A Transcriptional Atlas of Endothelial Cell Zonation Along the Pulmonary Vascular Tree

A Transcriptional Atlas of Endothelial Cell Zonation Along the Pulmonary Vascular Tree | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results A Transcriptional Atlas of Endothelial Cell Zonation Along the Pulmonary Vascular Tree View ORCID Profile Stefanie N. Sveiven , View ORCID Profile Carsten Knutsen , View ORCID Profile Fabio Zanini , View ORCID Profile David N. Cornfield , View ORCID Profile Cristina M. Alvira doi: https://doi.org/10.1101/2025.05.17.654540 Stefanie N. Sveiven 1 Division of Critical Care Medicine, Department of Pediatrics, University of California San Francisco , San Francisco, CA 94158, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stefanie N. Sveiven Carsten Knutsen 1 Division of Critical Care Medicine, Department of Pediatrics, University of California San Francisco , San Francisco, CA 94158, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carsten Knutsen Fabio Zanini 2 School of Clinical Medicine, UNSW Sydney , 2052, NSW, Australia 3 UNSW Cellular Genomics Futures Institute , 2052, NSW, Australia 4 UNSW Evolution & Ecology Research Centre , 2052, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Fabio Zanini David N. Cornfield 5 Center for Excellence in Pulmonary Biology, Stanford University School of Medicine , Stanford, CA 94305, USA 6 Division of Pulmonary, Asthma and Sleep Medicine, Department of Pediatrics, Stanford University School of Medicine , Stanford, CA 94305, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David N. Cornfield Cristina M. Alvira 1 Division of Critical Care Medicine, Department of Pediatrics, University of California San Francisco , San Francisco, CA 94158, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cristina M. Alvira For correspondence: cristina.alvira{at}ucsf.edu Abstract Full Text Info/History Metrics Preview PDF Abstract Background The lung vasculature is comprised of a series of branching vessels extending from the main pulmonary artery to the alveolar capillaries, then back to the pulmonary veins. Lung endothelial cells (EC) exist along this continuum, exposed to gradients of shear stress, oxygen tension and pressure. Single cell RNA sequencing (scRNA-seq) has identified lung EC subsets, but many aspects of the vascular continuum, including vessel size and capillary polarity remain undefined from transcriptomic data. Methods We created an endothelial-enriched scRNA-seq dataset from the P3 mouse lung. Using diffusion pseudotime across all lung EC, we developed an analytical framework to delineate transcriptomic gradients and assign vessel-size scores to categorize individual endothelial cells (EC) along the vascular continuum. We confirmed size-related gene expression patterns with fluorescence in situ hybridization. Results We categorized capillary 1, arterial and venous EC along two gradients: arterio-venous zonation and vessel size. This approach distinguished large arteries from arterioles, large veins from venules, and revealed arterio-venous polarity within the capillaries. Our data recapitulated previously established zonally defined cell signaling axes, such as high Cxcl12 - Cxcr4 signaling in arterioles. We also identified unique cellular communication occurring in large versus small arteries and veins, and localized injury-induced venous EC proliferation to vessels of specific size. This analytical framework was successfully applied to several published mouse and human datasets across different stages of lung development. Conclusions These findings provide a comprehensive transcriptional map of EC across the pulmonary vascular tree, enabling assignment of each individual cell to vessels with defined size and position. This framework offers spatial inferences and novel mechanistic insights from scRNA-seq data sets that may elucidate therapeutic targets to treat pulmonary vascular diseases affecting specific vascular segments. We speculate that similar frameworks could be applied to tissues outside the lung. Introduction Recent advances to profile the transcriptomes of single cells have transformed our understanding of biology, revealing previously underappreciated diversity across cellular subtypes, including the heterogeneity of pulmonary vascular cells during development and in response to injury 1 – 6 . Although most researchers categorize cell states into discrete clusters 7 , cell states often exist along continuums. Breaking these continuums into arbitrary groups may conceal important biological signals. For example, pulmonary endothelial cells (EC) lining the vascular continuum receive distinct physiologic signals (e.g. blood flow, pressure, pulsatility, and oxygen tension) depending on their precise location along the arterial-capillary-venous axis 8 . Thus, broad categorization of EC as artery, vein, or capillary likely obscures transcriptomic and phenotypic variation of cells across these physiological axes 9 , 10 . The entire cardiac output passes through the lungs with each heartbeat, allowing all the deoxygenated blood to pass through gas exchanging alveoli to ensure distribution of oxygen rich blood to the remaining organs. Pulmonary arterial (PAEC), capillary and venous (PVEC) EC lining the pulmonary circulation play distinct roles: modulating vascular tone to optimize ventilation and perfusion, performing gas exchange while maintaining a tight barrier to prevent fluid extravasation into the alveolus, and regulating leukocyte trafficking and immune responses to pathogens entering through the alveolar space 11 . In addition to their primary role in transporting oxygen and nutrients, EC also supply angiocrine signals that inform local cellular niches and influence the behavior of neighboring cells, regulating tissue development and repair 8 . Although single cell transcriptomics has identified distinct gene expression signatures exhibited by broad pulmonary EC subtypes 9 , 10 , EC located at proximal versus distal positions of the circulation encounter distinct microenvironments. Given that many pulmonary vascular diseases selectively affect specific locations within the circulation, developing computational methodologies to identify transcriptomic changes in pulmonary EC at specific locations will enhance our ability to understand alterations during lung development and disease 12 , 13 . Moreover, computational methods to infer cell-cell communications from single-cell data often propose interactions between cells that are spatially distant and therefore unlikely to directly interact. Transcriptional information on endothelial cell zonation can provide spatial information that increases the probability that specific cells are physically capable of communication. Using a strategy of endothelial enrichment and single cell RNA sequencing to create a high resolution, transcriptomic dataset from the developing mouse lung, we developed an analytical framework to assign vessel-size scores and categorize individual EC along a continuum of vessel size. We delineated a continuum of arterial to venous, and macro-to microvascular zones using these sized-based transcriptional signatures. Our strategy allowed identification of signaling axes previously established with spatial methodologies solely from transcriptomic data, and localization of disease relevant alterations in gene expression to specific segments of the vasculature. This vessel-size informed framework is robust across development and species and reveals how spatial EC heterogeneity underlies key processes in lung development and injury. Materials and Methods Sample preparation At birth, neonatal C57BL-6 (Charles River) mice were housed in room air (normoxia; N) or 80% O 2 (hyperoxia; H) for 72hr before euthanasia (N=6 per exposure; 3M 3F). Lungs were perfused with HBSS, excised, finely minced, pooled, and digested in Liberase TM (0.2mg/mL) plus DNaseI (0.01mg/mL) for 10min at 37.5 in a bacteriological shaker. The suspension was triturated 15 times. Liberase was inactivated using 2mM EDTA and cold FBS prior to pelleting the cell suspension. Cells were treated with 1X RBC Lysis for 5min then passed over a 30μm cell strainer before microbead enrichment (SmartStrainer, Miltenyi). In a parallel set of experiments, lungs were pressure fixed at 20cm H 2 O and paraffin embedded (FFPE) for in situ validation experiments. Enrichment by magnetic associated cell sorting (MACS) Immune cells were depleted using anti-CD45 coated Dynabeads (Invitrogen). The supernatant was pelleted, counted, and resuspended with blocking buffer (anti-mouse CD16/CD32 and anti-rat IgG; 1:100) for 10min. Mouse anti-BST1–APC (PVEC marker; Biolegend, clone BP-3) and rat anti-CD31–biotin (pan-EC marker; BD Pharminogen, clone MEC 13.1) antibodies were added to the cells (2μg/10M cells) and incubated on a HulaMixer for 20 minutes. To ensure sufficient PVEC for our analysis, following the manufacturer’s protocol, cells were first enriched for BST1 using the MACS anti-APC microbeads and then the flow through was enriched for CD31 using anti-biotin microbeads (Miltenyi). Chromium GEMX 3’ v4 gene expression Following the manufacturers protocol, enriched populations were then adjusted to 1500 cell/μL to obtain the 20,000 target cell input for gel bead emulsion formation (10X Genomics). Samples that met QC standards (Bioanalyzer, Stanford Protein and Nucleic Acid core) were used for barcoding, library construction and sequenced by NovoSeq Χ at a depth of 1B paired reads per sample (NovoGene Corporation Inc.). scRNA-seq Analysis Sequencing reads were aligned to the Grcm39 mouse genome using Cellranger (v9.0.0). SoupX was used to remove ambient RNA signal 14 . Gene expression count tables were turned into anndata objects and processed using scanpy when not otherwise specified 15 . Cells that had more than five median absolute deviations of either unique genes or UMIs detected were removed. Cells that had more than three median absolute deviations of percent mitochondrial or ribosomal UMIs were removed 7 . Scrublet was used to automatically detect and remove doublets. Counts were normalized and log transformed 16 . PCA was run prior to embedding and clustering. The Leiden algorithm was used for clustering, and Uniform Manifold Approximation and Projection for embedding. Lineage identity was assigned using canonical markers: Cdh5 (endothelial); Epcam (epithelial); Ptprc (immune); and Col1a1 (mesenchymal). Cell typing was performed after each lineage was re-clustered and embedded as detailed above. Cell types were assigned a second time after regressing out cell cycle genes 7 . Pseudotime analysis and branching was performed using Palantir 17 , and RNA velocity determined using velocyto to align reads, and scVelo to calculate trajectories 18 , 19 . Vessel size scoring was performed by finding shared genes within the top 50 genes positively or negatively correlating, using Pearson’s correlation, with pseudotime in PAEC or PVEC, using Cap1 EC as the root. Scores were generated for positively and negatively correlated genes separately, and the negative correlation score subtracted from the positive score, then scaled to be between 0-1. Vessel size categories were assigned from quartiles in the vessel size score. For external datasets, Cap1, PAEC and PVEC were subset from the dataset using the original annotation. These cells were re-embedded and clustered to assign consistent cell type names across datasets and remove low-quality cells. Vessel size scoring was done the same as described above. For more detail see https://github.com/CarstenKnutsen/Vessel_size_manuscript Validation by RNA in situ hybridization and immunofluorescence RNAScope MultiPlex v2 Assay (Advanced Cellular Diagnostics) and immunofluorescence was performed on 5μm FFPE lung sections from P3 mice. RNAScope probes were purchased from ACD and fluorophores from Akoya: Neonatal mice were given a single dose 20mg/kg of intragastric EdU dissolved in PBS 2hours before tissue collection. ClickiT EdU was performed after completion of the RNAScope protocol per the manufacturer’s instructions. Fluorescence images were captured using a Zeiss Axio Observer 7 equipped with Apotome for optical sectioning using a 20X objective. Images were quantified using CellProfiler (v4.2.6) 20 . A minimum of 10 images were taken per mouse, n=3-5 per group. From these images, vessels were cropped using the “IdentifyObjectsManually” module. Each cropped vessel was then measured for their signal intensity from each channel was using the “MeasureImageIntensity” module. For EdU+ PVEC quantification, nuclei, EdU signal and Slc6a2 signal were identified using the “IdentifyPrimaryObjects” module. Overlap of these signals was done using the “RelateObjects” module. Vessel diameter was determined using the outer diameter of the major axis. Quantification and statistical analysis To identify differentially expressed genes Wilcoxon rank sum tests were run on all genes’ expression between cell populations, with a false discovery rate (FDR) adjustment with Benjamini-Hochberg, significance was determined as having a FDR<0.05. Differences between two groups in image analysis were determined by Student’s t-test, and correlations were done using a Pearson correlation. Data Availability Data generated for this manuscript is deposited at the Gene Expression Omnibus under GSE315745. Postnatal mouse data was sourced from the Gene Expression Omnibus under code GSE151974. Adult mouse data was sourced from https://datasets.cellxgene.cziscience.com/e818d27c-62ae-4c19-97c7-6cd4d65b8f9b.h5ad . Human neonatal data was sourced from https://cellxgene.cziscience.com/collections/28e9d721-6816-48a2-8d0b-43bf0b0c0ebc . Human 1 month-3-year-old data was sourced from https://www.lungmap.net/dataset/?experiment_id=LMEX0000004400 . Public adult human data was sourced from https://figshare.com/articles/dataset/Tabula_Sapiens_v2/27921984 . Results Pulmonary endothelial cells exhibit gradients of gene expression that span macro-and microvascular transitions and delineate pre-and post-alveolar capillaries Pulmonary vascular EC exist along a structural continuum of large to small arteries and veins, including macrovascular to microvascular transitional zones where general capillaries/capillary 1 (gCap/Cap1) flank gas-exchanging aerocyte capillaries (aCap/Cap2) on either the pre-alveolar (arterial) or post-alveolar (venous) side of the circulation ( Fig. 1A ). To delineate EC diversity among the pulmonary vasculature, we performed single cell RNA sequencing (scRNA-seq) on pulmonary cells from mice at the saccular stage of development, a period of rapid vascular growth. Endothelial cells, from mice exposed to either normoxia or hyperoxia, an injury that disrupts microvascular growth and induces pathologic vascular remodeling 3 , 21 , were enriched to ensure representation of small subpopulations 3 , 21 . A total of 32,946 cells representing all lineages and cell types ( Fig. 1B ; SFig. 1A) , including 11,724 EC, were profiled with a median of 4,586 genes detected per cell. In the global lung UMAP ( Fig. 1C ), Cap1 embedded between PAEC and PVEC consistent with the physical proximity to macrovessels at pre-and post-capillary macro-micro transition zones 1 , 6 . Re-embedding these three populations provided additional resolution of these macro-microvascular transitions with a central Cap1 cluster positioned between PAEC and PVEC populations ( Fig. 1D ) , paralleling the structure of the pulmonary vascular tree. Download figure Open in new tab Figure 1: Pulmonary endothelial cells exhibit gradients of gene expression that span macro-and microvascular transitions and delineate pre and post alveolar capillaries. (A) Diagram of the lung vasculature demonstrating gradients of vessel size extending from the right heart through the pulmonary arteries, through progressively smaller vessels until gas exchange at the alveoli and venous size increasing throughout with return to the heart [made with BioRender]. (B) UMAP of all cells showing assigned lineage contingent upon expression of either Cdh5 (endothelial); Epcam (epithelial); Ptprc (immune); or Col1a1 (mesenchymal). (C) UMAP of vascular EC cell types highlighted: PAEC (blue), Cap1 (purple), and PVEC (red). (D) UMAP of PAEC, Cap1 and PVEC embedded alone. (E) UMAP colored by vascular EC cell type with RNA velocity vectors overlayed. Arrow of vector points in the direction of cellular trajectory. (F) UMAP colored by pseudotime (low to high: purple to yellow) with predicted branches overlayed. (G) UMAP with only Cap1 colored based on their arterio-venous sidedness. (H) UMAP feature plots of expression of arterial and venous marker genes (low to high: purple to yellow) showing markers largely restricted to macrovasculature, or (I) extending into polarized Cap1 EC. To investigate the dynamic progression of EC states along the vascular continuum, we performed trajectory inference analysis. RNA velocity vectors inferred that Cap1 cells can progress towards either PAEC or PVEC ( Fig. 1E ), while branched pseudotime analysis revealed progressively increasing pseudotime values from the Cap1 node towards the far edges of either PAEC or PVEC nodes ( Fig. 1F ) . This pattern suggested that spatial localization of Cap1 cells as either pre-alveolar (Cap1 A ; arterial-adjacent) or post-alveolar (Cap1 V ; venous-adjacent) could be inferred from the transcriptome ( Fig. 1G ). When examining the expression of arterial and venous marker genes, some canonical arterial ( Dkk2 , Gja5 ) and venous markers ( Slc6a2 ) were confined to their respective clusters, exhibiting little to no expression among Cap1 ( Fig. 1H ). However, other arterial and venous markers were enriched within each half of the Cap1 cluster, including elevated expression of canonical arterial ( Dll4 , and Hey1 ) or venous markers ( Nr2f2 , and Ackr3 ) within these putative Cap subsets ( Fig. 1I ) . Altogether, these data highlight transcriptional gradients of arterio-venous zonation, unique gene expression patterns among the vascular hierarchy, and a polarization of transcriptomic identities among Cap1 identified from our high-resolution scRNAseq dataset. The transcriptomic continuum of pulmonary endothelial cells aligns with vessel size The transcriptional polarization of Cap1 towards PAEC and PVEC nodes suggested that an endothelial transcriptional continuum may parallel the structural branching of the lung vasculature. One characteristic of pulmonary vascular patterning is the gradual decrease in the diameter of vessels as proximal vessels successively branch distally toward the capillaries, with associated differences in matrix and mural cell coverage, providing additional transcriptomic clues to infer vessel size. Thus, we next identified genes negatively and positively correlated with pseudotime for both the PVEC and PAEC, with approximately one-third of these genes shared between both groups ( Supplemental Fig. 2A ). Notable examples of genes correlated with increasing pseudotime included elastin ( Eln ) and von Willebrand Factor ( Vwf ) known to be expressed by macrovessels. In contrast, microvascular markers like proto-oncogene ( Kit ) and transmembrane protein 100 ( Tmem100 ), were among the genes that negatively correlated with pseudotime ( Fig. 2A ) 6 , 22 . Download figure Open in new tab Figure 2: The transcriptomic continuum of pulmonary endothelial cells aligns with vessel size size can be inferred in ECs from pulmonary vasculature . (A) Heatmaps of top, shared genes correlated with pseudotime in both arterial and venous branches. Pseudotime increases from left to right in each heatmap, with low gene expression in purple and high gene expression in yellow. (B) Pathway analyses showing select enriched pathways with shared pseudotime correlation. (C) UMAP of vessel size score based upon expression of genes in panel A, with low size score in white and high size score in orange. (D) UMAP colored by vessel size category, with size quartiles determined by the vessel size score. (E) Representative image of multiplexed in situ hybridization to detect expression of the endothelial marker gene Pecam1 (green) large vessel marker Fbln2 (red) and small vessel gene Tmem100 (yellow) in lung tissue from mice at P3 in normoxia. Calibration bar=20 μm (F) High magnification images of selected small (top) and large (bottom) vessels, showing each gene in separate images. Calibration bar=10 μm. Blinded quantification of (G) Fbln2 and (H) Tmem100 signal normalized to DAPI signal in vessels of various vessel sizes. Line in red is a linear regression on the log10 normalized vessel size, r and p value are from Pearson’s correlation. Each point represents a vessel. Vessels were imaged from 3 mice, with a total of n=97 vessels. Pathway analysis on the top positively and negatively correlated genes with pseudotime also supported size-dependent physiology and function ( Fig. 2B ; SFig. 2B) . Developmental and angiogenic pathways such as ‘tissue morphogenesis’, ‘blood vessel remodeling’, and ‘signaling by SCF-KIT’ were enriched in negatively correlated genes, while positively correlated genes included enrichment of pathways include ‘elastic fiber formation’, ‘striated muscle development’, and ‘negative regulation of angiogenesis’-pathways aligned with the structural and functional characteristics of larger vessels. We next used these shared pseudotime-correlated genes to calculate vessel size scores for each individual EC ( Fig. 2C ). Projecting these vessel-size scores onto the UMAP revealed a progressive distribution of vessel sizes along the vascular hierarchy, with the largest scores embedding along the terminal ends of the macrovascular clusters and the lowest scores embedding on the Cap1 node. We sorted scores into four bins representing ‘large’, ‘medium’, ‘small’, and ‘capillary’ based on quartile score ranges ( Fig. 2D ). The size nomenclature enabled us to visualize the assigned size distributions along the UMAP, such that the ‘capillary’ size embedded fully on the Cap1 cluster; the ‘small’ bin comprised the arms of PAEC and PVEC nodes connecting to the Cap1 cluster, while the ‘medium’ and ‘large’ cluster extended progressively outwards toward the distal edges of each macrovessel node. We then validated this transcriptomic scoring of vessel size using RNA fluorescence in situ hybridization. We detected the expression of a small vessel marker, Tmem100 , and a large vessel marker, Fbln2, in vessels of varying diameters ( Fig. 2E ). Images demonstrated the high expression of Fbln2 but minimal Tmem100 in larger vessels, and the reciprocal pattern in small vessels. Quantification confirmed that Fbln2 expression was positively correlated with vessel size, with increasing signal as vessel diameter increased ( Fig. 2F ) . Conversely, Tmem100 exhibited a negative correlation with diameter ( Fig. 2G ) . Together, these data support that our vessel-size analytical framework enables us to infer the relative size of the vessel from which an EC is derived based on gene expression, providing insights into spatial organization solely from transcriptomic data. Marked size-dependent transcriptomic heterogeneity within endothelial subtypes We next identified genes exhibiting size-related expression patterns common to both arteries and veins ( Fig. 3A ). EC from the largest arteries and veins exhibited high expression of Nr4a2 , an orphan nuclear receptor involved in vascular homeostasis and maturation, matrix Gla protein ( Mgp) , an essential inhibitor of vascular calcification and elastin ( Eln ), a protein providing elasticity to larger vessels 23 – 25 . Medium sized PAEC and PVEC highly expressed the endothelial quiescence transcription factor Foxo1 ; G-coupled protein receptor Adgrg6, which promotes angiogenesis by modulating VEGF signaling; and receptor protein tyrosine phosphatases Ptprr 26 – 28 . Small vessels had the highest expression of interferon-induced antiviral, transmembrane protein Ifitm3 29 ; neuropilin-1 ( Nrp1 ) a transducer of VEGF signaling that mediates angiogenic behavior 30 ; and the transcription factor Sox4 . Lastly, Cap1 EC highly express glucagon-like peptide-1 receptor ( Glp1r) , Ccdc85a , a gene which supports barrier function, and Kit , a gene expressed by progenitor cells and a known Cap1 marker 1 , 3 , 6 . Download figure Open in new tab Figure 3. Marked size-dependent transcriptomic heterogeneity within endothelial subtypes. (A) Dotplot showing gene expression associated with size shared across arteries and veins, with relative gene expression indicated by color (low to high: purple to yellow), and percent of cell population expressing each gene indicated by the size of the circle. (B) Dotplot showing gene expression of gene expression associated with size that are distinct among arteries and veins, with relative gene expression indicated by color (low to high: purple to yellow), and percent of cell population expressing each gene indicated by the size of the circle. (C) Representative image of multiplexed in situ hybridization to detect expression of the arterial marker gene Gja5 (white) and the large artery marker Dkk2 (yellow) in lung tissue from mice at P3 in normoxia. Calibration bar=10 μm. (D) Quantification of Dkk2 signal normalized by DAPI signal by vessel size. Each point represents a vessel. Vessels were imaged from 5 mice, with a total of 152 vessels. Line in red is a linear regression on the log10 normalized vessel size, r and p value are from Pearson’s correlation. (E) Representative image of multiplexed in situ hybridization to detect expression of the venous marker gene Slc6a2 (white) and the large vein marker Moxd1 (yellow) in lung tissue from mice at P3 in normoxia. (F) Quantification of Moxd1 signal normalized by DAPI signal by vessel size. Each point represents a vessel. Vessels were imaged from 4 mice, with a total of 144 vessels. Line in red is a linear regression on the log10 normalized vessel size, r and p value are from Pearson’s correlation. We then identified genes with distinct size-related expression patterns across the arterial and venous sides of the pulmonary circulation ( Fig. 3B ) . Consistent with our previous report, PAEC from large arteries showed highest expression of the Wnt-inhibitor Dkk2 3 and the transcription factor Sox6. PAEC from medium sized vessels were enriched for Lama3 , a laminin involved in basement membrane organization 31 , and Bdkrb2, encoding the bradykinin receptor B2, a regulator of vascular tone 32 . PAEC from small arteries were enriched for the HIF-responsive remodeling and stress-response genes Depp1 and Stc1 , Cap1 on the arterial side were enriched for Adam23, a regulator of integrin-mediated adhesion 33 , and Ntrk2 a gene uniquely induced in adult Cap1 following injury 34 . Cap1 on the venous side expressed Emid1 35 , and the muscarinic receptor, Chrm2 a regulator of vasomotor tone and angiogenic potential 36 . Among PVEC derived from small vessels, the muscarinic cholinergic receptor 3 ( Chrm3 ,) was increased 37 . EC from both small and medium sized veins highly expressed the retinoic acid receptor Rarb , a component of a pathway known to promote lung vascular and alveolar development 38 , 39 . Fads2b , a fatty acid desaturase important for polyunsaturated fatty acid metabolism, and glutamate receptor Gria3 , were also up-regulated in EC from medium veins. PVEC ascribed to large veins were enriched for the tumor suppressor Moxd1 , and Ptger3, encoding the prostaglandin E receptor 3( Fig. 3B ) . We validated these size specific gene expression signatures in arteries and veins using in situ hybridization. In arteries, we detected the large arterial marker, Dkk2 , in combination with the common arterial EC marker gene, Gja5 ( Fig. 3C ). Imaging confirmed high expression of Dkk2 restricted to large arteries, and quantification confirmed these results, demonstrating a positive correlation of Dkk2 with increasing vessel diameter ( Fig. 3D ) . Using the same approach in veins to identify the common venous EC marker, Slc6a2 with the larger vein marker , Moxd1, we found high expression of Moxd1 restricted to large veins, with quantification showing a strong positive correlation with increasing venous diameter ( Fig 3E-F ) . Taken together, these findings demonstrate that even within canonical lung endothelial subtypes, there is marked regional transcriptomic heterogeneity of EC corresponding to specific locations along the vascular tree. Transcriptomic determination of vessel size provides biologic insight into endothelial-derived cellular communication We next further validated our vessel-size analytical framework by determining if it could identify alterations in molecular signaling affecting specific segments of the pulmonary circulation previously identified using histologic data. For example, autocrine CXCL12-CXCR4 signaling promotes arteriolar patterning and branching during lung development 40 , 41 . Consistent with these data, we found the highest expression of Cxcl12 and Cxcr4 expression in small and medium sized PAEC ( Fig. 4A ). High Ackr3 expression, a decoy receptor that clears excess CXCL12, was found in PVEC from large veins, suggesting a role for pulmonary veins in restricting CXCL12-CXCR4 signaling to the pulmonary circulation ( Fig. 4B ). Dysregulated CXCL12-signaling is implicated in the pathobiology of PH, a disease which entails pathologic remodeling primarily affecting small, resistance pulmonary arteries. Recent evidence suggests that estrogen may regulate Cxcl12 expression 41 – 44 and abrogation of Esr2 reduces muscularization of small-medium arteries in experimental models of PH 45 . Download figure Open in new tab Figure 4. Transcriptomic determination of vessel size provides biologic insight into endothelial derived cellular communication. (A) UMAPs depicted gene expression (low to high: purple to yellow) of Cxcl12 and its two receptors, Cxcr4 and Ackr3 . (B) Dotplots showing Cxcl12 and Cxcr4 in arteries by vessel size, and Ackr3 in veins by vessel size. Relative gene expression is indicated by color (low to high, purple to yellow). Proportion of population expressing the gene is indicated by the size of the circle. (C) UMAPs colored by gene expression of Esr2 (low to high: purple to yellow). (D) Histogram of Esr2 gene expression across the arterial pseudotime branch. (E) Dotplots of select ligands expressed by PAEC exhibiting differential expression by vessel size, and corresponding dotplots of receptor expression for each ligand in mural cells. Relative gene expression is indicated by color (low to high: purple to yellow). (F) Dotplots of select ligands expressed by PVEC exhibiting differential expression by vessel size, and corresponding dotplots of receptor expression for each ligand in mural cells. Relative gene expression is indicated by color (low to high: purple to yellow). Proportion of population expressing the gene is indicated by the size of the circle for (E) and (F). (G) Dotplots of Adam23 expression in Cap1 A and Cap1 V , and Adam23 receptor expression in cells within vascular cell types in the alveolar niche. Relative gene expression is indicated by color (low to high: purple to yellow). Proportion of population expressing the gene is indicated by the size of the circle Thus, we examined whether the spatial distribution of the estrogen receptors, Esr1 and Esr2 aligned with Cxc12 - Cxcr4 enrichment in small arteries. Esr1 showed limited expression in vascular EC (SFig. 3A-C) . In contrast, Esr2 was restricted to the PAEC, with highest expression in EC from small and medium arteries ( Fig. 4C ), peaking in arterial EC with intermediate pseudotime values, similar to the expression pattern of Cxcl12 and Cxcr4 ( Fig. 4D ). We next investigated whether size based transcriptomic assignment of EC could identify putative cell-cell communication enriched at specific locations along the pulmonary vascular tree. We identified ligands that were differentially expressed in small versus large PAEC and determined the expression of putative receptors for each ligand in mural cells ( Fig. 4E ). Large PAEC highly expressed Dkk2 , and Efna5 , a high affinity ligand for Epha3 , highly expressed in a subset of VSMC. PAEC from medium sized arteries highly express Lama3 , a component of the basement membrane protein, laminin 5, likely promoting cell adhesion to mural cells highly expressing known interacting partners such as Itgb1 and Sdn2 46 , 47 . Arterial EC from small and medium arteries selectively express the Notch ligand Jag2 , a pathway shown to drive angiogenesis particularly in response to hypoxia 48 . Using a similar approach, we also identified location-specific differences in cell-cell communication originating from PVEC ( Fig. 4F ). EC from large veins highly expressed Col18a1 , the gene encoding the anti-angiogenic protein, endostatin, and Vcan encoding versican 49 . Both ligands can bind to integrins expressed by mural cells, and Versican-CD44 interactions likely support mural cell adhesion 50 .The Ig-superfamily adhesion receptor, Jam2 , was highly expressed by PVEC from medium sized veins, and small PVEC highly express the Notch ligand Dll1 , potentially signaling to Notch3 -expressing pericytes to promote vascular stabilization 51 . Spatially resolved cellular communication even revealed unique putative cellular communication along the arterial and venous sides of the capillaries ( Fig. 4G ) . For example, the atypical ADAM, Adam23 , which promotes cell adhesion, is only expressed by the arterial Cap1 EC 33 . Taken together, these data highlight that assigning vessel sizes across endothelial cells can identify physiologically relevant and distinct ligand-receptor interactions at specific segments of the pulmonary circulation. Vessel-size analytical framework reveals size-dependent responses to vascular injury We next evaluated whether this analytical framework could provide insights into the pulmonary vascular response to injuries that may not be uniform across the pulmonary circulation. Exposure of neonatal mice to chronic hyperoxia disrupts pulmonary vascular growth and pathologic vascular remodeling. We and others have previously reported that in contrast to suppressing proliferation of Cap1 EC, hyperoxia induces a proliferative response in PVEC 3 , 52 . However, it was unknown if this proliferative response was predominant in specific portions of the venous circulation. Examining the expression of a panel of cell-cycle genes confirmed heightened expression of pro-proliferation genes on the venous side of the pulmonary circulation, with peak expression in Cap1 V ( Fig. 5A ). By calculating proliferation scores among PVEC from different sized vessels, we found that the majority of normoxic EC exhibited low proliferation scores, with a small group of cells from each sized vein demonstrating higher scores. In response to hyperoxia, a greater number of PVEC from small and medium veins exhibited high proliferation scores ( Fig. 5B ). This was associated with a significant increase in top proliferation associated genes in venous-Cap1 and PVEC from small and medium veins ( Fig. 5C ). We validated these findings, using in vivo EdU incorporation assays ( Figure 5D ) . Smaller veins had the greatest percent proliferation, and the percent of EdU + PVEC negatively correlated with increasing diameter ( Fig. 5E ). Specifically, hyperoxia only significantly increased proliferation in PVEC in veins smaller than 50μm in diameter ( Fig. 5F ). Download figure Open in new tab Figure 5: Vessel-size analytical framework reveals size-dependent responses to vascular injury. (A) Dotplot of select proliferation marker genes in EC by vessel type and size. Relative gene expression is indicated by color (low to high: purple to yellow), and proportion of population expressing each gene is indicated by the size of the circle. (B) Kernel density estimate showing distribution of proliferation scores of PVEC by vessel size in normoxia and hyperoxia. (C) Dotplot of select proliferation marker genes in PVEC by vessel size in normoxia and hyperoxia. Relative gene expression is indicated by color (low to high: white to red), and proportion of population expressing each gene is indicated circle size. (D) Representative image of in situ hybridization to detect expression of the venous marker gene Slc6a2 (white) and EdU (Red) in lung tissue from mice at P3 in either normoxia or hyperoxia. Calibration bar=. (E) Quantification of the proportion of EdU+ PVECs by vessel size. Each point represents a vessel. A total of 265 vessels were imaged across normoxic and hyperoxic mice (n=4-5). Line in red is a linear regression on the log10 normalized vessel size, r and p value are from Pearson’s correlation. (F) Quantification of EdU+ PVECs binned by vessel size, less than or equal to or greater than 50 μm. Each point represents a mouse where PVEC nuclei and EdU+ PVEC nuclei counts were summed for each vessel size bin and total %EdU+ PVEC was calculated. The bar represents the mean of the mice from each condition. *p=0.036 by student’s t-test. We next examined whether hyperoxia induced distinct transcriptomic alterations of Cap1 EC located on either the arterial or venous side of the circulation. In the Cap1 A EC, hyperoxia increased genes associated with angiogenesis and TGFβ signaling ( Fig. 5G ). For example, hyperoxia increased Thsd7a a secreted glycoprotein that promotes filopodia formation and EC migration, Sox7 , a Sox family member essential for vasculogenesis and hypoxia-induced angiogenesis, Cd44 , a hyaluronan receptor that promotes EC proliferation, and Igf1r , which promotes stabilization of nascent blood vessels 53 – 56 . Hyperoxia also increased a number of genes that regulate BMP/TGFβ signaling including up-regulation of Acvr1b and Inhbb in the Cap1 A EC, which encode Alk4 and activin B, a pathway implicated in pathologic pulmonary vascular remodeling, and Smurf1 , an additional molecule that shifts homeostatic BMP signaling toward TGFβ-mediated remodeling 57 , 58 . Hyperoxia induced a distinct gene signature in Cap1 V EC ( Fig. 5H ), increasing the expression of Serpine2 and Pros1, regulators of coagulation that have also been shown to regulate vascular remodeling and barrier function 59 , 60 . Hyperoxia also increased Bax , a pro-apoptotic regulator induced by stress, and Mdm2 , a pro-survival factor that inhibits p53 signaling, a known pathway driving hyperoxia induced lung injury. Taken together these data suggest that Cap1 are comprised of two distinct subsets marked by gene signatures delineating their arterial versus venous localization and exhibiting divergent responses to hyperoxia-driven vascular injury. Transcriptomic gradients to identify endothelial cells by vessel size are conserved across development and species To determine the generalizability and external validity of our vessel size framework, we applied it to five additional lung-specific single-cell or nucleus RNAseq datasets spanning multiple ages, disease states, and species. These included two murine datasets: one overlapping with our developmental timepoints and experimental conditions 61 ( Fig. 6A ) and one from adult lung 62 ( Fig. 6B ); and three human datasets: a neonatal lung dataset from the first day of life 63 ( Fig. 6C ), a BPD cohort from 1 month to 3 years of age 64 ( Fig. 6D ), and an adult lung dataset 65 ( Fig. 6E ). We used the same approach as above, re-embedding PAEC, Cap1, and PVEC and utilizing pseudotime to approximate vessel size. We found that the terminal ends of the venous and arterial clusters exhibited the highest size scores, which gradually declined toward central Cap1 cluster enabling segmentation of vessels by approximate size ( Supplemental Fig. 4A-D ). Notably, we found that many of the size-related genes were consistent including Eln, which was highest in the largest vessels, and Col4a1, highest in the capillaries. Integration of these datasets and the present data demonstrate that cell type labels are conserved and embed together, even with multiple species and age cohorts ( Fig. 6E ) . Despite computing vessel size scores on each individual dataset, this score gradient is conserved in the integrated embedding. Using the developmental mouse dataset most similar to our own we see that many of the expression patterns, including size specific-gene expression, cell signaling, and changes in hyperoxia are also observable in this dataset 61 (Supplemental Fig. 5 A-D) . Download figure Open in new tab Figure 6: Transcriptomic gradients to identify endothelial cells by vessel size are conserved across development and species. UMAPs of EC cell subtype and vessel scores from datasets derived from (A) the developing mouse lung exposed to normoxia or hyperoxia 61 ; (B) the adult mouse lung 62 ; (C) one day old infant 63 (D) control and BPD infants 64 ; and (E) adult human lung 65 . UMAP of integrated datasets’ vascular endothelial cells vessel size scoring (low to high, white to orange). Together, these data demonstrate the reproducibility and consistency of this framework for attributing vessel-size scores to individual arterial, venous, and Cap1 EC across heterogenous sequencing datasets. This reproducibility is maintained despite variations in tissue preparation, sample sources, library preparation, and sequencing technology, highlighting that transcriptional continuums expressed along the vascular hierarchy are conserved across lifespan and species. Discussion Single cell transcriptomics has revealed robust EC heterogeneity but linking these molecular profiles to vascular architecture remains a major challenge. By relating gene expression to vessel size scores, we uncovered evolutionarily and developmentally conserved transcriptional gradients corresponding to vessel size and polarization along the arterial-venous axis. This method offers a scalable and biologically meaningful approach to stratify EC across the pulmonary vascular structural continuum. 61 – 65 Our framework also demonstrates conserved transcriptomic gradients across the pulmonary vascular tree across datasets, ages, disease states, and species, demonstrating highly generalizable and broadly reproducible vessel size-associated transcriptional patterns. Although all pulmonary EC share core transcriptional programs reflective of their common lineage, their identities are further shaped by cues from the local microenvironment (e.g. oxygen tension, flow dynamics, paracrine signals from adjacent cells). Our data shows that these differing cues produce a transcriptional continuum of gene expression rather than gene expression signatures restricted to separate endothelial subtypes. The main physiologic functions of discrete portions of the pulmonary circulation change across the vascular bed: pressure regulation in arteries, gas exchange in capillaries, and reservoir capacity in veins. By aligning gene expression patterns to inferred vessel size, our framework enables the identification of mechanisms directing regionally-distinct endothelial functions within the lung. For example, Foxo1 , a FOXO family transcription factor required for vascular growth and patterning in embryonic development was highly expressed by arterial and venous EC of medium sized vessels, highlighting its role in promoting quiescence of maturing vessels in this context 66 , 67 . The heightened expression of muscarinic receptors in small and medium sized veins likely promotes NO-mediated vasodilation, maintaining the obligatory low vascular tone on the post-capillary side of the circulation required to provide the transpulmonary gradient required for adequate pulmonary blood flow 68 . Examination of endothelial subtypes at greater resolution also allows the identification of gene expression by specialized subsets that would be obscured by ensemble averaging. For example, our data show that a small subset of Cap1 express the neurotrophin receptor Ntrk2 during normal development, a gene previously reported to be only induced in Cap1 EC after injury 34 , 69 . Whether these Ntrk2 + arterial Cap1 represents a specialized subset with the capacity that preferentially expands in response to injury remains to be determined. Many pulmonary vascular diseases induce endothelial dysfunction and vascular remodeling in select segments of the pulmonary circulation. For example, PAH is characterized by preferential muscularization of small arterioles, BPD often involves microvascular rarefication followed by aberrant angiogenesis, and pulmonary veno-occlusive disease (PVOD) preferentially affects small veins and venules 13 . To identify molecular mechanisms most relevant to specific vascular pathologies, focusing on the transcriptional and cell signaling aberrations occurring at the level of the vasculature most affected has the greatest chance of identifying disease specific, novel therapies. For example, although it is common for groups to report cell-cell communication across all cells within a single cell dataset, it is more likely that biologically relevant cellular communication occurs within specialized multi-cellular niches that are distinct at different levels of the circulation. This was clear in our dataset, which localized canonical pathways regulating vascular development and stability to discrete portions of the circulation, including highest expression of Efna5 in large PAEC, potentially modulating the contractility of surrounding VSMC to regulate vascular tone, and Jag2 and Dll1 expression by small PAEC and PVEC, respectively, likely promoting pericyte differentiation to promote vascular patterning and stabilization of nascent vessels 70 – 72 . We also demonstrated that our analytical framework allowed the identification of differential responses of pulmonary EC derived from different sized vessels to injury. Reports from our group and others have shown VEC proliferate in response to diverse lung injuries and we accurately localized the venous proliferative response to small veins 3 , 52 . We also found that by delineating the arterial and venous polarization of Cap1, we could identify enrichment of unique biologic responses in each subgroup in response to hyperoxia. In the Cap1 A , hyperoxia up-regulated genes that regulate angiogenesis and drive TGFβ-mediated vascular remodeling, where on the opposite side of the alveolus, the Cap1 V exhibited a heightened injury response with up-regulation of genes modulating coagulation, and p53-mediated signaling, likely reflecting a response to the markedly increased paO 2 experienced by these cells. Our study has several limitations. First, we made somewhat arbitrary divisions of cells in relation to vessel size, there is no ground-truth within the transcriptomic data relative to the diameter of a ‘small’ or ‘large’ vessel. Our analytic framework also performed optimally on datasets containing a high sampling of ECs with high sequencing depth across the vascular hierarchy, with less cells and/or lower sequencing depth offering less resolution. Finally, we limited this analysis to establishing the transcriptional gradients expressed along the endothelial hierarchy. However, VSMC and pericytes also exist along the same vascular continuum and it is likely that the mural cells would exhibit similar transcriptomic gradients. Identification of mural cell location from the transcriptome would further refine cell-cell communication predictions, particularly when considering either ligands or receptors that are membrane bound. Together these results highlight the power of applying a vessel-sized score to the interpretation of transcriptomic data. Signaling gradients are fundamental to organogenesis, providing spatial cues that guide cell fate decisions and tissue organization. Thus, it is unsurprising that ECs exhibit a transcriptomic continuum reflecting their position along physiologic axes. By relating gene expression to physiologically relevant locations, this approach revealed organized zones of endothelial specialization with potential clinical and therapeutic relevance. Although spatial transcriptomics and other in situ methods offer high-resolution views of gene expression within tissue, they are costly and low-throughout, restricted to predefined regions or gene panels, and still limited in their ability to resolve transcriptional gradients at cellular resolution across a complex hierarchy such as the vasculature. In contrast, our framework allows inference of spatial positioning directly from single cell transcriptomic data, enabling thousands of cells to be aligned along an anatomic continuum. In the future, additional physiological parameters such as blood oxygen concentration, flow dynamics, or stretch, could be integrated as complementary axes to further refine interpretations of transcriptomic changes in the pulmonary endothelium. Further, incorporating multi-modal datasets, such as single cell ATAC-seq, could illuminate the regulatory mechanisms underlying these gradients to refine our understanding of EC fate and phenotype transitions. We also anticipate that this strategy could be extended to other tissues and cells that exist along similar continua defined by size or other relevant gradients, with a similar paradigm already suggested in the brain 73 . Finally, applying this strategy to diverse datasets may enable resolution of common hierarchical regulatory systems driving key molecular mechanisms directing cell-fate, organ development, and disease. Author contributions S.N.S. contributed to study design, data collection, data analysis, interpretation, and writing of the manuscript. C.K. contributed to study design, data analysis, interpretation, and writing of the manuscript. F.Z. contributed to data analysis, interpretation, and writing of the manuscript. D.N.C. contributed to data interpretation and writing of the manuscript. C.M.A. supervised all aspects of the project, including the design, data analysis and interpretation, contributed to the writing and final approval of the manuscript. Sources of Funding This work was funded by NIH grants HL154002 (CMA), HL1558828 (CMA) and HL160018 (CMA and DNC). Disclosures The authors have no significant conflicts of interest to disclose. Download figure Open in new tab Supplemental Figure 1: Cell types detected in P3 murine dataset. (A) UMAP of P3 mouse dataset colored by cell type Download figure Open in new tab Supplemental Figure 2: Shared genes and pathways correlated with pseudotime in arteries and veins. (A) Venn diagrams showing overlap of top 50 genes correlated with pseudotime in Arterial and Venous EC branches. (B) All pathways enriched in shared genes between artery and vein negatively (top) and positively (bottom) correlated with pseudotime Download figure Open in new tab Supplemental Figure 3: Estrogen receptor expression in P3 murine dataset. (A) Dotplot of Esr1 and Esr2 expression across all cell types in P3 murine dataset. Average gene expression is indicated by color, in log counts per ten thousand, (low to high, white to red). Proportion of population expressing the gene is indicated by the size of the circle. (B) UMAP of vascular endothelial cells colored by Esr1 expression (low to high, purple to yellow). (C) Dotplot showing gene expression with distinct localization along the vascular hierarchy in PAECs and PVECs. Average gene expression is indicated by color (low to high, purple to yellow). Proportion of population expressing the gene is indicated by the size of the circle. Download figure Open in new tab Supplemental Figure 4: Vessel size scoring of mouse and human vascular endothelial cells across development. (A) UMAP of neonatal mouse vascular endothelial cells colored by cell type, vessel size category and gene gene expression (low to high, purple to yellow). of Eln , and Col4a1 . (B) UMAP of adult mouse vascular endothelial cells colored by cell type, vessel size category and gene gene expression (low to high, purple to yellow). of Eln , and Col4a1 . (C) UMAP of neonatal human vascular endothelial cells colored by cell type, vessel size category and gene gene expression (low to high, purple to yellow). of ELN , and COL4A1 . (D) UMAP of 1-month-3 year old human vascular endothelial cells colored by cell type, vessel size category and gene gene expression (low to high, purple to yellow). of ELN , and COL4A1 . (E) UMAP of adult human vascular endothelial cells colored by cell type, vessel size category and gene gene expression (low to high, purple to yellow). of ELN , and COL4A1 . (F) UMAP of integrated data colored by dataset Download figure Open in new tab Supplemental Figure 5: Vessel size transcriptomic patterns shown in Hurskainen et al scRNA-seq dataset. (A) Dotplot showing gene expression associated with size along the vascular hierarchy in Hurskainen et al.. Average gene expression is indicated by color (low to high, purple to yellow). Proportion of population expressing the gene is indicated by the size of the circle. (B) Dotplot showing gene expression with distinct localization along the vascular hierarchy in Hurskainen et al Average gene expression is indicated by color (low to high, purple to yellow). Proportion of population expressing the gene is indicated by the size of the circle. (C) UMAP of vascular endothelial cells of signaling genes Cxcl12 , Cxcr4 , Ackr3 , and Esr2 from Hurskainen et al colored by gene expression (low to high, purple to yellow). (D) Dotplot of select proliferation marker genes in P3 PVECs by vessel size in normoxia and hyperoxia. Average gene expression is indicated by color (low to high, white to red). Proportion of population expressing the gene is indicated by the size of the circle. Acknowledgments None Funder Information Declared NIH NHLBI , R01-HL154002 , HL1558828 , HL160018 Footnotes Reworked from short report to full mnauscript. Expanded from two figures to six. 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