3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Natural mitochondrial DNA (mtDNA) mutations enable the inference of clonal relationships among cells. mtDNA can be profiled along with measures of cell state, but has not yet been combined with the massively parallel approaches needed to tackle the complexity of human tissue. Here, we introduce a high-throughput, droplet-based mitochondrial single-cell Assay for Transposase Accessible Chromatin with sequencing (mtscATAC-seq), a method that combines high-confidence mtDNA mutation calling in thousands of single cells with their concomitant high-quality accessible chromatin profile. This enables the inference of mtDNA heteroplasmy, clonal relationships, cell state, and accessible chromatin variation in individual cells. We reveal single-cell variation in heteroplasmy of a pathologic mtDNA variant, which we associate with intra-individual chromatin variability and clonal evolution. We clonally trace thousands of cells from cancers, linking epigenomic variability to subclonal evolution and infer cellular dynamics of differentiating hematopoietic cells in vitro and in vivo. Taken together, our approach enables the study of cellular population dynamics and clonal properties in vivo.

          Related collections

          Most cited references73

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          BEDTools: a flexible suite of utilities for comparing genomic features

          Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Comprehensive Integration of Single-Cell Data

            Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              An Integrated Encyclopedia of DNA Elements in the Human Genome

              Summary The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure, and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall the project provides new insights into the organization and regulation of our genes and genome, and an expansive resource of functional annotations for biomedical research.
                Bookmark

                Author and article information

                Journal
                9604648
                20305
                Nat Biotechnol
                Nat Biotechnol
                Nature biotechnology
                1087-0156
                1546-1696
                20 July 2020
                12 August 2020
                April 2021
                14 April 2021
                : 39
                : 4
                : 451-461
                Affiliations
                [1 ]Division of Hematology / Oncology, Boston Children’s Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
                [2 ]Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
                [3 ]Division of Medical Sciences, Harvard University, Cambridge, MA 02138, USA
                [4 ]Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
                [5 ]Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
                [6 ]Department of Academic Haematology, UCL Cancer Institute, London, WC1E 6DD, UK
                [7 ]Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
                [8 ]Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
                [9 ]Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
                [10 ]Department of Pathology, Harvard Medical School, Boston, MA 02115, USA
                [11 ]Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
                [12 ]Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
                [13 ]Howard Hughes Medical Institute, Chevy Chase, MD 26309, USA
                [14 ]Department of Biology and Koch Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
                [15 ]Harvard Stem Cell Institute, Cambridge, MA 02138, USA
                Author notes
                [*]

                These authors contributed equally

                AUTHOR CONTRIBUTIONS

                C.A.L. and L.S.L. conceived and designed the project with guidance from A.R and V.G.S. C.A.L. developed the software and led data analysis. L.S.L. and C.M. developed the mtscATAC-seq experimental protocol. L.S.L led, designed, and performed experiments with assistance from C.M., W.L., and E.C. S.G. processed CLL patient samples with L.S.L. T.Z. performed the in situ genotyping experiments. Z.C. and J.M.V. analyzed data. K.P. processed the colorectal cancer specimen. D.R. and G.G. aided with exome sequencing. F.C., J.D.B., M.J.A., G.M.B., N.H., C.J.W., A.R., and V.G.S. each supervised various aspects of this work. A.R. and V.G.S. provided overall project oversight and acquired funding. C.A.L., L.S.L., A.R., and V.G.S. wrote the manuscript with input from all authors.

                Article
                NIHMS1613366
                10.1038/s41587-020-0645-6
                7878580
                32788668
                f7daf1a4-8e7f-4719-9c22-fb5763824d3a

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Categories
                Article

                Biotechnology
                Biotechnology

                Comments

                Comment on this article