
画像ベースの細胞プロファイリングでは、細胞集団の平均的な特徴を用いるが、これでは集団内の異質性を捉えられない。CytoSummaryNetは、自己教師あり対照学習と多重インスタンス学習を組み合わせることで、細胞集団の異質性を効果的に表現し、メカニズム予測の精度を大幅に向上させる。


coremsg

Image-based cell profiling is a powerful tool that compares perturbed cell populations by measuring thousands of single-cell features and summarizing them into profiles. Typically a sample is represented by averaging across cells, but this fails to capture the heterogeneity within cell populations. We introduce CytoSummaryNet: a Deep Sets-based approach that improves mechanism of action prediction by 30-68% in mean average precision compared to average profiling on a public dataset. CytoSummaryNet uses self-supervised contrastive learning in a multiple-instance learning framework, providing an easier-to-apply method for aggregating single-cell feature data than previously published strategies. Interpretability analysis suggests that the model achieves this improvement by downweighting small mitotic cells or those with debris and prioritizing large uncrowded cells. The approach requires only perturbation labels for training, which are readily available in all cell profiling datasets. CytoSummaryNet offers a straightforward post-processing step for single-cell profiles that can significantly boost retrieval performance on image-based profiling datasets.

### Competing Interest Statement

The Authors declare the following competing interests: S.S. and A.E.C. serve as scientific advisors for companies that use image-based profiling and Cell Painting (A.E.C: Recursion, SyzOnc, Quiver Bioscience; S.S.: Waypoint Bio, Dewpoint Therapeutics, Deepcell) and receive honoraria for occasional talks at pharmaceutical and biotechnology companies. R.v.D is an employee of CellVoyant. All other authors declare no competing interests.

Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning

画像ベースのプロファイリングにおける細胞集団の異質性を捉えるための対照学習を用いた表現の改善