Single-cell multi-omics typically include RNA sequencing (scRNA-seq) information, which reveals gene expression within individual cells, as well as ATAC sequencing (transposase-accessible chromatin with sequencing, scATAC-seq), which provides insights into the accessibility of packaged DNA in cells. Integration of scRNA-seq and scATAC-seq allow for identification of regulatory regions in the DNA linked to promoters of active genes at a single cell level. Single-cell multi-omics analysis has proven invaluable in unravelling critical information about cell types, fate, and functional states, offering deeper insights into cellular differentiation, tissue composition, and disease states such as tumours.
The analysis of such complex and massive datasets provides a multitude of inferential evidence, however, generating and sharing thousands of documents in different formats becomes challenging, limiting collaborative efforts. Moreover, genomic research data protection regulation is also often a hinder for international efforts in comparing findings within large cancer datasets.
Addressing these challenges Ankush Sharma, Akshay Akshay and Ragnhild Eskeland recently developed ShinyArchR.UiO1,2, a user-friendly, integrative, and open-source webtool specifically designed for the visualization and analysis of scATAC-seq data. ShinyArchR.UiO, based on ArchR3 analysis, simplifies data exploration process, provides a safe and comprehensive overview of the data, and facilitates the generation of publishable plots, empowering biologists to leverage the full potential of single-cell multi-omics research.
While single-cell multi-omics has become a powerful in annotating cell types, the mapping of gene expression and regulation information are not exactly “paired” per cell. The recent development of single-cell Multiome analysis has enabled simultaneous mapping of RNA-seq and ATAC-seq from the same cell. The Multiome data is commonly analysed by Seurat4 and Signac5 toolkits and can directly infer cell-type specific gene regulatory networks.
For visualisation and integration of single-cell Multiomes, Eskeland and colleagues have now developed ShinyMultiome.UiO1,2,6, with an intuitive web-based interface. ShinyMultiome.UiO is developed on R Shiny Framework, uses Seurat4 objects, and can be run locally or accessed through a hosted server2. This tool offers exploration of transcription based annotated cell-types combined with genome-wide exploration of DNA openness and gene expression using different plot types (UMAP, violin, ridge, pca, lsi, coverage) and high quality output data can be generated.
Master transcription factors (TFs) that normally regulate different cellular states are often aberrantly expressed and can play a major role in establishing gene expression programs driving cancer stem cell identities. ShinyMultiome.UiO enables the identification footprints of TFs in different cell-types, offering novel insights into normal and cancerous cellular regulatory processes.
Using these web tools, researchers can improve findability, accessibility, interoperability, and reusability (FAIR) in their single cell research projects. The availability of web-based data also facilitates collaborations and reduces carbon footprints.
Availability : github.com/EskelandLab Contributors: Akshay Akshay*, Ankush Sharma and Ragnhild Eskeland
*University of Bern