| First Author | Perdikari A | Year | 2018 |
| Journal | Cell Rep | Volume | 25 |
| Issue | 3 | Pages | 784-797.e4 |
| PubMed ID | 30332656 | Mgi Jnum | J:270769 |
| Mgi Id | MGI:6278702 | Doi | 10.1016/j.celrep.2018.09.044 |
| Citation | Perdikari A, et al. (2018) BATLAS: Deconvoluting Brown Adipose Tissue. Cell Rep 25(3):784-797.e4 |
| abstractText | 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. |
