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Direct, genome-wide assessment of DNA mutations in single cells.

Gundry M, Li W, Maqbool SB, Vijg J - Nucleic Acids Res. (2011)

Bottom Line: One way to circumvent this problem and simultaneously account for the mutational heterogeneity within tissues is whole genome sequencing of a representative number of single cells.Here, we show elevated mutation levels in single cells from Drosophila melanogaster S2 and mouse embryonic fibroblast populations after treatment with the powerful mutagen N-ethyl-N-nitrosourea.This method can be applied as a direct measure of exposure to mutagenic agents and for assessing genotypic heterogeneity within tissues or cell populations.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Albert Einstein College of Medicine, New York, NY 10461, USA.

ABSTRACT
DNA mutations are the inevitable consequences of errors that arise during replication and repair of DNA damage. Because of their random and infrequent occurrence, quantification and characterization of DNA mutations in the genome of somatic cells has been difficult. Random, low-abundance mutations are currently inaccessible by standard high-throughput sequencing approaches because they cannot be distinguished from sequencing errors. One way to circumvent this problem and simultaneously account for the mutational heterogeneity within tissues is whole genome sequencing of a representative number of single cells. Here, we show elevated mutation levels in single cells from Drosophila melanogaster S2 and mouse embryonic fibroblast populations after treatment with the powerful mutagen N-ethyl-N-nitrosourea. This method can be applied as a direct measure of exposure to mutagenic agents and for assessing genotypic heterogeneity within tissues or cell populations.

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Somatic mutation detection using single cell sequencing. (A) Somatic mutations in tissues are rare and therefore found only in single sequencing reads from which they are routinely filtered out as sequencing errors during post-alignment processing. Adopting a single cell approach overcomes this limitation by transforming each somatic event into a consensus variant call. (B) Schematic depiction of the single cell sequencing protocol used for Drosophila S2 cells.
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gkr949-F1: Somatic mutation detection using single cell sequencing. (A) Somatic mutations in tissues are rare and therefore found only in single sequencing reads from which they are routinely filtered out as sequencing errors during post-alignment processing. Adopting a single cell approach overcomes this limitation by transforming each somatic event into a consensus variant call. (B) Schematic depiction of the single cell sequencing protocol used for Drosophila S2 cells.

Mentions: In spite of the enormous progress in genomics, the field is still very much focused on averages. Virtually all genome-wide analysis methods are geared towards clonally derived genomic DNA. For example, in sequencing cancer genomes, typically only a very small fraction of all mutations present in the tumor are detected (1). The far majority of mutations are low-abundance mutations present in a limited number of cells. In principle, such mutations could be detected by sequencing a large number of times across the same locus, i.e. at great sequencing depth. However, the high error rate of current high-throughput sequencing platforms (0.1–1%) effectively masks low-abundance mutations (2,3). To account for sequencing errors, current protocols for mutation detection are based on a consensus model, i.e. finding the same event in multiple, independent reads from the same locus. This allows only the detection of clonally amplified mutations present in most or all of the cells in a tissue sample and essentially constrains access to low-abundance mutations. One way to circumvent this problem is to sequence genomes of individual cells after whole genome amplification (WGA) (4). Every mutation in that cell at a particular locus now acts as the consensus sequence. This is schematically depicted in Figure 1A.Figure 1.


Direct, genome-wide assessment of DNA mutations in single cells.

Gundry M, Li W, Maqbool SB, Vijg J - Nucleic Acids Res. (2011)

Somatic mutation detection using single cell sequencing. (A) Somatic mutations in tissues are rare and therefore found only in single sequencing reads from which they are routinely filtered out as sequencing errors during post-alignment processing. Adopting a single cell approach overcomes this limitation by transforming each somatic event into a consensus variant call. (B) Schematic depiction of the single cell sequencing protocol used for Drosophila S2 cells.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3300019&req=5

gkr949-F1: Somatic mutation detection using single cell sequencing. (A) Somatic mutations in tissues are rare and therefore found only in single sequencing reads from which they are routinely filtered out as sequencing errors during post-alignment processing. Adopting a single cell approach overcomes this limitation by transforming each somatic event into a consensus variant call. (B) Schematic depiction of the single cell sequencing protocol used for Drosophila S2 cells.
Mentions: In spite of the enormous progress in genomics, the field is still very much focused on averages. Virtually all genome-wide analysis methods are geared towards clonally derived genomic DNA. For example, in sequencing cancer genomes, typically only a very small fraction of all mutations present in the tumor are detected (1). The far majority of mutations are low-abundance mutations present in a limited number of cells. In principle, such mutations could be detected by sequencing a large number of times across the same locus, i.e. at great sequencing depth. However, the high error rate of current high-throughput sequencing platforms (0.1–1%) effectively masks low-abundance mutations (2,3). To account for sequencing errors, current protocols for mutation detection are based on a consensus model, i.e. finding the same event in multiple, independent reads from the same locus. This allows only the detection of clonally amplified mutations present in most or all of the cells in a tissue sample and essentially constrains access to low-abundance mutations. One way to circumvent this problem is to sequence genomes of individual cells after whole genome amplification (WGA) (4). Every mutation in that cell at a particular locus now acts as the consensus sequence. This is schematically depicted in Figure 1A.Figure 1.

Bottom Line: One way to circumvent this problem and simultaneously account for the mutational heterogeneity within tissues is whole genome sequencing of a representative number of single cells.Here, we show elevated mutation levels in single cells from Drosophila melanogaster S2 and mouse embryonic fibroblast populations after treatment with the powerful mutagen N-ethyl-N-nitrosourea.This method can be applied as a direct measure of exposure to mutagenic agents and for assessing genotypic heterogeneity within tissues or cell populations.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Albert Einstein College of Medicine, New York, NY 10461, USA.

ABSTRACT
DNA mutations are the inevitable consequences of errors that arise during replication and repair of DNA damage. Because of their random and infrequent occurrence, quantification and characterization of DNA mutations in the genome of somatic cells has been difficult. Random, low-abundance mutations are currently inaccessible by standard high-throughput sequencing approaches because they cannot be distinguished from sequencing errors. One way to circumvent this problem and simultaneously account for the mutational heterogeneity within tissues is whole genome sequencing of a representative number of single cells. Here, we show elevated mutation levels in single cells from Drosophila melanogaster S2 and mouse embryonic fibroblast populations after treatment with the powerful mutagen N-ethyl-N-nitrosourea. This method can be applied as a direct measure of exposure to mutagenic agents and for assessing genotypic heterogeneity within tissues or cell populations.

Show MeSH
Related in: MedlinePlus