研究揭示與肥胖代謝健康相關(guān)的脂肪細胞群體

作者：小柯機器人 發(fā)布時間：2024/12/19 16:13:32

瑞士蘇黎世聯(lián)邦理工學(xué)院Christian Wolfrum等研究人員，合作揭示了與肥胖代謝健康相關(guān)的脂肪細胞群體。這一研究成果于2024年12月17日在線發(fā)表在國際學(xué)術(shù)期刊《細胞—代謝》上。

研究人員生成了代謝健康和代謝不健康肥胖個體的，皮下和內(nèi)臟脂肪組織（AT）的綜合細胞圖譜。通過結(jié)合單細胞核RNA測序數(shù)據(jù)、大規(guī)模轉(zhuǎn)錄組學(xué)數(shù)據(jù)和臨床參數(shù)，研究人員發(fā)現(xiàn)間皮細胞、脂肪細胞和脂肪前體細胞與代謝疾病的關(guān)聯(lián)最強。

此外，研究人員揭示了特定細胞的轉(zhuǎn)錄程序，如間皮細胞向間充質(zhì)表型的轉(zhuǎn)變，這些程序在將肥胖與代謝疾病解耦中起到了重要作用。

總的來說，這些發(fā)現(xiàn)通過揭示臨床終點的生物學(xué)驅(qū)動因素，為深入了解肥胖與代謝疾病的關(guān)系提供了寶貴的見解。

據(jù)悉，盡管肥胖個體的代謝表型差異較大，精準(zhǔn)醫(yī)學(xué)仍未被視為肥胖治療的標(biāo)準(zhǔn)方法。AT功能障礙是影響代謝性疾病風(fēng)險變異性的重要因素之一，但關(guān)于不同細胞群體、細胞類型特異性轉(zhuǎn)錄程序，與疾病嚴(yán)重性之間的關(guān)聯(lián)了解較少。

附：英文原文

Title: Unveiling adipose populations linked to metabolic health in obesity

Author: Isabel Reinisch, Adhideb Ghosh, Falko Noé, Wenfei Sun, Hua Dong, Peter Leary, Arne Dietrich, Anne Hoffmann, Matthias Blüher, Christian Wolfrum

Issue&Volume: 2024-12-17

Abstract: Precision medicine is still not considered as a standard of care in obesity treatment, despite a large heterogeneity in the metabolic phenotype of individuals with obesity. One of the strongest factors influencing the variability in metabolic disease risk is adipose tissue (AT) dysfunction; however, there is little understanding of the link between distinct cell populations, cell-type-specific transcriptional programs, and disease severity. Here, we generated a comprehensive cellular map of subcutaneous and visceral AT of individuals with metabolically healthy and unhealthy obesity. By combining single-nucleus RNA-sequencing data with bulk transcriptomics and clinical parameters, we identified that mesothelial cells, adipocytes, and adipocyte-progenitor cells exhibit the strongest correlation with metabolic disease. Furthermore, we uncovered cell-specific transcriptional programs, such as the transitioning of mesothelial cells to a mesenchymal phenotype, that are involved in uncoupling obesity from metabolic disease. Together, these findings provide valuable insights by revealing biological drivers of clinical endpoints.

DOI: 10.1016/j.cmet.2024.11.006

Source: https://www.cell.com/cell-metabolism/abstract/S1550-4131(24)00452-2

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