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Friday, December 3 • 4:30pm - 4:40pm
OP 27 - Morphology and gene expression profiling provide complementary information for mapping cell state

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OP-27
Morphology and gene expression profiling provide complementary information for mapping cell state

Presenting Author: Gregory Way, University of Colorado Anschutz

Co-Author(s):
Ted Natoli, Broad Institute of MIT and Harvard
Adeniyi Adeboye, Broad Institute of MIT and Harvard
Lev Litichevskiy, Broad Institute of MIT and Harvard
Andrew Yang, Broad Institute of MIT and Harvard
Xiaodong Lu, Broad Institute of MIT and Harvard
Juan Caicedo, Broad Institute of MIT and Harvard
Beth Cimini, Broad Institute of MIT and Harvard
Kyle Karhohs, Broad Institute of MIT and Harvard
David Logan, Pfizer
Mohammad Rohban, Imaging Platform
Maria Kost-Alimova, Center for the Development of Therapeutics
Kate Hartland, Center for the Development of Therapeutics
Michael Bornholdt, Imaging Platform
Niranj Chandrasekaran, Imaging Platform
Marzieh Haghighi, Imaging Platform
Shantanu Singh, Imaging Platform
Aravind Subramanian, Cancer Program
Anne Carpenter, Imaging Platform

Abstract: Deep profiling of cell states can provide a broad picture of biological changes that occur in disease, mutation, or in response to drug or chemical treatments. Morphological and gene expression profiling, for example, can cost-effectively capture thousands of features in thousands of samples across perturbations, but it is unclear to what extent the two modalities capture overlapping versus complementary mechanistic information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb A549 lung cancer with 1,327 small molecules from the Drug Repurposing Hub across six doses. We determine that the two assays capture some shared and some complementary information in mapping cell state. We find that as compared to L1000, Cell Painting captures a higher proportion of reproducible compounds and mechanisms and has more diverse samples, but measures fewer distinct groups of features. In a deep learning analysis, L1000 predicted more compound mechanisms of action (MOA). In general, the two assays together provide a complementary view of drug mechanisms for follow up analyses. Our analysis answers fundamental biological questions comparing the two biological modalities and, given the numerous applications of profiling in biology, provides guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations.


Presenters
avatar for Gregory Way

Gregory Way

Assistant Professor, University of Colorado Anschutz


Friday December 3, 2021 4:30pm - 4:40pm MST
Ballroom Salon 1