Single-cell omics integration: from data harmonization to biological discovery

Single-cell omics technologies have rapidly reshaped how we study genome regulation, chromatin organization, and cellular heterogeneity. By profiling molecular features at the level of individual cells, these approaches capture cell‑to‑cell variation in transcriptional, epigenomic, and increasingly three‑dimensional genome states, offering new perspectives on how regulatory programs shape cellular identity, function, and fate.Recent methodological advances—spanning scRNA‑seq, scATAC‑seq, scChIP‑seq and CUT&Tag, single‑cell genome conformation assays such as scHi‑C, CITE‑seq, perturbation‑based readouts including CRISPR‑enabled profiling, and emerging single‑cell proteomic platforms—have greatly expanded the scope and resolution of single‑cell studies across development, physiology, and disease. Despite substantial technical progress, extracting robust and interpretable biological insight from these datasets remains a major challenge. Differences in data modality, resolution, scale, and noise structure, together with batch effects and incomplete correspondence across modalities, continue to complicate integrative analyses and limit our ability to link cell‑type–specific genome states to biological function.This Research Topic aims to advance computational and analytical approaches for integrating and interpreting single‑cell omics data, with particular emphasis on genome organization and dynamics. We are especially interested in studies that investigate how chromatin states, regulatory elements, and nuclear or 3D genome architecture change across cellular states and over time, and how these genome‑level changes relate to development and disease.We welcome contributions that develop or apply computational and statistical methods—including machine learning, deep learning, generative modeling, and integrative frameworks—to improve data harmonization across modalities and batches, enable scalable representation learning, and support robust inference of regulatory networks, cell populations,trajectories, and dynamic genome programs. Contributions addressing standards, benchmarking, data simulation, and interoperability are also encouraged, as they are essential for reproducibility and sustained progress in the field.The scope of this Research Topic includes computational, statistical, and analytical studies in any biological system or disease context, provided that genome regulation and/or genome organization and dynamics are a central focus. We welcome original research articles, methodological advances, benchmarks, resources, reviews, and biologically motivated applications that use integrated single‑cell (and spatial) omics approaches to illuminate genome‑centric regulatory principles.- Novel algorithms and frameworks for single-cell multi-omics integration relevant to genome regulation and chromatin organization (e.g., embeddings, probabilistic models, deep learning)- Computational methods for integrating single-cell datasets with bulk-level omics to map cell-type–specific regulatory and chromatin signals to population-level profiles- Inferring cell-type/cell-state–specific regulatory networks across single-cell omics modalities (e.g., linking chromatin accessibility and histone marks to transcription factor activity and gene expression)- Strategies for harmonizing and normalizing single-cell omics data across experiments or platforms, including robust correction of batch effects without erasing genome-state biology- Computational approaches for defining cell populations and inferring trajectories based on dynamic changes in chromatin states and gene regulation- Methods for integrating and modeling 3D genome / genome conformation data at single-cell resolution and linking chromatin architecture to gene regulation- Computational methods to simulate single-cell omics data for genome-centric benchmarking (including sparsity, modality mismatch, batch structure, and 3D genome features)- Applications of integrated single-cell (and spatial) omics analysis to genome organization in disease, development, environmental adaptation, and cell reprogramming- Benchmarking and reviews of state-of-the-art single-cell integration methods, with metrics relevant to genome regulation and chromatin architecture- Visualization and interpretation tools for genome-centric single-cell omics data (e.g., regulatory element activity, chromatin state dynamics, and 3D genome representations)- Emerging multimodal experimental technologies and new data types that enable integrative analysis of chromatin and nuclear organization Topic editors: Shilu Zhang, Alireza Fotuhi Siahpirani, Junha Shin Source: https://www.frontiersin.org/research-topics/79619/single-cell-omics-integration-from-data-harmonization-to-biological-discovery