Recent developments in single-cell multi-omics techniques, such as single-cell RNA-sequencing (scRNA-seq) and single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), scChIP-seq, scHi-C, provide high-resolution insights into the genomic, transcriptomic, and chromatin accessibility profiles of single cells within a complex biological system. These technologies have offered a comprehensive view of the cellular and molecular composition of various tissues, exceeding the capabilities of bulk sequencing approaches. Integrating these datasets is pivotal for advancing our understanding of complex biological systems, enabling insights into cellular heterogeneity, facilitating the identification of regulatory networks governing gene expression and other molecular processes at the single-cell level, enhancing our understanding of biological dynamics, and their implications in health and disease. Computational methods facilitate the integration of diverse datasets, allowing researchers to combine information from various omics layers (e.g., transcriptomics, chromatin accessibility) to gain a more comprehensive understanding of cellular processes or disease progression.

This Research Topic aims to address the challenges of utilizing single-cell multi-omics datasets. The latest advancements in single-cell technologies, coupled with the increasing volume and complexity of single-cell multi-omics data, require advanced computational tools to achieve a more comprehensive understanding of gene regulatory networks across diverse disease, environmental, and developmental contexts. An essential challenge in integrating single-cell multi-omics data arises from the absence of correspondence between cells, given that they originate from distinct populations. Current integrative approaches for single-cell datasets employ dimensionality reduction or clustering techniques to define cell populations using single cell transcriptome and epigenome data separately or jointly. However, there are ongoing challenges in integrating single-cell omics data across different platforms or modalities, sparsity and noise in single-cell data, enhancing integrative models to define cell populations, inferring cell trajectory, inferring gene regulatory networks, and modeling the dynamics of networks across diverse disease, environmental, and developmental contexts.

Contributors are invited to delve into diverse themes within the scope of computational approaches for integrating single-cell multi-omics datasets to unravel the regulatory mechanism in biological system. Topics of interest include but are not limited to:
1. Development of integrative approaches using single-cell multi-omics data
2. Development of computational methods that integrates single-cell multi-omics datasets to infer cell type-specific or cell state-specific TF-gene interactions and model dynamic changes across development process, reprograming process or conditions.
3. Computational methods to simulate single cell multi-omics data guided by gene regulatory networks
4. Benchmarking of the state-of-the-art integration methods using single-cell multi-omics datasets
5. Integrating single-cell multi-omics data to do trajectory inference

Manuscript submissions may be in one of the following forms:
• Developing novel computational methods including Machine learning, Deep learning and Embedding methods that are significantly improved compared to existing methods with applications in real biological system. These manuscripts may include original raw data.
• Benchmarking or Review articles of the state-of-the-art computational methods that integrate single-cell multi-omics data to define cell populations, infer cell trajectory and infer gene regulatory networks and the future directions of the field.

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Frontiers in Molecular Biosciences

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