Linking chromatin state to gene expression: cross-modal integration of epigenomic and transcriptomic data

This Research Topic is sponsored by Nurture Bio Labs Inc, who as part of this topic provide authors with open-access data hosting on its platform, Tellus, alongside free subscriptions for all authors participating in this Research Topic. Furthermore the first 5 accepted articles will benefit from a fee support of 25%.Gene expression is governed not only by the DNA sequence but also by the physical and chemical state of chromatin that surrounds it. Histone modifications, DNA methylation, chromatin accessibility, and three-dimensional genome architecture together shape when, where, and how strongly genes are transcribed. For decades, these regulatory layers were studied in isolation, with epigenomic assays and transcriptomic profiling generating parallel but disconnected datasets. The result was a fragmented picture: we could catalog chromatin features and quantify transcripts, yet we struggled to draw confident mechanistic links between the two.The rapid maturation of single-cell and multi-omic technologies has transformed what is now possible. Techniques such as single-cell ATAC-seq, CUT&Tag, multiome assays, and spatially resolved profiling enable the simultaneous capture of chromatin and expression signals from the same cells or from matched populations. This abundance of paired and unpaired data has created an urgent need for computational frameworks that integrate multiple modalities, reconcile their differing resolutions and noise structures, and translate correlation into testable regulatory models.Cross-modal integration is central to understanding development, cellular identity, disease progression, and therapeutic response. Mapping how chromatin states influence transcriptional output can distinguish causal regulatory signals from passive modifications. Yet, the field still lacks consensus on benchmarking strategies, model interpretability, and standardization. Open, shareable datasets and reproducible pipelines are therefore vital to scaling these approaches. This Research Topic aims to advance the integration of epigenomic and transcriptomic data into unified models that link chromatin state to gene expression. We seek contributions that develop innovative methods, establish rigorous benchmarks, and deliver biological insights while promoting a reproducible, collaborative community for cross-modal regulatory genomics.Computational contributions are encouraged to share code and to host supporting data openly on the Tellus platform, facilitating reproducibility and reuse across the community. Both experimental and purely computational studies fall within the scope of this topic, as do benchmarking efforts and resource papers that strengthen cross-laboratory integration initiatives.Submissions are invited across the following subthemes:• Computational integration methods: algorithms for joint embedding, alignment, and modality matching of paired or unpaired epigenomic and transcriptomic data.• Predictive modeling of expression from chromatin: machine learning and deep learning models that infer transcriptional output from accessibility, histone marks, or methylation.• Single-cell and spatial multi-omics: integration strategies for multiome, single-cell, and spatially resolved chromatin and expression assays.• Regulatory element and enhancer–promoter inference: linking distal regulatory elements to target genes through combined chromatin and expression signals.• Benchmarking, standards, and reproducibility: comparative evaluations, gold-standard datasets, and FAIR data practices for cross-modal analysis.• Biological and clinical applications: applying integration methods to development, differentiation, disease mechanisms, or therapeutic response.• Open data resources and tools: curated datasets, pipelines, and software, including resources shared via TellusThe Topic Editor Ryan Ward is an employee of Nurture Bio Labs Inc. All other Topic Editors declare no conflicts of interest. Topic editors: Mohammad Aasif Khan, Rahul Singh Jasrotia, Ryan David Ward, Sampara Vasishta, Hua Zhong Source: https://www.frontiersin.org/research-topics/86028/linking-chromatin-state-to-gene-expression-cross-modal-integration-of-epigenomic-and-transcriptomic-data