| Literature DB >> 30332656 |
Aliki Perdikari1, Germán Gastón Leparc2, Miroslav Balaz1, Nuno D Pires1, Martin E Lidell3, Wenfei Sun1, Francesc Fernandez-Albert2, Sebastian Müller1, Nassila Akchiche1, Hua Dong1, Lucia Balazova1, Lennart Opitz1, Eva Röder1, Holger Klein2, Patrik Stefanicka4, Lukas Varga5, Pirjo Nuutila6, Kirsi A Virtanen6, Tarja Niemi7, Markku Taittonen8, Gottfried Rudofsky9, Jozef Ukropec10, Sven Enerbäck3, Elia Stupka2, Heike Neubauer11, Christian Wolfrum12.
Abstract
Recruitment and activation of thermogenic adipocytes have received increasing attention as a strategy to improve systemic metabolic control. The analysis of brown and brite adipocytes is complicated by the complexity of adipose tissue biopsies. Here, we provide an in-depth analysis of pure brown, brite, and white adipocyte transcriptomes. By combining mouse and human transcriptome data, we identify a gene signature that can classify brown and white adipocytes in mice and men. Using a machine-learning-based cell deconvolution approach, we develop an algorithm proficient in calculating the brown adipocyte content in complex human and mouse biopsies. Applying this algorithm, we can show in a human weight loss study that brown adipose tissue (BAT) content is associated with energy expenditure and the propensity to lose weight. This online available tool can be used for in-depth characterization of complex adipose tissue samples and may support the development of therapeutic strategies to increase energy expenditure in humans.Entities:
Keywords: BAT content; deconvolution; gene signature; pure adipocyte populations
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Year: 2018 PMID: 30332656 DOI: 10.1016/j.celrep.2018.09.044
Source DB: PubMed Journal: Cell Rep Impact factor: 9.423