ena-STUDY-ETHZ-02-05-2017-09:27:35:328-288
BATLAS: Deconvoluting brown adipose tissue
Recruitment and activation of classical brown and inducible brite/beige adipocytes has received increasing attention in recent years as a strategy to improve systemic metabolic control.Nevertheless, the origin and expression signatures of brown and brite/beige adipocytes are still under debate, mainly due to the complexity of tissue biopsies. To study different adipocyte types in detail, we generated pure samples of brown, brite/beige, and white mature adipocytes by fluorescence activated cell sorting. Using this resource, we could demonstrate that on a transcriptional level, brown and brite/beige mature adipocytes are identical. Employing a machine learning approach for paired analysis of transcriptional data of pure mouse brown,brite/beige and white adipocytes and human brown and white whole adipose tissue obtained by PET-CT-guided biopsies, we were concomitantly able to identify a gene signature that can classify brown and white adipose tissue depots. Using the newly developed algorithm, we were able to predict the brown adipocyte content in a mixed population of adipocytes from different human biopsies that can be used for in-depth characterization of complex tissue samples from adipose tissue and might therefore support the development strategies to increase brown adipocyte formation in humans.
Recruitment and activation of classical brown and inducible brite/beige adipocytes has received increasing attention in recent years as a strategy to improve systemic metabolic control.Nevertheless, the origin and expression signatures of brown and brite/beige adipocytes are still under debate, mainly due to the complexity of tissue biopsies. To study different adipocyte types in detail, we generated pure samples of brown, brite/beige, and white mature adipocytes by fluorescence activated cell sorting. Using this resource, we could demonstrate that on a transcriptional level, brown and brite/beige mature adipocytes are identical. Employing a machine learning approach for paired analysis of transcriptional data of pure mouse brown,brite/beige and white adipocytes and human brown and white whole adipose tissue obtained by PET-CT-guided biopsies, we were concomitantly able to identify a gene signature that can classify brown and white adipose tissue depots. Using the newly developed algorithm, we were able to predict the brown adipocyte content in a mixed population of adipocytes from different human biopsies that can be used for in-depth characterization of complex tissue samples from adipose tissue and might therefore support the development strategies to increase brown adipocyte formation in humans.