A Framework for Analyzing the Epigenetic and Transcriptomic Landscape of Cancers Using Single-Cell Multiomic Data

Single-cell approaches to study gene regulation using multi-modal data (sc-Multiome), such as genomes, transcriptomes, and chromatin accessibility of tumor cells, offer great insights into the development trajectory. By enabling high-resolution barcoding, single-cell isolation facilitates the detection of subpopulations, uncovers molecular mechanisms, and characterizes cell types to reveal cellular heterogeneity within complex tissues. The pipeline for analyzing multiome data includes four main steps: processing Single-Cell Multiome ATAC + Gene Expression sequencing data, gene expression analysis using Seurat, chromatin accessibility analysis and the joint embedding of the expression and accessibility data using SnapATAC2. Here, we outline a comprehensive protocol for joint embedding of chromatin accessibility and transcriptomic data using best practices developed in our laboratories, along with detailed parameter tuning at each step to fully leveraging this pipeline. This approach enhances our understanding of the intricate tumor microenvironment and aids in determining cellular landscape. This chapter focuses on the integration of single-cell multi-omics methodologies, emphasizing their utility in cancer research.