A statistical framework for high-content phenotypic profiling using cellular feature distributions.
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
22 12 2022
22 12 2022
Historique:
received:
17
02
2022
accepted:
05
12
2022
entrez:
22
12
2022
pubmed:
23
12
2022
medline:
27
12
2022
Statut:
epublish
Résumé
High-content screening (HCS) uses microscopy images to generate phenotypic profiles of cell morphological data in high-dimensional feature space. While HCS provides detailed cytological information at single-cell resolution, these complex datasets are usually aggregated into summary statistics that do not leverage patterns of biological variability within cell populations. Here we present a broad-spectrum HCS analysis system that measures image-based cell features from 10 cellular compartments across multiple assay panels. We introduce quality control measures and statistical strategies to streamline and harmonize the data analysis workflow, including positional and plate effect detection, biological replicates analysis and feature reduction. We also demonstrate that the Wasserstein distance metric is superior over other measures to detect differences between cell feature distributions. With this workflow, we define per-dose phenotypic fingerprints for 65 mechanistically diverse compounds, provide phenotypic path visualizations for each compound and classify compounds into different activity groups.
Identifiants
pubmed: 36550289
doi: 10.1038/s42003-022-04343-3
pii: 10.1038/s42003-022-04343-3
pmc: PMC9780213
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1409Informations de copyright
© 2022. The Author(s).
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