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Single-cell transcriptogenomics reveals transcriptional exclusion of ENU-mutated alleles.

ABSTRACT: Recently, great progress has been made in single cell genomics and transcriptomics. Here, we present an integrative method, termed single-cell transcriptogenomics (SCTG), in which whole exome sequencing and RNA-seq is performed concurrently on single cells. This methodology enables one to track germline and somatic variants directly from the genome to the transcriptome in individual cells. Mouse embryonic fibroblasts were treated with the powerful mutagen ethylnitrosourea (ENU) and subjected to SCTG. Interestingly, while germline variants were found to be transcribed in an allelically balanced fashion, a significantly different pattern of allelic exclusion was observed for ENU-mutant variants. These results suggest that the adverse effects of induced mutations, in contrast to germline variants, may be mitigated by allelically biased transcription. They also illustrate how SCTG can be instrumental in the direct assessment of phenotypic consequences of genomic variants.

SUBMITTER: Li W

PROVIDER: S-EPMC4342853 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Single-cell transcriptogenomics reveals transcriptional exclusion of ENU-mutated alleles.

Li Wenge W Calder R Brent RB Mar Jessica C JC Vijg Jan J

Mutation research 20150201

Recently, great progress has been made in single cell genomics and transcriptomics. Here, we present an integrative method, termed single-cell transcriptogenomics (SCTG), in which whole exome sequencing and RNA-seq is performed concurrently on single cells. This methodology enables one to track germline and somatic variants directly from the genome to the transcriptome in individual cells. Mouse embryonic fibroblasts were treated with the powerful mutagen ethylnitrosourea (ENU) and subjected to ...[more]

PMID: 25733965

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Project description:Single-cell analysis has the potential to provide us with a host of new knowledge about biological systems, but it comes with the challenge of correctly interpreting the biological information. While emerging techniques have made it possible to measure inter-cellular variability at the transcriptome level, no consensus yet exists on the most appropriate method of data analysis of such single cell data. Methods for analysis of transcriptional data at the population level are well established but are not well suited to single cell analysis due to their dependence on population averages. In order to address this question, we have systematically tested combinations of methods for primary data analysis on single cell transcription data generated from two types of primary immune cells, neutrophils and T lymphocytes. Cells were obtained from healthy individuals, and single cell transcript expression data was obtained by a combination of single cell sorting and nanoscale quantitative real time PCR (qRT-PCR) for markers of cell type, intracellular signaling, and immune functionality. Gene expression analysis was focused on hierarchical clustering to determine the existence of cellular subgroups within the populations. Nine combinations of criteria for data exclusion and normalization were tested and evaluated. Bimodality in gene expression indicated the presence of cellular subgroups which were also revealed by data clustering. We observed evidence for two clearly defined cellular subtypes in the neutrophil populations and at least two in the T lymphocyte populations. When normalizing the data by different methods, we observed varying outcomes with corresponding interpretations of the biological characteristics of the cell populations. Normalization of the data by linear standardization taking into account technical effects such as plate effects, resulted in interpretations that most closely matched biological expectations. Single cell transcription profiling provides evidence of cellular subclasses in neutrophils and leukocytes that may be independent of traditional classifications based on cell surface markers. The choice of primary data analysis method had a substantial effect on the interpretation of the data. Adjustment for technical effects is critical to prevent misinterpretation of single cell transcript data.

Project description:BackgroundN-ethyl-N-nitrosourea (ENU) mutagenesis is a useful method for the genetic engineering of plants, and the production of functional mutants in animal models including mice and zebrafish. Grass carp reovirus (GCRV) is a haemorrhagic disease of grass carp which has caused noteworthy losses in fingerlings over the last few years. To overcome this problem, we used ENU mutant grass carp in an attempt to identify functional resistance genes for future hereditary rearing projects in grass carp.ResultsThis study used ENU-mutated grass carp to identify genetic markers associated with resistance to the haemorrhagic disease caused by GCRV. Bulked segregant analysis (BSA) was performed on two homozygous gynogenetic ENU grass carp groups who were susceptible or resistant to GCRV. This analysis identified 466,162 SNPs and 197,644 InDels within the genomes of these mixed pools with a total of 170 genes annotated in the associated region, including 49 genes with non-synonymous mutations at SNP sites and 25 genes with frame shift mutations at InDel sites. Of these 170 mutated genes, 5 randomly selected immune-related genes were shown to be more strongly expressed in the resistant group as compared to the susceptible animals. In addition, we found that one immune-related gene, EPHB2, presented with two heterozygous SNP mutations which altered the animal's responded to GCRV disease. These SNPs were found in the intron region of EPHB2 at positions 5859 (5859G > A) and 5968 (5968G > A) and were significantly (p = 0.002, 0.003) associated with resistance to GCRV. These SNP sites were also shown to correlate with the GCRV-resistant phenotype in these ENU grass carp. We also evaluated the mortality of the different ENU fish genotypes in response to GCRV and the SNPs in EPHB2. The outcomes of these evaluations will be useful in future selections of GCRV-resistant genes for genetic breeding in grass carp.ConclusionOur results provide a proof of concept for the application of BSA-sequence analysis in detecting genes responsible for specific functional genotypes and may help to develop better methods for marker-assisted selection, especially for disease resistance in response to GCRV.

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Single-cell transcriptogenomics reveals transcriptional exclusion of ENU-mutated alleles.

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Single-cell transcriptogenomics reveals transcriptional exclusion of ENU-mutated alleles.

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