ArticleNature communications2024
SPACe: an open-source, single-cell analysis of Cell Painting data.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Image-based, pooled phenotyping reveals multidimensional, disease-specific variant effects.Cell · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- High-content phenotyping reveals Golgi dynamics and their role in cell cycle regulation.The Journal of cell biology · 2026Article
- PhenoProfiler: advancing phenotypic learning for image-based drug discovery.Nature communications · 2025Article
- A machine learning-based analysis method for small molecule high content screening of three-dimensional cancer spheroid morphology.Molecular pharmacology · 2025Article
- Article
- A phenotypic screen identifies xanthohumol and other flavonoids as killers of bladder cancer.Pharmacological research. Natural products · 2025Article
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12 authors.
Funding
Abstract
Phenotypic profiling by high throughput microscopy, including Cell Painting, has become a leading tool for screening large sets of perturbations in cellular models. To efficiently analyze this big data, available open-source software requires computational resources usually not available to most laboratories. In addition, the cell-to-cell variation of responses within a population, while collected and analyzed, is usually averaged and unused. We introduce SPACe (Swift Phenotypic Analysis of Cells), an open-source platform for analysis of single-cell image-based morphological profiles produced by Cell Painting. We highlight several advantages of SPACe, including processing speed, accuracy in mechanism of action recognition, reproducibility across biological replicates, applicability to multiple models, sensitivity to variable cell-to-cell responses, and biological interpretability to explain image-based features. We illustrate SPACe in a defined screening campaign of cell metabolism small-molecule inhibitors tested in seven cell lines to highlight the importance of analyzing perturbations across models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.