Down syndrome iPSC model: endothelial perspective on tumor development

Abstract

Trisomy 21 (T21), known as Down syndrome (DS), is a widely studied chromosomal abnormality. Previous studies have shown that DS individuals have a unique cancer profile. While exhibiting low solid tumor prevalence, DS patients are at risk for hematologic cancers, such as acute megakaryocytic leukemia and acute lymphoblastic leukemia. We speculated that endothelial cells are active players in this clinical background. To this end, we hypothesized that impaired DS endothelial development and functionality, impacted by genome-wide T21 alterations, potentially results in a suboptimal endothelial microenvironment with the capability to prevent solid tumor growth.
To test this hypothesis, we assessed molecular and phenotypic differences of endothelial cells differentiated from Down syndrome and euploid iPS cells. Microarray, RNA-Seq, and bioinformatic analyses revealed that most significantly expressed genes belong to angiogenic, cytoskeletal rearrangement, extracellular matrix remodeling, and inflammatory pathways. Interestingly, the majority of these genes are not located on Chromosome 21. To substantiate these findings, we carried out functional assays. The obtained phenotypic results correlated with the molecular data and showed that Down syndrome endothelial cells exhibit decreased proliferation, reduced migration, and a weak TNF-α inflammatory response. Based on this data, we provide a set of genes potentially associated with Down syndrome’s elevated leukemic incidence and its unfavorable solid tumor microenvironment—highlighting the potential use of these genes as therapeutic targets in translational cancer research.

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www.oncotarget.com Oncotarget, 2020, Vol. 11, (No. 36), pp: 3387-3404
Down syndrome iPSC model: endothelial perspective on tumor
development
Mariana Perepitchka1,2, Yekaterina Galat1,2,3,*, Igor P. Beletsky3, Philip M. Iannaccone1,2,4,5
and Vasiliy Galat2,3,4,5,6
1Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
2Developmental Biology Program, Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital,
Chicago, IL, USA
3Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, Pushchino, Russia
4Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
5Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
6ARTEC Biotech Inc, Chicago, IL, USA
*Co-first author
Correspondence to: Mariana Perepitchka, email: mperepitchka@u.northwestern.edu
Yekaterina Galat, email: ygalat@luriechildrens.org
Vasiliy Galat, email: v-galat@northwestern.edu
Keywords: Down syndrome; iPSC-derived endothelial model; T21 genome-wide Implications; meta-analysis; tumor microenvironment
Received: July 10, 2019 Accepted: August 01, 2020 Published: September 08, 2020
Copyright: Perepitchka et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC
BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
ABSTRACT
Trisomy 21 (T21), known as Down syndrome (DS), is a widely studied chromosomal
abnormality. Previous studies have shown that DS individuals have a unique cancer profile.
While exhibiting low solid tumor prevalence, DS patients are at risk for hematologic
cancers, such as acute megakaryocytic leukemia and acute lymphoblastic leukemia.
We speculated that endothelial cells are active players in this clinical background. To
this end, we hypothesized that impaired DS endothelial development and functionality,
impacted by genome-wide T21 alterations, potentially results in a suboptimal endothelial
microenvironment with the capability to prevent solid tumor growth.
To test this hypothesis, we assessed molecular and phenotypic differences of
endothelial cells differentiated from Down syndrome and euploid iPS cells. Microarray,
RNA-Seq, and bioinformatic analyses revealed that most significantly expressed genes
belong to angiogenic, cytoskeletal rearrangement, extracellular matrix remodeling,
and inflammatory pathways. Interestingly, the majority of these genes are not located
on Chromosome 21. To substantiate these findings, we carried out functional assays.
The obtained phenotypic results correlated with the molecular data and showed that
Down syndrome endothelial cells exhibit decreased proliferation, reduced migration,
and a weak TNF-α inflammatory response. Based on this data, we provide a set of
genes potentially associated with Down syndrome’s elevated leukemic incidence and
its unfavorable solid tumor microenvironment—highlighting the potential use of these
genes as therapeutic targets in translational cancer research.
INTRODUCTION
Down syndrome (DS) is commonly evaluated on
the basis of physical and clinical traits resulting from
genomic alterations caused by a trisomy of Chromosome
21 (T21) [1]. DS occurs at a frequency of 1/700–1/800
births, and the frequency increases with maternal age [2].
Initially, the dominant perspective was that DS phenotypes
resulted from extra gene dosage effects solely relative
to T21. Furthermore, even though Chromosome 21 is
considered to host approximately 350 genes, research
efforts were directed toward a small subset of genes
clustered around the DS critical region (DSCR) [3]. More
recent studies, however, suggest that there are potentially
many causative genes in DS distributed over larger regions
of Chromosome 21 [4], and such gene dysregulation may
Research Paper

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impact up to one-third of disomic genes [5]. Additionally,
transcriptome and protein analyses have shown that
this Chromosome 21 dosage effect can induce gene
overexpression and/or underexpression on a genome-wide
level [6]. Such genome-wide expression dysregulation in
DS was actually noted in a study on fetal fibroblasts of
monozygotic twins discordant for T21 [7].
Studies utilizing fetal tissue and animal models have
provided valuable input into understanding DS clinical
and physical features. This being said, studies at the fetal
stages are limited by ethical and technical considerations,
and mouse models do not fully recapitulate human DS
developmental traits [8–10]. Pluripotent stem cell models
are a powerful alternative that can be employed to further
our understanding of the molecular and biochemical
effects of T21 on human development [11]. Our previous
research, amongst others’, has shown that induced
pluripotent stem cells (iPSCs) progress through major
embryonic developmental stages [12–14], and by utilizing
DS iPSCs, this opens a direct line into investigating DS
phenotypic traits and genotypic implications.
From a genotypic standpoint, previous studies
focusing on DSCR1 and DYRK1A genes, located on
the extra copy of Chromosome 21, showed that DS
individuals have a unique cancer profile. On one hand,
DS children have a 500-fold risk of developing acute
megakaryoblastic leukemia (AMKL) and a 20-fold risk
of being diagnosed with acute lymphoblastic leukemia
(ALL) [15–17]. On the other hand, the DS genotypic
profile is also associated with reduced solid tumor growth
[18, 19]. Such an unusual cancer profile may potentially
exemplify a dynamic interplay of genetic mutations and
the construction of a tissue-specific microenvironment that
hinders the expansion of the pre-metastatic solid tumor
niche.
Within this suboptimal microenvironment, the tumor
would be unable to employ strategies that involve the
use of and communication with host cellular machinery.
Examples include: secretion of extracellular vesicles,
enhancing transcription of extracellular matrix (ECM)
and cytoskeletal remodeling enzymes, induction of
angiogenesis, vessel co-option, up-regulation of cytokine
receptors, and recruitment of pro-inflammatory signaling
molecules [20–23]. Endothelial cells, which have
incredible plasticity/structural heterogeneity, support
hematopoietic stem cell maintenance, and promote
hematopoietic and immune processes, are highly employed
by the solid tumor niche. To this end, we hypothesized that
impaired DS endothelial development and functionality,
impacted by genome-wide T21 alterations, potentially
results in a suboptimal endothelial microenvironment with
the capability to prevent solid tumor growth.
This work is the first study of DS iPSC-produced
endothelial cells (iECs). Our group, as well as other
researchers, has developed robust technologies of
endothelial cell derivation from iPSCs [24–28]. To test our
hypothesis, we employed such technology to assess the
molecular and phenotypic differences of endothelial cells
differentiated from DS and euploid iPSCs. Our microarray,
RNA-Seq, and bioinformatic analyses revealed that
most of the differentially expressed genes belong to
proliferative (angiogenic), cytoskeletal rearrangement,
ECM remodeling, and inflammatory pathways. All
of these pathways incorporate crucial biochemical
mechanisms that the solid tumor niche potentially alters
for metastatic initiation and progression. Interestingly, the
majority of the significantly expressed genes within these
pathways were not located on Chromosome 21. These
findings, confirmed by functional assays, may prove to be
useful in ongoing DS clinical research and provide a new
perspective on tumor development, which can aid future
cancer-related studies.
RESULTS
Characterization and bioinformatic functional
assessment of iPSCs and iECs
To begin evaluating the DS endothelial genotype
and phenotype, cellular characterization experiments
were initially performed to verify stem cell pluripotency
prior to endothelial differentiation. Confirmation of
endothelial lineage commitment followed afterward.
For these experiments, the following cell lines were
employed: patient-derived skin fibroblasts (FB1,
FB2), a commercially purchased embryonic stem cell
line (H9-ESC), induced pluripotent stem cells (SR2-
iPSCs DSV-iPSCs, isoDSV-iPSCs), and endothelial
cells (commercially purchased HUVECs, DSV-iECs,
isoDSV-iECs, SR2-iECs, H9-iECs). The SR2-iPSCs
and DSV-iPSCs were established via over-expression
of Sox2, c-Myc, Oct4, and Nanog in commercially
purchased fibroblasts. Additional differentiation and
characterization information is provided in [13, 29, 30].
Regarding isoDSV-iPSCs, this cell line was established
as a result of the spontaneous loss of an extra copy of
Chromosome 21 in DSV-iPSCs. These three iPS cell lines
were differentiated into endothelial cells (SR2-iECs, DSV-
iECs, isoDSV-iECs), which were previously characterized
in [24, 31, 32].
For pluripotency verification, immunocytochemistry
was carried out to ensure pluripotency marker expression
in H9-ESCs, SR2-iPSCs, DSV-iPSCs, and isoDSV-
iPSCs. Co-expression of TRA-1-80/Nanog and TRA-
1-60/Oct4 was evident in all cell lines. TRA-1-60/Oct4
expression for DSV-iPSCs and isoDSV-iPSCs is provided
(Figure 1A). Following endothelial differentiation, FACS
analysis was performed to confirm endothelial lineage-
specific marker expression. The acquired endothelial cells
(H9-iECs, DSV-iECs, isoDSV-iECs, SR2-iECs) were
CD34+/CD31+(PECAM-1)/CD144+(VE-Cadherin). The
cell lines also displayed the characteristic cobblestone

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morphology, and an additional immunocytochemical
evaluation confirmed expression of von Willebrand
factor (VWF) and VE-Cadherin (Figure 1B). To further
ensure lineage commitment, a heatmap of characteristic
endothelial genes was generated from microarray data via
R Studio. The results show a clear delineation between
endothelial, iPSC, and fibroblast gene expression levels.
Additionally, by employing a clustering algorithm, the
heatmap provides another layer of distinction with regard
to disomy vs. trisomy. The disomic endothelial cells
(SR2-iECs, HUVECs, H9-iECs), trisomic and isogenic
endothelial pair (DSV-iECs, isoDSV-iECs), trisomic and
isogenic induced pluripotent stem cell pair (DSV-iPSCs,
isoDSV-iPSCs), and fibroblasts (FB1, FB2) have all been
appropriately paired (Figure 1C).
To complement these results, a bioinformatic
functional analysis was performed utilizing the Network
Analyst, a comprehensive gene analysis platform
[33]. The microarray dataset utilized in this analysis
incorporated DSV-iPSC, isoDSV-iPSC, DSV-iEC, and
isoDSV-iEC cell lines. First, we obtained an overview
of the number of iPSC-specific genes, iEC-specific
genes, and shared genes within the dataset (Figure 1D).
We then performed a functional enrichment analysis
on each gene group. The reference database was Gene
Ontology: Biological Pathways (GO:BP). The iPSC +
iEC functional processes reflect general cell regulatory
mechanisms critical to cell survival, which explains
the large gene number (8,887) shared amongst the cell
lines. The other data tables include functional processes
that specifically correlate with iPSC and iEC lineages,
respectively. All reported functional processes are
statistically significant (Figure 1E).
Comparative bioinformatic cancer gene analyses
of DSV-iECs
Following characterization, we performed
comparative gene analyses to study the potential genomic
impact of T21 with respect to aberrant DS endothelial
development, increased leukemic prevalence, and
decreased solid tumor incidence. We compared clinical
oncology RNA-Seq data (oncogene/tumor suppressor
perspective) and RNA-Seq data from DSV-iEC and
isoDSV-iEC cell lines. In order to generate the clinical
oncology gene expression dataset, we utilized The Cancer
Genome Atlas (TCGA) [34, 35] and the OncoKb database
[36]. To complement our DS endothelial RNA-Seq
expression data, we referenced the Cancer GeneticsWeb,
which integrates data from several databases such as
OMIM, PubMed, GO, GeneCards, and others [37].
Additionally, to further ensure an accurate cancer type-to-
gene correlation for both datasets, the OncoLnc database
was used as another resource. OncoLnc couples clinical
oncology data (21 cancer cases) with mRNA expression
levels [38].
Chromosome 21 cancer-related genes
Throughout the years, a significant number of
cancer-related genes, mapped along Chromosome
21, have been identified. We evaluated our DSV-iEC
model with respect to the gene expression levels of 26
commonly studied, cancer-related genes on Chromosome
21 [37]. Genes with non-significant p-values and fold
changes (FCs) < 0.5 were excluded from the analysis.
The resulting gene list consisted of 14 differentially
expressed genes (Figure 2A). We then utilized the
OncoLnc database to assess which of these genes are
predominately expressed in leukemias or solid tumors.
The RUNX1, U2AF1, ITGB2, DYRK1A, DONSON, and
SLC19A1 gene expression levels were highly elevated
in AML cases. Our RNA-Seq data correlates with this
clinical data by showing significantly up-regulated
expression levels for all six genes in DSV-iECs vs
isoDSV-iECs. The remaining 8 genes had elevated
mRNA expression levels across several solid tumor
cases, according to OncoLnc. In comparison to clinical
data, 6 out of the 8 genes (MX1, RCAN1, CSTB, ETS2,
COL18A1, CXADR) were significantly down-regulated
in DSV-iECs whereas TIAM1 and ADAMTS1 genes
were significantly up-regulated.
Leukemia and solid tumor–associated gene panels
To further investigate the genetic implications
of T21 relative to tumor formation, we also assessed
genome-wide differential expression between trisomic
DSV-iECs and disomic isoDSV-iECs. Our approach was
two-fold. First, we utilized TCGA to compile a list of 500
genes that are frequently altered in leukemias and 500
genes frequently altered in solid tumors. The OncoLnc
database supplemented this portion of the analysis by
providing additional confirmation as to which genes have
a strong correlation with either leukemic or solid tumor
types. We then organized the gene lists based on alteration
category: mutations, copy number alterations (CNAs), and
fusions. Afterward, we employed the OncoKb database to
ensure that our gene lists contain significantly expressed
and frequently altered oncogenes and tumor suppressors
associated with at least 3 leukemic or 3 solid tumor
types. Genes with non-significant p-values, FC < 0.5, and
genes displaying a relatively equal contribution toward
leukemic and solid tumor development were omitted
from the analysis. Following this, we selected the top 5
significantly expressed genes per alteration category.
The leukemia-associated gene panel contains 11
oncogenes and 4 tumor suppressors (Figure 2B). Out
of the 11 oncogenes, compared to isoDSV-iECs, DSV-
iECs exhibited up-regulated expression of 9 oncogenes
(mutations: GATA2, NOTCH1, IDH2; CNAs: WHSC1,
EZH2; fusions: MECOM, RUNX1T1, NSD1, KAT6A).
JAK2 (mutations) and BCR (fusions) oncogenes were
down-regulated. With regard to tumor suppressors, DSV–
iECs showed an equal divide: 2 up-regulated (mutations:

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Figure 1: Characterization of disomic and trisomic iPSCs and iECs. (A) (i) Phase contrast microscopy images of the trisomic
and isogenic pair: DSV-iPSCs and isoDSV-iPSCs. (ii) Immunocytochemistry images showing TRA-1-60 [green] and OCT4 [red] co-
expression in DSV-iPSCs and isoDSV-iPSCs. (B) Representative images: (1) flow cytometric analysis of iECs demonstrating homogeneity
of CD31 and CD144 co-expression; (2) Cobblestone endothelial morphology; (3) Immunocytochemistry showing positive expression
of cell-surface marker VE-Cadherin (CD144); (4) Immunocytochemistry data confirming positive expression of von Willebrand factor
(VWF). Cell nuclei were stained with DAPI [blue]. (C) Heatmap showing vascular-related gene expression correlation and cell line
clustering amongst HUVEC, disomic SR2-iECs, isoDSV-iECs, DSV-iECs, isoDSV-iPSCs, and DSV-iPSCs. FB1 and FB2 cell lines are
fibroblasts. The microarray expression data has been log-transformed. (D) Venn diagram generated via Network Analyst showing the
number of microarray genes unique to and shared between DSV-iPSC, isoDSV-iPSC, DSV-iEC, and isoDSV-iEC cell lines. (E) Statistically
significant functional processes that incorporate the genes in the venn diagram. Processes were obtained via Network Analyst’s enrichment
analysis (GO:BP database).

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DNMT3A; CNAs: FANCA) and 2 down-regulated
(CNAs: IRF1, BTG1) genes. Overall, it seems that DSV-
iECs are showing an up-regulated trend with regard to
leukemia-associated oncogenes.
With regard to the solid tumor-associated gene
panel, there are 10 oncogenes and 5 tumor suppressors
(Figure 2C). 8 of the oncogenes have a down-regulated
expression (mutations: EGFR, AR, IRS1, DDR2; CNAs:
INHBA, RAC1; fusions: DDIT3, RET). EWSR1 and
ETV4 (fusions) exhibit up-regulated expression. For
the tumor suppressors, there are 3 up-regulated genes
(mutations: POLE; CNAs: DNMT3B, RECQL4) and 2
down-regulated genes (CNAs: SDHA; fusions: ZFHX3).
In comparison to the leukemia-associated gene panel,
DSV-iECs potentially exhibit a greater inclination toward
down-regulating solid tumor-associated oncogenes.
Endothelial microenvironment: MetaCore
pathway analysis of DSV-iECs
Observing the variability in oncogene and tumor
suppressor expression levels in DSV-iECs vs isoDSV-
iECs, we decided to perform bioinformatic pathway
analysis utilizing the Clarivate Analytics MetaCore
program. Our pathways of interest involved endothelial
processes that solid tumors may exploit for growth and
metastasis:
Proliferation pathways
According to MetaCore’s enrichment analysis,
the pathway with the lowest p-value (1.987 × 10-23)
involved Hypoxia Inducible Factor-1 (HIF-1) signaling
(Supplementary Figure 1A). This significant p-value
reflects the summation of expression values across HIF-
1 pathway’s multiple gene targets. HIF-1 is a critical
regulator of a wide array of physiological processes,
including: angiogenesis, ECM remodeling, cytoskeletal
rearrangements, and inflammation [39–42]. With regard
to our dataset, the HIF-1 complex impacts downstream
Endothelin-1 (EDN1) gene expression (p-value = 0,
FC = 3.86), (Supplementary Figure 1B). Down-regulated
in DSV-iECs, EDN1 is an angiogenic factor involved
in cell proliferation, and its overexpression has been
linked to tumor growth and metastasis [43, 44]. Further
investigation of EDN1 signaling (EDN1/EDNRB)
showed that this pathway can induce downstream ERK1/2
signaling, which is also down-regulated in DSV-iECs
(Supplementary Figure 2A).
Since ERK1/2 is involved in cell proliferation,
migration, and survival [45], we investigated its up and
downstream targets in connection with the EDN1/EDNRB
pathway. By referencing MetaCore’s published pathways
and utilizing the Pathway Map Creator application, we
identified the Integrin beta-3 (ITGB3) gene. Integrin beta-
3 is activated by EDNR1/EDNRB and CCL2 pathways
with ERK1/2 at the crossroads (Supplementary Figure 4
and Supplementary Figure 5A). Based on p-value (3.93
× 10-275), FC (6.28), and its capability to increase cellular
survival and migratory potential, we selected ITGB3 as
another gene of interest. The ITGB3 gene, like EDN1,
is also down-regulated in DSV-iECs. Additionally,
ITGB3 expression is increased during oxidative stress,
which further implicates its involvement in the tumor
microenvironment (TME) [46].
Further analysis of ERK1/2 upstream and
downstream targets brought the Tissue factor (F3) gene
(p-value = 4.66 × 10-53, FC = 4.31) to our attention. An
upstream regulator of the ERK1/2 complex, the F3 gene
is down-regulated in DSV-iECs (Supplementary Figure
2B and Supplementary Figure 4). F3 signaling activates a
variety of molecular pathways through G-protein-coupled
receptors (ex., EGFR pathway) and contributes to cellular
proliferation. Overexpression of the F3 gene has also been
associated with cell migration/cytoskeletal reorganization
and tumor progression [47, 48]. As a result, F3 became an
additional gene of interest linked to tumor development
due to its proliferative and migratory impact.
Migration/cytoskeletal rearrangement and ECM
remodeling pathways
In the TME, expression of proliferative factors is
complemented by the initiation of the metastatic cascade:
cancer cell invasion, intravasation, and extravasation. This
motile phenotype stems from increased ECM elasticity,
permeability, and degradation [49, 50]. We explored
ECM remodeling pathways, and within the CCL25/CCR9
signaling pathway, we identified three downstream genes
that showed the most extensive changes in their differential
expression: MMP-1 (Matrix Metallopeptidase-1, p-value =
0, FC = 6.45), MMP–10 (Matrix Metallopeptidase-10,
p-value = 1.97 × 10-126, FC = 6.13), and HAPLN1
(p-value = 0, FC = 6.99). MMP-1 and MMP-10 are down-
regulated in DSV-iECs, while HAPLN1 is up-regulated.
Furthermore, all three genes are involved in ECM
composition and fluidity (Supplementary Figure 3A).
A discontinuous and unstable ECM composition
triggers actin cytoskeletal rearrangements, which initiate
changes in cell shape and promote migration. Analysis of
cytoskeletal remodeling pathways revealed that DSV-iECs
displayed a consistent down-regulation in the expression
levels of major downstream cytoskeletal complexes, such
as F-actin cytoskeleton and actomyosin (Supplementary
Figure 3B). When evaluating the expression levels of the
genes involved in the formation of these complexes, the
ACTG2 gene had the lowest p-value (5.67 × 10
-31
) and a
FC = 5.05. Additionally, the ACTG2 gene, as part of the
downstream actin complex, also plays a key role in the
TGF-β pathway, which is a major inducer of cell migration
[51], (Supplementary Figure 3B).
Inflammation pathways
Changes in proliferative capacity and ECM
composition or fluidity can stimulate expression of

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Figure 2: DS endothelial perspective on tumor development: chromosome 21 cancer-related genes, leukemia-associated
genes, solid tumor-associated genes. (A) Significantly expressed leukemia-associated [red] and solid tumor-associated [grey] genes
mapped along Chromosome 21. The blue star represents a tumor suppressor, and the red star highlights an oncogene. Red arrows symbolize
up-regulation, and green arrows are representative of down-regulation. (B and C) Top 5 significantly expressed, genome-wide leukemia
and solid tumor-associated oncogenes [red stars] and tumor suppressors [blue stars] per most frequent alteration type (mutations, CNAs,
fusions). Red arrows show up-regulated expression, and green arrows represent down-regulated expression. The p-value, fold change, and
expression data for all gene panels was obtained from RNA-Seq analysis. Additionally, every gene panel shows DSV-iEC expression values
compared to isogenic control (isoDSV-iECs).

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inflammatory response agents within a multitude of
pathways that intertwine via the transcription factor NF-
kB [52, 53]. As a result, we approached our inflammatory
signaling analysis by focusing on significantly expressed
up and downstream genes of pathways that incorporate
NF-kB, which is down-regulated in our DSV-iEC model.
More specifically, we focused on the crosstalk between
NF-kB and the CCL2, IL-33, IL-1, and EGFR signaling
pathways (Supplementary Figures 4–6). Our approach
was two-fold: (1) analyze gene expression relative to
each individual pathway; (2) study genes impacted by
the interconnectivity of these pathways. In the first part
of this analysis, the following upstream genes were the
most significantly expressed: CCL2 (p-value = 0, FC =
6.93), IL-33 (p-value = 2.39 × 10-238, FC = 10.48), and
IL-1β (p-value = 1.29 × 10
-61
, FC = 4.70), (Supplementary
Figure 5 and Supplementary Figure 6A). Regarding
downstream signaling, the APOE gene (p-value = 2.40 ×
10-88, FC = 3.72, IL-1 pathway) and SERPINB2 (PAI2)
gene (p-value = 1.55 × 10
-71
, FC = 6.88, EGFR pathway)
showed significant differential expression (Supplementary
Figure 6).
For the second part of the inflammatory analysis,
the interconnectivity of the EGFR, IL-1, CCL2, and
IL-33 pathways highlighted additional genes that are
simultaneously regulated by several of these pathways.
The extensive regulation of these genes correlates with
significant differential expression. More specifically,
the IL-6 gene (p-value = 9.12 × 10-32, FC = 6.97) was
impacted by CCL2, IL-1, and IL-33 pathways; the CXCL1
gene (p-value = 0, FC = 5.71) was part of the IL-1 and
IL-33 pathways; the IL-8 gene (p-value = 3.74 × 10-149,
FC = 10. 52) was present in the IL-1, IL-33, and EGFR
pathways (Supplementary Figures 5 and 6). As an added
factor for consideration, irregularities in the expression
levels of these upstream and downstream genes have been
implicated in tumorigenesis [54–58].
Visualization and expression assessment of select
pathway genes
As a summation of the pathway analysis, schematic
representations of the proliferation, migration, and
inflammation pathways were created. The schematics
show pathway interconnectivity and highlight the genes of
interest (Figure 3A and 3B). Following pathway analysis,
to better visualize the extent of differential expression,
we constructed a volcano plot showing the top 7,000
genes within our RNA-Seq dataset. All of the 15 selected
genes are significant with respect to p-value and FC
(Figure 3C). We also mapped the chromosomal locations
of all 15 genes. Interestingly, with regard to proliferation,
cytoskeletal rearrangement, ECM remodeling, and
inflammation pathways, these most significantly expressed
genes were not located on Chromosome 21 (Figure 3D).
This observation paved the way for another
assessment: evaluating how extensively endothelial
lineage effects the expression levels of the select
15 genes following iPSC differentiation. Utilizing
microarray data, we compared gene expression levels
between DSV-iECs/DSV-iPSCs and isoDSV-iECs/
isoDSV-iPSCs. DSV-iECs showed an up-regulation in
all genes, except ITGB3, ACTG2, IL-1β, IL-6, APOE,
and SERPINB2 (PAI2). isoDSV-iECs showed an up-
regulation in all genes, except HAPLN1 and APOE. This
result indicates that endothelial maturation promotes
an up-regulatory expression trend with regard to the
select 15 genes. This being said, the fact that DSV-
iECs exhibited fewer up-regulated genes in comparison
to isoDSV-iECs is worth to consider relative to T21
implications on endothelial development—potentially
leading to a suboptimal endothelial microenvironment.
rtPCR verification
Out of the 15 genes, we performed rtPCR
verification on CCL2, HAPLN1, and APOE genes.
This selection stems from our focus on gene expression
differences between iPSCs vs. iECs, up/downstream gene
targets, and the interconnectivity of pathway interactions.
With respect to our gene selection, CCL2 is a versatile
upstream gene, and its pathway impacts proliferative,
inflammatory, and cytoskeletal rearrangement
mechanisms. CCL2 also effects the expression levels of
important gene targets involved in tumorigenesis: VEGF,
TNF-α, INF-gamma, HIF1-A, etc. Furthermore, CCL2
expression is down-regulated in DSV-iECs. HAPLN1
impacts ECM composition via proteoglycan affiliation.
Its downstream signaling effects migratory cytoskeletal
rearrangements and ECM remodeling. HAPLN1 is up-
regulated in DSV-iECs. APOE is a downstream gene at
the intersection of the IL-1B and TNF-α pathways. These
pathways are both implicated in inflammatory regulation.
APOE is up-regulated in DSV-iECs. rtPCR results
confirmed the expression levels for all three genes (Figure
3E and 3F).
iEC functionality
iEC development
Based on pathway and bioinformatic analyses, our
gene expression data indicated that DSV-iECs have a
genome-wide expression dysregulation in comparison to
disomic control iECs. We were interested whether these
genetic differences effected the formation of endothelium
at the earliest stages of development. To gain more insight,
we compared endothelial differentiation efficiency of
trisomic and disomic cell lines. To account for variability
and epigenetic effects, we used isogenic iPSC lines as
well as iPSCs from different individuals. The cells were
differentiated using a monolayer culture, CHIR99021
induction protocol. Differentiation efficiency was assessed
by measuring the amount of CD34+/CD31+ cells via flow
cytometry. We found no significant differences between

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the percentages of endothelial cells generated at day 5
of trisomic (DSV-iPSC) vs. isogenic (isoDSV-iPSC) and
disomic (SR2-iPSC) differentiation. With some variability,
all cell lines had an endothelial differentiation efficiency
of about 15%. Figure 4A shows the differentiation results
for DSV-iPSCs and SR2-iPSCs.
Proliferation assays
To investigate how genome-wide expression
dysregulation effects endothelial cell function, the
differentiated iECs were subjected to functional assays
that target vasculogenic potential. The first assay measured
endothelial proliferative sensitivity to different VEGF
concentrations: 0.5 ng/mL, 2 ng/mL, and 20 ng/mL. These
values were chosen on the basis of free VEGF secretions
(ranging from 0.3 ng/mL to 17.5 ng/mL) in ascites, pleural
effusions, plasma, and serum of patients diagnosed with
various cancer types [59]. By comparing the effects of
such variable VEGF additions in DSV-iECs and control
HUVECs (Human Umbilical Vein Endothelial Cells), we
found that DSV-iECs were less proliferative and had a
diminished response to VEGF. At a concentration of 0.5
ng/mL VEGF, there was a significant increase in HUVEC
proliferation, while DSV-iECs had no significant response.
Notably, when the concentration of VEGF was increased
to 20 ng/mL, the difference in proliferative potential
between HUVECs and DSV-iECs became less significant
(Figure 4B).
As an additive confirmation, we performed an EdU
Proliferation Assay using DSV-iECs and the SR2-iECs.
The proliferation efficiency was assessed as the percentage
of cells in the G0/G1 and S/G2 phases of the cell cycle.
The results demonstrated that DSV-iECs, in comparison
to SR2-iECs, had a smaller percentage of cells in the S/
G2 phases for each treatment condition. Furthermore,
similarly to HUVECs, SR2-iECs exhibited a significant
proliferative response following 0.5 ng VEGF addition.
DSV-iECs were more responsive after the addition of 20
ng VEGF (Figure 4C and 4D).
Having obtained these results, we also screened our
RNA-Seq data for the expression levels of three VEGF
receptors: FLT-1 (VEGFR1), KDR (VEGFR2), and FLT-
4 (VEGFR3). The goal was to evaluate VEGF receptor
expression levels in light of the VEGF response sensitivity
of DSV-iECs. All three VEGF receptors are significantly
up-regulated in DSV-iECs (Figure 4E). In contrast to this
up-regulation, DSV-iECs still elicited a weaker VEGF
response compared to control SR2-iECs. This inverse
relationship is suggestive of a more widespread angiogenic
dysregulation in DS.
Tube formation and spheroid assays
We further evaluated the vasculogenic potential of
DSV-iECs and control SR2-iECs via the Tube Formation
Assay. We observed that DSV-iEC tubular extensions
exhibited a thinner density, covered a smaller area of
the culture wells, and had fewer branching points, loops,
and total number of tubes in comparison to SR2-iECs
(Figure 5A). DSV-iEC networks were also characterized
by decreased stability and integrity. During incubation at
37°C and 5% CO2, following the 6 hr mark, DSV-iECs
detached and degraded in a shorter time period vs. SR2-
iECs (data not shown).
These results correlate with the Spheroid Assay
data. SR2 endothelial spheroids demonstrated greater
migratory potential: more sprouts, “detached” cells, and
“edging” cells were noticeable in the culture well [60].
DSV-iEC spheroids, on the other hand, required an
additional 24 hrs and twice the number of cells in order
for spheroid area, sprout area, and cumulative sprout
length to resemble control SR2-iECs (Figure 5B). Like the
proliferation assays, these results also indicate that DSV-
iEC angiogenic potential is lower than that of the disomic
control.
Inflammation assay
To investigate the relationship between endothelial
dysregulation and immune response, we conducted the
Tumor Necrosis Factor-α (TNF-α) Inflammation Assay.
TNF-α initiates the endothelial inflammatory cascade by
activating transcription of selectins, cadherins, integrins,
and CAM proteins. We decided to focus on E-selectin
and VCAM-1 adhesion proteins because they play a key
role in vascular transmigration [61, 62], and both proteins
have also been implicated in tumor cell recruitment
during inflammation [62, 63]. According to our flow
cytometry data, following TNF-α stimulation, DSV-
iECs showed a decreased activation of surface proteins
VCAM-1 (CD106) and E-selectin (CD62E) compared to
control SR2-iECs (Figure 5C and 5D). Such a diminished
response possibly reflects the already present up-
regulation of VCAM-1 and E-selectin prior to TNF-α
stimulation. This could be indicative of endothelial
dysfunction and/or other mediators influencing VCAM-
1 and E-selectin expression.
DISCUSSION
The Down syndrome phenotype is characterized
by angiogenic, ECM-associated, and immune response
imbalances [18, 64]. All of these factors, which rely on
endothelial functionality, are key agents that tumors
employ to create a favorable niche for growth, and
ultimately, metastasis. Previous studies have shown that
T21 has the potential to induce endothelial dysfunction
as early as the progenitor stage, but the extent of this
biological impact varies between DS individuals [65, 66].
Our data shows no significant difference between trisomic
and disomic iPSC endothelial differentiation efficiency,
yet functional assays show that impairment is evident
in trisomic endothelial cells. These results acknowledge
the possibility that T21 alters endothelial functionality
throughout endothelial maturation-highlighting the

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consideration that T21 potentially utilizes a combination
of gene dosage and microenvironmental/biochemical cues
to elicit a temporally progressive endothelial impairment.
This mechanistic flexibility may form the
framework as to why DS patients are prone to exhibiting
a leukemic phenotype, yet are more resistant to solid
tumor development and metastasis. In our Chromosome
21 cancer-related gene panel, all leukemia-associated
genes were up-regulated, and the majority of solid tumor-
associated genes were down-regulated. Our genome-wide
bioinformatic analysis aligns with this expression trend.
Trisomic endothelial cells showed a greater predisposition
Figure 3: Bioinformatic pathway analysis and gene verification. (A and B) Schematic representations of proliferation +
inflammation pathways [left] and cytoskeletal rearrangement + ECM remodeling pathways [right]. Genes of interest are highlighted in
blue. Red stars symbolize pathway starting points. The dotted lines refer to activation [green arrows] and inhibition [red bar-headed lines]
mechanisms. (C) Volcano plot showing the statistical significance of the selected genes of interest. In relation to DSV-iECs, the down-
regulated genes are toward the right, and up-regulated genes are on the left. (D) Chromosome plot showing locations of selected genes.
Chromosome locations were obtained via RNA-Seq analysis and the UCSC Genome Browser. (E) rtPCR verification of both microarray and
RNA-Seq data with regard to APOE, HAPLN1, and CCL2 expression. (F) rtPCR data table with CCL2 removed to improve visualization
of APOE and HAPLN1 expression. The rtPCR tables include data, presented as mean ± SEM, from three experimental replicates.

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toward up-regulating leukemia-associated oncogenes
and down-regulating solid tumor-associated oncogenes
with a potential inclination toward tumor suppressor
up-regulation. These results present the possibility
that T21, on a genome-wide level, is challenging solid
tumor development in a dualistic fashion: pairing down-
regulation of oncogenes with up-regulation of tumor
suppressors.
To further evaluate T21 impact on solid tumor
development, bioinformatic pathway analysis was
performed to gain insight into how T21 gene expression
alterations may effect the endothelial microenvironment,
which is a crucial component of the tumor niche. The
more dynamic the tumor-niche interactions become, the
greater the likelihood of metastasis-inducing signaling
mechanisms. These mechanisms can lead to an increase in
tumor proliferation, ECM anchorage, and immune evasion
[67]. In consideration of this factor, statistical significance
(p-value, FC) of gene expression, and pathway
interconnectivity, we evaluated signaling pathways that
regulate proliferation, cytoskeletal rearrangement, ECM
remodeling, and inflammation. By thoroughly studying
these pathways (HIF-1, EDN1/EDNRB, F3, CCL25/
CCR9, actin-cytoskeleton, CCL2, IL-33, IL-1, EGFR),
we firstly observed that Chromosome 21-specific gene
expression levels did not exhibit as significant a FC
in expression compared to genes mapped along other
chromosomes (Chr. 1, Chr. 2, Chr. 4, Chr. 5, Chr. 6, Chr. 7,
Chr. 9, Chr. 11, Chr. 17, Chr. 18, Chr. 19). Additionally, 14
out of the 15 selected, most significantly expressed, non-
Chromosome 21 genes were down-regulated in DSV-iECs
compared to disomic iECs. These two aspects introduce
the possibility that T21 may regulate the endothelial
microenvironment to a greater extent via genome-wide
alterations vs. Chromosome 21-specific gene dosage
effects.
Evaluation of the endothelial microenvironment
from a proliferative perspective entails a focus on
angiogenic factors, which mediate crosstalk between
the tumor niche and endothelial cells. To promote tumor
vasculature support, the homeostatic balance of pro- and
anti-angiogenic signaling must shift in favor of pro-
angiogenic overexpression. This aspect underlies the
current interest in identifying key angiogenic players (ex.,
PDGF, SCF, ILs, TGF-β) that could serve as therapeutic
targets capable of shutting off the tumor’s angiogenic
“on” switch [68]. In DS, the presence of a possible anti-
angiogenic microenvironment leading to the suppression
of solid tumor growth was suggested in a study of a DS
mouse model, which showed DSCR1 suppression of
VEGF signaling [69].
Our DSV-iEC model revealed EDN1, ITGB3, and
F3 genes as most significantly expressed in angiogenic/
proliferative pathways. Additionally, the positive feedback
loops that EDN1, ITGB3, and F3 share with inflammatory
genes, such as IL-8, CXCL-1 (GRO-1), IL-33, CCL2, IL-
6, and IL-1β, further emphasizes the significant role of
this select gene set in angiogenesis. EDN1, ITGB3, and
F3 have been implicated in tumor progression [70–72], but
they have not been well studied with respect to DS. Their
down-regulated expression, as shown in our DSV-iECs,
also supports the potential presence of an anti-angiogenic
microenvironment that may prevent solid tumor growth.
Furthermore, this gene expression data, coupled with
bioinformatic pathway analysis, correlates with our
proliferation functional assays. DSV-iECs exhibited a
reduced proliferative rate following VEGF addition,
despite the increased gene expression levels of three
VEGF receptors. DSV-iECs also spent significantly more
time in the G0/G1 phases of the cell cycle in comparison
to disomic iECs.
By pairing proliferative potential with migratory
capability in the endothelial microenvironment, the pre-
metastatic tumor transitions toward a more invasive
phenotype. The extent of invasiveness is impacted by
the distance between primary and secondary tumor sites
and disruption in ECM composition, which has been
linked to metastatic onset. During cancer progression,
overexpression of MMPs—involved in degrading
collagen, laminin, fibronectin, and proteoglycans—can
induce porosity in the ECM basement membrane. This
enables cancer cells to more easily bypass the ECM and
achieve intravasation [73, 74]. Cancer cell protrusion
of the ECM is also accompanied by dynamic actin
polymerization and rearrangements. The cytoskeletal
protrusions adhere to the ECM, and actin contractile
machinery promotes cancer cell movement into the ECM
membrane [75].
In consideration of this interplay between ECM
composition and actin contractility, our DSV-iEC model
exhibits down-regulated expression of MMP-1 and MMP-
10 genes, as well as an up-regulation of HAPLN1, which
plays a key role in creating proteoglycan-hyaluronic acid
(HA) aggregates. This HA-based matrix imparts further
stiffness onto the ECM membrane, which is associated
with decreased migration [76]. Additionally, trisomic iEC
down-regulated expression of the ACTG2 gene, which is
part of the actin cytoskeletal complex, may be indicative of
limited actin contractility and cell motility. Our functional
assays support this possibility. The low integrity, density,
and number of DSV-iEC tube formations as well as
decreased spheroid area and sprout length offer support for
a potentially reduced ECM-cytoskeletal dynamic in DS,
which does not reflect the elevated migratory interactions
typically associated with metastasis.
In addition to proliferative and migratory
capabilities, the tumor niche also utilizes the endothelial
microenvironment to evade immune detection. Individuals
diagnosed with DS often have an impaired immune system
[77], and it was hypothesized that T21-induced interferon
signaling results in such chronic immune dysregulation
[78]. In addition to this, DS adults also exhibit increased

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cytokine production (TNF-α, IFNγ, etc.) from peripheral
blood mononuclear cells (PBMCs), which are highly
involved in inflammatory processes [79]. Previous cancer
research has shown that such inflammatory conditions
are linked with tumor onset, and hypoxia plays a key role
in this regard. Hypoxic conditions encourage oxidative
damage, mutations, and the survival of more resistant,
“stem-like” tumor cells that will undergo metastasis.
Furthermore, the more delayed the inflammatory response,
the greater is the opportunity for the tumor niche to release
cytokines, chemokines, and exosomes for the purpose of
priming secondary metastatic tissue sites [80].
Relative to hypoxia, our inflammatory genes of
interest (CCL2, IL-8, IL-6, IL-1β, SERPINB2 [PAI2], IL-
33, CXCL-1 [GRO-1], and APOE) are downstream of NF-
κB, which activates transcription of HIF-1. In DSV-iECs,
all of these genes are down-regulated with the exception
of APOE. Our TNF-α inflammatory response functional
Figure 4: Endothelial differentiation efficiency, VEGF response sensitivity, and proliferative potential of disomic
and trisomic iECs. (A) Flow cytometry results of DSV-iEC and SR2-iEC differentiation in the presence of VEGF. The endothelial
differentiation efficiency table incorporates expression data from three replicates. (B) DSV-iEC and HUVEC cell counts following
0.5 ng/mL, 2 ng/mL, and 20 ng/mL VEGF addition. The cell counts incorporated three replicates. (C) Flow cytometry data from the
EdU Proliferation Assay. For the SR2-iEC cell line, the data shows the G0/G1 cell percentage consistently decreasing as more cells
are transitioning into the S/G2 cell cycles. DSV-iECs, despite showing a growing cell percentage in the S/G2 cell cycle phases, have a
significantly larger percentage of cells in the G0/G1 phases. (D) All EdU flow cytometry data compiled into a histogram. Four replicates
were included in the assay. (E) RNA-Seq data showing up-regulated expression of three VEGF receptors in DSV-iECs. All statistical data
in the figure is presented as mean ± SEM.

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assay aligns with this result: compared to disomic
iECs, DSV-iECs were less responsive following TNF-α
stimulus. Another interesting aspect to note is that DSV-
iECs had elevated VCAM-1 and E-selectin expression
prior to TNF-α addition. This factor raises the possibility
that DSV-iEC inflammatory response may be impacted by
endothelial dysregulation and/or other mediators, such as
already up-regulated cytokine levels [79]. Furthermore,
APOE’s activating and inhibiting inflammatory activity
[81], combined with the down-regulation of mentioned
inflammatory response genes, is an additional point to
consider as this may potentially contribute toward a
prolonged state of inflammation.
Taking into account all of these aspects, this
work identified the following factors that may offer
insight into the question of why DS individuals exhibit
an elevated leukemic precedence and decreased solid
tumor growth: (1) decreased proliferative and migratory
capability; (2) a potentially prolonged inflammatory
state; (3) down-regulation of genome-wide solid tumor-
associated oncogenes; and (4) an up-regulation of
genome-wide leukemia-associated oncogenes. These
factors also highlight the widespread involvement of
the tumor niche during pre-metastatic phases of cancer
development and the importance of evaluating the
endothelial microenvironment from a variety of molecular
Figure 5: Tube formation, spheroid sprouting, and inflammatory response of disomic and trisomic iECs. (A) (i, ii) SR2-
iEC and DSV-iEC phase contrast microscopy images of the Tube Formation Assay: prior to and following WimTube software analysis;
(iii) SR2-iECs and DSV-iECs stained with cell-permeant dye Calcein-AM. The numerical values, reported in pixels (px), refer to total
tube length; (iv) Tube Formation data table showing mean values for loops, branching points, and total number of tubes; (B) (i, ii) Phase
contrast microscopy images of the Spheroid Assay: prior to and following WimSprout software analysis; (iii) Spheroid Assay data table
showing mean values, which are reported in pixels (px), for total spheroid area, total sprout area, and total sprout length. (C) Representative
image: flow cytometry results of iEC response to TNF-α stimulation. The sensitivity of the response was evaluated on the basis of VCAM-1
(CD106) and E-selectin (CD62E) cell surface expression. (D) Inflammatory Assay data incorporating four experimental replicates. Unlike
the SR2-iEC line, DSV-iECs have VCAM-1 and E-selectin moderately expressed prior to TNF-α activation. Following the addition of
TNF-α, DSV-iECs do not show as significant a difference in VCAM-1 and E-selectin expression like SR2-iECs. All statistical data in the
Figure is presented as mean ± SEM.

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perspectives. The use of iPSCs and directed differentiation
protocols provide a new and powerful tool to continue
gaining more insight into the biology of DS, endothelial
development, the solid tumor niche, and a wide array
of human diseases. As more differentiation models
become available, such as hematopoietic stem cells, the
types of experiments that can be done and their correct
interpretation will grow.
MATERIALS AND METHODS
Cell culture
Skin fibroblasts (FB1, FB2) were obtained from
patients (Caucasian females aged 2- and 3-y-old) at Ann
& Robert H. Lurie Children’s Hospital. The fibroblasts
were cultured in DMEM medium (Life Technologies)
supplemented with 10% FBS (Fisher Scientific), NEAA
(Fisher Scientific), and HEPES (Fisher Scientific).
Trisomic (DSV) and disomic (SR2) iPSCs were derived
via over-expression of Sox2, c-Myc, Oct4, and Nanog
in commercially purchased fibroblasts. Additional
information is provided in Galat et al. (2016, 2017). Prior
to differentiation, the cells were maintained on Matrigel-
coated culture dishes in mTeSR1 medium (STEMCELL
Technologies). The isoDSV-iPSCs were obtained via the
spontaneous loss of an extra copy of Chromosome 21
in DSV-iPSCs. H9-ESCs (WA09) were purchased from
WiCell. HUVECs were kindly provided by the Hendrix
laboratory, which purchased the cells from ATCC (PCS-
100-013).
Endothelial differentiation
Endothelial differentiation of DSV-iPSCs, isoDSV-
iPSCs, H9-ESCs, and SR2-iPSCs was induced via the
addition of CHIR99021 (STEMCELL Technologies)
and vascular endothelial growth factor (VEGF165)
(R&D Systems) to the culture medium. On day 4, the
differentiated iECs were isolated by immuno-selection
of CD31+CD144+ cells via a magnetic column (Miltenyi
Biotec). Following this, the iECs were grown on
fibronectin-coated (10 μg/mL) (BD Biosciences) plates
and cultured in VascuLife EnGS medium (LifeLine) at
37°C and 5% CO2.
Flow cytometry analysis
To verify the endothelial marker expression the
iECs were analyzed via flow cytometry. The cells
were harvested with StemPro Accutase (ThermoFisher
Scientific), washed with ice-cold FACS buffer (PBS +
1% FBS + 2 mM EDTA), and incubated with conjugated
antibodies CD31 PE, CD34 FITC, VE-Cadherin APC
(Miltenyi Biotech) for 30 minutes at 4°C. Following this,
the cells were washed with a 0.5% BSA/PBS solution.
Data collection was performed via the FACSCalibur (BD
Biosciences) and analyzed with the FlowJo software
(version 10.5.3).
Immunocytochemistry
The following procedures were all performed
at room temperature. iECs were fixed with 3.2%
paraformaldehyde for 30 min and permeabilized for 5 min
with 0.1% Triton-x-100 in PBS. The cells were then treated
with Dako Protein Block for 25 min in order to prevent
nonspecific antibody binding. Following this, iECs were
incubated with the following mouse anti-human, primary
antibodies: VE-Cadherin (BD Biosciences) (1 hr) and
VWF (R&D Systems) (3 hrs). After washing the cells 3×
with Dako Washing Buffer (WB), the appropriate Alexa
Fluor-conjugated secondary antibodies (Invitrogen) were
added to cell culture wells; the incubation time was 45
minutes. All antibody dilutions were performed according
to manufacturers’ instructions. Samples were then washed
once more with WB and incubated with DAPI (Sigma
Aldrich) for 3 minutes. The immunofluorescent cells
were visualized with Leica DM IRB inverted microscope
system (Leica, Germany) equipped with a digital camera
Retiga 4000R (Qlmaging, Canada), which was controlled
with Openlab software version 5.0.2 (Perkin-Elmer).
Proliferation assays
Assay #1: 30,000 control and trisomic cells were
seeded per well onto fibronectin-coated (10 μg/mL) (BD
Biosciences) 6-well plates. The cells were cultured in
VascuLife EnGS medium (LifeLine) containing varying
VEGF concentrations (0.5 ng/mL, 2 ng/mL, and 20 ng/
mL). When one of the cell lines reached confluence, all
cells for a particular VEGF concentration were harvested
with StemPro Accutase (ThermoFisher Scientific) and a
cell count was performed.
Assay #2: On day 6, utilizing the Click-iT EdU Flow
Cytometry Assay Kit (cat #: C10425) and following the
manufacturer’s protocol, the cells were harvested, labeled,
and analyzed via the Accuri flow cytometer. Endothelial
proliferation potential was assessed relative to cytometric
DNA synthesis measurement (G0/G1 and S/G2 cell cycle
phases).
Tube formation assay
Matrigel (Corning) was thawed overnight at 4°C.
The following morning, matrix coating was added to 12-
well cell culture plates, which were incubated for 30 min at
37°C and 5% CO2. iECs were seeded at a density of 2.75
× 105 cells per well and incubated for 6 hrs in VascuLife
EnGS medium (LifeLine). After the incubation period, the
cells were treated with the cell permeable dye Calcein-
AM (2 μg/mL) and incubated for 30 min at 37°C and 5%

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CO
2
. Afterwards, the 12-well cell culture plates were ready
for tube network visualization under the Leica DM IRB
inverted microscope system (Leica, Germany) equipped
with a digital camera Retiga 4000R (Qlmaging, Canada).
Spheroid assay
iECs were cultured in VascuLife EnGS complete
medium (LifeLine) on 6 cm dishes coated with fibronectin
(10 μg/mL) (BD Biosciences). Prior to harvesting the cells,
methocel solution was prepared by mixing methylcellulose
powder (4,000 cP) (Sigma-Aldrich) with preheated (60°C)
VascuLife EnGS basal medium (LifeLine). Following this,
an equivalent amount of Vasculife basal medium containing
5% FBS was added to the mixture and the solution was
stirred overnight at 4°C. The solution was stored at 4°C
until cell monolayers were grown. The confluent cells were
harvested with StemPro Accutase (ThermoFisher Scientific),
centrifuged, and resuspended in 20% methocel + 80%
VascuLife complete medium. Following this, 30 uL cell
suspensions were used to generate 3D spheroids according
to the JoVE hanging drop protocol. Next, a collagen-
neutral solution was prepared by mixing collagen (Type
I) (ThermoFisher Scientific) + 10× EBSS (ThermoFisher
Scientific) + 0.1 N NaOH + 0.1 N HCl. This mixture was
combined in a 1:1 ratio with Vasculife, containing 20%
FBS, 0.5% Methocel, and 100 ng/mL VEGF. Afterward,
the spheroids were plated in the following manner: the first
layer of the combined mixture was added to a 4-well culture
plate, followed by the spheroid, and then another layer of
the mixture. The spheroids were cultured at 37°C and 5%
CO2 for 3 days and imaged via a light microscopy Nikon
D100 digital SLR camera (Tokyo, Japan) on a Leica DM
IRB inverted microscope.
Inflammatory response assay
Confluent iEC monolayers were incubated with
TNF-α (10 ng/mL) (Biolegend) for 6 hrs at 37°C and
5% CO2. Following TNF-α treatment, the cells were
harvested with StemPro Accutase (ThermoFisher
Scientific), washed with ice-cold FACS buffer (PBS +
1% FBS + 2 mM EDTA), and incubated with E-selectin
APC (Miltenyi Biotech) and VCAM-1 FITC (eBioscience)
conjugated antibodies for 30 min at 4°C. Following this,
the cells were washed with a 0.5% BSA/PBS solution.
EC inflammatory response to TNF-α was measured via
flow cytometry in light of VCAM-1 and E-selectin surface
expression percentage. Data collection was performed
via the FACSCalibur cytometer (BD Biosciences) and
analyzed via the FlowJo software (version 10.5.3).
RNA isolation
Total RNA was extracted with the RNeasy
Mini Kit (Qiagen) via the instructions provided in the
manufacturer’s protocol. RNA quality and concentration
were assessed via the Nanodrop.
Microarray analysis
RNA aliquotes were submitted to University of
Chicago Genomics Facility. The RNA samples were
reverse transcribed into cDNA, which was hybridized onto
a HumanHT-12 v4BeadChip that was scanned by Illumina
iScan. The acquired data was processed and normalized
via the iScan Control software. Gene expression
comparisons were obtained using the R Studio software
(Bioconductor package).
RNA sequencing analysis
Aliquots of RNA were submitted to Northwestern
University’s NUSeq Core. The mRNA library was
prepared and the samples were analyzed using HiSeq
4000 Sequencing 50bp, Single Reads. The obtained list of
differentially expressed genes was further analyzed using
MetaCore (Clarivate Analytics version 19.4/build 69900)
and R Studio software (version 3.6.1). The gplots, pheatmap,
and enhancedvolcano packages were incorporated into the
R script in order to generate the heatmap and volcano plot.
Real time qPCR
The High-Capacity RNA-to-cDNA kit (Applied
Biosystems) was used to reverse transcribe the isolated
RNA. Each reaction tube included up to 2 ug of RNA. The
reverse transcription reaction was performed according to
manufacturer instructions via the MBS Satellite (0.2 G)
Thermal Cycler (ThermoFisher Scientific). The qPCR
reaction mix was prepared by adding 12 ng of cDNA
from each sample to the PowerUp SYBR Green Master
Mix (2×) (Applied Biosystems). qPCR was performed
(Standard Cycling Mode, primer Tm < 60°C) via the 7500
Fast Real-Time PCR system (Applied Biosystems). The
7500 v2.3 software was used for data collection and gene
expression comparisons (2-ΔΔCT method). Primer sequences
provided in Supplementary Table 1.
Abbreviations
ALL: acute lymphoblastic leukemia; AMKL:
acute megakaryocytic leukemia; DS: Down syndrome;
DSCR: Down syndrome critical region; CNAs: copy
number alterations; iECs: iPS-produced endothelial cells;
ECM: extracellular matrix; EDN1: Endothelin-1; ESCs:
embryonic stem cells; F3: Tissue factor; FC: fold change;
HA: hyaluronic acid; HIF-1: Hypoxia Inducible Factor-1;
iPSCs: induced pluripotent stem cells; HUVECs: human
umbilical vein endothelial cells; ITGB3: Integrin beta-
3; MMP: matrix metallopeptidase; PBMCs: peripheral
blood mononuclear cells; T21: trisomy of Chromosome

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21; TCGA: The Cancer Genome Atlas; TME: tumor
microenvironment; TNF-α: tumor necrosis factor-alpha;
VWF: von Willebrand factor.
Author contributions
Study conception and design: MP, YG, IPB, and
VG. Data Collection: MP and YG. Bioinformatic analysis:
MP, YG, and VG. Endothelial cell derivation: MP and
YG. Data assembly and interpretation: MP, YG, and VG.
Manuscript editing: MP, YG, IPB, PMI, and VG. All
authors have read and approved the final manuscript.
ACKNOWLEDGMENTS
We would like to acknowledge Dr. John Crispino
for the valuable discussion. Additionally, we would like
to thank the NUSeq Core: Center for Genetic Medicine:
Feinberg School of Medicine: Northwestern University
and the University of Chicago Genomics Facility for their
services.
CONFLICTS OF INTEREST
Authors have no conflicts of interest to declare.
FUNDING
Research was supported in part by NHLBI,
RC1HL100168, and Stanley Manne Children’s Research
Institute’s Grant to VG.
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