Investigating cellular heterogeneity at the single-cell level by the flexible and mobile extrachromosomal circular DNA

Extrachromosomal circular DNA (eccDNA) is a special class of DNA derived from linear chromosomes. It coexists independently with linear chromosomes in the nucleus. eccDNA has been identified in multiple organisms, including Homo sapiens, and has been shown to play important roles relevant to tumor progression and drug resistance. To date, computational tools developed for eccDNA detection are only applicable to bulk tissue. Investigating eccDNA at the single-cell level using a computational approach will elucidate the heterogeneous and cell-type-specific landscape of eccDNA within cellular context. Here, we performed the first eccDNA analysis at the single-cell level using data generated by single-cell Assay for Transposase-Accessible Chromatin with sequencing (scATAC-seq) in adult and pediatric glioblastoma (GBM) samples. Glioblastoma multiforme (GBM) is an aggressive tumor of the central nervous system with a poor prognosis. Our analysis provides an overview of cellular origins, genomic distribution, as well as the differential regulations between linear and circular genome under disease- and cell-type-specific conditions across the open chromatin regions in GBM. We focused on some eccDNA elements that are potential mobile enhancers acting in a trans-regulation manner. In summary, this pilot study revealed novel eccDNA features in the cellular context of brain tumor, supporting the strong need for eccDNA investigation at the single-cell level

MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment

Abstract Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common cell types and states in the tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy