A1 Refereed original research article in a scientific journal

BATLAS: Deconvoluting Brown Adipose Tissue




AuthorsAliki Perdikari, Germán Gastón Leparc, Miroslav Balaz, Nuno D. Pires, Martin E. Lidell, Wenfei Sun, Francesc Fernandez-Albert, Sebastian Müller, Nassila Akchiche, Hua Dong, Lucia Balazova, Lennart Opitz, Eva Röder, Holger Klein, Patrik Stefanicka, Lukas Varga, Pirjo Nuutila, Kirsi A. Virtanen, Tarja Niemi, Markku Taittonen, Gottfried Rudofsky, Jozef Ukropec, Sven Enerbäck, Elia Stupka, Heike Neubauer, Christian Wolfrum

PublisherCELL PRESS

Publication year2018

JournalCell Reports

Journal name in sourceCELL REPORTS

Journal acronymCELL REP

Volume25

Issue3

First page 784

Last page797.e4

Number of pages18

ISSN2211-1247

DOIhttps://doi.org/10.1016/j.celrep.2018.09.044(external)

Self-archived copy’s web addresshttps://research.utu.fi/converis/portal/detail/Publication/36509460(external)


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.

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