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Atlas of Computational Cell Reprogramming

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L2 · Proxy inverse design T

DIRECTEUR

Hamano M, Nakamura T, Ito R, Shimada Y, Iwata M, Takeshita JI, Eguchi R, Yamanishi Y

2024 · Bioinformatics (Oxford, England)

MOTIVATION: Direct reprogramming (DR) is a process that directly converts somatic cells to target cells.

Abstract

From the original paper, Bioinformatics (Oxford, England) · PubMed

MOTIVATION: Direct reprogramming (DR) is a process that directly converts somatic cells to target cells. Although DR via small molecules is safer than using transcription factors (TFs) in terms of avoidance of tumorigenic risk, the determination of DR-inducing small molecules is challenging. RESULTS: Here we present a novel in silico method, DIRECTEUR, to predict small molecules that replace TFs for DR. We extracted DR-characteristic genes using transcriptome profiles of cells in which DR was induced by TFs, and performed a variant of simulated annealing to explore small molecule combinations with similar gene expression patterns with DR-inducing TFs. We applied DIRECTEUR to predicting combinations of small molecules that convert fibroblasts into neurons or cardiomyocytes, and were able to reproduce experimentally verified and functionally related molecules inducing the corresponding conversions. The proposed method is expected to be useful for practical applications in regenerative medicine. AVAILABILITY AND IMPLEMENTATION: The code and data are available at the following link: https://github.com/HamanoLaboratory/DIRECTEUR.git.

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 DIRECTEUR 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
T
Intervention
Transcription factors
Framework
Machine learning

Software

Code
github.com/HamanoLaboratory/DIRECTEUR
Reproducibility
2 of 4
FAIR4RS
0 of 5

Last audited 2026-05-24

Citation

Hamano M et al. (2024). DIRECTEUR: transcriptome-based prediction of small molecules that replace transcription factors for direct cell conversion., Bioinformatics (Oxford, England).

DOI: 10.1093/bioinformatics/btae048

PMID: 38273708

BibTeX 
@article{directeur2024,
  title  = {DIRECTEUR: transcriptome-based prediction of small molecules that replace transcription factors for direct cell conversion.},
  author = {Hamano M et al.},
  year   = {2024},
  journal = {Bioinformatics (Oxford, England)},
  pmid = {38273708},
  doi  = {10.1093/bioinformatics/btae048}
}