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A Transcriptomic Comparative Study of Cranial Vasculature

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Abstract

In genetic studies of cerebrovascular diseases, the optimal vessels to use as controls remain unclear. Our goal is to compare the transcriptomic profiles among 3 different types of control vessels: superficial temporal artery (STA), middle cerebral arteries (MCA), and arteries from the circle of Willis obtained from autopsies (AU). We examined the transcriptomic profiles of STA, MCA, and AU using RNAseq. We also investigated the effects of using these control groups on the results of the comparisons between aneurysms and the control arteries. Our study showed that when comparing pathological cerebral arteries to control groups, all control groups presented similar responses in the activation of immunological processes, the regulation of intracellular signaling pathways, and extracellular matrix productions, despite their intrinsic biological differences. When compared to STA, AU exhibited upregulation of stress and apoptosis genes, whereas MCA showed upregulation of genes associated with tRNA/rRNA processing. Moreover, our results suggest that the matched case–control study design, which involves control STA samples collected from the same subjects of matched aneurysm samples in our study, can improve the identification of non-inherited disease-associated genes. Given the challenges associated with obtaining fresh intracranial arteries from healthy individuals, our study suggests that using MCA, AU, or paired STA samples as controls are feasible strategies for future large-scale studies investigating cerebral vasculopathies. However, the intrinsic differences of each type of control should be taken into consideration when interpreting the results. With the limitations of each control type, it may be most optimal to use multiple tissues as controls.

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Data Availability

The datasets generated during and/or analysed during the current study will be available in a public repository.

Abbreviations

IA:

Intracranial aneurysm

STA:

Superficial temporal artery

MCA:

Middle cerebral artery

AU:

Circle of Willis from autopsies

AVM:

Arteriovenous malformation

MMD:

Moyamoya disease

ECA:

External carotid artery

ICA:

Internal carotid artery

ANEU:

Aneurysm

CV:

Coefficient of variation

RRA:

Robust rank aggregation

References

  1. Portegies ML, Koudstaal PJ, Ikram MA. Cerebrovascular disease. Handb Clin Neurol. 2016;138:239–61. https://doi.org/10.1016/B978-0-12-802973-2.00014-8.

    Article  CAS  PubMed  Google Scholar 

  2. Kane JR. The role of brain vasculature in glioblastoma. Mol Neurobiol. 2019;56:6645–53. https://doi.org/10.1007/s12035-019-1561-y.

    Article  CAS  PubMed  Google Scholar 

  3. Sweeney MD, Kisler K, Montagne A, Toga AW, Zlokovic BV. The role of brain vasculature in neurodegenerative disorders. Nat Neurosci. 2018;21:1318–31. https://doi.org/10.1038/s41593-018-0234-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Tuo QZ, Zhang ST, Lei P. Mechanisms of neuronal cell death in ischemic stroke and their therapeutic implications. Med Res Rev. 2022;42:259–305. https://doi.org/10.1002/med.21817.

    Article  PubMed  Google Scholar 

  5. Winkler EA, Kim CN, Ross JM, Garcia JH, Gil E, Oh I, Chen LQ, Wu D, Catapano JS, Raygor K, et al. A single-cell atlas of the normal and malformed human brain vasculature. Science. 2022;375:eabi7377. https://doi.org/10.1126/science.abi7377.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Chalouhi N, Hoh BL, Hasan D. Review of cerebral aneurysm formation, growth, and rupture. Stroke. 2013;44:3613–22. https://doi.org/10.1161/STROKEAHA.113.002390.

    Article  PubMed  Google Scholar 

  7. Strange F, Gruter BE, Fandino J, Marbacher S. Preclinical intracranial aneurysm models: a systematic review. Brain Sci. 2020;10:134. https://doi.org/10.3390/brainsci10030134. 

  8. Li S, Han Y, Zhang Q, Tang D, Li J, Weng L. Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using machine learning methods in moyamoya disease. Front Mol Biosci. 2022;9:991425. https://doi.org/10.3389/fmolb.2022.991425.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Li L, Yang X, Jiang F, Dusting GJ, Wu Z. Transcriptome-wide characterization of gene expression associated with unruptured intracranial aneurysms. Eur Neurol. 2009;62:330–7. https://doi.org/10.1159/000236911.

    Article  CAS  PubMed  Google Scholar 

  10. Nakaoka H, Tajima A, Yoneyama T, Hosomichi K, Kasuya H, Mizutani T, Inoue I. Gene expression profiling reveals distinct molecular signatures associated with the rupture of intracranial aneurysm. Stroke. 2014;45:2239–45. https://doi.org/10.1161/STROKEAHA.114.005851.

    Article  CAS  PubMed  Google Scholar 

  11. Jiang Y, Zhang M, He H, Chen J, Zeng H, Li J, Duan R. MicroRNA/mRNA profiling and regulatory network of intracranial aneurysm. BMC Med Genomics. 2013;6:36. https://doi.org/10.1186/1755-8794-6-36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Aoki T, Koseki H, Miyata H, Itoh M, Kawaji H, Takizawa K, Kawashima A, Ujiie H, Higa T, Minamimura K, et al. RNA sequencing analysis revealed the induction of CCL3 expression in human intracranial aneurysms. Sci Rep. 2019;9:10387. https://doi.org/10.1038/s41598-019-46886-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Huang Q, Huang QY, Sun Y, Wu S. High-throughput data reveals novel circular RNAs via competitive endogenous RNA networks associated with human intracranial aneurysms. Med Sci Monit. 2019;25:4819–30. https://doi.org/10.12659/MSM.917081.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Fan J, Yu L, Zhao J. Comparative transcriptome analysis reveals involvement of TLR-2 signaling in the pathogenesis of intracranial aneurysm. J Clin Neurosci. 2018;47:258–63. https://doi.org/10.1016/j.jocn.2017.07.016.

    Article  CAS  PubMed  Google Scholar 

  15. Li H, Yue H, Hao Y, Li H, Wang S, Yu L, Zhang D, Cao Y, Zhao J. Expression profile of long noncoding RNAs in human cerebral aneurysms: a microarray analysis. J Neurosurg. 2017;127:1055–62. https://doi.org/10.3171/2016.9.JNS16839.

    Article  CAS  PubMed  Google Scholar 

  16. Yu L, Wang J, Wang S, Zhang D, Zhao Y, Wang R, Zhao J. DNA methylation regulates gene expression in intracranial aneurysms. World Neurosurg. 2017;105:28–36. https://doi.org/10.1016/j.wneu.2017.04.064.

    Article  PubMed  Google Scholar 

  17. Li H, Li H, Yue H, Wang W, Yu L, Shuo W, Cao Y, Zhao J. Comparison between smaller ruptured intracranial aneurysm and larger un-ruptured intracranial aneurysm: gene expression profile analysis. Neurosurg Rev. 2017;40:419–25. https://doi.org/10.1007/s10143-016-0799-3.

    Article  PubMed  Google Scholar 

  18. Wang W, Li H, Yu L, Zhao Z, Wang H, Zhang D, Zhang Y, Lan Q, Wang J, Zhao J. Aberrant expression of lncRNAs and mRNAs in patients with intracranial aneurysm. Oncotarget. 2017;8:2477–84. https://doi.org/10.18632/oncotarget.13908.

    Article  PubMed  Google Scholar 

  19. Li H, Wang W, Zhang L, Lan Q, Wang J, Cao Y, Zhao J. Identification of a long noncoding RNA-associated competing endogenous RNA network in intracranial aneurysm. World Neurosurg. 2017;97:684–92. https://doi.org/10.1016/j.wneu.2016.10.016.

    Article  PubMed  Google Scholar 

  20. Bekelis K, Kerley-Hamilton JS, Teegarden A, Tomlinson CR, Kuintzle R, Simmons N, Singer RJ, Roberts DW, Kellis M, Hendrix DA. MicroRNA and gene expression changes in unruptured human cerebral aneurysms. J Neurosurg. 2016;125:1390–9. https://doi.org/10.3171/2015.11.JNS151841.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Yu L, Fan J, Wang S, Zhang D, Wang R, Zhao Y, Zhao J. Gene expression profiles in intracranial aneurysms. Neurosci Bull. 2014;30:99–106. https://doi.org/10.1007/s12264-013-1398-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Marchese E, Vignati A, Albanese A, Nucci CG, Sabatino G, Tirpakova B, Lofrese G, Zelano G, Maira G. Comparative evaluation of genome-wide gene expression profiles in ruptured and unruptured human intracranial aneurysms. J Biol Regul Homeost Agents. 2010;24:185–95.

    CAS  PubMed  Google Scholar 

  23. Shi C, Awad IA, Jafari N, Lin S, Du P, Hage ZA, Shenkar R, Getch CC, Bredel M, Batjer HH, et al. Genomics of human intracranial aneurysm wall. Stroke. 2009;40:1252–61. https://doi.org/10.1161/STROKEAHA.108.532036.

    Article  PubMed  Google Scholar 

  24. Peters DG, Kassam AB, Feingold E, Heidrich-O’Hare E, Yonas H, Ferrell RE, Brufsky A. Molecular anatomy of an intracranial aneurysm: coordinated expression of genes involved in wound healing and tissue remodeling. Stroke. 2001;32:1036–42. https://doi.org/10.1161/01.str.32.4.1036.

    Article  CAS  PubMed  Google Scholar 

  25. Zhou S, Dion PA, Rouleau GA. Genetics of intracranial aneurysms. Stroke. 2018;49:780–7. https://doi.org/10.1161/STROKEAHA.117.018152.

    Article  PubMed  Google Scholar 

  26. Masuoka T, Hayashi N, Hori E, Kuwayama N, Ohtani O, Endo S. Distribution of internal elastic lamina and external elastic lamina in the internal carotid artery: possible relationship with atherosclerosis. Neurol Med Chir (Tokyo). 2010;50:179–82. https://doi.org/10.2176/nmc.50.179.

    Article  PubMed  Google Scholar 

  27. Reddy S, Kumar P, Prasad K. Histomorphometric and sympathetic innervation of the human superficial temporal artery. Indian J Plast Surg. 2011;44:127–33. https://doi.org/10.4103/0970-0358.81449.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Hitomi Y, Masaki N, Ishinoda Y, Kagami K, Yasuda R, Toya T, Namba T, Nagatomo Y, Takase B, Adachi T. Effectiveness of pulsatility index of carotid Doppler ultrasonography to predict cardiovascular events. J Med Ultrason. 2001;2022(49):95–103. https://doi.org/10.1007/s10396-021-01164-5.

    Article  Google Scholar 

  29. Kronick MD, Chopra A, Swamy S, Brar V, Jung E, Abraham CZ, Liem TK, Landry GJ, Moneta GL. Peak systolic velocity and color aliasing are important in the development of duplex ultrasound criteria for external carotid artery stenosis. J Vasc Surg. 2020;72:951–7. https://doi.org/10.1016/j.jvs.2019.10.099.

    Article  PubMed  Google Scholar 

  30. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21. https://doi.org/10.1093/bioinformatics/bts635.

    Article  CAS  PubMed  Google Scholar 

  31. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. https://doi.org/10.1186/s13059-014-0550-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Lê S, Josse J, Husson F. FactoMineR: An R package for multivariate analysis. J Stat Softw. 2008;25:1–18. https://doi.org/10.18637/jss.v025.i01.

    Article  Google Scholar 

  33. Kolde R, Laur S, Adler P, Vilo J. Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics. 2012;28:573–80. https://doi.org/10.1093/bioinformatics/btr709.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2:100141. https://doi.org/10.1016/j.xinn.2021.100141.

    Article  CAS  PubMed  Google Scholar 

  35. Wu G, Dawson E, Duong A, Haw R, Stein L. ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis. F1000Res. 2014;3:146. https://doi.org/10.12688/f1000research.4431.2.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman WH, Pages F, Trajanoski Z, Galon J. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009;25:1091–3. https://doi.org/10.1093/bioinformatics/btp101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Gillespie M, Jassal B, Stephan R, Milacic M, Rothfels K, Senff-Ribeiro A, Griss J, Sevilla C, Matthews L, Gong C, et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 2022;50:D687–92. https://doi.org/10.1093/nar/gkab1028.

    Article  CAS  PubMed  Google Scholar 

  38. Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, Khodadoust MS, Esfahani MS, Luca BA, Steiner D, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019;37:773–82. https://doi.org/10.1038/s41587-019-0114-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Yang XS, Liu SA, Liu JW, Yan Q. Fucosyltransferase IV enhances expression of MMP-12 stimulated by EGF via the ERK1/2, p38 and NF-kappaB pathways in A431 cells. Asian Pac J Cancer Prev. 2012;13:1657–62. https://doi.org/10.7314/apjcp.2012.13.4.1657.

    Article  PubMed  Google Scholar 

  40. Huang C. Roles of E3 ubiquitin ligases in cell adhesion and migration. Cell Adh Migr. 2010;4:10–8. https://doi.org/10.4161/cam.4.1.9834.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Salmi M, Hellman J, Jalkanen S. The role of two distinct endothelial molecules, vascular adhesion protein-1 and peripheral lymph node addressin, in the binding of lymphocyte subsets to human lymph nodes. J Immunol. 1998;160:5629–36.

    Article  CAS  PubMed  Google Scholar 

  42. Castillo K, Contreras GF, Pupo A, Torres YP, Neely A, Gonzalez C, Latorre R. Molecular mechanism underlying beta1 regulation in voltage- and calcium-activated potassium (BK) channels. Proc Natl Acad Sci U S A. 2015;112:4809–14. https://doi.org/10.1073/pnas.1504378112.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Morga R, Pera J. Transcriptomic studies on intracranial aneurysms. Genes. 2023;14:613.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Guo T, Hou D, Yu D. Bioinformatics analysis of gene expression profile data to screen key genes involved in intracranial aneurysms. Mol Med Rep. 2019;20:4415–24. https://doi.org/10.3892/mmr.2019.10696.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Bo L, Wei B, Wang Z, Li C, Gao Z, Miao Z. Bioinformatic analysis of gene expression profiling of intracranial aneurysm. Mol Med Rep. 2018;17:3473–80. https://doi.org/10.3892/mmr.2017.8367.

    Article  CAS  PubMed  Google Scholar 

  46. Zhu H, Tan J, Zhao Y, Wang Z, Wu Z, Li M. Potential role of the chemotaxis system in formation and progression of intracranial aneurysms through weighted gene co-expression network analysis. Int J Gen Med. 2022;15:2217–31. https://doi.org/10.2147/IJGM.S347420.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Jiang Y, Leng J, Lin Q, Zhou F. Epithelial-mesenchymal transition related genes in unruptured aneurysms identified through weighted gene coexpression network analysis. Sci Rep. 2022;12:225. https://doi.org/10.1038/s41598-021-04390-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Zheng X, Xue C, Luo G, Hu Y, Luo W, Sun X. Identification of crucial genes in intracranial aneurysm based on weighted gene coexpression network analysis. Cancer Gene Ther. 2015;22:238–45. https://doi.org/10.1038/cgt.2015.10.

    Article  CAS  PubMed  Google Scholar 

  49. Chen L, Fan Y, Wan J. Screening of key genes of unruptured intracranial aneurysms by using DNA microarray data analysis techniques. Genet Mol Res. 2014;13:758–67. https://doi.org/10.4238/2014.January.31.2.

    Article  CAS  PubMed  Google Scholar 

  50. Wei L, Gao YJ, Wei SP, Zhang YF, Zhang WF, Jiang JX, Sun ZY, Xu W. Transcriptome network-based method to identify genes associated with unruptured intracranial aneurysms. Genet Mol Res. 2013;12:3263–73. https://doi.org/10.4238/2013.September.3.2.

    Article  CAS  PubMed  Google Scholar 

  51. Sun R, Zhou Y, Cui Q. Comparative analysis of aneurysm subtypes associated genes based on protein-protein interaction network. BMC Bioinformatics. 2021;22:587. https://doi.org/10.1186/s12859-021-04513-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Laarman MD, Kleinloog R, Bakker MK, Rinkel GJE, Bakkers J, Ruigrok YM. Assessment of the most optimal control tissue for intracranial aneurysm gene expression studies. Stroke. 2019;50:2933–6. https://doi.org/10.1161/STROKEAHA.119.024881.

    Article  CAS  PubMed  Google Scholar 

  53. Etminan N, Rinkel GJ. Unruptured intracranial aneurysms: development, rupture and preventive management. Nat Rev Neurol. 2016;12:699–713. https://doi.org/10.1038/nrneurol.2016.150.

    Article  PubMed  Google Scholar 

  54. Fordyce SL, Kampmann ML, van Doorn NL, Gilbert MT. Long-term RNA persistence in postmortem contexts. Investig Genet. 2013;4:7. https://doi.org/10.1186/2041-2223-4-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Vezzani A, French J, Bartfai T, Baram TZ. The role of inflammation in epilepsy. Nat Rev Neurol. 2011;7:31–40. https://doi.org/10.1038/nrneurol.2010.178.

    Article  CAS  PubMed  Google Scholar 

  56. Kanamori F, Yokoyama K, Ota A, Yoshikawa K, Karnan S, Maruwaka M, Shimizu K, Ota S, Uda K, Araki Y, et al. Transcriptome-wide analysis of intracranial artery in patients with moyamoya disease showing upregulation of immune response, and downregulation of oxidative phosphorylation and DNA repair. Neurosurg Focus. 2021;51:E3. https://doi.org/10.3171/2021.6.FOCUS20870.

    Article  PubMed  Google Scholar 

  57. Bakker MK, van der Spek RAA, van Rheenen W, Morel S, Bourcier R, Hostettler IC, Alg VS, van Eijk KR, Koido M, Akiyama M, et al. Genome-wide association study of intracranial aneurysms identifies 17 risk loci and genetic overlap with clinical risk factors. Nat Genet. 2020;52:1303–13. https://doi.org/10.1038/s41588-020-00725-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kleinloog R, Verweij BH, van der Vlies P, Deelen P, Swertz MA, de Muynck L, Van Damme P, Giuliani F, Regli L, van der Zwan A, et al. RNA sequencing analysis of intracranial aneurysm walls reveals involvement of lysosomes and immunoglobulins in rupture. Stroke. 2016;47:1286–93. https://doi.org/10.1161/STROKEAHA.116.012541.

    Article  CAS  PubMed  Google Scholar 

  59. Li F, Yan K, Wu L, Zheng Z, Du Y, Liu Z, Zhao L, Li W, Sheng Y, Ren L, et al. Single-cell RNA-seq reveals cellular heterogeneity of mouse carotid artery under disturbed flow. Cell Death Discov. 2021;7:180. https://doi.org/10.1038/s41420-021-00567-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Barbier M, Gross MS, Aubart M, Hanna N, Kessler K, Guo DC, Tosolini L, Ho-Tin-Noe B, Regalado E, Varret M, et al. MFAP5 loss-of-function mutations underscore the involvement of matrix alteration in the pathogenesis of familial thoracic aortic aneurysms and dissections. Am J Hum Genet. 2014;95:736–43. https://doi.org/10.1016/j.ajhg.2014.10.018.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Sanchez-Infantes D, Nus M, Navas-Madronal M, Fite J, Perez B, Barros-Membrilla AJ, Soto B, Martinez-Gonzalez J, Camacho M, Rodriguez C, et al. Oxidative stress and inflammatory markers in abdominal aortic aneurysm. Antioxidants (Basel). 2021;10:602. https://doi.org/10.3390/antiox10040602.

  62. Hendrix P, Foreman PM, Harrigan MR, Fisher WS 3rd, Vyas NA, Lipsky RH, Lin M, Walters BC, Tubbs RS, Shoja MM, et al. Association of plasminogen activator inhibitor 1 (SERPINE1) polymorphisms and aneurysmal subarachnoid hemorrhage. World Neurosurg. 2017;105:672–7. https://doi.org/10.1016/j.wneu.2017.05.175.

    Article  PubMed  Google Scholar 

  63. Lin M, Griessenauer CJ, Starke RM, Tubbs RS, Shoja MM, Foreman PM, Vyas NA, Walters BC, Harrigan MR, Hendrix P, et al. Haplotype analysis of SERPINE1 gene: risk for aneurysmal subarachnoid hemorrhage and clinical outcomes. Mol Genet Genomic Med. 2019;7:e737. https://doi.org/10.1002/mgg3.737.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Denny JC, Ritchie MD, Basford MA, Pulley JM, Bastarache L, Brown-Gentry K, Wang D, Masys DR, Roden DM, Crawford DC. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics. 2010;26:1205–10. https://doi.org/10.1093/bioinformatics/btq126.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Denny JC, Bastarache L, Ritchie MD, Carroll RJ, Zink R, Mosley JD, Field JR, Pulley JM, Ramirez AH, Bowton E, et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol. 2013;31:1102–10. https://doi.org/10.1038/nbt.2749.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank Mel Feany for facilitating the procurement of autopsy vessels.

Funding

This study was supported by grants from the NIH (1R01 NS105675).

NIH,1R01 NS105675

Author information

Authors and Affiliations

  1. Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA

    Jianing Zhang, Jee-Yeon Ryu, Selena-Rae Tirado, M. Ali Aziz-Sultan, G. Rees Cosgrove, Kai U. Frerichs, Nirav J. Patel & Rose Du

  2. Department of Neurosurgery, Sutter Health, Danville, CA, USA

    Lawrence D. Dickinson & Tamar R. Binyamin

  3. Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, USA

    Aviva Abosch

  4. Department of Neurosurgery, Albany Medical Center, Albany, NY, USA

    Alan S. Boulos

  5. Department of Neurosurgery, Emory University, Atlanta, GA, USA

    Daniel L. Barrow & Brian M. Howard

  6. Department of Neurosurgery, University of Texas Southwestern, Dallas, TX, USA

    H. Hunt Batjer & Babu G. Welch

  7. Department of Neurosurgery, University of Texas Health Science Center, Houston, TX, USA

    Spiros L. Blackburn, P. Roc Chen & Arthur L. Day

  8. Department of Neurosurgery, University of California San Francisco, San Francisco, CA, USA

    Edward F. Chang

  9. Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA

    Edward F. Chang, Geoffrey P. Colby & Anthony C. Wang

  10. Department of Neurosurgery, University of North Carolina Chapel Hill, Chapel Hill, NC, USA

    Carlos A. David

  11. Department of Forensic Medicine, New York University Grossman School of Medicine, New York, NY, USA

    Rebecca D. Folkerth

  12. Department of Neurosurgery, Helsinki University and Helsinki University Hospital, Helsinki, Finland

    Behnam R. Jahromi & Mika Niemela

  13. Department of Neurosurgery, University of Colorado, Denver, CO, USA

    Steven G. Ojemann

  14. Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA

    R. Mark Richardson & Ziv Williams

  15. Department of Neurosurgery, Affiliated Fuxing Hospital, Capital Medical University, Beijing, China

    Xiangen Shi

  16. Department of Neurosurgery, NCH Baker Hospital, Naples, FL, USA

    Edison P. Valle-Giler

  17. Neuroscience Partners, Moraga, Oakland, CA, USA

    Edie E. Zusman

  18. Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Scott T. Weiss

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  1. Jianing Zhang
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GENIE Investigators

Contributions

JZ analyzed the data and drafted the initial manuscript. JYR and ST processed samples. RD designed and supervised the study, and obtained data and samples. LDD, AB, MAA, ASB, DLB, HHB, TRB, SLB, EFC, PRC, GPC, GRC, CAD, ALD, RDF, KUF, BMH, BRJ, MN, SGO, NJP, RMR, XS, EPV, ACW, BGW, ZW, EEZ obtained samples. All authors critically reviewed the manuscript and data, and approved the final manuscript version.

Corresponding author

Correspondence to Rose Du.

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Competing Interests

MAA is a proctor for Covidien and Codman. The other authors report no conflicts.

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Zhang, J., Ryu, JY., Tirado, SR. et al. A Transcriptomic Comparative Study of Cranial Vasculature. Transl. Stroke Res. (2023). https://doi.org/10.1007/s12975-023-01186-w

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  • DOI: https://doi.org/10.1007/s12975-023-01186-w

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