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Atlas of Computational Cell Reprogramming

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L2 · Proxy inverse design I D T

PC3T

Han L, Song B, Zhang P, Zhong Z, Zhang Y, Bo X, Wang H, Zhang Y, Cui X, Zhou W

2023 · Communications biology

Cellular transitions hold great promise in translational medicine research.

Abstract

From the original paper, Communications biology · PubMed

Cellular transitions hold great promise in translational medicine research. However, therapeutic applications are limited by the low efficiency and safety concerns of using transcription factors. Small molecules provide a temporal and highly tunable approach to overcome these issues. Here, we present PC3T, a computational framework to enrich molecules that induce desired cellular transitions, and PC3T was able to consistently enrich small molecules that had been experimentally validated in both bulk and single-cell datasets. We then predicted small molecule reprogramming of fibroblasts into hepatic progenitor-like cells (HPLCs). The converted cells exhibited epithelial cell-like morphology and HPLC-like gene expression pattern. Hepatic functions were also observed, such as glycogen storage and lipid accumulation. Finally, we collected and manually curated a cell state transition resource containing 224 time-course gene expression datasets and 153 cell types. Our framework, together with the data resource, is freely available at http://pc3t.idrug.net.cn/ . We believe that PC3T is a powerful tool to promote chemical-induced cell state transitions.

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 PC3T relates to the cross-modality inverse-design framework of the review.

Why this level

Level 2 because the method instantiates the inverse-design objective through an empirical or learned proxy response map F^proxy(PS,u)\hat F_{\mathrm{proxy}}(P_S, u) rather than a mechanistic intervention-dependent model. Representation family is signature / state-matching. Cited in §3.3 of the review. Editorial rationale pending review by the maintainer.

Classification

Level
L2
Representation
Signature / state-matching
Modalities
I, D, T
Intervention
Transcription factors
Framework
Machine learning

Software

Code
Not available
Reproducibility
Not audited
FAIR4RS

Last audited 2026-05-24

Citation

Han L et al. (2023). PC3T: a signature-driven predictor of chemical compounds for cellular transition., Communications biology.

DOI: 10.1038/s42003-023-05225-y

PMID: 37758874

BibTeX
@article{pc3t2023,
  title  = {PC3T: a signature-driven predictor of chemical compounds for cellular transition.},
  author = {Han L et al.},
  year   = {2023},
  journal = {Communications biology},
  pmid = {37758874},
  doi  = {10.1038/s42003-023-05225-y}
}