Detecting expressed cancer somatic mutations from single-cell RNA sequencing data

Abstract

Identifying expressed somatic mutations directly from single-cell RNA sequencing (scRNA-seq) data is challenging but highly valuable. Computational methods have been attempted but no reliable methods have been reported to identify somatic mutations with high fidelity. We present RESA -- Recurrently Expressed SNV Analysis, a computational framework that identifies expressed somatic mutations from scRNA-seq data with high precision. We test RESA in multiple cancer cell line datasets, where RESA demonstrates average area under the curve (AUC) of 0.9 on independently held out test sets, and achieves average precision of 0.71 when evaluated by bulk whole exome, which is substantially higher than previous approaches. In addition, RESA detects a median of 201 mutations per cell, 50 times more than what was reported in experimental technologies with simultaneous expression and mutation profiling. Furthermore, applying RESA to scRNA-seq from a melanoma patient, we demonstrate that RESA recovers the known BRAF driver mutation of the sample and melanoma dominating mutational signatures, identifies mutation associated expression signatures, reveals nondriver perturbed and stage specific cancer hallmarks, and unveils the complex relationship between genomic and transcriptomic intratumor heterogeneity. Therefore, RESA could provide novel views in the study of intratumor heterogeneity and relate genetic alterations to transcriptional changes at single cell level.

De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data

“…To our knowledge, LongSom is the first method combining de novo detection of SNVs and fusions from the same cell to reconstruct clonal heterogeneity. Besides nuclear SNVs, which are commonly obtained from RNA (Muyas et al 2023; Zhang et al 2023) and DNA seq (Zafar et al 2016), LongSom also calls mitochondrial SNVs. In the analyzed HGSOC dataset, the mitochondrial SNVs were called in most cancer and non-cancer cells, and some high-confidence fusion calls were expressed in most clones or subclones (P2: IGF2BP2::TESPA1, P1: SMG7::CH507-513H4.1, etc.…”

“…However, this is challenging with SR sequencing, as genetic variations are difficult to recover from SR scRNA-seq data due to capture bias, while scDNA- seq cannot assess gene expression. Recently, DNA-free de novo scRNA SNV (Muyas et al 2023; Zhang et al 2023) and CNA (Serin Harmanci et al 2020); (Gao et al 2021, 2023) calling methods were developed for SR sequencing, compensating the 3’ capture bias by pooling large amounts of cells or sequencing at very high read depths per cell. However, SR sequencing is unsuited to detect isoforms or gene fusions.…”

Combining bulk and single-cell RNA-sequencing data to reveal Gene expression pattern of smooth muscle cell in pulmonary arterial hypertension Running title：Smooth Muscle Cell Gene Expression in Pulmonary Arterial Hypertension

“…Notably, the study identi ed several lncRNAs signi cantly associated with the pathological state of PAH, such as MALAT1 and EPB41L4A-AS1. These lncRNAs may in uence the function of SMCs by regulating critical biological processes like metabolic pathways, extracellular matrix adhesion, and immune responses 11 . An in-depth analysis of the single-cell dataset GSE210248 aimed to reveal the gene expression differences and their biological signi cance in speci c cell types under healthy and diseased conditions.…”

“…This is particularly crucial for understanding how Pulmonary Artery Smooth Muscle Cells (SMCs) exhibit distinct behavioral traits across various PAH subtypes and stages of the disease. Recent studies have begun to harness this technology to delve into the gene expression patterns of SMCs in PAH, providing valuable insights, yet many unknowns and challenges remain to be addressed 11 . Notably, the integration and interpretation of the vast and complex data from both bulk RNA-seq and scRNA-seq pose signi cant challenges in achieving a deeper and more comprehensive understanding of PAH 12 .…”

Identification of Multi-landscape and Cell Interactions in the Tumor Microenvironment through High-Coverage Single-Cell Sequencing

“…This approach primarily detects short fragments located near the poly(A) tail or the 5' end of the transcript, leaving the remaining sequences of polyadenylated RNA molecules undetected. As a result, the accurate detection of single nucleotide variants (SNVs), gene fusions and alternative splicing events is limited [10][11][12][13]. Recently, several high-throughput scRNA-seq platforms for total transcriptome full-length sequencing have emerged [16,31,33].…”