To uncover changes on endothelial cells as result of exposure to culture conditions, we evaluated the transcriptome of endothelial cells isolated from human umbilical cord veins. Half of each participant’s cell preparation was freshly processed for RNA isolation (referred to as ‘cord’ or ‘in vivo’) while the other half was placed under culture conditions (referred to as ‘culture’ or ‘in vitro’). Cells were subsequently passaged and evaluated at ‘early’ passage (P 2–3) and ‘late’ passage (P 7–8) to capture transcriptional differences between cellular environments that were common amongst all seven patients regardless of fetal sex or genetic background (Figure 1A, Supplementary file 1). Patient demographics with paired maternal–fetal outcomes are provided in Table 1, and each patient had matched cord, early passage, and late passage paradigm. Principal component analysis (PCA) of bulk RNA sequencing (RNAseq) transcriptional profiles revealed that cord versus culture/in vitro environments were the dominant factor influencing measured expression levels (Figure 1B, C, Supplementary file 1). PC1 captured 47.4% of the total variance whereas PC2 only accounts for 11.1%. PC2 appeared to represent the differences between early and late passage but these conditions did not segregate from each other as clearly as cord versus in vitro culture. As such, all comparisons to culture conditions are subsequently made with early (in vitro) cells in culture.

Approximately half of the expressed genes were differentially expressed between cord and culture conditions (4532–4645 genes overlapping), whereas the transcriptomic signature was very similar between early and late cultures (11,706 genes overlapping) (Figure 1D). As such, we considered only differences between cord and culture signatures going forward (Figure 1E–G, Supplementary file 1, Figure 1—figure supplements 1–2).

Genes with robust changes in expression are highlighted in Figure 1E, F. Among several signatures, we observed that TGFβ and BMP target genes were reduced under culture. Some of the most in vivo-specific transcripts were related to the extracellular matrix; while several genes specific to the in vitro environment associated with the cell cycle (Supplementary file 1). We also found that the most highly expressed genes across patients and environments demonstrated minimal variation across individuals and considerable variation between environments (Figure 1F, Supplementary file 1). As expected, we found that endothelial cells lose expression of flow-responsive genes (KLF4, KLF2) once placed under culture conditions, whereas they quickly acquire proliferation-related genes (CCNB2, CCNA2, CDCA2). Perhaps more surprising was that transition into culture promotes a significant decrease in transcripts associated with extracellular matrix genes (COL23A1, MMP28, FBLN2, ELN, COL1A2, COL6A3), cytokine (CXCL2, SOCS3, TGFB3, CTGF), and early response genes (FOS, ZFP36, JUNB) (Supplementary file 1, Supplementary file 4). In addition to increased expression of cell cycle genes in culture, transcripts associated with survival and a pro-angiogenic phenotype were also upregulated (e.g., APLN, BAX, CCN, CCNB2, CCNBA1, CEPH1, CDCA7l, CDCA2, MDM2). Further, the significant increase of VEPH1 under culture conditions was of particular interest as the protein product of this gene is associated with suppression of TGFβ1, FOXO, and Wnt signaling (Shathasivam et al., 2015).

Gene Ontology (GO) term enrichment of differentially expressed genes was performed using GO biological processes in order of significance. Significant terms, defined using hypergeometric p values and enrichment factors, were hierarchically clustered based on similarities among gene members into networks (Figure 1G).

In the network, terms are represented by a node with its size proportional to the number of differentially expressed genes in that term. Focusing on genes expressed uniquely in cord relative to culture, we found enrichment of transcripts with documented involvement into blood vessel development, skeletal system development (mostly the TGFβ family), heart/blood vessel development, ossification (extracellular matrix genes), and cytokine production (Figure 1G).

To determine whether the identified changes were supported by similar drifts at the protein level, validation of the transcriptomic signature was performed by comparing cord and in vitro protein extracts by untargeted liquid chromatography–mass spectrometry (LC–MS)-based proteomics. PCA analysis of relative protein abundances was conducted for seven matched individuals from the cord and early culture. The analysis demonstrated clear separation of the experimental conditions (Figure 1H). In agreement to the RNA-level differences, there were significant changes in protein expression between the environments. Albeit not as remarkably different than the transcriptomic read-outs (likely due to depth of coverage and statistical power), we identified an -omics signature of proteins specific to cord (about 160/3000 proteins) and to early culture (about 411/3000 proteins) (Figure 1—figure supplement 2E). These differences are presented in Figure 1—figure supplement 2F.

To explore the degree of overlap between RNA and protein, we compared the results of differential transcripts using the cord versus culture analysis to that of protein cord versus culture analysis (Figure 1—figure supplement 2G). The relationships between t-statistics between cord and culture across -omic layers revealed significant correlation between RNA and protein signatures (r = 0.4, p = 1 × 10⁻⁰⁷) (Figure 1I). Consistent with prior findings, the data revealed low expression of cell cycle proteins and high expression of flow-responsive proteins in the cord (ex vivo) proteomics profile (Figure 1I, Supplementary files 2 and 9).

A large number of genes associated with transition from cord to culture appeared to be flow related, we explored the potential to ameliorate these differences by imposing shear stress on cultured cells. This approach is warranted by observations that once placed under laminar flow, endothelial cells significantly change their morphology and reduce proliferation resembling in vivo conditions (Chiu and Chien, 2011). Further, the onset of flow is associated with significant transcriptional increase in flow-responsive genes, like KLF2 (Chien, 2007; Nakajima and Mochizuki, 2017; Zhou et al., 2014), which is one of the cord-specific transcripts (Figures 1 and 2, Supplementary file 3). We thus performed two comparisons: (1) static culture to flow cultures (Figure 2A) and (2) each to cord endothelial cells (Figures 2 and 3, Supplementary file 3).

Indeed, we found that shear stress significantly rescued the expression of approximately 17% of genes including targets of BMP and Notch signaling known to be sensitive to flow. At the transcriptional level, the effect of physical forces, particularly laminar, oscillatory, and disturbed flow has been extensively investigated (Kim et al., 2017; Nakajima et al., 2017; Peng et al., 2019; Polacheck et al., 2017). These investigations have been instrumental to clarify the effect of shear stress on endothelial cells. Nonetheless, previously published studies focused in evaluating flow responses at short and long time points in relation to static cultures. We took advantage of having isolated RNA directly from cords, allowing for comparisons between in vivo and in vitro (static and flow) conditions using genetically identical backgrounds.

First, PCA of matched patients (n = 4, Table 1) demonstrated static cells in vitro and under 30 min of flow displayed relatively similar global transcriptional signatures. Differences were apparent on PC1 with flow, defined as 8–48 hr of laminar shear stress exposure (Figure 2B). Figure 2C provides a clear delineation and transcriptomic signature as a function of static (control and 30 min) versus longer time points (8, 24, and 48 hr of flow). Significant changes (log10FC) were noted between static and flow cultures (Figure 2D and Supplementary files 3 and 4), with IGFBP5, ELN, KLF4, ETPR1, and TGFBR3 significantly dependent on flow for their transcriptional increase. Correlation scatter plots of the cord versus culture (x-axis) were compared to time under flow (y-axis) and this analysis showed a time-dependent positive correlation to the cord transcriptome (vs. culture) (Figure 2E). Progressive time under flow from up to 48 hr of shear stress (flow) revealed that the transcriptional signature of cells correlates more specifically to that of the cord than with static cultures. Initially, the correlation coefficient was insignificant (r = −0.035, p = 0.004) with progressive changes to the point that by 48 hr of shear stress the correlational coefficient to cord reached r = 0.34, = −8.0 × 10⁻⁹, which is significantly different than static culture (Figure 2E). Collectively these data offer proof that drifts in the transcriptome of endothelial cells under culture can be partially rescued by exposure to laminar shear stress. To improve the data accessibility and analysis capabilities of the generated transcriptomic flow data, we implemented an open-source website, Flow Profiler, to display the data as a table, plots, and with some analysis functionalities (Figure 2—figure supplement 2).

A marked change toward the cord state was noted also by pathway analysis. Specifically, GO terms associated with blood vessel development (EDN1, BMP2, BMP4, TGFPR3, ITG1BP1, HES1, HEY1), regulation of cellular protein location (ITGA3, RACK1, PTPN9, SPTBN1), and cellular response to laminar fluid shear stress (ASS1, KLF2, KLF4, MAPK7, NFE2L2) were regained by long-term exposure to flow (Figure 2F). Gene set enrichment analysis of differential expressed genes in cultured endothelial cells under flow (vs. static) revealed gene annotations related to an acute inflammatory response, heart morphogenesis, second messenger-mediated signaling, and ossification (related to BMP and TGFβ responses). We also found tRNA and rRNA metabolic processes were silenced under flow (Supplementary files 3 and 4, Figure 2—figure supplement 1, Figure 3—figure supplement 1).

Superimposing the cord and culture signatures (from Figure 1) with the static versus flow experiments (from Figure 2) clarifies how the transcriptional profile of cultured cells under flow approximates the in vivo transcriptome better when compared to static states (Figure 3A, across PC1, Supplementary files 3 and 4). PC1 primarily displays the differences between cord and culture samples (we interpret the PC2 to represent differences between short-term and extended flow). Since the extended flow samples are in the middle position between cord and late culture, we interpret this as a partial rescue of the differences imparted by culture. This shift was also noted by evaluating total number of transcriptional changes up- or downregulated (Figure 3B). In fact, much of the cord signature overlapped with genes that were rescued or attenuated under flow and paralleled those expressed by the cord. Specifically, the incorporation of shear stress to the in vitro static conditions attenuated the variability between cord and culture with a drift recovery in 17% of the genes (Figure 3).

To identify cohorts of genes altered by shear stress and that approximate the in vivo environment (rescued), we performed a transcriptome-wide weighted gene co-expression network analysis (WGCNA). This approach led us to identify 36 co-expression modules, revealing gene groups that are co-enriched in either cord or culture environments, or in static versus flow conditions (Figure 3C, red: down; blue: up, Supplementary files 3 and 4, Figure 3—figure supplement 1). Relative expression values for the most expressed modules across each of the patients are illustrated in Figure 3—figure supplement 1D, E and then culminated in summary in Figure 3D.

Superimposing the cord transcriptome on the flow transcriptome, highlighted co-expressed modules with significant enriched directionality (concordance of up- or downregulation) in cord and culture transcriptomes (Figure 3C). Specifically, we compared the differential gene expression of the cord to that of the cultured cells under flow. Using the differential gene expression, GO network analysis of WGCNA demonstrated that differential modules were selectively increased (blue) and decreased (brown) by long-term exposure to shear stress (Figure 3E–H). The blue module (2185 genes, r = 0 .71, p = 3e−04) was increased in the cord and under flow conditions as compared to static culture. The blue module showed increased transcriptional concurrence with the cord and this was progressive with time under flow (r = 0.8, p = 4e−04). Although exposure to shear stress partially recapitulated the cord environment (Figure 3E), this was not the case for all the transcripts, highlighting signatures that are exquisitely flow dependent and others that are flow independent and likely regulated by alternative factors, such as heterotypic cell interactions or in vivo metabolites. Notably, the genes and GOs associated with this module included blood vessel development and leukocyte activation (Figure 3F). The brown module was decreased both in the cord and in flow as compared to static cells (1408 genes, r = −0.9, p = 3e−08). The brown module was defined by cell cycle and cell cycle checkpoints, was less expressed in cord (vs. culture) and in flow (vs. static, r = −0.62, p = 0.01). These genes gained expression in culture, yet flow reverted their phenotype to lower expression, as was evident in cord (Figure 3G, H, Table 2, Supplementary file 5). In summary, this network analysis uncovered co-expressed gene signatures that are sensitive to shear stress (induced, aka blue module and repressed, aka brown module) and represented in vivo.

In addition, the robust dataset identified transcripts previously unknown to be altered by laminar shear stress (Supplementary file 3). To confirm the reproducibility of a few transcripts at the protein level, we validated by western blot three examples found to be upregulated, downregulated, or unchanged that were further in relation to their levels in vivo (endothelial lysates from cord umbilical vein) (Figure 4, Figure 4—source data 1). Detailed evaluation of the time kinetics for transcripts uncovered important nuances, for example, the extended time course (48 hr) of shear stress is important for some transcripts. For example, thioredoxin-interacting protein (TXNIP), a stress-responsive protein that inhibits thioredoxin and previously thought to be reduced by exposure to 24 hr of shear stress [PMID:15696199], exhibits a drastic increased by 48 hr of laminar flow. A list of the top 30 transcripts that are altered from in vivo (cord) to in vitro is shown in Table 2. Table 3 shows the extent of concordant and discordant rescue by exposing cells to laminar shear stress. In addition introduces a platform ‘Flow Profiler’ to interrogate the behavior of any gene under flow.

Given these global differences between cord and culture, we asked whether the differential gene expression was also affected by heterotypic cell interactions, namely with SMCs. To address this question, we leveraged single-cell RNA sequencing (scRNAseq) technology to obtain transcriptomes of individual cells isolated from endothelial cells in a homogenous culture (mono-culture, MC) versus endothelial cells co-cultured with SMCs (co-culture, CC). The approach was aimed at further approximating contextual environment and obtain signatures responsive to those changes (Figure 5A). We profiled technical replicates of primary endothelial cells, primary SMCs (individual mono-cultures), and co-cultured endothelial cells and SMCs all plated to confluency using scRNAseq (Supplementary file 6, Figure 5—figure supplement 1). Endothelial cells and SMCs were isolated from the same cord eliminating potential confounding factors associated with genetic variations. In total, 51,000 cells were sequenced with an average of 3402 genes and 18,740 transcripts per cell. Individual samples were independently analyzed to confirm correlation between triplicates, normalized and then combined for analysis. Unsupervised clustering demonstrated the cells cluster by origin (Figure 5B–E). We then confirmed cell clusters as endothelial cells (PECAM1 and CDH5, Figure 5C, E) and SMCs (ACTA2 and TAGLN) (Figure 5—figure supplement 1C).

Transcriptomic profiles that defined each cluster was performed by Seurat and this information offered initial insight on transcriptional shifts that occurred consequently to heterotypic cell interactions. As shown by heatmap (Figure 5F), clear differences were noted when endothelial cells were in mono-culture (MC) versus co-culture (CC). Specifically, co-culture prompted a reduction in NOTCH target genes (FABP4, GJA4, FABP5, HEY) and a clear induction in TGFβ downstream targets (SERPINE1, IGFBP7, SOX4, TIMP1) (Figure 5F). Ingenuity pathway analysis provided further clarification as to the functional impact related to presence of SMCs. As shown in Figure 5G, the major signaling pathways and transcriptional regulators that prompted transcriptional drifts on endothelial cells by co-culture included TGFβ, VEGF, TP53, HTT, MYC, TNF, EDN1, SP1, and HGF. We calculated a module score using the expression of downstream targets for TFGβ1 and VEGFA identified by ingenuity pathway analysis and found a significant increase upon co-culture for both (Figure 5H). This is entirely surprising as SMCs provide a source for these two cytokines. Activation of the TGFβ pathway results in shifts in extracellular matrix proteins, MMPs, and integrins (Figure 5I) and it is further supported by transcriptional increases in TGFβ receptors ACVRL1 and ENG. Interestingly, co-culture conditions resulted in an increase of clathrin-related genes (AAK1, AP2B1, and CLTB) and a decrease in caveolin-related genes (CAV1 and CAV2) (Figure 5J). These changes occurred with no significant alterations in CDH5, ERG, NOTCH1, and JAG1 (Figure 5K).

Naturally, the next question focused on which signatures impacted by heterotypic cell interactions yield a rescue of the in vivo condition. To delineate these transcriptional relationships, we overlapped scRNA sequencing data obtained from cord-derived endothelial cells and compared them to the mono- and co-culture endothelial transcriptomes (Figure 6A–C, Supplementary files 7 and 8). Interestingly, global transcriptional profiling in uniform manifold approximation and projection (UMAP) showed a shift of co-culture toward cord (Figure 6A). In-depth analyses of the data using Seurat, GOs, and ingenuity pathways revealed cohorts of genes that were indeed rescued (either up- or downregulated) and genes that were not rescued by the co-culture condition. Examples of those categories are shown in Figure 6D and group analysis by dot blot as displayed in Figure 6E. Genes rescued by co-culture relate to NOTCH signaling (HES1, FABP4) and TGFβ (ENG). In addition, we found that clathrin pathways, noted to be increased by SMC co-culture (Figure 5) were indeed part of the in vivo signature displayed by endothelial cells in the cord (Figure 6F) with upregulation of transcripts for AAK1 and EPN2. Co-culture also was responsible for rescue of TJP1, responsible for tight junctions and two transmembrane proteins that regulate calcium homeostasis (TMEM165 and 203) (Figure 6F). Interestingly we noticed a decrease in IRF7 and VASH1 under co-culture that also approximated the cord condition. In summary, co-culture of endothelial cells with SMCs normalized networks related to cell growth and differentiation, clathrin-vesicle-related genes, and recovered targets downstream TGFβ, recovering approximately 9% of the original cord (in vivo) signature (Figure 7).

Human umbilical cords were collected under Institutional Review Board (UCLA IRB#16-001694) at time of birth. Umbilical cords were processed within 2–4 hr from time of birth and cells were isolated as previously described (Crampton et al., 2007). All samples were collected from participants who provided signed written informed consent and were de-identified immediately after cords were obtained. Table 1 describes the clinical details of the participants/donors. The umbilical cord was clamped bilaterally, cut at least into two fragments, and placed in HBSS at room temperature. The umbilical vein was cannulated with a 18 G animal feeding needle with a blunt tip in the direction of oxygenated blood flow from the placenta to the fetus. Subsequently, the umbilical vein was serially washed with 20 ml of Hanks' Balanced Salt Solution (HBSS) (HBBS) 3× to remove blood cells from the lumen. For collection of in vivo samples: 1 ml of RLT from RNeasy Micro Kit (QIAGEN, Germantown, MD) flushed through closed circuit and re-aspirated with the distal end of the umbilical cord clamped and stored in −80°C until all RNA was ready to be extraction. The length of time to obtained cells was approximately 30–60 min from cord clamping at delivery/birth.

For isolation of endothelial cells for culture, the other half of the umbilical cord was flushed with 8 ml of collagenase-2 (210 IU, Worthington Biochemical, Lakewood, NJ) and the exudate was further incubated at 37°C for 20 min to dissociate cells. Collagenase was inactivated with the addition of equivalent volume 10% fetal bovine serum (FBS) with MCDB 131 media (VEC Technologies, Rensselaer, NY). The cells were pelleted, resuspended in media, plated and cultured for 30 min on tissue culture treated dishes in humidified incubator at 37°C and 5% CO₂. After the short incubation period (30 min), the plates were washed to remove non-adherent cells. After cells became confluent, additional purification steps were conducted (Miltenyi Biotec #130-091-935) and the purity of the endothelial isolates was evaluated by FACs analysis, immunocytochemistry and, in the case of the in vivo counter parts (RNA from the umbilical cords) we also used scRNAseq. The information on the purity of the isolated cells is shown in Figure 1—figure supplement 1. For culture cells, they were passaged using 1× Trypsin (Fisher Scientific, Waltham, MA) and collected with RLT for RNA extraction at early passage (passage 2–3) and at late passage (passage 7–8). Subsequently all RNA was extracted in tandem using RNeasy Micro Kit (QIAGEN, Germantown, MD). Contamination with genomic DNA was eliminated with incubation of DNase I at room temperature. Agilent Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA) was used to assess RNA integrity and Qubit (Invitrogen, Carlsbad, CA) for RNA concentration and purity.

For co-culture experiments, primary umbilical SMCs were isolated from the same genetically identical cords after removal of the endothelium and expanded in vitro for two passages. The purity of the SMCs was tested by scRNAseq (Figure 1—figure supplement 1). SMCs were then seeded at 50% confluency onto the culture plate 24 hr prior to adding HUVECs to reach a confluent density. After an additional 24 hr co-cultured cells were trypsinized for scRNAseq experiments.

Library preparation was performed using TruSeq Total RNASeq Kit (Illumina, San Diego, CA) according to the manufacturer’s instruction. Sequencing was conducted on an Illumina HiSeq 4000 (RNAseq) and NovaSeq S2 (scRNAseq) instrument (Illumina, San Diego, CA) at the University California Los Angeles. Sequencing parameters were optimized for 50 bp single-end reads at a depth of 30,000 million reads/sample. Reads were mapped to the hg38 build of the human genome with Bowtie2 (Langmead and Salzberg, 2012) and RNAseq reads were mapped with STAR (Dobin et al., 2013). RNAseq experiments that measured accessibility and expression in different environment (cord versus culture) were all conducted at least twice. Benjamini–Hochberg false discovery rate (FDR) method was used to correct for all multiple testing in this study with a significance threshold of FDR <0.05. No explicit power analysis was used to compute sample size.

Gene expression analysis was conducted using R software. First, each raw count TPM gene expression profile was log10 transformed and rescaled to zero-mean and unit-resolution for both the cord versus culture and flow versus static datasets. Data were adjusted for batch effects using an empirical Bayes framework with the ComBat function from the sva package; no covariates were included in the model and the algorithm was set to use non-parametric adjustments. The expression of individuals genes was screened for associations with experimental treatments using biweight midcorrelation, a robust correlation measure, with the bicorAndPvalue function from the WGCNA package. Individual genes were also tested for associations with experimental treatments using Welch’s t-test using the base R t-test function, adopting a Bonferroni-corrected significance threshold (p < 2.5e−6). Principal components analysis (PCA) was conducted following gene-wise scaling to zero-mean and unit-variance. WGCNA was conducted using the blockwiseModulesvfunction from the WGCNA package; the network soft-thresholding power was set to 3, the network type was set to ‘signed hybrid’; and the entire gene set was used for module detection by adjusting the maxBlockSize. The data can be found on the Gene Expression Omnibus (GEO) under the GEO accession number GSE158081. Both STRINGv10 (Szklarczyk et al., 2015) and Metascape (Zhou et al., 2019) were used to generate differential gene expression figures.

The web application for viewing flow transciptomic data was built to display data as a table or plot and allows for easy accessibility to all investigators. The application was developed using html, JavaScript, and CSS. A plot is drawn for each gene, at the indicated times and the average value of all available samples is displayed. A searchable/filtrable table gather the average values per gene plus the origin and slope of the resulting curve. Origin and slope were computed using the linear regression functions from Excel 2019 (INTERCEPT and SLOPE). The website provides a set of functionalities supporting the analysis of gene profiles. In addition to basic tools allowing search, filter, and combination of multiple profiles on one plot, two tools allow to find the most similar or divergent profiles compared to a selection of one or more profiles. The similarity is defined by a value between 0 and 2. Then a range is defined for each available time point (the average value of the selected gene plus and minus the similarity value). All profiles with all of their timespoints within this range are filtered. Divergence works in a comparable way, but only profiles with an average value outside the defined range are filtered. The two functions can also be used with an artificial curve as the comparison basis (the user can draw the desired curve on the plot).

Protein samples were reduced and alkylated using 5 mM Tris (2-carboxyethyl) phosphine and 10 mM iodoacetamide, respectively, and digested by the sequential addition of trypsin and lys-C proteases, as described (Wohlschlegel, 2009; Florens et al., 2006). The digested peptides were desalted using Pierce C18 tips (Thermo Fisher Scientific, Waltham, MA), dried and resuspended in 5% formic acid, and fractionated online using a 25 cm long, 75 µM inner diameter fused silica capillary packed in-house with bulk C18 reversed phase resin (1.9 µM, 100 A pores, Dr. Maisch GmbH). The 140-min water–acetonitrile gradient was delivered using a Dionex Ultimate 3000 UHPLC system (Thermo Fisher Scientific, Waltham, MA) at a flow rate of 300 nl/min (Buffer A: water with 3% Dimethyl sulfoxide (DMSO) and 0.1% formic acid and Buffer B: acetonitrile with 3% DMSO and 0.1% formic acid). Peptides were ionized by the application of a distal 2.2 kV and introduced into the Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific, Waltham, MA) and analyzed by tandem mass spectrometry (MS/MS). Data were acquired using a Data-Dependent Acquisition (DDA) method comprised of a full MS1 scan (Resolution = 120,000) followed by sequential MS2 scans (Resolution = 15,000) to utilize the remainder of the 3-s cycle time. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al., 2019) partner repository with the dataset identifier PXD020958 and 10.6019/PXD020958. Data analysis was performed using the MSGF+ search engine (Kim and Pevzner, 2014) via the target-decoy strategy against the EMBL Human reference proteome (UP000005640 9606). The identification false detection rates (FDRs) at the peptide-spectrum-match (PSM) were defined using Percolator, protein identification confidence was estimated via the stand-alone implementation of FIDO such that analytes had respective q-values at or below 0.01 at both PSM and protein level (Serang et al., 2010; Granholm et al., 2014; The et al., 2016). Extracted ion chromatograms were calculated for each peptide using Skyline (MacLean et al., 2010). The MSStats R-package was used to normalize across runs using quantile normalization, summarize peptide-level intensities into a protein-level abundance, and perform statistical testing to compare protein abundance across conditions (Choi et al., 2014).

Confluent endothelial monolayers were grown on tissue culture treated 6-well plates (Falcon #08-772-1B) in complete MCDB-131 media (VEC Technologies # MCDB131-WOFBS) plus 10% FBS (Omega Scientific #FB-11) containing 4% dextran (Sigma-Aldrich #31392) for approximately 12–18 hr and then subjected to shear stress (130 rpm) in new medium containing 4% dextran (Sigma-Aldrich #31392) for indicated time intervals and cultured alongside static controls. Orbital shear stress (130 rpm) was applied to confluent cell cultures by using an orbital shaker positioned inside the incubator as previously discussed (Dardik et al., 2005). The shear stress within the cell culture well corresponds to arterial magnitudes (11.5 dynes/cm²) of shear stress. To reduce issues associated with uniformity of shear stress, the endothelial cell monolayers in 6-well plates were lysed after removing center region using cell scraper (BD Falcon #35-3085) and washing with 1× HBSS (Corning #21-022-CV). A 1.8 cm blade was used circumferentially to remove the center of the monolayer that did not see the higher shear stress.

Single cells were isolated from umbilical cord flushes as described above. To keep the processing time between tissue harvesting and single-cell lysis at a minimum, no further cell type enrichment step was performed. For the generation of single-cell gel beads in emulsion, cells were loaded on a Chromium single-cell instrument (10× Genomics, Pleasanton, CA) with an estimated targeted cell recovery of ~5000 cells as per manufacturer’s protocol. In brief, single-cell suspension of cells in 0.4% bovine serum albumin–phosphate-buffered saline were added to each channel on the 10× chip. Cells were partitioned with Gel Beads into emulsion in the Chromium instrument where cell lysis and barcoded reverse transcription of RNA occurred following amplification. Single-cell RNAseq libraries were prepared by using the Chromium single cell 3′ library and gel bead kit v3 (10× Genomics, Pleasanton, CA). Sequencing was performed (as described above) and the digital expression matrix was generated by demultiplexing, barcode processing, and gene unique molecular index counting by using the Cell Ranger pipeline (10× Genomics, Pleasanton, CA). The data can be found under the GEO accession number: GSE156939.

To identify different cell types and find signature genes for each cell type, the R package Seurat (version 3.1.2) was used to analyze the digital expression matrix. Cells with less than 500 unique molecular identifiers (UMIs) and greater than 50% mitochondrial expression were removed from further analysis. Seurat function NormalizeData was used to normalize the raw counts. Variable genes were identified using the FindVariableGenes function; genes with normalized expression values between 0.1 and 5 and with a dispersion of at least 0.5 were considered variable. The Seurat ScaleData function was used to scale and center expression values in the dataset for dimensional reduction. PCA, t-distributed stochastic neighbor embedding (t-SNE), and UMAP were used to reduce the dimensions of the data, and the first two dimensions were used in plots. A graph-based clustering approach was later used to cluster the cells; then signature genes were found and used to define cell types for each cluster. ECs were selected based on high expression of PECAM1 and CDH5 genes. SMCs were identified by the high expression of ACTA2 and TAGLN genes. Module scores were calculated using the AddModuleScore function with default parameters.

Endothelial cells were lysed in modified Radioimmunoprecipitation assay (RIPA) buffer (50 mM Tris pH 8, 0.1% sodium dodecyl sulfate (SDS), 0.5% sodium desoxycholate, 1% Triton X-100, 150 mM NaCl, 1× protease inhibitor cocktail). Proteins were separated by SDS–polyacrylamide gel electrophoresis gradient (4–20%) gel and transferred onto nitrocellulose membranes and incubated overnight at 4°C with primary antibodies. The following primary antibodies were used in this study: PLVAP (DSHB, Cat#MECA-32); PTGS1 (Cell Signaling Technologies, Cat#9896S); TXNIP (Cell Signaling, Cat#71632); FOXP1 (Cell Signaling Technologies Cat#4402S); TFRC (DSHB, Cat#G1/221/12); ZFP36L2 (Cell Signaling, Cat#2119); ACTN1 (Sigma, Cat#A5044); GAPDH (Millipore Sigma, Cat#MAB374); VCL (Millipore Sigma, Cat#V-9131). Secondary antibodies included: Amersham ECL Rabbit IgG HRP-Linked Whole Antibody (Cat#NA934) and Amersham ECL Mouse IgG, HRP-Linked Whole Antibody (Cat#NA931) both from Cytiva. Immuno-complexes were detected by enhanced chemiluminescence with SuperSignal West Pico PLUS (Thermo Fisher Scientific #PI34580) and Femto Maximum Sensitivity Substrate (Thermo Fisher Scientific #PI34096) using ChemiDoc Touch Imaging System (Bio-Rad Laboratories). Quantification of bands by densitometry analysis was performed using ImageLab Software (Bio-Rad Laboratories).