Skip to main content
SpringerLink
Log in

Genetic variation in retinal vascular patterning predicts variation in pial collateral extent and stroke severity

  • Original Paper
  • Published:
Angiogenesis Aims and scope Submit manuscript

Abstract

The presence of a native collateral circulation in tissues lessens injury in occlusive vascular diseases. However, differences in genetic background cause wide variation in collateral number and diameter in mice, resulting in large variation in protection. Indirect estimates of collateral perfusion suggest that wide variation also exists in humans. Unfortunately, methods used to obtain these estimates are invasive and not widely available. We sought to determine whether differences in genetic background in mice result in variation in branch patterning of the retinal arterial circulation, and whether these differences predict strain-dependent differences in pial collateral extent and severity of ischemic stroke. Retinal patterning metrics, collateral extent, and infarct volume were obtained for 10 strains known to differ widely in collateral extent. Multivariate regression was conducted, and model performance was assessed using K-fold cross-validation. Twenty-one metrics varied with strain (p < 0.01). Ten metrics (e.g., bifurcation angle, lacunarity, optimality) predicted collateral number and diameter across seven regression models, with the best model closely predicting (p < 0.0001) number (±1.2–3.4 collaterals, K-fold R 2 = 0.83–0.98), diameter (±1.2–1.9 μm, R 2 = 0.73–0.88), and infarct volume (±5.1 mm3, R 2 = 0.85–0.87). An analogous set of the most predictive metrics, obtained for the middle cerebral artery (MCA) tree in a subset of the above strains, also predicted (p < 0.0001) collateral number (±3.3 collaterals, K-fold R 2 = 0.78) and diameter (±1.6 μm, R2  = 0.86). Thus, differences in arterial branch patterning in the retina and the MCA trees are specified by genetic background and predict variation in collateral extent and stroke severity. If also true in human, and since genetic variation in cerebral collaterals extends to other tissues at least in mice, a similar “retinal predictor index” could serve as a non- or minimally invasive biomarker for collateral extent in brain and other tissues. This could aid prediction of severity of tissue injury in the event of an occlusive event or development of obstructive disease and in patient stratification for treatment options and clinical studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abbreviations

A:

Artery

ACA:

Anterior cerebral artery

AIC:

Akaike’s Information Criterion

AVR:

Artery-to-vein diameter ratio

B6:

The C57BL/6 inbred mouse strain

BIC:

Bayesian Information Criterion

CAD:

Coronary artery disease

CFI:

Collateral flow index

COL:

Collateral

COL-D:

Average diameter of all pial collaterals between the MCA and ACA trees

COL-N:

The number of pial collaterals between the MCA and ACA trees

CRAE:

Central retinal artery equivalent

CRVE:

Central retinal vein equivalent

E1:

Embryonic day 1 (2, 3…etc.)

FD:

Fractal dimension

IZ:

Inner zone (of the retina)

IQR:

Interquartile range

Lac:

Lacunarity

MCA:

Middle cerebral artery

MCAO:

MCA occlusion

MV:

Marginal vein of the retina

OD:

Optic disk

OZ:

Outer zone (of the retina)

P1:

Postnatal day 1 (2, 3…etc.)

PAD:

Peripheral artery disease

PBS:

Phosphate-buffered saline

PFA:

Paraformaldehyde

RM:

Regression modeling (stepwise multivariate)

ROI:

Region of interest

RPIn or d :

Retinal predictor index for collateral number or diameter

RPM:

Retinal patterning metric

V:

Vein

References

  1. Shuaib A, Butcher K, Mohammad AA, Saqqur M, Liebeskind DS (2011) Collateral blood vessels in acute ischaemic stroke: a potential therapeutic target. Lancet Neurol 10:909–921

    Article  PubMed  Google Scholar 

  2. Meier P, Hemingway H, Lansky AJ, Knapp G, Pitt B, Seiler C (2012) The impact of the coronary collateral circulation on mortality: a meta-analysis. Eur Heart J 33:614–621

    Article  PubMed  Google Scholar 

  3. Zhang H, Prabhakar P, Sealock RW, Faber JE (2010) Wide genetic variation in the native pial collateral circulation is a major determinant of variation in severity of stroke. J Cerebral Blood Flow Metab 30:923–934

    Article  CAS  Google Scholar 

  4. Chalothorn D, Faber JE (2010) Strain-dependent variation in native collateral function in mouse hindlimb. Physiol Genomics 42:469–479

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  5. Christoforidis GA, Karakasis C, Mohammad Y, Caragine LP, Yang M, Slivka AP (2009) Predictors of hemorrhage following intra-arterial thrombolysis for acute ischemic stroke: the role of pial collateral formation. AJNR Am J Neuroradiol 30:165–170

    Article  CAS  PubMed  Google Scholar 

  6. Miteff F, Levi CR, Bateman GA, Spratt N, McElduff P, Parsons MW (2009) The independent predictive utility of computed tomography angiographic collateral status in acute ischaemic stroke. Brain 132:2231–2238

    Article  PubMed  Google Scholar 

  7. Lima FO, Furie KL, Silva GS, Lev MH, Camargo EC, Singhal AB, Harris GJ, Halpern EF, Koroshetz WJ, Smith WS, Yoo AJ, Nogueira RG (2010) The pattern of leptomeningeal collaterals on CT angiography is a strong predictor of long-term functional outcome in stroke patients with large vessel intracranial occlusion. Stroke 41:2316–2322

    Article  PubMed  Google Scholar 

  8. Jung S, Gilgen M, Slotboom J, El-Koussy M, Zubler C, Kiefer C, Luedi R, Mono ML, Heldner MR, Weck A, Mordasini P, Schroth G, Mattle HP, Arnold M, Gralla J, Fischer U (2013) Factors that determine penumbral tissue loss in acute ischaemic stroke. Brain 136:3554–3560

    Article  PubMed  Google Scholar 

  9. Nambiar V, Sohn SI, Almekhlafi MA, Chang HW, Mishra S, Qazi E, Eesa M, Demchuk AM, Goyal M, Hill MD, Menon BK (2014) Collateral status and response to recanalization in patients with acute ischemic stroke. AJNR Am J Neuroradiol 35:884–890

    Article  CAS  PubMed  Google Scholar 

  10. Meier P, Gloekler S, Zbinden R, Beckh S, de Marchi SF, Zbinden S, Wustmann K, Billinger M, Vogel R, Cook S, Wenaweser P, Togni M, Windecker S, Meier B, Seiler C (2007) Beneficial effect of recruitable collaterals: a 10-year follow-up study in patients with stable coronary artery disease undergoing quantitative collateral measurements. Circulation 116:975–983

    Article  PubMed  Google Scholar 

  11. Traupe T, Ortmann J, Stoller M, Baumgartner I, de Marchi SF, Seiler C (2013) Direct quantitative assessment of the peripheral artery collateral circulation in patients undergoing angiography. Circulation 128:737–744

    Article  PubMed  Google Scholar 

  12. Wang S, Zhang H, Wiltshire T, Sealock R, Faber JE (2012) Genetic dissection of the Canq1 locus governing variation in extent of the collateral circulation. PLoS One 7:31910

    Article  Google Scholar 

  13. Keum S, Marchuk DA (2009) A locus mapping to mouse chromosome 7 determines infarct volume in a mouse model of ischemic stroke. Circ Cardiovasc Genet 2:591–598

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  14. Sealock R (2014) Zhang, Lucitti J, Moore S, Faber J. Congenic fine-mapping identifies a major causal locus for variation in the native collateral circulation and ischemic injury in brain and lower extremity. Circ Res 114:660–671

    Article  CAS  PubMed  Google Scholar 

  15. Lee Y, Menon B, Huang D, Wilhelmsen K, Jovin T, Parsons M, Ribo M, Selim M, Sheth K, Al-Ali F, Marshall R, Shuaib A, Demchuk A, Powers W, Liebeskind D, Faber J (2014) GENEtic Determinants of Collateral Status in Stroke—The GENEDCSS Study. AHA/ASA International Stroke Conference

  16. Chalothorn D, Faber JE (2010) Formation and maturation or the murine native cerebral collateral circulation. J Molec Cell Cardiol 49:251–259

    Article  CAS  Google Scholar 

  17. Lucitti JL, Mackey J, Morrison JC, Haigh JJ, Adams RH, Faber JE (2012) Formation of the collateral circulation is regulated by vascular endothelial growth factor-A and A disintegrin and metalloprotease family members 10 and 17. Circ Res 111:1539–1550

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Chalothorn D, Clayton JA, Zhang H, Pomp D, Faber JE (2007) Collateral density, remodeling and VEGF-A expression differ widely between mouse strains. Physiol Genomics 30:179–191

    Article  CAS  PubMed  Google Scholar 

  19. Clayton JA, Chalothorn D, Faber JE (2008) Vascular endothelial growth factor-A specifies formation of native collaterals and regulates collateral growth in ischemia. Circ Res 103:1027–1036

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  20. Chalothorn D, Zhang H, Smith JE, Edwards JC, Faber JE (2009) Chloride intracellular channel-4 is a determinant of native collateral formation in skeletal muscle and brain. Circ Res 105:89–98

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  21. Dai X, Faber JE (2010) eNOS deficiency causes collateral vessel rarefaction and impairs activation of a cell cycle gene network during arteriogenesis. Circ Res 106:1870–1881

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. MacGabhann F, Peirce SM (2010) Collateral capillary arterialization following ligation in murine skeletal muscle. Microcirc. 17:333–347

    Google Scholar 

  23. Moore SM, Waters MR, Faber JE (2013) Hypertension and other cardiovascular risk factors lead to premature rarefaction of the native collateral circulation. J Vasc Surg 57(5):79

    Article  Google Scholar 

  24. Liew G, Wang JJ, Mitchell P, Wong TY (2008) Retinal vascular imaging. Circ Cardiovasc Imaging 1:156–161

    Article  PubMed  Google Scholar 

  25. Patton N, Aslam T, MacGillivray T, Pattie A, Deary IJ, Dhillon B (2005) Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures. J Anat 206:319–348

    Article  PubMed Central  PubMed  Google Scholar 

  26. Witt N, Wong TY, Hughes AD, Chaturvedi N, Klein BE, Evans R, McNamara M, Thom SA, Klein R (2006) Abnormalities of retinal microvascular structure and risk of mortality from ischemic heart disease and stroke. Hypertension 47:975–981

    Article  CAS  PubMed  Google Scholar 

  27. Patton N, Aslam TM, MacGillivray T, Deary IJ, Dhillon B, Eikelboom RH, Yogesan K, Constable IJ (2006) Retinal image analysis: concepts, applications and potential. Prog Retin Eye Res 25:99–127

    Article  PubMed  Google Scholar 

  28. Hughes AD, Wong TY, Witt N, Evans R, Thom SA, Klein BE, Chaturvedi N, Klein R (2009) Determinants of retinal microvascular architecture in normal subjects. Microcirculation 16:159–166

    Article  PubMed  Google Scholar 

  29. Witt NW, Chapman N, Thom SA, Stanton AV, Parker KH, Hughes AD (2010) A novel measure to characterize optimality of diameter relationships at retinal vascular bifurcations. Artery Res 4:75–80

    Article  PubMed Central  PubMed  Google Scholar 

  30. Knudtson MD, Lee KE, Hubbard LD, Wong TY, Klein R, Klein BE (2003) Revised formulas for summarizing retinal vessel diameters. Curr Eye Res 27:143–149

    Article  PubMed  Google Scholar 

  31. Arganda-Carreras I, Fernandez-Gonzalez R, Munoz-Barrutia A, Ortiz-De-Solorzano C (2010) 3D reconstruction of histological sections: application to mammary gland tissue. Microsc Res Tech 73:1019–1029

    Article  PubMed  Google Scholar 

  32. Steyerberg E (2009) Validation of prediction models. In: clinical prediction models. Springer Science + Business Media, LLC. Chapter 17, pp 299–311

  33. Doubal FN, MacGillivray TJ, Patton N, Dhillon B, Dennis MS, Wardlaw JM (2010) Fractal analysis of retinal vessels suggests that a distinct vasculopathy causes lacunar stroke. Neurology 74:1102–1107

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  34. Cristofaro B, Shi Y, Faria M, Suchting S, Leroyer AS, Trindade A, Duarte A, Zovein AC, Iruela-Arispe ML, Nih LR, Kubis N, Henrion D, Loufrani L, Todiras M, Schleifenbaum J, Gollasch M, Zhuang ZW, Simons M, Eichmann A, le Noble F (2013) Dll4-Notch signaling determines the formation of native arterial collateral networks and arterial function in mouse ischemia models. Development 140:1720–1729

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  35. Saint-Geniez M, D’Amore PA (2004) Development and pathology of the hyaloid, choroidal and retinal vasculature. Int J Dev Biol 48:1045–1058

    Article  PubMed  Google Scholar 

  36. Fruttiger M (2007) Development of the retinal vasculature. Angiogenesis 10:77–88

    Article  PubMed  Google Scholar 

  37. Gielecki J, Zurada A, Kozłowska H, Nowak D, Loukas M (2009) Morphometric and volumetric analysis of the middle cerebral artery in human fetuses. Acta Neurobiol Exp (Wars) 69:129–137

    Google Scholar 

  38. Okudera T, Ohta T, Huang YP, Yokota A (1988) Developmental and radiological anatomy of the superficial cerebral convexity vessels in the human fetus. J Neuroradiol 15:205–224

    CAS  PubMed  Google Scholar 

  39. Provis JM (2001) Development of the primate retinal vasculature. Prog Ret Eye Res. 20:799–821

    Article  CAS  Google Scholar 

  40. Fielder AR, Quinn GE (2005) Retinopathy of prematurity (chapter 51). In: Taylor D, Hoyt CS (eds) Pediatric ophthalmology and strabismus, 3rd edn. Elsevier Saunders, Edinburgh, pp 506–530

    Google Scholar 

  41. Faber JE, Dai X, Lucitti J (2011) Genetic and environmental mechanisms controlling formation and maintenance of the native collateral circulation ch 1. In: Deindl IE, Schaper W (eds) Arteriogenesis—molecular regulation, pathophysiology and therapeutics. Shaker Verlag, Aachen, pp 1–22

    Google Scholar 

  42. Fahy SJ, Sun C, Zhu G, Healey PR, Spector TD, Martin NG, Mitchell P, Wong TY, Mackey DA, Hammond CJ, Andrew T (2011) The relationship between retinal arteriolar and venular calibers is genetically mediated, and each is associated with risk of cardiovascular disease. Invest Ophthalmol Vis Sci 52:975–981

    Article  PubMed Central  PubMed  Google Scholar 

  43. Taarnhoj NC, Larsen M, Sander B, Kyvik KO, Kessel L, Hougaard JL, Sørensen TI (2006) Heritability of retinal vessel diameters and blood pressure: a twin study. Invest Opthalmol Vis Sci 47:3539–3544

    Article  Google Scholar 

  44. Lee KE, Klein BE, Klein R, Knudtson MD (2004) Familial aggregation of retinal vessel caliber in the beaver dam eye study. Invest Ophthalmol Vis Sci 45:3929–3933

    Article  PubMed  Google Scholar 

  45. Liu YP, Kuznetsova T, Jin Y, Thijs L, Asayama K, Gu YM, Bochud M, Verhamme P, Struijker-Boudier HA, Staessen JA (2013) Heritability of the retinal microcirculation in Flemish families. Am J Hypertens 26:392–399

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  46. Taarnhoj NC, Munch IC, Sander B, Kessel L, Hougaard JL, Kyvik K, Sørensen TI, Larsen M (2008) Straight versus tortuous retinal arteries in relation to blood pressure and genetics. Br J Ophthalmol 92:1055–1060

    Article  CAS  PubMed  Google Scholar 

  47. Ikram MK, Sim X, Jensen RA, Cotch MF, Hewitt AW, Ikram MA, Wang JJ, Klein R, Klein BE, Breteler MM, Cheung N, Liew G, Mitchell P, Uitterlinden AG, Rivadeneira F, Hofman A, de Jong PT, van Duijn CM, Kao L, Cheng CY, Smith AV, Glazer NL, Lumley T, McKnight B, Psaty BM, Jonasson F, Eiriksdottir G, Aspelund T, Global BPgen Consortium, Harris TB, Launer LJ, Taylor KD, Li X, Iyengar SK, Xi Q, Sivakumaran TA, Mackey DA, Macgregor S, Martin NG, Young TL, Bis JC, Wiggins KL, Heckbert SR, Hammond CJ, Andrew T, Fahy S, Attia J, Holliday EG, Scott RJ, Islam FM, Rotter JI, McAuley AK, Boerwinkle E, Tai ES, Gudnason V, Siscovick DS, Vingerling JR, Wong TY (2010) Four novel Loci (19q13, 6q24, 12q24, and 5q14) influence the microcirculation in vivo. PLoS Genet 6:e1001184

    Article  PubMed Central  PubMed  Google Scholar 

  48. Wong TY, Klein R, Couper DJ, Cooper LS, Shahar E, Hubbard LD, Wofford MR, Sharrett AR (2001) Retinal microvascular abnormalities and incident stroke: the atherosclerosis risk in communities study. Lancet 358(9288):1134–1140

    Article  CAS  PubMed  Google Scholar 

  49. Doubal FN, Hokke PE, Wardlaw JM (2009) Retinal microvascular abnormalities and stroke: a systematic review. J Neurol Neurosurg Psychiatr 80:158–165

    Article  CAS  PubMed  Google Scholar 

  50. Lindley RI, Wang JJ, Wong M, Mitchell P, Liew G, Hand P, Wardlaw J, De Silva DA, Baker M, Rochtchina E, Chen C, Hankey GJ, Chang HM, Fung VS, Gomes L, Wong TY (2009) Retinal microvasculature in acute lacunar stroke: a cross-sectional study. Lancet Neurol 8:628–634

    Article  PubMed  Google Scholar 

  51. Kwa VI, Lopez OL (2010) Fractal analysis of retinal vessels: peeping at the tree of life? Neurology 74:1088–1089

    Article  PubMed  Google Scholar 

  52. Faber JE, Zhang H, Lassance-Soares RM, Prabhakar P, Najafi AH, Burnett MS, Epstein SE (2011) Aging causes collateral rarefaction and increased severity of ischemic injury in multiple tissues. Arterioscler Thromb Vasc Biol 31:1748–1756

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  53. Menon BK, Smith EE, Coutts SB, Welsh DG, Faber JE, Damani Z, Goyal M, Hill MD, Demchuk AM, Hee Cho K-H, Chang H-W, Hong J-H, Sohn SI (2013) Leptomeningeal collaterals are associated with modifiable metabolic risk factors. Ann Neurol 74:241–248

    CAS  PubMed Central  PubMed  Google Scholar 

  54. Threadgill DW, Miller DR, Churchill GA, de Villena FP (2011) The collaborative cross: a recombinant inbred mouse population for the systems genetic era. ILAR J 52:24–31

    Article  CAS  PubMed  Google Scholar 

  55. Smolock EM, Ilyushkina IA, Ghazalpour A, Gerloff J, Murashev AN, Lusis AJ, Korshunov VA (2012) Genetic locus on mouse chromosome 7 controls elevated heart rate. Physiol Genomics 44:689–698

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  56. Stanton AV, Wasan B, Cerutti A, Ford S, Marsh R, Sever PP, Thom SA, Hughes AD (1995) Vascular network changes in the retina with age and hypertension. J Hypertens 13:1724–1728

    Article  CAS  PubMed  Google Scholar 

  57. de Marchi SF, Gloekler S, Meier P, Traupe T, Steck H, Cook S, Vogel R, Seiler C (2011) Determinants of preformed collateral vessels in the human heart without coronary artery disease. Cardiology 118:198–206

    Article  PubMed  Google Scholar 

  58. Vickerman MB, Keith PA, McKay TL, Gedeon DJ, Watanabe M, Montano M, Karunamuni G, Kaiser PK, Sears JE, Ebrahem Q, Ribita D, Hylton AG, Parsons-Wingerter P (2009) VESGEN 2D: automated, user-interactive software for quantification and mapping of angiogenic and lymphangiogenic trees and networks. Anat Rec (Hoboken) 292:320–332

    Article  PubMed Central  Google Scholar 

  59. Cheung CY, Hsu W, Lee ML, Wang JJ, Mitchell P, Lau QP, Hamzah H, Ho M, Wong TY (2010) A new method to measure peripheral retinal vascular caliber over an extended area. Microcirculation 17:495–503

    PubMed  Google Scholar 

  60. Calleja AI, Cortijo E, García-Bermejo P, Gómez RD, Pérez-Fernández S, Del Monte JM, Muñoz MF, Fernández-Herranz R, Arenillas JF (2013) Collateral circulation on perfusion-computed tomography-source images predicts the response to stroke intravenous thrombolysis. Eur J Neurol 20:795–802

    Article  CAS  PubMed  Google Scholar 

  61. Wang S, Zhang H, Dai X, Sealock R, Faber JE (2010) Genetic architecture underlying variation in extent and remodeling of the collateral circulation. Circ Res 107:558–568

    Article  CAS  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgments

We thank Dr. Stephen Lockett, director of the Optical Microscopy and Analysis Laboratory Leidos Biomedical Research, Inc, Frederick National Laboratory for Cancer Research for supporting Dr. Chen’s contributions, Dr. Mary Hartnett and Nicarter Gordon for advice on retinal flat-mounting, Jonathan Alexander, Sadana Rangarao, Deepanshu Singh, and Dr. Siddharth Srivastava for assistance in image analysis, and Dr. Jennifer Lucitti for providing VEGFAhi/+, VEGFAlo/+, CD1 and CLIC4−/− mice. NIH Grants HL090655, HL111070 and NS083633 (J.E.F.), T35-DK007386 (P.P.). Dr. Chen’s role in this project has been funded in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.

Conflict of interest

None.

Author information

Authors and Affiliations

  1. Department of Cell Biology and Physiology, McAllister Heart Institute, 6309 MBRB, University of North Carolina, Chapel Hill, NC, 27599-7545, USA

    Pranay Prabhakar, Hua Zhang & James E. Faber

  2. Optical Microscopy and Analysis Laboratory Leidos Biomedical Research, Inc, Frederick National Laboratory for Cancer Research, Frederick, MD, USA

    De Chen

Authors
  1. Pranay Prabhakar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. De Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. James E. Faber
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to James E. Faber.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 2175 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prabhakar, P., Zhang, H., Chen, D. et al. Genetic variation in retinal vascular patterning predicts variation in pial collateral extent and stroke severity. Angiogenesis 18, 97–114 (2015). https://doi.org/10.1007/s10456-014-9449-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10456-014-9449-y

Keywords

Search

Navigation