| Summary: | Summary: 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. : By combining mouse and human transcriptome data, Perdikari et al. identify a gene signature that can classify brown and white adipocytes. Using a machine-learning-based cell deconvolution approach, they develop an algorithm proficient in calculating the brown adipocyte content in complex biopsies. This web tool allows in-depth characterization of adipose tissue samples. Keywords: pure adipocyte populations, gene signature, deconvolution, BAT content
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