Abstract
The vascular endothelium features unique molecular and functional properties across different vessel types, such as between arteries, veins and capillaries, as well as between different organs, such as the leaky sinusoidal endothelium of the liver versus the impermeable vessels of the brain. However, the transcriptional networks governing endothelial organ specialization remain unclear. Here we profile the accessible chromatin and transcriptional landscapes of the endothelium from the mouse liver, lung, heart, kidney, brain and retina, across developmental time, to identify potential transcriptional regulators of endothelial heterogeneity. We then determine which of these putative regulators are conserved in human brain endothelial cells, and using single-cell transcriptomic profiling, we define which regulatory networks are active during brain maturation. Finally, we show that the putative transcriptional regulators identified by these three approaches molecularly and functionally reprogram naive endothelial cells. Thus, this resource can be used to identify potential transcriptional regulators controlling the establishment and maintenance of organ-specific endothelial specialization.
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Data availability
Data within this paper were mapped to the mouse (mm10, https://genome.ucsc.edu/cgi-bin/hgTracks?db=mm10&chromInfoPage=&pix=1821) or human (hg38, https://genome.ucsc.edu/cgi-bin/hgGateway) genome. Datasets generated in this study were deposited at the Gene Expression Omnibus: mouse bulk ATAC-seq and RNA-seq data, accession GSE185345, and human dataset, accession GSE187565. Mouse ATAC-seq data can be visualized at https://genome.ucsc.edu/s/mguiterr/ATAC_Summary_Final_CyVerse. For human ATAC-seq data, tracks can be visualized at https://genome.ucsc.edu/s/mguiterr/hg38_hCMEC_ATAC. The scRNA-seq data can be explored directly using the following web-based resources: https://wythelab.shinyapps.io/ScRNA_Brain_EC_Development/ or https://singlecell.broadinstitute.org/single_cell/reviewer_access/a4af66e3-f699-4ace-995a-60e56e3bdf7f, code: MFQN6BNPDU. The R studio object for single-cell data is available via figshare at https://doi.org/10.6084/m9.figshare.28266635 (ref. 196). All plasmids described in this study are available via Addgene at https://www.addgene.org/Joshua_Wythe/. Source data are provided with this paper.
Code availability
The code workflow used to generate data in the paper is available via GitHub at https://github.com/wythelab/Cantu_Hill_EC_Diversity.
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Acknowledgements
We thank J. Fish (University Health Network, Toronto, Canada) for critical comments on the paper and K. Berman de Ruiz (Baylor College of Medicine, Houston, Texas) and A. Herman (Baylor College of Medicine, Houston, Texas) for assistance with mouse husbandry and organ isolation. This study was supported by grants from the National Institutes of Health (HL127717, HL130804 and HL118761, J.F.M.; HL159159, J.D.W.; F31 HL136065, M.C.H.; T32 HL-007284, G.E.L.); the Vivian L. Smith Foundation (J.F.M.); the American Heart Association (19PRE34410104, M.E.C.G.; 16GRNT31330023, J.D.W.); the Caroline Wiess Law Fund, the Curtis Hankamer Basic Research Fund and the ARCO Foundation Young Teacher-Investigator Award (J.D.W.); the Cancer Research and Prevention Institute of Texas (RP200402) (J.D.W.); and institutional startup funds from the CVRI at Baylor College of Medicine (J.D.W.) and from the University of Virginia School of Medicine (J.D.W.). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the paper.
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M.E.C.G., M.C.H. and J.D.W. were responsible for the conception, design, execution and interpretation of the experiments. M.E.C.G. and J.D.W. wrote the original draft. G.E.L. and W.B.G. were involved in the design, execution and analysis of the experiments. J.F.M. contributed reagents and resources, supervised M.C.H., interpreted the experiments and edited the paper. All authors revised the paper and consented to its contents.
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J.F.M. is a cofounder of and owns shares in Yap Therapeutics. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Lymphatic marker expression in RNA-Seq and ATAC-Seq.
a) Expression levels of endothelial markers (top) and lymphatic markers (bottom) as determined by RNA-Sequencing of adult endothelial and input samples (normalized expression). b) Genome browser tracks of lymphatic markers in all ATAC-seq tissue samples reveals no substantial difference in genome accessibility at loci for lymphatic markers between input and endothelial enriched samples.
Extended Data Fig. 2 Transcription factor expression and motif presence across all organs.
a) Expression levels of common transcription factors (related to Fig. 2) across the adult endothelium (normalized expression). b) Enriched motifs identified by HOMER (one-tailed Fisher’s exact test) from all organs, with all timepoints condensed into one sample per organ. Size of the bubble and the color represent the p-value. The top 50 motifs are shown (values represent the -log10 of the p-value). c) Expression levels of enriched transcription factors across the adult endothelium (normalized expression).
Extended Data Fig. 3 Chromatin Accessibility Changes Across Time in the Heart Endothelium.
a) Differential chromatin accessibility determined by ATAC-Seq peaks in the heart endothelium (11,079 peaks) at E12.5, postnatal day 6 (P6) and adult (2-month-old) mice. b) Biological processes from expressed genes and with accessible chromatin in each timepoint. c) Top 10 transcription factor motifs ranked by gene expression for each age. Log2 expression over input indicated in the y-axis. Motif enrichment p-value (one-tailed Fisher’s exact test) is shown according to the dot size.
Extended Data Fig. 4 Chromatin Accessibility Changes Across Time in the Liver Endothelium.
a) Differential chromatin accessibility determined by ATAC-Seq peaks in the liver endothelium (8,666 peaks) at E12.5, postnatal day 6 (P6) and adult (2-month-old) mice. b) Biological processes from expressed genes and with accessible chromatin in each timepoint. c) Top 10 transcription factor motifs ranked by gene expression for each age. Log2 expression over input indicated in the y-axis. Motif enrichment p-value (one-tailed Fisher’s exact test) is shown according to the dot size.
Extended Data Fig. 5 Chromatin Accessibility Changes Across Time in the Lung Endothelium.
a) Differential chromatin accessibility determined by ATAC-Seq peaks in the lung endothelium (1,731 peaks) at E12.5, postnatal day 6 (P6) and adult (2-month-old) mice. b) Biological processes from expressed genes and with accessible chromatin in each timepoint. c) Top 10 transcription factor motifs ranked by gene expression for each age. Log2 expression over input indicated in the y-axis. Motif enrichment p-value (one-tailed Fisher’s exact test) is shown according to the dot size.
Extended Data Fig. 6 Chromatin Accessibility Changes Across Time in the Kidney Endothelium.
a) Differential chromatin accessibility determined by ATAC-Seq peaks in the kidney endothelium (3,035 peaks) at E12.5, postnatal day 6 (P6) and adult (2-month-old) mice. b) Biological processes from expressed genes and with accessible chromatin in each timepoint. c) Top 10 transcription factor motifs ranked by gene expression for each age. Log2 expression over input indicated in the y-axis. Motif enrichment p-value (one-tailed Fisher’s exact test) is shown according to the dot size.
Extended Data Fig. 7 Novel potential gene regulatory elements in organ specific transcription factors.
Genome browser tracks from ATAC-seq data highlight transcription factor DNA-binding motifs in accessible chromatin regions enriched in endothelial samples from the mouse (a) brain, (b) heart and (c) liver. Motifs present and their location are highlighted in black, while the genomic loci are indicated below each panel in blue (boxes represent exons, arrows indicate the direction of transcription).
Extended Data Fig. 8 Cell to Cell Communication Changes in the Neurovascular Unit Over Time.
a) Schematic representation of the harvesting and isolation of endothelial cells from E9.5, E12.5, E16.5, P8 and adult mice. Cells were purified using Magnetic Isolation Cells Sorting (MACS) and processed for downstream sequencing and analysis following the 10x Genomics protocol. b) UMAP of cells clustered by collection timepoint. c) UMAP of cells with the identity of each cluster indicated. d) Feature plot showing expression of the endothelial marker Cdh5 (encodes VE-Cadherin) superimposed on the UMAP to identity endothelial cells (normalized expression values). e) Circos plot of differentially expressed ligands in non-EC cells within our dataset, as well as their target genes expressed in the CNS endothelium between E9.5 and Adult. f) Unbiased analysis of top predicted interactions of differentially expressed ligands and receptors between ECs and pericytes in E9.5 and adult using the Cell-Cell Interactions (CCInx using adjusted p-value, and average log2 fold change). Wilcoxon rank sum test, two-sided P-value.
Extended Data Fig. 9 Differential gene expression in endothelial cells across time.
a) UMAP of only endothelial cells (from Fig. 6) colored coded by timepoint and clustered using Monocle 3 to show maturation across time. b) Feature plots denoting normalized expression of endothelial marker genes, Pecam1 and Cdh5, and canonical lymphatic marker genes, Lyve1 and Prox1, demonstrate the preferential enrichment of blood endothelial cell transcriptomes rather than lymphatic endothelial transcriptomes. c) Heatmap of differential gene expression analysis of single cell RNA-seq results from brain endothelial cells profiled across developmental time (row z-score). Genes in red have a known role in blood brain barrier function. The top 10 differentially expressed genes for each time point are shown, followed by the pan-endothelial transcripts Pecam1, Cldn5 and Kdr (normalized expression). d) Expression of BBB-related genes over developmental time.
Extended Data Fig. 10 Gene Ontology analysis of endothelial maturation within the mouse brain.
The differentially expressed gene signatures from the single cell endothelial subtypes of the developing and adult mouse brain that were used for pseudotime analysis by Moncole3 were processed for Gene Ontology analysis. The heatmap reflects which pathways and processes are upregulated in each of the unique endothelial cell subtypes (values represent the -log10 of the p-value). Colored by significance of pathway enrichment (2-sided p-value).
Supplementary information
Supplementary Information
Supplementary Figs. 1–3.
Supplementary Tables
Supplementary Tables 1–49.
Supplementary Data 1
TEER data and Transwell leak data (related to Supplementary Fig. 2).
Supplementary Data 2
qRT–PCR data (related to Supplementary Fig. 3).
Source data
Source Data Fig. 6
Raw and uncropped imaging data for Fig. 6.
Source Data Fig. 7
Raw and uncropped imaging data for Fig. 7.
Source Data Fig. 8
Raw and uncropped imaging data for Fig. 8.
Source Data Fig. 8
Raw TEER, Transwell and qRT–PCR statistical data for Fig. 8.
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Cantu Gutierrez, M.E., Hill, M.C., Largoza, G.E. et al. Mapping the transcriptional and epigenetic landscape of organotypic endothelial diversity in the developing and adult mouse. Nat Cardiovasc Res 4, 473–495 (2025). https://doi.org/10.1038/s44161-025-00618-0
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DOI: https://doi.org/10.1038/s44161-025-00618-0
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