be controlled by a voltage button. In the panel, the reset position button can reset the stage position, and the GetPosition button updates the current stage position. Motor Stop button is for emergency stop. Shut Down button is for closing the program. A B C D E 124 A.2. Scanning/wide-field two-photon control program This program is developed for scanning two-photon excited fluorescence imaging in multi-mode optical imaging system. In order to execute this program, two photon.exe file should be opened. Then, a Server Name panel is opened as shown in Figure A-3. Here, same IP address shown in Figure A-2 should be inserted. After inserting the IP address, OK button should be clicked. Then, a Port Number panel is shown again. After inserting the same port number inserted in main system control program, click OK button again. If then, the scanning/wide-field two-photon imaging panel is opened. Figure A-3. Server connection panel. Figure A-4 shows the scanning/wide-field two-photon imaging panel. Here, after the selection of Image size, click a scan button. If then, the image acquisition is started. If the CONTINUE Switch is off, the image acquisition is done only once, but if the CONTINUE Switch is on, the image acquisition is performed continuously. The image acquired is shown in Scanned Image box. For the save of an image, click the Save button after inserting a file name in save name box. For the wide-field two-photon image acquisition, click the WTEF. 125 Figure A-4. Scanning two-photon excited fluorescence imaging panel. A.3. Joystick/mFLIM program In order to execute this program, Joystick.exe file should be opened after the connection of Joystick with PC. Main server connection procedures are same with those demonstrated in the section A.2. If the procedures are preceded, the Joystick/FLIM panel is shown as Figure A-5. If the panel is shown, the multimode system can be controlled with Joystick in different locations. Here, FLIM button is for the start of mFLIM, Figure A-5. Joystick/FLIM panel. 126 A.4. 3D fluorescence imaging program This program allows us to take fluorescence image with different angle views. In order to execute this program, 3D Reconstruction.exe file should be opened. Main server connection procedures are also same with those demonstrated in the section A.2. If the procedures are preceded, the 3D fluorescence imaging panel is shown as Figure A-5. . The Snap button generates an image only once, but Grab button offers continuous image acquisition. Particularly, for the acquisition of fluorescence image with different angle views, click the SCAN button after an exposure time and a degree step in EXPOSURE BOX and Degree Step is inserted respectively. Here, if you click a SAVE button after writing a file name in the Save Name box, Images is automatically saved while the image acquisition. Figure A-6. 3D fluorescence imaging panel. A.4. Source code The source code will be provided through CD. 127 APPENDIX B Fluorescence lifetime imaging and analysis B.1. First-order exponential fitting method for fluorescence lifetimes The analysis program for the construction of fluorescence lifetime image was developed using Matlab 7.1. This program is comprised of two files, first order exponential fitting for FLIM.m and Exponential_curve.m. In the first order exponential fitting for FLIM.m file, several functions are performed, including load file, input parameters (time step, time range, and threshold_value), a routine of first-order exponential fitting, and generation of FLIM image. Also, In Exponential_curve.m file, exponential curve fitting is performed and lifetime coefficient is returned. B.2. Source code First order exponential fitting for FLIM.m file close all clear all %%%%%%%%%%%%%%%%%Input parameter%%%%%%%% time_range=4800; % the time range of the acquired images. time_step=200; % Time step threshold_value=1500; % Thresholding fluorescence intensity of the fist image in order to reduce analysis errors. %%%%%%%%%%loading Image Stack%%%%%%%%%%%%%%%% info = imfinfo('C:\Documents and Settings\Jae youn Hwang\My Documents\research\optics\chemotherapy cell\data\Mar_12_2009\mouse1\FLIM\Mar_12_2009\mouse1_double_injection_day2.tif'); 128 nFrames = length(info); fWidth = info(1).Width; fHeight= info(1).Height; test = zeros([fHeight fWidth nFrames],'double') for frame=1:nFrames [test(:,:,frame),map] = imread('C:\Documents and Settings\Jae youn Hwang\My Documents\research\optics\chemotherapy cell\data\Mar_12_2009\mouse1\FLIM\Mar_12_2009\mouse1_double_injection_day2.tif',frame); end %sz=size(test); t=[0:time_step:time_range]; %%%%%%%%%%%%First-order exponential Fitting routine%%%%%%%%%%%%%% t=t'; for a=1:fHeight, for b=1:fWidth, tempvalue=squeeze([test(a,b,:)]); if tempvalue(1)<threshold_value; Tau(a,b)=0; end if tempvalue(1)>threshold_value; Norm_value=tempvalue./max(tempvalue); Time_constant= exponentional_curve(t,Norm_value); Tau(a,b)=Time_constant(2); end end end load('MyColormaps','mycmap')%load color coding map Tau_2=1./Tau;% Fluorescence lifetime Final_Tau=[Tau_2 .* (Tau_2 >200&Tau_2<10000)]; %%%%%%%%%%Fluorescence lifetim image display%%%%%%%%%%%%%%%% figure imshow(Final_Tau,map); colormap(mycmap);caxis([0 2500]);Title('Mouse1 Her-Ga double injection 2days'); Exponential_curve.m file function [coe]=exponentional_curve(x,y) %EXPONENTIONAL CURVE Create plot of datasets and fits % EXPONENTIONAL CURVE(X,Y) % Creates a plot, similar to the plot in the main curve fitting % window, using the data that you provide as input. You can 129 % apply this function to the same data you used with cftool % or with different data. You may want to edit the function to % customize the code and this help message. % % Number of datasets: 1 % Number of fits: 1 % Data from dataset "y vs. x": % X = x: % Y = y: % Unweighted % --- Create fit "fit 1" ok_ = ~(isnan(x) | isnan(y)); st_ = [1 0.001 0.2]; ft_ = fittype('a*exp(-b*x)+c', 'dependent',{'y'},'independent',{'x'}, 'coefficients',{'a', 'b', 'c'}); % Fit this model using new data cf_ = fit(x(ok_),y(ok_),ft_,'Startpoint',st_); coe=coeffvalues(cf_); %return time constant.

Abstract (if available)

Abstract This thesis reports advanced optical imaging technology in a stand-alone system that enables functional mesoscopic imaging (whole-body or endoscopic imaging with microscopic resolution) of small animals in vivo, and provides for quantitative, dynamic, and functional monitoring of chemo- and nanoconstruct therapy. Many currently available imaging approaches have been applied to preclinical studies of cancer, stem cells, and pharmaceutical outcomes. Moreover, useful in vivo imaging may require several, preferably combined, and advanced imaging modalities to examine different but complementary characteristics of molecules, cells, or tissues. Although commercial systems perform well for standard imaging of small animals, they have limitations stemming from being single-modality instruments. Thus, a new multimode optical imaging system that is designed to be application-optimizable, with higher sensitivity and specificity has been developed here in order to overcome these limitations. The instrument combines various in vivo imaging modes, including fluorescence intensity, spectral, lifetime, intra-vital confocal, two-photon excited fluorescence, and bioluminescence imaging. Also this system is a unique and comprehensive imaging platform for analyzing kinetic, quantitative, environmental, and other highly-relevant information with macro- to micro-scopic resolution. This system can be optimized for various applications, and the combination of multiple imaging modes for increased contrast and complementary/synergetic information in chemotherapy assessment and cancer detection is emphasized here.

Linked assets

Development of a multi-mode optical imaging system for preclinical applications in vivo

Conceptually similar

ZIP

Development of a multi-mode optical imaging system for preclinical applications in vivo [program]

PDF

Analysis and visualization of multi-mode optical spectral-signature bioimaging data for in vivo applications

PDF

A high frequency array- based photoacoustic microscopy imaging system

PDF

Development of novel optical materials for whispering gallery mode resonator for nonlinear optics

PDF

Hyperspectral optical imaging for detection, diagnosis and staging of cancer

PDF

Metabolic profiling of single hematopoietic stem cells for developing novel ex vivo culture strategies

PDF

Application of optical forces in microphotonic systems

PDF

Intravascular imaging on high-frequency ultrasound combined with optical modalities

PDF

Development of back-end processing system for high frequency ultrasound b-mode imaging

PDF

Multi-modality intravascular imaging by combined use of ultrasonic and opticial techniques

PDF

In vivo study with a novel liposomal formulation of a recombinant ADAM disintegrin-like domain showing both anti-angiogenic and tumor growth inhibition activities

PDF

A new approach for assessing early osteoarthritis in the rat

PDF

Optimal defect-tolerant SRAM designs in terms of yield-per-area under constraints on soft-error resilience and performance

PDF

High-frequency ultrasound array-based imaging system for biomedical applications

PDF

A computational framework for exploring the role of speech production in speech processing from a communication system perspective

PDF

Electrodeposition of platinum-iridium coatings and nanowires for neurostimulating applications: fabrication, characterization and in-vivo retinal stimulation/recording EIS studies of hexavalent a...