A statistical framework for high-content phenotypic profiling using cellular feature distributions

Yanthe E. Pearson, Stephan Kremb, Glenn L. Butterfoss, Xin Xie, Hala Fahs, Kristin C. Gunsalus

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number1409
JournalCommunications Biology
Issue number1
StatePublished - Dec 2022

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences


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