TY - JOUR
T1 - A statistical framework for high-content phenotypic profiling using cellular feature distributions
AU - Pearson, Yanthe E.
AU - Kremb, Stephan
AU - Butterfoss, Glenn L.
AU - Xie, Xin
AU - Fahs, Hala
AU - Gunsalus, Kristin C.
N1 - Funding Information:
The authors thank Nikolaos Giakoumidis (NYUAD Core Technology Platforms) for the maintenance of the High-Throughput Screening Platform; Fathima Shaffra Mohammed Refai and Julie Connelly (NYUAD Center for Genomics and Systems Biology) for assistance with experiments; and Paul Selzer (Novartis, Basel, CH), Roger Linington (Simon Fraser University, Vancouver, CA), and Marc Bickle (Roche, Basel, CH) for technical advice and useful discussions. This work was supported by Tamkeen by an NYUAD Research Institute grant to the NYUAD Center for Genomics and Systems Biology (ADHPG-CGSB).
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
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U2 - 10.1038/s42003-022-04343-3
DO - 10.1038/s42003-022-04343-3
M3 - Article
C2 - 36550289
AN - SCOPUS:85144561142
SN - 2399-3642
VL - 5
JO - Communications Biology
JF - Communications Biology
IS - 1
M1 - 1409
ER -