Single-cell analysis of white adipose tissue reveals the tumor-promoting adipocyte subtypes

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Abstract

Background

The tumor-adipose microenvironment (TAME) is characterized by the enrichment of adipocytes, and is considered a special ecosystem that supports cancer progression. However, the heterogeneity and diversity of adipocytes in TAME remains poorly understood.

Methods

We conducted a single-cell RNA sequencing analysis of adipocytes in mouse and human white adipose tissue (WAT). We analyzed several adipocyte subtypes to evaluate their relationship and potential as prognostic factors for overall survival (OS). The potential drugs are screened by using bioinformatics methods. The tumor-promoting effects of a typical adipocyte subtype in breast cancer are validated by performing in vitro functional assays and immunohistochemistry (IHC) in clinical samples.

Results

We profiled a comprehensive single-cell atlas of adipocyte in mouse and human WAT and described their characteristics, origins, development, functions and interactions with immune cells. Several cancer-associated adipocyte subtypes, namely DPP4 ⁺ adipocytes in visceral adipose and ADIPOQ ⁺ adipocytes in subcutaneous adipose, are identified. We found that high levels of these subtypes are associated with unfavorable outcomes in four typical adipose-associated cancers. Some potential drugs including Trametinib, Selumetinib and Ulixertinib are discovered. Emphatically, knockdown of adiponectin receptor 1 (AdipoR1) and AdipoR2 impaired the proliferation and invasion of breast cancer cells. Patients with AdipoR2-high breast cancer display significantly shorter relapse-free survival (RFS) than those with AdipoR2-low breast cancer.

Conclusion

Our results provide a novel understanding of TAME at the single-cell level. Based on our findings, several adipocyte subtypes have negative impact on prognosis. These cancer-associated adipocytes may serve as key prognostic predictor and potential targets for treatment in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-023-04256-7.

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Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

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Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.

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The Cancer Genome Atlas Pan-Cancer analysis project.

John N Weinstein, Eric Collisson, Gordon Mills … (2014)

The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.

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Author and article information

Contributors

Si Sun: 59333173@qq.com

Sheng-Rong Sun: SunSR137@whu.edu.cn

Qi Wu:

ORCID: http://orcid.org/0000-0001-6182-5274

waiwai@whu.edu.cn

Journal

Journal ID (nlm-ta): J Transl Med

Journal ID (iso-abbrev): J Transl Med

Title: Journal of Translational Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1479-5876

Publication date (Electronic): 15 July 2023

Publication date PMC-release: 15 July 2023

Publication date Collection: 2023

Volume: 21

Electronic Location Identifier: 470

Affiliations

[1 ]GRID grid.412632.0, ISNI 0000 0004 1758 2270, Department of Breast and Thyroid Surgery, , Renmin Hospital of Wuhan University, ; Wuhan, Hubei People’s Republic of China

[2 ]GRID grid.412632.0, ISNI 0000 0004 1758 2270, Department of Pathology, , Renmin Hospital of Wuhan University, ; Wuhan, Hubei People’s Republic of China

[3 ]Department of Breast and Thyroid Surgery, Huangshi Central Hospital, Hubei Polytechnic University, Huangshi, Hubei People’s Republic of China

[4 ]Department of Oncology, Shanghai Artemed Hospital, Shanghai, People’s Republic of China

[5 ]GRID grid.412632.0, ISNI 0000 0004 1758 2270, Department of Clinical Laboratory, , Renmin Hospital of Wuhan University, ; Wuhan, Hubei People’s Republic of China

[6 ]GRID grid.412538.9, ISNI 0000 0004 0527 0050, Tongji University Cancer Center, , Shanghai Tenth People’s Hospital, Tongji University School of Medicine, ; Shanghai, People’s Republic of China

Author information

Qi Wu http://orcid.org/0000-0001-6182-5274

Article

Publisher ID: 4256

DOI: 10.1186/s12967-023-04256-7

PMC ID: 10349475

PubMed ID: 37454080

SO-VID: ddd8eb1f-40b5-43a9-9d0e-127fe76d96ff

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 3 March 2023

Date accepted : 9 June 2023

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 82203629

Award Recipient : Qi Wu

Funded by: Shanghai Pujiang Program

Award ID: 22PJD054

Award Recipient : Qi Wu

Funded by: Foundation of Huangshi Central Hospital

Award ID: ZX2023605

Award Recipient : Hong-Fang Feng

Funded by: FundRef http://dx.doi.org/10.13039/501100012226, Fundamental Research Funds for the Central Universities;

Award ID: 2042021kf0098

Award Recipient : Yuan Wang

Funded by: Research foundation of Shanghai Artemed Hospital

Award ID: ATM2022YJ01

Award Recipient : Wen-Ge Li

Custom metadata

ScienceOpen disciplines: Medicine

Keywords: tumor-adipose microenvironment,single-cell rna sequencing,cancer-associated adipocytes,adiponectin receptor

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ScienceOpen disciplines: Medicine

Keywords: tumor-adipose microenvironment, single-cell rna sequencing, cancer-associated adipocytes, adiponectin receptor

Single-cell analysis of white adipose tissue reveals the tumor-promoting adipocyte subtypes

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Abstract

Background

Methods

Results

Conclusion

Supplementary Information

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Karger: Nephrology

Most cited references 55

Integrated analysis of multimodal single-cell data

Inference and analysis of cell-cell communication using CellChat

The Cancer Genome Atlas Pan-Cancer analysis project.

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