Tumor heterogeneity presents a challenge for inferring clonal evolution and driver gene identification. Here, we describe a method for analyzing the cancer genome at a single-cell nucleotide level. To perform our analyses, we first devised and validated a high-throughput whole-genome single-cell sequencing method using two lymphoblastoid cell line single cells. We then carried out whole-exome single-cell sequencing of 90 cells from a JAK2-negative myeloproliferative neoplasm patient. The sequencing data from 58 cells passed our quality control criteria, and these data indicated that this neoplasm represented a monoclonal evolution. We further identified essential thrombocythemia (ET)-related candidate mutations such as SESN2 and NTRK1, which may be involved in neoplasm progression. This pilot study allowed the initial characterization of the disease-related genetic architecture at the single-cell nucleotide level. Further, we established a single-cell sequencing method that opens the way for detailed analyses of a variety of tumor types, including those with high genetic complex between patients.
SUMMARY The utility of genome editing technologies for disease modeling and developing cellular therapies has been extensively documented, but the impact of these technologies on mutational load at the whole-genome level remains unclear. We performed whole-genome sequencing to evaluate the mutational load at single-base resolution in individual gene-corrected human induced pluripotent stem cells (hiPSCs) clones in three different disease models. Single-cell clones gene correction by helper-dependent adenoviral vector (HDAdV) or Transcription Activator-Like Effector Nuclease (TALEN) exhibited few off-target effects and a low level of sequence variation, comparable to that accumulated in routine hiPSC culture. The sequence variants were randomly distributed and unique to individual clones. We also combined both technologies and developed a TALEN-HDAdV hybrid vector, which significantly increased gene-correction efficiency in hiPSCs. Therefore, with careful monitoring via whole genome sequencing it is possible to apply genome editing to human pluripotent cells with minimal impact on genomic mutational load.
Single-cell sequencing is a powerful tool for delineating clonal relationship and identifying key driver genes for personalized cancer management. Here we performed single-cell sequencing analysis of a case of colon cancer. Population genetics analyses identified two independent clones in tumor cell population. The major tumor clone harbored APC and TP53 mutations as early oncogenic events, whereas the minor clone contained preponderant CDC27 and PABPC1 mutations. The absence of APC and TP53 mutations in the minor clone supports that these two clones were derived from two cellular origins. Examination of somatic mutation allele frequency spectra of additional 21 wholetissue exome-sequenced cases revealed the heterogeneity of clonal origins in colon cancer. Next, we identified a mutated gene SLC12A5 that showed a high frequency of mutation at the single-cell level but exhibited low prevalence at the population level. Functional characterization of mutant SLC12A5 revealed its potential oncogenic effect in colon cancer. Our study provides the first exome-wide evidence at single-cell level supporting that colon cancer could be of a biclonal origin, and suggests that low-prevalence mutations in a cohort may also play important protumorigenic roles at the individual level.
By using genomic, transcriptome, and epigenomic comparisons of EBV infected vs noninfected gastric cancer cells and tumor samples, we identified alterations in genes, gene expression, and methylation that affect different signaling networks. These might be involved in EBV-associated gastric carcinogenesis.
BackgroundThe applications of massively parallel sequencing technology to fetal cell-free DNA (cff-DNA) have brought new insight to non-invasive prenatal diagnosis. However, most previous research based on maternal plasma sequencing has been restricted to fetal aneuploidies. To detect specific parentally inherited mutations, invasive approaches to obtain fetal DNA are the current standard in the clinic because of the experimental complexity and resource consumption of previously reported non-invasive approaches.MethodsHere, we present a simple and effective non-invasive method for accurate fetal genome recovery-assisted with parental haplotypes. The parental haplotype were firstly inferred using a combination strategy of trio and unrelated individuals. Assisted with the parental haplotype, we then employed a hidden Markov model to non-invasively recover the fetal genome through maternal plasma sequencing.ResultsUsing a sequence depth of approximately 44X against a an approximate 5.69% cff-DNA concentration, we non-invasively inferred fetal genotype and haplotype under different situations of parental heterozygosity. Our data show that 98.57%, 95.37%, and 98.45% of paternal autosome alleles, maternal autosome alleles, and maternal chromosome X in the fetal haplotypes, respectively, were recovered accurately. Additionally, we obtained efficient coverage or strong linkage of 96.65% of reported Mendelian-disorder genes and 98.90% of complex disease-associated markers.ConclusionsOur method provides a useful strategy for non-invasive whole fetal genome recovery.
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