دورية أكاديمية
Decoding myofibroblast origins in human kidney fibrosis.
| العنوان: | Decoding myofibroblast origins in human kidney fibrosis. |
|---|---|
| المؤلفون: | Kuppe C; Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany.; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany., Ibrahim MM; Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany.; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany.; Bayer Pharma AG, Berlin, Germany., Kranz J; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany.; Department of Urology and Paediatric Urology, St Antonius Hospital, Eschweiler, Germany.; Department of Urology, Kidney Transplantation Centre, Martin-Luther-University, Halle, Germany., Zhang X; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany., Ziegler S; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany., Perales-Patón J; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany.; Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, BioQuant, Heidelberg, Germany.; Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany., Jansen J; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany.; Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.; Department of Pediatric Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Amalia Children's Hospital, Nijmegen, The Netherlands., Reimer KC; Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany.; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany.; Department of Cell Biology, Institute for Biomedical Technologies, RWTH Aachen University, Aachen, Germany., Smith JR; Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK., Dobie R; Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK., Wilson-Kanamori JR; Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK., Halder M; Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany.; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany., Xu Y; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany., Kabgani N; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany., Kaesler N; Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany.; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany., Klaus M; III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany., Gernhold L; III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany., Puelles VG; III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.; Department of Anatomy and Developmental Biology, Monash Biomedical Discovery Institute, Monash University, Melbourne, Victoria, Australia., Huber TB; III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany., Boor P; Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany.; Department of Pathology, RWTH Aachen University, Aachen, Germany., Menzel S; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany., Hoogenboezem RM; Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands., Bindels EMJ; Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands., Steffens J; Department of Urology and Paediatric Urology, St Antonius Hospital, Eschweiler, Germany., Floege J; Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany., Schneider RK; Department of Cell Biology, Institute for Biomedical Technologies, RWTH Aachen University, Aachen, Germany.; Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands., Saez-Rodriguez J; Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, BioQuant, Heidelberg, Germany.; Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany.; Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, Heidelberg University, Heidelberg, Germany., Henderson NC; Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK.; MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK., Kramann R; Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany. rkramann@gmx.net.; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany. rkramann@gmx.net.; Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam, The Netherlands. rkramann@gmx.net. |
| المصدر: | Nature [Nature] 2021 Jan; Vol. 589 (7841), pp. 281-286. Date of Electronic Publication: 2020 Nov 11. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 0410462 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-4687 (Electronic) Linking ISSN: 00280836 NLM ISO Abbreviation: Nature Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Basingstoke : Nature Publishing Group Original Publication: London, Macmillan Journals ltd. |
| مواضيع طبية MeSH: | Cell Lineage*, Fibrosis/*pathology , Kidney Tubules/*pathology , Myofibroblasts/*pathology , Renal Insufficiency, Chronic/*pathology, Adaptor Proteins, Signal Transducing/metabolism ; Animals ; Calcium-Binding Proteins/metabolism ; Case-Control Studies ; Cell Differentiation ; Extracellular Matrix/metabolism ; Extracellular Matrix/pathology ; Female ; Fibroblasts/cytology ; Fibroblasts/metabolism ; Humans ; Male ; Mesoderm/cytology ; Mesoderm/pathology ; Mice ; Myofibroblasts/metabolism ; Pericytes/cytology ; Pericytes/pathology ; RNA-Seq ; Receptor, Platelet-Derived Growth Factor alpha/metabolism ; Receptor, Platelet-Derived Growth Factor beta/metabolism ; Single-Cell Analysis ; Transcriptome |
| مستخلص: | Kidney fibrosis is the hallmark of chronic kidney disease progression; however, at present no antifibrotic therapies exist 1-3 . The origin, functional heterogeneity and regulation of scar-forming cells that occur during human kidney fibrosis remain poorly understood 1,2,4 . Here, using single-cell RNA sequencing, we profiled the transcriptomes of cells from the proximal and non-proximal tubules of healthy and fibrotic human kidneys to map the entire human kidney. This analysis enabled us to map all matrix-producing cells at high resolution, and to identify distinct subpopulations of pericytes and fibroblasts as the main cellular sources of scar-forming myofibroblasts during human kidney fibrosis. We used genetic fate-tracing, time-course single-cell RNA sequencing and ATAC-seq (assay for transposase-accessible chromatin using sequencing) experiments in mice, and spatial transcriptomics in human kidney fibrosis, to shed light on the cellular origins and differentiation of human kidney myofibroblasts and their precursors at high resolution. Finally, we used this strategy to detect potential therapeutic targets, and identified NKD2 as a myofibroblast-specific target in human kidney fibrosis. |
| التعليقات: | Comment in: Nat Rev Nephrol. 2021 Mar;17(3):151. (PMID: 33268843) Comment in: Kidney Int. 2021 Jun;99(6):1259-1261. (PMID: 33647325) Comment in: J Urol. 2021 Aug;206(2):480-482. (PMID: 33975458) |
| References: | Duffield, J. S. Cellular and molecular mechanisms in kidney fibrosis. J. Clin. Invest. 124, 2299–2306 (2014). (PMID: 248927034038570) Kramann, R., DiRocco, D. P. & Humphreys, B. D. Understanding the origin, activation and regulation of matrix-producing myofibroblasts for treatment of fibrotic disease. J. Pathol. 231, 273–289 (2013). (PMID: 24006178) Friedman, S. L., Sheppard, D., Duffield, J. S. & Violette, S. Therapy for fibrotic diseases: nearing the starting line. Sci. Transl. Med. 5, 167sr1 (2013). (PMID: 23303606) Falke, L. L., Gholizadeh, S., Goldschmeding, R., Kok, R. J. & Nguyen, T. Q. Diverse origins of the myofibroblast—implications for kidney fibrosis. Nat. Rev. Nephrol. 11, 233–244 (2015). (PMID: 25584804) Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018). (PMID: 300935976104812) Kang, H. M. et al. Defective fatty acid oxidation in renal tubular epithelial cells has a key role in kidney fibrosis development. Nat. Med. 21, 37–46 (2015). (PMID: 25419705) Naba, A. et al. The extracellular matrix: Tools and insights for the “omics” era. Matrix Biol. 49, 10–24 (2016). (PMID: 26163349) Fan, Y. et al. Comparison of kidney transcriptomic profiles of early and advanced diabetic nephropathy reveals potential new mechanisms for disease progression. Diabetes 68, 2301–2314 (2019). (PMID: 315781936868471) Kriz, W., Kaissling, B. & Le Hir, M. Epithelial-mesenchymal transition (EMT) in kidney fibrosis: fact or fantasy? J. Clin. Invest. 121, 468–474 (2011). (PMID: 213705233026733) Huang, S. & Susztak, K. Epithelial plasticity versus EMT in kidney fibrosis. Trends Mol. Med. 22, 4–6 (2016). (PMID: 26700490) Elices, M. J. et al. VCAM-1 on activated endothelium interacts with the leukocyte integrin VLA-4 at a site distinct from the VLA-4/fibronectin binding site. Cell 60, 577–584 (1990). (PMID: 1689216) Kang, H. M. et al. Sox9-positive progenitor cells play a key role in renal tubule epithelial regeneration in mice. Cell Rep. 14, 861–871 (2016). (PMID: 267765204854207) Ramachandran, P. et al. Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature 575, 512–518 (2019). (PMID: 315971606876711) Wang, Y.-Y. et al. Macrophage-to-myofibroblast transition contributes to interstitial fibrosis in chronic renal allograft injury. J. Am. Soc. Nephrol. 28, 2053–2067 (2017). (PMID: 282098095491278) Henderson, N. C. et al. Targeting of αv integrin identifies a core molecular pathway that regulates fibrosis in several organs. Nat. Med. 19, 1617–1624 (2013). (PMID: 24216753) Wernig, G. et al. Unifying mechanism for different fibrotic diseases. Proc. Natl Acad. Sci. USA 114, 4757–4762 (2017). (PMID: 284242505422830) Venkatachalam, M. A., Weinberg, J. M., Kriz, W. & Bidani, A. K. Failed tubule recovery, AKI-CKD transition, and kidney disease progression. J. Am. Soc. Nephrol. 26, 1765–1776 (2015). (PMID: 258104944520181) Kramann, R. et al. Parabiosis and single-cell RNA sequencing reveal a limited contribution of monocytes to myofibroblasts in kidney fibrosis. JCI Insight 3, e99561 (2018). (PMID: 6012505) Gerstein, M. B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91–100 (2012). (PMID: 229556194154057) Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013). (PMID: 240972673959825) Palumbo-Zerr, K. et al. Orphan nuclear receptor NR4A1 regulates transforming growth factor-β signaling and fibrosis. Nat. Med. 21, 150–158 (2015). (PMID: 25581517) Schubert, M. et al. Perturbation-response genes reveal signaling footprints in cancer gene expression. Nat. Commun. 9, 20 (2018). (PMID: 292959955750219) Zhao, S. et al. NKD2, a negative regulator of Wnt signaling, suppresses tumor growth and metastasis in osteosarcoma. Oncogene 34, 5069–5079 (2015). (PMID: 255791774802362) Li, C. et al. Myristoylated Naked2 escorts transforming growth factor α to the basolateral plasma membrane of polarized epithelial cells. Proc. Natl Acad. Sci. USA 101, 5571–5576 (2004). (PMID: 15064403397424) Moerman, T. et al. GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks. Bioinformatics 35, 2159–2161 (2019). (PMID: 30445495) Lemos, D. R. et al. Interleukin-1β activates a MYC-dependent metabolic switch in kidney stromal cells necessary for progressive tubulointerstitial fibrosis. J. Am. Soc. Nephrol. 29, 1690–1705 (2018). (PMID: 297398136054344) Tsukui, T. et al. Collagen-producing lung cell atlas identifies multiple subsets with distinct localization and relevance to fibrosis. Nat. Commun. 11, 1920 (2020). (PMID: 323176437174390) Adams, T. S. et al. Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis. Sci. Adv. 6, eaba1983 (2020). (PMID: 328325997439502) Kramann, R. et al. Perivascular Gli1 + progenitors are key contributors to injury-induced organ fibrosis. Cell Stem Cell 16, 51–66 (2015). (PMID: 25465115) Takasoto, M. et al. Kidney organoids from human iPS cells contain multiple lineages and model human nephrogenesis. Nature 526, 564–568 (2015). Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017). (PMID: 288460905623106) Srivastava, A., Malik, L., Smith, T., Sudbery, I. & Patro, R. Alevin efficiently estimates accurate gene abundances from dscRNA-seq data. Genome Biol. 20, 65 (2019). (PMID: 309178596437997) Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017). (PMID: 282639595600148) Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 47 (D1), D766–D773 (2019). (PMID: 30357393) Lun, A. T. L., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000 Res. 5, 2122 (2016). Scrucca, L., Fop, M., Murphy, T. B. & Raftery, A. E. mclust 5: clustering, classification and density estimation using gaussian finite mixture models. R J. 8, 289–317 (2016). (PMID: 278187915096736) Fortunato, S. & Barthélemy, M. Resolution limit in community detection. Proc. Natl Acad. Sci. USA 104, 36–41 (2007). (PMID: 17190818) Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014.e22 (2018). (PMID: 300963146086934) Lake, B. B. et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat. Commun. 10, 2832 (2019). (PMID: 312493126597610) Clark, J. Z. et al. Representation and relative abundance of cell-type selective markers in whole-kidney RNA-Seq data. Kidney Int. 95, 787–796 (2019). (PMID: 308260167466803) Wu, C. et al. BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol. 10, R130 (2009). (PMID: 199196823091323) Durinck, S. et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21, 3439–3440 (2005). (PMID: 16082012) Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009). (PMID: 196178893159387) Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019). (PMID: 311781186687398) Xu, C. & Su, Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31, 1974–1980 (2015). (PMID: 258057226280782) Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). (PMID: 260004884481139) Alter, O., Brown, P. O. & Botstein, D. Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl Acad. Sci. USA 97, 10101–10106 (2000). (PMID: 1096367327718) Rosvall, M. & Bergstrom, C. T. Maps of random walks on complex networks reveal community structure. Proc. Natl Acad. Sci. USA 105, 1118–1123 (2008). (PMID: 182162672234100) Dahlin, J. S. et al. A single-cell hematopoietic landscape resolves 8 lineage trajectories and defects in Kit mutant mice. Blood 131, e1–e11 (2018). (PMID: 295882785969381) Ibrahim, M. M. & Kramann, R. genesorteR: feature ranking in clustered single cell data. Preprint at https://doi.org/10.1101/676379 (2019). Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018). (PMID: 296081776152897) Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008). Lun, A. T. L., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016). (PMID: 27122128) Rand, W. M. Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66, 846–850 (1971). Yang, L., Liu, J., Lu, Q., Riggs, A. D. & Wu, X. SAIC: an iterative clustering approach for analysis of single cell RNA-seq data. BMC Genomics 18 (Suppl. 6), 689 (2017). (PMID: 289842045629617) Karaiskos, N. et al. The Drosophila embryo at single-cell transcriptome resolution. Science 358, 194–199 (2017). (PMID: 28860209) McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018). Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018). (PMID: 299143546007078) Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005). (PMID: 161995171239896) Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011). (PMID: 215463933106198) Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012). (PMID: 224554633339379) The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000). (PMID: 3037419) Holland, C. H. et al. Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data. Genome Biol. 21, 36 (2020). (PMID: 320510037017576) Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). (PMID: 260004884481139) Yang, J. et al. Single cell transcriptomics reveals unanticipated features of early hematopoietic precursors. Nucleic Acids Res. 45, 1281–1296 (2017). (PMID: 28003475) Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016). (PMID: 27207943) Bushnell, B. BBMap: a Fast, Accurate, Splice-Aware Aligner https://www.osti.gov/biblio/1241166 (2014). Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013). (PMID: 23104886) Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009). (PMID: 195059432723002) Quinlan, A. R. BEDTools: the Swiss-army tool for genome feature analysis. Curr. Protoc. Bioinformatics 47, 11.12.1–11.12.34 (2014). Adey, A. et al. Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition. Genome Biol. 11, R119 (2010). (PMID: 211438623046479) Ibrahim, M. M., Lacadie, S. A. & Ohler, U. JAMM: a peak finder for joint analysis of NGS replicates. Bioinformatics 31, 48–55 (2015). (PMID: 25223640) Luehr, S., Hartmann, H. & Söding, J. The XXmotif web server for eXhaustive, weight matriX-based motif discovery in nucleotide sequences. Nucleic Acids Res. 40, W104–W109 (2012). (PMID: 226932183394272) Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011). (PMID: 213302903065696) Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44 (W1), W160–W165 (2016). (PMID: 270799754987876) Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011). (PMID: 212210953346182) Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017). (PMID: 289918925937676) Schacht, T., Oswald, M., Eils, R., Eichmüller, S. B. & König, R. Estimating the activity of transcription factors by the effect on their target genes. Bioinformatics 30, i401–i407 (2014). (PMID: 251612264147899) Alvarez, M. J. et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat. Genet. 48, 838–847 (2016). (PMID: 273225465040167) Garcia-Alonso, L., Holland, C. H., Ibrahim, M. M., Turei, D. & Saez-Rodriguez, J. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Res. 29, 1363–1375 (2019). (PMID: 313409856673718) de Kanter, J. K., Lijnzaad, P., Candelli, T., Margaritis, T. & Holstege, F. C. P. CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing. Nucleic Acids Res. 47, e95 (2019). (PMID: 312262066895264) Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020). (PMID: 32103204) Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000 Res. 4, 1521 (2015). Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015). (PMID: 256057924402510) Korotkevich, G., Sukhov, V. & Sergushichev, A. Fast gene set enrichment analysis. Preprint at https://doi.org/10.1101/060012 (2019). |
| معلومات مُعتمدة: | 104366/Z/14/Z United Kingdom WT_ Wellcome Trust; United Kingdom MRC_ Medical Research Council; 219542/Z/19/Z United Kingdom WT_ Wellcome Trust; United Kingdom WT_ Wellcome Trust; 677448 International ERC_ European Research Council |
| المشرفين على المادة: | 0 (Adaptor Proteins, Signal Transducing) 0 (Calcium-Binding Proteins) 0 (NKD2 protein, human) EC 2.7.10.1 (Receptor, Platelet-Derived Growth Factor alpha) EC 2.7.10.1 (Receptor, Platelet-Derived Growth Factor beta) |
| تواريخ الأحداث: | Date Created: 20201111 Date Completed: 20210225 Latest Revision: 20230127 |
| رمز التحديث: | 20240628 |
| مُعرف محوري في PubMed: | PMC7611626 |
| DOI: | 10.1038/s41586-020-2941-1 |
| PMID: | 33176333 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1476-4687 |
|---|---|
| DOI: | 10.1038/s41586-020-2941-1 |