351
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Lin DS, Kan A, Gao J, Crampin EJ, Hodgkin PD, Naik SH. DiSNE Movie Visualization and Assessment of Clonal Kinetics Reveal Multiple Trajectories of Dendritic Cell Development. Cell Rep 2019. [PMID: 29514085 DOI: 10.1016/j.celrep.2018.02.046] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
A thorough understanding of cellular development is incumbent on assessing the complexities of fate and kinetics of individual clones within a population. Here, we develop a system for robust periodical assessment of lineage outputs of thousands of transient clones and establishment of bona fide cellular trajectories. We appraise the development of dendritic cells (DCs) in fms-like tyrosine kinase 3 ligand culture from barcode-labeled hematopoietic stem and progenitor cells (HSPCs) by serially measuring barcode signatures and visualize these multidimensional data using developmental interpolated t-distributed stochastic neighborhood embedding (DiSNE) time-lapse movies. We identify multiple cellular trajectories of DC development that are characterized by distinct fate bias and expansion kinetics and determine that these are intrinsically programmed. We demonstrate that conventional DC and plasmacytoid DC trajectories are largely separated already at the HSPC stage. This framework allows systematic evaluation of clonal dynamics and can be applied to other steady-state or perturbed developmental systems.
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Affiliation(s)
- Dawn S Lin
- Molecular Medicine Division, Walter and Eliza Hall Institute, Parkville, VIC 3052, Australia; Immunology Division, Walter and Eliza Hall Institute, Parkville, VIC 3052, Australia; Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - Andrey Kan
- Immunology Division, Walter and Eliza Hall Institute, Parkville, VIC 3052, Australia; Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - Jerry Gao
- Molecular Medicine Division, Walter and Eliza Hall Institute, Parkville, VIC 3052, Australia
| | - Edmund J Crampin
- Systems Biology Laboratory, University of Melbourne, Parkville, VIC 3010, Australia; Centre for Systems Genomics, University of Melbourne, Parkville, VIC 3010, Australia; ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Melbourne School of Engineering, University of Melbourne, Parkville, VIC 3010, Australia
| | - Philip D Hodgkin
- Immunology Division, Walter and Eliza Hall Institute, Parkville, VIC 3052, Australia; Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - Shalin H Naik
- Molecular Medicine Division, Walter and Eliza Hall Institute, Parkville, VIC 3052, Australia; Immunology Division, Walter and Eliza Hall Institute, Parkville, VIC 3052, Australia; Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia.
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352
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Abstract
Abstract
The development of clustered regularly interspaced short-palindromic repeat (CRISPR)-Cas systems for genome editing has transformed the way life science research is conducted and holds enormous potential for the treatment of disease as well as for many aspects of biotechnology. Here, I provide a personal perspective on the development of CRISPR-Cas9 for genome editing within the broader context of the field and discuss our work to discover novel Cas effectors and develop them into additional molecular tools. The initial demonstration of Cas9-mediated genome editing launched the development of many other technologies, enabled new lines of biological inquiry, and motivated a deeper examination of natural CRISPR-Cas systems, including the discovery of new types of CRISPR-Cas systems. These new discoveries in turn spurred further technological developments. I review these exciting discoveries and technologies as well as provide an overview of the broad array of applications of these technologies in basic research and in the improvement of human health. It is clear that we are only just beginning to unravel the potential within microbial diversity, and it is quite likely that we will continue to discover other exciting phenomena, some of which it may be possible to repurpose as molecular technologies. The transformation of mysterious natural phenomena to powerful tools, however, takes a collective effort to discover, characterize, and engineer them, and it has been a privilege to join the numerous researchers who have contributed to this transformation of CRISPR-Cas systems.
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353
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Kebschull JM. DNA sequencing in high-throughput neuroanatomy. J Chem Neuroanat 2019; 100:101653. [PMID: 31173871 DOI: 10.1016/j.jchemneu.2019.101653] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Revised: 03/30/2019] [Accepted: 05/24/2019] [Indexed: 01/15/2023]
Abstract
Mapping brain connectivity at single cell resolution is critical for understanding brain structure. For decades, such mapping has been principally approached with microscopy techniques, aiming to visualize neurons and their connections. However, these techniques are often very labor intensive and do not scale well to the complexity of mammalian brains. We recently leveraged the speed and parallelization of DNA sequencing to map the projections of thousands of single neurons in single experiments, and to map cortical mesoscale connectivity in single mice. Here, I review the state of sequencing-based neuroanatomy, and discuss future directions in synaptic connectivity mapping and comparative connectomics.
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354
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Chan MM, Smith ZD, Grosswendt S, Kretzmer H, Norman TM, Adamson B, Jost M, Quinn JJ, Yang D, Jones MG, Khodaverdian A, Yosef N, Meissner A, Weissman JS. Molecular recording of mammalian embryogenesis. Nature 2019; 570:77-82. [PMID: 31086336 PMCID: PMC7229772 DOI: 10.1038/s41586-019-1184-5] [Citation(s) in RCA: 251] [Impact Index Per Article: 41.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 04/10/2019] [Indexed: 01/04/2023]
Abstract
Ontogeny describes the emergence of complex multicellular organisms from single totipotent cells. This field is particularly challenging in mammals, owing to the indeterminate relationship between self-renewal and differentiation, variation in progenitor field sizes, and internal gestation in these animals. Here we present a flexible, high-information, multi-channel molecular recorder with a single-cell readout and apply it as an evolving lineage tracer to assemble mouse cell-fate maps from fertilization through gastrulation. By combining lineage information with single-cell RNA sequencing profiles, we recapitulate canonical developmental relationships between different tissue types and reveal the nearly complete transcriptional convergence of endodermal cells of extra-embryonic and embryonic origins. Finally, we apply our cell-fate maps to estimate the number of embryonic progenitor cells and their degree of asymmetric partitioning during specification. Our approach enables massively parallel, high-resolution recording of lineage and other information in mammalian systems, which will facilitate the construction of a quantitative framework for understanding developmental processes.
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Affiliation(s)
- Michelle M Chan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Zachary D Smith
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Stefanie Grosswendt
- Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Helene Kretzmer
- Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Thomas M Norman
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Britt Adamson
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Molecular Biology, Lewis Sigler Institute, Princeton University, Princeton, NJ, USA
| | - Marco Jost
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Jeffrey J Quinn
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Dian Yang
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Matthew G Jones
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
- Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA, USA
| | - Alex Khodaverdian
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Nir Yosef
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- Ragon Institute of Massachusetts General Hospital, MIT and Harvard University, Cambridge, MA, USA
| | - Alexander Meissner
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
- Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany.
| | - Jonathan S Weissman
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA.
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355
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Kim T, Lu TK. CRISPR/Cas-based devices for mammalian synthetic biology. Curr Opin Chem Biol 2019; 52:23-30. [PMID: 31136835 DOI: 10.1016/j.cbpa.2019.04.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 04/13/2019] [Accepted: 04/16/2019] [Indexed: 12/26/2022]
Abstract
Since its first demonstration for mammalian gene editing, CRISPR/Cas technology has been widely adopted in research, industry, and medicine. Beyond indel mutations induced by Cas9 activity, recent advances in CRISPR/Cas have enabled DNA or RNA base editing. In addition, multiple orthogonal methods for the spatiotemporal regulation of CRISPR/Cas activity and repurposed Cas proteins for the visualization and relocation of specific genomic loci in living cells have been described. By harnessing the versatility of CRISPR/Cas-based devices and gene circuits, synthetic biologists are developing memory devices for lineage tracing and technologies for unbiased, high-throughput interrogation of combinatorial gene perturbations. We envision that such approaches will enable researchers to gain deeper insights into the translation of genotypes to phenotypes in healthy and diseased states.
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Affiliation(s)
- Tackhoon Kim
- Research Laboratory of Electronics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Timothy K Lu
- Research Laboratory of Electronics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
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356
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Pei W, Wang X, Rössler J, Feyerabend TB, Höfer T, Rodewald HR. Using Cre-recombinase-driven Polylox barcoding for in vivo fate mapping in mice. Nat Protoc 2019; 14:1820-1840. [PMID: 31110297 DOI: 10.1038/s41596-019-0163-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 03/12/2019] [Indexed: 01/02/2023]
Abstract
Fate mapping is a powerful genetic tool for linking stem or progenitor cells with their progeny, and hence for defining cell lineages in vivo. The resolution of fate mapping depends on the numbers of distinct markers that are introduced in the beginning into stem or progenitor cells; ideally, numbers should be sufficiently large to allow the tracing of output from individual cells. Highly diverse genetic barcodes can serve this purpose. We recently developed an endogenous genetic barcoding system, termed Polylox. In Polylox, random DNA recombination can be induced by transient activity of Cre recombinase in a 2.1-kb-long artificial recombination substrate that has been introduced into a defined locus in mice (Rosa26Polylox reporter mice). Here, we provide a step-by-step protocol for the use of Polylox, including barcode induction and estimation of induction efficiency, barcode retrieval with single-molecule real-time (SMRT) DNA sequencing followed by computational barcode identification, and the calculation of barcode-generation probabilities, which is key for estimations of single-cell labeling for a given number of stem cells. Thus, Polylox barcoding enables high-resolution fate mapping in essentially all tissues in mice for which inducible Cre driver lines are available. Alternative methods include ex vivo cell barcoding, inducible transposon insertion and CRISPR-Cas9-based barcoding; Polylox currently allows combining non-invasive and cell-type-specific labeling with high label diversity. The execution time of this protocol is ~2-3 weeks for experimental data generation and typically <2 d for computational Polylox decoding and downstream analysis.
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Affiliation(s)
- Weike Pei
- Division of Cellular Immunology, German Cancer Research Center, Heidelberg, Germany
| | - Xi Wang
- Division of Theoretical Systems Biology, German Cancer Research Center, Heidelberg, Germany.,Bioquant Center, University of Heidelberg, Heidelberg, Germany
| | - Jens Rössler
- Division of Theoretical Systems Biology, German Cancer Research Center, Heidelberg, Germany.,Bioquant Center, University of Heidelberg, Heidelberg, Germany
| | - Thorsten B Feyerabend
- Division of Cellular Immunology, German Cancer Research Center, Heidelberg, Germany.
| | - Thomas Höfer
- Division of Theoretical Systems Biology, German Cancer Research Center, Heidelberg, Germany. .,Bioquant Center, University of Heidelberg, Heidelberg, Germany.
| | - Hans-Reimer Rodewald
- Division of Cellular Immunology, German Cancer Research Center, Heidelberg, Germany.
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357
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Stolper J, Ambrosio EM, Danciu DP, Buono L, Elliott DA, Naruse K, Martínez-Morales JR, Marciniak-Czochra A, Centanin L. Stem cell topography splits growth and homeostatic functions in the fish gill. eLife 2019; 8:e43747. [PMID: 31090541 PMCID: PMC6534379 DOI: 10.7554/elife.43747] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 05/14/2019] [Indexed: 11/13/2022] Open
Abstract
While lower vertebrates contain adult stem cells (aSCs) that maintain homeostasis and drive un-exhaustive organismal growth, mammalian aSCs display mainly the homeostatic function. Here, we use lineage analysis in the medaka fish gill to address aSCs and report separate stem cell populations for homeostasis and growth. These aSCs are fate-restricted during the entire post-embryonic life and even during re-generation paradigms. We use chimeric animals to demonstrate that p53 mediates growth coordination among fate-restricted aSCs, suggesting a hierarchical organisation among lineages in composite organs like the fish gill. Homeostatic and growth aSCs are clonal but differ in their topology; modifications in tissue architecture can convert the homeostatic zone into a growth zone, indicating a leading role for the physical niche defining stem cell output. We hypothesise that physical niches are main players to restrict aSCs to a homeostatic function in animals with fixed adult size.
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Affiliation(s)
- Julian Stolper
- Centre for Organismal StudiesHeidelberg UniversityHeidelbergGermany
- Murdoch Children’s Research InstituteRoyal Children’s HospitalParkvilleAustralia
| | | | | | - Lorena Buono
- Centro Andaluz de Biología del DesarrolloUniversidad Pablo de OlavideSevilleSpain
| | - David A Elliott
- Murdoch Children’s Research InstituteRoyal Children’s HospitalParkvilleAustralia
| | - Kiyoshi Naruse
- Laboratory of BioresourcesNational Institute for Basic Biology, National Institutes of Natural SciencesOkazakiJapan
| | | | - Anna Marciniak-Czochra
- Institute of Applied MathematicsHeidelberg UniversityHeidelbergGermany
- Bioquant CenterHeidelberg UniversityHeidelbergGermany
| | - Lazaro Centanin
- Centre for Organismal StudiesHeidelberg UniversityHeidelbergGermany
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358
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Arlotta P. Organoids required! A new path to understanding human brain development and disease. Nat Methods 2019; 15:27-29. [PMID: 29298289 DOI: 10.1038/nmeth.4557] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Our ability to study the developing human brain has recently been dramatically advanced by the development of human 'brain organoids', three-dimensional culture systems that recapitulate selected aspects of human brain development in reductionist, yet complex, tissues in vitro. Here I discuss the promises and challenges this new model system presents.
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Affiliation(s)
- Paola Arlotta
- Department of Stem Cell and Regenerative Biology, Harvard University
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359
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Gohl DM, Magli A, Garbe J, Becker A, Johnson DM, Anderson S, Auch B, Billstein B, Froehling E, McDevitt SL, Beckman KB. Measuring sequencer size bias using REcount: a novel method for highly accurate Illumina sequencing-based quantification. Genome Biol 2019; 20:85. [PMID: 31036053 PMCID: PMC6489363 DOI: 10.1186/s13059-019-1691-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 04/09/2019] [Indexed: 01/15/2023] Open
Abstract
Quantification of DNA sequence tags from engineered constructs such as plasmids, transposons, or other transgenes underlies many functional genomics measurements. Typically, such measurements rely on PCR followed by next-generation sequencing. However, PCR amplification can introduce significant quantitative error. We describe REcount, a novel PCR-free direct counting method. Comparing measurements of defined plasmid pools to droplet digital PCR data demonstrates that REcount is highly accurate and reproducible. We use REcount to provide new insights into clustering biases due to molecule length across different Illumina sequencers and illustrate the impacts on interpretation of next-generation sequencing data and the economics of data generation.
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Affiliation(s)
- Daryl M. Gohl
- University of Minnesota Genomics Center, Minneapolis, MN 55455 USA
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455 USA
| | - Alessandro Magli
- Department of Medicine, University of Minnesota, Minneapolis, MN 55455 USA
- Stem Cell Institute, University of Minnesota, Minneapolis, MN 55455 USA
| | - John Garbe
- University of Minnesota Genomics Center, Minneapolis, MN 55455 USA
| | - Aaron Becker
- University of Minnesota Genomics Center, Minneapolis, MN 55455 USA
| | | | - Shea Anderson
- University of Minnesota Genomics Center, Minneapolis, MN 55455 USA
| | - Benjamin Auch
- University of Minnesota Genomics Center, Minneapolis, MN 55455 USA
| | - Bradley Billstein
- University of Minnesota Genomics Center, Minneapolis, MN 55455 USA
- Present Address: Illumina, Inc, San Diego, CA 92122 USA
| | - Elyse Froehling
- University of Minnesota Genomics Center, Minneapolis, MN 55455 USA
| | - Shana L. McDevitt
- Vincent J. Coates Genomics Sequencing Laboratory, University of California, Berkeley, CA 94720 USA
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360
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Sun J. CRISPR-Mediated Recording of Mouse Development. CRISPR J 2019; 1:314-316. [PMID: 31021271 DOI: 10.1089/crispr.2018.29036.jls] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Jianlong Sun
- School of Life Science and Technology, ShanghaiTech University , Shanghai, China
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361
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Sebo ZL, Rodeheffer MS. Assembling the adipose organ: adipocyte lineage segregation and adipogenesis in vivo. Development 2019; 146:dev172098. [PMID: 30948523 PMCID: PMC6467474 DOI: 10.1242/dev.172098] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Adipose tissue is composed of anatomically distinct depots that mediate several important aspects of energy homeostasis. The past two decades have witnessed increased research effort to elucidate the ontogenetic basis of adipose form and function. In this Review, we discuss advances in our understanding of adipose tissue development with particular emphasis on the embryonic patterning of depot-specific adipocyte lineages and adipocyte differentiation in vivo Micro-environmental cues and other factors that influence cell identity and cell behavior at various junctures in the adipocyte lineage hierarchy are also considered.
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Affiliation(s)
- Zachary L Sebo
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06520-8103, USA
| | - Matthew S Rodeheffer
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06520-8103, USA
- Department of Comparative Medicine, Yale School of Medicine, New Haven, CT 06520-8016, USA
- Yale Stem Cell Center, Yale School of Medicine, New Haven, CT 06520-8073, USA
- Program in Integrative Cell Signaling and Neurobiology of Metabolism, Yale School of Medicine, New Haven, CT 06510, USA
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362
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Lu QR, Qian L, Zhou X. Developmental origins and oncogenic pathways in malignant brain tumors. WILEY INTERDISCIPLINARY REVIEWS-DEVELOPMENTAL BIOLOGY 2019; 8:e342. [PMID: 30945456 DOI: 10.1002/wdev.342] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 02/20/2019] [Accepted: 03/08/2019] [Indexed: 12/21/2022]
Abstract
Brain tumors such as adult glioblastomas and pediatric high-grade gliomas or medulloblastomas are among the leading causes of cancer-related deaths, exhibiting poor prognoses with little improvement in outcomes in the past several decades. These tumors are heterogeneous and can be initiated from various neural cell types, contributing to therapy resistance. How such heterogeneity arises is linked to the tumor cell of origin and their genetic alterations. Brain tumorigenesis and progression recapitulate key features associated with normal neurogenesis; however, the underlying mechanisms are quite dysregulated as tumor cells grow and divide in an uncontrolled manner. Recent comprehensive genomic, transcriptomic, and epigenomic studies at single-cell resolution have shed new light onto diverse tumor-driving events, cellular heterogeneity, and cells of origin in different brain tumors. Primary and secondary glioblastomas develop through different genetic alterations and pathways, such as EGFR amplification and IDH1/2 or TP53 mutation, respectively. Mutations such as histone H3K27M impacting epigenetic modifications define a distinct group of pediatric high-grade gliomas such as diffuse intrinsic pontine glioma. The identification of distinct genetic, epigenomic profiles and cellular heterogeneity has led to new classifications of adult and pediatric brain tumor subtypes, affording insights into molecular and lineage-specific vulnerabilities for treatment stratification. This review discusses our current understanding of tumor cells of origin, heterogeneity, recurring genetic and epigenetic alterations, oncogenic drivers and signaling pathways for adult glioblastomas, pediatric high-grade gliomas, and medulloblastomas, the genetically heterogeneous groups of malignant brain tumors. This article is categorized under: Gene Expression and Transcriptional Hierarchies > Gene Networks and Genomics Adult Stem Cells, Tissue Renewal, and Regeneration > Stem Cell Differentiation and Reversion Signaling Pathways > Cell Fate Signaling.
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Affiliation(s)
- Q Richard Lu
- Brain Tumor Center, Division of Experimental Hematology and Cancer Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Lily Qian
- Brain Tumor Center, Division of Experimental Hematology and Cancer Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Xianyao Zhou
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, Sichuan University, Chengdu, China.,Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, China
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363
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Fischer DS, Fiedler AK, Kernfeld EM, Genga RMJ, Bastidas-Ponce A, Bakhti M, Lickert H, Hasenauer J, Maehr R, Theis FJ. Inferring population dynamics from single-cell RNA-sequencing time series data. Nat Biotechnol 2019; 37:461-468. [PMID: 30936567 PMCID: PMC7397487 DOI: 10.1038/s41587-019-0088-0] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 02/28/2019] [Indexed: 11/09/2022]
Abstract
Recent single-cell RNA-sequencing studies have suggested that cells follow continuous transcriptomic trajectories in an asynchronous fashion during development. However, observations of cell flux along trajectories are confounded with population size effects in snapshot experiments and are therefore hard to interpret. In particular, changes in proliferation and death rates can be mistaken for cell flux. Here we present pseudodynamics, a mathematical framework that reconciles population dynamics with the concepts underlying developmental trajectories inferred from time-series single-cell data. Pseudodynamics models population distribution shifts across trajectories to quantify selection pressure, population expansion, and developmental potentials. Applying this model to time-resolved single-cell RNA-sequencing of T-cell and pancreatic beta cell maturation, we characterize proliferation and apoptosis rates and identify key developmental checkpoints, data inaccessible to existing approaches.
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Affiliation(s)
- David S Fischer
- Institute of Computational Biology, Helmholz Zentrum München, Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Anna K Fiedler
- Institute of Computational Biology, Helmholz Zentrum München, Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, Garching bei München, Germany
| | - Eric M Kernfeld
- Program in Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Medical School, Worcester, MA, USA
| | - Ryan M J Genga
- Program in Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Medical School, Worcester, MA, USA
| | - Aimée Bastidas-Ponce
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Stem Cell Research, Helmholtz Zentrum München, Neuherberg, Germany
- Medical Faculty, Technical University of Munich, Munich, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Mostafa Bakhti
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Stem Cell Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Heiko Lickert
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Stem Cell Research, Helmholtz Zentrum München, Neuherberg, Germany
- Medical Faculty, Technical University of Munich, Munich, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholz Zentrum München, Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, Garching bei München, Germany
| | - Rene Maehr
- Program in Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Medical School, Worcester, MA, USA
| | - Fabian J Theis
- Institute of Computational Biology, Helmholz Zentrum München, Neuherberg, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
- Department of Mathematics, Technical University of Munich, Garching bei München, Germany.
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364
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Liszczak G, Muir TW. Barcoding mit Nukleinsäuren: Anwendung der DNA‐Sequenzierung als molekulares Zählwerk. Angew Chem Int Ed Engl 2019. [DOI: 10.1002/ange.201808956] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Glen Liszczak
- Department of ChemistryPrinceton University Princeton NJ 08544 USA
- Aktuelle Adresse: Department of BiochemistryUT Southwestern Medical Center Dallas TX 75390 USA
| | - Tom W. Muir
- Department of ChemistryPrinceton University Princeton NJ 08544 USA
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365
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DeWitt WS, Mesin L, Victora GD, Minin VN, Matsen FA. Using Genotype Abundance to Improve Phylogenetic Inference. Mol Biol Evol 2019; 35:1253-1265. [PMID: 29474671 PMCID: PMC5913685 DOI: 10.1093/molbev/msy020] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Modern biological techniques enable very dense genetic sampling of unfolding evolutionary histories, and thus frequently sample some genotypes multiple times. This motivates strategies to incorporate genotype abundance information in phylogenetic inference. In this article, we synthesize a stochastic process model with standard sequence-based phylogenetic optimality, and show that tree estimation is substantially improved by doing so. Our method is validated with extensive simulations and an experimental single-cell lineage tracing study of germinal center B cell receptor affinity maturation.
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Affiliation(s)
- William S DeWitt
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA.,Department of Genome Sciences, University of Washington, Seattle, WA
| | - Luka Mesin
- Laboratory of Lymphocyte Dynamics, The Rockefeller University, New York, NY
| | - Gabriel D Victora
- Laboratory of Lymphocyte Dynamics, The Rockefeller University, New York, NY
| | | | - Frederick A Matsen
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA
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366
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Hwang B, Lee W, Yum SY, Jeon Y, Cho N, Jang G, Bang D. Lineage tracing using a Cas9-deaminase barcoding system targeting endogenous L1 elements. Nat Commun 2019; 10:1234. [PMID: 30874552 PMCID: PMC6420643 DOI: 10.1038/s41467-019-09203-z] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Accepted: 02/26/2019] [Indexed: 11/30/2022] Open
Abstract
Determining cell lineage and function is critical to understanding human physiology and pathology. Although advances in lineage tracing methods provide new insight into cell fate, defining cellular diversity at the mammalian level remains a challenge. Here, we develop a genome editing strategy using a cytidine deaminase fused with nickase Cas9 (nCas9) to specifically target endogenous interspersed repeat regions in mammalian cells. The resulting mutation patterns serve as a genetic barcode, which is induced by targeted mutagenesis with single-guide RNA (sgRNA), leveraging substitution events, and subsequent read out by a single primer pair. By analyzing interspersed mutation signatures, we show the accurate reconstruction of cell lineage using both bulk cell and single-cell data. We envision that our genetic barcode system will enable fine-resolution mapping of organismal development in healthy and diseased mammalian states.
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Affiliation(s)
- Byungjin Hwang
- Department of Chemistry, Yonsei University, Seoul, 03722, Korea
| | - Wookjae Lee
- Department of Chemistry, Yonsei University, Seoul, 03722, Korea
| | - Soo-Young Yum
- Laboratory of Theriogenology and Biotechnology, Department of Veterinary Clinical Sciences, College of Veterinary Medicine, the Research Institute of Veterinary Science, and BK21 PLUS Program for Creative Veterinary Science Research, Seoul National University, Seoul, 08826, Korea
| | - Yujin Jeon
- Department of Chemistry, Yonsei University, Seoul, 03722, Korea
| | - Namjin Cho
- Department of Chemistry, Yonsei University, Seoul, 03722, Korea
| | - Goo Jang
- Laboratory of Theriogenology and Biotechnology, Department of Veterinary Clinical Sciences, College of Veterinary Medicine, the Research Institute of Veterinary Science, and BK21 PLUS Program for Creative Veterinary Science Research, Seoul National University, Seoul, 08826, Korea.
| | - Duhee Bang
- Department of Chemistry, Yonsei University, Seoul, 03722, Korea.
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367
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Ludwig LS, Lareau CA, Ulirsch JC, Christian E, Muus C, Li LH, Pelka K, Ge W, Oren Y, Brack A, Law T, Rodman C, Chen JH, Boland GM, Hacohen N, Rozenblatt-Rosen O, Aryee MJ, Buenrostro JD, Regev A, Sankaran VG. Lineage Tracing in Humans Enabled by Mitochondrial Mutations and Single-Cell Genomics. Cell 2019; 176:1325-1339.e22. [PMID: 30827679 PMCID: PMC6408267 DOI: 10.1016/j.cell.2019.01.022] [Citation(s) in RCA: 344] [Impact Index Per Article: 57.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 11/29/2018] [Accepted: 01/09/2019] [Indexed: 01/22/2023]
Abstract
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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Affiliation(s)
- Leif S Ludwig
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA.
| | - Caleb A Lareau
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA 02129, USA; Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02115, USA
| | - Jacob C Ulirsch
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02115, USA
| | - Elena Christian
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Christoph Muus
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Lauren H Li
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Karin Pelka
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Will Ge
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yaara Oren
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Alison Brack
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Travis Law
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Jonathan H Chen
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Genevieve M Boland
- Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Nir Hacohen
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | | | - Martin J Aryee
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA 02129, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jason D Buenrostro
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Society of Fellows, Harvard University, Cambridge, MA 02138, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Department of Biology and Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Vijay G Sankaran
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA.
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368
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Liu K, Petree C, Requena T, Varshney P, Varshney GK. Expanding the CRISPR Toolbox in Zebrafish for Studying Development and Disease. Front Cell Dev Biol 2019; 7:13. [PMID: 30886848 PMCID: PMC6409501 DOI: 10.3389/fcell.2019.00013] [Citation(s) in RCA: 99] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 01/24/2019] [Indexed: 12/13/2022] Open
Abstract
The study of model organisms has revolutionized our understanding of the mechanisms underlying normal development, adult homeostasis, and human disease. Much of what we know about gene function in model organisms (and its application to humans) has come from gene knockouts: the ability to show analogous phenotypes upon gene inactivation in animal models. The zebrafish (Danio rerio) has become a popular model organism for many reasons, including the fact that it is amenable to various forms of genetic manipulation. The RNA-guided CRISPR/Cas9-mediated targeted mutagenesis approaches have provided powerful tools to manipulate the genome toward developing new disease models and understanding the pathophysiology of human diseases. CRISPR-based approaches are being used for the generation of both knockout and knock-in alleles, and also for applications including transcriptional modulation, epigenome editing, live imaging of the genome, and lineage tracing. Currently, substantial effort is being made to improve the specificity of Cas9, and to expand the target coverage of the Cas9 enzymes. Novel types of naturally occurring CRISPR systems [Cas12a (Cpf1); engineered variants of Cas9, such as xCas9 and SpCas9-NG], are being studied and applied to genome editing. Since the majority of pathogenic mutations are single point mutations, development of base editors to convert C:G to T:A or A:T to G:C has further strengthened the CRISPR toolbox. In this review, we provide an overview of the increasing number of novel CRISPR-based tools and approaches, including lineage tracing and base editing.
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Affiliation(s)
| | | | | | | | - Gaurav K. Varshney
- Functional and Chemical Genomics Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, United States
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369
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Wu SH(S, Lee JH, Koo BK. Lineage Tracing: Computational Reconstruction Goes Beyond the Limit of Imaging. Mol Cells 2019; 42:104-112. [PMID: 30764600 PMCID: PMC6399003 DOI: 10.14348/molcells.2019.0006] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 01/18/2018] [Accepted: 01/20/2019] [Indexed: 02/07/2023] Open
Abstract
Tracking the fate of individual cells and their progeny through lineage tracing has been widely used to investigate various biological processes including embryonic development, homeostatic tissue turnover, and stem cell function in regeneration and disease. Conventional lineage tracing involves the marking of cells either with dyes or nucleoside analogues or genetic marking with fluorescent and/or colorimetric protein reporters. Both are imaging-based approaches that have played a crucial role in the field of developmental biology as well as adult stem cell biology. However, imaging-based lineage tracing approaches are limited by their scalability and the lack of molecular information underlying fate transitions. Recently, computational biology approaches have been combined with diverse tracing methods to overcome these limitations and so provide high-order scalability and a wealth of molecular information. In this review, we will introduce such novel computational methods, starting from single-cell RNA sequencing-based lineage analysis to DNA barcoding or genetic scar analysis. These novel approaches are complementary to conventional imaging-based approaches and enable us to study the lineage relationships of numerous cell types during vertebrate, and in particular human, development and disease.
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Affiliation(s)
- Szu-Hsien (Sam) Wu
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna Biocenter (VBC), 1030 Vienna,
Austria
| | - Ji-Hyun Lee
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna Biocenter (VBC), 1030 Vienna,
Austria
| | - Bon-Kyoung Koo
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna Biocenter (VBC), 1030 Vienna,
Austria
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370
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Liszczak G, Muir TW. Nucleic Acid-Barcoding Technologies: Converting DNA Sequencing into a Broad-Spectrum Molecular Counter. Angew Chem Int Ed Engl 2019; 58:4144-4162. [PMID: 30153374 DOI: 10.1002/anie.201808956] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Indexed: 12/17/2022]
Abstract
The emergence of high-throughput DNA sequencing technologies sparked a revolution in the field of genomics that has rippled into many branches of the life and physical sciences. The remarkable sensitivity, specificity, throughput, and multiplexing capacity that are inherent to parallel DNA sequencing have since motivated its use as a broad-spectrum molecular counter. A key aspect of extrapolating DNA sequencing to non-traditional applications is the need to append nucleic-acid barcodes to entities of interest. In this review, we describe the chemical and biochemical approaches that have enabled nucleic-acid barcoding of proteinaceous and non-proteinaceous materials and provide examples of downstream technologies that have been made possible by DNA-encoded molecules. As commercially available high-throughput sequencers were first released less than 15 years ago, we believe related applications will continue to mature and close by proposing new frontiers to support this assertion.
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Affiliation(s)
- Glen Liszczak
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA.,Present address: Department of Biochemistry, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Tom W Muir
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA
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371
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Thaler DS, Head MG, Horsley A. Precision public health to inhibit the contagion of disease and move toward a future in which microbes spread health. BMC Infect Dis 2019; 19:120. [PMID: 30727964 PMCID: PMC6364421 DOI: 10.1186/s12879-019-3715-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2018] [Accepted: 01/10/2019] [Indexed: 12/15/2022] Open
Abstract
Antimicrobial resistance continues to outpace the development of new chemotherapeutics. Novel pathogens continue to evolve and emerge. Public health innovation has the potential to open a new front in the war of "our wits against their genes" (Joshua Lederberg). Dense sampling coupled to next generation sequencing can increase the spatial and temporal resolution of microbial characterization while sensor technologies precisely map physical parameters relevant to microbial survival and spread. Microbial, physical, and epidemiological big data could be combined to improve prospective risk identification. However, applied in the wrong way, these approaches may not realize their maximum potential benefits and could even do harm. Minimizing microbial-human interactions would be a mistake. There is evidence that microbes previously thought of at best "benign" may actually enhance human health. Benign and health-promoting microbiomes may, or may not, spread via mechanisms similar to pathogens. Infectious vaccines are approaching readiness to make enhanced contributions to herd immunity. The rigorously defined nature of infectious vaccines contrasts with indigenous "benign or health-promoting microbiomes" but they may converge. A "microbial Neolithic revolution" is a possible future in which human microbial-associations are understood and managed analogously to the macro-agriculture of plants and animals. Tradeoffs need to be framed in order to understand health-promoting potentials of benign, and/or health-promoting microbiomes and infectious vaccines while also discouraging pathogens. Super-spreaders are currently defined as individuals who play an outsized role in the contagion of infectious disease. A key unanswered question is whether the super-spreader concept may apply similarly to health-promoting microbes. The complex interactions of individual rights, community health, pathogen contagion, the spread of benign, and of health-promoting microbiomes including infectious vaccines require study. Advancing the detailed understanding of heterogeneity in microbial spread is very likely to yield important insights relevant to public health.
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Affiliation(s)
- David S. Thaler
- Biozentrum, University of Basel, Klingelbergstrasse 50/70, CH-4056 Basel, Switzerland
| | - Michael G. Head
- Clinical Informatics Research Unit, Faculty of Medicine, University of Southampton, University Hospital Southampton, Coxford Road, Southampton, SO16 6YD UK
| | - Andrew Horsley
- Research School of Physics and Engineering, The Australian National University, Mills Rd., Canberra, ACT 2601 Australia
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372
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Binan L, Drobetsky EA, Costantino S. Exploiting Molecular Barcodes in High-Throughput Cellular Assays. SLAS Technol 2019; 24:298-307. [PMID: 30707854 DOI: 10.1177/2472630318824337] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Multiplexing strategies, which greatly increase the number of simultaneously measured parameters in single experiments, are now being widely implemented by both the pharmaceutical industry and academic researchers. Color has long been used to identify biological signals and, when combined with molecular barcodes, has substantially enhanced the depth of multiplexed sample characterization. Moreover, the recent advent of DNA barcodes has led to an explosion of innovative cell sequencing approaches. Novel barcoding strategies also show great promise for encoding spatial information in transcriptomic studies, and for precise assessment of molecular abundance. Both color- and DNA-based barcodes can be conveniently analyzed with either a microscope or a cytometer, or via DNA sequencing. Here we review the basic principles of several technologies used to create barcodes and detail the type of samples that can be identified with such tags.
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Affiliation(s)
- Loïc Binan
- 1 Research Center of the Maisonneuve-Rosemont Hospital, Montreal, QC, Canada.,2 Department of Ophthalmology, Université de Montréal, Montreal, QC, Canada
| | - Elliot A Drobetsky
- 1 Research Center of the Maisonneuve-Rosemont Hospital, Montreal, QC, Canada.,3 Department of Medicine & Molecular Biology Program, University of Montreal, Montreal, QC, Canada
| | - Santiago Costantino
- 1 Research Center of the Maisonneuve-Rosemont Hospital, Montreal, QC, Canada.,2 Department of Ophthalmology, Université de Montréal, Montreal, QC, Canada
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373
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Hicks DG, Speed TP, Yassin M, Russell SM. Maps of variability in cell lineage trees. PLoS Comput Biol 2019; 15:e1006745. [PMID: 30753182 PMCID: PMC6388934 DOI: 10.1371/journal.pcbi.1006745] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 02/25/2019] [Accepted: 01/02/2019] [Indexed: 11/19/2022] Open
Abstract
New approaches to lineage tracking have allowed the study of differentiation in multicellular organisms over many generations of cells. Understanding the phenotypic variability observed in these lineage trees requires new statistical methods. Whereas an invariant cell lineage, such as that for the nematode Caenorhabditis elegans, can be described by a lineage map, defined as the pattern of phenotypes overlaid onto the binary tree, a traditional lineage map is static and does not describe the variability inherent in the cell lineages of higher organisms. Here, we introduce lineage variability maps which describe the pattern of second-order variation in lineage trees. These maps can be undirected graphs of the partial correlations between every lineal position, or directed graphs showing the dynamics of bifurcated patterns in each subtree. We show how to infer these graphical models for lineages of any depth from sample sizes of only a few pedigrees. This required developing the generalized spectral analysis for a binary tree, the natural framework for describing tree-structured variation. When tested on pedigrees from C. elegans expressing a marker for pharyngeal differentiation potential, the variability maps recover essential features of the known lineage map. When applied to highly-variable pedigrees monitoring cell size in T lymphocytes, the maps show that most of the phenotype is set by the founder naive T cell. Lineage variability maps thus elevate the concept of the lineage map to the population level, addressing questions about the potency and dynamics of cell lineages and providing a way to quantify the progressive restriction of cell fate with increasing depth in the tree.
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Affiliation(s)
- Damien G. Hicks
- Centre for Micro-Photonics, Department of Physics and Astronomy, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia
- Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
| | - Terence P. Speed
- Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
| | - Mohammed Yassin
- Peter MacCallum Cancer Centre, Parkville, Victoria 3052, Australia
| | - Sarah M. Russell
- Centre for Micro-Photonics, Department of Physics and Astronomy, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia
- Peter MacCallum Cancer Centre, Parkville, Victoria 3052, Australia
- Department of Pathology and Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria 3050, Australia
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374
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Salvador-Martínez I, Grillo M, Averof M, Telford MJ. Is it possible to reconstruct an accurate cell lineage using CRISPR recorders? eLife 2019; 8:e40292. [PMID: 30688650 PMCID: PMC6349403 DOI: 10.7554/elife.40292] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 01/11/2019] [Indexed: 02/02/2023] Open
Abstract
Cell lineages provide the framework for understanding how cell fates are decided during development. Describing cell lineages in most organisms is challenging; even a fruit fly larva has ~50,000 cells and a small mammal has >1 billion cells. Recently, the idea of applying CRISPR to induce mutations during development, to be used as heritable markers for lineage reconstruction, has been proposed by several groups. While an attractive idea, its practical value depends on the accuracy of the cell lineages that can be generated. Here, we use computer simulations to estimate the performance of these approaches under different conditions. We incorporate empirical data on CRISPR-induced mutation frequencies in Drosophila. We show significant impacts from multiple biological and technical parameters - variable cell division rates, skewed mutational outcomes, target dropouts and different sequencing strategies. Our approach reveals the limitations of published CRISPR recorders, and indicates how future implementations can be optimised. Editorial note This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
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Affiliation(s)
- Irepan Salvador-Martínez
- Centre for Life’s Origins and Evolution, Department of Genetics Evolution and EnvironmentUniversity College LondonLondonUnited Kingdom
| | - Marco Grillo
- Institut de Génomique Fonctionnelle de Lyon (IGFL)École Normale Supérieure de LyonLyonFrance
- Centre National de la Recherche Scientifique (CNRS)ParisFrance
| | - Michalis Averof
- Institut de Génomique Fonctionnelle de Lyon (IGFL)École Normale Supérieure de LyonLyonFrance
- Centre National de la Recherche Scientifique (CNRS)ParisFrance
| | - Maximilian J Telford
- Centre for Life’s Origins and Evolution, Department of Genetics Evolution and EnvironmentUniversity College LondonLondonUnited Kingdom
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375
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Mokalled MH, Poss KD. A Regeneration Toolkit. Dev Cell 2019; 47:267-280. [PMID: 30399333 DOI: 10.1016/j.devcel.2018.10.015] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 10/10/2018] [Accepted: 10/11/2018] [Indexed: 12/13/2022]
Abstract
The ability of animals to replace injured body parts has been a subject of fascination for centuries. The emerging importance of regenerative medicine has reinvigorated investigations of innate tissue regeneration, and the development of powerful genetic tools has fueled discoveries into how tissue regeneration occurs. Here, we present an overview of the armamentarium employed to probe regeneration in vertebrates, highlighting areas where further methodology advancement will deepen mechanistic findings.
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Affiliation(s)
- Mayssa H Mokalled
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.
| | - Kenneth D Poss
- Department of Cell Biology, Duke University Medical Center, Durham, NC 27710, USA; Regeneration Next, Duke University, Durham, NC 27710, USA.
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376
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Song Y, Xu X, Wang W, Tian T, Zhu Z, Yang C. Single cell transcriptomics: moving towards multi-omics. Analyst 2019; 144:3172-3189. [DOI: 10.1039/c8an01852a] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Single-cell multi-omics analysis helps characterize multiple layers of molecular features at a single-cell scale to provide insights into cellular processes and functions.
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Affiliation(s)
- Yanling Song
- Institute of Molecular Medicine
- Renji Hospital
- Shanghai Jiao Tong University
- School of Medicine
- Shanghai
| | - Xing Xu
- State Key Laboratory of Physical Chemistry of Solid Surfaces
- Key Laboratory for Chemical Biology of Fujian Province
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation
- Department of Chemical Biology
- College of Chemistry and Chemical Engineering
| | - Wei Wang
- Institute of Molecular Medicine
- Renji Hospital
- Shanghai Jiao Tong University
- School of Medicine
- Shanghai
| | - Tian Tian
- State Key Laboratory of Physical Chemistry of Solid Surfaces
- Key Laboratory for Chemical Biology of Fujian Province
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation
- Department of Chemical Biology
- College of Chemistry and Chemical Engineering
| | - Zhi Zhu
- State Key Laboratory of Physical Chemistry of Solid Surfaces
- Key Laboratory for Chemical Biology of Fujian Province
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation
- Department of Chemical Biology
- College of Chemistry and Chemical Engineering
| | - Chaoyong Yang
- Institute of Molecular Medicine
- Renji Hospital
- Shanghai Jiao Tong University
- School of Medicine
- Shanghai
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377
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Multilayered Heterogeneity of Glioblastoma Stem Cells: Biological and Clinical Significance. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1139:1-21. [DOI: 10.1007/978-3-030-14366-4_1] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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378
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Giladi A, Amit I. Single-Cell Genomics: A Stepping Stone for Future Immunology Discoveries. Cell 2018; 172:14-21. [PMID: 29328909 DOI: 10.1016/j.cell.2017.11.011] [Citation(s) in RCA: 127] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 11/01/2017] [Accepted: 11/06/2017] [Indexed: 02/08/2023]
Abstract
The immunology field has invested great efforts and ingenuity to characterize the various immune cell types and elucidate their functions. However, accumulating evidence indicates that current technologies and classification schemes are limited in their ability to account for the functional heterogeneity of immune processes. Single-cell genomics hold the potential to revolutionize the way we characterize complex immune cell assemblies and study their spatial organization, dynamics, clonal distribution, pathways, function, and crosstalks. In this Perspective, we consider recent and forthcoming technological and analytical advances in single-cell genomics and the potential impact of those advances on the future of immunology research and immunotherapy.
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Affiliation(s)
- Amir Giladi
- Department of Immunology, Weizmann Institute, Rehovot 76100, Israel
| | - Ido Amit
- Department of Immunology, Weizmann Institute, Rehovot 76100, Israel.
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379
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The dynamics of adaptive genetic diversity during the early stages of clonal evolution. Nat Ecol Evol 2018; 3:293-301. [PMID: 30598529 DOI: 10.1038/s41559-018-0758-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 11/19/2018] [Indexed: 12/17/2022]
Abstract
The dynamics of genetic diversity in large clonally evolving cell populations are poorly understood, despite having implications for the treatment of cancer and microbial infections. Here, we combine barcode lineage tracking, sequencing of adaptive clones and mathematical modelling of mutational dynamics to understand adaptive diversity changes during experimental evolution of Saccharomyces cerevisiae under nitrogen and carbon limitation. We find that, despite differences in beneficial mutational mechanisms and fitness effects, early adaptive genetic diversity increases predictably, driven by the expansion of many single-mutant lineages. However, a crash in adaptive diversity follows, caused by highly fit double-mutant 'jackpot' clones that are fed from exponentially growing single mutants, a process closely related to the classic Luria-Delbrück experiment. The diversity crash is likely to be a general feature of asexual evolution with clonal interference; however, both its timing and magnitude are stochastic and depend on the population size, the distribution of beneficial fitness effects and patterns of epistasis.
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380
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Mukherjee P, Nathamgari SSP, Kessler JA, Espinosa HD. Combined Numerical and Experimental Investigation of Localized Electroporation-Based Cell Transfection and Sampling. ACS NANO 2018; 12:12118-12128. [PMID: 30452236 PMCID: PMC6535396 DOI: 10.1021/acsnano.8b05473] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/14/2023]
Abstract
Localized electroporation has evolved as an effective technology for the delivery of foreign molecules into cells while preserving their viability. Consequently, this technique has potential applications in sampling the contents of live cells and the temporal assessment of cellular states at the single-cell level. Although there have been numerous experimental reports on localized electroporation-based delivery, a lack of a mechanistic understanding of the process hinders its implementation in sampling. In this work, we develop a multiphysics model that predicts the transport of molecules into and out of the cell during localized electroporation. Based on the model predictions, we optimize experimental parameters such as buffer conditions, electric field strength, cell confluency, and density of nanochannels in the substrate for successful delivery and sampling via localized electroporation. We also identify that cell membrane tension plays a crucial role in enhancing both the amount and the uniformity of molecular transport, particularly for macromolecules. We qualitatively validate the model predictions on a localized electroporation platform by delivering large molecules (bovine serum albumin and mCherry-encoding plasmid) and by sampling an exogeneous protein (tdTomato) in an engineered cell line.
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Affiliation(s)
- Prithvijit Mukherjee
- Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Theoretical and Applied Mechanics Program, Northwestern University, Evanston, Illinois 60208, United States
| | - S. Shiva P. Nathamgari
- Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Theoretical and Applied Mechanics Program, Northwestern University, Evanston, Illinois 60208, United States
| | - John A. Kessler
- Department of Neurology, Northwestern University, Chicago, Illinois 60611, United States
| | - Horacio D. Espinosa
- Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Theoretical and Applied Mechanics Program, Northwestern University, Evanston, Illinois 60208, United States
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381
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Biddy BA, Kong W, Kamimoto K, Guo C, Waye SE, Sun T, Morris SA. Single-cell mapping of lineage and identity in direct reprogramming. Nature 2018; 564:219-224. [PMID: 30518857 PMCID: PMC6635140 DOI: 10.1038/s41586-018-0744-4] [Citation(s) in RCA: 247] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2017] [Accepted: 10/03/2018] [Indexed: 12/19/2022]
Abstract
Direct lineage reprogramming involves the conversion of cellular identity. Single-cell technologies are useful for deconstructing the considerable heterogeneity that emerges during lineage conversion. However, lineage relationships are typically lost during cell processing, complicating trajectory reconstruction. Here we present 'CellTagging', a combinatorial cell-indexing methodology that enables parallel capture of clonal history and cell identity, in which sequential rounds of cell labelling enable the construction of multi-level lineage trees. CellTagging and longitudinal tracking of fibroblast to induced endoderm progenitor reprogramming reveals two distinct trajectories: one leading to successfully reprogrammed cells, and one leading to a 'dead-end' state, paths determined in the earliest stages of lineage conversion. We find that expression of a putative methyltransferase, Mettl7a1, is associated with the successful reprogramming trajectory; adding Mettl7a1 to the reprogramming cocktail increases the yield of induced endoderm progenitors. Together, these results demonstrate the utility of our lineage-tracing method for revealing the dynamics of direct reprogramming.
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Affiliation(s)
- Brent A Biddy
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Wenjun Kong
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Kenji Kamimoto
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Chuner Guo
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Sarah E Waye
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Tao Sun
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Nanomedicine Research Center, Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Samantha A Morris
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA.
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA.
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382
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Zhang D, Osborne JM, Abu-Bonsrah KD, Cheeseman BL, Landman KA, Jurkowicz B, Newgreen DF. Stochastic clonal expansion of “superstars” enhances the reserve capacity of enteric nervous system precursor cells. Dev Biol 2018; 444 Suppl 1:S287-S296. [DOI: 10.1016/j.ydbio.2018.01.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 01/25/2018] [Accepted: 01/28/2018] [Indexed: 10/18/2022]
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383
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Xu M, Wang J, Guo X, Li T, Kuang X, Wu QF. Illumination of neural development by in vivo clonal analysis. CELL REGENERATION (LONDON, ENGLAND) 2018; 7:33-39. [PMID: 30671228 PMCID: PMC6326247 DOI: 10.1016/j.cr.2018.09.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2018] [Revised: 08/22/2018] [Accepted: 09/18/2018] [Indexed: 01/22/2023]
Abstract
Single embryonic and adult neural stem cells (NSCs) are characterized by their self-renewal and differentiation potential. Lineage tracing via clonal analysis allows for specific labeling of a single NSC and tracking of its progeny throughout development. Over the past five decades, a plethora of clonal analysis methods have been developed in tandem with integration of chemical, genetic, imaging and sequencing techniques. Applications of these approaches have gained diverse insights into the heterogeneous behavior of NSCs, lineage relationships between cells, molecular regulation of fate specification and ontogeny of complex neural tissues. In this review, we summarize the history and methods of clonal analysis as well as highlight key findings revealed by single-cell lineage tracking of stem cells in developing and adult brains across different animal models.
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Affiliation(s)
- Mingrui Xu
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100101, China
| | - Jingjing Wang
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Xize Guo
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100101, China
| | - Tingting Li
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100101, China
| | - Xia Kuang
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Qing-Feng Wu
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100101, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100101, China
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384
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Brody Y, Kimmerling RJ, Maruvka YE, Benjamin D, Elacqua JJ, Haradhvala NJ, Kim J, Mouw KW, Frangaj K, Koren A, Getz G, Manalis SR, Blainey PC. Quantification of somatic mutation flow across individual cell division events by lineage sequencing. Genome Res 2018; 28:1901-1918. [PMID: 30459213 PMCID: PMC6280753 DOI: 10.1101/gr.238543.118] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 10/27/2018] [Indexed: 02/06/2023]
Abstract
Mutation data reveal the dynamic equilibrium between DNA damage and repair processes in cells and are indispensable to the understanding of age-related diseases, tumor evolution, and the acquisition of drug resistance. However, available genome-wide methods have a limited ability to resolve rare somatic variants and the relationships between these variants. Here, we present lineage sequencing, a new genome sequencing approach that enables somatic event reconstruction by providing quality somatic mutation call sets with resolution as high as the single-cell level in subject lineages. Lineage sequencing entails sampling single cells from a population and sequencing subclonal sample sets derived from these cells such that knowledge of relationships among the cells can be used to jointly call variants across the sample set. This approach integrates data from multiple sequence libraries to support each variant and precisely assigns mutations to lineage segments. We applied lineage sequencing to a human colon cancer cell line with a DNA polymerase epsilon (POLE) proofreading deficiency (HT115) and a human retinal epithelial cell line immortalized by constitutive telomerase expression (RPE1). Cells were cultured under continuous observation to link observed single-cell phenotypes with single-cell mutation data. The high sensitivity, specificity, and resolution of the data provide a unique opportunity for quantitative analysis of variation in mutation rate, spectrum, and correlations among variants. Our data show that mutations arrive with nonuniform probability across sublineages and that DNA lesion dynamics may cause strong correlations between certain mutations.
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Affiliation(s)
- Yehuda Brody
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Robert J Kimmerling
- MIT Department of Biological Engineering, Cambridge, Massachusetts 02139, USA
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts 02139, USA
| | - Yosef E Maruvka
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- MGH Cancer Center and Department of Pathology, Boston, Massachusetts 02114, USA
| | - David Benjamin
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Joshua J Elacqua
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- MIT Department of Biological Engineering, Cambridge, Massachusetts 02139, USA
| | - Nicholas J Haradhvala
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- MGH Cancer Center and Department of Pathology, Boston, Massachusetts 02114, USA
| | - Jaegil Kim
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Kent W Mouw
- Harvard Medical School, Boston, Massachusetts 02115, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA
| | - Kristjana Frangaj
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA
| | - Amnon Koren
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA
| | - Gad Getz
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- MGH Cancer Center and Department of Pathology, Boston, Massachusetts 02114, USA
| | - Scott R Manalis
- MIT Department of Biological Engineering, Cambridge, Massachusetts 02139, USA
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts 02139, USA
| | - Paul C Blainey
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- MIT Department of Biological Engineering, Cambridge, Massachusetts 02139, USA
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385
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Lyne AM, Kent DG, Laurenti E, Cornils K, Glauche I, Perié L. A track of the clones: new developments in cellular barcoding. Exp Hematol 2018; 68:15-20. [DOI: 10.1016/j.exphem.2018.11.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 11/09/2018] [Accepted: 11/13/2018] [Indexed: 11/30/2022]
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386
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Lau CH. Applications of CRISPR-Cas in Bioengineering, Biotechnology, and Translational Research. CRISPR J 2018; 1:379-404. [PMID: 31021245 DOI: 10.1089/crispr.2018.0026] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
CRISPR technology is rapidly evolving, and the scope of CRISPR applications is constantly expanding. CRISPR was originally employed for genome editing. Its application was then extended to epigenome editing, karyotype engineering, chromatin imaging, transcriptome, and metabolic pathway engineering. Now, CRISPR technology is being harnessed for genetic circuits engineering, cell signaling sensing, cellular events recording, lineage information reconstruction, gene drive, DNA genotyping, miRNA quantification, in vivo cloning, site-directed mutagenesis, genomic diversification, and proteomic analysis in situ. It has also been implemented in the translational research of human diseases such as cancer immunotherapy, antiviral therapy, bacteriophage therapy, cancer diagnosis, pathogen screening, microbiota remodeling, stem-cell reprogramming, immunogenomic engineering, vaccine development, and antibody production. This review aims to summarize the key concepts of these CRISPR applications in order to capture the current state of play in this fast-moving field. The key mechanisms, strategies, and design principles for each technological advance are also highlighted.
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Affiliation(s)
- Cia-Hin Lau
- Department of Biomedical Engineering, City University of Hong Kong , Hong Kong, SAR, China
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387
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Abstract
The growing scale and declining cost of single-cell RNA-sequencing (RNA-seq) now permit a repetition of cell sampling that increases the power to detect rare cell states, reconstruct developmental trajectories, and measure phenotype in new terms such as cellular variance. The characterization of anatomy and developmental dynamics has not had an equivalent breakthrough since groundbreaking advances in live fluorescent microscopy. The new resolution obtained by single-cell RNA-seq is a boon to genetics because the novel description of phenotype offers the opportunity to refine gene function and dissect pleiotropy. In addition, the recent pairing of high-throughput genetic perturbation with single-cell RNA-seq has made practical a scale of genetic screening not previously possible.
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Affiliation(s)
- Kenneth D Birnbaum
- Center for Genomics and Systems Biology, New York University, New York, NY 10003, USA;
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388
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Abstract
Measuring biological data across time and space is critical for understanding complex biological processes and for various biosurveillance applications. However, such data are often inaccessible or difficult to directly obtain. Less invasive, more robust and higher-throughput biological recording tools are needed to profile cells and their environments. DNA-based cellular recording is an emerging and powerful framework for tracking intracellular and extracellular biological events over time across living cells and populations. Here, we review and assess DNA recorders that utilize CRISPR nucleases, integrases and base-editing strategies, as well as recombinase and polymerase-based methods. Quantitative characterization, modelling and evaluation of these DNA-recording modalities can guide their design and implementation for specific application areas.
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Affiliation(s)
- Ravi U Sheth
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA
- Integrated Program in Cellular, Molecular and Biomedical Studies, Columbia University, New York, NY, USA
| | - Harris H Wang
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA.
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA.
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389
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Raj B, Gagnon JA, Schier AF. Large-scale reconstruction of cell lineages using single-cell readout of transcriptomes and CRISPR-Cas9 barcodes by scGESTALT. Nat Protoc 2018; 13:2685-2713. [PMID: 30353175 PMCID: PMC6279253 DOI: 10.1038/s41596-018-0058-x] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Lineage relationships among the large number of heterogeneous cell types generated during development are difficult to reconstruct in a high-throughput manner. We recently established a method, scGESTALT, that combines cumulative editing of a lineage barcode array by CRISPR-Cas9 with large-scale transcriptional profiling using droplet-based single-cell RNA sequencing (scRNA-seq). The technique generates edits in the barcode array over multiple timepoints using Cas9 and pools of single-guide RNAs (sgRNAs) introduced during early and late zebrafish embryonic development, which distinguishes it from similar Cas9 lineage-tracing methods. The recorded lineages are captured, along with thousands of cellular transcriptomes, to build lineage trees with hundreds of branches representing relationships among profiled cell types. Here, we provide details for (i) generating transgenic zebrafish; (ii) performing multi-timepoint barcode editing; (iii) building scRNA-seq libraries from brain tissue; and (iv) concurrently amplifying lineage barcodes from captured single cells. Generating transgenic lines takes 6 months, and performing barcode editing and generating single-cell libraries involve 7 d of hands-on time. scGESTALT provides a scalable platform to map lineage relationships between cell types in any system that permits genome editing during development, regeneration, or disease.
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Affiliation(s)
- Bushra Raj
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA.
| | - James A Gagnon
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA
- Department of Biology, University of Utah, Salt Lake City, UT, USA
| | - Alexander F Schier
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA
- Biozentrum, University of Basel, Basel, Switzerland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Stem Cell Institute, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
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390
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Crow M, Gillis J. Co-expression in Single-Cell Analysis: Saving Grace or Original Sin? Trends Genet 2018; 34:823-831. [PMID: 30146183 PMCID: PMC6195469 DOI: 10.1016/j.tig.2018.07.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 07/05/2018] [Accepted: 07/25/2018] [Indexed: 01/04/2023]
Abstract
As a fundamental unit of life, the cell has rightfully been the subject of intense investigation throughout the history of biology. Technical innovations now make it possible to assay cellular features at genomic scale, yielding breakthroughs in our understanding of the molecular organization of tissues, and even whole organisms. As these data accumulate we will soon be faced with a new challenge: making sense of the plethora of results. Early investigations into the replicability of cell type profiles inferred from single-cell RNA sequencing data have indicated that this is likely to be surprisingly straightforward due to consistent gene co-expression. In this opinion article we discuss the evidence for this claim and its implications for interpreting cell type-specific gene expression.
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Affiliation(s)
- Megan Crow
- Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724, USA
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724, USA.
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391
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Sakuma T, Yamamoto T. Acceleration of cancer science with genome editing and related technologies. Cancer Sci 2018; 109:3679-3685. [PMID: 30315615 PMCID: PMC6272086 DOI: 10.1111/cas.13832] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 08/30/2018] [Accepted: 10/09/2018] [Indexed: 12/26/2022] Open
Abstract
Genome editing includes various edits of the genome, such as short insertions and deletions, substitutions, and chromosomal rearrangements including inversions, duplications, and translocations. These variations are based on single or multiple DNA double-strand break (DSB)-triggered in cellulo repair machineries. In addition to these "conventional" genome editing strategies, tools enabling customized, site-specific recognition of particular nucleic acid sequences have been coming into wider use; for example, single base editing without DSB introduction, epigenome editing with recruitment of epigenetic modifiers, transcriptome engineering using RNA editing systems, and in vitro detection of specific DNA and RNA sequences. In this review, we provide a quick overview of the current state of genome editing and related technologies that multilaterally contribute to cancer science.
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Affiliation(s)
- Tetsushi Sakuma
- Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, Hiroshima, Japan
| | - Takashi Yamamoto
- Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, Hiroshima, Japan
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392
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Kebschull JM, Zador AM. Cellular barcoding: lineage tracing, screening and beyond. Nat Methods 2018; 15:871-879. [PMID: 30377352 DOI: 10.1038/s41592-018-0185-x] [Citation(s) in RCA: 133] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Accepted: 09/26/2018] [Indexed: 01/14/2023]
Abstract
Cellular barcoding is a technique in which individual cells are labeled with unique nucleic acid sequences, termed barcodes, so that they can be tracked through space and time. Cellular barcoding can be used to track millions of cells in parallel, and thus is an efficient approach for investigating heterogeneous populations of cells. Over the past 25 years, cellular barcoding has been used for fate mapping, lineage tracing and high-throughput screening, and has led to important insights into developmental biology and gene function. Driven by plummeting sequencing costs and the power of synthetic biology, barcoding is now expanding beyond traditional applications and into diverse fields such as neuroanatomy and the recording of cellular activity. In this review, we discuss the fundamental principles of cellular barcoding, including the underlying mathematics, and its applications in both new and established fields.
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Affiliation(s)
- Justus M Kebschull
- Watson School of Biological Sciences, Cold Spring Harbor, NY, USA.,Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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393
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Zhao L, Liu Z, Levy SF, Wu S. Bartender: a fast and accurate clustering algorithm to count barcode reads. Bioinformatics 2018; 34:739-747. [PMID: 29069318 DOI: 10.1093/bioinformatics/btx655] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Accepted: 10/18/2017] [Indexed: 11/14/2022] Open
Abstract
Motivation Barcode sequencing (bar-seq) is a high-throughput, and cost effective method to assay large numbers of cell lineages or genotypes in complex cell pools. Because of its advantages, applications for bar-seq are quickly growing-from using neutral random barcodes to study the evolution of microbes or cancer, to using pseudo-barcodes, such as shRNAs or sgRNAs to simultaneously screen large numbers of cell perturbations. However, the computational pipelines for bar-seq clustering are not well developed. Available methods often yield a high frequency of under-clustering artifacts that result in spurious barcodes, or over-clustering artifacts that group distinct barcodes together. Here, we developed Bartender, an accurate clustering algorithm to detect barcodes and their abundances from raw next-generation sequencing data. Results In contrast with existing methods that cluster based on sequence similarity alone, Bartender uses a modified two-sample proportion test that also considers cluster size. This modification results in higher accuracy and lower rates of under- and over-clustering artifacts. Additionally, Bartender includes unique molecular identifier handling and a 'multiple time point' mode that matches barcode clusters between different clustering runs for seamless handling of time course data. Bartender is a set of simple-to-use command line tools that can be performed on a laptop at comparable run times to existing methods. Availability and implementation Bartender is available at no charge for non-commercial use at https://github.com/LaoZZZZZ/bartender-1.1. Contact sasha.levy@stonybrook.edu or song.wu@stonybrook.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lu Zhao
- Department of Applied Mathematics and Statistics
| | - Zhimin Liu
- Laufer Center for Physical and Quantitative Biology.,Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Sasha F Levy
- Laufer Center for Physical and Quantitative Biology.,Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Song Wu
- Department of Applied Mathematics and Statistics
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394
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Al’Khafaji AM, Deatherage D, Brock A. Control of Lineage-Specific Gene Expression by Functionalized gRNA Barcodes. ACS Synth Biol 2018; 7:2468-2474. [PMID: 30169961 PMCID: PMC6661167 DOI: 10.1021/acssynbio.8b00105] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Lineage tracking delivers essential quantitative insight into dynamic, probabilistic cellular processes, such as somatic tumor evolution and differentiation. Methods for high diversity lineage quantitation rely on sequencing a population of DNA barcodes. However, manipulation of specific individual lineages is not possible with this approach. To address this challenge, we developed a functionalized lineage tracing tool, Control of Lineages by Barcode Enabled Recombinant Transcription (COLBERT), that enables high diversity lineage tracing and lineage-specific manipulation of gene expression. This modular platform utilizes expressed barcode gRNAs to both track cell lineages and direct lineage-specific gene expression.
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Affiliation(s)
- Aziz M. Al’Khafaji
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Daniel Deatherage
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas 78712, United States
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395
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Elsner M. Barcodes galore for developmental biology. Nat Biotechnol 2018; 36:936. [PMID: 30307925 DOI: 10.1038/nbt.4269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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396
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Schmidt F, Cherepkova MY, Platt RJ. Transcriptional recording by CRISPR spacer acquisition from RNA. Nature 2018; 562:380-385. [DOI: 10.1038/s41586-018-0569-1] [Citation(s) in RCA: 83] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 08/21/2018] [Indexed: 12/11/2022]
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397
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398
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Biase FH, Wu Q, Calandrelli R, Rivas-Astroza M, Zhou S, Chen Z, Zhong S. Rainbow-Seq: Combining Cell Lineage Tracing with Single-Cell RNA Sequencing in Preimplantation Embryos. iScience 2018; 7:16-29. [PMID: 30267678 PMCID: PMC6135740 DOI: 10.1016/j.isci.2018.08.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 08/01/2018] [Accepted: 08/10/2018] [Indexed: 02/06/2023] Open
Abstract
We developed the Rainbow-seq technology to trace cell division history and reveal single-cell transcriptomes. With distinct fluorescent protein genes as lineage markers, Rainbow-seq enables each single-cell RNA sequencing (RNA-seq) experiment to simultaneously decode the lineage marker genes and read single-cell transcriptomes. We triggered lineage tracking in each blastomere at the 2-cell stage, observed microscopically inequivalent contributions of the progeny to the two embryonic poles at the blastocyst stage, and analyzed every single cell at either 4- or 8-cell stage with deep paired-end sequencing of full-length transcripts. Although lineage difference was not marked unequivocally at a single-gene level, it became clear when the transcriptome was analyzed as a whole. Moreover, several groups of novel transcript isoforms with embedded repeat sequences exhibited lineage difference, suggesting a possible link between DNA demethylation and cell fate decision. Rainbow-seq bridged a critical gap between division history and single-cell RNA-seq assays.
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Affiliation(s)
- Fernando H Biase
- Department of Bioengineering, University of California San Diego, San Diego, CA 92130, USA
| | - Qiuyang Wu
- Department of Bioengineering, University of California San Diego, San Diego, CA 92130, USA; Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
| | - Riccardo Calandrelli
- Department of Bioengineering, University of California San Diego, San Diego, CA 92130, USA
| | - Marcelo Rivas-Astroza
- Department of Bioengineering, University of California San Diego, San Diego, CA 92130, USA
| | - Shuigeng Zhou
- School of Computer Science, Fudan University, Shanghai 200433, China
| | - Zhen Chen
- Department of Diabetes Complications and Metabolism, City of Hope, Duarte, CA 91010, USA
| | - Sheng Zhong
- Department of Bioengineering, University of California San Diego, San Diego, CA 92130, USA.
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Abstract
Two articles recently described the development of CRISPR technologies that have the potential to fundamentally transform the barcoding and tracing of mammalian cells.
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Affiliation(s)
- Thomas Gaj
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Pablo Perez-Pinera
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- Carl R Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- Carle Illinois College of Medicine, Champaign, IL, 61820, USA.
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
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400
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Liu DS, Xu TL. Cell-Type Identification in the Autonomic Nervous System. Neurosci Bull 2018; 35:145-155. [PMID: 30171526 DOI: 10.1007/s12264-018-0284-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Accepted: 05/31/2018] [Indexed: 11/25/2022] Open
Abstract
The autonomic nervous system controls various internal organs and executes crucial functions through sophisticated neural connectivity and circuits. Its dysfunction causes an imbalance of homeostasis and numerous human disorders. In the past decades, great efforts have been made to study the structure and functions of this system, but so far, our understanding of the classification of autonomic neuronal subpopulations remains limited and a precise map of their connectivity has not been achieved. One of the major challenges that hinder rapid progress in these areas is the complexity and heterogeneity of autonomic neurons. To facilitate the identification of neuronal subgroups in the autonomic nervous system, here we review the well-established and cutting-edge technologies that are frequently used in peripheral neuronal tracing and profiling, and discuss their operating mechanisms, advantages, and targeted applications.
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Affiliation(s)
- Di-Shi Liu
- Department of Anatomy and Physiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Tian-Le Xu
- Department of Anatomy and Physiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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