BATLAS: Deconvoluting Brown Adipose Tissue



: Aliki 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

Publisher: CELL PRESS

: 2018

: Cell Reports

: CELL REPORTS

: CELL REP

: 25

: 3

: 784

: 797.e4

: 18

: 2211-1247

DOI: https://doi.org/10.1016/j.celrep.2018.09.044

: https://research.utu.fi/converis/portal/detail/Publication/36509460


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.

Last updated on 2024-26-11 at 22:55