The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Single-cell analysis of human adipose tissue identifies depot- and disease-specific cell types
Open accessAbstract
The complex relationship between metabolic disease risk and body fat distribution in humans involves cellular characteristics that are specific to body fat compartments. Here we show depot-specific differences in the stromal vascular fraction of visceral and subcutaneous adipose tissue by performing single-cell RNA sequencing of tissue specimens from obese individuals. We characterize multiple immune cells, endothelial cells, fibroblasts, adipose and haematopoietic stem cell progenitors. Subpopulations of adipose-resident immune cells are metabolically active and associated with metabolic disease status, including a population of potential dysfunctional CD8+ T cells that express metallothioneins. We identify multiple types of adipocyte progenitors that are common across depots, including a subtype enriched in individuals with type 2 diabetes. Depot-specific analysis reveals a class of adipocyte progenitors unique to visceral adipose tissue, which shares common features with beige pre-adipocytes. Our human single-cell transcriptome atlas across fat depots provides a resource to dissect the functional genomics of metabolic disease. Adipose tissue varies depending on localization. Vijay et al. perform single-cell RNA sequencing in multiple adipose tissue depots from obese individuals and identify distinct subpopulations of endothelial cells, immune cells and pre-adipocytes.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Trimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn923 · Cited by 14,108
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Aran, D., Looney, A. P., Liu, L., Wu, E., Fong, V., Hsu, A., Chak, S., Naikawadi, R. P., Wolters, P. J., Abate, A. R., et al. (2019) · Nature Immunology · doi:10.1038/s41590-018-0276-y · Cited by 4,984
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944
Mechanisms of fatty acid-induced inhibition of glucose uptake.
Boden, G., Chen, X., Ruiz, J., White, J. V., & Rossetti, L. (1994) · Journal of Clinical Investigation · doi:10.1172/jci117252 · Cited by 3,705
Cited by
The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth
de Visser, K. E., & Joyce, J. A. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.02.016 · Cited by 4,059
Cells of the adult human heart
Litviňuková, M., Talavera-López, C., Maatz, H., Reichart, D., Worth, C. L., Lindberg, E. L., Kanda, M., Polanski, K., Heinig, M., Lee, M., et al. (2020) · Nature · doi:10.1038/s41586-020-2797-4 · Cited by 1,729
Transfer learning enables predictions in network biology
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al. (2023) · Nature · doi:10.1038/s41586-023-06139-9 · Cited by 1,193
A single-cell atlas of human and mouse white adipose tissue
Emont, M. P., Jacobs, C., Essene, A. L., Pant, D., Tenen, D., Colleluori, G., Di Vincenzo, A., Jørgensen, A. M., Dashti, H., Stefek, A., et al. (2022) · Nature · doi:10.1038/s41586-022-04518-2 · Cited by 840
Neeland, I. J., Lim, S., Tchernof, A., Gastaldelli, A., Rangaswami, J., Ndumele, C. E., Powell-Wiley, T. M., & Després, J.-P. (2024) · Nature Reviews Disease Primers · doi:10.1038/s41572-024-00563-5 · Cited by 463
Self-citation
Metadata
- Authors
- Vijay, J., Gauthier, M.-F., Biswell, R. L., Louiselle, D. A., Johnston, J. J., Cheung, W. A., Belden, B., Pramatarova, A., Biertho, L., Gibson, M., Simon, M.-M., Djambazian, H., Staffa, A., Bourque, G., Laitinen, A., Nystedt, J., Vohl, M.-C., Fraser, J. D., Pastinen, T., Tchernof, A., & Grundberg, E.
- Venue
- Nature Metabolism
- Published
- 2019-12-23
- Volume
- 2
- Issue
- 1
- Pages
- 97-109
- ISSN
- 2522-5812 (ISSN-L)
- OpenAlex
- W2994702596
- Cited by
- 455
- References
- 65
- FWCI
- 24.14
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Single-cell analysis of human adipose tissue identifies depot- and disease-specific cell types. https://app.cochat.ai/papers/10.1038/s42255-019-0152-6
- The paper
- Vijay, J., Gauthier, M.-F., Biswell, R. L., Louiselle, D. A., Johnston, J. J., Cheung, W. A., Belden, B., Pramatarova, A., Biertho, L., Gibson, M., Simon, M.-M., Djambazian, H., Staffa, A., Bourque, G., Laitinen, A., Nystedt, J., Vohl, M.-C., Fraser, J. D., Pastinen, T., . . . Grundberg, E. (2019). Single-cell analysis of human adipose tissue identifies depot- and disease-specific cell types. Nature Metabolism. https://doi.org/10.1038/s42255-019-0152-6
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.