Single-Cell Exome Sequencing Reveals Single-Nucleotide Mutation Characteristics of a Kidney Tumor

Abstract

Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and has very few mutations that are shared between different patients. To better understand the intratumoral genetics underlying mutations of ccRCC, we carried out single-cell exome sequencing on a ccRCC tumor and its adjacent kidney tissue. Our data indicate that this tumor was unlikely to have resulted from mutations in VHL and PBRM1. Quantitative population genetic analysis indicates that the tumor did not contain any significant clonal subpopulations and also showed that mutations that had different allele frequencies within the population also had different mutation spectrums. Analyses of these data allowed us to delineate a detailed intratumoral genetic landscape at a single-cell level. Our pilot study demonstrates that ccRCC may be more genetically complex than previously thought and provides information that can lead to new ways to investigate individual tumors, with the aim of developing more effective cellular targeted therapies.

Low-grade chromosomal mosaicism in human somatic and embryonic stem cell populations

“…It is important to bear in mind that these studies are based on the analysis of DNA from extractions from large numbers of cells, which only allows detection of CNVs if they are present in at least 5-10% of the cells 11,12 . In contrast, the recent development of new methods for the comprehensive study of the single cell genome [13][14][15][16] , epigenome 17 and transcriptome 18 is leading to a new understanding of cellular diversity. For instance, the study of single breast cancer cells revealed a significant number of de novo DNA copy number changes that appeared after only one cell cycle 15 and the analysis of large numbers of single cells has provided interesting insight in the evolution of genetically different cell populations within one tumour [14][15][16] .…”

“…In contrast, the recent development of new methods for the comprehensive study of the single cell genome [13][14][15][16] , epigenome 17 and transcriptome 18 is leading to a new understanding of cellular diversity. For instance, the study of single breast cancer cells revealed a significant number of de novo DNA copy number changes that appeared after only one cell cycle 15 and the analysis of large numbers of single cells has provided interesting insight in the evolution of genetically different cell populations within one tumour [14][15][16] . In the field of hiPSC, a recent study described low-grade mosaicism in neurons and differentiated hiPSCs at high resolution using array-based comparative genomic hybridization (aCGH) and single-cell sequencing 19 .…”

Le rôle des phénomènes aléatoires dans le cancer

“…En effet, ce n'est que l'analyse des génomes de cellules individuelles qui permet de révéler l'ampleur de l'hétérogénéité génétique intratumorale. Diverses études de séquençage du génome de cellules cancéreuses individuelles dans des cas de carcinomes [7] comme de leucémies [8], ont montré qu'il existe des sous-populations distinctes avec des profils génétiques complexes, sans sous-population intermé-diaire, ni événement génétique initial ; -enfin ces études de cellules individuelles ne permettent parfois même pas de caractériser de sous-population particulière, ni de mutation clonale [9]. Cette hétérogénéité génétique à l'échelle de la cellule individuelle questionne l'origine génétique du processus.…”

A simple and novel method for RNA‐seq library preparation of single cell cDNA analysis by hyperactive Tn5 transposase

“…However, recent advances in molecular biology has enabled us to carry out single cell level analysis of expression profiles using single cell cDNA construction (Saitou et al, 2002;Kurimoto et al, 2006;Tougan et al, 2008;Tang et al, 2010;Islam et al, 2011) as well as analysis of genomic organizational changes using whole genome amplification (Navin et al, 2011;Hou et al, 2012;Xu et al, 2012). Using these cDNA or genomic libraries from a single cell as ''amplicons'' by means of polymerase chain reaction (PCR), linear amplification with T7 polymerase or multiple displacement amplification can be analyzed with deep sequencing (Tougan et al, 2008;Tang et al, 2010;Navin et al, 2011;Hou et al, 2012;Xu et al, 2012). Single-cell analysis has the potential to reveal critical information which is masked by the heterogeneity of cell populations studied thus far (Saitou et al, 2002;Navin et al, 2011;Hou et al, 2012;Xu et al, 2012).…”

“…Using these cDNA or genomic libraries from a single cell as ''amplicons'' by means of polymerase chain reaction (PCR), linear amplification with T7 polymerase or multiple displacement amplification can be analyzed with deep sequencing (Tougan et al, 2008;Tang et al, 2010;Navin et al, 2011;Hou et al, 2012;Xu et al, 2012). Single-cell analysis has the potential to reveal critical information which is masked by the heterogeneity of cell populations studied thus far (Saitou et al, 2002;Navin et al, 2011;Hou et al, 2012;Xu et al, 2012). The recent increasing demand for single cell level analysis requires the protocols to be simplified and to be more reproducible.…”