Back

Single-Nucleus Analysis of Human White Adipose Tissue Reveals Adipocyte Subsets with Distinct Metabolic Profiles

Efthymiou, V.; Ghosh, A.; Kodani, S. D.; Caubit, X.; Fasano, L.; Ali, W.; Poulos, L. S.; Camara, H.; Gupta, A.; Belaidouni, Y.; Booeshaghi, S. A.; Yang, S.; Rastogi, R.; Shamsi, F.; Vernon, A.; Streets, A. M.; Tseng, Y.-H.; Patti, M. E.

2025-09-16 cell biology
10.1101/2025.09.14.673351 bioRxiv
Show abstract

Anatomic location of white adipose tissue is a determinant of cardiometabolic risk. To understand differences within/between adipose depots, we generated 65,668 single-nucleus transcriptomes from human subcutaneous or intraabdominal adipose tissue (SAT/IAT). Unsupervised analysis revealed 26 adipose-resident cell clusters including two subpopulations of mature adipocytes, characterized by high vs. low expression of adipocyte maturation genes (ADIPOMAThi vs. ADIPOMATlo). ADIPOMATlo adipocytes demonstrate a low-differentiation, pro-inflammatory, and pro-fibrotic transcriptome. IAT-resident ADIPOMATlo were more abundant in higher BMI donors, while SAT-resident ADIPOMATlo associated with impaired glycemia. TSHZ3 was identified as a candidate regulator of ADIPOMATlo transcriptome. TSHZ3 knockdown in adipogenic progenitors inhibited differentiation, with downregulation of early adipogenic regulators (e.g. CEBPA/B, PPARG) and mature adipocyte genes. Heterozygous deletion of Tshz3 in mice reduced SAT and IAT weight. Here, we show that adipocyte subsets with distinct transcriptomic signature reside in human WAT; altered TSHZ3-mediated transcriptional regulation may contribute to low-maturation subpopulation linked to metabolic disease.

Published in Nature Communications (predicted rank #1) · training set

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.