An integrated single cell and spatial transcriptomic map of human white adipose tissue

. 2023 Mar 15 ; 14 (1) : 1438. [epub] 20230315

Jazyk angličtina Země Anglie, Velká Británie Médium electronic

Typ dokumentu metaanalýza, časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/pmid36922516
Odkazy

PubMed 36922516
PubMed Central PMC10017705
DOI 10.1038/s41467-023-36983-2
PII: 10.1038/s41467-023-36983-2
Knihovny.cz E-zdroje

To date, single-cell studies of human white adipose tissue (WAT) have been based on small cohort sizes and no cellular consensus nomenclature exists. Herein, we performed a comprehensive meta-analysis of publicly available and newly generated single-cell, single-nucleus, and spatial transcriptomic results from human subcutaneous, omental, and perivascular WAT. Our high-resolution map is built on data from ten studies and allowed us to robustly identify >60 subpopulations of adipocytes, fibroblast and adipogenic progenitors, vascular, and immune cells. Using these results, we deconvolved spatial and bulk transcriptomic data from nine additional cohorts to provide spatial and clinical dimensions to the map. This identified cell-cell interactions as well as relationships between specific cell subtypes and insulin resistance, dyslipidemia, adipocyte volume, and lipolysis upon long-term weight changes. Altogether, our meta-map provides a rich resource defining the cellular and microarchitectural landscape of human WAT and describes the associations between specific cell types and metabolic states.

Center for Infectious Medicine Department of Medicine Huddinge Karolinska Institutet Karolinska University Hospital Huddinge SE 141 52 Huddinge Sweden

Childhood Cancer Research Unit Department of Women's and Children's Health Karolinska Institutet SE 171 77 Stockholm Sweden

Department of Clinical Science Intervention and Technology Unit of Renal Medicine Karolinska Institutet Karolinska University Hospital Huddinge SE 141 86 Huddinge Sweden

Department of Human Biology NUTRIM School of Nutrition and Translational Research in Metabolism Maastricht University Medical Centre Maastricht the Netherlands

Department of Medicine Huddinge Karolinska Institutet Karolinska University Hospital Huddinge SE 141 83 Huddinge Sweden

Department of Metabolic Health Nestle Institute of Health Sciences Nestle Research Lausanne Switzerland

Department of Obesity and Nutritional Sciences The Novo Nordisk Foundation Hellerup Denmark

Franco Czech Laboratory for Clinical Research on Obesity 3rd Faculty of Medicine Charles University Prague and Université Toulouse 3 Paul Sabatier Toulouse France

Helmholtz Institute for Metabolic Obesity and Vascular Research of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig Leipzig Germany

Institut Universitaire de France Paris France

Institute of Metabolic and Cardiovascular Diseases Université de Toulouse UMR1297 Toulouse France

Laboratoire de biochimie Centre Hospitalier Universitaire de Toulouse Toulouse France

Laboratory of Translational Nutrition Biology Institute of Food Nutrition and Health Department of Health Sciences and Technology ETH Zurich Schwerzenbach Switzerland

Medical Department 3 Endocrinology Nephrology Rheumatology University of Leipzig Medical Center Leipzig Germany

Science for Life Laboratory Department of Gene Technology KTH Royal Institute of Technology SE 171 65 Solna Sweden

Zobrazit více v PubMed

Sakers A, De Siqueira MK, Seale P, Villanueva CJ. Adipose-tissue plasticity in health and disease. Cell. 2022;185:419–446. doi: 10.1016/j.cell.2021.12.016. PubMed DOI PMC

Shao M, et al. De novo adipocyte differentiation from Pdgfrbeta(+) preadipocytes protects against pathologic visceral adipose expansion in obesity. Nat. Commun. 2018;9:890. doi: 10.1038/s41467-018-03196-x. PubMed DOI PMC

Schwalie PC, et al. A stromal cell population that inhibits adipogenesis in mammalian fat depots. Nature. 2018;559:103–108. doi: 10.1038/s41586-018-0226-8. PubMed DOI

Hepler C, et al. Identification of functionally distinct fibro-inflammatory and adipogenic stromal subpopulations in visceral adipose tissue of adult mice. Elife. 2018;7:e39636. doi: 10.7554/eLife.39636. PubMed DOI PMC

Dong H, et al. Identification of a regulatory pathway inhibiting adipogenesis via RSPO2. Nat. Metab. 2022;4:90–105. doi: 10.1038/s42255-021-00509-1. PubMed DOI PMC

Jaitin DA, et al. Lipid-associated macrophages control metabolic homeostasis in a Trem2-dependent manner. Cell. 2019;178:686–698.e614. doi: 10.1016/j.cell.2019.05.054. PubMed DOI PMC

Backdahl J, et al. Spatial mapping reveals human adipocyte subpopulations with distinct sensitivities to insulin. Cell Metab. 2021;33:1869–1882.e1866. doi: 10.1016/j.cmet.2021.07.018. PubMed DOI

Sun W, et al. snRNA-seq reveals a subpopulation of adipocytes that regulates thermogenesis. Nature. 2020;587:98–102. doi: 10.1038/s41586-020-2856-x. PubMed DOI

Emont MP, et al. A single-cell atlas of human and mouse white adipose tissue. Nature. 2022;603:926–933. doi: 10.1038/s41586-022-04518-2. PubMed DOI PMC

Vijay J, et al. Single-cell analysis of human adipose tissue identifies depot and disease specific cell types. Nat. Metab. 2020;2:97–109. doi: 10.1038/s42255-019-0152-6. PubMed DOI PMC

Hildreth AD, et al. Single-cell sequencing of human white adipose tissue identifies new cell states in health and obesity. Nat. Immunol. 2021;22:639–653. doi: 10.1038/s41590-021-00922-4. PubMed DOI PMC

Acosta JR, et al. Single cell transcriptomics suggest that human adipocyte progenitor cells constitute a homogeneous cell population. Stem Cell Res. Ther. 2017;8:250. doi: 10.1186/s13287-017-0701-4. PubMed DOI PMC

Merrick D, et al. Identification of a mesenchymal progenitor cell hierarchy in adipose tissue. Science. 2019;364:eaav2501. doi: 10.1126/science.aav2501. PubMed DOI PMC

Karunakaran D, et al. RIPK1 gene variants associate with obesity in humans and can be therapeutically silenced to reduce obesity in mice. Nat. Metab. 2020;2:1113–1125. doi: 10.1038/s42255-020-00279-2. PubMed DOI PMC

Angueira AR, et al. Defining the lineage of thermogenic perivascular adipose tissue. Nat. Metab. 2021;3:469–484. doi: 10.1038/s42255-021-00380-0. PubMed DOI PMC

Hao Y, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184:3573–3587.e3529. doi: 10.1016/j.cell.2021.04.048. PubMed DOI PMC

Li B, et al. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nat. Methods. 2022;19:662–670. doi: 10.1038/s41592-022-01480-9. PubMed DOI

Dominguez Conde C, et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science. 2022;376:eabl5197. doi: 10.1126/science.abl5197. PubMed DOI PMC

Eto H, et al. Characterization of structure and cellular components of aspirated and excised adipose tissue. Plast. Reconstr. Surg. 2009;124:1087–1097. doi: 10.1097/PRS.0b013e3181b5a3f1. PubMed DOI

Denisenko E, et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol. 2020;21:130. doi: 10.1186/s13059-020-02048-6. PubMed DOI PMC

Forcato M, Romano O, Bicciato S. Computational methods for the integrative analysis of single-cell data. Brief Bioinform. 2021;22:20–29. doi: 10.1093/bib/bbaa042. PubMed DOI PMC

Peng M, Li Y, Wamsley B, Wei Y, Roeder K. Integration and transfer learning of single-cell transcriptomes via cFIT. Proc. Natl. Acad. Sci. USA. 2021;118:e2024383118. doi: 10.1073/pnas.2024383118. PubMed DOI PMC

Xu C, et al. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Mol. Syst. Biol. 2021;17:e9620. doi: 10.15252/msb.20209620. PubMed DOI PMC

Polanski K, et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics. 2020;36:964–965. doi: 10.1093/bioinformatics/btz625. PubMed DOI PMC

Korsunsky I, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods. 2019;16:1289–1296. doi: 10.1038/s41592-019-0619-0. PubMed DOI PMC

Gayoso A, et al. A Python library for probabilistic analysis of single-cell omics data. Nat. Biotechnol. 2022;40:163–166. doi: 10.1038/s41587-021-01206-w. PubMed DOI

Luecken MD, et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods. 2022;19:41–50. doi: 10.1038/s41592-021-01336-8. PubMed DOI PMC

Tran HTN, et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 2020;21:12. doi: 10.1186/s13059-019-1850-9. PubMed DOI PMC

Hubert L, Arabie P. Comparing partitions. J. Classif. 1985;2:193–218. doi: 10.1007/BF01908075. DOI

Buttner M, Miao Z, Wolf FA, Teichmann SA, Theis FJ. A test metric for assessing single-cell RNA-seq batch correction. Nat. Methods. 2019;16:43–49. doi: 10.1038/s41592-018-0254-1. PubMed DOI

Simpson EH. Measurement of diversity. Nature. 1949;163:688–688. doi: 10.1038/163688a0. DOI

Arner P, et al. The epigenetic signature of systemic insulin resistance in obese women. Diabetologia. 2016;59:2393–2405. doi: 10.1007/s00125-016-4074-5. PubMed DOI PMC

Krieg L, et al. Multiomics reveal unique signatures of human epiploic adipose tissue related to systemic insulin resistance. Gut. 2021;71:2179–2193. doi: 10.1136/gutjnl-2021-324603. PubMed DOI PMC

Chen L, et al. ITLN1 inhibits tumor neovascularization and myeloid derived suppressor cells accumulation in colorectal carcinoma. Oncogene. 2021;40:5925–5937. doi: 10.1038/s41388-021-01965-5. PubMed DOI

Johnson MD, et al. Inhibition of angiogenesis by tissue inhibitor of metalloproteinase. J. Cell Physiol. 1994;160:194–202. doi: 10.1002/jcp.1041600122. PubMed DOI

Medina RJ, et al. Molecular analysis of endothelial progenitor cell (EPC) subtypes reveals two distinct cell populations with different identities. BMC Med. Genomics. 2010;3:18. doi: 10.1186/1755-8794-3-18. PubMed DOI PMC

Keighron C, Lyons CJ, Creane M, O’Brien T, Liew A. Recent advances in endothelial progenitor cells toward their use in clinical translation. Front. Med. (Lausanne) 2018;5:354. doi: 10.3389/fmed.2018.00354. PubMed DOI PMC

Wang QA, Tao C, Gupta RK, Scherer PE. Tracking adipogenesis during white adipose tissue development, expansion and regeneration. Nat. Med. 2013;19:1338–1344. doi: 10.1038/nm.3324. PubMed DOI PMC

Borrelli MR, et al. The antifibrotic adipose-derived stromal cell: grafted fat enriched with CD74+ adipose-derived stromal cells reduces chronic radiation-induced skin fibrosis. Stem Cells Transl. Med. 2020;9:1401–1413. doi: 10.1002/sctm.19-0317. PubMed DOI PMC

Buechler MB, et al. Cross-tissue organization of the fibroblast lineage. Nature. 2021;593:575–579. doi: 10.1038/s41586-021-03549-5. PubMed DOI

Ehrlund A, et al. Transcriptional dynamics during human adipogenesis and its link to adipose morphology and distribution. Diabetes. 2017;66:218–230. doi: 10.2337/db16-0631. PubMed DOI PMC

Khan A, et al. SNEV(hPrp19/hPso4) regulates adipogenesis of human adipose stromal cells. Stem Cell Rep. 2017;8:21–29. doi: 10.1016/j.stemcr.2016.12.001. PubMed DOI PMC

Tini G, et al. DNA methylation during human adipogenesis and the impact of fructose. Genes Nutr. 2020;15:21. doi: 10.1186/s12263-020-00680-2. PubMed DOI PMC

Shao M, et al. Pathologic HIF1alpha signaling drives adipose progenitor dysfunction in obesity. Cell Stem Cell. 2021;28:685–701.e687. doi: 10.1016/j.stem.2020.12.008. PubMed DOI PMC

Harms MJ, et al. Mature human white adipocytes cultured under membranes maintain identity, function, and can transdifferentiate into brown-like adipocytes. Cell Rep. 2019;27:213–225.e215. doi: 10.1016/j.celrep.2019.03.026. PubMed DOI

Forrest AR, et al. A promoter-level mammalian expression atlas. Nature. 2014;507:462–470. doi: 10.1038/nature13182. PubMed DOI PMC

Gupta A, et al. Characterization of transcript enrichment and detection bias in single-nucleus RNA-seq for mapping of distinct human adipocyte lineages. Genome Res. 2022;32:242–257. doi: 10.1101/gr.275509.121. PubMed DOI PMC

Jin S, et al. Inference and analysis of cell-cell communication using CellChat. Nat. Commun. 2021;12:1088. doi: 10.1038/s41467-021-21246-9. PubMed DOI PMC

Timokhina I, Kissel H, Stella G, Besmer P. Kit signaling through PI 3-kinase and Src kinase pathways: an essential role for Rac1 and JNK activation in mast cell proliferation. EMBO J. 1998;17:6250–6262. doi: 10.1093/emboj/17.21.6250. PubMed DOI PMC

Huizer K, et al. Periostin is expressed by pericytes and is crucial for angiogenesis in glioma. J. Neuropathol. Exp. Neurol. 2020;79:863–872. doi: 10.1093/jnen/nlaa067. PubMed DOI

Rosen ED, Spiegelman BM. What we talk about when we talk about fat. Cell. 2014;156:20–44. doi: 10.1016/j.cell.2013.12.012. PubMed DOI PMC

Pasarica M, et al. Reduced adipose tissue oxygenation in human obesity: evidence for rarefaction, macrophage chemotaxis, and inflammation without an angiogenic response. Diabetes. 2009;58:718–725. doi: 10.2337/db08-1098. PubMed DOI PMC

Kerr AG, Andersson DP, Ryden M, Arner P, Dahlman I. Long-term changes in adipose tissue gene expression following bariatric surgery. J. Intern. Med. 2020;288:219–233. doi: 10.1111/joim.13066. PubMed DOI

Petrus P, et al. Transforming growth factor-beta3 regulates adipocyte number in subcutaneous white adipose tissue. Cell Rep. 2018;25:551–560.e555. doi: 10.1016/j.celrep.2018.09.069. PubMed DOI

Lenz M, Arts ICW, Peeters RLM, de Kok TM, Ertaylan G. Adipose tissue in health and disease through the lens of its building blocks. Sci. Rep. 2020;10:10433. doi: 10.1038/s41598-020-67177-1. PubMed DOI PMC

Norreen-Thorsen M, et al. A human adipose tissue cell-type transcriptome atlas. Cell Rep. 2022;40:111046. doi: 10.1016/j.celrep.2022.111046. PubMed DOI

Hoffstedt J, et al. Long-term protective changes in adipose tissue after gastric bypass. Diabetes Care. 2017;40:77–84. doi: 10.2337/dc16-1072. PubMed DOI

Mileti E, et al. Human white adipose tissue displays selective insulin resistance in the obese state. Diabetes. 2021;70:1486–1497. doi: 10.2337/db21-0001. PubMed DOI

Ryden M, et al. The adipose transcriptional response to insulin is determined by obesity, not insulin sensitivity. Cell Rep. 2016;16:2317–2326. doi: 10.1016/j.celrep.2016.07.070. PubMed DOI

Slyper M, et al. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat. Med. 2020;26:792–802. doi: 10.1038/s41591-020-0844-1. PubMed DOI PMC

Fleming, S. J., Marioni, J. C. & Babadi, M. CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets. bioRxiv, https://www.biorxiv.org/content/10.1101/791699v1 (2019). DOI

Germain PL, Lun A, Garcia Meixide C, Macnair W, Robinson MD. Doublet identification in single-cell sequencing data using scDblFinder. F1000Research. 2022;10:979. doi: 10.12688/f1000research.73600.2. PubMed DOI PMC

Team, R. C. R. A Language and Environment for Statistical Computing, (Vienna, Austria, 2018).

Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 2019;20:296. doi: 10.1186/s13059-019-1874-1. PubMed DOI PMC

Hyvarinen A, Oja E. Independent component analysis: algorithms and applications. Neural Netw. 2000;13:411–430. doi: 10.1016/S0893-6080(00)00026-5. PubMed DOI

Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol.37, 38–44 (2018). PubMed

Gustavsen JA, Pai S, Isserlin R, Demchak B, Pico AR. RCy3: network biology using cytoscape from within R. F1000Res. 2019;8:1774. doi: 10.12688/f1000research.20887.2. PubMed DOI PMC

Jew B, et al. Accurate estimation of cell composition in bulk expression through robust integration of single-cell information. Nat. Commun. 2020;11:1971. doi: 10.1038/s41467-020-15816-6. PubMed DOI PMC

Arner E, et al. Adipose tissue microRNAs as regulators of CCL2 production in human obesity. Diabetes. 2012;61:1986–1993. doi: 10.2337/db11-1508. PubMed DOI PMC

Arner P, Andersson DP, Backdahl J, Dahlman I, Ryden M. Weight gain and impaired glucose metabolism in women are predicted by inefficient subcutaneous fat cell lipolysis. Cell Metab. 2018;28:45–54.e43. doi: 10.1016/j.cmet.2018.05.004. PubMed DOI

Imbert A, et al. Network analyses reveal negative link between changes in adipose tissue GDF15 and BMI during dietary-induced weight loss. J. Clin. Endocrinol. Metab. 2022;107:e130–e142. doi: 10.1210/clinem/dgab621. PubMed DOI

Armenise C, et al. Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects. Am. J. Clin. Nutr. 2017;106:736–746. doi: 10.3945/ajcn.117.156216. PubMed DOI

Schwarzer, G., Carpenter, J. R. & Rücker, G. Meta-Analysis with R (Springer Cham, 2015).

Acosta JR, et al. Increased fat cell size: a major phenotype of subcutaneous white adipose tissue in non-obese individuals with type 2 diabetes. Diabetologia. 2016;59:560–570. doi: 10.1007/s00125-015-3810-6. PubMed DOI

Kleshchevnikov V, et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 2022;40:661–671. doi: 10.1038/s41587-021-01139-4. PubMed DOI

Andersson A, et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol. 2020;3:565. doi: 10.1038/s42003-020-01247-y. PubMed DOI PMC

Elosua-Bayes M, Nieto P, Mereu E, Gut I, Heyn H. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 2021;49:e50. doi: 10.1093/nar/gkab043. PubMed DOI PMC

Cable DM, et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 2022;40:517–526. doi: 10.1038/s41587-021-00830-w. PubMed DOI PMC

Biancalani T, et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat. Methods. 2021;18:1352–1362. doi: 10.1038/s41592-021-01264-7. PubMed DOI PMC

Lopez R, et al. DestVI identifies continuums of cell types in spatial transcriptomics data. Nat. Biotechnol. 2022;40:1360–1369. doi: 10.1038/s41587-022-01272-8. PubMed DOI PMC

Noguchi S, et al. FANTOM5 CAGE profiles of human and mouse samples. Sci. Data. 2017;4:170112. doi: 10.1038/sdata.2017.112. PubMed DOI PMC

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...