Identification of New Markers of Angiogenic Sprouting Using Transcriptomics: New Role for RND3
Arteriosclerosis, Thrombosis, and Vascular Biology
Abstract
BACKGROUND:
New blood vessel formation requires endothelial cells to transition from a quiescent to an invasive phenotype. Transcriptional changes are vital for this switch, but a comprehensive genome-wide approach focused exclusively on endothelial cell sprout initiation has not been reported.
METHODS:
Using a model of human endothelial cell sprout initiation, we developed a protocol to physically separate cells that initiate the process of new blood vessel formation (invading cells) from noninvading cells. We used this model to perform multiple transcriptomics analyses from independent donors to monitor endothelial gene expression changes.
RESULTS:
Single-cell population analyses, single-cell cluster analyses, and bulk RNA sequencing revealed common transcriptomic changes associated with invading cells. We also found that collagenase digestion used to isolate single cells upregulated the Fos proto-oncogene transcription factor. Exclusion of Fos proto-oncogene expressing cells revealed a gene signature consistent with activation of signal transduction, morphogenesis, and immune responses. Many of the genes were previously shown to regulate angiogenesis and included multiple tip cell markers. Upregulation of SNAI1 (snail family transcriptional repressor 1), PTGS2 (prostaglandin synthase 2), and JUNB (JunB proto-oncogene) protein expression was confirmed in invading cells, and silencing JunB and SNAI1 significantly reduced invasion responses. Separate studies investigated rounding 3, also known as RhoE, which has not yet been implicated in angiogenesis. Silencing rounding 3 reduced endothelial invasion distance as well as filopodia length, fitting with a pathfinding role for rounding 3 via regulation of filopodial extensions. Analysis of in vivo retinal angiogenesis in Rnd3 heterozygous mice confirmed a decrease in filopodial length compared with wild-type littermates.
CONCLUSIONS:
Validation of multiple genes, including rounding 3, revealed a functional role for this gene signature early in the angiogenic process. This study expands the list of genes associated with the acquisition of a tip cell phenotype during endothelial cell sprout initiation.
Graphical Abstract
Highlights
•
Transcriptomic analyses identified 39 candidate genes that were upregulated at the onset of endothelial sprouting.
•
The gene signature includes signal transduction, morphogenesis, and immune responses.
•
Newly identified rounding 3 is associated with filopodial extension and pathfinding in 3 dimensions.
Angiogenesis is a multistep process involving careful coordination of distinct endothelial cell behaviors that ultimately accomplishes new blood vessel growth. Quiescent endothelial cells lining mature blood vessels are arranged as a single layer with stable junctions to maintain vascular integrity.1 In contrast, activated sprouting endothelial cells have vastly different characteristics.2,3 Given the known complexities in cell phenotypes,3–5 phenotype switching,6–9 and the abundance of signaling pathways involved in angiogenesis,10–14 gaining further insights into phenotypic changes displayed by individual endothelial cells during early sprout initiation will be invaluable to ultimately design effective strategies that antagonize, enhance, or normalize angiogenic vasculature.12
To a large extent, gene expression determines the identity, fate, functional response, and ultimately phenotype of individual cells.15,16 Unprecedented discoveries monitoring gene expression changes have been made possible by the development of high throughput sequencing (seq) technologies, including bulk RNA sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq). Bulk RNA-seq analyzes large amounts of RNA from a single sample to discover differences in expression, splicing, sequence variation, and methylation events. One drawback, however, is that bulk RNA-seq does not distinguish between individual cells or cell types in a heterogeneous sample. This limitation has been overcome by scRNA-seq, which detects RNA transcripts from individual cells,17,18 even when originating from heterogeneous starting material.19 Both bulk and single-cell RNA-seq have provided insights into vascular development, heterogeneity, and differentiation.20–24 In addition, endothelial cell expression profiles in lymph nodes25,26 and tumors,27,28 as well as during arteriovenous specification,29 coronary artery development,30 and various conditions of disease progression5,31 have been reported.
Transcriptional profiling provides an unprecedented opportunity to gain insights into genetic signatures that regulate biological processes. Our understanding of angiogenic sprouting has been aided by proteomics,32–34 knockout and transgenic model organisms,35–41 and to a lesser-degree transcriptomic analyses.5,28,31 To our knowledge, a comprehensive, high-resolution transcriptional analysis focusing solely on endothelial cell sprout initiation has not been reported. The current study applies to both single-cell and bulk transcriptomic analyses for increased resolution of transcriptional changes that distinguish invading from noninvading endothelial cells. We discovered that 39 candidate genes were commonly upregulated in activated, invading endothelial cells when comparing the transcriptomic data sets. The majority of these candidate genes have been previously implicated in angiogenesis, enhancing confidence in the data set. Gene ontology pathways most highly represented include signal transduction, morphogenesis, and immune evasion. Validation studies showed fundamental roles for multiple genes, including newly identified rounding 3. Rounding 3 knockdown and haploinsufficiency in mice resulted in reduced filopodia length during retinal angiogenesis compared with wild-type (WT) littermates. Altogether, this study defines early events in endothelial sprout initiation more clearly by expanding the list of potential genetic markers upregulated early in the process of endothelial sprouting.
METHODS
Data Availability
Single-cell sequencing data and materials have been made publicly available in National Center for Biotechnology Information’s Gene Expression Omnibus,42 and can be accessed at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE254777.
Normalized reads for Bulk RNA-seq data are available in Data Set 3.
Code Availability
Single-cell RNA-seq analyses using Cell Ranger were performed by the Texas A&M Institute for Genome Sciences and Society Bioinformatics Core. Projections were generated using 10× Genomics Loupe Browser 6.
Endothelial Cell Culture
Human umbilical vein endothelial cells (HUVECs) from 6 independent donors (see Major Resources Table) were used for transcriptomic analyses and validation. Cells were cultured at passages 3 to 6 in 75 cm2 flasks (Corning, NY) coated with 1 mg/mL sterile gelatin (Sigma, MO). Growth medium was previously described in detail43 and consisted of Medium 199 (M199; Gibco, MA), supplemented with fetal bovine serum (Gibco, MA), bovine hypothalamic extract (Pel-Freeze Biologicals, AK), heparin (Sigma, MO), antibiotics (Gibco, MA), and gentamycin (Gibco, MA).
Single-Cell RNA Sequencing
Confluent HUVECs (passage 3–6) were used in invasion assays.43 Type 1 collagen isolated from rat tails (Pel-Freeze Biologicals, AK) was used to prepare collagen matrices at 2.5 mg/mL with 1 µmol/L S1P (sphingosine 1-phosphate; Sigma, MO) in 96-well half-area plates (Costar, NY). The mixture (25 µL) was equilibrated for 45 minutes before seeding HUVEC (30 000 cells per well) in media containing M199 (Gibco, MA) supplemented with 1× RSII, ascorbic acid and 40 ng/mL bFGF (basic fibroblast growth factor) and VEGF (vascular endothelial growth factor; R&D Systems, MN). For single-cell RNA-seq, cells were placed in an invasion assay for 6 hours before noninvading, invading, and total cell populations were collected.
Noninvading Cell Collection
Medium was removed, collagen matrices were rinsed with 100 µL of warm 1× Hepes saline+5 mmol/L EDTA for 1 minute at room temperature, 50 µL 0.25% trypsin-EDTA (Gibco, MA) was added per well, and cells were incubated at 37 °C for 2 to 3 minutes. Plates were tapped to dislodge cells and 50 µL fetal bovine serum (Invitrogen, MA) was added to each well to neutralize the trypsin. Liberated cells were collected, and wells were rinsed with 100 µL 1× Hepes saline+5 mmol/L EDTA to collect the remaining nonadherent cells. Cells were centrifuged at 500g for 5 minutes and resuspended in 0.1% BSA in 1× PBS.
Invading Cell Collection
Following removal of the noninvading cell population, the remaining collagen matrices (containing invading cells) were digested in M199 containing 1 mg/mL collagenase (Sigma, MO) at 37°C for 5 minutes. Digests were centrifuged at 500g for 5 minutes and resuspended in 0.1% BSA in 1× PBS.
Total Cell Collection
At 6 hours of incubation, the medium was removed, and matrices containing invading and noninvading populations were collected and digested in M199 containing 1 mg/mL collagenase at 37°C for 5 minutes. Samples were centrifuged at 500g for 5 minutes and resuspended in 0.1% BSA in 1× PBS.
Collagenase Digestion Versus Direct Lysis
Endothelial cells seeded onto collagen matrices were collected after 6 hours of invasion into RLT lysis buffer directly (no collagenase exposure). Alternatively, collagen matrices were digested with collagenase, centrifuged, and then resuspended in RLT lysis buffer. The maximum time cells were exposed to collagenase was 10 minutes at 37 °C.
Generation of Single-Cell Libraries
Single-cell libraries for each sample were generated at the Texas A&M Institute for Genome Sciences and Society (College Station, TX). Single-cell suspensions collected using the above protocol were loaded in the 10× chromium controller using the chromium single-cell 3’ v3 Kit per the manufacturer’s protocol. All samples were processed in the same batch. Sequencing was performed with Illumina NextSeq 500.
Computational Analysis of Single-Cell Libraries
The scRNA-seq analyses were performed with the 10× Genomics Cell Ranger (v4.0) pipeline using the default settings. Cell Ranger computations were performed by the Texas A&M Institute for Genome Sciences and Society core facility. The raw Illumina NextSeq 500 base call file outputs were demultiplexed into FASTQ files by use of cellranger mkfastq. Then, cellranger aggr was used to normalize read counts, align reads to the human genome GRCh38 and generate single-cell feature counts.
For the population analysis, libraries from two different cell donors of noninvading and invading populations were aggregated, and read depth was normalized using cellranger aggr. The same method was applied for the two cell donors aggregated in the total sample Cluster Analysis. Loupe Cell Browser (v6) was used to filter cells with high mitochondrial gene expression (10% or 15% threshold) and excessively high or low unique molecular identifier counts. For the aggregated biological replicates, Loupe was used to calculate differential gene expression, producing relative comparisons of gene expression between noninvading and invading populations. To regress the impact of collagenase exposure, cells with Fos proto-oncogene (FOS) expression >0 were excluded using Loupe before clustering. Cluster analysis was done on the total samples using Kmeans clustering in Loupe software. The clusters representing the invading and noninvading cell populations, as defined by the Cluster Analysis, were compared. Per the default methods of Loupe, P values were calculated using the sSeq variant of the negative binomial exact test and were corrected for false discovery rate using Benjamini-Hochberg method for multiple comparisons.44 A false discovery rate–corrected P value threshold of 0.1 was used to determine statistical significance in the single-cell analysis.
Bulk RNA-Seq
Samples analyzed for bulk RNA-seq included control and activated cells. Control (nonactivated) cells were collected after seeding onto collagen matrices without S1P, bFGF, or VEGF for 1 and 6 hours (1 HR control, 6 HR control). Invading (activated) cells were treated with S1P and growth factors identically to scRNA-seq experiments before collection at 1 and 6 hours (1 HR treated, 6HR treated). We have previously shown that endothelial cells activated with both S1P and growth factors undergo robust invasion.45–47 Thus, the control groups at 1 and 6 HR consist of noninvading cells, while invading cells are present in the treated group only.45,46 HUVECs from 3 individual donors were used for these experiments. At the specified time point, media was removed, and samples were digested in 1 mg/mL collagenase/M199 and incubated at 37 °C for 5 minutes. Cells were centrifuged at 500g for 5 minutes. The supernatant was removed, and cell pellets were resuspended in RLT lysis buffer.
RNA was extracted, treated with DNase on the column using Qiagen’s RNeasy Kit per manufacturer’s instructions, and processed using Texas A&M AgriLife Research Genomics & Bioinformatics Services for sequencing using a 125-PE HiSeq platform with Illumina’s TruSeq RNA Library Prep. STAR2.3.148 was used to align the reads to the reference genome hg19 with mapping rates between 93% and 94%. Differential expression analysis, normalization, and transformation of read count data were performed using the DESeq R package version 1.38.0.49 Two comparisons were performed: 1 HR control versus 1 HR treated, and 6 HR control versus 6 HR treated to detect changes in mRNA expression between control (noninvading) and treated (invading) populations.
Validation of mRNA Expression Using qPCR
Invading and noninvading cells were collected after 6 hours of incubation as described for scRNA-seq. Cell pellets were resuspended in the RLT Lysis Buffer, and RNA was extracted with Qiagen’s RNeasy Kit. Total gels were collected at 1, 3, or 6 hours of invasion for qPCR (quantitative polymerase chain reaction) analysis of gene expression during siRNA invasion assays to confirm gene knockdown. cDNA (complementary DNA) was made using Bio-Rad’s iSCRIPT cDNA synthesis kit with 0.5 to 1 µg RNA as a starting template. qPCR was performed using iTaq Universal SYBR Green Supermix (Bio-Rad, CA), 0.5 µmol/L forward and reverse primers and 1-µL cDNA template, diluted 1 to 100, on a StepOnePlus Real-time System (ABI, MA) for data analysis as 2−ΔΔCT. RPLP0 was used as the housekeeping gene. Primer sequences are listed in Table S1.
Immunofluorescence and Imaging
Invading cultures at the time points indicated were fixed in 4% paraformaldehyde (Electron Microscopy Sciences, PA) for 20 minutes, washed twice in 25 mmol/L Tris with 200 mmol/L glycine for 15 minutes, permeabilized with 0.5% Triton X-100 in PBS for 30 minutes, and incubated in blocking buffer (0.1% Triton X-100, 1% BSA, 0.2% sodium azide, 5% goat serum) at 4 °C overnight. Primary antibodies (1:200 in blocking buffer) were applied for 3 hours at RT. Samples were washed 4× for 30 minutes each in 0.1% Triton X-100 in PBS. Goat anti-rabbit or anti-mouse secondary antibodies conjugated to Alexa 488 or Alexa 594 (Invitrogen, MA) were added (1:600 in blocking buffer) for 1 hour at RT. Samples were washed twice for 30 minutes in 0.1% Triton X-100 in PBS and again overnight. A final wash containing 10 µmol/L DAPI was performed before capturing images with a Nikon Eclipse TI equipped with NIS (Nikon Imaging Software) Elements AR software. Antibodies are listed in the Major Resources Table. For quantifying JUNB (JunB proto-oncogene) nuclear intensity, image stacks were collected to identify invading and noninvading cells. JunB nuclear intensity was determined by identifying the center of the nucleus and recording JunB and DAPI (4′,6-diamidino-2-phenylindole) signaling intensities. Data were separated into invading and noninvading groups for analysis.
siRNA Transfection
Silencer select siRNAs (Ambion 4392420) to SNAI1 (snail family transcriptional repressor 1): ID No. s13187: CACUGGUAUUUAUAUUUCAtt; β2M: ID No. s1852: GAAUGGAGAGAGAAUUGAAtt; rounding 3: ID No. s1577: GAACGUGAAAUGCAAGAUAtt, ID No. s1579: GAUCCUAAUCAGAACGUGAtt and Negative Control No. 2 (catalog no. 4390846) were used at 50 to 100 nmol/L to transfect HUVEC (P3–P6) that were seeded at 50% confluency onto gelatin (1 mg/mL) coated flasks with siPORT Amine (Ambion, MA) per manufacturer’s instructions. Cells were used in invasion assays and collected for Western blotting and RNA extraction 48 to 72 hours after transfection.
ASO Transfection
Custom antisense oligonucleotides were designed for JUNB (5′-TCTGGCGCGATAGCTT-3′) and a negative control scramble (5′-AACACGTCTATACGCG-3′). HUVECs, passage 3, were transfected using siPORT Amine (Ambion, MA) with 50-nmol/L antisense oligonucleotide, per manufacturer’s instructions. Briefly, P3 HUVECs were seeded onto gelatin-coated flasks (1 mg/mL) at 90% confluency. The next day, cells were transfected with 12.5-µL siPORT Amine and 50-nmol/L antisense oligonucleotide in 3-mL Opti-MEM I Reduced-Serum Medium (no antibiotic) containing 5% fetal bovine serum. After overnight incubation, cells were rinsed with Opti-MEM (no antibiotic) and fed with antibiotic-free growth media. The following day, cells were used in invasion assays and extracts were collected for Western blotting and RNA extraction.
Evaluation and Quantification of Invasion Responses
Overnight invasion cultures were fixed in 3% glutaraldehyde (Sigma, MO) and stained with 0.1% toluidine blue in 30% methanol. Toluidine blue-stained collagen matrices were cut and imaged from the side on an Olympus CKX41 microscope fitted with a Q Color 3 camera to document invasion responses. Separate samples were stained with DAPI before capturing confocal images with a Nikon Eclipse T1 microscope and rendered in the volume view with Alpha depth coding selected. Nuclear invasion distance was quantified using NIS Elements AR software. A minimum of 25 structures from 3 separate experiments were analyzed. The data shown are from a representative experiment.
Morphogenesis Assay
HUVECs were transfected in T25s with siRNA-targeting RND3 or siNEG2 (siRNA negative control 2) control. Morphogenesis assays were performed 48 hours later. Cells were mixed with 3.75 mg/mL collagen, seeded into 96-well half-area plates, and equilibrated for 30 minutes. Growth media contained 40 ng/mL bFGF, 40 ng/mL VEGF, ascorbic acid, 1× RSII, and 50 ng/mL phorbol ester. Lumen diameter and filopodia length were measured using Image-Pro Analyzer.
Protein Extracts and Western Blotting
To quantify protein levels in cells, pellets were lysed directly with hot 1.5× Laemmli sample buffer with 2% mercaptoethanol. Protein extracts were incubated at 95 °C for 5 minutes before loading onto SDS-PAGE gels (7%–12%) for electrophoresis. Proteins were transferred to 0.45 µm Protran Membrane (GE, IL) for 90 minutes at 140 V. Membranes were blocked for 1 hour in 5% milk before the addition of primary antibodies and incubated at 4 °C overnight. Blots were washed 3× (5 minutes per wash in Tween-20 saline) before the addition of horse radish peroxidase–conjugated secondary antibodies (1:5000). After a 1-hour incubation with the secondary antibody, blots were again washed 3× (5 minutes per wash in Tween-20 saline) before the addition of ECL (Millipore, MA) for chemiluminescent detection on a Bio-Rad Image Analyzer using Image Lab to capture and quantify signal intensity. Densitometry values were obtained using Image Lab software to normalize the signal intensity of investigated proteins to CD31 as indicated. Analyses were performed with at least 3 independent replicates. Antibodies are listed in the Major Resources Table. Uncropped Western Blots are included in Figure S1.
Retinal Angiogenesis Assays
Animal studies were approved by the Texas A&M Institutional Animal Care and Use Committee (AUP no. 2023-0043-H). Rnd3 haploinsufficient mice were generated as previously reported.50 Briefly, the targeting vector was inserted at Rnd3 intron 2 in the ES cell line with C57BL/6 N background. The chimeras were inbred with WT C57BL/6J (Jackson Laboratory) for >30 generations. The age-matched WT littermates were used as control. Retinas were isolated at postnatal day 5 and stained with 10 μg/mL FITC (fluorescein isothiocyanate)-conjugated lectin from Griffonia simplicifolia (isolectin-B4)51 (L9381, Sigma, MO). Images were collected from each lobe and analyzed by an observer blinded to mouse genotype. Data from at least 5 fields per retina were pooled and averaged to provide a value for each retina. Data from 2 male and 4 female mice per genotype were pooled, as sex is not a determinant in retinal angiogenesis in newborn mice.
GOnet Pathway Analysis
GOnet software (http://tools.dice-database.org/GOnet/) was used for gene ontology term annotation analysis on the list of commonly upregulated genes in invading endothelial cells by selecting bio processes with go annotation in csv output.
Statistical Analysis
GraphPad Prism version 6.07 for Windows (GraphPad Software, La Jolla, CA, www.graphpad.com) was used to perform post hoc statistical analyses for invasion, protein quantification, and retinal angiogenesis. Raw data values were analyzed for normality and equal variation before the application of parametric or nonparametric statistical tests. Individual tests applied are indicated in each figure, along with exact P values. Error values in all figures represent SD.
RESULTS
Three-Dimensional Assays Allowed Separation of Invading and Noninvading Endothelial Cell Populations for Single-Cell RNA Sequencing
A 3-dimensional (3D) model of primary human endothelial sprout initiation was employed to find genes important for the initiation of angiogenesis. Cells were seeded as a monolayer onto 3D collagen matrices and supplied with proangiogenic factors to induce sprouting. After 24 hours of incubation, a population of endothelial cells has completed the invasion process (Figure 1A). We chose the 6-hour time point where cells had just begun the invasion process, and we hypothesized that genes important for the initiation of sprouting angiogenesis/angiogenic switch would be upregulated at this time. At 6 hours, noninvading and invading cells are clearly distinguishable, physically separable, and the latter extended processes into collagen matrices (Figure 1A). We performed both single-cell and bulk RNA transcriptomics to identify changes in gene expression between noninvading and invading cells. In scRNA-seq experiments, two independent donors were used to collect separate samples of noninvading and invading cells for a population analysis (Figure 1B). Additionally, samples containing all invading and noninvading cells (TOTAL) were collected and evaluated using scRNA-seq cluster analysis. The schematic in Figure 1C illustrates the separation of noninvading and invading populations used for scRNA-seq. The noninvading population was liberated by treating the monolayer of 6-hour cultures with trypsin. With the noninvading population removed, invading cells were collected from collagen matrices by collagenase treatment. Photographs of cell monolayers (Figure S2A) and a side view of invading cells (Figure S2B) before and after trypsinization reveal that the invading cells that extended processes into collagen matrices remain behind. This method was used to collect a viable sample of single cells from noninvading and invading populations suitable for scRNA-seq (Figure S2C).
Collagenase Dissociation Artificially Induced FOS Expression
The initial scRNA-seq population analysis comparing noninvading to invading endothelial cells revealed substantial upregulation of various early response genes (ERGs) including EGR1, FOS, FOSB, JUNB, and ZFP36. Recent reports have shown dissociation of tissue with collagenase can cause spurious gene expression of ERGs.52–54 Due to this potential artifact in our expression data, we compared direct lysis of invading cells versus 10-minute collagenase exposure before lysis. A qPCR analysis revealed that FOS expression was substantially upregulated with collagenase digestion compared with cells not exposed to collagenase but other ERGs evaluated were not (Figure S3A). Based on this result, FOS-expressing cells were excluded from single-cell data sets and reclustered. In doing so, 5394 and 4117 cells were removed from the aggregated Population and Cluster Analyses, respectively before proceeding to additional downstream analyses. A summary of scRNA-seq parameters metrics, including number of cells analyzed, and reads per cell, is shown in Table S2.
Population Analyses Derived From scRNA-Seq of Invading and Noninvading Cell Populations Revealed Significant Upregulation of 180 Genes in the Invading Population
We first performed a Population Comparison of noninvading and invading endothelial cells that generated 180 mRNAs (Data Set 1). Precise clustering of the population of noninvading (blue), invading (red), and aggregated (red and blue), from both biological replicates (donors A and B) revealed that single-cell sequencing yielded two populations with distinct transcriptional expression profiles (Figure 1D). These data show the successful separation of invading from noninvading endothelial cells for downstream scRNA-seq analysis. A heat map of the top 50 differentially expressed genes from the population analysis is shown (Figure 1E).
Cluster Analyses Derived From scRNA-Seq of a Total Cell Population Identified an Invading Cluster Enriched With Tip Cell Markers
A second transcriptomic analysis used total samples containing a mix of invading and noninvading cells (Figure 1B). A cluster analysis was performed to determine if invading and noninvading populations could be efficiently identified from a mixed population using scRNA-seq. Guided by the gene set obtained from the population comparison that assigned markers to invading and noninvading groups (Data Set 1), we identified a noninvading cluster (blue) and an invading cluster (red) from total samples (Figure 1F). This analysis generated 1176 differentially expressed genes with upregulated expression in the invading cluster. Data set 2 shows a complete list of the local Cluster Analysis comparison between the invading (red) and noninvading (blue) clusters. A heat map of the top 50 differentially expressed genes from the Cluster Analysis is shown (Figure 1G). We found the entire invading cluster is enriched with multiple tip cell markers, including CD34, CXCR4, DLL4, and others, consistent with our goal of identifying markers of angiogenic sprouting. We also observe that cells are not substantially segregating based on library of origin, but rather on expression profiles categorizing them as either invading or noninvading (Figure 1F).
Bulk RNA-seq of Invading and Noninvading Cell Populations Revealed Early Transcriptional Changes Consistent With a Sprouting Phenotype
Bulk RNA-seq experiments using 3 independent endothelial cell donors were performed to complement the scRNA-seq analysis (Figure 2A). Experimental groups included nonactivated control samples and samples treated with proangiogenic stimuli that were activated (treated). The control group showed no invasion, while the treated group showed robust invasion initiation responses after 1 and 6 hours of incubation.47 RNA was collected from nonactivated and thus an entirely noninvading cell population (1 HR control and 6 HR control) for comparison with activated samples that contained a mixture of invading and noninvading cells (1 HR treated and 6 HR treated). Cells were collected and sequenced, and the relative expression of individual genes was determined. The summary of bulk RNA-seq metrics, data, and differentially expressed genes identified from the bulk RNA-seq analysis are included in Table S3 and data sets 3 and 4, respectively. This analysis generated 453 differentially expressed genes that were significantly upregulated at 1 or 6 hours of invasion in treated versus control samples. No adjustments for collagenase treatment were included, as both the control and treated cell populations were collagenase digested. A heat map of the top 50 differentially expressed genes from the bulk RNA-seq analysis is shown (Figure 2B).
Integration of Population, Cluster, and Bulk RNA-Seq Analyses Revealed 39 Candidate Genes Consistently Upregulated With Invasion
To establish a unique and validated list of genes upregulated in sprout initiation, we integrated information from the scRNA-seq population comparison (yellow), cluster analyses (blue), and bulk RNA-seq data (red) and looked for overlap among the data sets (Figure 2C). The Venn diagram shows that 39 candidate genes were commonly upregulated in all 3 analyses (Figure 2C). The cluster comparison yielded 83 genes in common with the population comparison. Considering the bulk RNA-seq comparisons, 56 of the 180 genes were upregulated in activated, invading cells at either 1 or 6 hours. The 39 candidate mRNAs ultimately common to all 3 data sets are listed in the Table. A comparison of all data sets (population, cluster, and bulk RNA-seq analyses) can be found in data set 5. Of these, we observed genes that have been reported to play a role in angiogenic responses, as well as those uninvestigated in angiogenesis (Table). To ensure that collagenase digestion was not responsible for the elevated expression of these candidate genes, we chose one gene from each category in the Table and performed qPCR on samples that were extracted with or without collagenase. We did not see a significant increase in expression of the candidate genes selected in response to collagenase treatment (Figure S3B), indicating that changes in gene expression are associated with alterations in phenotype between invading and noninvading cells. Ultimately, these analyses converged to 39 commonly upregulated transcripts found in invading endothelial cells.
| Function | Gene | Gene name | Known role | Related role |
|---|---|---|---|---|
| Adhesion and migration | NEDD9* | Neural precursor cell expressed, developmentally downregulated 9* | NO* | Cancer cell invasion55 |
| NUAK2* | NUAK family SNF1-like kinase 2* | NO* | Proliferation | |
| RAPH1* | Ras-associated and pleckstrin homology domains-containing protein 1* | NO* | Cell invasion56,57 | |
| RND1 | Rho-related GTP-binding protein Rho6 | YES58 | Notch transcript58 | |
| RND3* | Rho family GTPase 3* | NO* | Invasion, proliferation59 | |
| SEMA7A | Semaphorin 7A | YES60 | ||
| Immunosuppression | CD200* | Cluster of differentiation 200* | NO* | Immune evasion61 |
| STC1 | Stanniocalcin 1 | YES62,63 | Calcium regulation | |
| Inflammatory response | ICAM1 | Intercellular adhesion molecule 1 | YES64,65 | Leukocyte recruitment |
| IL11 | Interleukin 11 | YES66 | Endothelial recruitment | |
| SELE | Selectin E | YES67,68 | Leukocyte recruitment | |
| PTGS2 | Prostaglandin-endoperoxide synthase 2 | YES69 | ||
| VCAM1 | Vascular cell adhesion molecule 1 | YES70 | Leukocyte recruitment | |
| Ion channel | KCNN2* | Potassium calcium-activated channel subfamily N member 2* | NO* | Hypertension |
| Notch signaling | JAG1 | Jagged canonical notch ligand 1 | YES71 | Tip and stalk cell fate |
| Proteases/enzymes | ADAMTS1 | ADAM metallopeptidase with thrombospondin type 1 motif 1 | YES72 | |
| ADAMTS4 | ADAM metallopeptidase with thrombospondin type 1 motif 4 | YES72 | Aggrecanase-1 | |
| ADAMTS9 | ADAM metallopeptidase with thrombospondin type 1 motif 9 | YES72 | ||
| F3 | Coagulation factor III (tissue factor) | YES73 | ||
| MMP10 | Matrix metalloproteinase-10 (stromelysin 2) | YES74 | ||
| NDST1 | N-deacetylase and N-sulfotransferase 1 | YES75 | ||
| PLA2G4C* | Phospholipase A2 group IV C* | NO* | Lipid metabolism | |
| ST6GAL1 | Beta-galactoside alpha-2,6-Sialyltransferase 1 | YES76 | ||
| Proliferation | DUSP16* | Dual specificity phosphatase 16* | NO* | MAP kinase signaling |
| DUSP8* | Dual specificity phosphatase 8* | NO* | MAP kinase signaling | |
| NEK10* | Nima-related kinase 10* | NO* | Cell cycle arrest | |
| TNFSF15 | Tumor necrosis factor ligand superfamily member 15 | YES77 | VEGF receptor signaling | |
| Transcription factors | BCL6B | B-cell CLL/lymphoma 6 member B protein | YES78 | |
| CREB5 | Cyclic AMP-responsive element-binding protein 5 | YES79 | Thrombospondin repression | |
| EGR1 | Early growth response 1 | YES80 | ||
| EGR3 | Early growth response 3 | YES81 | ||
| ELL2* | Elongation factor for RNA polymerase II 2* | NO* | Transcription | |
| JUNB | JunB proto-oncogene | YES82,83 | ||
| KDM6B* | Lysine-specific demethylase 6B* | NO* | ||
| NR4A2 | Nuclear receptor subfamily 4 group A member 2 | YES84 | VEGF, EGR1 induced | |
| NR4A3 | Nuclear receptor subfamily 4 group A member 3 | YES85 | ||
| SNAI1 | Snail family transcriptional repressor 1 | YES86 | ||
| ZFP36 | Zfp36 ring finger protein | YES87 | JAG1 degradation | |
| Other | ZSWIM4* | Zinc finger swim domain-containing protein 4* | NO* | Chromatin organization |
Genes are listed alphabetically in functional categories based on Gene Ontology terms, identified by gene symbol and name. ADAM indicates a disintegrin-like and metalloproteinase; AMP, adenosine monophosphate; CLL, chronic lymphocytic leukemia; GTP, guanosine triphosphate; MAP, mitogen-activated protein kinase signaling; NUAK, adenosine monophosphate kinase regulated novel kinase; SNF1, family sucrose nonfermenting; and VEGF, vascular endothelial growth factor.
*
Genes have not been shown to play a role in angiogenic responses.
Gene Ontology Pathway Analysis Identified Expected Changes in Signal Transduction and Morphogenesis Along With a Strong Proinflammatory Profile
Analysis of the genes commonly upregulated with endothelial invasion revealed the majority regulate signal transduction and developmental processes (Figure 3A). Interestingly, almost half (46%) of the commonly upregulated genes are also involved in immune system processes, including BCL6B, CD200, EGR1, EGR3, ICAM1, IL11, JAG1, JUNB, KDM6B, NEDD9, NR4A3, SELE, SEMA7A, ST6GAL1, STC1, TNFSF15, VCAM1, and ZFP36. Differential gene expression of immune-related transcripts between invading and noninvading cells in the Population, Cluster, and bulk RNA-seq analyses is depicted with heat maps in Figure 3B. A stark upregulation in expression of the genes associated with immune regulation and inflammatory responses is displayed from the bulk RNA-seq data, where increased expression occurs with activation at either 1 or 6 hours of invasion (Figure 3C). Together, these data reveal anticipated changes in signaling processes known to be associated with angiogenic responses, such as signal transduction, differentiation, and motility, as well as an unexpected (and previously unrecognized) upregulation of markers of inflammation and immune evasion.
Differentially Expressed Genes Generated From All Comparisons Showed High Congruency With Independent Experiments
We selected genes from each category listed in the Table to test for congruency of gene expression. Independent samples of invading and noninvading cells (6-hour incubation) were analyzed with qPCR (Figure 4A). All gene candidates showed higher expression in invading compared with noninvading in at least one donor. No significant upregulation was observed for the CDH5 control. Bulk RNA expression shown in Figure 4B indicated independent expression in individual donors in control and treated groups at 1 and 6 hours.
For further validation of transcriptomic data, we analyzed changes in protein expression of two upregulated genes that are known to be associated with angiogenic responses. Protein lysates collected from noninvading and invading endothelial cells (ECs) after 6 hours of incubation were analyzed for PTGS2 (prostaglandin synthase 2), SNAI1, and ERK1/2 (extracellular signal-regulated kinase 1/2; Figure 5A). Normalization of PTGS2, SNAI1, and ERK1/2 to CD31 loading controls revealed significant upregulation of PTGS2 and SNAI1, but no change in ERK1/2 controls (Figure 5B). These results were reinforced by immunofluorescence, showing the increased staining intensity of PTGS2 and SNAI1 in invading cells compared with noninvading cells (Figure 5C). Counterstaining with tubulin (red) allowed visualization of sprouting structures. No increases in expression of the ERK1/2 control were seen in invading structures. These results confirmed that expression of PTGS2 and SNAI1 proteins increased at the onset of endothelial cell invasion.
SNAI1 Knockdown Reduced EC Invasion Distance
Because SNAI1 has previously been reported to promote endothelial-mesenchymal transition (EndMT) during angiogenesis,86,88 and several of the genes listed in the Table are associated with EndMT or epithelial-mesenchymal transition (Table S4), we next tested for a role for SNAI1 in initiating invasion. We compared siRNA-mediated knockdown of SNAI1 (siRNA-targeting SNAI1) to a siRNA-targeting beta 2 microglobulin negative control. Previous studies using siRNA targeting beta 2 microglobulin revealed no negative effects on endothelial sprouting.45,47,89–93 Successful knockdown was observed for SNAI1 (Figure 6A and 6B) that was associated with modestly reduced invasion density (Figure 6C) and significantly reduced invasion distance (Figure 6D), consistent with a prior report.94 SNAI1 expression was upregulated rapidly at the onset of sprout initiation in invading cells (Figure 4B) and was required for optimal invasion responses (Figure 6), providing a correlation between rapid induction of SNAI1 and angiogenesis.
Loss of JunB Reduced EC Invasion Density and Distance
JUNB is an AP-1 transcription factor that has recently been implicated in retinal angiogenesis.83 To determine if changes in JUNB protein expression follow transcriptional upregulation, endothelial cells invading collagen matrices for 3 hours were stained with antibodies to JUNB and tubulin. After categorizing cells as invading or noninvading based on tubulin staining (Figure 7A), JUNB nuclear intensity was normalized to DAPI intensity. The results showed that invading endothelial cells have significantly higher expression of JUNB compared with noninvading cells (Figure 7B). Because silencing of JUNB was inefficient when using siRNAs (data not shown), antisense oligonucleotides were designed to target JUNB. Targeting JUNB significantly reduced JUNB protein (Figure 7C) and mRNA expression (Figure 7D). As expected, the loss of JUNB resulted in a significant decrease in invasion density (Figure 7E and 7F) and invasion distance (Figure 7E and 7G). These data confirm that JUNB is required for successful invasion responses and agree with a prior report.83
Rounding 3 Contributed to EC Sprout Initiation and Filopodia Extension
Sprout initiation is associated with changes in endothelial cell motility and multiple genes involved in regulating these processes were upregulated. Although RND1 and SEMA7A have previously been shown to play a role in endothelial motility and angiogenesis,58,60 few data are available on the role of NEDD9, NUAK2, RAPH1, or RND3 (Table). Rounding 3 is an atypical Rho GTPase (also known as RhoE) that has previously been implicated in cell rounding, loss of stress fibers95,96 and barrier function.97,98 Thus, we performed siRNA-mediated silencing to determine if RND3 was required for endothelial cell invasion. siRNA directed to RND3 significantly reduced mRNA expression compared with the control treatment (Figure 8A). While RND3 silencing did not change invasion density (Figure 8B), the loss of RND3 resulted in a significant reduction in nuclear invasion distance (Figure 8C and 8D). A closer analysis of the morphology of invading cells revealed shorter overall processes and filopodia that appeared to be stunted (Figure 8E). Quantitation of filopodia length revealed a significant decrease with siRNA-targeting RND3 treatment compared with siNEG2 control (Figure 8F). These data agree with the original report of rounding 3 overexpression driving filopodia formation99 and suggest rounding 3 upregulation may be necessary for filopodia extension, which is known to be required for tip cell migration.100
To evaluate RND3’s role in additional angiogenesis assays, we measured changes in proliferation and migration in endothelial monolayers and saw no changes in proliferation or migration with loss of rounding 3 (Figure S4). In morphogenesis assays comparing siRNA-targeting RND3 to siNEG2 controls, we observed visibly reduced lumen formation with loss of rounding 3 (Figure 8G and 8H). Consistent with invasion assays, loss of rounding 3 resulted in significantly reduced filopodia length (Figure 8I). These data reinforce a requirement for rounding 3 and filopodia extension during sprouting in 3D.
To confirm a role for rounding 3 in filopodial extension in vivo, we used mice heterozygous for Rnd3. Haplosufficient Rnd3 mice were necessary because homozygous mice are born at a frequency of 0.9% of live births.50 No significant reductions were seen in retinal outgrowth between WT and Rnd3+/− P5 mice (Figure 9A and 9B). To analyze filopodial extensions, additional analyses focused on the retinal front at P5, where it appeared filopodial extensions were shorter in Rnd3+/- tip cells (Figure 9C). Quantification of filopodial length revealed that RND3+/- mice displayed significantly shorter filopodia than WT littermates (Figure 9D), confirming the reduction in filopodia lengths observed in vitro (Figure 8). Perhaps not surprisingly, since Rnd3+/− mice retain one copy of Rnd3, this may explain why no differences in retinal outgrowth are observed. Regardless, Rnd3 upregulation appears to be required for filopodial extension, which is a key component of successful angiogenic responses.100
DISCUSSION
This study analyzed multiple independent biological replicates with 3 transcriptomic analyses to reveal gene expression changes that distinguish invading from noninvading endothelial cells, and it provides a new set of markers to characterize mRNAs that are upregulated at the onset of angiogenic sprouting. Using combined scRNA-seq population and cluster analyses, along with bulk RNA-seq, we discovered 39 candidate genes that were rapidly upregulated within 6 hours of initiating invasion. The majority are associated with morphogenesis, developmental processes, or EndMT, supporting a pronounced and rapid change in phenotype at the onset of angiogenic sprout initiation. Our findings suggest nearly half of the upregulated genes regulate immune responses or are associated with immune evasion, providing evidence that significant phenotypic changes occur in endothelial cells within hours after activation and sprout initiation. We validated a role for 3 of these genes (SNAI1, JUNB, RND3) in our 3D model by demonstrating that silencing resulted in significantly impaired cell invasion. Notably, RND3 knockdown reduced filopodia length. Additionally, we successfully verified with qPCR, the congruency of expression of a subset of the genes in independent invading and noninvading samples. Furthermore, we validated an upregulation of protein expression in invading cells for SNAI1, PTGS2, and JUNB. Finally, we validated in vitro findings with a mouse retinal angiogenesis model and found that haploinsufficiency of Rnd3 retarded filopodia growth. These data provide a new set of markers to characterize early sprouting events in angiogenesis.
Our data revealed a role for rounding 3 as an early marker of angiogenic sprouting that enhances filopodia extension and endothelial invasion in 3D. We found that silencing rounding 3 expression shortened filopodia length and reduced invasion distance in vitro. Shortened filopodia are consistent with early reports that showed rounding 3 overexpression promoted substantial filopodial extensions.101 A connection between filopodia, which are prominently displayed on tip cells, and cell migration has been established. The absence of filopodia slowed tip cell migration during retinal angiogenesis.102 More recently, endothelial cell filopodia were demonstrated to rapidly and actively perceive the environment and accelerate decisions for tip cell differentiation,2,103,104 and filopodia are needed for faster searching105,106 as loss of filopodia led to a slower selection of tip cells.100,104,107–109 Together, these reports link filopodia extensions with tip cell motility. Although we did not observe a decrease in the speed of the outgrowth of retinal angiogenic vessels in P5 mice with Rnd3 haploinsufficiency, this may be due to incomplete loss of endothelial Rnd3 expression. Decreased density of cardiac vessels in Rnd3+/- mice compared with WT littermates was observed with pressure overload,110 so perhaps a challenge is necessary. While we saw no ability of rounding 3 to regulate endothelial proliferation or migration in 2 dimensions, the filopodial phenotype was consistently observed in multiple angiogenesis assays. Additional studies are needed, ideally with endothelial-specific silencing of Rnd3 to prove a requirement for rounding 3 in retinal angiogenesis in vivo.
A substantial number of upregulated transcripts (18 of 39 genes) are associated with immune responses and immune evasion. Angiogenesis is well accepted to occur alongside inflammation.111 Resident cells such as macrophages, dendritic cells, lymphocytes, and microglia promote angiogenesis and anastomosis during zebrafish development,112 collateral vessel growth,113 macular degeneration,114 chick CAM assays,115,116 retinal angiogenesis,112 and developmental and pathological angiogenesis.117,118 Leukocytes secrete angiogenic factors, including chemokines and VEGF. The presence of these proangiogenic factors promotes endothelial growth and migration toward the initial source (ie, leukocytes), and direct macrophage/endothelial cell interactions have been documented elegantly by Ruhrberg and colleagues.112 Others have shown angiogenic endothelial cells navigate the extravascular environment often encountering macrophages, monocytes, CD45+ cells, and dendritic cells.112,119–121 It makes sense that endothelial cells should be equipped to display the proper cell surface markers to avoid immune recognition by individual resident immune cells. In addition to well-characterized markers of endothelial activation such as SELE, ICAM1, and VCAM1, two prominent candidates are identified here: CD200 and STC1 (stanniocalcin 1). STC1 has long been known to correlate with angiogenic responses, but its exact function has remained elusive.122 Recently, STC1 was shown to promote intracellular retention of CALR (calreticulin), an eat me signal, and promote immune evasion in liver cancer.123 It is worth noting that both STC1 and CALR are highly abundant endothelial transcripts (Data Set 3). In addition to STC1, CD200 is well described in the cancer literature to mediate immune evasion.124 CD200 and multiple receptors are also expressed by invading trophoblasts and tissues in the decidua in mice and humans,125 where they are anticipated to promote immune tolerance in pregnancy.126 Thus, our data indicate that rapid upregulation of STC1 and CD200 expression along with well-characterized markers of endothelial activation occur to allow ECs to avoid immune recognition and activation of resident immune cells as angiogenesis proceeds.
Our results showed that collagenase treatment to dissociate cells artificially activated FOS expression in endothelial cells. These findings reinforce recent reports that collagenase dissociation upregulates the expression of ERGs and affects clustering by exaggerating differential gene expression.53,54,127 In prior reports, digests were typically performed for one hour, whereas in this study, cells were exposed to collagenase for 10 minutes. We observed a 3-fold increase in FOS expression in collagenase-treated cells compared with nontreated cells. It is possible that we observed a minimal impact of collagenase digestion on gene expression because of the limited exposure time to collagenase compared with longer digestion times in other studies.53,54 Based on the observed FOS upregulation with collagenase treatment (Figure S3A), cells with high FOS expression were excluded to avoid skewing Cluster Analysis. We confirmed no significant upregulation of candidate genes occurred in response to collagenase treatment (Figure S3B). Our studies reveal that even limited treatment with collagenase can upregulate the FOS ERG and potentially skew clustering results.
The clustering analysis aided an unbiased analysis of sprouting responses in 3D. Clustering analysis is inherently dependent on input parameters and can be highly sensitive to thresholding and filtering.128 When separating the total cell sample into two clusters, the results aligned with the independent results from the population comparison and bulk RNA-seq analyses. The results of the clustering analysis are further strengthened by reproducibility within the noninvading and invading cluster profiles of both biological replicates. In agreement with an early phenotypic change, several tip cell markers were significantly upregulated in the cluster analyses. Angiogenic tip cells are characterized as highly migratory cells that extend multiple filopodia and lead the way during new blood vessel growth.129 The integrated comparison that evaluated invading and noninvading endothelial cells (Table) revealed significantly higher expression of known tip cell markers ADAMTS1,130 JUNB,83 BCL6B,131 RND1,58,131 and VCAM1131 in invading endothelial cells. The abundant literature reporting tip cell markers have been collected using a variety of models.32,132–135 Often the new blood vessel growth studied in these models is at an advanced stage of progression, where tip, stalk, and phalanx cells are present.5,27,31,32 In the current study, endothelial cells are still in the early or nascent stages of emerging from an existing monolayer. Although cells extend filopodia into the collagen matrix, junctions are intact, and nuclei largely remain on the surface of the collagen matrix, raising the possibility that all cells in the invading population are not yet fully differentiated or perhaps committed, because endothelial cells are highly plastic and oscillate between tip, stalk, and other cell phenotypes.31,107,109,136 These data indicate the existence of an emerging tip cell population based on the upregulation of multiple tip cell markers in both the Population and Cluster Analyses. Further study is needed to address the functional role of the previously unidentified candidate genes in controlling tip cell behaviors.
PTGS2 was validated at the protein level as being upregulated in invading endothelial cells. PTGS enzymes are also known as COX (cyclooxygenase) proteins. While PTGS1 (prostaglandin synthase 1; COX-1) is constitutively expressed in endothelial cells, PTGS2 (prostaglandin synthase 2; COX-2) is upregulated in angiogenic tissues, atherosclerosis, and during inflammation.137–139 It has been known for some time that cyclooxygenase inhibitors slow tumor growth and dramatically decrease tumor angiogenesis.140–143 The proangiogenic action of PTGS2 is not completely clear, but it may act through peroxidase activity within the enzyme142 or indirectly through upregulation of prostaglandin E2, a downstream prostanoid that promotes angiogenesis.144 Prostaglandin E2 also has the ability to indirectly promote angiogenesis by upregulating VEGF and FGF.142,145,146 While we do not observe upregulation of prostanoid synthases in transcriptomic data, PTGES3, is the sole prostanoid synthase expressed at high levels constitutively in the bulk RNA-seq data (Data Set 3), suggesting PTGES3 (prostaglandin E synthase 3) would likely be available to generate prostaglandin E2 and has been shown to do so in cooperation with PTGS1.147 As an alternate mechanism, PTGS2 arrests endothelial and other cells in G0/G1 stages of the cell cycle,148 which is consistent with a decrease in proliferation rates associated with tip cell formation.149 Although the specific role of PTGS2 is not revealed in these studies, the identification of PTGS2 as a marker of invading endothelial cells with a tip cell phenotype is consistent with what is known in angiogenesis.
Our final list of 39 candidate genes is consistent with EndMT occurring at the onset of invasion. The transitory state of partial EndMT has been proposed as a method for endothelial cells to acquire invasive properties while still maintaining an endothelial identity.88 SNAI1, a known activator of EndMT, had a significant impact on endothelial invasion distance, as knockout cells exhibited impaired migration when compared with controls. The Hughes laboratory reported that SNAI1 and Slug expression increase with time during endothelial cell invasion in 3D fibrin matrices and are essential for endothelial cell migration and sprouting, concluding that they do not act redundantly.94 We observe low levels of Slug expression, perhaps because of differences in experimental conditions (the addition of serum or phorbol ester), the 3D matrix used (fibrin versus collagen), or timing (6 hours of invasion versus 3–6 days). The data here indicate that SNAI1 is activated quickly (1 hour) on collagen matrices and that exposure to S1P and growth factors amplifies this upregulation. Other genes upregulated with invasion that have been previously implicated in EndMT include NDST1,150 RND3,151 SEMA7A,152 STC1,153 and VCAM1154 reinforcing an EndMT phenotype at the onset of angiogenic sprouting.
Our data raise the possibility that the JUNB transcription factor promotes an invasive tip cell phenotype consistent with EndMT in angiogenesis. JUNB has been shown to be required for vascular development in the embryo, placenta, and retina.83,155 Specific evidence for a requirement for JUNB in endothelial cells has been provided by both constitutive156 and inducible83 endothelial knockouts. The Hla laboratory has recently shown that AP-1 transcription sites open with increased sprouting, and JunB drives retinal angiogenic endothelial cell invasion into the deep retinal layer.83 In that study, JUNB upregulation was associated with the acquisition of neural guidance cues during angiogenesis.83 Further, elevated JUNB expression in endothelial cells promotes a tip cell phenotype.155 Consistent with these reports, JUNB has previously been shown to regulate invasion of various cancers,157–159 trophoblasts,160,161 endometrial cells,162 and lymphatic endothelial cells.163 Given that an AP-1 binding site exists in the promoter region of SNAI1164 and Slug,165 JUNB may be rapidly upregulated to promote EndMT, which will contribute to an invasive phenotype in a variety of systems, including angiogenic endothelium.
Interpreting data from large transcriptomic experiments necessitates prioritization of hits, as endless lists of differentially expressed genes are often generated. Consistent with others,31 we employed a 3-tiered analysis approach using scRNA-seq and bulk RNA-seq data from 5 individual donors in which invading cells were efficiently separated from noninvading cells. From these analyses, we identified 39 candidate genes upregulated in activated, invading endothelial cells. Evaluation of these genes highlights a stark and rapid change in plasticity that occurs in endothelial cells as diverse transcriptional pathways are activated to accomplish rapid, coordinated convergence leading to the initiation of angiogenic sprouting.
ARTICLE INFORMATION
Supplemental Material
Figures S1–S4
Major Resources Table
Tables S1–S4
Data Sets 1–5
Acknowledgments
Research reported in this publication was supported by the Texas A&M University T3 grant 02-247008 (K.J. Bayless) and the Department of Molecular and Cellular Medicine. K.J. Bayless, C.A. Abbey, C.L. Duran, and J. Chang participated in experimental design. C.A. Abbey, Z. Chen, Y. Chen, S. Roy, K.J. Bayless, C.L. Duran, and A. Coffell participated in data collection. C.A. Abbey, A. Coffell, and K.J. Bayless participated in data analysis. K.J. Bayless, C.A. Abbey, G.B. Wells, J. Chang, S. Chakraborty, T.M. Sveeggen, and A. Coffell participated in article drafting. All authors performed article editing and approval.
Footnote
Nonstandard Abbreviations and Acronyms
- 3D
- 3-dimensional
- ASO
- antisense oligonucleotide
- bFGF
- basic fibroblast growth factor
- CALR
- calreticulin
- COX
- cyclooxygenase
- DAPI
- 4′,6-diamidino-2-phenylindole
- EndMT
- endothelial-mesenchymal transition
- ERG
- early response gene
- ERK1/2
- extracellular signal-regulated kinase 1/2
- FOS
- Fos proto-oncogene
- HUVEC
- human umbilical vein endothelial cell
- JUNB
- JunB proto-oncogene
- PTGES3
- prostaglandin E synthase 3
- PTGS1
- prostaglandin synthase 1
- PTGS2
- prostaglandin synthase 2
- RNA-seq
- RNA sequencing
- S1P
- sphingosine 1-phosphate
- scRNA-seq
- single-cell RNA sequencing
- SNAI1
- snail family transcriptional repressor 1
- STC1
- stanniocalcin 1
- VEGF
- vascular endothelial growth factor
- WT
- wild-type
- ZFP36
- zinc finger protein 36
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© 2024 American Heart Association, Inc.
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Received: 18 December 2023
Accepted: 28 February 2024
Published online: 14 March 2024
Published in print: May 2024
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This work was supported by a T3 pilot grant from Texas A&M University (K.J.B.), and NHLBI R01HL141215, NHLBI R01HL150124, NHLBI R01HL148133, and 23TPA1142716 (J.C.).
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