Accurate identification of single-nucleotide variants in whole-genome-amplified single cells

Dong, Xiao; Zhang, Lei; Milholland, Brandon; Lee, Moonsook; Maslov, Alexander Y.; Wang, Tao; Vijg, Jan

doi:10.1038/nmeth.4227

Cited by 207 publications

(278 citation statements)

References 19 publications

(33 reference statements)

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“…Most mutations were found by sequencing runs of both exome capture kits, suggesting they are unlikely to be sequencing errors. Therefore, these mutations were either real biological mutations acquired during sphere culture or potential artefacts introduced by the WGA process as discussed by previous studies [26][27][28]. The median variant allele fractions in most cells were low, which could be caused by either WGA artefacts or additional copy number variations.…”

Section: Discussionmentioning

confidence: 79%

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Comparison of SureSelect and Nextera Exome Capture Performance in Single-Cell Sequencing

Huss

Glenn

et al. 2018

Hum Hered

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Background: Advances in single-cell sequencing provide unprecedented opportunities for clinical examination of circulating tumor cells, cancer stem cells, and other rare cells responsible for disease progression and drug resistance. On the genomic level, single-cell whole exome sequencing (scWES) started to gain popularity with its unique potentials in characterizing mutational landscapes at a single-cell level. Currently, there is little known about the performance of different exome capture kits in scWES. Nextera rapid capture (NXT; Illumina, Inc.) has been the only exome capture kit recommended for scWES by Fluidigm C1, a widely accessed system in single-cell preparation. Results: In this study, we compared the performance of NXT following Fluidigm’s protocol with Agilent SureSelectXT Target Enrichment System (AGL), another exome capture kit widely used for bulk sequencing. We created DNA libraries of 192 single cells isolated from spheres grown from a melanoma specimen using Fluidigm C1. Twelve high-yield cells were selected to perform dual-exome capture and sequencing using AGL and NXT in parallel. After mapping and coverage analysis, AGL outperformed NXT in coverage uniformity, mapping rates of reads, exome capture rates, and low PCR duplicate rates. For germline variant calling, AGL achieved better performance in overlap with known variants in dbSNP and transition-transversion ratios. Using calls from high coverage bulk sequencing from blood DNA as the golden standard, AGL-based scWES demonstrated high positive predictive values, and medium to high sensitivity. Lastly, we evaluated somatic mutation calling by comparing single-cell data with the matched blood sequence as control. On average, 300 mutations were identified in each cell. In 10 of 12 cells, higher numbers of mutations were identified using AGL than NXT, probably caused by coverage depth. When mutations are adequately covered in both AGL and NXT data, the two methods showed very high concordance (93–100% per cell). Conclusions: Our results suggest that AGL can also be used for scWES when there is sufficient DNA, and it yields better data quality than the current Fluidigm’s protocol using NXT.

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Section: Discussionmentioning

confidence: 79%

“…Using SCS to track the dynamic mutational profile on the DNA level, as one of its most potentially useful applications, has been slow to develop due to technical difficulties such as allelic dropout, non-uniform coverage, and WGA artefacts [26,27]. There is an urgent need for developing protocols that overcome these SCS-specific challenges.…”

Section: Discussionmentioning

confidence: 99%

Comparison of SureSelect and Nextera Exome Capture Performance in Single-Cell Sequencing

Huss

Glenn

et al. 2018

Hum Hered

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“…Mutations identified using whole genome sequencing of single human, primary fibroblasts after amplification and in unamplified clones from single fibroblasts taken from the same population were compared. After Dong et al 26 (A color version of this figure is available in the online journal.) and humans, we observed an almost two orders of magnitude higher somatic mutation frequency.…”

Section: Single-cell Methods To Analyze Somatic Mutationsmentioning

confidence: 99%

A high-fidelity method for genomic sequencing of single somatic cells reveals a very high mutational burden

Vijg

Dong

Zhang

2017

Exp Biol Med (Maywood)

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Somatic mutations cause cancer, possibly other diseases and aging. Yet, very little is known about the frequency of such mutations in vivo, their distribution across the genome, and their possible functional consequences other than cancer. Even in cancer, we do not know the heterogeneity of mutations within a tumor and if seemingly normal cells in its surroundings already have elevated mutation frequencies. Here, we review a new, whole genome amplification system that allows accurate quantification and characterization of single-cell mutational landscapes in human cells and tissues in relation to disease. AbstractPostzygotic mutations in somatic cells lead to genome mosaicism and can be the cause of cancer, possibly other human diseases and aging. Somatic mutations are difficult to detect in bulk tissue samples. Here, we review the available assays for measuring somatic mutations, with a focus on recent single-cell, whole genome sequencing methods.

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“…We recently developed a new protocol, i.e. single-cell multiple displacement amplification (SCMDA), with a single-cell variant caller (SCcaller), to accurately identify somatic mutations across the genome from a single cell after WGA [47]. The procedure was validated by directly comparing mutation frequency and spectrum between amplified single cells and unamplified clones derived from cells in the same population of early passage, human primary fibroblasts.…”

Section: Functional Impact Of Somatic Mutationsmentioning

confidence: 99%

Genome instability: a conserved mechanism of ageing?

Vijg

Dong

Milholland

et al. 2017

Essays in Biochemistry

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DNA is the carrier of genetic information and the primary template from which all cellular information is ultimately derived. Changes in the DNA information content through mutation generate diversity for evolution through natural selection but are also a source of deleterious effects. It has since long been hypothesized that mutation accumulation in somatic cells of multicellular organisms could causally contribute to age-related cellular degeneration and death. Assays to detect different types of mutations, from base substitutions to large chromosomal aberrations, have been developed and show unequivocally that mutations accumulate in different tissues and cell types of ageing humans and animals. More recently, next-generation sequencing-based methods have been developed to accurately determine the complete landscape of base substitution mutations in single cells. The first results show that the somatic mutation rate is much higher than the germline mutation rate and that base substitution loads in somatic cells are high enough to potentially affect cellular function.

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Accurate identification of single-nucleotide variants in whole-genome-amplified single cells

Cited by 207 publications

References 19 publications

Comparison of SureSelect and Nextera Exome Capture Performance in Single-Cell Sequencing

Comparison of SureSelect and Nextera Exome Capture Performance in Single-Cell Sequencing

A high-fidelity method for genomic sequencing of single somatic cells reveals a very high mutational burden

Genome instability: a conserved mechanism of ageing?

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