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Single cell lineage tracing by endogenous mitochondrial DNA mutations in ATAC-seq data

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Homo sapiens

PROVIDER: GSE122578 | GEO | 2019/04/11

REPOSITORIES: GEO

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Single cell lineage tracing by endogenous mitochondrial DNA mutations in ATAC-seq data [HSC]

Project description:Simultaneous measurement of lineage relationship and information on cell fate is desireable for human tissues. Here we describe a new strategy to track cell lineage using endogenous mitochondrial DNA variants in ATAC-seq data, which simultaneously measures the chromatin profiling. We first provide a proof of principle that somatic mutations in the mitochondrial genome can be used as a genetic marker to depict cellular lineage relationships and further quantified the power of lineage definition with these genetic markers. Using this method, we demonstrated the genetic and epigenetic heterogeneity of hematopoietic stem cells from patient with acute myeloid leukemia.

2019-04-11 | GSE122577 | GEO

Single-cell lineage tracing by endogenous mutations enriched in transposase accessible mitochondrial DNA.

Project description:Simultaneous measurement of cell lineage and cell fates is a longstanding goal in biomedicine. Here we describe EMBLEM, a strategy to track cell lineage using endogenous mitochondrial DNA variants in ATAC-seq data. We show that somatic mutations in mitochondrial DNA can reconstruct cell lineage relationships at single cell resolution with high sensitivity and specificity. Using EMBLEM, we define the genetic and epigenomic clonal evolution of hematopoietic stem cells and their progenies in patients with acute myeloid leukemia. EMBLEM extends lineage tracing to any eukaryotic organism without genetic engineering.

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Single cell lineage tracing by endogenous mitochondrial DNA mutations in ATAC-seq data [stem cells and blasts]

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Single cell lineage tracing by endogenous mitochondrial DNA mutations in ATAC-seq data

Project description:Single cell lineage tracing by endogenous mitochondrial DNA mutations in ATAC-seq data

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Single cell lineage tracing by endogenous mitochondrial DNA mutations in ATAC-seq data [HSC]

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Human lineage tracing enabled by mitochondrial mutations and single cell genomics

Project description:This SuperSeries is composed of the SubSeries listed below.

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Lineage Tracing in Humans Enabled by Mitochondrial Mutations and Single-Cell Genomics.

Project description:Lineage tracing provides key insights into the fate of individual cells in complex organisms. Although effective genetic labeling approaches are available in model systems, in humans, most approaches require detection of nuclear somatic mutations, which have high error rates, limited scale, and do not capture cell state information. Here, we show that somatic mutations in mtDNA can be tracked by single-cell RNA or assay for transposase accessible chromatin (ATAC) sequencing. We leverage somatic mtDNA mutations as natural genetic barcodes and demonstrate their utility as highly accurate clonal markers to infer cellular relationships. We track native human cells both in vitro and in vivo and relate clonal dynamics to gene expression and chromatin accessibility. Our approach should allow clonal tracking at a 1,000-fold greater scale than with nuclear genome sequencing, with simultaneous information on cell state, opening the way to chart cellular dynamics in human health and disease.

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Single cell lineage tracing by endogenous mitochondrial DNA mutations in ATAC-seq data [stem cells and blasts]

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Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data.

Project description:Recent studies combine two novel technologies, single-cell RNA-sequencing and CRISPR-Cas9 barcode editing for elucidating developmental lineages at the whole organism level. While these studies provided several insights, they face several computational challenges. First, lineages are reconstructed based on noisy and often saturated random mutation data. Additionally, due to the randomness of the mutations, lineages from multiple experiments cannot be combined to reconstruct a species-invariant lineage tree. To address these issues we developed a statistical method, LinTIMaT, which reconstructs cell lineages using a maximum-likelihood framework by integrating mutation and expression data. Our analysis shows that expression data helps resolve the ambiguities arising in when lineages are inferred based on mutations alone, while also enabling the integration of different individual lineages for the reconstruction of an invariant lineage tree. LinTIMaT lineages have better cell type coherence, improve the functional significance of gene sets and provide new insights on progenitors and differentiation pathways.

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