Using single‐cell multiple omics approaches to resolve tumor heterogeneity

Abstract

It has become increasingly clear that both normal and cancer tissues are composed of heterogeneous populations. Genetic variation can be attributed to the downstream effects of inherited mutations, environmental factors, or inaccurately resolved errors in transcription and replication. When lesions occur in regions that confer a proliferative advantage, it can support clonal expansion, subclonal variation, and neoplastic progression. In this manner, the complex heterogeneous microenvironment of a tumour promotes the likelihood of angiogenesis and metastasis. Recent advances in next-generation sequencing and computational biology have utilized single-cell applications to build deep profiles of individual cells that are otherwise masked in bulk profiling. In addition, the development of new techniques for combining single-cell multi-omic strategies is providing a more precise understanding of factors contributing to cellular identity, function, and growth. Continuing advancements in single-cell technology and computational deconvolution of data will be critical for reconstructing patient specific intra-tumour features and developing more personalized cancer treatments.

Methods for Copy Number Aberration Detection from Single-cell DNA Sequencing Data

“…Moreover, HMMcopy was designed for aCGH data originally, and thus does not take into account the specific error profiles that characterize single-cell sequencing data, such as low and uneven coverage, or the computational challenges that arise due to biological processes such as aneuploidy in a tumor single cell. While these three methods have been widely applied to analyze single-cell data [27,[31][32][33][34][37][38][39][40][41][42][43][44][45][46][47][48], a comprehensive study of their performance is currently lacking. While Knouse et al [32] assessed the performance of CBS and HMM-based methods on single-cell DNA sequencing data, their evaluation is limited to CNVs in brain and skin cells.…”

Using Single Nucleotide Variations in Single-Cell RNA-Seq to Identify Subpopulations and Genotype-phenotype Linkage

“…Rather than being considered the "by-products" of scRNA-seq, the SNVs not only have the potential to improve the accuracy of identifying subpopulations compared to GE, but also offer unique opportunities to study the genetic events (genotype) associated with gene expression (phenotype) 17,18 . Moreover, when the coupled DNA-and RNA-based single-cell sequencing techniques become mature, the computational methodology proposed in this report can be adopted as well 19 .…”

Developmental origins and oncogenic pathways in malignant brain tumors

“…Single‐cell sequencing, which allows profiling of single cells as opposed to bulk tumor tissue, has also been utilized to define the heterogeneity of gliomas (Khoo et al, ; Levitin, Yuan, & Sims, ; Ortega et al, ). This approach has enabled the dissection of heterogeneous cellular populations within tumors by identifying potential malignantly transformed cells (e.g., stem‐like subpopulations and sublineages of developmentally related cells) and cell types comprising tumor microenvironments (e.g., infiltrating immune populations and other stromal populations) in different types of brain tumors (Filbin et al, ; Patel et al, ; Tirosh & Suva, ).…”