Deep sequencing reveals cell-type specific patterns of single cell transcriptome variation
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus Rattus norvegicus
PROVIDER: GSE56638 | GEO | 2015/05/21
SECONDARY ACCESSION(S): PRJNA244374
REPOSITORIES: GEO
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Project description:We present high quality deep read-depth single cell RNA sequencing for 91 cells from five mouse tissues and 18 cells from two rat tissues, along with 30 control samples of bulk RNA diluted to single-cell levels. We find that transcriptomes differ globally across tissues with regard to the number of genes expressed, the average expression patterns, and within cell-type variation patterns. We develop methods to filter genes for reliable quantification and to calibrate biological variation. All cell types include genes with high variability in expression, in a tissue-specific manner. We also find evidence that single cell variability of neuronal genes in mice is correlated with that in rats consistent with the hypothesis that levels of variation may be conserved. From an initial 143 cells we identified 107 high quality samples with deep genic coverage, including 13 brown adipocytes, 19 cardiomyocytes, 19 cortical pyramidal neurons and 18 hippocampal pyramidal neurons from embryonic mouse, 8 cortical pyramidal neurons and 8 hippocampal pyramidal neurons from embryonic rat, and 22 serotonergic neurons from adult mouse. While mouse data is collected from three strains of mice at different ages, each cell type dataset is internally consistent. There are no technical replicates of single-cell samples. We additionally prepared 30 control samples, amplifications of bulk total cardiomyocyte RNA diluted to single cell quantities.
2015-05-21 | E-GEOD-56638 | biostudies-arrayexpress
Project description:Cell-to-cell expression variation is a prevalent feature of cell populations, but the environmental, cellular and biochemical conditions under which variability in the level of one gene can or cannot be propagated to affect that of other genes in the underlying network within cells remain poorly understood. Here we explore this issue using single-cell qPCR and bulk-level RNAseq, CAGE, and ChIPseq analysis of human macrophages exposed to different cytokine environments. Our analyses reveal that cellular adaptation to different environments could involve tuning the extent of such variability (or information) propagation (IP) in the network, thereby shaping environment-dependent patterns of gene-gene correlations and phenotypic heterogeneity across single cells. We find that IP can be regulated through modification of multiple network and signaling parameters, such as the degree of transcription factor-chromatin interactions. Our findings thus suggest that adaptive IP may be a widespread, yet underexplored, aspect of cellular adaptation to distinct environments.
2017-03-27 | GSE80726 | GEO
Project description:Cell-to-cell expression variation is a prevalent feature of cell populations, but the environmental, cellular and biochemical conditions under which variability in the level of one gene can or cannot be propagated to affect that of other genes in the underlying network within cells remain poorly understood. Here we explore this issue using single-cell qPCR and bulk-level RNAseq, CAGE, and ChIPseq analysis of human macrophages exposed to different cytokine environments. Our analyses reveal that cellular adaptation to different environments could involve tuning the extent of such variability (or information) propagation (IP) in the network, thereby shaping environment-dependent patterns of gene-gene correlations and phenotypic heterogeneity across single cells. We find that IP can be regulated through modification of multiple network and signaling parameters, such as the degree of transcription factor-chromatin interactions. Our findings thus suggest that adaptive IP may be a widespread, yet underexplored, aspect of cellular adaptation to distinct environments.
2017-03-27 | GSE80720 | GEO
Project description:BACKGROUND: The transcript levels of many genes exhibit significant variation in tissue samples from inbred laboratory mice. A microarray experiment was designed to separate transcript abundance variation across samples from adipose, heart, kidney, and liver tissues of C57BL/6J mice into within-mouse and between-mouse components. Within-mouse variance captures variation due to heterogeneity of gene expression within tissues, RNA-extraction, and array processing. Between-mouse variance reflects differences in transcript levels between these genetically identical mice. Many biological sources can contribute to heterogeneous transcript levels within a tissue sample including inherent stochasticity of biochemical processes such as intrinsic and extrinsic noise within cells and differences in cell-type composition which can result from heterogeneity of stem and progenitor cell populations. Differences in global signaling patterns between individuals and micro-environmental influences such as interactions with pathogens and cage mates can also contribute to variation, but are likely to contribute more to the between-mouse variance component. RESULTS: The nature and extent of transcript abundance variation differs across tissues. Adipose has the largest total variance and the largest within-mouse variance. Liver has the smallest total variance, but it has the most between-mouse variation. Genes with high variability can be classified into groups with correlated patterns of expression that are enriched for specific biological functions. Variation between mice is associated with circadian rhythm, growth hormone signaling, immune response, androgen regulation, lipid metabolism, and the extracellular matrix. Genes showing correlated patterns of within mouse variation were also associated with biological functions, spatial connectivity of stochastic variation and heterogeneity of cell types within tissues. CONCLUSIONS: Genetically identical mice are individuals and they can experience different outcomes for medically important traits. This is reflected in the stochastic variation in gene expression observed between genetically identical mice. Much of the stochasticity has organismal, tissue, or spatial connectivity. Prior knowledge of the genes and functional classes of genes that are likely to vary in the absence of experimental perturbations, whether these are genetic or environmental, can inform experimental design decisions and the interpretation of gene expression data. Variation in gene expression in genetically identical mice sheds light on the impact of stochastic and micro-environmental factors and their phenotypic consequences.
2010-10-01 | GSE20121 | GEO
Project description:Natural mitochondrial DNA (mtDNA) sequence variation plays a fundamental role in human disease and enables the clonal tracing of native human cells. While various genotyping approaches revealed mutational heterogeneity in human tissues and single cells, current methodologies are limited by scale. Here, we introduce a high-throughput, droplet-based mitochondrial single-cell Assay for Transposase Accessible Chromatin with sequencing (mtscATAC-seq) protocol and computational framework that facilitate high-confidence mtDNA mutation calling in thousands of single cells. Further, the concomitant high-quality accessible chromatin readout enables the paired inference of individual cell mtDNA heteroplasmy, clonal lineage, cell state, and accessible chromatin regulatory features. Our multi-omic analyses reveals single-cell variation in heteroplasmy of a pathologic mtDNA variant (m.8344A>G), which we tie to intra-individual chromatin variability and clonal evolution. Further, using somatic mtDNA mutations, we clonally trace thousands of hematopoietic cells in vitro and in patients with chronic lymphocytic leukemia, linking epigenomic variability to subclonal evolution in vivo.
2020-07-13 | GSE142742 | GEO
Project description:Natural mitochondrial DNA (mtDNA) sequence variation plays a fundamental role in human disease and enables the clonal tracing of native human cells. While various genotyping approaches revealed mutational heterogeneity in human tissues and single cells, current methodologies are limited by scale. Here, we introduce a high-throughput, droplet-based mitochondrial single-cell Assay for Transposase Accessible Chromatin with sequencing (mtscATAC-seq) protocol and computational framework that facilitate high-confidence mtDNA mutation calling in thousands of single cells. Further, the concomitant high-quality accessible chromatin readout enables the paired inference of individual cell mtDNA heteroplasmy, clonal lineage, cell state, and accessible chromatin regulatory features. Our multi-omic analyses reveals single-cell variation in heteroplasmy of a pathologic mtDNA variant (m.8344A>G), which we tie to intra-individual chromatin variability and clonal evolution. Further, using somatic mtDNA mutations, we clonally trace thousands of hematopoietic cells in vitro and in patients with chronic lymphocytic leukemia, linking epigenomic variability to subclonal evolution in vivo.
2020-07-13 | GSE148509 | GEO
Project description:Natural mitochondrial DNA (mtDNA) sequence variation plays a fundamental role in human disease and enables the clonal tracing of native human cells. While various genotyping approaches revealed mutational heterogeneity in human tissues and single cells, current methodologies are limited by scale. Here, we introduce a high-throughput, droplet-based mitochondrial single-cell Assay for Transposase Accessible Chromatin with sequencing (mtscATAC-seq) protocol and computational framework that facilitate high-confidence mtDNA mutation calling in thousands of single cells. Further, the concomitant high-quality accessible chromatin readout enables the paired inference of individual cell mtDNA heteroplasmy, clonal lineage, cell state, and accessible chromatin regulatory features. Our multi-omic analyses reveals single-cell variation in heteroplasmy of a pathologic mtDNA variant (m.8344A>G), which we tie to intra-individual chromatin variability and clonal evolution. Further, using somatic mtDNA mutations, we clonally trace thousands of hematopoietic cells in vitro and in patients with chronic lymphocytic leukemia, linking epigenomic variability to subclonal evolution in vivo.
2020-07-13 | GSE148508 | GEO
Project description:Natural mitochondrial DNA (mtDNA) sequence variation plays a fundamental role in human disease and enables the clonal tracing of native human cells. While various genotyping approaches revealed mutational heterogeneity in human tissues and single cells, current methodologies are limited by scale. Here, we introduce a high-throughput, droplet-based mitochondrial single-cell Assay for Transposase Accessible Chromatin with sequencing (mtscATAC-seq) protocol and computational framework that facilitate high-confidence mtDNA mutation calling in thousands of single cells. Further, the concomitant high-quality accessible chromatin readout enables the paired inference of individual cell mtDNA heteroplasmy, clonal lineage, cell state, and accessible chromatin regulatory features. Our multi-omic analyses reveals single-cell variation in heteroplasmy of a pathologic mtDNA variant (m.8344A>G), which we tie to intra-individual chromatin variability and clonal evolution. Further, using somatic mtDNA mutations, we clonally trace thousands of hematopoietic cells in vitro and in patients with chronic lymphocytic leukemia, linking epigenomic variability to subclonal evolution in vivo.
2020-07-13 | GSE142744 | GEO
Project description:Natural mitochondrial DNA (mtDNA) sequence variation plays a fundamental role in human disease and enables the clonal tracing of native human cells. While various genotyping approaches revealed mutational heterogeneity in human tissues and single cells, current methodologies are limited by scale. Here, we introduce a high-throughput, droplet-based mitochondrial single-cell Assay for Transposase Accessible Chromatin with sequencing (mtscATAC-seq) protocol and computational framework that facilitate high-confidence mtDNA mutation calling in thousands of single cells. Further, the concomitant high-quality accessible chromatin readout enables the paired inference of individual cell mtDNA heteroplasmy, clonal lineage, cell state, and accessible chromatin regulatory features. Our multi-omic analyses reveals single-cell variation in heteroplasmy of a pathologic mtDNA variant (m.8344A>G), which we tie to intra-individual chromatin variability and clonal evolution. Further, using somatic mtDNA mutations, we clonally trace thousands of hematopoietic cells in vitro and in patients with chronic lymphocytic leukemia, linking epigenomic variability to subclonal evolution in vivo.
2020-07-13 | GSE142743 | GEO
Project description:Natural mitochondrial DNA (mtDNA) sequence variation plays a fundamental role in human disease and enables the clonal tracing of native human cells. While various genotyping approaches revealed mutational heterogeneity in human tissues and single cells, current methodologies are limited by scale. Here, we introduce a high-throughput, droplet-based mitochondrial single-cell Assay for Transposase Accessible Chromatin with sequencing (mtscATAC-seq) protocol and computational framework that facilitate high-confidence mtDNA mutation calling in thousands of single cells. Further, the concomitant high-quality accessible chromatin readout enables the paired inference of individual cell mtDNA heteroplasmy, clonal lineage, cell state, and accessible chromatin regulatory features. Our multi-omic analyses reveals single-cell variation in heteroplasmy of a pathologic mtDNA variant (m.8344A>G), which we tie to intra-individual chromatin variability and clonal evolution. Further, using somatic mtDNA mutations, we clonally trace thousands of hematopoietic cells in vitro and in patients with chronic lymphocytic leukemia, linking epigenomic variability to subclonal evolution in vivo.
2020-07-13 | GSE142741 | GEO