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 McGonagle; Adam Stefek; Josée Dupuis; Beth A Cimini; Hans Hauner; Miriam S Udler; Anne E Carpenter; Jose C Florez; Cecilia Lindgren; Suzanne B R Jacobs; Melina Claussnitzer

doi:10.1016/j.xgen.2023.100346

Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler

Cell Genom. 2023 Jun 20;3(7):100346. doi: 10.1016/j.xgen.2023.100346. eCollection 2023 Jul 12.

Authors

Samantha Laber^{1

2

3}, Sophie Strobel^{1

4}, Josep M Mercader^{1

5

6}, Hesam Dashti^{1

6

7}, Felipe R C Dos Santos^{1

7}, Phil Kubitz^{1

8

7}, Maya Jackson^{1

7}, Alina Ainbinder¹, Julius Honecker⁸, Saaket Agrawal¹, Garrett Garborcauskas¹, David R Stirling⁹, Aaron Leong^{1

5

6}, Katherine Figueroa^{1

5}, Nasa Sinnott-Armstrong^{1

10}, Maria Kost-Alimova⁹, Giacomo Deodato¹, Alycen Harney¹, Gregory P Way⁹, Alham Saadat¹, Sierra Harken¹, Saskia Reibe-Pal², Hannah Ebert¹¹, Yixin Zhang¹², Virtu Calabuig-Navarro^{1

11}, Elizabeth McGonagle¹, Adam Stefek¹, Josée Dupuis^{12

13}, Beth A Cimini¹, Hans Hauner^{4

8

14}, Miriam S Udler^{1

5

6}, Anne E Carpenter⁹, Jose C Florez^{1

5

6}, Cecilia Lindgren^{1

2

3}, Suzanne B R Jacobs^{1

5}, Melina Claussnitzer^{1

15

5

6

7}

Affiliations

¹ Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
² Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK.
³ Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.
⁴ Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, 85354 Freising-Weihenstephan, Germany.
⁵ Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.
⁶ Department of Medicine, Harvard Medical School, Boston, MA 02114, USA.
⁷ The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
⁸ Else Kröner-Fresenius-Centre for Nutritional Medicine, School of Life Sciences, Technical University of Munich, 85354 Freising-Weihenstephan, Germany.
⁹ Imaging Platform, Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
¹⁰ Department of Genetics, Stanford University, San Francisco, CA, USA.
¹¹ Institute of Nutritional Science, University Hohenheim, 70599 Stuttgart, Germany.
¹² Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA.
¹³ Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1G1, Canada.
¹⁴ German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany.
¹⁵ Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.

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.

Keywords: cardio-metabolic disease-oriented image-based profiling; high-dimensional mapping of cellular phenotypes; variant-to-function studies.

Abstract

Grants and funding