Class C0 records a score of target features or observed screen outcomes. That score does not by itself settle representation, controllability, or evaluability. Boundary conditions.
Iterative TF screening
Liu S, Li L, Zhang F, Garcia-Corral M, Meyer K, Fortuna PRJ, van Sambeek B, Appleton E, Ng AHM, Khoshakhlagh P, Lu YR, Cameron J, Ramirez RN, Chen Y, Wu CT, Huang JY, Tan Y, Chao G, Aach J, Lim ET, Tam JM, Raychaudhuri S, Church GM
2025 · Nature communications
Differentiation of induced pluripotent stem cells (iPSCs) into specialized cell types is essential for uncovering cell-type specific molecular mechanisms and interrogating cellular function.
Abstract
From the original paper, Nature communications · PubMed
Differentiation of induced pluripotent stem cells (iPSCs) into specialized cell types is essential for uncovering cell-type specific molecular mechanisms and interrogating cellular function. Transcription factor screens have enabled efficient production of a few cell types; however, engineering cell types that require complex transcription factor combinations remains challenging. Here, we report an iterative, high-throughput single-cell transcription factor screening method that enables the identification of transcription factor combinations for specialized cell differentiation, which we validated by differentiating human microglia-like cells. We found that the expression of six transcription factors, SPI1, CEBPA, FLI1, MEF2C, CEBPB, and IRF8, is sufficient to differentiate human iPSC into cells with transcriptional and functional similarity to primary human microglia within 4 days. Through this screening method, we also describe a novel computational method allowing the exploration of single-cell RNA sequencing data derived from transcription factor perturbation assays to construct causal gene regulatory networks for future cell fate engineering.
Summary
Editorial summary pending review by the maintainer. The paper's own abstract appears above; the Atlas summary in the maintainer's voice will explain how Iterative TF screening relates to the inverse-design framework of the review.
Why this class
Level 0 because the method scores features against a target/background contrast without modelling an intervention . The output is a ranked list of candidate identity determinants, not an intervention whose effect has been computed. Representation family is signature / state-matching. Cited in §3.1 of the review. Editorial rationale pending review by the maintainer.