Stellar quality control for single-cell image-based profiling with coSMicQC

Abstract

Over the past twenty years, high-content imaging has transformed our ability to measure cell phenotypes. The need to bioinformatically process these phenotypes led to the development of a research field called image-based profiling. However, because the standard image-based profiling approach involves averaging data, single-cell quality control (QC) has been historically ignored. The conventional approach of aggregating single cells into bulk profiles conserves computational resources and reduces, but does not completely remove, the impact of low-quality single cells. As software scalability improves, researchers are increasingly turning to single-cell image-based profiling to reveal important signals of phenotypic heterogeneity. Therefore, this evolution toward single cells compels single-cell QC standards to ensure that observed morphology differences are driven by biology and not technical interference. We address these challenges with coSMicQC (Single cell Morphology Quality Control), a reproducible Python package with comprehensive tutorials that supports systematic, human-in-the-loop filtering of low-quality single cells. CoSMicQC integrates seamlessly into standard image-based profiling protocols, providing an interactive, Jupyter-compatible user interface to set thresholds and flag technical outliers. Applied to four real-world datasets, coSMicQC achieves data-quality gains comparable to labor-intensive manual annotation, at a fraction of the effort. We show how coSMicQC optimizes assay conditions, outperforms the alternative outlier detector PyOD at single-cell phenotype classification, detects mycoplasma contamination, and rescues lead compounds in a large-scale drug screen that would otherwise have been missed. Overall, coSMicQC is a reliable, scalable method for removing technical outliers, reducing noise, and strengthening image-based profiling insights.