Callum Tromans-Coia, Nasim Jamali, Hamdah Shafqat Abbasi, Kenneth A Giuliano, Mai Hagimoto, Kevin Jan, Erika Kaneko, Stefan Letzsch, Alexander Schreiner, Jonathan Z Sexton, Mahomi Suzuki, O Joseph Trask, Mitsunari Yamaguchi, Fumiki Yanagawa, Michael Yang, Anne E Carpenter, Beth A Cimini
{"title":"Assessing the performance of the Cell Painting assay across different imaging systems.","authors":"Callum Tromans-Coia, Nasim Jamali, Hamdah Shafqat Abbasi, Kenneth A Giuliano, Mai Hagimoto, Kevin Jan, Erika Kaneko, Stefan Letzsch, Alexander Schreiner, Jonathan Z Sexton, Mahomi Suzuki, O Joseph Trask, Mitsunari Yamaguchi, Fumiki Yanagawa, Michael Yang, Anne E Carpenter, Beth A Cimini","doi":"10.1002/cyto.a.24786","DOIUrl":null,"url":null,"abstract":"<p><p>Quantitative microscopy is a powerful method for performing phenotypic screens from which image-based profiling can extract a wealth of information, termed profiles. These profiles can be used to elucidate the changes in cellular phenotypes across cell populations from different patient samples or following genetic or chemical perturbations. One such image-based profiling method is the Cell Painting assay, which provides morphological insight through the imaging of eight cellular compartments. Here, we examine the performance of the Cell Painting assay across multiple high-throughput microscope systems and find that all are compatible with this assay. Furthermore, we determine independently for each microscope system the best performing settings, providing those who wish to adopt this assay an ideal starting point for their own assays. We also explore the impact of microscopy setting changes in the Cell Painting assay and find that few dramatically reduce the quality of a Cell Painting profile, regardless of the microscope used.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":"915-926"},"PeriodicalIF":2.5000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10841730/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cytometry Part A","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1002/cyto.a.24786","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/10/3 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Quantitative microscopy is a powerful method for performing phenotypic screens from which image-based profiling can extract a wealth of information, termed profiles. These profiles can be used to elucidate the changes in cellular phenotypes across cell populations from different patient samples or following genetic or chemical perturbations. One such image-based profiling method is the Cell Painting assay, which provides morphological insight through the imaging of eight cellular compartments. Here, we examine the performance of the Cell Painting assay across multiple high-throughput microscope systems and find that all are compatible with this assay. Furthermore, we determine independently for each microscope system the best performing settings, providing those who wish to adopt this assay an ideal starting point for their own assays. We also explore the impact of microscopy setting changes in the Cell Painting assay and find that few dramatically reduce the quality of a Cell Painting profile, regardless of the microscope used.
期刊介绍:
Cytometry Part A, the journal of quantitative single-cell analysis, features original research reports and reviews of innovative scientific studies employing quantitative single-cell measurement, separation, manipulation, and modeling techniques, as well as original articles on mechanisms of molecular and cellular functions obtained by cytometry techniques.
The journal welcomes submissions from multiple research fields that fully embrace the study of the cytome:
Biomedical Instrumentation Engineering
Biophotonics
Bioinformatics
Cell Biology
Computational Biology
Data Science
Immunology
Parasitology
Microbiology
Neuroscience
Cancer
Stem Cells
Tissue Regeneration.