Dataset Information

SMOOTH-seq: single-cell genome sequencing of human cells on a third-generation sequencing platform.

ABSTRACT: There is no effective way to detect structure variations (SVs) and extra-chromosomal circular DNAs (ecDNAs) at single-cell whole-genome level. Here, we develop a novel third-generation sequencing platform-based single-cell whole-genome sequencing (scWGS) method named SMOOTH-seq (single-molecule real-time sequencing of long fragments amplified through transposon insertion). We evaluate the method for detecting CNVs, SVs, and SNVs in human cancer cell lines and a colorectal cancer sample and show that SMOOTH-seq reliably and effectively detects SVs and ecDNAs in individual cells, but shows relatively limited accuracy in detection of CNVs and SNVs. SMOOTH-seq opens a new chapter in scWGS as it generates high fidelity reads of kilobases long.

SUBMITTER: Fan X

PROVIDER: S-EPMC8247186 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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SMOOTH-seq: single-cell genome sequencing of human cells on a third-generation sequencing platform.

Fan Xiaoying X Yang Cheng C Li Wen W Bai Xiuzhen X Zhou Xin X Xie Haoling H Wen Lu L Tang Fuchou F

Genome biology 20210630 1

There is no effective way to detect structure variations (SVs) and extra-chromosomal circular DNAs (ecDNAs) at single-cell whole-genome level. Here, we develop a novel third-generation sequencing platform-based single-cell whole-genome sequencing (scWGS) method named SMOOTH-seq (single-molecule real-time sequencing of long fragments amplified through transposon insertion). We evaluate the method for detecting CNVs, SVs, and SNVs in human cancer cell lines and a colorectal cancer sample and show ...[more]

PMID: 34193237

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Project description:Since the first description of a commensal seminal microbiome using sequencing, less than a decade ago, interest in the composition of this microbiome and its relationship with fertility has been growing. Articles using next-generation sequencing techniques agree on the identification of the most abundant bacterial phyla. However, at the genus level, there is still no consensus on which bacteria are most abundant in human seminal plasma. This discrepancy may be due to methodological variability such as sample collection, bacterial DNA extraction methodology, which hypervariable regions of 16S rRNA gene have been amplified, or bioinformatic analysis. In the present work, seminal microbiota of 14 control samples and 42 samples of idiopathic infertile patients were characterized based on full-length sequencing of the 16S rRNA gene using MinION platform from Oxford Nanopore. These same samples had been analyzed previously using Illumina's MiSeq sequencing platform. Comparison between the results obtained with the two platforms has been used to analyze the impact of sequencing method on the study of the seminal microbiome's composition. Seminal microbiota observed with MinION were mainly composed of the phyla Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria, with the most abundant genera being Peptoniphilus, Finegoldia, Staphylococcus, Anaerococcus, Campylobacter, Prevotella, Streptococcus, Lactobacillus, Ezakiella and Enterococcus. This composition was similar to that found by the Illumina platform, since these 10 most abundant genera were also among the most abundant genera detected by the Nanopore platform. In both cases, the top 10 genera represented more than 70% of the classified reads. However, relative abundance of each bacterium did not correlate between these two platforms, with intraindividual variations of up to 50 percentage points in some cases. Results suggest that the effect of the sequencing platform on the characterization of seminal microbiota is not very large at the phylum level, with slightly variances in Firmicutes and Actinobacteria, but presents differences at the genus level. These differences could alter the composition and diversity of bacterial profiles or posterior analyses. This indicates the importance of conducting multi-platform studies to better characterize seminal microbioma.

Project description:BackgroundSchizophrenia (SCZ) is a heterogeneous psychiatric disorder, with significant contribution from genetic factors particularly for chronic cases with negative symptoms and cognitive deficits. To date, Genome Wide Association Studies (GWAS) and exome sequencing have associated SCZ with a number of single nucleotide polymorphisms (SNPs) and copy number variants (CNVs), but there is still missing heritability. Medium-sized structural variants (SVs) are difficult to detect using SNP arrays or second generation sequencing, and may account for part of the missing heritability of SCZ.Aims and objectivesTo identify SVs associated with severe chronic SCZ across the whole genome.Study design10 multiplex families with probands suffering from chronic SCZ with negative symptoms and cognitive deficits were recruited, with all their affected members demonstrating uni-lineal inheritance. Control subjects comprised one affected member from the affected lineage, and unaffected members from each paternal and maternal lineage.MethodsThird generation sequencing was applied to peripheral blood samples from 10 probands and 5 unaffected controls. Bioinformatic tools were used to identify SVs from the long sequencing reads, with confirmation of findings in probands by short-read Illumina sequencing, Sanger sequencing and visual manual validation with Integrated Genome Browser.ResultsIn the 10 probands, we identified and validated 88 SVs (mostly in introns and medium-sized), within 79 genes, which were absent in the 5 unaffected control subjects. These 79 genes were enriched in 20 biological pathways which were related to brain development, neuronal migration, neurogenesis, neuronal/synaptic function, learning/memory, and hearing. These identified SVs also showed evidence for enrichment of genes that are highly expressed in the adolescent striatum.ConclusionA substantial part of the missing heritability in SCZ may be explained by medium-sized SVs detectable only by third generation sequencing. We have identified a number of such SVs potentially conferring risk for SCZ, which implicate multiple brain-related genes and pathways. In addition to previously-identified pathways involved in SCZ such as neurodevelopment and neuronal/synaptic functioning, we also found novel evidence for enrichment in hearing-related pathways and genes expressed in the adolescent striatum.

Dataset Information

SMOOTH-seq: single-cell genome sequencing of human cells on a third-generation sequencing platform.

Publications

SMOOTH-seq: single-cell genome sequencing of human cells on a third-generation sequencing platform.

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