Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler
Samantha Laber, Sophie Strobel, Josep M. Mercader, Hesam Dashti, Felipe R. C. dos Santos, Phil Kubitz, Maya Jackson, Alina Ainbinder, Julius Honecker, Saaket Agrawal, Garrett Garborcauskas, David R. Stirling, Aaron Leong, Katherine Figueroa, Nasa Sinnott-Armstrong, Maria Kost‐Alimova, Giacomo Deodato, Alycen Harney, Gregory P. Way, Alham Saadat, Sierra Harken, Saskia Reibe-Pal, Hannah Ebert, Yixin Zhang, Virtu Calabuig-Navarro, Elizabeth R. McGonagle, Adam Štefek, Josée Dupuis, Beth A. Cimini, Hans Hauner, Miriam S. Udler, Anne E. Carpenter, José C. Florez, Cecilia M. Lindgren, Suzanne B.R. Jacobs, Melina Claussnitzer
Abstract
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.