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强大的单核RNA测序揭示了肥胖期间脂肪组织重塑中特定储存库的细胞群体动态变化。

Robust single-nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity.

作者信息

So Jisun, Strobel Olivia, Wann Jamie, Kim Kyungchan, Paul Avishek, Acri Dominic J, Dabin Luke C, Kim Jungsu, Peng Gang, Roh Hyun Cheol

机构信息

Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, United States.

Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, United States.

出版信息

Elife. 2025 Jan 13;13:RP97981. doi: 10.7554/eLife.97981.

Abstract

Single-nucleus RNA sequencing (snRNA-seq), an alternative to single-cell RNA sequencing (scRNA-seq), encounters technical challenges in obtaining high-quality nuclei and RNA, persistently hindering its applications. Here, we present a robust technique for isolating nuclei across various tissue types, remarkably enhancing snRNA-seq data quality. Employing this approach, we comprehensively characterize the depot-dependent cellular dynamics of various cell types underlying mouse adipose tissue remodeling during obesity. By integrating bulk nuclear RNA-seq from adipocyte nuclei of different sizes, we identify distinct adipocyte subpopulations categorized by size and functionality. These subpopulations follow two divergent trajectories, adaptive and pathological, with their prevalence varying by depot. Specifically, we identify a key molecular feature of dysfunctional hypertrophic adipocytes, a global shutdown in gene expression, along with elevated stress and inflammatory responses. Furthermore, our differential gene expression analysis reveals distinct contributions of adipocyte subpopulations to the overall pathophysiology of adipose tissue. Our study establishes a robust snRNA-seq method, providing novel insights into the biological processes involved in adipose tissue remodeling during obesity, with broader applicability across diverse biological systems.

摘要

单细胞核RNA测序(snRNA-seq)作为单细胞RNA测序(scRNA-seq)的替代方法,在获取高质量细胞核和RNA方面面临技术挑战,持续阻碍其应用。在此,我们提出了一种强大的技术,用于分离各种组织类型中的细胞核,显著提高了snRNA-seq数据质量。采用这种方法,我们全面表征了肥胖期间小鼠脂肪组织重塑过程中各种细胞类型的储存库依赖性细胞动态。通过整合来自不同大小脂肪细胞核的大量核RNA测序数据,我们鉴定出按大小和功能分类的不同脂肪细胞亚群。这些亚群遵循两条不同的轨迹,即适应性轨迹和病理性轨迹,其流行程度因储存库而异。具体而言,我们确定了功能失调的肥大脂肪细胞的一个关键分子特征,即基因表达全面关闭,同时应激和炎症反应升高。此外,我们的差异基因表达分析揭示了脂肪细胞亚群对脂肪组织整体病理生理学的不同贡献。我们的研究建立了一种强大的snRNA-seq方法,为肥胖期间脂肪组织重塑所涉及的生物学过程提供了新的见解,在不同生物系统中具有更广泛的适用性。

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