DOI: 10.17044/scilifelab.28202864.v1
title: Single-cell morphological profiling reveals insights into cell death

description: Dataset description:The data Organization of files:1) Features: features.tar.gzsinglecell_features_CellProfiler.parquet: This file contains the single-cell profiles extracted with CellProfiler used for the analysis in this publication. Features are normalised and filtered according to Fig. 1B, C in the paper.singlecell_features_DeepProfiler.parquet: This file contains the single-cell profiles extracted with DeepProfiler used for the analysis in this publication. Features are normalised and filtered according to Fig. 1B, C in the paper.singlecell_features_DINO.parquet: This file contains the single-cell profiles extracted with DINO used for the analysis in this publication. Features are normalised and filtered according to Fig. 1B, C in the paper (number of profiles here is smaller than in the other two approaches as outlined in the paper).2) Metadata: metadata_celldeath_paper.csv. This file contains the metadata used in the orgiinal cell painting experiment. It contains, plate, well, site (field-of-view), compounds, moa as well used concentrations and treatment conditions.3). Grit scores: grit_scores.tar.gz. This zipped folder contains the grit scores for the compound concentrations for all three feature extractors. This info is provided for all the compounds concentrations for which grit could be computed. One file each for CellProfiler, DeepProfiler and DINO. 4.) E-distance: edistance.tar.gz. This zipped folder contains the edistances and etest results for the compound concentrations for all three feature extractors. This info is provided for all the compounds concentrations for which they could be computed. One file each for CellProfiler, DeepProfiler, and DINO. The file names indicate the number of samples and permutations used in the permutation test.5.) Splits: splits.tar.gz. This zipped folder contains the splits used in the supervised model training. One file for each CellProfiler, DeepProfiler, and DINO. As described in the paper, splits were performed based on the wells of plates. Each file contains moa, compound, plate, well as well as split and the fraction of cells in each well.Publication:The data in this repository supports the following publication:"Single-cell morphological profiling reveals insights into cell death" by Frey et al.Abstract:Analysis of single-cell data has emerged as a powerful tool for studying biological processes andresponse to perturbations. However, its application in morphological profiling is less explored. In thisstudy, we profile six cell death subtypes induced by 50 small molecule drugs across six concentrationsusing the Cell Painting assay. We evaluate the performance of three feature extraction methodsat single-cell and aggregated level and apply supervised and unsupervised analyses to uncoverfactors contributing to cell death mechanisms. Our results show that a bagged LightGBMXT model,trained on single-cell DeepProfiler achieved classification accuracy of 77.23%, with a top overallperformance of 89.97% for corresponding aggregated profiles. Furthermore, self-supervised learningusing the transformer-based DINO network revealed highly resolved and biologically meaningfulsubpopulations, shedding light on perturbation- and concentration-specific molecular targets anddose-dependant morphological changes. Our findings demonstrate the potential of studying phenomicdata on single-cell level to enhance the characterization of cell death pathways, advancing ourunderstanding of perturbation effects at a granular level.