Deep learning enables the quantification of browning capacity of human adipose samples

Abstract

BackgroundThe recruitment of thermogenic adipocytes in human fat depots markedly improves metabolic disorders such as type 2 diabetes mellitus (T2DM). However, identification and quantification of thermogenic cells in human fats, especially in metabolic disorders patients, remains a major challenge. Here, we aim to provide a stringent validation of human thermogenic adipocyte signature genes, and construct transcriptome-based models to quantify the browning degree of human fats.MethodsEvidence from RNA-seq, microarray analyses and experimental approaches were integrated to isolate robust human brown-like fat signature genes. Meta-analysis was employed to validate the performance of known human brown-like fat marker genes. Autoencoder was used to reveal the browning levels of human adipose samples for supervised machine learning. Ensemble machine learning was applied to devised molecular metrics for quantifying browning degree of human fats. Obesity and T2DM datasets were used to validate the performance of the molecular metrics in adipose-related metabolic disorders.ResultsHuman brown-like adipocytes were heterogeneous populations which showed distinct transcriptional patterns and biological features. Only DHRS11, REEP6 and STX11 were robust signature genes that were consistently up-regulated in different human brown-like fats, especially in creatine-induced UCP1-independent adipocytes. The molecular metrices based on the expression patterns of the three signature genes, named human browning capacity index (HBI) and absolute HBI (absHBI), were superior to 26 traditional human brown-like fat marker genes and previously reported browning classifier in prediction of browning levels of human adipocytes and adipose tissues as well as primary cell cultures upon various physiological and pharmacological stimuli. Notably, these molecular metrics also reflected the insulin sensitivity and glucose-lipid metabolic activity of human adipose samples from obesity and T2DM patients.ConclusionsIn summary, this study provides promising signatures and computational tools for evaluating browning levels of human adipose samples in response to physiological and medical intervention. The metrices construction pipeline provides an alternative approach for training machine learning models using unlabeled samples.