Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Resource
  • Published:

Profiling the mouse brain endothelial transcriptome in health and disease models reveals a core blood–brain barrier dysfunction module

Nature Neuroscience volume 22, pages 1892–1902 (2019)Cite this article

Subjects

Abstract

Blood vessels in the CNS form a specialized and critical structure, the blood–brain barrier (BBB). We present a resource to understand the molecular mechanisms that regulate BBB function in health and dysfunction during disease. Using endothelial cell enrichment and RNA sequencing, we analyzed the gene expression of endothelial cells in mice, comparing brain endothelial cells with peripheral endothelial cells. We also assessed the regulation of CNS endothelial gene expression in models of stroke, multiple sclerosis, traumatic brain injury and seizure, each having profound BBB disruption. We found that although each is caused by a distinct trigger, they exhibit strikingly similar endothelial gene expression changes during BBB disruption, comprising a core BBB dysfunction module that shifts the CNS endothelial cells into a peripheral endothelial cell-like state. The identification of a common pathway for BBB dysfunction suggests that targeting therapeutic agents to limit it may be effective across multiple neurological disorders.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Endothelial reporter mouse.
Fig. 2: BBB leakage and inflammation following different disease models.
Fig. 3: Cerebrovascular transcriptional changes following disease.
Fig. 4: Pathways altered in the CNS endothelial cells following neurological disease.
Fig. 5: Identification of the BBB dysfunction module.
Fig. 6: Endothelial transcriptional regulation by activated beta-catenin.

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author upon request. RNA-sequencing files were deposited in the Gene Expression Omnibus repository and are available to use. Specific experimental details are also organized in the accompanying Nature Research Reporting Summary.

References

  1. Daneman, R. The blood–brain barrier in health and disease. Ann. Neurol. 72, 648–672 (2012).

    CAS  PubMed  Google Scholar 

  2. Zlokovic, B. V. The blood–brain barrier in health and chronic neurodegenerative disorders. Neuron 57, 178–201 (2008).

    CAS  PubMed  Google Scholar 

  3. Rubin, L. L. & Staddon, J. M. The cell biology of the blood–brain barrier. Annu. Rev. Neurosci. 22, 11–28 (1999).

    CAS  PubMed  Google Scholar 

  4. Janzer, R. C. & Raff, M. C. Astrocytes induce blood–brain barrier properties in endothelial cells. Nature 325, 253–257 (1987).

    CAS  PubMed  Google Scholar 

  5. Stewart, P. A. & Wiley, M. J. Developing nervous tissue induces formation of blood–brain barrier characteristics in invading endothelial cells: a study using quail-chick transplantation chimeras. Dev. Biol. 84, 183–192 (1981).

    CAS  PubMed  Google Scholar 

  6. Armulik, A. et al. Pericytes regulate the blood–brain barrier. Nature 468, 557–561 (2010).

    CAS  PubMed  Google Scholar 

  7. Daneman, R., Zhou, L., Kebede, A. A. & Barres, B. A. Pericytes are required for blood–brain barrier integrity during embryogenesis. Nature 468, 562–566 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Rubin, L. L. et al. A cell culture model of the blood–brain barrier. J. Cell Biol. 115, 1725–1735 (1991).

    CAS  PubMed  Google Scholar 

  9. Weidenfeller, C., Svendsen, C. N. & Shusta, E. V. Differentiating embryonic neural progenitor cells induce blood–brain barrier properties. J. Neurochem. 101, 555–565 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Daneman, R. et al. Wnt/beta-catenin signaling is required for CNS, but not non-CNS, angiogenesis. Proc. Natl Acad. Sci. USA 106, 641–646 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Liebner, S. et al. Wnt/beta-catenin signaling controls development of the blood–brain barrier. J. Cell Biol. 183, 409–417 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Stenman, J. M. et al. Canonical Wnt signaling regulates organ-specific assembly and differentiation of CNS vasculature. Science 322, 1247–1250 (2008).

    CAS  PubMed  Google Scholar 

  13. Wang, Y. et al. Norrin/Frizzled4 signaling in retinal vascular development and blood brain barrier plasticity. Cell 151, 1332–1344 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhou, Y. & Nathans, J. Gpr124 controls CNS angiogenesis and blood–brain barrier integrity by promoting ligand-specific canonical Wnt signaling. Dev. Cell 31, 248–256 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Zhou, Y. et al. Canonical WNT signaling components in vascular development and barrier formation. J. Clin. Invest. 124, 3825–3846 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Pinzon-Daza, M. L. et al. The cross-talk between canonical and non-canonical Wnt-dependent pathways regulates P-glycoprotein expression in human blood–brain barrier cells. J. Cereb. Blood Flow Metab. 34, 1258–1269 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Ioannidou, S. et al. An in vitro assay reveals a role for the diaphragm protein PV-1 in endothelial fenestra morphogenesis. Proc. Natl Acad. Sci. USA 103, 16770–16775 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Keuschnigg, J. et al. The prototype endothelial marker PAL-E is a leukocyte trafficking molecule. Blood 114, 478–484 (2009).

    CAS  PubMed  Google Scholar 

  19. Shue, E. H. et al. Plasmalemmal vesicle associated protein-1 (PV-1) is a marker of blood–brain barrier disruption in rodent models. BMC Neurosci. 9, 29 (2008).

    PubMed  PubMed Central  Google Scholar 

  20. Doring, A., Wild, M., Vestweber, D., Deutsch, U. & Engelhardt, B. E- and P-selectin are not required for the development of experimental autoimmune encephalomyelitis in C57BL/6 and SJL mice. J. Immunol. 179, 8470–8479 (2007).

    PubMed  Google Scholar 

  21. Love, S. & Barber, R. Expression of P-selectin and intercellular adhesion molecule-1 in human brain after focal infarction or cardiac arrest. Neuropathol. Appl. Neurobiol. 27, 465–473 (2001).

    CAS  PubMed  Google Scholar 

  22. Okada, Y. et al. P-selectin and intercellular adhesion molecule-1 expression after focal brain ischemia and reperfusion. Stroke 25, 202–211 (1994).

    CAS  PubMed  Google Scholar 

  23. Mayadas, T. N., Johnson, R. C., Rayburn, H., Hynes, R. O. & Wagner, D. D. Leukocyte rolling and extravasation are severely compromised in P selectin-deficient mice. Cell 74, 541–554 (1993).

    CAS  PubMed  Google Scholar 

  24. Huang, J. et al. Postischemic cerebrovascular E-selectin expression mediates tissue injury in murine stroke. Stroke 31, 3047–3053 (2000).

    CAS  PubMed  Google Scholar 

  25. Jin, A. Y. et al. Reduced blood brain barrier breakdown in P-selectin deficient mice following transient ischemic stroke: a future therapeutic target for treatment of stroke. BMC Neurosci. 11, 12 (2010).

    PubMed  PubMed Central  Google Scholar 

  26. Bowes, M. P., Zivin, J. A. & Rothlein, R. Monoclonal antibody to the ICAM-1 adhesion site reduces neurological damage in a rabbit cerebral embolism stroke model. Exp. Neurol. 119, 215–219 (1993).

    CAS  PubMed  Google Scholar 

  27. Connolly, E. S. Jr. et al. Cerebral protection in homozygous null ICAM-1 mice after middle cerebral artery occlusion. Role of neutrophil adhesion in the pathogenesis of stroke. J. Clin. Invest. 97, 209–216 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Henninger, D. D. et al. Cytokine-induced VCAM-1 and ICAM-1 expression in different organs of the mouse. J. Immunol. 158, 1825–1832 (1997).

    CAS  PubMed  Google Scholar 

  29. Lyck, R. et al. T-cell interaction with ICAM-1/ICAM-2 double-deficient brain endothelium in vitro: the cytoplasmic tail of endothelial ICAM-1 is necessary for transendothelial migration of T cells. Blood 102, 3675–3683 (2003).

    CAS  PubMed  Google Scholar 

  30. Sharief, M. K., Noori, M. A., Ciardi, M., Cirelli, A. & Thompson, E. J. Increased levels of circulating ICAM-1 in serum and cerebrospinal fluid of patients with active multiple sclerosis. Correlation with TNF-ɑ and blood–brain barrier damage. J. Neuroimmunol. 43, 15–21 (1993).

    CAS  PubMed  Google Scholar 

  31. Derada Troletti, C., de Goede, P., Kamermans, A. & de Vries, H. E. Molecular alterations of the blood–brain barrier under inflammatory conditions: the role of endothelial to mesenchymal transition. Biochim. Biophys. Acta 1862, 452–460 (2016).

    CAS  PubMed  Google Scholar 

  32. Zhou, T. et al. Microvascular endothelial cells engulf myelin debris and promote macrophage recruitment and fibrosis after neural injury. Nat. Neurosci. 22, 421–435 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Ley, K. The role of selectins in inflammation and disease. Trends Mol. Med. 9, 263–268 (2003).

    CAS  PubMed  Google Scholar 

  34. Conductier, G., Blondeau, N., Guyon, A., Nahon, J. L. & Rovere, C. The role of monocyte chemoattractant protein MCP1/CCL2 in neuroinflammatory diseases. J. Neuroimmunol. 224, 93–100 (2010).

    CAS  PubMed  Google Scholar 

  35. Aggarwal, B. B., Shishodia, S., Ashikawa, K. & Bharti, A. C. The role of TNF and its family members in inflammation and cancer: lessons from gene deletion. Curr. Drug Targets Inflamm. Allergy 1, 327–341 (2002).

    CAS  PubMed  Google Scholar 

  36. Xing, C. et al. Neuronal production of lipocalin-2 as a help-me signal for glial activation. Stroke 45, 2085–2092 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Ferreira, A. C. et al. From the periphery to the brain: lipocalin-2, a friend or foe? Prog. Neurobiol. 131, 120–136 (2015).

    CAS  PubMed  Google Scholar 

  38. Cheng, B., Chen, J., Bai, B. & Xin, Q. Neuroprotection of apelin and its signaling pathway. Peptides 37, 171–173 (2012).

    CAS  PubMed  Google Scholar 

  39. Ma, S., Kwon, H. J., Johng, H., Zang, K. & Huang, Z. Radial glial neural progenitors regulate nascent brain vascular network stabilization via inhibition of Wnt signaling. PLoS Biol. 11, e1001469 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Sun, C. et al. Conditional ablation of neuroprogenitor cells in adult mice impedes recovery of poststroke cognitive function and reduces synaptic connectivity in the perforant pathway. J. Neurosci. 33, 17314–17325 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Akamatsu, Y. et al. Impaired leptomeningeal collateral flow contributes to the poor outcome following experimental stroke in the type 2 diabetic mice. J. Neurosci. 35, 3851–3864 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Semple, B. D. et al. Early gelatinase activity is not a determinant of long-term recovery after traumatic brain injury in the immature mouse. PLoS One 10, e0143386 (2015).

    PubMed  PubMed Central  Google Scholar 

  43. Semple, B. D., Blomgren, K., Gimlin, K., Ferriero, D. M. & Noble-Haeusslein, L. J. Brain development in rodents and humans: identifying benchmarks of maturation and vulnerability to injury across species. Prog. Neurobiol. 106-107, 1–16 (2013).

    PubMed  Google Scholar 

  44. Daneman, R. et al. The mouse blood–brain barrier transcriptome: a new resource for understanding the development and function of brain endothelial cells. PLoS One 5, e13741 (2010).

    PubMed  PubMed Central  Google Scholar 

  45. Lui, J. H. et al. Radial glia require PDGFD-PDGFRβ signalling in human but not mouse neocortex. Nature 515, 264–268 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    PubMed  PubMed Central  Google Scholar 

  47. Oldham, M. C. et al. Functional organization of the transcriptome in human brain. Nat. Neurosci. 11, 1271–1282 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank N. Lescano and A. Schroeder and the Parnassus Flow Cytometry Core, T. Rambaldo and the UCSD Veterans Hospital Flow Cytometry Core, R. Chadwick and the Gladstone Genomics Core, A. Williams and The Gladstone Bioinformatics Core and K. Jepsen and the UCSD Institute for Genomics Medicine Genomics Center. R.D. is funded by the National Institutes of Health (NIH)/National Institue of Neurological Disorders and Stroke (NINDS) (grant no. R01 NS091281-01A1), a National Multiple Sclerosis Society Pilot Grant and Takeda Pharmaceuticals New Frontier Science Program. R.N.M. was funded by the UCSF Department of Clinical Pharmacology and Therapeutics (grant no. GM007546) and the Department of Anesthesia and Perioperative Care (grant no. GM008440) NIH T32 grants. M.C.O. is funded by the NIH/National Institue of Mental Health (grant no. R01 MH113896). L.J.N.-H. is funded by the NIH/NINDS (grant nos. R01 NS050159 and NS077767). T.H. is funded by the NIH/NINDS (grant nos. R01NS055876 and R01NS082280) and the Barrow Neurological Foundation grant.

Author information

Author notes

  1. These authors contributed equally: Roeben Nocon Munji, Allison Luen Soung.

Authors and Affiliations

  1. Departments of Pharmacology and Neurosciences, University of California, San Diego, San Diego, CA, USA

    Roeben Nocon Munji, Allison Luen Soung, Geoffrey Aaron Weiner, Fabien Sohet, Sidar Aydin, Austin Batugal, Anne Christelle Cabangcala & Richard Daneman

  2. Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA

    Bridgette Deanne Semple, Alpa Trivedi, Kayleen Gimlin, Patrick Georg Schupp, Michael Clark Oldham & Linda J. Noble-Haeusslein

  3. Department of Neurosurgery and Neurobiology, Barrow Aneurysm and AVM Research Center, Barrow Neurological Institute, Phoenix, AZ, USA

    Masakazu Kotoda, Masaaki Korai & Tomoki Hashimoto

Authors
  1. Roeben Nocon Munji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Allison Luen Soung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Geoffrey Aaron Weiner
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fabien Sohet
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bridgette Deanne Semple
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alpa Trivedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kayleen Gimlin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Masakazu Kotoda
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Masaaki Korai
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Sidar Aydin
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Austin Batugal
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Anne Christelle Cabangcala
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Patrick Georg Schupp
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Michael Clark Oldham
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Tomoki Hashimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Linda J. Noble-Haeusslein
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Richard Daneman
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.D. designed and participated in the analysis of all experiments. R.N.M. designed the seizure and Wnt signaling studies, performed cell purification of health and Wnt signaling models, and participated in the analysis of health, disease and Wnt signaling studies and writing of the manuscript. A.L.S. performed the endothelial cell purification and analysis of health and disease models. G.A.W. performed the alignment and normalization of RNA-sequencing data. P.G.S. and M.C.O. designed and performed the gene coexpression analysis. F.S. participated in the design and analysis of the EAE model. L.J.N.-H., B.D.S., A.T. and K.G. participated in the design and analysis of all TBI experiments. T.H., M.Korai and M.Kotoda participated in the design and analysis of all MCAO experiments. S.A. participated in the design and analysis of the Wnt signaling study. A.B. and A.C.C. participated in the design and analysis of disease models.

Corresponding author

Correspondence to Richard Daneman.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Britta Engelhardt and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Cell markers in endothelial cell samples.

Comparison of the expression levels of endothelial cell markers and non-endothelial cell markers in endothelial cells enriched from brain tissue. A) The brain vascular (black bars; samples-red dots) and brain endothelial samples (gray bars; samples-blue dots) are enriched with RNA of markers for CNS endothelial cells but not RNA of markers for CNS pericytes, astrocytes, neurons, oligodendrocytes (oligoden) and microglia (micrg). B) A graph representing the same data in A with the y-axis set to 220CPM and excluding the highly enriched endothelial cell markers to demonstrate the lack of enrichment of pericyte cell marker expression in the brain endothelial samples compared to the brain vascular samples. A,B) Data is presented as mean ±SEM; n=2 mice per condition.

Supplementary Figure 2 Quantification of blood-brain barrier biotin permeability.

Quantified pixel intensity of biotin:streptavidin-alexa-488 fluorescence on brain/spinal cord tissue sections from seizure, EAE, stroke and TBI disease models and corresponding controls during acute, subacute and chronic timepoints. Biotin was intravenously perfused in anesthetized mice. Data is presented as median fold change of control with arbitrary units (AU). Box plot: 25th–75th percentile box, minimum-maximum whiskers. Statistical test: t test (unpaired, parametric, equal standard deviation, two-tailed), * P-value<0.05, ** P-value<0.01, *** P-value<0.001, **** P-value<0.0001. n=number of mice (Control/Acute/Subacute/Chronic): Seizure: 5/3/4/4, EAE: 4/4/5/5, Stroke: 9/3/3/3, TBI: 8/3/3/3.

Supplementary Figure 3 Quantification of blood-brain barrier fibrinogen permeability.

A-H) Representative images of brain/spinal cord sections immunostained for the endogenous serum component fibrinogen (red) and vessel marker CD31 (green) in control and subacute mice. Fibrinogen is increased in seizure, EAE, stroke and TBI disease models (B,D,F,H) compared to controls (A,C,E,G). B/D/F/H-prime) Higher magnification of boxed regions in subacute showing colocalization of fibrinogen with CD31+ vessels (arrowheads highlight examples). Scale bars: 100 microns; 200 microns in E and G. n=number of independent experiments: Seizure: 2, EAE:2, Stroke: 3, TBI: 4. I) Quantified pixel intensity of fibrinogen immunostaining with alexa-594 fluorophore on tissue sections from seizure, EAE, stroke and TBI disease models and corresponding controls during the subacute timepoint. Data is presented as median fold change of control with arbitrary units (AU). Box plot: 25th-75th percentile box, minimum-maximum whiskers. Statistical test: t test (unpaired, parametric, equal standard deviation, two-tailed), ** P-value<0.01, *** P-value<0.001. n=number of mice (Control/Subacute): Seizure: 3/4, EAE: 4/3 Stroke: 4/4, TBI: 3/3.

Supplementary Figure 4 Immunoglobulin G localization in the CNS.

Representative images of brain/spinal cord sections immunostained for endogenous immunoglobulin G (IgG) in control and subacute mice. Peripherally synthesized IgG is increased in seizure, EAE, stroke and TBI disease models (B,D,F,H) compared to controls (A,C,E,G). Scale bars: 200 microns; 100 microns in EAE. n=number of mice (Control/Subacute): Seizure: 3/5, EAE: 2/3, Stroke: 4/4, TBI: 3/3.

Supplementary Figure 5 Claudin 5 organization in CNS vessels.

Representative images of brain/spinal cord sections immunostained for the tight junction protein Caludin 5 (red) and vessel marker CD31 (green) in control and subacute mice. Claudin 5 localization in vessels is disorganized in hypertrophic vessels of EAE, stroke and TBI disease models (D,F,H) compard to controls (C,E,G) (arrowheads highlight examples). Hypertrophic vessels in the seizure model was observed but was less common. Scale bar: 100 microns. n=number of mice (Control/Subacute): Seizure: 3/7, EAE: 3/3, Stroke: 4/4, TBI: 3/3.

Supplementary Figure 6 Expression of collagen I in the CNS.

Representative images of brain/spinal cord sections immunostained for extracellular matrix protein collagen I (red) and vessel marker CD31 (green) in control and subacute mice. Collagen I colocalized with CD31 is increased in seizure, EAE, stroke and TBI disease models (B,D,F,H) compared to controls (A,C,E,G). Arrow in seizure model control (A) highlight an example of a medium size vessel without collagen expression. Dense DAPI labeling in EAE subacute identify a lesion. Arrowheads highlight examples of ectopic expression of collagen I colocalized with CD31+ vessels. Scale bars: 50 microns. n=number of mice (Control/Subacute): Seizure: 3/4, EAE: 4/3, Stroke: 4/4, TBI: 3/3.

Supplementary Figure 7 Quantification of the expression of BBB dysfunction module genes.

Quantified vessel length expressing the gene of interest (GOI). Expression of extracellular matrix genes collagen I, collagen III, decorin, lumican and SPP1 is increased in brain/spinal cord from seizure, EAE, stroke and TBI disease models compared to corresponding controls. Data is presented as mean percent vascular length (±SEM) expressing the GOI of the total length of CD31 positive vessels. Statistical test: t test (equal standard deviation, two-tailed, unpaired: Seizure and EAE, paired ipsilateral/contralateral: MCAO and TBI), * P-value<0.05, ** P-value<0.01, *** P-value<0.001, **** P-value<0.0001. n=number of mice (Control/Subacute): Seizure: 3/3, EAE: 3/3, Stroke: 4/4, TBI: 3/3.

Supplementary Figure 8 Endothelial cell enrichment.

A graphical representation of the key steps of the endothelial cell enrichment procedure used in this study. The regions of interests in health or disease experiments for brain, heart, lung, liver, kidney or spinal cord are identified in box A. The blue flow path (B) is the standard procedure. The violet flow path (C) adds the second enzymatic digestion step to dissociate remaining adherent cells used in the health “Brain endothelial” samples. The red flow path (D) adds the modifications for all peripheral organs. The green flow path (E) further adds modifications to the peripheral organ procedures for the activated beta-catenin studies.

Supplementary Figure 9 FACS gating strategy for endothelial cell enrichment.

Representative sample. (A) Gating for single cells with side scatter (SSC) and forward scatter (FSC). (B) Gating to exclude dead cells (DAPI+), and immune cells and pericytes (A488+ or FITC+). (C) Gating for endothelial cells (tdTomato+). (D) Number of events detected and percent of events relative to parent population (All Events) or previous gate.

Supplementary information

Supplementary Figs. 1–9, Supplementary Results & Discussion and Supplementary Tables 1–5

Supplementary Figs. 1–9, Supplementary Results & Discussion and Supplementary Tables 1–5.

Reporting Summary

Supplementary File 1

RNA sequencing of brain endothelial cells in health and disease. Master excel spreadsheet containing the expression values in counts per million (c.p.m.) for each annotated gene in samples of brain vascular cells, brain endothelial cells, heart endothelial cells, kidney endothelial cells, lung endothelial cells, liver endothelial cells and whole brain, as well as control, acute, subacute and chronic timepoints in CNS endothelial cells following the kainic acid model of seizures, EAE model of multiple sclerosis, MCAO model of stroke and impact model of pediatric TBI. The log2 fold comparisons are given for each gene for critical pair-wise comparisons in health and for each disease. Statistics are displayed on additional tabs: The P value and false discovery rate (FDR) values are given for the critical comparisons in health (second tab, M-AU) and diseases (third tab, E-CC). For each gene we also identify whether it is enriched given comparisons in health (second tab, AW-BM) and disease (third tab, CE-DJ). In the fourth tab we present relevant metrics from gene coexpression network analysis. ‘Seed’ genes (column C) met statistical criteria for initial module assignments (Methods). After identifying coexpression modules, module membership strength (kME) was calculated for all genes with respect to all modules. Columns H-BW report the kME value and corresponding nominal P value for each gene with respect to each module eigengene. Columns F and G indicate the module with the most significant, positive kME value for that gene meeting either Bonferroni- (column F) or false discovery rate- (column G) corrected levels of significance. We also report the mean expression value for each gene over all samples (column D), as well as its percentile rank (column E). Statistical tests: Wald test for P values and Benjamini–Hochberg for FDR. n = 2 mice each condition for healthy organs as source of enriched endothelial cells and 4 mice for whole brain tissue. n = 3 mice each for disease model control and experimental conditions as source of enriched endothelial cells with exception of n = 2 mice for TBI control subacute and chronic conditions.

Supplementary File 2

Mural cell genes. The list of brain mural cell-enriched genes identified as those genes enriched in the brain vascular over brain endothelial samples (BV c.p.m. > 5, log2 fold > 1.5, P < 0.05, BE < 10 c.p.m.). Statistical tests: Wald test for P values and Benjamini–Hochberg for FDR. n = 2 mice each condition as source of enriched endothelial cells and 4 mice for whole brain tissue.

Supplementary File 3

BBB-enriched genes. The list of BBB-enriched genes identified as genes with at least 5 counts per million (c.p.m.) in brain endothelial cells, and at log2 fold > 1.00 enrichment in brain endothelial cells compared with each peripheral endothelial cell with P < 0.05. Mural cell genes were excluded by identifying genes with brain vascular c.p.m. > brain endo c.p.m., log2 fold > 1.00, brain vascular c.p.m. > 5, P < 0.05). Statistical tests: Wald test for P values and Benjamini–Hochberg for FDR. n = 2 mice each condition as source of enriched endothelial cells and 4 mice for whole brain tissue.

Supplementary File 4

Tight junction proteins expressed in brain endothelial cells. Excel spreadsheet of the tight junction proteins (bicellular and tricellular), as annotated by mouse genome informatics (MGI), with at least 5 c.p.m. expression value in the brain endothelial cell sample. Statistical test: Wald test. n = 2 mice each condition as source of enriched endothelial cells and 4 mice for whole brain tissue.

Supplementary File 5

Peripheral endothelial-enriched genes. The list of peripheral endothelial-enriched genes identified as those genes enriched (c.p.m. > 5, log2 fold > –1.00, P < 0.05) in at least three of the peripheral endothelial samples compared with the brain endothelial cells. Statistical tests: Wald test for P values and Benjamini–Hochberg for FDR. n = 2 mice each condition as source of enriched endothelial cells and 4 mice for whole brain tissue.

Supplementary File 6

BBB dysfunction module. The list of genes that are upregulated (log2 fold > 1.00, c.p.m. > 5 at subacute timepoint, P < 0.05) in at least three of the four diseases at the subacute timepoint. Statistical tests: Wald test for P values and Benjamini–Hochberg for FDR. n = 3 mice each condition as source of enriched endothelial cells with exception of n = 2 mice for TBI control subacute and chronic conditions.

Supplementary File 7

RNA sequencing of peripheral endothelial cells following activated beta-catenin signaling. Master excel spreadsheet containing the expression values in counts per million (c.p.m.) for each annotated gene in samples taken from liver and lung endothelial cells of control (VE-Cadherin-CreERT2) and mice with activated beta-catenin induced in their endothelial cells (Rosa-Bcat-GOF; VE-Cadherin-CreERT2). log2 fold comparisons, P values and false discovery rate (FDR) values are given for each gene comparing controls with activated beta-catenin conditions of liver and lung samples. Additional tabs are given listing genes that are up- and downregulated due to activated beta-catenin in the liver endothelial cells, lung endothelial cells and both as given by P < 0.05 and an absolute value of >10 c.p.m. in the sample with the greater value. Statistical tests: Wald test for P values and Benjamini–Hochberg for FDR. n = 4 mice each condition as source of enriched endothelial cells.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Munji, R.N., Soung, A.L., Weiner, G.A. et al. Profiling the mouse brain endothelial transcriptome in health and disease models reveals a core blood–brain barrier dysfunction module. Nat Neurosci 22, 1892–1902 (2019). https://doi.org/10.1038/s41593-019-0497-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-019-0497-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing