ArticleCell reports2019
Rational Reprogramming of Cellular States by Combinatorial Perturbation.
Article in Cell reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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Who cites it
16 citing papers in PubMed.
- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- Combinatorial transcription factor interactions drive modular gene regulatory networks.bioRxiv : the preprint server for biology · 2026Article
- Redefining cellular reprogramming with advanced genomic technologies.Nature reviews. Genetics · 2026Review
- Dissecting the impact of transcription factor dose on cell reprogramming heterogeneity using scTF-seq.Nature genetics · 2025Article
- Epiregulon: Single-cell transcription factor activity inference to predict drug response and drivers of cell states.Nature communications · 2025Article
- The peripheral Atf3Acta biochimica et biophysica Sinica · 2025Article
- Direct Cardiac Reprogramming in the Age of Computational Biology.Journal of cardiovascular development and disease · 2024Review
- Review
- Computational analysis of synergism in small networks with different logic.Journal of biological physics · 2023Article
- Review
- Modeling Human TBX5 Haploinsufficiency Predicts Regulatory Networks for Congenital Heart Disease.Developmental cell · 2021Article
- Computational Stem Cell Biology: Open Questions and Guiding Principles.Cell stem cell · 2021Review
- Single-Cell Genomics: Catalyst for Cell Fate Engineering.Frontiers in bioengineering and biotechnology · 2021Review
- Neuronal differentiation strategies: insights from single-cell sequencing and machine learning.Development (Cambridge, England) · 2020Review
- Transcriptional Reprogramming of Distinct Peripheral Sensory Neuron Subtypes after Axonal Injury.Neuron · 2020Article
- Mapping regulators of cell fate determination: Approaches and challenges.APL bioengineering · 2020Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Ectopic expression of transcription factors (TFs) can reprogram cell state. However, because of the large combinatorial space of possible TF cocktails, it remains difficult to identify TFs that reprogram specific cell types. Here, we develop Reprogram-Seq to experimentally screen thousands of TF cocktails for reprogramming performance. Reprogram-Seq leverages organ-specific cell-atlas data with single-cell perturbation and computational analysis to predict, evaluate, and optimize TF combinations that reprogram a cell type of interest. Focusing on the cardiac system, we perform Reprogram-Seq on MEFs using an undirected library of 48 cardiac factors and, separately, a directed library of 10 epicardial-related TFs. We identify a combination of three TFs, which efficiently reprogram MEFs to epicardial-like cells that are transcriptionally, molecularly, morphologically, and functionally similar to primary epicardial cells. Reprogram-Seq holds promise to accelerate the generation of specific cell types for regenerative medicine.
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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.