Cell GenomicsGenomicsPeer-reviewed journal article
Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler
Samantha LaberiD, Sophie StrobeliD, Josep Maria MercaderiD, Hesam DashtiiD, Felipe R. C. dos SantosiD, Phil KubitziD, Maya Jackson, Alina AinbinderiD, Julius HoneckeriD, Saaket AgrawaliD, Garrett GarborcauskasiD, David R. StirlingiD, Aaron LeongiD, Katherine FigueroaiD, Nasa Sinnott-ArmstrongiD, Maria Kost‐AlimovaiD, Giacomo Deodato, Alycen M. HarneyiD, Gregory P. WayiD, Alham Saadat, Sierra Harken, Saskia ReibeiD, Hannah Ebert, Yixin ZhangiD, Virtu Calabuig-NavarroiD, , Melina ClaussnitzeriD
Abstract
A primary obstacle in translating genetics and genomics data into therapeutic strategies is elucidating the cellular programs affected by genetic variants and genes associated with human diseases. Broadly applicable high-throughput, unbiased assays offer a path to rapidly characterize gene and variant function and thus illuminate disease mechanisms. Here, we report LipocyteProfiler, an unbiased high-throughput, high-content microscopy assay that is amenable to large-scale morphological and cellular profiling of lipid-accumulating cell types. We apply LipocyteProfiler to adipocytes and hepatocytes and demonstrate its ability to survey diverse cellular mechanisms by generating rich context-, and process-specific morphological and cellular profiles. We then use LipocyteProfiler to identify known and novel cellular programs altered by polygenic risk of metabolic disease, including insulin resistance, waist-to-hip ratio and the polygenic contribution to lipodystrophy. LipocyteProfiler paves the way for large-scale forward and reverse phenotypic profiling in lipid-storing cells, and provides a framework for the unbiased identification of causal relationships between genetic variants and cellular programs relevant to human disease.
- organism: human
- assay: imaging
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: experimental assay
- Citations
- 42
- FWCI
- 4.48
- Field percentile
- top 4%
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