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Bioinformatics Tools for Bulk Gene Expression Deconvolution in Diabetic Retinopathy

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Diabetic Retinopathy

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2678))

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Abstract

Retinal neovascularization is one of the leading causes of vision loss and a hallmark of proliferative diabetic retinopathy (PDR). The immune system is observed to be involved in the pathogenesis of diabetic retinopathy (DR). The specific immune cell type that contributes to retinal neovascularization can be identified via a bioinformatics analysis of RNA sequencing (RNA-seq) data, known as deconvolution analysis. Previous study has identified the infiltration of macrophages in the retina of rats with hypoxia-induced retinal neovascularization and patients with PDR through a deconvolution algorithm, known as CIBERSORTx. Here, we describe the protocols of using CIBERSORTx to perform the deconvolution analysis and downstream analysis of RNA-seq data.

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References

  1. Campochiaro PA (2013) Ocular neovascularization. J Mol Med (Berl) 91(3):311–321. https://doi.org/10.1007/s00109-013-0993-5

    Article  CAS  PubMed  Google Scholar 

  2. Ishibazawa A, Nagaoka T, Yokota H, Takahashi A, Omae T, Song Y-S, Takahashi T, Yoshida A (2016) Characteristics of retinal neovascularization in proliferative diabetic retinopathy imaged by optical coherence tomography angiography. Invest Ophthalmol Vis Sci 57(14):6247–6255. https://doi.org/10.1167/iovs.16-20210

    Article  PubMed  Google Scholar 

  3. Pan WW, Lin F, Fort PE (2021) The innate immune system in diabetic retinopathy. Prog Retin Eye Res 84:100940

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Kong D, Gong L, Arnold E, Shanmugam S, Fort PE, Gardner TW, Abcouwer SF (2016) Insulin-like growth factor 1 rescues R28 retinal neurons from apoptotic death through ERK-mediated BimEL phosphorylation independent of Akt. Exp Eye Res 151:82–95

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Mukai R, Okunuki Y, Husain D, Kim CB, Lambris JD, Connor KM (2018) The complement system is critical in maintaining retinal integrity during aging. Front Aging Neurosci 10:15

    Article  PubMed  PubMed Central  Google Scholar 

  6. Rübsam A, Parikh S, Fort PE (2018) Role of inflammation in diabetic retinopathy. Int J Mol Sci 19(4):942

    Article  PubMed  PubMed Central  Google Scholar 

  7. Demircan N, Safran B, Soylu M, Ozcan A, Sizmaz S (2006) Determination of vitreous interleukin-1 (IL-1) and tumour necrosis factor (TNF) levels in proliferative diabetic retinopathy. Eye 20(12):1366–1369

    Article  CAS  PubMed  Google Scholar 

  8. Murugeswari P, Shukla D, Rajendran A, Kim R, Namperumalsamy P, Muthukkaruppan V (2008) Proinflammatory cytokines and angiogenic and anti-angiogenic factors in vitreous of patients with proliferative diabetic retinopathy and Eales’ disease. Retina 28(6):817–824

    Article  PubMed  Google Scholar 

  9. Boss JD, Singh PK, Pandya HK, Tosi J, Kim C, Tewari A, Juzych MS, Abrams GW, Kumar A (2017) Assessment of neurotrophins and inflammatory mediators in vitreous of patients with diabetic retinopathy. Invest Ophthalmol Vis Sci 58(12):5594–5603

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Akhtar-Schäfer I, Wang L, Krohne TU, Xu H, Langmann T (2018) Modulation of three key innate immune pathways for the most common retinal degenerative diseases. EMBO Mol Med 10(10):e8259

    Article  PubMed  PubMed Central  Google Scholar 

  11. Yuan G-C, Cai L, Elowitz M, Enver T, Fan G, Guo G, Irizarry R, Kharchenko P, Kim J, Orkin S, Quackenbush J, Saadatpour A, Schroeder T, Shivdasani R, Tirosh I (2017) Challenges and emerging directions in single-cell analysis. Genome Biol 18(1):84. https://doi.org/10.1186/s13059-017-1218-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63. https://doi.org/10.1038/nrg2484

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Li X, Wang C-Y (2021) From bulk, single-cell to spatial RNA sequencing. Int J Oral Sci 13(1):36. https://doi.org/10.1038/s41368-021-00146-0

    Article  PubMed  PubMed Central  Google Scholar 

  14. Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, Mason CE, Socci ND, Betel D (2013) Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol 14(9):3158. https://doi.org/10.1186/gb-2013-14-9-r95

    Article  CAS  Google Scholar 

  15. Jin H, Liu Z (2021) A benchmark for RNA-seq deconvolution analysis under dynamic testing environments. Genome Biol 22(1):102. https://doi.org/10.1186/s13059-021-02290-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Avila Cobos F, Vandesompele J, Mestdagh P, De Preter K (2018) Computational deconvolution of transcriptomics data from mixed cell populations. Bioinformatics 34(11):1969–1979. https://doi.org/10.1093/bioinformatics/bty019

    Article  CAS  PubMed  Google Scholar 

  17. Abbas AR, Wolslegel K, Seshasayee D, Modrusan Z, Clark HF (2009) Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus. PLoS One 4(7):e6098. https://doi.org/10.1371/journal.pone.0006098

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Shen-Orr SS, Tibshirani R, Khatri P, Bodian DL, Staedtler F, Perry NM, Hastie T, Sarwal MM, Davis MM, Butte AJ (2010) Cell type-specific gene expression differences in complex tissues. Nat Methods 7(4):287–289. https://doi.org/10.1038/nmeth.1439

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Gong T, Hartmann N, Kohane IS, Brinkmann V, Staedtler F, Letzkus M, Bongiovanni S, Szustakowski JD (2011) Optimal deconvolution of transcriptional profiling data using quadratic programming with application to complex clinical blood samples. PLoS One 6(11):e27156. https://doi.org/10.1371/journal.pone.0027156

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kuhn A, Thu D, Waldvogel HJ, Faull RL, Luthi-Carter R (2011) Population-specific expression analysis (PSEA) reveals molecular changes in diseased brain. Nat Methods 8(11):945–947. https://doi.org/10.1038/nmeth.1710

    Article  CAS  PubMed  Google Scholar 

  21. Qiao W, Quon G, Csaszar E, Yu M, Morris Q, Zandstra PW (2012) PERT: a method for expression deconvolution of human blood samples from varied microenvironmental and developmental conditions. PLoS Comput Biol 8(12):e1002838. https://doi.org/10.1371/journal.pcbi.1002838

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ahn J, Yuan Y, Parmigiani G, Suraokar MB, Diao L, Wistuba II, Wang W (2013) DeMix: deconvolution for mixed cancer transcriptomes using raw measured data. Bioinformatics 29(15):1865–1871. https://doi.org/10.1093/bioinformatics/btt301

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Quon G, Haider S, Deshwar AG, Cui A, Boutros PC, Morris Q (2013) Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction. Genome Med 5(3):29. https://doi.org/10.1186/gm433

    Article  PubMed  PubMed Central  Google Scholar 

  24. Zhong Y, Wan Y-W, Pang K, Chow LML, Liu Z (2013) Digital sorting of complex tissues for cell type-specific gene expression profiles. BMC Bioinformatics 14(1):89. https://doi.org/10.1186/1471-2105-14-89

    Article  PubMed  PubMed Central  Google Scholar 

  25. Liebner DA, Huang K, Parvin JD (2014) MMAD: microarray microdissection with analysis of differences is a computational tool for deconvoluting cell type-specific contributions from tissue samples. Bioinformatics 30(5):682–689. https://doi.org/10.1093/bioinformatics/btt566

    Article  CAS  PubMed  Google Scholar 

  26. Angelova M, Charoentong P, Hackl H, Fischer ML, Snajder R, Krogsdam AM, Waldner MJ, Bindea G, Mlecnik B, Galon J, Trajanoski Z (2015) Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy. Genome Biol 16(1):64. https://doi.org/10.1186/s13059-015-0620-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA (2015) Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 12(5):453–457. https://doi.org/10.1038/nmeth.3337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Corliss BA, Azimi MS, Munson JM, Peirce SM, Murfee WL (2016) Macrophages: an inflammatory link between angiogenesis and lymphangiogenesis. Microcirculation 23(2):95–121. https://doi.org/10.1111/micc.12259

    Article  PubMed  PubMed Central  Google Scholar 

  29. Aran D, Hu Z, Butte AJ (2017) xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol 18(1):220. https://doi.org/10.1186/s13059-017-1349-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Steen CB, Liu CL, Alizadeh AA, Newman AM (2020) Profiling cell type abundance and expression in bulk tissues with CIBERSORTx. Methods Mol Biol 2117:135–157. https://doi.org/10.1007/978-1-0716-0301-7_7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, Khodadoust MS, Esfahani MS, Luca BA, Steiner D, Diehn M, Alizadeh AA (2019) Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol 37(7):773–782. https://doi.org/10.1038/s41587-019-0114-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Wang J-H, Kumar S, Liu G-S (2021) Bulk gene expression deconvolution reveals infiltration of M2 macrophages in retinal neovascularization. Invest Ophthalmol Vis Sci 62(14):22–22. https://doi.org/10.1167/iovs.62.14.22

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A (2012) NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res 41(D1):D991–D995. https://doi.org/10.1093/nar/gks1193

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wang JH, Kumar S, Liu GS (2021) Bulk gene expression deconvolution reveals infiltration of M2 macrophages in retinal neovascularization. Invest Ophthalmol Vis Sci 62(14):22. https://doi.org/10.1167/iovs.62.14.22

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA (2018) Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol Biol 1711:243–259. https://doi.org/10.1007/978-1-4939-7493-1_12

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Team RStudio (2021) RStudio: integrated development for R. RStudio, PBC, Boston. 2020

    Google Scholar 

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Acknowledgments

This work was supported by grants from the National Health and Medical Research Council of Australia (GNT1185600). The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian Government.

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Correspondence to Guei-Sheung Liu or Jiang-Hui Wang .

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© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Teh, R.Q., Liu, GS., Wang, JH. (2023). Bioinformatics Tools for Bulk Gene Expression Deconvolution in Diabetic Retinopathy. In: Liu, GS., Wang, JH. (eds) Diabetic Retinopathy. Methods in Molecular Biology, vol 2678. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3255-0_7

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  • DOI: https://doi.org/10.1007/978-1-0716-3255-0_7

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3254-3

  • Online ISBN: 978-1-0716-3255-0

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