Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Endocrine, Metabolic & Immune Disorders-Drug Targets (Formerly Current Drug Targets - Immune, Endocrine & Metabolic Disorders)
  4. Volume 24, Issue 14
  5. Article
Volume 24, Issue 14
  • ISSN: 1871-5303
  • E-ISSN: 2212-3873

Abstract

Background

Diabetic retinopathy (DR) is a major cause of vision loss in working-age individuals worldwide. Cell-to-cell communication between retinal cells and retinal pigment epithelial cells (RPEs) in DR is still unclear, so this study aimed to generate a single-cell atlas and identify receptor‒ligand communication between retinal cells and RPEs.

Methods

A mouse single-cell RNA sequencing (scRNA-seq) dataset was retrieved from the GEO database (GSE178121) and was further analyzed with the R package Seurat. Cell cluster annotation was performed to further analyze cell‒cell communication. The differentially expressed genes (DEGs) in RPEs were explored through pathway enrichment analysis and the protein‒protein interaction (PPI) network. Core genes in the PPI were verified by quantitative PCR in ARPE-19 cells.

Results

We observed an increased proportion of RPEs in STZ mice. Although some overall intercellular communication pathways did not differ significantly in the STZ and control groups, RPEs relayed significantly more signals in the STZ group. In addition, THBS1, ITGB1, COL9A3, ITGB8, VTN, TIMP2, and FBN1 were found to be the core DEGs of the PPI network in RPEs. qPCR results showed that the expression of ITGB1, COL9A3, ITGB8, VTN, TIMP2, and FBN1 was higher and consistent with scRNA-seq results in ARPE-19 cells under hyperglycemic conditions.

Conclusion

Our study, for the first time, investigated how signals that RPEs relay to and from other cells underly the progression of DR based on scRNA-seq. These signaling pathways and hub genes may provide new insights into DR mechanisms and therapeutic targets.

© 2024 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/emiddt/10.2174/0118715303286652240214110511
2024-02-21
2025-01-09

  • From This Site

    /content/journals/emiddt/10.2174/0118715303286652240214110511
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. W, B. The role of the gut microbiota in the pathogenesis of diabetes.Int. J. Mol. Sci.2022231480
     [Google Scholar]
  2. ElafrosM.A. Patient and health care provider knowledge of diabetes and diabetic microvascular complications: A comprehensive literature review.Rev. Endocr. Metab. Disord.202224222123910.1007/s11154‑022‑09754‑536322296
     [Google Scholar]
  3. SalzmannJ. LimbG.A. KhawP.T. GregorZ.J. WebsterL. ChignellA.H. CharterisD.G. Matrix metalloproteinases and their natural inhibitors in fibrovascular membranes of proliferative diabetic retinopathy.Br. J. Ophthalmol.200084101091109610.1136/bjo.84.10.109111004090
     [Google Scholar]
  4. WangW. LoA. Diabetic retinopathy: pathophysiology and treatments.Int. J. Mol. Sci.2018196181610.3390/ijms1906181629925789
     [Google Scholar]
  5. RoyS. KimD. LimR. Cell-cell communication in diabetic retinopathy.Vision Res.201713911512210.1016/j.visres.2017.04.01428583293
     [Google Scholar]
  6. HuangH. Pericyte-endothelial interactions in the retinal microvasculature.Int. J. Mol. Sci.20202119741310.3390/ijms2119741333049983
     [Google Scholar]
  7. LiuC. GeH.M. LiuB.H. DongR. ShanK. ChenX. YaoM.D. LiX.M. YaoJ. ZhouR.M. ZhangS.J. JiangQ. ZhaoC. YanB. Targeting pericyte–endothelial cell crosstalk by circular RNA-cPWWP2A inhibition aggravates diabetes-induced microvascular dysfunction.Proc. Natl. Acad. Sci.2019116157455746410.1073/pnas.181487411630914462
     [Google Scholar]
  8. KimD. MouritzenU. LarsenB.D. RoyS. Inhibition of Cx43 gap junction uncoupling prevents high glucose-induced apoptosis and reduces excess cell monolayer permeability in retinal vascular endothelial cells.Exp. Eye Res.2018173859010.1016/j.exer.2018.05.00329750972
     [Google Scholar]
  9. OkuH. KodamaT. SakagamiK. PuroD.G. Diabetes-induced disruption of gap junction pathways within the retinal microvasculature.Invest. Ophthalmol. Vis. Sci.20014281915192011431461
     [Google Scholar]
  10. MutoT. TienT. KimD. SarthyV.P. RoyS. High glucose alters Cx43 expression and gap junction intercellular communication in retinal Müller cells: promotes Müller cell and pericyte apoptosis.Invest. Ophthalmol. Vis. Sci.20145574327433710.1167/iovs.14‑1460624938518
     [Google Scholar]
  11. González-CasanovaJ. SchmachtenbergO. MartínezA.D. SanchezH.A. HarchaP.A. Rojas-GomezD. An update on connexin gap junction and hemichannels in diabetic retinopathy.Int. J. Mol. Sci.2021226319410.3390/ijms2206319433801118
     [Google Scholar]
  12. TienT. MutoT. ZhangJ. SohnE.H. MullinsR.F. RoyS. Association of reduced Connexin 43 expression with retinal vascular lesions in human diabetic retinopathy.Exp. Eye Res.201614610310610.1016/j.exer.2015.12.01126738943
     [Google Scholar]
  13. TienT. BarretteK.F. ChronopoulosA. RoyS. Effects of high glucose-induced Cx43 downregulation on occludin and ZO-1 expression and tight junction barrier function in retinal endothelial cells.Invest. Ophthalmol. Vis. Sci.201354106518652510.1167/iovs.13‑1176324008412
     [Google Scholar]
  14. SatoT. HaimoviciR. KaoR. LiA.F. RoyS. Downregulation of connexin 43 expression by high glucose reduces gap junction activity in microvascular endothelial cells.Diabetes20025151565157110.2337/diabetes.51.5.156511978657
     [Google Scholar]
  15. TuZ. LiY. SmithD.S. SheibaniN. HuangS. KernT. LinF. Retinal pericytes inhibit activated T cell proliferation.Invest. Ophthalmol. Vis. Sci.201152129005901010.1167/iovs.11‑800822003106
     [Google Scholar]
  16. XiaT. RizzoloL.J. Effects of diabetic retinopathy on the barrier functions of the retinal pigment epithelium.Vision Res.2017139728110.1016/j.visres.2017.02.00628347688
     [Google Scholar]
  17. PonnalaguM. SubramaniM. JayadevC. ShettyR. DasD. Retinal pigment epithelium-secretome: A diabetic retinopathy perspective.Cytokine20179512613510.1016/j.cyto.2017.02.01328282610
     [Google Scholar]
  18. YangJ. LiY. HanY. FengY. ZhouM. ZongC. HeX. JiaR. XuX. FanJ. Single-cell transcriptome profiling reveals intratumoural heterogeneity and malignant progression in retinoblastoma.Cell Death Dis.20211212110010.1038/s41419‑021‑04390‑434815392
     [Google Scholar]
  19. VallejoJ. CochainC. ZerneckeA. LeyK. Heterogeneity of immune cells in human atherosclerosis revealed by scRNA-Seq.Cardiovasc. Res.202111713cvab26010.1093/cvr/cvab26034343272
     [Google Scholar]
  20. ZhengK. LinL. JiangW. ChenL. ZhangX. ZhangQ. RenY. HaoJ. Single-cell RNA-seq reveals the transcriptional landscape in ischemic stroke.J. Cereb. Blood Flow Metab.2022421567310.1177/0271678X21102677034496660
     [Google Scholar]
  21. XiaD. WangY. XiaoY. LiW. Applications of single-cell RNA sequencing in atopic dermatitis and psoriasis.Front. Immunol.202213103874410.3389/fimmu.2022.103874436505405
     [Google Scholar]
  22. ZhangM.G. KuznetsoffJ.N. OwensD.A. GalloR.A. KalahastyK. CruzA.M. KurtenbachS. CorreaZ.M. PelaezD. HarbourJ.W. Early mechanisms of chemoresistance in retinoblastoma.Cancers 20221419496610.3390/cancers1419496636230889
     [Google Scholar]
  23. VoigtA.P. MulfaulK. MullinN.K. Flamme-WieseM.J. GiacaloneJ.C. StoneE.M. TuckerB.A. ScheetzT.E. MullinsR.F. Single-cell transcriptomics of the human retinal pigment epithelium and choroid in health and macular degeneration.Proc. Natl. Acad. Sci.201911648241002410710.1073/pnas.191414311631712411
     [Google Scholar]
  24. MenonM. MohammadiS. Davila-VelderrainJ. GoodsB.A. CadwellT.D. XingY. Stemmer-RachamimovA. ShalekA.K. LoveJ.C. KellisM. HaflerB.P. Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration.Nat. Commun.2019101490210.1038/s41467‑019‑12780‑831653841
     [Google Scholar]
  25. Van HoveI. De GroefL. BoeckxB. ModaveE. HuT.T. BeetsK. EtienneI. Van BergenT. LambrechtsD. MoonsL. FeyenJ.H.M. PorcuM. Single-cell transcriptome analysis of the Akimba mouse retina reveals cell-type-specific insights into the pathobiology of diabetic retinopathy.Diabetologia202063102235224810.1007/s00125‑020‑05218‑032734440
     [Google Scholar]
  26. HuZ. MaoX. ChenM. WuX. ZhuT. LiuY. ZhangZ. FanW. XieP. YuanS. LiuQ. Single-cell transcriptomics reveals novel role of microglia in fibrovascular membrane of proliferative diabetic retinopathy.Diabetes202271476277310.2337/db21‑055135061025
     [Google Scholar]
  27. LvK. YingH. HuG. HuJ. JianQ. ZhangF. Integrated multi-omics reveals the activated retinal microglia with intracellular metabolic reprogramming contributes to inflammation in STZ-induced early diabetic retinopathy.Front. Immunol.20221394276810.3389/fimmu.2022.94276836119084
     [Google Scholar]
  28. NiuT. FangJ. ShiX. ZhaoM. XingX. WangY. ZhuS. LiuK. Pathogenesis study based on high-throughput single-cell sequencing analysis reveals novel transcriptional landscape and heterogeneity of retinal cells in type 2 diabetic mice.Diabetes20217051185119710.2337/db20‑083933674409
     [Google Scholar]
  29. SunL. WangR. HuG. LiuH. LvK. DuanY. ShenN. WuJ. HuJ. LiuY. JinQ. ZhangF. XuX. Single cell RNA sequencing (scRNA-Seq) deciphering pathological alterations in streptozotocin-induced diabetic retinas.Exp. Eye Res.202121010871810.1016/j.exer.2021.10871834364890
     [Google Scholar]
  30. JisnaV.A. JayarajP.B. Protein structure prediction: conventional and deep learning perspectives.Protein J.202140452254410.1007/s10930‑021‑10003‑y34050498
     [Google Scholar]
  31. McDermaidA. MonierB. ZhaoJ. LiuB. MaQ. Interpretation of differential gene expression results of RNA-seq data: review and integration.Brief. Bioinform.20192062044205410.1093/bib/bby06730099484
     [Google Scholar]
  32. TomarN. DeR.K. Comparing methods for metabolic network analysis and an application to metabolic engineering.Gene2013521111410.1016/j.gene.2013.03.01723537990
     [Google Scholar]
  33. IssaN.T. StathiasV. SchürerS. DakshanamurthyS. Machine and deep learning approaches for cancer drug repurposing.Semin. Cancer Biol.20216813214210.1016/j.semcancer.2019.12.01131904426
     [Google Scholar]
  34. QianX.B. ChenT. XuY.P. ChenL. SunF.X. LuM.P. LiuY.X. A guide to human microbiome research: Study design, sample collection, and bioinformatics analysis.Chin. Med. J.2020133151844185510.1097/CM9.000000000000087132604176
     [Google Scholar]
  35. HuangY. PengJ. LiangQ. Identification of key ferroptosis genes in diabetic retinopathy based on bioinformatics analysis.PLoS One2023181e028054810.1371/journal.pone.028054836689408
     [Google Scholar]
  36. YoungbloodH. RobinsonR. SharmaA. SharmaS. Proteomic biomarkers of retinal inflammation in diabetic retinopathy.Int. J. Mol. Sci.20192019475510.3390/ijms2019475531557880
     [Google Scholar]
  37. WangN. WeiL. LiuD. ZhangQ. XiaX. DingL. XiongS. Identification and validation of autophagy-related genes in diabetic retinopathy.Front. Endocrinol.20221386760010.3389/fendo.2022.86760035574010
     [Google Scholar]
  38. WangN. DingL. LiuD. ZhangQ. ZhengG. XiaX. XiongS. Molecular investigation of candidate genes for pyroptosis-induced inflammation in diabetic retinopathy.Front. Endocrinol.20221391860510.3389/fendo.2022.91860535957838
     [Google Scholar]
  39. KaurS. BronsonS.M. Pal-NathD. MillerT.W. Soto-PantojaD.R. RobertsD.D. Functions of thrombospondin-1 in the tumor microenvironment.Int. J. Mol. Sci.2021229457010.3390/ijms2209457033925464
     [Google Scholar]
  40. MesquitaJ. Castro-de-SousaJ.P. Vaz-PereiraS. NevesA. PassarinhaL.A. TomazC.T. Vascular endothelial growth factors and placenta growth factor in retinal vasculopathies: Current research and future perspectives.Cytokine Growth Factor Rev.20183910211510.1016/j.cytogfr.2017.11.00529248329
     [Google Scholar]
  41. UemuraA. FruttigerM. D’AmoreP.A. De FalcoS. JoussenA.M. SennlaubF. BrunckL.R. JohnsonK.T. LambrouG.N. RittenhouseK.D. LangmannT. VEGFR1 signaling in retinal angiogenesis and microinflammation.Prog. Retin. Eye Res.20218410095410.1016/j.preteyeres.2021.10095433640465
     [Google Scholar]
  42. WuG. MaZ. ChengY. HuW. DengC. JiangS. LiT. ChenF. YangY. Targeting Gas6/TAM in cancer cells and tumor microenvironment.Mol. Cancer20181712010.1186/s12943‑018‑0769‑129386018
     [Google Scholar]
  43. BiasellaF. StrunzT. KielC. WeberB.H.F. FriedrichU. Vitronectin and its interaction with PAI-1 suggests a functional link to vascular changes in amd pathobiology.Cells20221111176610.3390/cells1111176635681461
     [Google Scholar]
  44. FilippouP.S. KaragiannisG.S. ConstantinidouA. Midkine (MDK) growth factor: A key player in cancer progression and a promising therapeutic target.Oncogene202039102040205410.1038/s41388‑019‑1124‑831801970
     [Google Scholar]
  45. JeonC.J. StrettoiE. MaslandR.H. The major cell populations of the mouse retina.J. Neurosci.199818218936894610.1523/JNEUROSCI.18‑21‑08936.19989786999
     [Google Scholar]
  46. SpencerB.G. EstevezJ.J. LiuE. CraigJ.E. FinnieJ.W. Pericytes, inflammation, and diabetic retinopathy.Inflammopharmacology202028369770910.1007/s10787‑019‑00647‑931612299
     [Google Scholar]
  47. YangJ. LiuZ. Mechanistic pathogenesis of endothelial dysfunction in diabetic nephropathy and retinopathy.Front. Endocrinol.20221381640010.3389/fendo.2022.81640035692405
     [Google Scholar]
  48. XiaM. JiaoL. WangX.H. TongM. YaoM.D. LiX.M. YaoJ. LiD. ZhaoP.Q. YanB. Single-cell RNA sequencing reveals a unique pericyte type associated with capillary dysfunction.Theranostics20231382515253010.7150/thno.8353237215579
     [Google Scholar]
  49. ZarkadaG. HowardJ.P. XiaoX. ParkH. BizouM. LeclercS. KünzelS.E. BoisseauB. LiJ. CagnoneG. JoyalJ.S. AndelfingerG. EichmannA. DubracA. Specialized endothelial tip cells guide neuroretina vascularization and blood-retina-barrier formation.Dev. Cell2021561522372251.e610.1016/j.devcel.2021.06.02134273276
     [Google Scholar]
  50. SanguinetiR. PudduA. NicolòM. TraversoC.E. CorderaR. VivianiG.L. MaggiD. miR-126 mimic counteracts the increased secretion of vegf-a induced by high glucose in ARPE-19 cells.J. Diabetes Res.202120211710.1155/2021/664922233709000
     [Google Scholar]
  51. ShinD.H. JoJ.Y. KimS.H. ChoiM. HanC. ChoiB.K. KimS.S. Midkine is a potential therapeutic target of tumorigenesis, angiogenesis, and metastasis in non-small cell lung cancer.Cancers2020129240210.3390/cancers1209240232847073
     [Google Scholar]
  52. ShengB. WeiZ. WuX. LiY. LiuZ. USP12 promotes breast cancer angiogenesis by maintaining midkine stability.Cell Death Dis.20211211107410.1038/s41419‑021‑04102‑y34759262
     [Google Scholar]
  53. KosugiT. YuzawaY. SatoW. KawaiH. MatsuoS. TakeiY. MuramatsuT. KadomatsuK. Growth factor midkine is involved in the pathogenesis of diabetic nephropathy.Am. J. Pathol.2006168191910.2353/ajpath.2006.05048816400005
     [Google Scholar]
  54. ErkocR. CikrikciogluM.A. AintabE. Erek ToprakA. KilicU. GokO. Yasin CetinA.I. ZorluM. KiskacM. CakircaM. ErkalS.N. IsenH.C. KaratoprakC. GAS6 intron 8 c.834 + 7G > A gene polymorphism in diabetic nephropathy.Ren. Fail.201537586687010.3109/0886022X.2015.103460625869052
     [Google Scholar]
  55. WuW. XuH. MengZ. ZhuJ. XiongS. XiaX. LeiH. Axl is essential for in vitro angiogenesis induced by vitreous from patients with proliferative diabetic retinopathy.Front. Med.2021878715010.3389/fmed.2021.78715035004753
     [Google Scholar]
  56. SaikO.V. KlimontovV.V. Bioinformatic reconstruction and analysis of gene networks related to glucose variability in diabetes and its complications.Int. J. Mol. Sci.20202122869110.3390/ijms2122869133217980
     [Google Scholar]
  57. LauwenS. BaerenfaengerM. RuigrokS. de JongE.K. WesselsH.J.C.T. den HollanderA.I. LefeberD.J. Loss of the AMD-associated B3GLCT gene affects glycosylation of TSP1 without impairing secretion in retinal pigment epithelial cells.Exp. Eye Res.202121310879810.1016/j.exer.2021.10879834695439
     [Google Scholar]
  58. ChenC.Y. MeloE. JakobP. FriedleinA. ElsässerB. GoettigP. KueppersV. N-Terminomics identifies HtrA1 cleavage of thrombospondin-1 with generation of a proangiogenic fragment in the polarized retinal pigment epithelial cell model of age-related macular degeneration.Matrix Biol.20187084101
     [Google Scholar]
  59. PopescuM. BogdanC. PinteaA. RuginăD. IonescuC. Antiangiogenic cytokines as potential new therapeutic targets for resveratrol in diabetic retinopathy.Drug Des. Devel. Ther.2018121985199610.2147/DDDT.S15694130013318
     [Google Scholar]
  60. Miyajima-UchidaH. HayashiH. BeppuR. KurokiM. FukamiM. Production and accumulation of thrombospondin-1 in human retinal pigment epithelial cells.Invest. Ophthalmol. Vis. Sci.2000412561567
     [Google Scholar]
  61. BhattacharyyaS. MarinicT.E. KrukovetsI. HoppeG. SteninaO.I. Cell type-specific post-transcriptional regulation of production of the potent antiangiogenic and proatherogenic protein thrombospondin-1 by high glucose.J. Biol. Chem.200828395699570710.1074/jbc.M70643520018096704
     [Google Scholar]
  62. RoyS. BaeE. AminS. KimD. Extracellular matrix, gap junctions, and retinal vascular homeostasis in diabetic retinopathy.Exp. Eye Res.2015133586810.1016/j.exer.2014.08.01125819455
     [Google Scholar]
  63. KotajimaN. KandaT. YuukiN. KimuraT. KishiS. FukumuraY. TamuraJ. KobayashiI. Type IV collagen serum and vitreous fluid levels in patients with diabetic retinopathy.J. Int. Med. Res.200129429229610.1177/14732300010290040511675902
     [Google Scholar]
  64. RoyS. MaielloM. LorenziM. Increased expression of basement membrane collagen in human diabetic retinopathy.J. Clin. Invest.199493143844210.1172/JCI1169798282817
     [Google Scholar]
  65. ArkkilaP.E.T. RönnemaaT. KoskinenP.J. KantolaI.M. SeppänenE. ViikariJ.S.A. Biochemical markers of type III and I collagen: association with retinopathy and neuropathy in Type 1 diabetic subjects.Diabet. Med.2001181081682110.1046/j.1464‑5491.2001.00576.x11678972
     [Google Scholar]
  66. HosodaY. OkadaM. MatsumuraM. OginoN. HondaY. NagaiY. Intravitreal neovascular tissue of proliferative diabetic retinopathy: An immunohistochemical study.Ophthalmic Res.199224526026410.1159/0002671761282231
     [Google Scholar]
  67. JerdanJ.A. GlaserB.M. Retinal microvessel extracellular matrix: An immunofluorescent study.Invest. Ophthalmol. Vis. Sci.19862721942033510998
     [Google Scholar]
  68. MasmiquelL.L. BurgosR. MateoC. MartíR. SeguraR.M. SimóR. Effect of panretinal photocoagulation on serum levels of laminin in patients with diabetes: A prospective study.Br. J. Ophthalmol.19998391056105910.1136/bjo.83.9.105610460775
     [Google Scholar]
  69. PietschmannP. SchernthanerG. SchnackC.H. GaubeS. Serum concentrations of laminin P1 in diabetics with advanced nephropathy.J. Clin. Pathol.1988419929932
     [Google Scholar]
  70. MasmiquelL. SeguraR.M. MateoC. CalatayudM. MartíR. MesaJ. SimóR. Serum laminin as a marker of diabetic retinopathy development: A 4-year follow-up study.Am. J. Ophthalmol.2000129334735210.1016/S0002‑9394(99)00361‑X10704551
     [Google Scholar]
  71. GrantD.S. KleinmanH.K. MartinG.R. The role of basement membranes in vascular development.Ann. N. Y. Acad. Sci.19905881617210.1111/j.1749‑6632.1990.tb13197.x2192650
     [Google Scholar]
  72. OshitariT. BrownD. RoyS. SiRNA strategy against overexpression of extracellular matrix in diabetic retinopathy.Exp. Eye Res.2005811323710.1016/j.exer.2005.01.00615978252
     [Google Scholar]
  73. QinD. ZhangG. XuX. WangL. The PI3K/Akt signaling pathway mediates the high glucose-induced expression of extracellular matrix molecules in human retinal pigment epithelial cells.J. Diabetes Res.2015201511110.1155/2015/92028025695094
     [Google Scholar]
  74. Casaroli MaranoR.P. PreissnerK.T. VilaróS. Fibronectin, laminin, vitronectin and their receptors at newly-formed capillaries in proliferative diabetic retinopathy.Exp. Eye Res.199560151710.1016/S0014‑4835(05)80079‑X7536680
     [Google Scholar]
  75. BrownleeM. Glycation products and the pathogenesis of diabetic complications.Diabetes Care199215121835184310.2337/diacare.15.12.18351464241
     [Google Scholar]
  76. HammesH.P. WeissA. HessS. ArakiN. HoriuchiS. BrownleeM. PreissnerK.T. Modification of vitronectin by advanced glycation alters functional properties in vitro and in the diabetic retina.Lab. Invest.19967533253388804356
     [Google Scholar]
  77. EsserP. BresgenM. WellerM. HeimannK. WiedemannP. The significance of vitronectin in proliferative diabetic retinopathy.Graefes Arch. Clin. Exp. Ophthalmol.1994232847748110.1007/BF001953577523258
     [Google Scholar]
  78. LeeJ. KimK.E. ChoiD.K. JangJ.Y. JungJ.J. KiyonariH. ShioiG. ChangW. SudaT. MochizukiN. NakaokaY. KomuroI. YooO.J. KohG.Y. Angiopoietin-1 guides directional angiogenesis through integrin αvβ5 signaling for recovery of ischemic retinopathy.Sci. Transl. Med.20135203203ra12710.1126/scitranslmed.300666624048525
     [Google Scholar]
  79. FriedlanderM. TheesfeldC.L. SugitaM. FruttigerM. ThomasM.A. ChangS. ChereshD.A. Involvement of integrins alpha v beta 3 and alpha v beta 5 in ocular neovascular diseases.Proc. Natl. Acad. Sci.199693189764976910.1073/pnas.93.18.97648790405
     [Google Scholar]
  80. BiasellaF. PlösslK. KarlC. WeberB.H.F. FriedrichU. Altered protein function caused by AMD-associated variant rs704 links vitronectin to disease pathology.Invest. Ophthalmol. Vis. Sci.20206114210.1167/iovs.61.14.233259607
     [Google Scholar]
  81. MilewiczD.M. BravermanA.C. De BackerJ. MorrisS.A. BoileauC. MaumeneeI.H. JondeauG. EvangelistaA. PyeritzR.E. Marfan syndrome.Nat. Rev. Dis. Primers2021716410.1038/s41572‑021‑00298‑734475413
     [Google Scholar]
  82. LuísC. FernandesR. SoaresR. von HafeP. A state of the art review on the novel mediator asprosin in the metabolic syndrome.Porto Biomed. J.202056e10810.1097/j.pbj.000000000000010833324783
     [Google Scholar]
  83. OrucY. CelikF. OzgurG. BeyazyildizE. UgurK. YardimM. SahinI. AkkocR.F. AydinS. Altered blood and aqueous humor levels of asprosin, 4-hydroxynonenal, and 8-hydroxy-deoxyguanosine in patients with diabetes mellitus and cataract with and without diabetic retinopathy.Retina202040122410241610.1097/IAE.000000000000277632091490
     [Google Scholar]
  84. AtlıH. OnalanE. YakarB. KaymazT. DuzenciD. KarakulakK. DönderE. GürsuM.F. DayananR. The relationship of serum asprosin level with diabetic and non-diabetic retinopathy.Eur. Rev. Med. Pharmacol. Sci.20222662117212335363361
     [Google Scholar]
  85. LiangG. QinZ. LuoY. YinJ. ShiZ. WeiR. MaW. Exosomal microRNA-133b-3p from bone marrow mesenchymal stem cells inhibits angiogenesis and oxidative stress via FBN1 repression in diabetic retinopathy.Gene Ther.2022291271071910.1038/s41434‑021‑00310‑535125496
     [Google Scholar]
  86. LiX. WangC.Y. From bulk, single-cell to spatial RNA sequencing.Int. J. Oral Sci.20211313610.1038/s41368‑021‑00146‑034782601
     [Google Scholar]
  87. SadikanM.Z. Abdul NasirN.A. LambukL. MohamudR. ReshidanN.H. LowE. SingarS.A. Mohmad SabereA.S. IezhitsaI. AgarwalR. Diabetic retinopathy: A comprehensive update on in vivo, in vitro and ex vivo experimental models.BMC Ophthalmol.202323142110.1186/s12886‑023‑03155‑137858128
     [Google Scholar]
  88. DuhE.J. SunJ.K. StittA.W. Diabetic retinopathy: Current understanding, mechanisms, and treatment strategies.JCI Insight2017214e9375110.1172/jci.insight.9375128724805
     [Google Scholar]
/content/journals/emiddt/10.2174/0118715303286652240214110511
Loading
Single-Cell RNA Sequencing Reveals Transcriptional Signatures and Cell-Cell Communication in Diabetic Retinopathy
Endocr Metab Immune Disord Drug Targets 24, 1651 (2024); https://doi.org/10.2174/0118715303286652240214110511
/content/journals/emiddt/10.2174/0118715303286652240214110511
/content/journals/emiddt/10.2174/0118715303286652240214110511
Loading

Data & Media loading...

Supplements

file format download Download

  • Article Type:     Research Article
Keyword(s): biomarker; cell-cell communication; Diabetic retinopathy; retinal pigment epithelial cells; single-cell RNA sequencing; therapeutic targets

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test