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Atlas of Computational Cell Reprogramming

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L2 · Proxy inverse design I D T P

SiPER

Zheng M, Xie B, Okawa S, Liew SY, Deng H, del Sol A

2023 · Stem cell reports

Cellular conversion can be induced by perturbing a handful of key transcription factors (TFs).

Abstract

From the original paper, Stem cell reports · PubMed

Cellular conversion can be induced by perturbing a handful of key transcription factors (TFs). Replacement of direct manipulation of key TFs with chemical compounds offers a less laborious and safer strategy to drive cellular conversion for regenerative medicine. Nevertheless, identifying optimal chemical compounds currently requires large-scale screening of chemical libraries, which is resource intensive. Existing computational methods aim at predicting cell conversion TFs, but there are no methods for identifying chemical compounds targeting these TFs. Here, we develop a single cell-based platform (SiPer) to systematically prioritize chemical compounds targeting desired TFs to guide cellular conversions. SiPer integrates a large compendium of chemical perturbations on non-cancer cells with a network model and predicted known and novel chemical compounds in diverse cell conversion examples. Importantly, we applied SiPer to develop a highly efficient protocol for human hepatic maturation. Overall, SiPer provides a valuable resource to efficiently identify chemical compounds for cell conversion.

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 SiPER 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 regulatory-network influence. Cited in §3.3 of the review. Editorial rationale pending review by the maintainer.

Classification

Level
L2
Representation
Regulatory-network influence
Modalities
D, I, P, T
Intervention
Transcription factors
Framework
Machine learning

Software

Web tool
siper.uni.lu/
Reproducibility
4 of 4
FAIR4RS
1 of 5

Last audited 2026-05-24

Citation

Zheng M et al. (2023). A single cell-based computational platform to identify chemical compounds targeting desired sets of transcription factors for cellular conversion., Stem cell reports.

DOI: 10.1016/j.stemcr.2022.10.013

PMID: 36400030

BibTeX
@article{siper2023,
  title  = {A single cell-based computational platform to identify chemical compounds targeting desired sets of transcription factors for cellular conversion.},
  author = {Zheng M et al.},
  year   = {2023},
  journal = {Stem cell reports},
  pmid = {36400030},
  doi  = {10.1016/j.stemcr.2022.10.013}
}