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Abstract
PDGFRA+ cells have been identified as adipocyte stem cells (ASCs) that differentiate into beige adipocytes in white adipose tissue (WAT) following thermogenic stimuli. To elucidate the molecular heterogeneity of ASCs, we conducted single-cell transcriptomic profiling of PDGFRA+ cells isolated from the inguinal WAT (iWAT) of mice treated with the beta3 adrenergic receptor agonist CL316243. Single-cell RNA-seq revealed nine major clusters, which were categorized into four groups: resting, proliferating, differentiating, and adipogenic factor-expressing cells (AFECs). Trajectory analysis revealed sequential activation of molecular pathways, including the Hedgehog and Notch signaling pathways, during beige adipogenesis. AFECs expressed Dpp4 and did not differentiate into adipocytes in culture or after transplantation. Furthermore, genetic lineage tracing studies indicated that DPP4+ cells did not differentiate into adipocytes in iWAT during CL316243-induced beige adipogenesis. However, high-fat diet feeding led to the recruitment of adipocytes from DPP4+ cells in iWAT. Overall, this study improved our understanding of the dynamic molecular basis of beige adipogenesis and the potential contribution of DPP4+ adipocyte lineages to the pathological expansion of WAT during diet-induced obesity.
DPP4+ Adipocyte Lineages: Role in Diet-Induced Adipose Tissue Expansion
This research examines beige adipogenesis, or the creation of ‘beige’ fat cells that burn energy and could help fight obesity. The scientists discovered a group of cells, identifed by specific markers PDGFRA and DPP4, which serve as a source for beige adipogenesis but don’t turn into beige fat cells themselves. They also found that these cells can change to become fat cells under certain situations, like a high-fat diet. The study also showed that the Hedgehog and Notch signaling pathways are vital in the transformation of PDGFRA+ cells into beige fat cells. These discoveries could be important for developing anti-obesity therapeutics.
This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.
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1 Seoul National University, Laboratory of Developmental Biology and Genomics, College of Veterinary Medicine, Interdisciplinary Program for Bioinformatics and Program for Cancer Biology, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905)
2 DGIST, Department of New Biology, Daegu, Republic of Korea (GRID:grid.417736.0) (ISNI:0000 0004 0438 6721)
3 Kangwon National University, Division of Chemical Engineering and Bioengineering, College of Art, Culture and Engineering, Chuncheon, Republic of Korea (GRID:grid.412010.6) (ISNI:0000 0001 0707 9039)
4 College of Pharmacy and Research Institute of Pharmaceutical Sciences Seoul National University, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905)
5 Seoul National University, School of Biological Sciences, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905)
6 DGIST, Department of New Biology, Daegu, Republic of Korea (GRID:grid.417736.0) (ISNI:0000 0004 0438 6721); Pohang University of Science and Technology (POSTECH), Department of Life Sciences, Pohang, Republic of Korea (GRID:grid.49100.3c) (ISNI:0000 0001 0742 4007)