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The protective role of progenitor cells: cell proliferation relates to cognition and white matter microstructure

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The protective role of progenitor cells: cell proliferation relates to cognition and white matter microstructure

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Content Copyright 2021 Anisa Marshall
THE PROTECTIVE ROLE OF PROGENITOR CELLS:
CELL PROLIFERATION RELATES TO COGNITION AND WHITE MATTER
MICROSTRUCTURE
by
Anisa Marshall
A Thesis Presented to the
FACULTY OF THE USC DORNSIFE COLLEGE OF LETTERS, ARTS AND SCIENCES
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree of
MASTER OF ARTS
PSYCHOLOGY
May 2021
ii
Table of Contents
List of Tables………………………………………………………………………………..……iii
List of Figures………………………………………………………………………………….....iv
Abstract………………………………………………………………………...……………….…v
Introduction…………………………………………………………………………….…………1
Methods…………………………………………………………………..……………………….6
Participants………………………………………………………………………………..6
Measures…………………………………………………………………………………..7
Analyses…………………………………………………………………………………...9
Results…………………………………………………………………………………..………..13
Discussion……………………………………………………………………………..………....14
References……………………………………………………………………….……………….27

iii
List of Tables
Table 1. Demographics and clinical characteristics………………………………………...……20
Table 2. CFU-Hill colony counts predict cognition…………………………………………...…21
Table 3. Pearson’s product-moment correlations of FA values and CFU-Hill colony counts…..22

iv
List of Figures
Figure 1. WM ROIs based on the JHU DTI-based white matter atlas………………………..…23
Figure 2. CFU-Hill colony counts are attenuated with cognitive impairment………………...…24
Figure 3. Low vs. High CFU-Hill colony proliferation and FA values……………………….....25
Figure 4. Scatterplots of cingulum and corpus callosum FA and CFU-Hill log counts…………26

v
Abstract
Background: Neurodegeneration is an early and prevalent cause of cognitive decline across
dementia subtypes. Progenitor cells may mobilize in response to age-related white matter
degeneration and ameliorate cognitive consequences of injury.
Objective: To evaluate endothelial progenitor cell proliferation in vitro in older adults, and
determine the relationship between cell proliferation, white matter microstructure, and cognitive
function.
Method: Sixty-three dementia-free older adults with low vascular risk were recruited from the
community. Participants underwent venipuncture, a comprehensive neuropsychological battery,
and structural MRI scanning, including DTI. Blood samples were obtained to determine
progenitor reserve using in vitro cell culture using the CFU-Hill colony assay.
Results: Participants with mild cognitive dysfunction (Clinical Dementia Rating; CDR = 0.5; n
= 11) exhibited depleted CFU-Hill colony counts relative to their cognitively normal
counterparts (CDR 0; n = 45) after controlling for age, sex, and education. Greater CFU-Hill
colony proliferation predicted better memory, executive functioning, and language performance,
and was associated with decreased fractional anisotropy (FA) in the majority of examined white
matter regions.
Conclusions: Progenitor cell proliferation is associated with increased cognitive functioning and
decreased FA throughout the brain. These data suggest progenitor cells may mobilize to areas of
white matter injury in response to damage and protect against related cognitive dysfunction.

Keywords: Progenitor cells, white matter microstructure, diffusion imaging, cognitive
dysfunction

1
Introduction
Vascular risk factors contribute to the breakdown of brain structural-functional integrity
and are robustly associated with increased risk of mild cognitive impairment (MCI) and
Alzheimer’s disease (AD) (de la Torre, 2006). Indeed, the neurovascular dysfunction and
endothelial activation propagated by these risk factors are thought to play key roles in the
pathogenesis and cognitive decline in AD (de la Torre, 2004; Grammas, 2011). While the
mechanism by which these vascular factors impair cognition remains to be fully elucidated, it
has been shown that endothelial dysfunction and cerebral blood flow (CBF) dysregulation due to
vascular pathology negatively impacts brain structural integrity critical to cognitive functioning
(De Silva & Faraci, 2016). Vascular risk factors may affect the white matter in particular, which
is particularly susceptible to ischemia from small vessel disease (SVD), a prevalent vascular
neuropathology that impacts white matter structural integrity and is a risk factor for MCI and AD
(Arvanitakis, Capuano, Leurgans, Bennett, & Schneider, 2016). Although the impact of vascular
risk factors on age-related cognitive decline risk is well established, less is known about vascular
protection and its role in moderating cognitive decline trajectory in healthy aging, MCI, and AD.
Investigation into vascular protective factors and its relation to age-related cognitive decline and
white matter integrity may be critical for efforts to identify new therapeutic targets aimed at
ameliorating risk for cognitive impairment associated with vascular dysfunction.
One potential protective vascular factor involves progenitor cells that originate from bone
marrow and other tissue compartments and are present in adult peripheral blood (Asahara,
Kawamoto, & Masuda, 2011). Vascular protective cells, termed endothelial progenitor cells
(EPCs), are crucial in blood vessel and capillary formation and healing, and also have been
shown to mediate neuronal repair mechanisms following brain injury (Herrmann et al., 2014).

2
Two types of time dependent EPCs exist, named accordingly as early EPCs and late EPCs [also
called endothelial colony forming cells (ECFCs)] based on the timing of their appearance in vitro
(Hur et al., 2004; Ma, Morancho, Montaner, & Rosell, 2015; Yoo et al., 2005). Early EPCs, a
heterogeneous combination of monocytes/macrophages (Park, 2011) and T-lymphocytes (Liman
& Endres, 2012), are also called “circulating angiogenic cells”, “myeloid angiogenic cells”
(Chambers, O’Neill, O’Doherty, Medina, & Stitt, 2013; Medina et al., 2011), “proangiogenic
cells” , “hematopoietic endothelial progenitor cells” (Asahara et al., 2011), “CFU-Hill cells”
(Asahara et al., 1997; Medina et al., 2010; Medina et al., 2017), and “small EPCs”. Although
some endothelial phenotypes are similar in both early EPCs and late EPCs, they differ in terms
of morphology, proliferation rate, survival features, gene expression, and functional expression
(Hristov & Weber, 2004). The differing functional roles found in early and late EPCs
synergistically contribute to neovasculogenesis in vivo (Hur et al., 2004). Early EPCs support
brain health by homing to sites of neural or cerebrovascular injury and secreting angiogenic
growth factors and cytokines (e.g., vascular endothelial growth factor; VEGF), thereby activating
adjacent endothelial cells and promoting angiogenesis by regenerating mature endothelial cells
and neurogenesis by supporting neural cell growth (Imitola et al., 2004; Schänzer et al., 2004).
Early EPCs appear to be further protective against neurovascular injury by secreting a number of
neurotropic factors in addition to VEGF (He et al., 2004), facilitating neuronal tissue repair (Li et
al., 2018; Park, K. J., Park, Liu, & Baker, 2014; Wang, T., Fang, & Yin, 2018), and contributing
to the neurogenic (Shen et al., 2004), neurovascular (Ma et al., 2015), and oligovascular niche
(Arai & Lo, 2009; Pham et al., 2012; Shen et al., 2004). In contrast, late EPCs enhance
vasculogenesis via their high proliferation potency upon stimulation by early EPCs (Basile &
Yoder, 2014; Hao et al., 2011; Shen et al., 2004; Simard et al., 2017; Szmitko et al., 2003; Wang,

3
P. et al., 2011). Despite their unique roles, early and late EPCs play an interactive role in
neuroprotection through their combined contributions to angiogenesis, neurogenesis, and
neovasculogeneisis (Hur et al., 2004). As such, these cells may help stave off the delirious
effects of neurodegenerative processes and delay, or prevent, age-related cognitive decline.
Putative early EPCs are cultured from peripheral blood mononuclear cells (PBMCs) and
support angiogenesis and endothelial functioning. In vitro colony-forming assays measuring
angiogenic activity have been used historically to define putative EPCs. These assays afford
evaluation of colony-forming units (CFU-Hill colonies) indicative of EPC proliferative potential.
CFU-Hill colonies consist of hematopoietic progenitor cells and T lymphocytes (Timmermans et
al., 2007; Yoder et al., 2007) and are involved in stimulating, supporting, and regulating
angiogenesis (Asosingh et al., 2008; Hur et al., 2004; Li et al., 2018; Yoon et al., 2005).
Evidence corroborating protective effects of EPCs in brain injury and neurodegeneration
comes from studies using cell surface EPCs markers (CD34+, CD133+, CD309+) and
investigations into the EPC angiogenic secretome in individuals with MCI and/or AD (Asahara
et al., 2011). Neurogenesis (Moreno-Jiménez et al., 2019) and angiogenesis (Vagnucci & Li,
2003) are both impaired in AD, and circulating EPCs are accordingly depleted in AD (Lee et al.,
2010; Xiao-dong et al., 2011). AD patients also present with reduced angiogenic activity of the
EPC secretome (Lee et al., 2010). More specifically, circulating CD34+ levels are reduced in
AD and related to AD neuropathology [i.e., amyloid-beta (Ab) levels] in the cerebrospinal fluid
(CSF) (Maler et al., 2006). Although case-control comparisons of progenitor levels in AD and
cognitively normal older individuals have yielded mixed results (Bigalke et al., 2011; Breining et
al., 2016; Xiao-dong et al., 2011; Stellos et al., 2010; Taguchi et al., 2008), the extent to which
progenitor cells are mobilized in AD consistently relates to cognitive functioning. For example,

4
patients with AD exhibiting higher CD34+CD133+ cell levels and greater progenitor cell
proliferation in vitro have better cognitive functioning (Lee et al., 2009).
A similar profile of EPC depletion appears in patients with increased cardiovascular and
stroke risk (Ghani et al., 2005; Hill et al., 2003; Werner et al., 2005). Despite the inverse
relationship between EPCs and vascular risk, when EPCs mobilize in response to
cerebrovascular and white matter injury and home to sites of ischemia, levels of EPCs increase
(Asahara et al., 1997). EPCs levels are significantly affected by stroke onset, and the degree to
which they are mobilized relate to stroke outcome and infarct severity (Arai, Jin, Navaratna, &
Lo, 2009). Specifically, greater EPC proliferation correlates with better functional recovery and
reduced infarct growth after vascular injury caused by acute ischemic stroke, suggesting EPCs
may support neurorepair after ischemic stroke (Sobrino et al., 2007). This neuroprotective
support is corroborated by transplantation studies in animal models, where EPC-based therapy
leads to recovery of ischemic cerebral injury (Zhang, Zhang, Jiang, & Chopp, 2002; Zhao et al.,
2013). The association between increased EPC proliferation, good functional outcome, and
attenuated infarct growth and maturation suggest EPC mobilization as a key process for
angiogenic recovery and subsequent brain tissue repair following ischemia (Arai, Jin, Navaratna,
& Lo, 2009). EPCs may specifically support oligodendrocyte precursor cell proliferation in the
oligovascular niche (Yuan et al., 2018), suggesting a potential role in protecting white matter
integrity. Higher degrees of circulating and proliferating EPCs may thus indicate a protective,
compensatory response when disease severity is low and following acute episodes vascular
injury. Furthermore, the mobilization of EPCs after vascular injury indicate that these cells may
play a role in repairing white matter.

5
Novel imaging techniques could shed light on the neurological benefits of progenitor
cells in the aging brain by delineating their relationship to white matter structural integrity.
Conventional MRI is important for identifying macrostructural white matter markers of
microvascular disease, including white matter lesions (i.e., white matter hyperintensities; WML)
and cerebral microbleeds (CMBs). Both WMLs and CMBs confer risk of MCI and AD
(Defrancesco et al., 2013; Smith et al., 2008; Yates et al., 2014). However, complementary
assessment of microstructural damage in white matter using diffusion imaging is also crucial for
detecting structural abnormalities that may result in functional impairment, particularly during
the early phases of the disease course (Maier-Hein et al., 2015; Sexton, Kalu, Filippini, Mackay,
& Ebmeier, 2011). Microstructural white matter abnormalities in normal appearing white matter
on conventional MRI can be present years before WML develop, suggesting that visually
identified WMLs are indicative of more advanced white matter pathology (De Groot et al.,
2013). Diffusion tensor imaging (DTI) is one such method of advanced structural imaging that
can elucidate axonal integrity, a measure correlated with functional recovery of brain injury
(Stinear et al., 2007), myelination, cerebrovascular health (Sam et al., 2016), and early age-
related cognitive decline (Charlton et al., 2006). Given the putative protective role EPCs have on
the neurovascular and oligovascular niche, measuring axonal integrity with DTI may provide
insights into how EPCs could protect brain structure and functioning.
The present study investigated the link between in vitro EPC proliferation and axonal
integrity in relation to cognitive functioning in a cohort of community dwelling older adults.
Based on prior studies in stroke and AD (Sobrino et al., 2007; Yip et al., 2008), we hypothesized
that EPC proliferation may be potentiated by white matter damage indicated by DTI, and that the
degree of EPC proliferation would be related to better cognitive function.

6
Methods
Following participants’ informed consent and approval by the University of Southern
California (USC) Institutional Review Board (IRB), the study protocol was implemented at the
Vascular Senescence and Cognition (VaSC) Laboratory in the USC Department of Psychology.
Participants
Sixty-three community dwelling older adults (Mage = 70.94; SDage = 7.39), free of
dementia or clinical stroke, underwent venipuncture, neuropsychological testing, and brain MRI.
Participants were recruited from the community via university platforms, word-of-mouth, flyers,
and community outreach events. Inclusion criteria for participants consisted of being a minimum
age of 55 years and independently living. Exclusion criteria included history of dementia,
clinical stroke, learning disability, traumatic brain injury, or other systemic or neurological
illness or treatment that may affect central nervous system functioning. Vascular risk factors
were determined by clinical interview and objective measures, including blood pressure, body
mass index (weight in kg/ height in m
2
), dyslipidemia, (i.e., history of elevated total cholesterol,
low density lipoprotein cholesterol or triglycerides, or reduced high density cholesterol), diabetes
(history of type I or II), history of cardiovascular disease (angina, intermittent claudication,
myocardial infarction, stent placement, coronary artery bypass graft), or history of transient
ischemic attack. Blood pressure was measured twice from each arm on two separate days, and
systolic and diastolic pressures were averaged across these time points. Pulse pressure was
calculated as difference between systolic and diastolic pressure, and mean arterial pressure was
calculated as the sum of diastolic blood pressure and one-third of the pulse pressure.
Procedures
Blood draw

7
Fasting venipuncture was performed prior to neuropsychological testing during
participants’ first visit, with approximately 25 mL of blood being drawn into EDTA coated
tubes. Samples were centrifuged and peripheral blood mononuclear cells (PBMCs) were isolated
from the plasma for performing cellular assays as described below. The separated plasma was
stored at -80 degrees Celsius prior to performing cellular assays.
CFU-Hill in vitro culture
PBMCs were isolated from anticoagulated whole blood by density gradient centrifugation
in Histopaque-1077 (Sigma Aldrich). Cells were first seeded on 12-well fibronectin-coated plates
(Day 0) at 2.5 x 10
6
cells/well in CFU-Hill Liquid Medium (Stem Cell Tech). Non-adherent cells
were then collected and replated onto 24-well fibronectin-coated plates at 1.0 x 10
6
cells/well
(Day 2). CFU colonies were next scored as a central core of round cells surrounded by radiating
spindle-shaped endothelial-like cells (Day 5) (Figure 2a, b). The depletion of early adherent cells
in the CFU-Hill assay is necessary in order to exclude activated macrophages and mature
endothelial cells. Following day 5 in culture, CFU-Hill colonies were counted, and mean colony
number calculated.
ApoE genotyping
ApoE genotyping was derived from plasma separation of the blood cell pellet fraction, in
which the PureLink Genomic DNA Mini Kit (Thermo Fisher) isolated DNA. Isolated DNA was
genotyped using the TaqMan SNP Genotyping Assay (Thermo Fisher) on an Applied
Biosystems 7300 Real Time PCR System. ApoE gene SNPs were analyzed for dbSNP IDs
rs429358 and rs7412. Allelic discrimination was determined using qPCR software. The ApoE e4
allele was labeled as rs429358-C + rs7412-C.

8
Cognitive testing
The cognitive test battery in the present study consisted of several neuropsychological
tests in each cognitive domain of interest, in addition to a global measure of cognitive and
functional performance related to the deficits in AD and other dementias. Cognitive domains for
this study included memory (Verbal memory: WMS-IV Logical memory I and II; Visual
memory: WMS-IV Visual Reproduction I and II), executive functioning (DKEFS Trail Making
Test B, FAS Fluency, Golden Stroop), attention (DKEFS Trail Making Test A, Digit Span
Forward), and language (Naming: Multilingual Naming Test (MiNT), Fluency: Animal and
Fruits & Vegetables Naming.
The Clinical Dementia Rating Scale (CDR) was used as a measure of cognitive and
functional impairment indicating MCI and/or preclinical AD. The CDR is a 5-point scale
characterizing six cognitive and functional domains applicable to AD and similar dementias,
including memory, orientation, judgement & problem solving, community affairs, home &
hobbies, and personal care. Functional domains were based on a participant and informant semi-
structured interview administered by a trained research associate, while cognitive domains were
evaluated by administering a participant and informant semi-structured interview and brief
cognitive tests. A global CDR score was calculated by scoring each domain and totaling the
CDR Sum of Boxes (CDR-SOB) based on criteria detailed elsewhere (Morris, 1993). A global
score of zero indicates no clinically meaningful cognitive or functional impairment, while a
global score of 0.5 indicates mild cognitive impairment (MCI) and/or very mild dementia. While
different criteria are used to diagnose MCI, previous studies have used a global CDR of 0.5 as a
diagnostic criterion (Chang et al., 2011; Duara et al., 2010; Meguro et al., 2004). The CDR has
additionally been shown to be informative in diagnosing MCI (Stephan et al., 2013).

9
Imaging acquisition
Participants were scanned at Dana and David Dornsife Cognitive Neuroscience Imaging Center
at USC on a Siemens 3T trio with TIM scanner with a 20-channel head coil. All eligible
participants were scanned with a T1-weighted magnetization prepared rapid gradient-echo
(MPRAGE) sequence (Scan parameters: R = 2300ms, TE = 2.98ms, TI = 900ms, slice thickness
= 1.20 mm, flip angle = 9°, field of view = 256mm). Diffusion tensor imaging was acquired in a
subset of our sample (n = 46) using two 64-direction diffusion weighted echo planar imaging
sequences with gradient values of b = 0 and b = 1000 s/mm
2
and the following parameters: TR =
8100 ms; TE = 69 ms; 70 slices with 2mm thickness. A subset of our sample (n = 51)
additionally underwent T2-fluid attenuated inversion recovery (FLAIR) sequence with the follow
scan parameters: TR = 10000 ms, TE = 91 ms, TI = 2500 ms, slice thickness = 5.0 mm, flip
angle = 150°, field of view = 220 mm) for evaluation of SVD lesion burden.
Analyses
DTI preprocessing
DTI data processing and analysis were conducted using FMRIB Software Library (FSL
v6.0) and MRtrix3 (Tournier et al., 2019). Each volume was quality controlled using methods
described elsewhere and visually examined for anomalies (Andersson, Graham, Zsoldos, &
Sotiropoulos, 2016). Diffusion-weighted image pre-processing included image denoising,
followed by Gibbs ringing, eddy current distortion, and B1 bias field correction. Outlier slices
from one subject with corrupted data were removed from further imaging analyses. Non-brain
tissue was deleted from whole head images using brain masks generated from each b=0 image
via FMRIB’s Brain Extraction Tool (BET v2) (Smith, 2002). FMRIB’s Diffusion Toolbox (FDT
2.0) was then used to fit diffusion tensors and calculate fractional anisotropy (FA).

10
FA images were subsequently registered into FMRIB58_FA standard space and voxel-
wise analyses were conducted using Tract-Based Spatial Statistics [TBSS v1.2; (Smith et al.,
2006)]. Likely outliers were first removed from the diffusion tensor fitting by eroding the FA
images slightly and zeroing the end slices. Nonlinear registration then aligned all FA images to
the 1x1x1mm FMRIB58_FA standard space. Nonlinear transforms from the previous step were
subsequently applied to all subjects to bring them into standard space. Registrations were
evaluated by visually inspecting the warped structural and FA images in standard space. No
misregistrations were identified. The mean FA image was generated following registration and
thinned to create the mean FA skeleton, which was further thresholded at FA > 0.2 to mitigate
partial volume effect. FA volumes for each individual were then projected onto this skeleton to
create a skeletonized FA map. A voxelwise cross-subject statistical analysis with 500
permutations was subsequently conducted on a group comparison of high versus low CFU-Hill
colony counts using a median split of the log counts. The significance threshold of p < 0.5 was
corrected for multiple comparisons using family-wise error (FWE) correction. Skeletal regions
showing FA measure differences between groups was labeled using the John Hopkins University
(JHU) white matter atlas. Mean FA values for each ROI derived from the JHU white matter atlas
were also extracted for partial correlational analyses, correcting for age, sex, and education. A
subset of our total participant sample (n = 37) that had both DTI and cell culture data were
included in these analyses.
Small vessel disease quantification
A subset of our sample (n = 51) were classified for probable small vessel disease (SVD) burden.
White matter T2 hyperintense lesions (WML), lacunes, and microbleeds were identified for each
individual subject and the degree of lesion burden was quantified according to previously

11
detailed methods (Amin Al Olama et al., 2020; Staals et al., 2015). Fazekas score was calculated
to quantify WML (Fazekas, 1987). Lesion burden was quantified as a score ranked on a scale
from zero to three according to degree of probable SVD. A score of zero indicated absent WML,
lacunes, and microbleeds. A score of one indicated no WMLs [i.e., Fazekas score of 0: “absent"
(Fazekas, 1987)], and the presence of one or more microbleeds and 1-2 lacunes (n = 17). A score
of one indicated a minimal degree of WMLs [i.e., Fazekas score of 1: “smooth halo around
ventricles or beginning of confluence of foci” (Fazekas, 1987)], one or more microbleeds, and 1-
2 lacunes (n = 9). A score of two indicated a moderate degree of WMLs [i.e., Fazekas score of 2:
“smooth halo around ventricles or beginning of confluence of foci” (Fazekas, 1987)], one or
more microbleeds, and 3-5 lacunes (n = 5). Finally, a score of three represented the worse degree
of lesion burden, as indicated by significant presence of WMLs [i.e., Fazekas score of 3:
“irregular periventricular hyperintensity extending into the deep white matter or large confluent
areas" (Fazekas, 1987)], one or more microbleeds, and greater than 5 lacunes (n = 1). The SVD
simple score was then quantified by dichotomizing the original SVD score (score of 0 versus
score greater than 0) to account for unequal sample sizes between groups (SVD simple score of
0: n = 28; SVD simple score of 1: n = 23).
Statistical analyses
Data were first examined for outliers and deviations from normality using skewness and
kurtosis measures. Log10-transformation was used to normalize the distribution of progenitor
cell proliferation.
Participant groups (CDR 0 versus CDR 0.5) were compared across demographic
characteristics and vascular risk factors using independent sample t-tests, chi-square tests, and
one-way analysis of variance (ANOVA). Potential contributors to progenitor cell proliferation

12
(i.e., age, sex, and vascular risk factors) were additionally analyzed. CFU-Hill colony counts
were evaluated between groups using Univariate Analysis of Covariance (ANCOVA),
controlling for age, sex, and education level. Hierarchical multiple linear regression analyses
were run to determine if the addition of CFU-Hill counts improved the prediction of
neuropsychological test performance above age, sex and education level alone. Age, sex and
education level were thus included in the first model of each test, followed by CFU-Hill log
counts in the second model. ANCOVA compared CFU-Hill counts to CDR score and SVD
burden score.
The JHU DTI-based white matter atlas was used to select 21 WM bilateral regions of
interest (ROIs): corticospinal tract; medial lemniscus; cerebral peduncle (inferior, superior, and
main); internal capsule (anterior and posterior limb, and retrolenticular part); corona radiata
(anterior, superior, and posterior); posterior thalamic radiation; sagittal striatum, external
capsule, cingulum (cingulate gyrus and parahippocampus); fornix (cres) / stria terminalis;
superior longitudinal fasciculus; superior fronto-occipital fasciculus; uncinate fasciculus, and
tapetum. Cerebellum (middle cerebellar peduncle, pontine crossing tract, inferior and superior
cerebellar peduncle), corpus callosum (the genu, body, and splenium), and fornix (column and
body, (crus)/stria terminalis) were also included as ROIs in our analyses (Figure 1). Limbic and
cortical white matter ROIs were selected due to their role in early MCI and subsequent AD, and
additional ROIs were studied to elucidate the association of CFU-Hill colony proliferation and
white matter integrity. Pearson’s product-moment correlations were run to examine the
relationship between CFU-Hill colony counts and white matter ROIs in the subset of participants
with DTI and cell culture data. Multiple comparisons were corrected for by decreasing the false

13
discovery rate (FDR) using the Benjamini-Hochberg procedure (Benjamini and Hochberg,
1995).

Results
Demographics and vascular risk factors
Participants with indications of mild cognitive and/or functional impairment as measured
by the CDR (i.e., CDR of 0.5) were significantly older than participants who had a CDR of 0 (p
< 0.001) but did not differ on other demographic or vascular risk factors (Table 1).
CFU-Hill colony proliferation and cognitive dysfunction
CFU-Hill colony log counts were significantly depleted in participants with a CDR of 0.5
(M = 0.47, SD = 0.46) compared to participants with a CDR of 0 (M = 0.78, SD = 0.35); t(2.45)=,
p = 0.02. After correcting for age, sex, and education, CFU-Hill colony log counts remained
significantly depleted in participants with a CDR of 0.5 compared to those with a CDR of 0, F(1,
51) = 5.84, p = 0.02 (Figure 1c, d).
Lower CFU-Hill colony counts predicted worse performances on tests of executive
functioning, verbal memory, and language after controlling for age, sex, and education [(1)
Animals: F(4, 60) = 4.10, p = 0.005; R
2
= 0.16; β = 0.41; DF = 12.51, p = 0.001; (2) Fruits and
Vegetables: F(4, 60) = 4.00, p = 0.006; R
2
= 0.21; β = 0.35; DF = 9.01, p = 0.004; (3) FAS: F(4,
60) = 7.81, p < 0.001; R
2
= 0.09; β = 0.30; DF = 7.04, p = 0.007; (4) Trails B: F(4, 60) = 5.13; p
= 0.001; β = 0.25; R
2
= 0.26; DF = 5.07, p = 0.03; (5) WMS-IV Logical Memory I: F(4, 60)
=9.17; p < 0.001; β = .28; R
2
= 0.38; DF = 7.92, p = 0.02] (Table 2). CFU-Hill colony counts did
not significantly predict other cognitive tests in our study after controlling for age, sex, and
education (p > 0.5; Table 2).

14
CFU-Hill colony proliferation and SVD
No significant relationship was found between progenitor cell colony counts and SVD
dichotomized scores after adjustment for age, sex, and education [F(1,46) = 1.77 , p = 0.19].
CFU-Hill colony proliferation and white matter regions
In the subsample that underwent DTI (n = 37), individuals with greater CFU-Hill colony
counts exhibited significantly lower FA values for the vast majority of ROIs after controlling for
age, sex, education, and multiple comparisons (Table 3) There was a highly significant negative
correlation between CFU-Hill colony counts in several cortical and limbic white matter ROIs,
including corticocortical connections through the external capsule (right: r(31) = -.58, p <0.001;
left: r(31) = -.61, p <0.001), thalamocortical and corticothalamic connections through the
anterior internal capsule (right: r(31) = -.52, p = 0.002; left: r(31) = -.50, p = 0.003) and
thalamic radiation (right: r(31) = -.56, p = 0.001; left: r(31) = -.62, p <0.001), and cingulum
bundle (cingulate gyrus (right): r(31) = -.55, p = 0.001; cingulate gyrus (left): r(31) = -.60, p
<0.001; hippocampal cingulum (right): r(31) = -.62, p <0.001; hippocampal cingulum (left):
r(31) = -.52, p = 0.002) (Figures 3, 4b). Greater CFU-Hill colony counts was also associated
with significantly lower FA in the subregions of the corpus callosum (genu: r(31) = -.58, p
<0.001; body: r(31) = -.59, p <0.001; splenium: r(31) = -.66, p <0.001) (Figures 3, 4a).

Discussion
The present study suggests depletion of progenitor cells in vitro are related to cognitive
dysfunction in older adults, specifically clinical measures of dementia (i.e., CDR score),
memory, executive functioning, and language. Findings suggest greater proliferation of
progenitor cells may additionally be linked to diminished white matter integrity, indicating

15
progenitor cells may be mobilized in response to white matter injury and may protect against
vascular-related white matter injury and resulting cognitive dysfunction. The measures of
cognitive dysfunction related to progenitor cell proliferation are indicators of pathological aging,
including immediate memory, executive dysfunction and fluency, all of which are linked to
increased risk of MCI and AD.
The present findings indicating in vitro EPC proliferation in relation to cognitive function
are consistent with prior studies of circulating progenitor cells showing reduced cell counts in
older adults with cognitive impairment (Hajjar, Goldstein, Waller, Moss, & Quyyumi, 2016;
Nation et al., 2018), while further highlighting the links among cognitive functioning, white
matter integrity and progenitor cell proliferation. Prior studies have demonstrated depletion of
progenitor cells in AD (Kong et al., 2011; Lee et al., 2009; Maler et al., 2006), early stage AD
(Maler et al., 2006), MCI (Nation et al., 2018) and vascular cognitive impairment (Taguchi et al.,
2008). The present study did not assess for AD biomarkers, thereby limiting conclusions
regarding the role of progenitor cells in the etiology of AD. However, the low vascular risk
burden in our study cohort suggests that EPC proliferative capacity is related to cognitive
dysfunction and integrity of white matter regions implicated in MCI and AD beyond that which
can be explained by vascular risk factors. Our participants had no reported history of
cardiovascular disease or stroke, and levels of SVD indicators were relatively low. CFU-Hill
counts were additionally not associated with SVD, and the vast majority of other vascular risk
factors. EPCs may thus mobilize in response to white matter damage to support cognitive
functioning independent of vascular risk.
Memory and language deficits, executive dysfunction, and decreased white matter
integrity in limbic and cortical regions are all indicative of early stages of MCI and AD (Baudic

16
et al., 2006; Bondi et al., 2008; Di Paola et al., 2010; McCullough et al., 2019; Schroeter, Stein,
Maslowski, & Neumann, 2009; Zhuang et al., 2012; Zhuang et al., 2013), suggesting attenuated
EPC proliferation may be a harbinger to prodromal AD. Previous studies demonstrating the
association of circulating CD34+ cell depletion in AD dementia and CSF Ab levels corroborate
the finding that EPCs may be related to AD pathology (Maler et al., 2006). Proliferative capacity
of progenitor cells may thus be associated with prodromal indicators of AD, including early
microstructural changes in white matter. Loss of white matter integrity involves a myriad of
pathological processes including axonal loss, demyelination, oligodendrocyte death, and glial
interactions (Assaf & Pasternak, 2008). Microstructural white matter changes as indicated with
FA, including demyelination and axonal damage, have already occurred prior to the
macrostructural white matter atrophy present in the more advanced stages of AD (Maier-Hein et
al., 2015). Thus, EPCs may mobilize to protect against cognitive consequences of injury due to
microstructural damage of injured white matter during prodromal AD, and later and attenuate in
progressive stages of the disease course as a result of increased AD pathology burden.
A number of possible mechanisms underlie the association between EPC proliferation
and WM microstructure, but not WM macrostructure. An intricate interplay of several regulatory
factors moderate EPC proliferation and mobilization, and expressions of these factors can be
altered with disease (Alexandra, Zeiher Andreas, & Stefanie, 2005). For instance, cytokines,
chemokines and other proteins in the EPC secretome may impact the proliferative capacity of
EPCs by supporting EPC growth and recruitment. C-X-C motif chemokine ligand (CXCR4) and
its associated chemokine, stromal cell–derived factor-1 (SDF-1), are expressed in the majority of
EPCs and have been shown to promote EPC migration. CXCR4 may mediate neuroprotection
and augment angiogenic following stroke in animal models (Fan et al., 2010). SDF-1 has been

17
shown to play an integral role in the mobilization of hematopoietic stem cells and EPCs,
mediates vascularization by augmenting EPC recruitment (Cheng & Qin, 2012). Vascular
endothelial growth factor (VEGF) has similarly shown to be important in mobilizing EPCs
(Tilling, Chowienczyk, & Clapp, 2009). Higher levels of VEGF and decreased SDF-1α in
individuals with silent brain infarctions are associated with a more favorable prognosis following
acute lacunar stroke, suggesting that the reparative function of EPCs is highly dependent on its
secretome (Kwon et al., 2015). The EPC secretome also appears to enhance the proliferation and
maturation of oligodendrocyte precursor cells in addition to supporting endothelial cell
proliferation in an animal model of cerebral hypoperfusion, demonstrating that factors secreted
by EPCs may protect against injury and degeneration in diseases involving white matter
(Takakuni et al., 2018), including AD and vascular contributions to cognitive impairment and
dementia. As the present study did not examine the EPC secretome in relation to cognition, SVD
score, or FA, further studies are warranted to delineate the association between levels of EPC
regulatory factors and white matter integrity, and the effects on age-related cognitive decline.
Progenitor cells have successfully improved functional outcomes and structural integrity
of neurological disorders including stroke and traumatic brain injury (TBI) (Mahmood et al.,
2001). White matter recovery is recognized as an essential process for the brain to repair after
injury and neurodegeneration (Benowitz & Carmichael, 2010). Supporting the entire
oligovascular niche, where cerebral endothelial cells support the proliferation of oligodendrocyte
precursor cells, in addition to the neurovascular unit, is thus crucial for optimizing repair
processes and functional outcome following white matter injury (Itoh, Maki, Lok, & Arai, 2015)
The complex interplay between remyelination and blood vessel regeneration during the rewiring
process of white matter tracts has been demonstrated in vivo in imaging studies of animal models

18
of stroke recovery (Jiang, Zhang, & Chopp, 2010). Crosstalk between oligodendrocytes and
cerebral endothelium mediate angiogenesis, a process that EPCs may support (Pham et al.,
2012). EPCs may also repair blood-brain barrier (BBB) breakdown linked to TBI via supporting
the interplay of endothelial cells, pericytes, astrocytes, neurons, and smooth muscle cells. These
interactions in the neurovascular unit are integral in maintaining BBB integrity. Macrostructural
vascular abnormalities may be mediated by the upregulation of angiogenesis, a developmental
process prevalent in the cerebrovasculature of individuals with MCI/AD (Vagnucci & Li, 2003)
and occurring in tandem with reduced vascular density of cortical areas, hippocampus, and basal
forebrain seen in AD (Miyakawa, 2010). This seemingly contradictory association may be due to
aberrant angiogenesis in AD, where new vessels are poorly formed and thus prone to premature
degeneration (Nakajima et al., 2003; Zlokovic, 2008). Future studies may evaluate whether
progenitor cells play a role in protecting the neurovascular unit against injury, thereby repairing
vulnerable blood vessels.
While the cross-sectional design of this study precludes any interpretations regarding
causality, the present findings provide additional support for a relationship between progenitor
cell proliferative capacity and age-related cognitive dysfunction independent of multiple AD risk
factors. Age is the greatest risk factor of MCI and AD, which was reflected in our samples as the
CDR = 0.5 group was significantly older than the CDR = 0 group. However, age was not related
to progenitor cell proliferation, suggesting that depletion of progenitor cell colony counts in the
CDR = 0.5 group cannot be explained by solely demographic factors. Progenitor cells may be
neuroprotective for the aging brain by ameliorating cognitive consequences of structural white
matter injury and degeneration, thus reflecting valuable therapeutic targets in preventative
clinical trials. However, much more research is needed to delineate whether these associations in

19
the present study are causal. Assessing progenitor cell proliferation longitudinally in relation to
age-related cognitive decline will provide crucial information of the potential for in vitro
progenitor cell levels as an early indicator of age-related cognitive decline. Further studies of the
interplay among EPCs, regulatory factors in the EPC secretome, white matter integrity, and
cognitive dysfunction are warranted to evaluate a potential protective role for EPCs in
individuals at risk of age-related cognitive decline, with implications for dementia risk
assessment and prevention.

20
Table 1. Demographics and clinical characteristics for the CDR 0 and 0.5 group

CDR 0 CDR 0.5
Demographics p-value
n 45 11
Age 69.5 (6.4) 76.6 (5.8) 0.001
Sex, % male 15 (33.3%) 7 (63.6%) 0.09
APOE4 carriers 8 (21.6%) 2 (20.0%) 1.00
Education 16.5 (2.2) 16.5 (1.6) 0.96
Vascular Risk Factors
Hypertension 19 (42.2%) 5 (45.5%) 1.00
Diabetes 6 (13.3%) 2 (18.2%) 0.65
Smoking (Current) % 5 (11.1%) 1 (9.1%) 1.00
Smoking (History) % 20 (44.4%) 7 (63.6%) 0.32
Dyslipidemia % 25 (55.6%) 5 (45.5%) 0.74
Transient Ischemic Attack % 1 (2.3%) 1 (9.1%) 0.36
Body Fat % 36.3 (7.0) 32.7 (7.3) 0.17
Systolic Blood Pressure (mmHg) 131.3 (13.2) 133.4 (8.9) 0.64
Diastolic Blood Pressure (mmHg) 78.0 (9.2) 78.5 (9.1) 0.86
Mean Arterial Pressure (mmHg) 113.5 (10.9) 115.1 (8.0) 0.68
Pulse Pressure (mmHg) 53.4 (10.4) 54.9 (6.3) 0.67

21
Table 2. CFU-Hill colony counts predict short-term verbal memory, executive functioning, and
language

Cognitive
domain Cognitive test/subtest
b R
2
DR
2
p-value
Memory WMS-IV Logical Memory I 0.28 0.38 0.07 0.009**

WMS-IV Logical Memory II 0.17 0.28 0.03 0.13

WMS-IV Visual Reproduction I 0.19 0.11 0.03 0.13

WMS-IV Visual Reproduction
II 0.18 0.09 0.03 0.15
Executive
function
Trails B
0.25 0.26 0.06 0.03*

Phonological fluency (FAS) 0.30 0.32 0.09 0.007**

Golden Stroop -0.04 0.28 0.00 0.75
Digit span backward 0.14 0.12 0.02 0.25
Attention Trails A 0.21 0.10 0.04 0.09
Digit span forward -0.07 0.16 0.00 0.59
Language MiNT visual naming test 0.17 0.11 0.02 0.46

Semantic fluency (Animals) 0.41 0.22 0.16 0.001**

Semantic fluency
(Fruits/vegetables) 0.35 0.21 0.12 0.004**

Note. N = 65. All values are based on statistics from model 2 of the hierarchical multiple
regression model (where model 1 predictors included age, sex, education, and model 2
predictors included age, sex, education, and CFU-Hill log counts). *P < 0.05; **P < 0.01; ***P
< 0.001.

22
Table 3. Pearson’s product-moment correlations of FA values for each ROI and CFU-Hill log-
transformed counts

FA

CFU-Hill
log counts p-value
CFU-Hill
log counts p-value
Middle cerebellar peduncle
-0.561 0.001*

Superior corona radiata R
-0.542 0.001*
Pontine crossing tract
-0.454 0.008*

Superior corona
radiata L -0.623 < 0.001*
Genu of corpus callosum
-0.58 < 0.001*

Posterior corona radiata R
-0.538 0.001*
Body of corpus callosum
-0.592 < 0.001*

Posterior corona radiata L
-0.522 0.002*
Splenium of corpus
callosum -0.66 < 0.001*

Posterior thalamic radiation
(include optic radiation) R -0.556 0.001*
Fornix (column and body
of fornix)
-0.167 0.352

Posterior thalamic radiation
(include optic radiation) L
-0.618 < 0.001*
Corticospinal tract R
-0.538 < 0.001*

Sagittal stratum R
-0.624 < 0.001*
Corticospinal tract L
-0.587 < 0.001*

Sagittal stratum L
-0.627 < 0.001*
Medial lemniscus R
-0.644 < 0.001*

External capsule R
-0.58 < 0.001*
Medial lemniscus L
-0.619 < 0.001*

External capsule L
-0.606 < 0.001*
Inferior cerebellar peduncle
R -0.574 < 0.001*

Cingulum (cingulate gyrus) R
-0.547 0.001*
Inferior cerebellar peduncle
L -0.602 < 0.001*

Cingulum (cingulate gyrus) L
-0.601 < 0.001*
Superior cerebellar
peduncle R -0.568 0.001*

Cingulum (hippocampus) R
-0.618 < 0.001*
Superior cerebellar
peduncle L -0.563 0.001*

Cingulum (hippocampus) L
-0.518 0.002*
Cerebral peduncle R
-0.588 < 0.001*

Fornix (cres) / Stria terminalis
R -0.633 < 0.001*
Cerebral peduncle L
-0.67 < 0.001*

Fornix (cres) / Stria terminalis
L -0.628 < 0.001*
Anterior limb of internal
capsule R
-0.518 0.002*

Superior longitudinal
fasciculus R
-0.704 < 0.001*
Anterior limb of internal
capsule L
-0.504 0.003*

Superior longitudinal
fasciculus L
-0.667 < 0.001*
Posterior limb of internal
capsule R
-0.637 < 0.001*

Superior fronto-occipital
fasciculus R
-0.446 0.009*
Posterior limb of internal
capsule L
-0.702 < 0.001*

Superior fronto-occipital
fasciculus L
-0.541 0.001*
Retrolenticular part of
internal capsule R
-0.623 < 0.001*

Uncinate fasciculus R
-0.429 0.013*
Retrolenticular part of
internal capsule L
-0.641 < 0.001*

Uncinate fasciculus L
-0.545 0.001*
Anterior corona radiata R
-0.527 0.002*

Tapetum R -0.307 0.08
Anterior corona radiata L
-0.551 0.001* Tapetum L -0.283 0.11

Note. N=37. * significant after FDR correction.

23
Figure 1. WM ROIs based on the JHU DTI-based white matter atlas

Note. JHU DTI-based White Matter (WM) Atlas ROIs. ACR, anterior corona radiata; ATR,
anterior thalamic radiation; ALIC, anterior limb of internal capsule; bCC, body of corpus
callosum, gCC, genu of corpus callosum, CgH, cingulum (hippocampus); CgC, cingulum
(cingulate gyrus); CST, corticospinal tract; CP, cerebral peduncle; EC, external capsule; FX,
fornix; ICP, inferior cerebellar peduncle; IFOF, inferior fronto-occipital fasciculus; MCP,
middle cerebellar peduncle; PCR, posterior corona radiata; PCT, pontine crossing tract; PLIC,
posterior limb of internal capsule; PTR, posterior thalamic radiation, RIC, Retrolenticular part
of internal capsule; SCP, superior cerebral peduncle; SCR, superior corona radiata; SFOF,
Superior fronto-occipital fasciculus; SLF, superior longitudinal fasciculus; SS, sagittal
striatum; TAP, tapetum; UF, uncinate fasciculus.

24
Figure 2. CFU-Hill colony counts are attenuated with cognitive impairment on the CDR

Note. A. CFU-Hill colony cultured from blood in vitro; B. Spindle-shaped cell (putative early
EPC); C. CFU-Hill colony log counts are depleted in individuals with greater cognitive
dysfunction on CDR; D. CFU-Hill colony raw counts are depleted in individuals with greater
cognitive dysfunction on CDR.

A. B.
D.
C.

25
Figure 3. Differences in FA between individuals with low vs. high CFU-Hill colony proliferation

Note. A. FA skeleton and B. cortical surface projection of voxelwise analysis of FA in individuals
with high versus low CFU-Hill colony counts.

0.05
0.02
p-value
Left
Right
<0.001
A.
B.
p-value
<0.001
0.02
0.05

26
Figure 4. Scatterplots of cingulum and corpus callosum FA and CFU-Hill log counts

Note. Relationship between CFU-Hill log counts and fractional anisotropy of (A) corpus
callosum (genu, body, and splenium) and (B) cingulum bundle (cingulate gyrus and
hippocampal) white matter ROIs.

0.0 0.5 1.0 1.5
0.4
0.5
0.6
0.7
0.8
0.9
CFU-Hill log counts
FA
Genu of corpus callosum
Body of corpus callosum
Splenium of corpus callosum
A.
0.0 0.5 1.0 1.5
0.3
0.4
0.5
0.6
0.7
CFU-Hill log counts
FA
Cingulum (cingulate gyrus) R
Cingulum (cingulate gyrus) L
Cingulum (hippocampus) R
Cingulum (hippocampus) L
B.

27
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Abstract (if available)

Abstract Background: Neurodegeneration is an early and prevalent cause of cognitive decline across dementia subtypes. Progenitor cells may mobilize in response to age-related white matter degeneration and ameliorate cognitive consequences of injury. ❧ Objective: To evaluate endothelial progenitor cell proliferation in vitro in older adults, and determine the relationship between cell proliferation, white matter microstructure, and cognitive function. ❧ Method: Sixty-three dementia-free older adults with low vascular risk were recruited from the community. Participants underwent venipuncture, a comprehensive neuropsychological battery, and structural MRI scanning, including DTI. Blood samples were obtained to determine progenitor reserve using in vitro cell culture using the CFU-Hill colony assay. ❧ Results: Participants with mild cognitive dysfunction (Clinical Dementia Rating; CDR = 0.5; n = 11) exhibited depleted CFU-Hill colony counts relative to their cognitively normal counterparts (CDR 0; n = 45) after controlling for age, sex, and education. Greater CFU-Hill colony proliferation predicted better memory, executive functioning, and language performance, and was associated with decreased fractional anisotropy (FA) in the majority of examined white matter regions. ❧ Conclusions: Progenitor cell proliferation is associated with increased cognitive functioning and decreased FA throughout the brain. These data suggest progenitor cells may mobilize to areas of white matter injury in response to damage and protect against related cognitive dysfunction.

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Creator Marshall, Anisa (author)

Core Title The protective role of progenitor cells: cell proliferation relates to cognition and white matter microstructure

School College of Letters, Arts and Sciences

Degree Master of Arts

Degree Program Psychology

Degree Conferral Date 2021-05

Publication Date 05/07/2023

Defense Date 05/01/2020

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

Tag cognitive dysfunction,diffusion imaging,OAI-PMH Harvest,progenitor cells,white matter microstructure

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Language English

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Advisor Thames, April (committee chair), Monterosso, John (committee member), Nation, Daniel (committee member)

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