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Evaluation of preservatives in blood collection tubes for cell-free RNA transcriptional profiles in human plasma

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Evaluation of preservatives in blood collection tubes for cell-free RNA transcriptional profiles in human plasma

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Content Evaluation of Preservatives in Blood Collection Tubes for CellFree RNA Transcriptional Profiles in Human Plasma
By
Chen-Tzu Yu
A Thesis Presented to the
FACULTY OF THE USC KECK SCHOOL OF MEDICINE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(BIOCHEMISTRY AND MOLECULAR MEDICINE)
August 2024

ii
ACKNOWLEDGEMENTS
First and foremost, I would like to express my sincere gratitude to my exceptional advisor
and committee chair, Dr. Amir Goldkorn; I am very grateful to have the opportunity to do
Master’s research in your lab. Throughout the past two years, you have not only provided me
with constant guidance for my study and research but also shared your tremendous wisdom and
immense knowledge of both science and life. The support and encouragement from you thrive on
me as both a researcher and an individual. Also, you made the days of the lab full of happiness
(e.g., Friday pizza lunch, holiday dinner, summer BBQ, and so on). If I had another chance to
choose a mentor for my Master's degree, I would not hesitate to join your lab again.
In addition to my advisors, I would like to thank the rest of my thesis committee, Dr.
Suhn K. Rhie and Dr. Daniel Weisenberger. Thank you for your encouragement, insightful
comments, and thought-provoking questions. I am sincerely appreciative for taking time off from
your busy schedules to attend my dissertation committee meetings.
I must also thank my fellow lab members, Daniel Bsteh, Tong Xu, Emmanuelle Hodara,
Smruthi Maganti, Maheen Iqbal, Nathan Lin, Alex Renn, Marcos, Cecile, and Milan for being
the best lab mates I could have asked for, and creating the lab environment that made this work
possible. I want to express my gratitude toward Daniel Bsteh for being an amazing and
outstanding mentor for my work, for your willingness to share your knowledge, and for
answering and solving all my questions.
Finally, I have to acknowledge my parents for always providing me with unwavering
support from Taiwan, words cannot express my gratitude.

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TABLE OF CONTENTS
Acknowledgments……………………………………………………..………………………….ii
List of Tables………………………………………………………………………...…………….v
List of Figures…………………………………………………………………………………….vi
Abbreviations…………………………………………………………………………………….vii
Abstract………………………………………………………………………………………….viii
Chapter 1: Introduction……………………………………………………...…………………….1
1.1 Liquid Biopsy in Cancer Management…………………………….………………….1
1.2 Challenges of Transcription Profiles in Liquid Biopsy……………………………….6
1.3 Inferred Gene Expression from Cell-free DNA in Liquid Biopsy…………..……..….9
1.4 Preservatives for cfRNA……………………………………………………….…….12
Chapter 2: Materials and Methods……………………………………………………………….16
2.1 Blood Sample Collection and Whole Blood Processing……………………………..16
2.2 cell-free RNA Extraction…………………………………………………………….19
2.3 Buffy Coat RNA Extraction and Quantification……………………………………..19
2.4 cell-free RNA Quantification………………………………………………………...19
2.5 PBMC Genes Primer Efficiency Test and RT-qPCR………………………………...20
2.6 Cell Culture and cell-free RNA from PCa Cells Supernatant………………………..22
2.7 RNA-seq Library Preparation………………………………………………………..23
Chapter 3: Results………………………………………………………………………………..25
3.1 Primer Efficiency Test of PBMC Genes …………………………….………………25
3.2 cfRNA quantification …………………………………………………..……………27
3.3 qRT-PCR for PBMC expression……………………………………..………………36

iv
3.4 RNA-seq Libraries …………………………………………………………………..38
Chapter 4: Discussion……………………………………………………………………...…….40
References………………………………………………………………………………….…….44

v
LIST OF TABLES
Table 1. Features of commercially available blood preservation tubes and centrifuge
parameters………………………………..…………………………………..…………..14
Table 2. Primer Sequence Information…………………………………..…………..…………..21
Table 3. Primer efficiency percentage for ENTPD7, SASS6, ATP1A1, and RBM33……….…..27
Table 4. Extracted cfRNA amount from 1.5mL plasma and quantified with 5S rRNA using RTqPCR……………………………………………………………………………………..29
Table 5. Extracted cfRNA amount from 1.5mL plasma and quantified with Bioanalyzer…..…..35
Table 6. RNA-seq libraries preparation results…………………………………………………..39

vi
LIST OF FIGURES
Figure 1. Various clinical applications of liquid biopsy using CTCs, circulating nucleic acids or
other tumor-derived materials in the bloodstream ……………………….….……......…..5
Figure 2. Correlation of PFE with gene expression levels in PBMC…………………….….…..11
Figure 3. A scatterplot depicts the relationship between plasma cfDNA PFE versus leukocyte
RNA expression levels (TPM) ………………………………….….…………..………..15
Figure 4. Workflow of whole blood processing and the pre-analytical steps to examine various
commercially available blood preservation tubes………………………………………..18
Figure 5. Primer efficiency test for PBMC gene targets in serial diluted buffy coat RNA….…..26
Figure 6. qRT-PCR Quantification method for plasma cfRNA amount extracted from different
blood preservation tubes………………………………….….…………..……….….…..28
Figure 7. Electropherogram from Agilent RNA Bioanalyzer of extracted plasma cfRNA….…..30
Figure 8. Extracted cfRNA amount from 1.5 mL plasma across 0hr, 24hr, 48hr & 72hr quantified
by Agilent 2100 Bioanalyzer with RNA 6000 Pico Series kit…..……………………….36
Figure 9. Plasma cfRNA expression extracted from different blood preservation tubes…….…..37

vii
LIST OF ABBREVIATIONS
PCa Prostate Cancer
mCRPC Metastatic castration-resistant prostate cancer
CTCs circulating tumor cells
cfDNA cell-free DNA
cfRNA cell-free RNA
miRNA Micro RNA
EVs extracellular vesicles
RT Room temperature
WBC White Blood Cell
PBMC Peripheral Blood Mononuclear Cell
qRT-PCR Real-Time (Quantitative) Reverse Transcriptase Polymerase Chain
Reaction
EpCAM Epithelial Cell Adhesion Molecule
TSS Transcriptional Starting Site
Ct quantification cycle
RIN RNA Integrity Number
EDTA Ethylenediaminetetraacetic acid

viii
ABSTRACT
Background: Detection and management of cancer has greatly advanced in recent decades;
however, obtaining tissue biopsies from metastases remains a challenge and cannot be routinely
performed. Liquid biopsy emerges as a possible solution, facilitating the non-invasive
assessment of malignant tissue presence in peripheral blood. Furthermore, liquid biopsy holds
the potential for disease detection, monitoring tumor heterogeneity, and identifying therapeutic
targets. Analyzing cell-free DNA (cfDNA) and cell-free RNA (cfRNA), key analytes of liquid
biopsy, allows for the exploration of genomic and transcriptomic profiles. In collaboration with
ThermoFisher and CHLA, the Goldkorn Lab has developed “HERCULES”, the first prostate
cancer (PC) specific targeted sequencing panel capable of parallel genomic profiling of plasma
cell-free circulating tumor DNA (ctDNA) and single CTCs. Beyond cfDNA mutational profiling,
transcriptional profiling offers a deeper understanding of the molecular mechanisms underlying
cancer progression and therapy resistance. However, this approach faces significant challenges
primarily related to the inherent instability of RNA analytes. RNA molecules are highly prone to
degradation, and their preservation poses a significant obstacle in obtaining reliable and highquality data. Additionally, the impact of fixatives used during sample processing can further
exacerbate RNA degradation, leading to compromised yield and quality of RNA extracted from
liquid biopsy samples. To address the challenges, we are optimizing cfRNA-based transcriptional
profiling workflow, establishing reliable quantification methods and assessment of blood tubes in
their capacity to preserve cfRNA.
Methods: For cfRNA workflow optimization, we extracted plasma from whole blood samples
obtained from 2 healthy donors, which were drawn into EDTA, Streck, and Norgen tubes. Tubes
were stored in room temperature and extracted at 0-hour, 24-hour, 48-hour and 72-hour after

ix
blood draw to observe RNA degradation over time. The extracted cfRNA samples underwent
quantification strategies using both real-time quantitative reverse-transcription PCR (qRT-PCR)
and Agilent 2100 Bioanalyzer with RNA 6000 Pico series kit. Evaluation of the blood tubes were
conducted by performing qRT-PCR on cfRNA samples targeting RBM33, ATP1A1, ENTPD7 and
SASS6. Given that most cfRNA were of hematopoietic origin, we hypothesize that the selected
PBMC genes from the scatterplot could be used to evaluate the preservation of cfRNA after
blood draw, and therefore gene selection for qRT-PCR was depended on peripheral blood
mononuclear cell (PBMC) RNA expression level (TPM) around 2.5 and 7.5. We further
performed RNA-seq on healthy donor cfRNA and spike-in cancer cell line cfRNA admixtures to
investigate the extent of increasing leukocyte-derived cellular RNAs diluting cancer-derived
cfRNA over time in EDTA and Streck tubes.
Results: We validated qRT-PCR for measuring gene expression in purified plasma cfRNA,
comparing blood preservation tubes to EDTA tubes. Buffy coat RNA underwent a 10-fold
dilution series, and qRT-PCR on eight PBMC genes identified four primers with efficiencies
close to 100%. cfRNA quantification using qRT-PCR and Bioanalyzer showed most fragments
were under 200 bp. We identified an upward trend in cfRNA amounts in EDTA and Streck tubes
after 72 hours at room temperature suggestive of leukocyte RNA release, while Norgen and
PAXgene tubes did not show this trend. However, qRT-PCR lacked sensitivity to detect
expression changes across different preservation tubes and storage times. Furthermore, we
designed an RNA-seq workflow with healthy donor cfRNA and spike-in cancer cell line cfRNA
admixtures to investigate the extent of increasing leukocyte-derived cellular RNAs diluting
cancer-derived cfRNA over time. The cDNA libraries showed good concentrations and were sent
for sequencing.

x
Conclusions: Our study reveals that cfRNA quantification using both qRT-PCR and Bioanalyzer
shows most fragments are under 200 bp, with the Bioanalyzer providing consistent
fragmentation results across different preservation methods. However, qPCR lacks sensitivity to
detect gene expression changes, while RNA-seq offers a more comprehensive profiling. This
emphasizes the importance of choosing appropriate preservation methods to maintain cfRNA
integrity for reliable downstream applications.

Chapter 1
INTRODUCTION
1.1 Liquid Biopsy in Cancer Management
Prostate cancer (PCa) is the most common cancer in American men, with an estimated
288,300 new diagnoses in 2023, and the second lethal cancer in American men, with an
estimated 34,700 deaths in 2023. Current treatment used for PCa therapy shows a potent antitumor effect but often leads to resistance, causing progression to incurable metastatic forms.
While the management of PCa has greatly advanced in recent decades, 5% of PCa patients suffer
from the incidence of metastatic disease, including metastatic castration-resistant prostate cancer
(mCRPC), and an estimated 30-40% of the patients develop biochemical recurrence after
treatment (1). Due to the high level of genetic and phenotypic heterogeneity within a given tumor
and the rapidly changing therapeutic landscape in mCRPC, early detection and delay of
progression are crucial in these patients (2). Current treatments for metastatic prostate cancer are
chemotherapies, hormonal agents, and targeted therapies, which can delay progression and
extend survival. However, the emerging issue of studying metastatic diseases lies in the
challenges associated with obtaining tissue samples from metastatic sites; namely, tumor biopsies
of metastatic lesions are not routinely performed and typically represent only a single site at a
single time point (3-4). In the past decade, disease profiling of PCa has expanded to include not
only tumor tissue, but also liquid biopsies of cells and genetic material circulating in the blood (5
(Morrison & Goldkorn, 2018)).
The term liquid biopsy refers to the analysis of peripheral blood or other body fluids to obtain
clinical or biological information about solid malignancy. Liquid biopsy provides insight into

2
malignant tissue with non-invasive procedures compared to traditional tumor biopsy, and liquid
biopsy can be studied repeatedly and non-invasively as the disease progresses (6-7).
Liquid biopsy is increasingly used for cancer molecular profiling in the precision oncology
field. It holds significant promise in transforming early-stage and advanced-stage cancer through
its applications in diagnosis, prognosis, and therapeutics, and thus enhances screening and early
detection, symptomatic and precision medicine, as well as patient treatment (8-9 (Ignatiadis et
al., 2021) (Connal et al., 2023)). Liquid biopsy provides prognostic information and helps the
assessment of the risk of disease progression. Moreover, it detects minimal residual disease
(MRD), which is crucial for predicting relapse in cancer patients.
Possible analytes of liquid biopsy range from circulating tumor cells (CTCs), cell-free DNA
(cfDNA), cell-free RNA (cfRNA), circulating extracellular vesicles (EVs), proteins and
metabolites. CTCs are cancer cells shed by primary or metastatic tumors into the bloodstream.
Enrichment technologies are utilized to overcome the rarity of the CTCs in the bloodstream.
CellSearch (Menarini Silicon Biosystems Inc., Bologna, Italy) is an EpCAM-affinity based assay
using immunomagnetic beads to enrich for CTCs. Non-affinity based techniques such as CTCiChip utilize microfluid technology to enhance the capture of CTCs with lower EpCAM
expression (10). High-content immunofluorescence microscopy is a more recent technology,
such as RareCyte (Rarecyte Inc., Seattle, WA), to scan slides with millions of cells and identify
candidate CTCs using morphometric and immunofluorescence calling algorithms. CTC
enumeration has shown to be correlated with prognosis and disease burden in mCRPC patients
(11-14). CTC clusters show worse prognosis comparing to individual CTCs, whereby a
homotypic cluster suggests a higher possibility of epithelial-mesenchymal transition (EMT) and
a heterotypic cluster may hold a stronger menace to immune system evasion. CTC molecular

3
profiling also may be useful in predicting and monitoring patients after receiving specific
therapies (15).
cfDNA refers to DNA fragments found in body fluids, typically from chromatin
fragmentation accompanying the death of normal cells or tumor cells. cfDNA fragments are
typically around 146-167 bp, but if shed from tumor cells, they tend to be shorter, ranging from
134-144 bp (16). Evidence has proven that cfDNA provides a comprehensive view of the tumor
genome as it is released directly from the tumor region (17-18). High depth of sequencing for
genomic profiling of cfDNA allows the tracking of genomic alterations, the assessment of
intratumor heterogeneity and the detection of subclonal mutations (19-20). While mutational
analysis of cfDNA has several clinical applications, there is a growing interest in evaluating
cfDNA beyond mutations, such as epigenetic alterations, which are even more frequent than
somatic mutations in cancer (21). This encompasses chromosomal rearrangements, copy number
variations, DNA methylation, fragmentation, read depth coverage, virus sequences detection, etc
(22-23). Epigenetic features of cfDNA such as 5-methycytosine, 5-hydroxymethylcytosine, or
cfDNA-protected signatures may provide further information about the tissue of origin for pancancer detection (24-26).
Current studies hypothesize that cfRNAs are released from cells by active secretion or
through apoptosis and necrosis. cfRNAs includes extracellular RNAs (exRNAs), micro RNAs
(miRNA), and long non-coding RNAs. cfRNAs comprise exosomes, lipoproteins, microvesicles,
and ribonucleoprotein (RNP) complexes. miRNAs raised the earliest and most interest among all
circulating RNAs in liquid biopsy due to their surprising stability in plasma and serum, which
have been related to their containment and preservation in EVs (27-29). Changes in RNA
expression of tumor cells are a dynamic process that can reflect tissue and diseases. Furthermore,

4
studies of cfRNA expand beyond the differential expression of genes and include other features
such as pathogenic alternative splicing or A-to-I RNA editing. These changes are observable only
in the transcriptome and not in the genome. Due to the uniqueness of transcriptomics, there has
been a rising interest in the field of cfRNA in the last few years (30-31).
Circulating EVs are lipid bilayer membrane-delimited nanoparticles secreted by all types of
cells into the extracellular spaces and body fluids (32). EVs comprise a variety of actively
released vesicle subtypes including exosomes, microvesicles, apoptotic bodies, and large
oncosomes. EVs contain important components like proteins, DNA, mRNA, lncRNA, lipids,
etc., and EVs play a critical role in intercellular communication. Recent studies suggest EVs hold
promise for the discovery of biomarkers for cancer diagnosis and cancer treatment (33-36).
Nonetheless, in order to distinguish the overlapping physical features from other lipidic
components such as HDL, LDL, or lipoproteins, techniques like ultracentrifugation and sizeexclusion chromatography (SEC) are utilized to isolate EVs from the body fluids before
extracting molecular components from EVs (37-38).
Together, these liquid biopsy analytes have the potential to provide additional information
about heterogeneous features of primary tumors or metastases that pathologists usually obtain. At
the early stage of disease development, liquid biopsy approaches could be used for cancer
screening; for instance, utilizing cfDNA sequencing to detect DNA methylation profiles and
identify occult disease in at-risk healthy populations (64). During the cancer progression, these
analytes have the potential to evaluate the risk of disease and the underlying biology that drives
resistance and progression. Furthermore, recent studies have shown that identifications of tumorspecific changes in cfDNA enable the detection of minimal residual disease and prediction of
recurrence in colon, breast, and lung cancers (65-67).

5
Figure 1 Various clinical applications of liquid biopsy using CTCs, circulating nucleic
acids or other tumor-derived materials in the bloodstream. (1) Early detection of cancer;
liquid biopsy approaches may be used to further investigate abnormalities detected on imaging
examinations such as mammography or lung CT. (2) Surveillance for micrometastatic disease
following curative-intent treatment of a primary tumour in order to evaluate the risk of disease
recurrence and enable timely management of recurrent disease, if needed. (3) Guiding the
selection of the most appropriate treatment and/or monitoring treatment responses in patients
with overt metastatic disease through dynamic characterization of changes in tumour burden
and disease biology. Figure taken from Ignatiadis et al. (2021) (8)

6
1.2 Challenges of Transcription Profiles in Liquid Biopsy
Precision oncology has been in rapid progress since the growing use of next-generation
sequencing (NGS) to identify significant mutations in cancer-related genes. Building on these
advancements, recent research has started to examine genome-wide tumor changes across
various “omics” fields and utilize transcriptomics data to advance patient treatment.
Whole-genome sequencing (WGS) of tumors at a depth of 60-100x and matched germline
DNA sequencing at 30x depth is currently the most accurate and comprehensive platform for
analyzing DNA mutations. (42-43). While RNA-seq analysis effectively detects expression
driver fusion and aberrantly expressed genes related to drug sensitivity in cell lines or patients
(44-45), its application in liquid biopsy, particularly cfRNA profiling, offers unique advantages.
Although high-depth sequencing of cfDNA allows for the tracking of genomic alterations,
assessment of intratumor heterogeneity, and detection of subclonal mutations, it has limited
ability to identify the transcriptional programs governing cancer phenotypes and their dynamic
changes during the course of the disease (46). To address these limitations, transcriptional
profiling through cfRNA is gaining attention for its potential to provide real-time insights into
gene expression.
Transcriptional profiling allows the identification of gene expression patterns associated with
cancers and thus helps in the understanding of tumor biology and patient profile. The
comprehension of individual gene expression profiles allows us to develop personalized
treatment strategies based on the patient’s specific genetic and transcriptomic patterns (39-41).
Analysis methods of RNA in liquid biopsy include qRT-PCR, digital droplet PCR (ddPCR),
microarrays, and RNA-seq. PCR-based methods are quick and easy to interpret, but they lack
high-throughput and can only analyze a small amount of pre-determined RNAs. Microarrays

7
have the advantage of analyzing large number of biomarkers in parallel; however, they tend to
have lower sensitivity and specificity. RNA-seq allows the detection of high-throughput analysis,
and the capacity to identify novel fusions, but at a cost of higher complexity of analysis and the
larger amount of sample input.
Recent studies showed that plasma or serum circulating RNAs (cfRNAs and miRNAs) have
shown promise as biomarkers for early detection, distinguishing malignancies, non-cancerous
states, and pre-cancerous conditions (47-48). Circulating miRNAs suggested a significant
diagnostic and prognostic values and showed potential in guiding therapy. In triple-negative
breast cancer (TNBC), microarray analysis has revealed that miR-199a-5p was restored when
compared to non-TNBC, suggesting its diagnostic value in guiding therapy (49). For
osteosarcoma, low level of miR-497 identified through qRT-PCR was correlated with aggressive
progression and poor prognosis (50). In bladder cancer, miR-205 was significantly expressed
compared to healthy individuals (51). Additionally, in ovarian cancer, the decreased expression
of miR-148a predicted poor prognosis and was associated with tumor growth and metastasis
(52).
cfRNA holds the potential ability to reflect a systemic response to growing malignancies.
Previous studies showed that circulating cfRNA transcriptome reflected the molecular changes of
fibrosis-associated genes in patients with nonalcoholic fatty liver disease (NAFLD) and holds
promising potential for fibrosis staging in clinical trials and practice (47). Also, cfRNA profiling
demonstrated a proof of principle for using cfRNA transcripts to distinguish between
hepatocellular carcinoma, multiple myeloma, their pre-cancerous states, and non-cancerous
states (48).

8
One of the main challenges of using transcriptional profile from the liquid biopsy is the preanalytical variables of RNA from the body fluids, including factors involved in blood collection,
plasma separation, and RNA purification process. The blood collection process involves
variables like anticoagulants, blood collection tubes, blood volume, and storage factors; while
performing plasma separation might consider centrifugation speed, duration, temperature, and
storage. RNA purification methods, plasma input volume, and temperature also impact final
cfRNA yield. The interpretation and replication of results on cfRNA abundance in plasma is
challenging, as the quantification technologies, including PCR and sequencing, are strongly
impacted by these pre-analytical variables (53). In addition to these technical challenges, data
analysis procedures protocols are not standardized.
Studies focusing on cfRNAs offer valuable insights into transcriptional profiles derived from
liquid biopsies, addressing the limitations of cfDNA in identifying the transcriptional programs
driving cancer phenotypes and their dynamic changes during the development of the disease.
However, there remains much to learn about effectively integrating transcriptome data and highthroughput sequencing into personalized therapeutic strategies.

9
1.3 Inferring Gene Expression from Cell-free DNA in Liquid Biopsy
cfDNAs are released into the bloodstream via apoptotic or necrotic events and active release
mechanisms, which carry the genetic and epigenetic information of its origin tumor tissues.
cfDNA fragmentomics refers to cfDNA molecules that are primarily nucleosome-associated
fragments, reflecting the distinctive chromatin configurations of their origins. Genomic regions
of cfDNA densely associated with nucleosomes are protected against endonucleases, while open
chromatin regions are prone to degradation.
Several studies identified that specific chromatin fragmentation features as potentially useful
for the classification of tissue-of-origin (22, 54-55). The length distribution of cfDNA has
revealed that cancer patients show a more fragmented pattern and shorter fragments compared to
healthy donors (56). There is now growing evidence suggesting that the modal size of normal
individuals is 167 bp and that of individuals with cancer is shorter than 145 bp with a 10 bp
periodicity. The 10-bp periodic oscillation observed might correspond to the wrapping and
protecting of the DNA from enzymatic cleavage around the nucleosome (57). Since genomewide DNA hypomethylation is a hallmark of many solid tumors (58), chromatin structure may be
more open, making nucleosome-bound cfDNAs more accessible to endonucleases during
apoptosis, resulting in alternative cleavage and cancer-specific fragmentation patterns.
Apart from using non-random distribution of cfDNA fragments to distinguish cancer patients
from non-cancerous conditions, their coverage signal around the transcriptional starting sites
(TSS) correlates with gene expression and has the potential to infer cancer driver gene
expression. By generating maps of genome-wide in vivo nucleosome occupancy, cfDNA
fragments harbor footprints of transcription factors (REF). The cfDNA nucleosome occupancies
correlate with the nuclear architecture, gene structure, and expression observed in cells,

10
suggesting that they could inform the cell type of origin. (54). Furthermore, the utilization of
whole-exome sequencing (WES) demonstrated that cfDNA coverage downstream of TSSs
reflects the silenced and highly expressed gene pattern. (59). Nucleosome depletion at the
promoters is the feature of active transcribed genes, and results in more diverse digested cfDNA
fragments (60).
While fragmentation of cfDNA shows promise in obtaining gene expression patterns from
cfDNA, it requires a high depth of whole-genome sequencing or whole-exome sequencing to
capture the majority of cfDNA fragments and achieve high accuracy of inferred gene expression.
In addition, the proportions of cfDNA released from tumor and non-tumor cells impact the
ability to infer expression. A study pointed out that more than 75% of cfDNA fragments for a
given TSS must be released by tumor cells to be able to retrieve gene expression status (61).
EPIC-seq, Epigenetic Expression Inference from cell-free DNA-sequencing, is a
fragmentomic method developed at Stanford (62). Focusing on fragment size and variability at
promoter regions, it used “promoter fragmentation entropy” (PFE) as the key feature to indicate
greater accessibility and transcription initiation at a given gene promoter.
In the Goldkorn lab, Dr. Daniel Bsteh obtained cfDNA from 2 healthy donors and 2
advanced mCRPC patients plasmas following the preparation of WGS libraries. The libraries
were pooled and sequenced for 30 X coverage. I recapitulated the bioinformatic pipeline written
by Dr. Daniel Bsteh, with the PFE determination pipeline originating from the EPIC-seq method
(62) (Figure 2).

11
Figure 2. Correlation of PFE and gene expression level in PBMCPBMCs. The scatterplots
depict the relationship between plasma cfDNA, obtaining from healthy donors (A) and
mCRPC patients (B), PFE versus leukocyte RNA expression levels (TPM). Pearson (r)
correlation coefficient is reported, P<2.2e-16.
To achieve the necessary deep coverage (~2000X) for prostate cancer-specific single gene
expression analysis, the Goldkorn lab is currently collaborating with the Rhie Lab at USC to
design a targeted probe panel. This panel enriches cfDNA fragments of genes of interest at ultradeep coverage, focusing on key genes and pathways associated with aggressive prostate cancer
phenotypes, therapy response, and clinical outcomes. Additionally, genes whose genetic
alterations are linked to these outcomes were included in the probe pull-down panel. To avoid
skewed results from leukocyte-derived cfDNA, genes highly expressed in leukocytes were
filtered out. This ongoing study in which the targeted approach ensures that only the relevant
cfDNA fragments are enriched, thereby facilitating precise analysis and deeper understanding of
prostate cancer-specific gene expression.
R = 0.88, p < 2.2e−16
0.20
0.25
0.30
0.35
−5 0 5 10
Gene expression level in PBMC (log2)
PFE
−5
0
5
10
TPM_log2
EPICseq healthy cfDNA
R = 0.88, p < 2.2e−16
0.20
0.25
0.30
0.35
−5 0 5 10
Gene expression level in PBMC (log2)
PFE
−5
0
5
10
TPM_log2
EPICseq mCRPC cfDNA A B

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1.4 Preservatives for cfRNA
Cell-free RNA (cfRNA) has emerged as a valuable biomarker for various clinical
applications including early cancer detection, monitoring of disease progression, and assessment
of therapeutic responses, contributing more transcriptomic information other than mutational
profile. Effective preservation of cfRNA in blood samples is critical to ensure the integrity and
reliability of downstream molecular and data analyses. The choice of blood preservation tubes
plays a pivotal role in maintaining the cfRNA quality, as improper preservation can lead to RNA
degradation or loss of crucial RNA information. However, currently, there is no standardized
protocol to retrieve cfRNA, and the yield as well as the integrity of RNA fragments are divergent
between individuals.
Obtaining whole blood in the clinic often involves a delay before processing in the
laboratory. Therefore, it is crucial to use preservatives in the blood collection tube to prevent
cfRNA degradation by ribonuclease (RNase) and contamination by cellular RNA during ex vivo
incubation. Ethylenediaminetetraacetic acid (EDTA) is widely used as the main anticoagulant in
blood collection tubes. EDTA effectively chelates calcium ions, magnesium, and other bivalent
metal ions, thereby inhibiting enzymatic reactions, such as blood clotting or DNA degradation
due to DNases. However, EDTA tubes lack additional preservatives, which can result in the
degradation of cfRNA and contamination of released intracellular RNAs over time. The
degradation is primarily due to ribonuclease (RNase) activity and other biochemical processes
that continue to occur in the absence of specific RNA-stabilizing agents.
During the temporary storage of blood samples, blood cells may undergo alterations
including cell apoptosis or stress response. The release of cellular RNAs from the degradation
and lysis of PBMCs may impact the expression pattern of the original transcriptome in the

13
plasma (68). Plasma cfRNA contains transcripts from other tissues that are crucial for
distinguishing disease states. cfRNA cannot be replenished by leukocytes, and the release of
cellular RNAs may dilute plasma cfRNAs, making them difficult to be detected in subsequent
analysis (69).
Short-term storage and transportation of blood samples from the collection site to the
laboratory are commonly required during research applications. To address the issue of RNA
preservation and degradation, commercially available blood preservation tubes (Table 1): Streck
BCT RNA, PAXgene Blood ccfDNA Tubes, and Norgen cfDNA/cfRNA Preservative Tubes
incorporate proprietary preservatives designed to maintain cfRNA stability in the whole blood.
Streck BCT RNA intends to reduce both blood cell lysis and nuclease activity by adding
preservative agent (polyamine), enzyme inhibitors (EDTA), nuclease inhibitors and metabolic
inhibitors (eg. Glyceraldehyde) with protective agents (the formaldehyde releasers, eg.
diazolidinyl urea). PAXgene Blood ccfDNA Tube aims to stabilize cfRNA by apoptosis inhibitor,
preferably a caspase inhibitor (a modified caspase specific peptide such as Q-VD-OPh), an
uncharged hypertonic agent (such as dihydroxyacetone), a chelating agent (EDTA) and an
organic compound with stabilizing effect. Norgen cfDNA/cfRNA Preservative Tubes are
designed to reduce cell lysis and preserve nucleic acids. Osmotic agents (such as NaCl), enzyme
inhibitors (EDTA), metabolic inhibitor (sodium azide) and volume-excluding polymer
(polyethylene glycol(PEG)) are added to the tubes. These tubes often contain chemical agents
that inhibit RNase activity and other enzymatic processes resulting in cfRNA degradation or
cellular RNA release, and osmotic agents to induce cell shrinking by mild hypertonic effects,
thereby increasing the cell stability. While these preservatives can significantly enhance RNA
stability and prevent degradation, they may also pose challenges. Specifically, the preservatives

14
could leave the stabilizing mechanism unclear, potentially complicating the retrieval and
interpretation of RNA expression.
Table 1. Features of commercially available blood preservation tubes and centrifuge
parameters.
We selected PBMC genes from a scatterplot depicting the relationship between plasma
cfDNA PFE versus leukocyte RNA expression levels (TPM) from a published study (62) (Figure
3.). Here, we hypothesize that the selected PBMC genes from the scatterplot could be used to
evaluate the preservation of cfRNA after blood draw, given that most cfRNA and cfDNA were of
hematopoietic origin.
By examining the dynamic changes in cfRNA expression patterns over time and under
different preservative and storage conditions, we aim to provide insights into the best practices
for cfRNA preservation and highlight potential trade-offs associated with the use of proprietary
preservatives. Through these analyses, we aim to determine which preservative maintains the
best quality of cfRNA, ensuring reliable and reproducible results in downstream applications.
cfNA Preservation Volume Storage Condition Preservative Centrifugation Condition
EDTA DNA, RNA 10 mL 5 days at 4˚C K3EDTA (without cfRNA preservatives)
Streck RNA 10 mL 7 days at RT
enzyme inhibitors
metabolic inhibitors
protective agents (polyamine, formaldehyde releasers, e.g.
diazolidinyl urea)
a. 1800 x g , 15min, RT
b. 2800 x g, 15min, RT
Norgen DNA, RNA 8.4 mL 30 days at RT
enzyme inhibitors
metabolic inhibitors
osmotic agents
volume-excluding polymer (e.g. PEG)
425 x g, 20min, RT
PAXgene DNA, RNA 10mL 10 days at RT
enzyme inhibitors
caspase inhibitors
uncharged hypertonic agent
organic compound with stabilizing effect
3000 x g, 15min, RT

15
Figure 3. A scatterplot depicts the relationship between plasma cfDNA PFE versus
leukocyte RNA expression levels (TPM). Figure taken from Esfahani et al. (2022)

16
Chapter 2
Materials and Methods
2.1 Blood Sample Collection and Whole Blood Processing
Donor Sample Collection.
Blood samples from non-cancer donors and patients with mCRPC were obtained from the
Norris Comprehensive Cancer Center at the University of Southern California. All samples were
collected under institutional review board (IRB) approved protocols by the University of
Southern California. Participants provided written informed consent to take part in the study.
Individuals who had no recorded previous history of cancer were considered to be non-cancer
donors.
For the direct comparison of commercially available cfRNA preservative tubes, we
collected peripheral whole blood from two healthy individuals. For each donor, blood was
collected into K2EDTA tubes (BD Vacutainer®) without cfRNA preservatives and into cfRNA
preservation tubes, namely, RNA Complete BCT for cell-free RNA from Streck (Catalog #,
Streck, La Vista, NE, USA), PAXgene Blood ccfDNA Tubes from Qiagen (Catalog #768115,
PreAnalytiX, Qiagen, Hilden, Germany), and cfDNA/cfRNA preservative tubes from Norgen
(Catalog #63950, Norgen Biotek, Thorold, ON, Canada).
For each donor, blood was collected into two EDTA tubes, two RNA Complete BCT
Streck tubes for cell-free RNA (Streck), two PAXgene Blood ccfDNA tubes (PreAnalytiX,
Qiagen) and four cfDNA/cfRNA preservative tubes (Norgen Biotek), for a grand total of 10
blood collection tubes. All tubes were filled completely and inverted immediately following
manufacturers’ recommendations after blood collection (Table 1.).

17
For EDTA tubes, Streck tubes, and PAX tubes, whole blood tubes were aliquoted into
four FalconTM 15mL conical centrifuge tubes and were left at room temperature (RT) for 0hr,
24hr, 48hr, and 72hr. For Norgen tubes, blood was drawn into preservative tubes, gently inverted
the tubes 5 times, with one inversion being a complete tum of the wrist 180 degrees and back,
and let the tubes stand in an upright position for 30 minutes before processing. The Norgen tubes
were not mixed and aliquoted because the manufacturer’s protocol advises against mixing the
contents before plasma separation to avoid hemolysis and contamination of the recovered plasma
with cellular genomic DNA/RNA. The preservation mechanism of the Norgen tubes differs from
other tubes, as it transforms the red blood cell layer into a gel-like texture.
Plasma Isolation.
Each whole blood content was centrifuged according to manufacturers’ protocols. (Table
1.) Separated plasma supernatant with 1.5 mL volume was transferred into a new FalconTM 15mL
conical centrifuge tubes at specific time points (0hr, 24hr, 48hr, and 72hr after blood drawn) and
proceeded to cfRNA extraction. The plasma section was centrifuged 3500 x g for10 min to
collect the buffy coat layer containing white blood cells (WBCs) and PBMCs.

18
Figure 4. Workflow of whole blood processing and the pre-analytical steps to examine
various commercially available blood preservation tubes.

19
2.2 cell-free RNA Extraction
Total cfRNA extraction was performed by using Plasma/Serum RNA Purification kit
(Norgen Biotek, Catalog#56100) according to the manufacturer’s protocol. 1.5 mL of human
plasma was used from each tube and each time point. To digest contaminating residual DNA,
cfRNA samples were treated with RNase-Free DNase I kit (Norgen Biotek, Catalog#25710)
according to the manufacturer’s protocol. The final eluted cfRNA was stored immediately at -
80°C.
2.3 Buffy coat RNA extraction and quantification.
Buffy coat RNA was extracted from the separated buffy coat layer from human whole
blood using Monarch® Total RNA Miniprep kit (New England BioLabs Inc., Catalog#T2010S).
To reduce contaminating genomic DNA, in-tube DNase I treatment included in the kit was
performed. The concentration of total buffy coat RNA was measured via NanoDrop
Spectrophotometry.
2.4 cell-free RNA Quantification
Standard curve generated from serial diluted buffy coat RNA
A standard curve using a serial diluted buffy coat, concentration from 50 pg/µL was
generated to quantify the unknown concentration of the samples. Starting from a 50 pg/µL buffy
coat RNA, we performed a six 1:10 serial dilution using the nuclease-free water, generating 5
pg/µL, 0.5 pg/µL, 50 fg/µL, 5 fg/µL, 0.5 fg/µL, and 50 ag/µL RNA standards. qRT-PCRs were
performed for 40 cycles using Luna® Universal One-Step qRT-PCR Kit (New England BioLabs

20
Inc., Catalog #E3005, Ipswich, MA) via the BioRad CFX 96 Real-Time PCR Detection System
as previously described.
qRT-PCR targeting 5S rRNA quantification.
30pg of cfRNA from each sample in triplicate was reverse transcribed and amplified
using Luna® Universal One-Step qRT-PCR Kit (New England BioLabs Inc., Catalog #E3005,
Ipswich, MA) via the BioRad CFX 96 Real-Time PCR Detection System. The cfRNA
quantification using qRT-PCR analysis was performed targeting housekeeping gene 5S rRNA.
Forward and reverse primer sequences of 5S rRNA can be found in Table 2.
To determine the unknown concentration of cfRNA samples, we used the linear equation
from the standard curve: � = �� + �, where � is the Ct value, � is the slope, and � is the Yintercept. To solve for �, the unknown concentration of cfRNA sample, using the known values
of �, � and �. The unknown cfRNA concentration is then obtained: RNA concentration = 10!.
Agilent Bioanalyzer RNA 6000 Pico chip. A second quantification strategy was conducted
using the Agilent RNA 6000 Pico Kit (Agilent, Catalog#5067-1513), with 3 µL of the sample
loaded.
2.5 PBMC Genes Primer Efficiency Test and qRT-PCR
Serial dilution of purified buffy coat RNA, previously described in Section 2.3.4,
generated a standard curve to calculate the efficiency of primers and r2 between serial dilution
concentrations. The concentrations of the buffy coat RNA samples were 10 ng/µL, 1 ng/µL, and
0.1 ng/µL, using 10-fold dilution steps. The prepared standard samples were analyzed by qRTPCR measuring the quantification cycle (Cq), reverse transcribed, and amplified using Luna®

21
Universal One-Step qRT-PCR Kit (New England BioLabs Inc., Catalog #E3005, Ipswich, MA)
via the BioRad CFX 96 Real-Time PCR Detection System. Forward and reverse sequences of
tested primers targeting PBMC genes can be found in Table 2. A plot of the Cq values versus the
logarithm of the concentrations was constructed and was expected to be linear with a negative
slope. All qRT-PCR experiments were performed in technical triplicates.
Table 2. Primer Sequence Information
qRT-PCR
Primers Forward Reverse
5S rRNA GCCATACCACCCTGAACG AGCCTACAGCACCCGGTATT
ENTPD7 Pair 1
GCTTCATTACCACGAGATAG
GC TGATGTCCAGCAAGTCATGGG
ENTPD7 Pair 2
TGTGATGTGCAACACACTGA
A GGATTGTCGGGACTCAGACCT
ICOSLG Pair 1
GCAGCCTTCGAGCTGATACT
C GTTTTCGACTCACTGGTTTGC
ICOSLG Pair 2
GGGATTCCAGGAGGTTTTGA
G GGGGTAGCCGTTTATGGATGT
SYT17 Pair 1
ATGGCGTACATCCAGTTGGA
A GGACTCGTAGCACTTCTGACA
SYT17 Pair 2 ATTCCCCGGATGGAAGACG CGCCAAACTCGATGGGTTTAAT
A
SASS6 Pair 1
GCAGGCTGTTTGAAATGTAG
C
TCTTTCGTGTAAAGTCCAGTTG
C
SASS6 Pair 2
GCGGCTAATAAAGACTTAAC
CGA CTTCTTGCTTAGTCCGCTGTAG
Beta Actin ACAGAGCCTCGCCTTTGCC GATATCATCATCCATGGTGAGC
TGG
GAPDH
TCAAGGCTGAGAACGGGAA
G GGACTCCACGACGTACTCAG
NCOR1
ACACCGCAGTATTGTCCAAA
T
CACCTGGTTTGTCTTGATGTTC
T
CDK17
AAGAGAAGGCTATCCCTCAC
AC ATAGGCTCATTATCCTTGCTGC
RBM33
CACATCAACCCGCACTTCAA
A GAACAGGTCCAGGTGTATGCT
ATP1A1
ACAGACTTGAGCCGGGGATT
A TCCATTCAGGAGTAGTGGGAG

22
2.6 Cell Culture and cell-free RNA from PCa Cells Supernatant
Human prostate cancer cell lines, LNCaP, were maintained in the lab in RPMI 1640
(Corning, Catalog #10-040-CV), supplemented with 10% heat-inactivated fetal bovine serum
(Omega) and 1% Penicillin/Streptomycin (100 units/mL, Invitrogen) at 37°C, 5% CO2.
LNCaP cells were cultured to confluency in two 15 cm plates. After reaching confluency,
the cells were washed with PBS and trypsinized using 0.25% trypsin. Post-trypsinization, the
cells were centrifuged and replated in a new medium, with each plate containing 12 mL of
growth medium. The cells were allowed to grow for 12 hours, followed by a medium
replacement with 12 mL of fresh growth medium. The cells were then incubated for an additional
24 hours. Following this incubation period, the growth medium from each flask was collected
into 15 mL nuclease-free conical tubes, centrifuged at 1000 × g for 10 minutes, and the
supernatant was transferred to new nuclease-free 15 mL tubes (Figure 4).
cfRNA was extracted from 12 mL of the collected cell culture medium using the QIAmp
Circulating Nucleic Acid Kit (Qiagen, Catalog#55114), following manufacturer’s protocol,
eluted into 50 µL of elution buffer, and immediately stored at -80°C. The cfRNA samples were
then DNase I treated, ensuring the removal of contaminating DNA, and further concentrated
from 50 µL to 15 µL using RNA Clean & ConcentratorTM-5 kit (Zymo Research,
Catalog#R1014). Final eluted cfRNA was stored immediately at -80°C. cfRNA quantification
was conducted using the Agilent RNA 6000 Pico Kit (Agilent, Catalog#5067-1513), with 3 µL of
the sample loaded.

23
2.6 RNA-seq Library Preparation
For plasma cfRNA, we collected the whole blood from one healthy donor into two EDTA
tubes and two Streck tubes. The tubes were left at RT for 0 hours and 48 hours, respectively
(Figure 5). All tubes were filled completely and inverted immediately following manufacturers’
recommendations after blood collection. Each whole blood content was centrifuged according to
manufacturers’ protocols. For each centrifuged plasma, we extracted cfRNA from 1.5mL plasma
using Plasma/Serum RNA Purification kit (Norgen Biotek, Catalog#56100) according to the
manufacturer’s protocol. The final eluted cfRNAs were verified via the Agilent RNA 6000 Pico
Kit (Agilent, Catalog#5067-1513), with 3 µL of the sample loaded.
We prepared stranded RNA-Seq libraries using SMARTer Stranded Total RNA-Seq Kit
v2 - Pico Input Mammalian from Takara Bio (Catalog #634411) according to the manufacturer’s
instructions. For cDNA synthesis, we used option 2 (without fragmentation), starting from highly
degraded RNA. The input of starting with 3µL of cfRNA from healthy plasma and 133.25 pg of
cfRNA from LNCaP cell supernatant were combined to generate cDNA libraries suitable for
next- generation sequencing. For the addition of adapters and indexes, we employed SMARTer
RNA unique dual index kit −96 U. SMARTer RNA unique dual index of each 5’ and 3’ PCR
primer were added to each sample to distinguish pooled libraries from each other. The amplified
RNA-seq library was purified by immobilization onto the AMPure XP PCR purification system
(Beckman Coulter). The library fragments originating from rRNA and mitochondrial rRNA were
treated with ZapR v2 and R-Probes according to the manufacturer’s protocols. For final RNAseq library amplification, 16 cycles of PCR were performed and eluted into 12 ul Tris buffer,
quantified with Qubit HS dsDNA, following amplified RNA-seq library purification. Positive

24
control RNA (250 pg/uL) included in the library preparation kit was included to ensure the
process of library preparation.
The cDNA libraries were quantified with Qubit dsDNA HS assay kit to ensure the yield
will provide enough material for further library validation and sequencing. The amplified RNAseq library was stored at −20 °C prior to sequencing. Libraries were barcoded and pooled, and a
total of 200ng from each cDNA library was sequenced using Illumina Sequencing of 20M reads
(PE150bp) per library (6GB) for a total of 200M (60 GB) for the entire submitted pool.
Figure 5. Workflow of cfRNA preparation for RNA-seq

25
Chapter 3
Results
3.1 Primer Efficiency Test of PBMC Genes
We were interested in using qRT-PCR to measure the expression of PBMC genes in
cfRNA recovered from purified plasma and verifying the effect and impact of blood preservation
tubes versus EDTA tubes, which have no preservatives for cfRNA but only anticoagulant.
Therefore we first aimed to establish the primer efficiency test of the PBMC genes on buffy coat
RNAs in order to validate the efficiency of selected PBMC genes.
To assess the performance of qRT-PCR assay and determine the dynamic range and the
limit of detection and quantification range, the buffy coat RNA samples were diluted in a 10-fold
dilution series. Following the serial dilution, qRT-PCR was performed on the 8 selected PBMC
genes with technical triplicates. The selected PBMC genes represented high or low expression
levels, summarized as transcript per million (TPM). A plot of the qRT-PCR results, Ct values,
versus the logarithm of the serial dilution concentrations was constructed. It was expected to be
linear with a negative slope (Figure 5). For a 10-fold dilution series, the slope is −3.33 when
efficiency = 100%, where the efficiency of qPCR is defined as the fraction of target molecules
that are copied in one PCR cycle. A properly designed primer should amplify target RNA with at
least 90% efficiency. Therefore, four primers with an efficiency closest to 100% were selected
for the following experiment to measure the expression of plasma cfRNA (Table 3).

26
Figure 5. Primer efficiency test for PBMC gene targets in serial diluted buffy coat RNAs.
Simple linear regression analysis depicted primer efficiencies of ENTPD7 (A), SASS6 (B),
ATP1A1 (C), and RBM33 (D). High correlation, indicated by R2 values close to 1,
demonstrates strong association between buffy coat RNA concentration and Ct values.
A B
A
C
A
D
A

27
Table 3. Primer efficiency percentage for ENTPD7, SASS6, ATP1A1, and RBM33.
Efficiency rates exceeding 80% indicate effective targeting PBMC genes, providing metrics
for measuring plasma cfRNA relative expression.
3.2 cfRNA Quantification
Following the blood collection from healthy donors and the processing of whole blood
samples, we extracted cfRNA from the separated plasma originally stored in various
commercially available blood preservation tubes. Since different quantification assays will
strongly impact the yield, we attempted to use two strategies: qRT-PCR and Bioanalyzer to
verify purified cfRNA. A standard curve plot generated from PBMCs was used to determine the
unknown cfRNA amount by calculating the corresponding Cq values (Figure 6). We found that
there is an upward trend of cfRNA stored at room temperature after the blood drawing process.
We then tested the cfRNA yield by using the Agilent Bioanalyzer RNA 6000 Pico Chip,
and the electropherograms generated from Bioanalyzer suggested that the majority of extracted
cfRNA are short fragments under 200bp (Figure 7). We summarized the RNA Integrity Number
(RIN), concentration, and total extracted cfRNA from 1.5 mL healthy donor plasma (Table 5). as
well as plotting the extracted RNA amount in the bar graph to observe the trend (Figure 8). As
seen in Figure 8, an upward trend of the cfRNA amount was observed in EDTA and Streck
tubes, but a downward trend was suggested in the Norgen tubes. As for PAXgene tubes, no
identical trend was observed between biological replicates. The results suggested that in EDTA

28
and Streck tubes, the release of cellular RNAs from leukocytes was diluting cfRNA in the
plasma, causing a few folds of increasing RNA amount after 72-hour whole blood room
temperature storage.
A
Ct value

29
Figure 6. QRT-PCR Quantification method for plasma cfRNA amount extracted from
different blood preservation tubes. (A) Simple linear regression plot generated from qRTPCR Ct values targeting 5S rRNAn. (B,C) Extracted cfRNA amount from 1.5mL plasma and
quantified with 5S rRNA using RTqRT-PCR. Amount of extracted cfRNA from blood tubes
calculated from given Ct values and linear regression formula from (A).
B
C

30
Figure 7. electropherogram from Agilent RNA Bioanalyzer of extracted plasma cfRNA.
The ladder is a set of six transcripts, 0.2, 0.5, 1.0, 2.0, 4.0 and 6.0 kb in length, designed for
use with the Agilent 2100 Bioanalyzer instrument (A). Representative electropherogram from
Agilent Bioanalyzer using RNA Pico 6000 Kit under different preservatives and storage time
at room temperature, EDTA tube (B), Streck tube (C), PAXgene tube (D), and Norgen tube
(E).
A

31
B

32
C

33
D

34

35
Table 5. Extracted cfRNA amount from 1.5mL plasma and quantified with Bioanalyzer.
Tube types Healthy donors Time points RIN Concentration Extracted cfRNA from
1.5 mL plasma (pg)
48hr 1.8 228 10260
72hr 1.8 248 11160
0hr 1.7 154 6930
24hr 1.9 226 10170
48hr 2.1 251 11295
72hr 2.4 318 14310
0hr 1.3 156 7020
24hr 1.4 201 9045
48hr 2.3 317 14265
72hr 1.3 199 8955
0hr 2.1 275 12375
24hr 1.8 227 10215
48hr 2.0 221 9945
72hr 2.6 327 14715
0hr 2.1 262 11790
24hr 2.3 240 10800
48hr 2.3 179 8055
72hr 2.5 280 12600
0hr 2.4 256 11520
24hr 2.5 531 23895
48hr 2.5 269 12105
72hr 2.5 218 9810
0hr 2.6 493 22185
24hr 2.4 411 18495
48hr 2.3 259 11655
72hr 2.1 252 11340
0hr 1.2 171 7695
24hr 1.1 112 5040
48hr 1.1 127 5715
72hr 1.9 209 9405
0hr 1.4 123 5535
PAXgene
Healthy donor 1
Healthy donor 2
Norgen
Healthy donor 1
Healthy donor 2
Healthy donor 1
Healthy donor 2
EDTA
Streck
Healthy donor 1
Healthy donor 2

36
Figure 8. Extracted cfRNA amount from 1.5 mL plasma across 0hr, 24hr, 48hr & 72hr
quantified using the Agilent 2100 Bioanalyzer with RNA 6000 Pico Series kit.
3.3 qRT-PCR for Plasma cfRNA Expression
Having established two different quantification assays to verify cfRNA yield from
healthy donor plasma, we selected Agilent Bioanalyzer verified results for the following qRTPCR input calculation. PBMC gene expression of cfRNA was determined through four selected
PBMC genes. We found that the selected PBMC genes did not demonstrate enough sensitivity to
detect the cfRNA expression changes upon different blood preservation tubes and room
temperature whole blood storage time (Figure 9).

37
Figure 9. Plasma cfRNA expression extracted from different blood preservation tubes.
Four PBMC genes display high Ct values across four time points of cfRNA extracted from
EDTA tube (A), Streck tube (B), Norgen tube (C), and PAXgene tube (D).
A
B

38
3.4 RNA-seq Libraries
The concentrations of cfRNAs from healthy plasma and LNCaP cell supernatant were
verified by Bioanalyzer RNA Pico 6000 chip. With 3.5 uL of RNA-seq library preparation input
volume, the amount of cfRNAs were calculated. Next, we controlled the input amount of spikein cfRNA admixtures from LNCaP cell supernatant as 133.25 pg. Finally, it came to the total
amounts of RNA for library input (Table 6). After the preparation of libraries, the eluted cDNA
C
D

39
libraries were quantified with Qubit dsDNA HS assay kit and calculated into a total amount with
12uL elution (Table 6). *200ng from each cDNA library was pooled and sent for sequencing
using Illumina Sequencing of 20M reads (PE150bp) per library (6GB) for a total of 200M (60
GB) for the entire submitted pool.
Table 6. RNA-seq libraries preparation results. (A) Each row represented the individual
tube differentiated by tube types, storage hours, and duplicate. Concentration was measured
with Bioanalyzer. cfRNA amount was calculated with 3.5 uL volume. Input amount of spike-in
cfRNA from LNCaP cancer cell supernatant was controlled as 133.25 pg. The ratio of healthy
plasma cfRNA to cancer cell cfRNA was calculated. (B) The concentrations of cDNA libraries
were measured with Qubit, and the amount was calculated with 12uL elution.
A
B

40
Chapter 4
Discussion
The design of the probe pull-down panel combined with EPIC-seq bioinformatic pipeline
analyses offers a significant opportunity to validate inferred gene expression profiles for various
applications in subsequent studies. These applications include comparing inferred gene
expression profiles from cfDNA to RNA-seq gene expression profiles from prostate cancer cell
lines, as well as comparing prostate cancer blood samples to those of healthy donors.
Additionally, this approach holds the potential to compare cfDNA gene expression profiles to
patient tissue biopsy RNA-seq data.
The unstandardized pre-analytical issues of cfRNA are a major and current obstacle in the
analysis of using circulating RNA for transcriptional profiles in liquid biopsy. Therefore, a better
understanding of the technical variables involved is necessary to obtain an intact and
comprehensive RNA material for downstream analysis.
In this study, we first demonstrated the impacts of blood collection tubes, including
EDTA or commercial proprietary preservatives, and storage time on the cfRNA from the
separated plasma samples. The primer efficiency test is crucial for validating the qRT-PCR
assay's ability to accurately measure gene expression levels. The standard curve is used to assess
the performance of qPCR assay by estimating its efficiency and determining the assay dynamic
range, limit of detection, and limit of quantification.
For a 10-fold dilution series, the slope is −3.33 when E = 100%. This follows from the
assumption of a perfect doubling of the number of DNA template molecules in each step of the
PCR. The efficiency (E) of PCR is defined as the fraction of target molecules that are copied in
one PCR cycle. Primers with high efficiency, 90%-100%, ensure that the assay can reliably

41
quantify RNA levels, even in samples with low RNA yield, such as cfRNA from plasma. By
establishing these parameters, we can confidently proceed with experiments that compare the
effects of different blood preservation tubes on cfRNA integrity and gene expression profiles.
The successful identification of efficient primers will enable more accurate and sensitive
detection of PBMC gene expression in cfRNA, facilitating the assessment of pre-analytical
variables and their impact on RNA integrity. This is important for optimizing liquid biopsy
protocols and improving the reliability of cfRNA-based transcriptional profiling.
The results from 2.4.2 cfRNA Quantification Strategies suggested that different
quantification assays strongly influence cfRNA yield determination, emphasizing the importance
of selecting appropriate quantification methods. The observed variations in cfRNA yield and
integrity among different blood preservation tubes underscore the need for standardized
protocols to minimize pre-analytical variability. The underlying mechanisms of qRT-PCR and
Bioanalyzer result in significantly different quantification outcomes. qRT-PCR measures relative
expression levels and targets the 5S rRNA housekeeping gene. However, this gene might not be
present in every cfRNA fragment. On the other hand, Bioanalyzer, a microfluidics-based
platform, analyzes nucleic acids by employing microcapillary electrophoresis to separate DNA or
RNA fragments based on size. This process allows Bioanalyzer to directly measure the size and
quantity of RNA fragments in a sample, based on their migration through a gel matrix during
electrophoresis. The resulting electropherogram provides detailed information about fragment
size distribution and RNA integrity. Bioanalyzer results are more sensitive to RNA degradation
or fragmentation compared to qRT-PCR, which focuses on the 5S rRNA housekeeping gene.
Understanding these mechanisms enables us to select the most suitable technique, thereby
enhancing the accuracy and reliability of cfRNA quantification and characterization.

42
We observed an upward trend in EDTA and Streck tube after 72-hour storage, suggesting
the possibility of hemolysis or lysis of white blood cells. This indicates that RNA quantities
increased rather than decreased due to degradation. Limited information available online about
the preservatives in Streck BCT RNA tubes suggests they contain polyamine (a preservative
agent), EDTA (an enzyme inhibitor), nuclease inhibitors, metabolic inhibitors (e.g.,
glyceraldehyde), and protective agents (e.g., diazolidinyl urea, a formaldehyde releaser).
The enzyme and nuclease inhibitors in these tubes prevent nucleases from degrading
cfRNA, thereby preserving RNA quantity and quality. Protective agents, such as polyamines or
formaldehyde releasers, bind to nucleic acids to prevent degradation and stabilize blood cells,
preventing the leakage of cellular RNA into the plasma. Additionally, metabolic inhibitors reduce
cell metabolism.
Compared to Norgen and PAXgene tubes, which use hypertonic agents to prevent blood
cells from leaking cellular RNA, the Streck tube does not. This difference might explain the
observed increase in total plasma cfRNA after 72 hours of room temperature storage.
Using qRT-PCR on purified plasma cfRNA targeting PBMC genes did not exhibit
sufficient sensitivity to detect dynamic changes in RNA expression. Due to the low yield of
cfRNA, even with a twofold increase in PBMC gene expression, the Ct values decreased by only
1. Additionally, since most cfRNA is presumably of hematopoietic origin, qRT-PCR analysis on
plasma cfRNA with primers targeting PBMC genes couldn't clearly differentiate between cfRNA
degradation and the release of cellular RNAs from blood cells. Over and above that, the RNA
integrity number (RIN) generated from the Bioanalyzer analysis suggested cfRNA fragments
between 1.5 and 3.0, indicating cfRNA as highly fragmented molecules. Study has pointed out

43
that intact to moderately degraded RNA was often lose the reliable quantification of gene
expression by qRT-PCR (RIN <5.0) (72).
Based on the caveat from previous experimental design and results from RT-qPCR, we
further conducted an RNA-seq experiment on plasma cfRNA from EDTA and Streck tubes,
stored at room temperature for 0 hours and 48 hours, to investigate the extent of increasing
leukocyte-derived cellular RNAs diluting cancer-derived cfRNA over time in different BCTs. We
are currently awaiting from RNA sequencing results and will analyze the readouts. Since we
controlled the spike-in amount of cancer cell supernatant-derived cfRNA, we anticipate
observing a diluted ratio of plasma cfRNA to cancer cell cfRNA, owing to the release of cellular
RNAs. The key advantages of RNA-seq are the increased discovery power for detecting novel
transcripts, enhanced sensitivity for detecting rare variants and lowly expressed genes, and
higher throughput for simultaneous sequencing of multiple genes across multiple samples.
In summary, our study underscores the critical importance of selecting appropriate blood
collection tubes and quantification methods for cfRNA analysis in liquid biopsy applications. We
have demonstrated that the type of preservative used in blood collection tubes significantly
influences cfRNA yield and integrity over storage time. Furthermore, our results highlight the
limitations of qRT-PCR in detecting dynamic changes in cfRNA expression due to its sensitivity
to RNA fragmentation and degradation. The application of RNA-seq offers a promising
alternative, providing increased sensitivity and throughput for comprehensive transcriptomic
profiling. Future studies should focus on standardizing pre-analytical protocols and further
exploring the use of advanced sequencing technologies to enhance the reliability and accuracy of
cfRNA-based diagnostics. These efforts will pave the way for optimizing liquid biopsy protocols
and improving the early detection and monitoring of diseases.

44
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Abstract (if available)

Abstract Background: Liquid biopsy, a non-invasive method for cancer assessment, analyzes cell-free DNA (cfDNA) and cell-free RNA (cfRNA) in blood. While cfRNA profiling provides insights into cancer progression and therapy resistance, RNA instability and degradation during processing pose significant challenges. This study aims to optimize cfRNA transcriptional profiling workflows and evaluate blood tube efficacy in preserving cfRNA.

Methods: Plasma was extracted from blood samples of two healthy donors drawn into EDTA, Streck, and Norgen tubes. Samples were stored at room temperature and analyzed at 0, 24, 48, and 72 hours post-draw. Quantification was performed using qRT-PCR and Agilent 2100 Bioanalyzer. We targeted specific PBMC genes for qRT-PCR and conducted RNA-seq on cfRNA from healthy donors and spike-in cancer cell line admixtures to examine leukocyte RNA dilution over time.

Results: qRT-PCR validated gene expression in purified plasma cfRNA, with most RNA fragments under 200 bp. EDTA and Streck tubes showed increased cfRNA at 72 hours, indicating leukocyte RNA release, unlike Norgen and PAXgene tubes. qRT-PCR lacked sensitivity for expression changes across preservation tubes, whereas RNA-seq provided comprehensive profiling. The cDNA libraries showed good concentrations and were sent for sequencing.

Conclusions: cfRNA quantification using qRT-PCR and Bioanalyzer showed consistent fragmentation, but qRT-PCR lacked sensitivity for expression changes. RNA-seq offered comprehensive profiling, emphasizing the need for appropriate preservation methods to maintain cfRNA integrity for reliable analysis.

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Asset Metadata

Creator Yu, Chen-Tzu (author)

Core Title Evaluation of preservatives in blood collection tubes for cell-free RNA transcriptional profiles in human plasma

School Keck School of Medicine

Degree Master of Science

Degree Program Biochemistry and Molecular Medicine

Degree Conferral Date 2024-08

Publication Date 07/26/2024

Defense Date 06/23/2024

Publisher Los Angeles, California (original), University of Southern California (original), University of Southern California. Libraries (digital)

Tag blood collection tubes,cell-free RNA,human plasma,liquid biopsy,OAI-PMH Harvest,preservatives,transcriptional profile

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Goldkorn, Amir (committee chair), Rhie, Suhn Kyong (committee member), Weisenberger, Daniel (committee member)

Creator Email bettyyu2000@gmail.com,chentzuy@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113998H4M

Unique identifier UC113998H4M

Identifier etd-YuChenTzu-13293.pdf (filename)

Legacy Identifier etd-YuChenTzu-13293

Document Type Thesis

Format theses (aat)

Rights Yu, Chen-Tzu

Internet Media Type application/pdf

Type texts

Source 20240730-usctheses-batch-1188 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu