Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 42242982

Identifying fate-determining transcription factors with single-cell omics.

Xi Xi·Chen Li·Lei Wei·Xuegong Zhang
View original source ↗Export metadata ↓

Abstract

Single-cell sequencing enables the systematic discovery of cell fate-determining transcription factors (TFs), or key TFs, that define cellular identity or drive cell state transitions. A wide range of computational methods have been developed for this goal, but they differ substantially in the input data and the biological questions they address. In this article, we systematically review computational approaches for key TF identification and organize them from three perspectives: whether they identify TFs defining cell state identity or driving state transitions, whether transitions are modeled as discrete or continuous processes, and whether TFs act individually or combinatorially. We summarize key features and application scenarios of relevant methods to guide tool selection and discuss emerging trends in this field toward programmable and active control of cell fate.

Explore related subjects

Transcription FactorsSingle-Cell AnalysisHumansAnimalsComputational BiologyCell DifferentiationSingle-Cell Gene Expression AnalysisGenomicscell identitycell state transitionsingle-celltranscription factorsJournal ArticleReview

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xi Xi, Chen Li, Lei Wei, Xuegong Zhang. 2026-06-04. Identifying fate-determining transcription factors with single-cell omics.. https://doi.org/10.1016/j.tig.2026.05.005

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

A transcription factor regulatory atlas for activity inference and perturbation prediction.

Inferring transcription factor (TF) activity from transcriptomes and predicting transcriptome-wide responses to TF perturbations remain challenging, in part because available TF-mRNA resources often face a trade-off between precision and coverage and typically lack signed regulatory information. Here, we present TFActProfiler, a TF-mRNA resource and computational framework that learns signed, quantitative TF-mRNA regulatory coefficients by integrating heterogeneous prior evidence (ChIP-based, motif-based, and curated TF-mRNA annotations) with large-scale bulk and single-cell RNA-seq atlases. TFActProfiler contains 2 606 176 signed TF-mRNA interactions and improves TF activity inference in TF knockdown benchmarks relative to widely used regulon resources while retaining broad TF and target coverage. In addition, because the same learned regulatory coefficients can be used to model downstream transcriptional effects, TFActProfiler enables prediction of transcriptome-wide gene expression responses to TF knockdown without training on task-matched perturbation data. When perturbation datasets are available, TFActProfiler can be further refined to achieve performance comparable to state-of-the-art machine-learning baselines. By providing a direction-aware representation of TF-mRNA regulation for both activity inference and perturbation-response modeling, TFActProfiler supports systematic dissection of gene regulatory programs across diverse cellular contexts.

Transcription Factors↗
PubMed2026

Motif-Cluster: Motif driven prioritization of transcription factor binding clusters.

Genome-wide analyses of transcription factor (TF) motif binding sites have largely emphasized individual high-affinity sites, while overlooking the regulatory importance of locally repetitive motif clusters. Such clusters, including combinations of weak and strong binding sites, can collectively enhance TF occupancy and regulatory activity. Here we present Motif-Cluster, an open-source framework for motif-driven prioritization and visualization of TF binding clusters using sequence information alone. Motif-Cluster integrates a density-based clustering strategy with flexible modeling of binding-site gaps and affinity signals, enabling the identification and ranking of candidate regulatory regions without requiring experimental binding data. Through simulations and multiple real-data analyses, we show that combining gap distributions with binding affinity effectively balances cluster size and signal strength while reducing noise from weak sites. Application to ZNF410 successfully recovers the previously characterized binding clusters in the CHD4 promoter, which are conserved between human and mouse. Additional case studies involving PHB1, TWIST1, and EGR1 further demonstrate the general applicability of the method across diverse transcription factors. Motif-Cluster also provides intuitive visualization and reproducible workflows to facilitate interpretation of spatially dense motif patterns. Overall, Motif-Cluster offers a robust and flexible approach for prioritizing transcription factor regulatory regions from genome-wide motif scans, enabling biological discovery and guiding experimental design, particularly in settings where direct genome-wide binding assays are unavailable.

Transcription Factors↗
PubMed2026

Positional grammar of transcription factor binding partitions developmental and stress-response regulation in plants.

Understanding how transcription factor binding site (TFBS) position influences gene regulation remains a fundamental challenge in plants. Here, we integrate conserved multiDAP TFBS maps for 244 transcription factors (TFs) with single-nucleus chromatin accessibility, cell type-resolved gene expression, and hormone-response datasets across Brassicaceae species to determine how TFBS position relates to regulatory function. Although conserved TFBSs are enriched near transcription start sites (TSSs), TSS-proximal accessibility poorly predicts cell type-specific expression. Instead, cell type-specific expression correlates best with conserved TFBSs embedded in cell type-restricted chromatin, with TF family-specific distributions across distal promoters and introns. In contrast, TSS-proximal TFBSs in broadly accessible chromatin are associated with rapid transcriptional responses to abiotic and biotic stress hormones. Coding sequence TFBSs mark a distinct regulatory context in which the same DNA sequence encodes both amino acid sequence and TF motifs, including evidence that CDS-localized ABR1 binding may contribute to repression during hormone response. Finally, distal upstream regions contain conserved multi-family TF clusters with enhancer-like features overlapping rare cell type-specific accessible chromatin and enriched near genes controlling embryonic, meristematic, and hormone-dependent developmental patterning. Together, these results support a positional grammar in which TFBS position and chromatin context jointly partition developmental, stress-responsive, and repressive regulatory output in plants.

Transcription Factors↗