ArticleNature communications2023
Revealing invisible cell phenotypes with conditional generative modeling.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 31 citations in OpenAlex.
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- Large scale compound selection guided by cell painting reveals activity cliffs and functional relationships.Communications biology · 2026Article
- Confounder-aware foundation modeling for accurate phenotype profiling in cell imaging.Npj imaging · 2025Article
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- Reprogramming of iPSCs to NPCEC-like cells by biomimetic scaffolds for zonular fiber reconstruction.Bioactive materials · 2025Article
- Predicting cell morphological responses to perturbations using generative modeling.Nature communications · 2025Article
- Towards generative digital twins in biomedical research.Computational and structural biotechnology journal · 2024Review
- Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization.Nature communications · 2024Article
- Visual interpretability of bioimaging deep learning models.Nature methods · 2024Article
- SST-editing: in silico spatial transcriptomic editing at single-cell resolution.Bioinformatics (Oxford, England) · 2024Article
- Exploring self-supervised learning biases for microscopy image representation.Biological imaging · 2024Article
- Development of AI-assisted microscopy frameworks through realistic simulation with pySTED.Nature machine intelligence · 2024Article
- Revealing invisible cell phenotypes with conditional generative modeling.Nature communications · 2023Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biological sciences, drug discovery and medicine rely heavily on cell phenotype perturbation and microscope observation. However, most cellular phenotypic changes are subtle and thus hidden from us by natural cell variability: two cells in the same condition already look different. In this study, we show that conditional generative models can be used to transform an image of cells from any one condition to another, thus canceling cell variability. We visually and quantitatively validate that the principle of synthetic cell perturbation works on discernible cases. We then illustrate its effectiveness in displaying otherwise invisible cell phenotypes triggered by blood cells under parasite infection, or by the presence of a disease-causing pathological mutation in differentiated neurons derived from iPSCs, or by low concentration drug treatments. The proposed approach, easy to use and robust, opens the door to more accessible discovery of biological and disease biomarkers.
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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.