使用脂肪细胞分析器发现代谢特征的内在和外在驱动因素的细胞程序。
Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler.
作者信息
Laber Samantha, Strobel Sophie, Mercader Josep M, Dashti Hesam, Dos Santos Felipe R C, Kubitz Phil, Jackson Maya, Ainbinder Alina, Honecker Julius, Agrawal Saaket, Garborcauskas Garrett, Stirling David R, Leong Aaron, Figueroa Katherine, Sinnott-Armstrong Nasa, Kost-Alimova Maria, Deodato Giacomo, Harney Alycen, Way Gregory P, Saadat Alham, Harken Sierra, Reibe-Pal Saskia, Ebert Hannah, Zhang Yixin, Calabuig-Navarro Virtu, McGonagle Elizabeth, Stefek Adam, Dupuis Josée, Cimini Beth A, Hauner Hans, Udler Miriam S, Carpenter Anne E, Florez Jose C, Lindgren Cecilia, Jacobs Suzanne B R, Claussnitzer Melina
机构信息
Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK.
出版信息
Cell Genom. 2023 Jun 20;3(7):100346. doi: 10.1016/j.xgen.2023.100346. eCollection 2023 Jul 12.
A primary obstacle in translating genetic associations with disease into therapeutic strategies is elucidating the cellular programs affected by genetic risk variants and effector genes. Here, we introduce LipocyteProfiler, a cardiometabolic-disease-oriented high-content image-based profiling tool that enables evaluation of thousands of morphological and cellular profiles that can be systematically linked to genes and genetic variants relevant to cardiometabolic disease. We show that LipocyteProfiler allows surveillance of diverse cellular programs by generating rich context- and process-specific cellular profiles across hepatocyte and adipocyte cell-state transitions. We use LipocyteProfiler to identify known and novel cellular mechanisms altered by polygenic risk of metabolic disease, including insulin resistance, fat distribution, and the polygenic contribution to lipodystrophy. LipocyteProfiler paves the way for large-scale forward and reverse deep phenotypic profiling in lipocytes and provides a framework for the unbiased identification of causal relationships between genetic variants and cellular programs relevant to human disease.
将与疾病相关的基因关联转化为治疗策略的一个主要障碍是阐明受遗传风险变异和效应基因影响的细胞程序。在此，我们介绍LipocyteProfiler，这是一种面向心脏代谢疾病的基于高内涵图像的分析工具，它能够评估数千种形态学和细胞特征，这些特征可以系统地与心脏代谢疾病相关的基因和遗传变异联系起来。我们表明，LipocyteProfiler通过在肝细胞和脂肪细胞状态转变过程中生成丰富的特定背景和过程的细胞特征，允许对多种细胞程序进行监测。我们使用LipocyteProfiler来识别由代谢疾病的多基因风险改变的已知和新的细胞机制，包括胰岛素抵抗、脂肪分布以及对脂肪营养不良的多基因贡献。LipocyteProfiler为脂肪细胞中的大规模正向和反向深度表型分析铺平了道路，并为无偏识别与人类疾病相关的遗传变异和细胞程序之间的因果关系提供了一个框架。
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