Dataset Information

Selection of Optimal Cell Lines for High-Content Phenotypic Screening.

ABSTRACT: High-content microscopy offers a scalable approach to screen against multiple targets in a single pass. Prior work has focused on methods to select "optimal" cellular readouts in microscopy screens. However, methods to select optimal cell line models have garnered much less attention. Here, we provide a roadmap for how to select the cell line or lines that are best suited to identify bioactive compounds and their mechanism of action (MOA). We test our approach on compounds targeting cancer-relevant pathways, ranking cell lines in two tasks: detecting compound activity ("phenoactivity") and grouping compounds with similar MOA by similar phenotype ("phenosimilarity"). Evaluating six cell lines across 3214 well-annotated compounds, we show that optimal cell line selection depends on both the task of interest (e.g., detecting phenoactivity vs inferring phenosimilarity) and distribution of MOAs within the compound library. Given a task of interest and a set of compounds, we provide a systematic framework for choosing optimal cell line(s). Our framework can be used to reduce the number of cell lines required to identify hits within a compound library and help accelerate the pace of early drug discovery.

SUBMITTER: Heinrich L

PROVIDER: S-EPMC10127200 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Selection of Optimal Cell Lines for High-Content Phenotypic Screening.

Heinrich Louise L Kumbier Karl K Li Li L Altschuler Steven J SJ Wu Lani F LF

ACS chemical biology 20230315 4

High-content microscopy offers a scalable approach to screen against multiple targets in a single pass. Prior work has focused on methods to select "optimal" cellular readouts in microscopy screens. However, methods to select optimal cell line models have garnered much less attention. Here, we provide a roadmap for how to select the cell line or lines that are best suited to identify bioactive compounds and their mechanism of action (MOA). We test our approach on compounds targeting cancer-relev ...[more]

PMID: 36920184

Similar Datasets

Project description:Coxiella burnetii is the agent of the emerging zoonosis Q fever. This pathogen invades phagocytic and non-phagocytic cells and uses a Dot/Icm secretion system to co-opt the endocytic pathway for the biogenesis of an acidic parasitophorous vacuole where Coxiella replicates in large numbers. The study of the cell biology of Coxiella infections has been severely hampered by the obligate intracellular nature of this microbe, and Coxiella factors involved in host/pathogen interactions remain to date largely uncharacterized. Here we focus on the large-scale identification of Coxiella virulence determinants using transposon mutagenesis coupled to high-content multi-phenotypic screening. We have isolated over 3000 Coxiella mutants, 1082 of which have been sequenced, annotated and screened. We have identified bacterial factors that regulate key steps of Coxiella infections: 1) internalization within host cells, 2) vacuole biogenesis/intracellular replication, and 3) protection of infected cells from apoptosis. Among these, we have investigated the role of Dot/Icm core proteins, determined the role of candidate Coxiella Dot/Icm substrates previously identified in silico and identified additional factors that play a relevant role in Coxiella pathogenesis. Importantly, we have identified CBU_1260 (OmpA) as the first Coxiella invasin. Mutations in ompA strongly decreased Coxiella internalization and replication within host cells; OmpA-coated beads adhered to and were internalized by non-phagocytic cells and the ectopic expression of OmpA in E. coli triggered its internalization within cells. Importantly, Coxiella internalization was efficiently inhibited by pretreating host cells with purified OmpA or by incubating Coxiella with a specific anti-OmpA antibody prior to host cell infection, suggesting the presence of a cognate receptor at the surface of host cells. In summary, we have developed multi-phenotypic assays for the study of host/pathogen interactions. By applying our methods to Coxiella burnetii, we have identified the first Coxiella protein involved in host cell invasion.

Dataset Information

Selection of Optimal Cell Lines for High-Content Phenotypic Screening.

Publications

Selection of Optimal Cell Lines for High-Content Phenotypic Screening.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets