Refate identifies chemical compounds to target trans-regulatory networks for cellular conversion
Xiao, D.; Sahadevan, S.; Mangala, M. M.; Kim, H. J.; Fredericks, A.; Huang, H.; Jothi, R.; Tam, P.; Gonzalez-Cordero, A.; Zyner, K. G.; Yang, P.
Show abstract
Identifying chemical compounds that target trans-regulatory networks (TRNs) underlying molecular programs of cells for directed cellular conversion (i.e. differentiation, reprogramming, transdifferentiation, and dedifferentiation) is a key step towards advancing regenerative medicine. Recent innovations in single-cell omics technologies enabled high-resolution profiling of TRNs that govern cell identity and cell-fate decisions. Here, we introduce Refate, a computational framework that integrates large-scale multimodal single-cell atlas data to quantify cell propensity of genes, together with six drug databases, to identify chemical compounds that target TRNs for directed cellular conversion. The reconstructed TRNs, including protein-protein interactions and gene regulatory networks, alongside chemical compounds that drive the cellular conversion provide greater biological interpretability and improve efficiency and efficacy. We evaluated Refate by testing its ability to uncover known transcription factors and chemical compounds validated in experimental conversions of various cell types. Furthermore, we experimentally validated the attribute of several novel chemical compounds identified by Refate for enhancing the conversion of human embryonic stem cells to human cranial neural crest cells. Together, these findings demonstrate Refate as an effective tool for discovering chemical compounds that target TRNs to enable cellular conversion, advancing efforts towards regenerative medicine. HighlightsO_LIRefate quantifies genes for cellular conversion using multimodal single-cell atlases C_LIO_LIRefate uncovers trans-regulatory networks (TRNs) underlying cellular conversion C_LIO_LIRefate identifies chemical compounds that target TRNs for cellular conversion C_LIO_LIValidation of Refate identified chemical compounds for hESC to hCNCC conversion C_LI
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Automatic identification of small molecules that promote cell conversion and reprogramming 97%
- High resolution multi-scale profiling of embryonic germ cell-like cells derivation reveals pluripotent state transitions in humans 96%
- Self-Organizing Neural Networks in Organoids Reveal Principles of Forebrain Circuit Assembly 93%
Similar papers in this journal
Similar papers in this journal
- CellUntangler: separating distinct biological signals in single-cell data with deep generative models 94%
- Effects of somatic mutations on cellular differentiation in iPSC models of neurodevelopment 94%
- Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.