Single-cell multi-omic analysis of cancer interactome

Cancer is a heterogeneous disease characterized by diverse cellular compositions and complex molecular regulatory networks. Recent advancements in single-cell technologies, such as single-cell and single-nucleus RNA and ATAC sequencing, have opened up new avenues for systems biologists to delve deeper into cancer interactomes. By integrating transcriptomic and epigenomic data at single-cell resolution, researchers now can uncover cell type-specific regulatory modalities, leading to a more precise understanding of cancer initiation, progression, and evolution. These tools can revolutionize cancer research by enabling the discovery of novel biomarkers and the development of personalized therapies.
This special collection invites submissions that develop innovative computational methods to integrate single-cell multi-omic (scRNA- and scATAC-seq) data or profile samples using both technologies to uncover novel insights into cancer. We prioritize studies that leverage existing atlas-level or landmark datasets or focus on identifying cell type-specific interactions through integrative analysis. Benchmark studies of prior multi-omic data integration algorithms, highlighting their strengths and limitations across diverse cancer types and single-cell data characteristics, are likewise preferred. Analysis guided or jointly analyzed with bulk sequencing profiles from the same cancer type is encouraged but not required. Submissions must leverage both scRNA-seq and scATAC-seq data to investigate cellular heterogeneity and regulatory mechanisms. Researchers are welcome to submit original research or review articles on topics that include, but are not limited to:

• Novel computational approaches for integrating scRNA-seq and scATAC-seq data
• Comparative analysis of existing integration methods
• Benchmarking integration methods on diverse cancer types and data characteristics
• Inferring cell type-specific regulatory networks
• Identifying cancer cell hierarchies and lineage relationships
• Characterizing tumor microenvironment and immune cell interactions
• Reverse-engineering cancer interactomes across regulatory modalities
• Understanding the interplay between transcription and chromatin accessibility in cancer
• Concurrent RNA and chromatin accessibility profiling of cancer
• Uncovering novel regulatory mechanisms underlying cancer initiation and progression
• Identifying potential therapeutic targets and biomarkers
• Predicting patient response to treatment and drug resistance