ArticleMolecular biology of the cell2023
Predicting reprogramming-related gene expression from cell morphology in human induced pluripotent stem cells.
Article in Molecular biology of the cell, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- Prediction of cellular morphology changes under perturbations with a transcriptome-guided diffusion model.Nature communications · 2025Article
- Deep learning identifies heterogeneous subpopulations in breast cancer cell lines.npj biomedical innovations · 2025Article
- A comprehensive analysis of induced pluripotent stem cell (iPSC) production and applications.Frontiers in cell and developmental biology · 2025Review
- Human Pluripotent Stem Cell Colony Migration Is Related to Culture Environment and Morphological Phenotype.Life (Basel, Switzerland) · 2024Article
- Morphological profiling for drug discovery in the era of deep learning.Briefings in bioinformatics · 2024Review
- High-volume, label-free imaging for quantifying single-cell dynamics in induced pluripotent stem cell colonies.PloS one · 2024Article
- Morphological Signal Processing for Phenotype Recognition of Human Pluripotent Stem Cells Using Machine Learning Methods.Biomedicines · 2023Article
- SynapseCLR: Uncovering features of synapses in primary visual cortex through contrastive representation learning.Patterns (New York, N.Y.) · 2023Article
- Morphology and gene expression profiling provide complementary information for mapping cell state.Cell systems · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
Purification is essential before differentiating human induced pluripotent stem cells (hiPSCs) into cells that fully express particular differentiation marker genes. High-quality iPSC clones are typically purified through gene expression profiling or visual inspection of the cell morphology; however, the relationship between the two methods remains unclear. We investigated the relationship between gene expression levels and morphology by analyzing live-cell, phase-contrast images and mRNA profiles collected during the purification process. We employed these data and an unsupervised image feature extraction method to build a model that predicts gene expression levels from morphology. As a benchmark, it was confirmed that the method can predict the gene expression levels from tissue images for cancer genes, performing as well as state-of-the-art methods. We then applied the method to iPSCs and identified two genes that are well predicted from cell morphology. Although strong batch (or possibly donor) effects resulting from the reprogramming process preclude the ability to use the same model to predict across batches, prediction within a reprogramming batch is sufficiently robust to provide a practical approach for estimating expression levels of a few genes and monitoring the purification process.
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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.