1
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Weiler P, Lange M, Klein M, Pe'er D, Theis F. CellRank 2: unified fate mapping in multiview single-cell data. Nat Methods 2024; 21:1196-1205. [PMID: 38871986 PMCID: PMC11239496 DOI: 10.1038/s41592-024-02303-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 05/09/2024] [Indexed: 06/15/2024]
Abstract
Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature we use to recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm development. Moreover, we enable estimating cell-specific transcription and degradation rates from metabolic-labeling data, which we apply to an intestinal organoid system to delineate differentiation trajectories and pinpoint regulatory strategies.
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Affiliation(s)
- Philipp Weiler
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Marius Lange
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Michal Klein
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Machine Learning Research, Apple, Paris, France
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Fabian Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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2
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Lee CY, Clatworthy MR, Withers DR. Decoding changes in tumor-infiltrating leukocytes through dynamic experimental models and single-cell technologies. Immunol Cell Biol 2024. [PMID: 38853634 DOI: 10.1111/imcb.12787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/13/2024] [Accepted: 05/13/2024] [Indexed: 06/11/2024]
Abstract
The ability to characterize immune cells and explore the molecular interactions that govern their functions has never been greater, fueled in recent years by the revolutionary advance of single-cell analysis platforms. However, precisely how immune cells respond to different stimuli and where differentiation processes and effector functions operate remain incompletely understood. Inferring cellular fate within single-cell transcriptomic analyses is now omnipresent, despite the assumptions typically required in such analyses. Recently developed experimental models support dynamic analyses of the immune response, providing insights into the temporal changes that occur within cells and the tissues in which such transitions occur. Here we will review these approaches and discuss how these can be combined with single-cell technologies to develop a deeper understanding of the immune responses that should support the development of better therapeutic options for patients.
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Affiliation(s)
- Colin Yc Lee
- Cambridge Institute of Therapeutic Immunology and Infection Disease, University of Cambridge, Cambridge, UK
| | - Menna R Clatworthy
- Cambridge Institute of Therapeutic Immunology and Infection Disease, University of Cambridge, Cambridge, UK
| | - David R Withers
- Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
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3
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Aalam SMM, Nguyen LV, Ritting ML, Kannan N. Clonal tracking in cancer and metastasis. Cancer Metastasis Rev 2024; 43:639-656. [PMID: 37910295 DOI: 10.1007/s10555-023-10149-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 10/16/2023] [Indexed: 11/03/2023]
Abstract
The eradication of many cancers has proven challenging due to the presence of functionally and genetically heterogeneous clones maintained by rare cancer stem cells (CSCs), which contribute to disease progression, treatment refractoriness, and late relapse. The characterization of functional CSC activity has necessitated the development of modern clonal tracking strategies. This review describes viral-based and CRISPR-Cas9-based cellular barcoding, lineage tracing, and imaging-based approaches. DNA-based cellular barcoding technology is emerging as a powerful and robust strategy that has been widely applied to in vitro and in vivo model systems, including patient-derived xenograft models. This review also highlights the potential of these methods for use in the clinical and drug discovery contexts and discusses the important insights gained from such approaches.
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Affiliation(s)
| | - Long Viet Nguyen
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Megan L Ritting
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Nagarajan Kannan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
- Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Rochester, MN, USA.
- Center for Regenerative Biotherapeutics, Mayo Clinic, Rochester, MN, USA.
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4
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Jindal K, Adil MT, Yamaguchi N, Yang X, Wang HC, Kamimoto K, Rivera-Gonzalez GC, Morris SA. Single-cell lineage capture across genomic modalities with CellTag-multi reveals fate-specific gene regulatory changes. Nat Biotechnol 2024; 42:946-959. [PMID: 37749269 PMCID: PMC11180607 DOI: 10.1038/s41587-023-01931-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 07/31/2023] [Indexed: 09/27/2023]
Abstract
Complex gene regulatory mechanisms underlie differentiation and reprogramming. Contemporary single-cell lineage-tracing (scLT) methods use expressed, heritable DNA barcodes to combine cell lineage readout with single-cell transcriptomics. However, reliance on transcriptional profiling limits adaptation to other single-cell assays. With CellTag-multi, we present an approach that enables direct capture of heritable random barcodes expressed as polyadenylated transcripts, in both single-cell RNA sequencing and single-cell Assay for Transposase Accessible Chromatin using sequencing assays, allowing for independent clonal tracking of transcriptional and epigenomic cell states. We validate CellTag-multi to characterize progenitor cell lineage priming during mouse hematopoiesis. Additionally, in direct reprogramming of fibroblasts to endoderm progenitors, we identify core regulatory programs underlying on-target and off-target fates. Furthermore, we reveal the transcription factor Zfp281 as a regulator of reprogramming outcome, biasing cells toward an off-target mesenchymal fate. Our results establish CellTag-multi as a lineage-tracing method compatible with multiple single-cell modalities and demonstrate its utility in revealing fate-specifying gene regulatory changes across diverse paradigms of differentiation and reprogramming.
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Affiliation(s)
- Kunal Jindal
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Mohd Tayyab Adil
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Naoto Yamaguchi
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Xue Yang
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Helen C Wang
- Department of Pediatrics, Division of Hematology and Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Kenji Kamimoto
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Guillermo C Rivera-Gonzalez
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Samantha A Morris
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA.
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5
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Yoon B, Kim H, Jung SW, Park J. Single-cell lineage tracing approaches to track kidney cell development and maintenance. Kidney Int 2024; 105:1186-1199. [PMID: 38554991 DOI: 10.1016/j.kint.2024.01.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 12/06/2023] [Accepted: 01/09/2024] [Indexed: 04/02/2024]
Abstract
The kidney is a complex organ consisting of various cell types. Previous studies have aimed to elucidate the cellular relationships among these cell types in developing and mature kidneys using Cre-loxP-based lineage tracing. However, this methodology falls short of fully capturing the heterogeneous nature of the kidney, making it less than ideal for comprehensively tracing cellular progression during kidney development and maintenance. Recent technological advancements in single-cell genomics have revolutionized lineage tracing methods. Single-cell lineage tracing enables the simultaneous tracing of multiple cell types within complex tissues and their transcriptomic profiles, thereby allowing the reconstruction of their lineage tree with cell state information. Although single-cell lineage tracing has been successfully applied to investigate cellular hierarchies in various organs and tissues, its application in kidney research is currently lacking. This review comprehensively consolidates the single-cell lineage tracing methods, divided into 4 categories (clustered regularly interspaced short palindromic repeat [CRISPR]/CRISPR-associated protein 9 [Cas9]-based, transposon-based, Polylox-based, and native barcoding methods), and outlines their technical advantages and disadvantages. Furthermore, we propose potential future research topics in kidney research that could benefit from single-cell lineage tracing and suggest suitable technical strategies to apply to these topics.
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Affiliation(s)
- Baul Yoon
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Hayoung Kim
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Su Woong Jung
- Division of Nephrology, Department of Internal Medicine, College of Medicine, Kyung Hee University, Seoul, Republic of Korea; Division of Nephrology, Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, Seoul, Republic of Korea.
| | - Jihwan Park
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea.
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6
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Holze H, Talarmain L, Fennell KA, Lam EY, Dawson MA, Vassiliadis D. Analysis of synthetic cellular barcodes in the genome and transcriptome with BARtab and bartools. CELL REPORTS METHODS 2024; 4:100763. [PMID: 38670101 PMCID: PMC11133760 DOI: 10.1016/j.crmeth.2024.100763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/25/2024] [Accepted: 04/03/2024] [Indexed: 04/28/2024]
Abstract
Cellular barcoding is a lineage-tracing methodology that couples heritable synthetic barcodes to high-throughput sequencing, enabling the accurate tracing of cell lineages across a range of biological contexts. Recent studies have extended these methods by incorporating lineage information into single-cell or spatial transcriptomics readouts. Leveraging the rich biological information within these datasets requires dedicated computational tools for dataset pre-processing and analysis. Here, we present BARtab, a portable and scalable Nextflow pipeline, and bartools, an open-source R package, designed to provide an integrated end-to-end cellular barcoding analysis toolkit. BARtab and bartools contain methods to simplify the extraction, quality control, analysis, and visualization of lineage barcodes from population-level, single-cell, and spatial transcriptomics experiments. We showcase the utility of our integrated BARtab and bartools workflow via the analysis of exemplar bulk, single-cell, and spatial transcriptomics experiments containing cellular barcoding information.
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Affiliation(s)
- Henrietta Holze
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Laure Talarmain
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Katie A Fennell
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Enid Y Lam
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Mark A Dawson
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia; The University of Melbourne Centre for Cancer Research, The University of Melbourne, Melbourne, VIC 3000, Australia.
| | - Dane Vassiliadis
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia.
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7
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Siniscalco A, Perera RP, Greenslade JE, Masters A, Doll H, Raj B. Barcoding Notch signaling in the developing brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.10.593533. [PMID: 38766256 PMCID: PMC11100830 DOI: 10.1101/2024.05.10.593533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Developmental signaling inputs are fundamental for shaping cell fates and behavior. However, traditional fluorescent-based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER-seq, a CRISPR-Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single-cell transcriptomics. We applied SABER-seq to record Notch signaling in developing zebrafish brains. SABER-seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch-dependent manner with inducible control while the recorder accumulates mutations that represent Notch activity in founder cells. We combine SABER-seq with an expanded juvenile brain atlas to define cell types whose fates are determined downstream of Notch signaling. We identified examples wherein Notch signaling may have differential impact on terminal cell fates. SABER-seq is a novel platform for rapid, scalable and high-resolution mapping of signaling activity during development.
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Affiliation(s)
- Abigail Siniscalco
- Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Roshan Priyarangana Perera
- Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Jessie E. Greenslade
- Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Aiden Masters
- Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Hannah Doll
- Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Bushra Raj
- Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Institute for Regenerative Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
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8
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Sánchez Rivera FJ, Dow LE. How CRISPR Is Revolutionizing the Generation of New Models for Cancer Research. Cold Spring Harb Perspect Med 2024; 14:a041384. [PMID: 37487630 PMCID: PMC11065179 DOI: 10.1101/cshperspect.a041384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
Cancers arise through acquisition of mutations in genes that regulate core biological processes like cell proliferation and cell death. Decades of cancer research have led to the identification of genes and mutations causally involved in disease development and evolution, yet defining their precise function across different cancer types and how they influence therapy responses has been challenging. Mouse models have helped define the in vivo function of cancer-associated alterations, and genome-editing approaches using CRISPR have dramatically accelerated the pace at which these models are developed and studied. Here, we highlight how CRISPR technologies have impacted the development and use of mouse models for cancer research and discuss the many ways in which these rapidly evolving platforms will continue to transform our understanding of this disease.
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Affiliation(s)
- Francisco J Sánchez Rivera
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
| | - Lukas E Dow
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York 10065, USA
- Department of Biochemistry, Weill Cornell Medicine, New York, New York 10065, USA
- Department of Medicine, Weill Cornell Medicine, New York, New York 10065, USA
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9
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Wang K, Hou L, Wang X, Zhai X, Lu Z, Zi Z, Zhai W, He X, Curtis C, Zhou D, Hu Z. PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes. Nat Biotechnol 2024; 42:778-789. [PMID: 37524958 DOI: 10.1038/s41587-023-01887-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 06/28/2023] [Indexed: 08/02/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR-Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.
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Affiliation(s)
- Kun Wang
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- School of Mathematical Sciences, Xiamen University, Xiamen, China
| | - Liangzhen Hou
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Faculty of Health Sciences, University of Macau, Taipa, Macau, China
| | - Xin Wang
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiangwei Zhai
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Zhaolian Lu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhike Zi
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Weiwei Zhai
- CAS Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Xionglei He
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Christina Curtis
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Da Zhou
- School of Mathematical Sciences, Xiamen University, Xiamen, China.
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
| | - Zheng Hu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
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10
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Manso BA, Rodriguez y Baena A, Forsberg EC. From Hematopoietic Stem Cells to Platelets: Unifying Differentiation Pathways Identified by Lineage Tracing Mouse Models. Cells 2024; 13:704. [PMID: 38667319 PMCID: PMC11048769 DOI: 10.3390/cells13080704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 04/17/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
Platelets are the terminal progeny of megakaryocytes, primarily produced in the bone marrow, and play critical roles in blood homeostasis, clotting, and wound healing. Traditionally, megakaryocytes and platelets are thought to arise from multipotent hematopoietic stem cells (HSCs) via multiple discrete progenitor populations with successive, lineage-restricting differentiation steps. However, this view has recently been challenged by studies suggesting that (1) some HSC clones are biased and/or restricted to the platelet lineage, (2) not all platelet generation follows the "canonical" megakaryocytic differentiation path of hematopoiesis, and (3) platelet output is the default program of steady-state hematopoiesis. Here, we specifically investigate the evidence that in vivo lineage tracing studies provide for the route(s) of platelet generation and investigate the involvement of various intermediate progenitor cell populations. We further identify the challenges that need to be overcome that are required to determine the presence, role, and kinetics of these possible alternate pathways.
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Affiliation(s)
- Bryce A. Manso
- Institute for the Biology of Stem Cells, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
- Department of Biomolecular Engineering, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
| | - Alessandra Rodriguez y Baena
- Institute for the Biology of Stem Cells, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
- Program in Biomedical Sciences and Engineering, Department of Molecular, Cell, and Developmental Biology, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
| | - E. Camilla Forsberg
- Institute for the Biology of Stem Cells, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
- Department of Biomolecular Engineering, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
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11
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Liu Z, Zeng H, Xiang H, Deng S, He X. Achieving single-cell-resolution lineage tracing in zebrafish by continuous barcoding mutations during embryogenesis. J Genet Genomics 2024:S1673-8527(24)00074-2. [PMID: 38621643 DOI: 10.1016/j.jgg.2024.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 04/03/2024] [Accepted: 04/07/2024] [Indexed: 04/17/2024]
Abstract
Unraveling the lineage relationships of all descendants from a zygote is fundamental to advancing our understanding of developmental and stem cell biology. However, existing cell barcoding technologies in zebrafish lack the resolution to capture the majority of cell divisions during embryogenesis. A recently developed method, a substitution mutation-aided lineage-tracing system (SMALT), successfully reconstructed high-resolution cell phylogenetic trees for Drosophila melanogaster. Here, we implement the SMALT system in zebrafish, recording a median of 14 substitution mutations on a one-kilobase-pair barcoding sequence for one-day post-fertilization embryos. Leveraging this system, we reconstruct four cell lineage trees for zebrafish fin cells, encompassing both original and regenerated fin. Each tree consists of hundreds of internal nodes with a median bootstrap support of 99%. Analysis of the obtained cell lineage trees reveals that regenerated fin cells mainly originate from cells in the same part of the fins. Through multiple times sampling germ cells from the same individual, we confirm the stability of the germ cell pool and the early separation of germ cell and somatic cell progenitors. Our system offers the potential for reconstructing high-quality cell phylogenies across diverse tissues, providing valuable insights into development and disease in zebrafish.
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Affiliation(s)
- Zhan Liu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China
| | - Hui Zeng
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China
| | - Huimin Xiang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China
| | - Shanjun Deng
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China
| | - Xionglei He
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China.
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12
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Feng DC, Zhu WZ, Wang J, Li DX, Shi X, Xiong Q, You J, Han P, Qiu S, Wei Q, Yang L. The implications of single-cell RNA-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy. Mil Med Res 2024; 11:21. [PMID: 38605399 PMCID: PMC11007901 DOI: 10.1186/s40779-024-00526-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 03/25/2024] [Indexed: 04/13/2024] Open
Abstract
In recent years, advancements in single-cell and spatial transcriptomics, which are highly regarded developments in the current era, particularly the emerging integration of single-cell and spatiotemporal transcriptomics, have enabled a detailed molecular comprehension of the complex regulation of cell fate. The insights obtained from these methodologies are anticipated to significantly contribute to the development of personalized medicine. Currently, single-cell technology is less frequently utilized for prostate cancer compared with other types of tumors. Starting from the perspective of RNA sequencing technology, this review outlined the significance of single-cell RNA sequencing (scRNA-seq) in prostate cancer research, encompassing preclinical medicine and clinical applications. We summarize the differences between mouse and human prostate cancer as revealed by scRNA-seq studies, as well as a combination of multi-omics methods involving scRNA-seq to highlight the key molecular targets for the diagnosis, treatment, and drug resistance characteristics of prostate cancer. These studies are expected to provide novel insights for the development of immunotherapy and other innovative treatment strategies for castration-resistant prostate cancer. Furthermore, we explore the potential clinical applications stemming from other single-cell technologies in this review, paving the way for future research in precision medicine.
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Affiliation(s)
- De-Chao Feng
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Division of Surgery & Interventional Science, University College London, London, WC1E 6BT, UK.
| | - Wei-Zhen Zhu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jie Wang
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Deng-Xiong Li
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xu Shi
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Qiao Xiong
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jia You
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Ping Han
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Shi Qiu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Qiang Wei
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Lu Yang
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China.
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13
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Derényi I, Demeter MC, Pérez-Jiménez M, Grajzel D, Szöllősi GJ. How mutation accumulation depends on the structure of the cell lineage tree. Phys Rev E 2024; 109:044407. [PMID: 38755817 DOI: 10.1103/physreve.109.044407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 03/08/2024] [Indexed: 05/18/2024]
Abstract
All the cells of a multicellular organism are the product of cell divisions that trace out a single binary tree, the so-called cell lineage tree. Because cell divisions are accompanied by replication errors, the shape of the cell lineage tree is a key determinant of how somatic evolution, which can potentially lead to cancer, proceeds. Carcinogenesis requires the accumulation of a certain number of driver mutations. By mapping the accumulation of mutations into a graph theoretical problem, we present an exact numerical method to calculate the probability of collecting a given number of mutations and show that for low mutation rates it can be approximated with a simple analytical formula, which depends only on the distribution of the lineage lengths, and is dominated by the longest lineages. Our results are crucial in understanding how natural selection can shape the cell lineage trees of multicellular organisms and curtail somatic evolution.
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Affiliation(s)
- Imre Derényi
- ELTE Eötvös University, Department of Biological Physics, Pázmány Péter Sétány 1A, H-1117 Budapest, Hungary and MTA-ELTE Statistical and Biological Physics Research Group, Pázmány Péter Sétány 1A, H-1117 Budapest, Hungary
| | - Márton C Demeter
- ELTE Eötvös University, Department of Biological Physics, Pázmány Péter Sétány 1A, H-1117 Budapest, Hungary and MTA-ELTE "Lendület" Evolutionary Genomics Research Group, Pázmány Péter Sétány 1A, H-1117 Budapest, Hungary
| | - Mario Pérez-Jiménez
- ELTE Eötvös University, Department of Biological Physics, Pázmány Péter Sétány 1A, H-1117 Budapest, Hungary and MTA-ELTE "Lendület" Evolutionary Genomics Research Group, Pázmány Péter Sétány 1A, H-1117 Budapest, Hungary
| | - Dániel Grajzel
- ELTE Eötvös University, Department of Biological Physics, Pázmány Péter Sétány 1A, H-1117 Budapest, Hungary and MTA-ELTE "Lendület" Evolutionary Genomics Research Group, Pázmány Péter Sétány 1A, H-1117 Budapest, Hungary
| | - Gergely J Szöllősi
- ELTE Eötvös University, Department of Biological Physics, Pázmány Péter Sétány 1A, H-1117 Budapest, Hungary; MTA-ELTE "Lendület" Evolutionary Genomics Research Group, Pázmány Péter Sétány 1A, H-1117 Budapest, Hungary; HUN-REN Centre for Ecological Research, Institute of Evolution, H-1113 Budapest, Hungary; and Model-Based Evolutionary Genomics Unit, Okinawa Institute of Science and Technology Graduate University, 904-0412 Okinawa, Japan
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14
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Mitic N, Neuschulz A, Spanjaard B, Schneider J, Fresmann N, Novoselc KT, Strunk T, Münster L, Olivares-Chauvet P, Ninkovic J, Junker JP. Dissecting the spatiotemporal diversity of adult neural stem cells. Mol Syst Biol 2024; 20:321-337. [PMID: 38365956 PMCID: PMC10987636 DOI: 10.1038/s44320-024-00022-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 01/31/2024] [Accepted: 02/01/2024] [Indexed: 02/18/2024] Open
Abstract
Adult stem cells are important for tissue turnover and regeneration. However, in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here, we dissected the diversity of neural stem cells in the adult zebrafish brain, an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single-cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze the regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells, with some subtypes being restricted to a single brain region, while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation, with different states being involved in proliferative and non-proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for the functional manipulation of neural stem cell subtypes.
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Affiliation(s)
- Nina Mitic
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Anika Neuschulz
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
- Humboldt Universität zu Berlin, Institute for Biology, Berlin, Germany
| | - Bastiaan Spanjaard
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Julia Schneider
- Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Stem Cell Research, Munich, Germany
- Biomedical Center Munich (BMC), Department of Cell Biology and Anatomy, Medical Faculty, LMU, Munich, Germany
- Graduate School of Systemic Neurosciences, LMU, Munich, Germany
| | - Nora Fresmann
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Klara Tereza Novoselc
- Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Stem Cell Research, Munich, Germany
- Biomedical Center Munich (BMC), Department of Cell Biology and Anatomy, Medical Faculty, LMU, Munich, Germany
- Graduate School of Systemic Neurosciences, LMU, Munich, Germany
| | - Taraneh Strunk
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Lisa Münster
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Pedro Olivares-Chauvet
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Jovica Ninkovic
- Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Stem Cell Research, Munich, Germany
- Biomedical Center Munich (BMC), Department of Cell Biology and Anatomy, Medical Faculty, LMU, Munich, Germany
| | - Jan Philipp Junker
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany.
- Charité - Universitätsmedizin Berlin, Berlin, Germany.
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15
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Eisele AS, Tarbier M, Dormann AA, Pelechano V, Suter DM. Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets. Nat Commun 2024; 15:2744. [PMID: 38553478 PMCID: PMC10980719 DOI: 10.1038/s41467-024-47158-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 03/20/2024] [Indexed: 04/02/2024] Open
Abstract
Assigning single cell transcriptomes to cellular lineage trees by lineage tracing has transformed our understanding of differentiation during development, regeneration, and disease. However, lineage tracing is technically demanding, often restricted in time-resolution, and most scRNA-seq datasets are devoid of lineage information. Here we introduce Gene Expression Memory-based Lineage Inference (GEMLI), a computational tool allowing to robustly identify small to medium-sized cell lineages solely from scRNA-seq datasets. GEMLI allows to study heritable gene expression, to discriminate symmetric and asymmetric cell fate decisions and to reconstruct individual multicellular structures from pooled scRNA-seq datasets. In human breast cancer biopsies, GEMLI reveals previously unknown gene expression changes at the onset of cancer invasiveness. The universal applicability of GEMLI allows studying the role of small cell lineages in a wide range of physiological and pathological contexts, notably in vivo. GEMLI is available as an R package on GitHub ( https://github.com/UPSUTER/GEMLI ).
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Affiliation(s)
- A S Eisele
- Ecole Polytechnique Fédérale de Lausanne, School of Life Sciences, Institute of Bioengineering, Lausanne, Switzerland.
| | - M Tarbier
- Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Solna, Sweden
| | - A A Dormann
- Ecole Polytechnique Fédérale de Lausanne, School of Life Sciences, Institute of Bioengineering, Lausanne, Switzerland
| | - V Pelechano
- Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Solna, Sweden
| | - D M Suter
- Ecole Polytechnique Fédérale de Lausanne, School of Life Sciences, Institute of Bioengineering, Lausanne, Switzerland.
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16
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Mai U, Chu G, Raphael BJ. Maximum Likelihood Inference of Time-scaled Cell Lineage Trees with Mixed-type Missing Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583638. [PMID: 38496496 PMCID: PMC10942411 DOI: 10.1101/2024.03.05.583638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Recent dynamic lineage tracing technologies combine CRISPR-based genome editing with single-cell sequencing to track cell divisions during development. A key computational problem in dynamic lineage tracing is to infer a cell lineage tree from the measured CRISPR-induced mutations. Three features of dynamic lineage tracing data distinguish this problem from standard phylogenetic tree inference. First, the CRISPR-editing process modifies a genomic location exactly once. This non-modifiable property is not well described by the time-reversible models commonly used in phylogenetics. Second, as a consequence of non-modifiability, the number of mutations per time unit decreases over time. Third, CRISPR-based genome-editing and single-cell sequencing results in high rates of both heritable and non-heritable (dropout) missing data. To model these features, we introduce the Probabilistic Mixed-type Missing (PMM) model. We describe an algorithm, LAML (Lineage Analysis via Maximum Likelihood), to search for the maximum likelihood (ML) tree under the PMM model. LAML combines an Expectation Maximization (EM) algorithm with a heuristic tree search to jointly estimate tree topology, branch lengths and missing data parameters. We derive a closed-form solution for the M-step in the case of no heritable missing data, and a block coordinate ascent approach in the general case which is more efficient than the standard General Time Reversible (GTR) phylogenetic model. On simulated data, LAML infers more accurate tree topologies and branch lengths than existing methods, with greater advantages on datasets with higher ratios of heritable to non-heritable missing data. We show that LAML provides unbiased time-scaled estimates of branch lengths. In contrast, we demonstrate that maximum parsimony methods for lineage tracing data not only underestimate branch lengths, but also yield branch lengths which are not proportional to time, due to the nonlinear decay in the number of mutations on branches further from the root. On lineage tracing data from a mouse model of lung adenocarcinoma, we show that LAML infers phylogenetic distances that are more concordant with gene expression data compared to distances derived from maximum parsimony. The LAML tree topology is more plausible than existing published trees, with fewer total cell migrations between distant metastases and fewer reseeding events where cells migrate back to the primary tumor. Crucially, we identify three distinct time epochs of metastasis progression, which includes a burst of metastasis events to various anatomical sites during a single month.
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Affiliation(s)
| | | | - Benjamin J. Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
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17
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Shlyakhtina Y, Bloechl B, Moran KL, Portal MM. Protocol to study the inheritance and propagation of non-genetically encoded states using barcode decay lineage tracing. STAR Protoc 2024; 5:102809. [PMID: 38180835 PMCID: PMC10801334 DOI: 10.1016/j.xpro.2023.102809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 11/21/2023] [Accepted: 12/15/2023] [Indexed: 01/07/2024] Open
Abstract
Here, we present a protocol to perform barcode decay lineage tracing followed by single-cell transcriptome analysis (BdLT-Seq). We describe steps for BdLT-Seq experimental design, building barcoded episome reporters, performing episome transfection, and barcode retrieval. We then describe procedures for sequencing library construction while providing options for sample multiplexing and data analysis. This BdLT-Seq technique enables the assessment of clonal evolution in a directional manner while preserving isogeneity, thus allowing the comparison of non-genetic molecular features between isogenic cell lineages. For complete details on the use and execution of this protocol, please refer to Shlyakhtina et al. (2023).1.
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Affiliation(s)
- Yelyzaveta Shlyakhtina
- Cell Plasticity & Epigenetics Lab, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK; Cell Plasticity & Epigenetics Lab, Cancer Research UK - Cancer Research UK Scotland Institute, The University of Glasgow, Glasgow G61 1BD, UK
| | - Bianca Bloechl
- Cell Plasticity & Epigenetics Lab, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK; Cell Plasticity & Epigenetics Lab, Cancer Research UK - Cancer Research UK Scotland Institute, The University of Glasgow, Glasgow G61 1BD, UK
| | - Katherine L Moran
- Cell Plasticity & Epigenetics Lab, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK
| | - Maximiliano M Portal
- Cell Plasticity & Epigenetics Lab, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK; Cell Plasticity & Epigenetics Lab, Cancer Research UK - Cancer Research UK Scotland Institute, The University of Glasgow, Glasgow G61 1BD, UK.
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18
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Pacesa M, Pelea O, Jinek M. Past, present, and future of CRISPR genome editing technologies. Cell 2024; 187:1076-1100. [PMID: 38428389 DOI: 10.1016/j.cell.2024.01.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 03/03/2024]
Abstract
Genome editing has been a transformative force in the life sciences and human medicine, offering unprecedented opportunities to dissect complex biological processes and treat the underlying causes of many genetic diseases. CRISPR-based technologies, with their remarkable efficiency and easy programmability, stand at the forefront of this revolution. In this Review, we discuss the current state of CRISPR gene editing technologies in both research and therapy, highlighting limitations that constrain them and the technological innovations that have been developed in recent years to address them. Additionally, we examine and summarize the current landscape of gene editing applications in the context of human health and therapeutics. Finally, we outline potential future developments that could shape gene editing technologies and their applications in the coming years.
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Affiliation(s)
- Martin Pacesa
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Station 19, CH-1015 Lausanne, Switzerland
| | - Oana Pelea
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Martin Jinek
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.
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19
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Qin X, Tape CJ. Functional analysis of cell plasticity using single-cell technologies. Trends Cell Biol 2024:S0962-8924(24)00006-0. [PMID: 38355348 DOI: 10.1016/j.tcb.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/09/2024] [Accepted: 01/15/2024] [Indexed: 02/16/2024]
Abstract
Metazoan organisms are heterocellular systems composed of hundreds of different cell types, which arise from an isogenic genome through differentiation. Cellular 'plasticity' further enables cells to alter their fate in response to exogenous cues and is involved in a variety of processes, such as wound healing, infection, and cancer. Recent advances in cellular model systems, high-dimensional single-cell technologies, and lineage tracing have sparked a renaissance in plasticity research. Here, we discuss the definition of cell plasticity, evaluate state-of-the-art model systems and techniques to study cell-fate dynamics, and explore the application of single-cell technologies to obtain functional insights into cell plasticity in healthy and diseased tissues. The integration of advanced biomimetic model systems, single-cell technologies, and high-throughput perturbation studies is enabling a new era of research into non-genetic plasticity in metazoan systems.
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Affiliation(s)
- Xiao Qin
- MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Oxford, OX3 9DS, UK.
| | - Christopher J Tape
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, 72 Huntley Street, London, WC1E 6DD, UK.
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20
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Sun W, Perkins M, Huyghe M, Faraldo MM, Fre S, Perié L, Lyne AM. Extracting, filtering and simulating cellular barcodes using CellBarcode tools. NATURE COMPUTATIONAL SCIENCE 2024; 4:128-143. [PMID: 38374363 PMCID: PMC10899113 DOI: 10.1038/s43588-024-00595-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/16/2024] [Indexed: 02/21/2024]
Abstract
Identifying true DNA cellular barcodes among polymerase chain reaction and sequencing errors is challenging. Current tools are restricted in the diversity of barcode types supported or the analysis strategies implemented. As such, there is a need for more versatile and efficient tools for barcode extraction, as well as for tools to investigate which factors impact barcode detection and which filtering strategies to best apply. Here we introduce the package CellBarcode and its barcode simulation kit, CellBarcodeSim, that allows efficient and versatile barcode extraction and filtering for a range of barcode types from bulk or single-cell sequencing data using a variety of filtering strategies. Using the barcode simulation kit and biological data, we explore the technical and biological factors influencing barcode identification and provide a decision tree on how to optimize barcode identification for different barcode settings. We believe that CellBarcode and CellBarcodeSim have the capability to enhance the reproducibility and interpretation of barcode results across studies.
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Affiliation(s)
- Wenjie Sun
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France.
| | - Meghan Perkins
- Institut Curie, Laboratory of Genetics and Developmental Biology, PSL Research University, INSERM U934, CNRS UMR3215, Paris, France
| | - Mathilde Huyghe
- Institut Curie, Laboratory of Genetics and Developmental Biology, PSL Research University, INSERM U934, CNRS UMR3215, Paris, France
| | - Marisa M Faraldo
- Institut Curie, Laboratory of Genetics and Developmental Biology, PSL Research University, INSERM U934, CNRS UMR3215, Paris, France
| | - Silvia Fre
- Institut Curie, Laboratory of Genetics and Developmental Biology, PSL Research University, INSERM U934, CNRS UMR3215, Paris, France
| | - Leïla Perié
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France.
| | - Anne-Marie Lyne
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France.
- INSERM U900, Paris, France.
- MINES ParisTech, CBIO-Centre for Computational Biology, PSL Research University, Paris, France.
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21
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Wang L, Dong W, Yin Z, Sheng J, Ezeana CF, Yang L, Yu X, Wong SSY, Wan Z, Danforth RL, Han K, Gao D, Wong STC. Charting Single Cell Lineage Dynamics and Mutation Networks via Homing CRISPR. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.05.574236. [PMID: 38260351 PMCID: PMC10802354 DOI: 10.1101/2024.01.05.574236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Single cell lineage tracing, essential for unraveling cellular dynamics in disease evolution is critical for developing targeted therapies. CRISPR-Cas9, known for inducing permanent and cumulative mutations, is a cornerstone in lineage tracing. The novel homing guide RNA (hgRNA) technology enhances this by enabling dynamic retargeting and facilitating ongoing genetic modifications. Charting these mutations, especially through successive hgRNA edits, poses a significant challenge. Our solution, LINEMAP, is a computational framework designed to trace and map these mutations with precision. LINEMAP meticulously discerns mutation alleles at single-cell resolution and maps their complex interrelationships through a mutation evolution network. By utilizing a Markov Process model, we can predict mutation transition probabilities, revealing potential mutational routes and pathways. Our reconstruction algorithm, anchored in the Markov model's attributes, reconstructs cellular lineage pathways, shedding light on the cell's evolutionary journey to the minutiae of single-cell division. Our findings reveal an intricate network of mutation evolution paired with a predictive Markov model, advancing our capability to reconstruct single-cell lineage via hgRNA. This has substantial implications for advancing our understanding of biological mechanisms and propelling medical research forward.
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Affiliation(s)
- Lin Wang
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Wenjuan Dong
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Zheng Yin
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
- Biostatistics and Bioinformatics Shared Resource, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Jianting Sheng
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Chika F. Ezeana
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Li Yang
- T.T. and W. F. Chao Center for BRAIN, Houston Methodist Research Institute, Houston, Texas 77030
| | - Xiaohui Yu
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | | | - Zhihao Wan
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Rebecca L. Danforth
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Kun Han
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Dingcheng Gao
- Department of Cell & Development Biology, Weill Cornell Medical College, New York, NY 10065
| | - Stephen T. C. Wong
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
- Departments of Radiology, Pathology and Genomic Medicine, Houston Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030
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22
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Beumer J, Clevers H. Hallmarks of stemness in mammalian tissues. Cell Stem Cell 2024; 31:7-24. [PMID: 38181752 PMCID: PMC10769195 DOI: 10.1016/j.stem.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/03/2023] [Accepted: 12/08/2023] [Indexed: 01/07/2024]
Abstract
All adult tissues experience wear and tear. Most tissues can compensate for cell loss through the activity of resident stem cells. Although the cellular maintenance strategies vary greatly between different adult (read: postnatal) tissues, the function of stem cells is best defined by their capacity to replace lost tissue through division. We discuss a set of six complementary hallmarks that are key enabling features of this basic function. These include longevity and self-renewal, multipotency, transplantability, plasticity, dependence on niche signals, and maintenance of genome integrity. We discuss these hallmarks in the context of some of the best-understood adult stem cell niches.
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Affiliation(s)
- Joep Beumer
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Basel, Switzerland.
| | - Hans Clevers
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Basel, Switzerland.
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23
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Yang C, Lei Y, Ren T, Yao M. The Current Situation and Development Prospect of Whole-Genome Screening. Int J Mol Sci 2024; 25:658. [PMID: 38203828 PMCID: PMC10779205 DOI: 10.3390/ijms25010658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/22/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
High-throughput genetic screening is useful for discovering critical genes or gene sequences that trigger specific cell functions and/or phenotypes. Loss-of-function genetic screening is mainly achieved through RNA interference (RNAi), CRISPR knock-out (CRISPRko), and CRISPR interference (CRISPRi) technologies. Gain-of-function genetic screening mainly depends on the overexpression of a cDNA library and CRISPR activation (CRISPRa). Base editing can perform both gain- and loss-of-function genetic screening. This review discusses genetic screening techniques based on Cas9 nuclease, including Cas9-mediated genome knock-out and dCas9-based gene activation and interference. We compare these methods with previous genetic screening techniques based on RNAi and cDNA library overexpression and propose future prospects and applications for CRISPR screening.
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Affiliation(s)
| | | | | | - Mingze Yao
- Shanxi Provincial Key Laboratory for Medical Molecular Cell Biology, Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education and Institute of Biomedical Sciences, Shanxi University, Taiyuan 030006, China; (C.Y.); (Y.L.); (T.R.)
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24
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Li Z, Yang W, Wu P, Shan Y, Zhang X, Chen F, Yang J, Yang JR. Reconstructing cell lineage trees with genomic barcoding: approaches and applications. J Genet Genomics 2024; 51:35-47. [PMID: 37269980 DOI: 10.1016/j.jgg.2023.05.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/19/2023] [Accepted: 05/20/2023] [Indexed: 06/05/2023]
Abstract
In multicellular organisms, developmental history of cell divisions and functional annotation of terminal cells can be organized into a cell lineage tree (CLT). The reconstruction of the CLT has long been a major goal in developmental biology and other related fields. Recent technological advancements, especially those in editable genomic barcodes and single-cell high-throughput sequencing, have sparked a new wave of experimental methods for reconstructing CLTs. Here we review the existing experimental approaches to the reconstruction of CLT, which are broadly categorized as either image-based or DNA barcode-based methods. In addition, we present a summary of the related literature based on the biological insight provided by the obtained CLTs. Moreover, we discuss the challenges that will arise as more and better CLT data become available in the near future. Genomic barcoding-based CLT reconstructions and analyses, due to their wide applicability and high scalability, offer the potential for novel biological discoveries, especially those related to general and systemic properties of the developmental process.
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Affiliation(s)
- Zizhang Li
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Wenjing Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Peng Wu
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yuyan Shan
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xiaoyu Zhang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Feng Chen
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Junnan Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Jian-Rong Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, Guangdong 510080, China.
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25
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Zhang Y, Liu F. The evolving views of hematopoiesis: from embryo to adulthood and from in vivo to in vitro. J Genet Genomics 2024; 51:3-15. [PMID: 37734711 DOI: 10.1016/j.jgg.2023.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 09/13/2023] [Accepted: 09/13/2023] [Indexed: 09/23/2023]
Abstract
The hematopoietic system composed of hematopoietic stem and progenitor cells (HSPCs) and their differentiated lineages serves as an ideal model to uncover generic principles of cell fate transitions. From gastrulation onwards, there successively emerge primitive hematopoiesis (that produces specialized hematopoietic cells), pro-definitive hematopoiesis (that produces lineage-restricted progenitor cells), and definitive hematopoiesis (that produces multipotent HSPCs). These nascent lineages develop in several transient hematopoietic sites and finally colonize into lifelong hematopoietic sites. The development and maintenance of hematopoietic lineages are orchestrated by cell-intrinsic gene regulatory networks and cell-extrinsic microenvironmental cues. Owing to the progressive methodology (e.g., high-throughput lineage tracing and single-cell functional and omics analyses), our understanding of the developmental origin of hematopoietic lineages and functional properties of certain hematopoietic organs has been updated; meanwhile, new paradigms to characterize rare cell types, cell heterogeneity and its causes, and comprehensive regulatory landscapes have been provided. Here, we review the evolving views of HSPC biology during developmental and postnatal hematopoiesis. Moreover, we discuss recent advances in the in vitro induction and expansion of HSPCs, with a focus on the implications for clinical applications.
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Affiliation(s)
- Yifan Zhang
- Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Shandong University, Qingdao, Shandong 266237, China
| | - Feng Liu
- Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Shandong University, Qingdao, Shandong 266237, China; State Key Laboratory of Membrane Biology, Institute of Zoology, Institute for Stem Cell and Regeneration, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100101, China.
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26
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Kim IS. DNA Barcoding Technology for Lineage Recording and Tracing to Resolve Cell Fate Determination. Cells 2023; 13:27. [PMID: 38201231 PMCID: PMC10778210 DOI: 10.3390/cells13010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 12/18/2023] [Accepted: 12/20/2023] [Indexed: 01/12/2024] Open
Abstract
In various biological contexts, cells receive signals and stimuli that prompt them to change their current state, leading to transitions into a future state. This change underlies the processes of development, tissue maintenance, immune response, and the pathogenesis of various diseases. Following the path of cells from their initial identity to their current state reveals how cells adapt to their surroundings and undergo transformations to attain adjusted cellular states. DNA-based molecular barcoding technology enables the documentation of a phylogenetic tree and the deterministic events of cell lineages, providing the mechanisms and timing of cell lineage commitment that can either promote homeostasis or lead to cellular dysregulation. This review comprehensively presents recently emerging molecular recording technologies that utilize CRISPR/Cas systems, base editing, recombination, and innate variable sequences in the genome. Detailing their underlying principles, applications, and constraints paves the way for the lineage tracing of every cell within complex biological systems, encompassing the hidden steps and intermediate states of organism development and disease progression.
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Affiliation(s)
- Ik Soo Kim
- Department of Microbiology, Gachon University College of Medicine, Incheon 21999, Republic of Korea
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27
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Sashittal P, Schmidt H, Chan M, Raphael BJ. Startle: A star homoplasy approach for CRISPR-Cas9 lineage tracing. Cell Syst 2023; 14:1113-1121.e9. [PMID: 38128483 PMCID: PMC11257033 DOI: 10.1016/j.cels.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 10/31/2023] [Accepted: 11/17/2023] [Indexed: 12/23/2023]
Abstract
CRISPR-Cas9-based genome editing combined with single-cell sequencing enables the tracing of the history of cell divisions, or cellular lineage, in tissues and whole organisms. Although standard phylogenetic approaches may be applied to reconstruct cellular lineage trees from this data, the unique features of the CRISPR-Cas9 editing process motivate the development of specialized models that describe the evolution of CRISPR-Cas9-induced mutations. Here, we introduce the "star homoplasy" evolutionary model that constrains a phylogenetic character to mutate at most once along a lineage, capturing the "non-modifiability" property of CRISPR-Cas9 mutations. We derive a combinatorial characterization of star homoplasy phylogenies and use this characterization to develop an algorithm, "Startle", that computes a maximum parsimony star homoplasy phylogeny. We demonstrate that Startle infers more accurate phylogenies on simulated lineage tracing data compared with existing methods and finds parsimonious phylogenies with fewer metastatic migrations on lineage tracing data from mouse metastatic lung adenocarcinoma.
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Affiliation(s)
- Palash Sashittal
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Henri Schmidt
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Michelle Chan
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
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28
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Pan X, Li H, Putta P, Zhang X. LinRace: cell division history reconstruction of single cells using paired lineage barcode and gene expression data. Nat Commun 2023; 14:8388. [PMID: 38104156 PMCID: PMC10725445 DOI: 10.1038/s41467-023-44173-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 12/03/2023] [Indexed: 12/19/2023] Open
Abstract
Lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes in single cells, which allows for inference of cell lineage and cell types at the whole organism level. While most state-of-the-art methods for lineage reconstruction utilize only the lineage barcode data, methods that incorporate gene expressions are emerging. Effectively incorporating the gene expression data requires a reasonable model of how gene expression data changes along generations of divisions. Here, we present LinRace (Lineage Reconstruction with asymmetric cell division model), which integrates lineage barcode and gene expression data using asymmetric cell division model and infers cell lineages and ancestral cell states using Neighbor-Joining and maximum-likelihood heuristics. On both simulated and real data, LinRace outputs more accurate cell division trees than existing methods. With inferred ancestral states, LinRace can also show how a progenitor cell generates a large population of cells with various functionalities.
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Affiliation(s)
- Xinhai Pan
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Hechen Li
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Pranav Putta
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Xiuwei Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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29
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Bobrovskikh AV, Zubairova US, Doroshkov AV. Fishing Innate Immune System Properties through the Transcriptomic Single-Cell Data of Teleostei. BIOLOGY 2023; 12:1516. [PMID: 38132342 PMCID: PMC10740722 DOI: 10.3390/biology12121516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/20/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023]
Abstract
The innate immune system is the first line of defense in multicellular organisms. Danio rerio is widely considered a promising model for IIS-related research, with the most amount of scRNAseq data available among Teleostei. We summarized the scRNAseq and spatial transcriptomics experiments related to the IIS for zebrafish and other Teleostei from the GEO NCBI and the Single-Cell Expression Atlas. We found a considerable number of scRNAseq experiments at different stages of zebrafish development in organs such as the kidney, liver, stomach, heart, and brain. These datasets could be further used to conduct large-scale meta-analyses and to compare the IIS of zebrafish with the mammalian one. However, only a small number of scRNAseq datasets are available for other fish (turbot, salmon, cavefish, and dark sleeper). Since fish biology is very diverse, it would be a major mistake to use zebrafish alone in fish immunology studies. In particular, there is a special need for new scRNAseq experiments involving nonmodel Teleostei, e.g., long-lived species, cancer-resistant fish, and various fish ecotypes.
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Affiliation(s)
- Aleksandr V. Bobrovskikh
- Department of Physics, Novosibirsk State University, 630090 Novosibirsk, Russia
- The Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia; (U.S.Z.); (A.V.D.)
| | - Ulyana S. Zubairova
- The Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia; (U.S.Z.); (A.V.D.)
- Department of Information Technologies, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Alexey V. Doroshkov
- The Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia; (U.S.Z.); (A.V.D.)
- Department of Genomics and Bioinformatics, Institute of Fundamental Biology and Biotechnology, Siberian Federal University, 660036 Krasnoyarsk, Russia
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30
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Affiliation(s)
- Bushra Raj
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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31
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Li L, Bowling S, McGeary SE, Yu Q, Lemke B, Alcedo K, Jia Y, Liu X, Ferreira M, Klein AM, Wang SW, Camargo FD. A mouse model with high clonal barcode diversity for joint lineage, transcriptomic, and epigenomic profiling in single cells. Cell 2023; 186:5183-5199.e22. [PMID: 37852258 DOI: 10.1016/j.cell.2023.09.019] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 07/11/2023] [Accepted: 09/19/2023] [Indexed: 10/20/2023]
Abstract
Cellular lineage histories and their molecular states encode fundamental principles of tissue development and homeostasis. Current lineage-recording mouse models have insufficient barcode diversity and single-cell lineage coverage for profiling tissues composed of millions of cells. Here, we developed DARLIN, an inducible Cas9 barcoding mouse line that utilizes terminal deoxynucleotidyl transferase (TdT) and 30 CRISPR target sites. DARLIN is inducible, generates massive lineage barcodes across tissues, and enables the detection of edited barcodes in ∼70% of profiled single cells. Using DARLIN, we examined fate bias within developing hematopoietic stem cells (HSCs) and revealed unique features of HSC migration. Additionally, we established a protocol for joint transcriptomic and epigenomic single-cell measurements with DARLIN and found that cellular clonal memory is associated with genome-wide DNA methylation rather than gene expression or chromatin accessibility. DARLIN will enable the high-resolution study of lineage relationships and their molecular signatures in diverse tissues and physiological contexts.
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Affiliation(s)
- Li Li
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Sarah Bowling
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Sean E McGeary
- Department of Systems Biology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Qi Yu
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Bianca Lemke
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Karel Alcedo
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Yuemeng Jia
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Xugeng Liu
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Mark Ferreira
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Allon M Klein
- Department of Systems Biology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Shou-Wen Wang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; School of Science, Westlake University, Hangzhou, Zhejiang 310024, China.
| | - Fernando D Camargo
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
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32
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Boers R, Boers J, Tan B, van Leeuwen ME, Wassenaar E, Sanchez EG, Sleddens E, Tenhagen Y, Mulugeta E, Laven J, Creyghton M, Baarends W, van IJcken WFJ, Gribnau J. Retrospective analysis of enhancer activity and transcriptome history. Nat Biotechnol 2023; 41:1582-1592. [PMID: 36823354 PMCID: PMC10635829 DOI: 10.1038/s41587-023-01683-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 01/20/2023] [Indexed: 02/25/2023]
Abstract
Cell state changes in development and disease are controlled by gene regulatory networks, the dynamics of which are difficult to track in real time. In this study, we used an inducible DCM-RNA polymerase subunit b fusion protein which labels active genes and enhancers with a bacterial methylation mark that does not affect gene transcription and is propagated in S-phase. This DCM-RNA polymerase fusion protein enables transcribed genes and active enhancers to be tagged and then examined at later stages of development or differentiation. We apply this DCM-time machine (DCM-TM) technology to study intestinal homeostasis, revealing rapid and coordinated activation of enhancers and nearby genes during enterocyte differentiation. We provide new insights in absorptive-secretory lineage decision-making in intestinal stem cell (ISC) differentiation and show that ISCs retain a unique chromatin landscape required to maintain ISC identity and delineate future expression of differentiation-associated genes. DCM-TM has wide applicability in tracking cell states, providing new insights in the regulatory networks underlying cell state changes.
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Affiliation(s)
- Ruben Boers
- Department of Developmental Biology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Joachim Boers
- Department of Developmental Biology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Beatrice Tan
- Department of Developmental Biology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Marieke E van Leeuwen
- Department of Developmental Biology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Evelyne Wassenaar
- Department of Developmental Biology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Erlantz Gonzalez Sanchez
- Department of Developmental Biology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Esther Sleddens
- Department of Developmental Biology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Yasha Tenhagen
- Department of Developmental Biology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Eskeatnaf Mulugeta
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Joop Laven
- Department of Obstetrics and Gynaecology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Menno Creyghton
- Department of Developmental Biology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Willy Baarends
- Department of Developmental Biology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Wilfred F J van IJcken
- Erasmus Center for Biomics, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Joost Gribnau
- Department of Developmental Biology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands.
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33
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Xie L, Liu H, You Z, Wang L, Li Y, Zhang X, Ji X, He H, Yuan T, Zheng W, Wu Z, Xiong M, Wei W, Chen Y. Comprehensive spatiotemporal mapping of single-cell lineages in developing mouse brain by CRISPR-based barcoding. Nat Methods 2023; 20:1244-1255. [PMID: 37460718 DOI: 10.1038/s41592-023-01947-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 06/06/2023] [Indexed: 08/09/2023]
Abstract
A fundamental interest in developmental neuroscience lies in the ability to map the complete single-cell lineages within the brain. To this end, we developed a CRISPR editing-based lineage-specific tracing (CREST) method for clonal tracing in Cre mice. We then used two complementary strategies based on CREST to map single-cell lineages in developing mouse ventral midbrain (vMB). By applying snapshotting CREST (snapCREST), we constructed a spatiotemporal lineage landscape of developing vMB and identified six progenitor archetypes that could represent the principal clonal fates of individual vMB progenitors and three distinct clonal lineages in the floor plate that specified glutamatergic, dopaminergic or both neurons. We further created pandaCREST (progenitor and derivative associating CREST) to associate the transcriptomes of progenitor cells in vivo with their differentiation potentials. We identified multiple origins of dopaminergic neurons and demonstrated that a transcriptome-defined progenitor type comprises heterogeneous progenitors, each with distinct clonal fates and molecular signatures. Therefore, the CREST method and strategies allow comprehensive single-cell lineage analysis that could offer new insights into the molecular programs underlying neural specification.
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Affiliation(s)
- Lianshun Xie
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hengxin Liu
- University of Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Zhiwen You
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Luyue Wang
- University of Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Yiwen Li
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Xinyue Zhang
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoshan Ji
- Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China
| | - Hui He
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Tingli Yuan
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Wenping Zheng
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Ziyan Wu
- UniXell Biotechnology, Shanghai, China
| | - Man Xiong
- State Key Laboratory of Medical Neurobiology-Ministry of Education Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Wu Wei
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
- Center for Biomedical Informatics, Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.
- Lingang Laboratory, Shanghai, China.
| | - Yuejun Chen
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China.
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34
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Dudchenko O, Ordovas-Montanes J, Bingle CD. Respiratory epithelial cell types, states and fates in the era of single-cell RNA-sequencing. Biochem J 2023; 480:921-939. [PMID: 37410389 PMCID: PMC10422933 DOI: 10.1042/bcj20220572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/07/2023]
Abstract
Standalone and consortia-led single-cell atlases of healthy and diseased human airways generated with single-cell RNA-sequencing (scRNA-seq) have ushered in a new era in respiratory research. Numerous discoveries, including the pulmonary ionocyte, potentially novel cell fates, and a diversity of cell states among common and rare epithelial cell types have highlighted the extent of cellular heterogeneity and plasticity in the respiratory tract. scRNA-seq has also played a pivotal role in our understanding of host-virus interactions in coronavirus disease 2019 (COVID-19). However, as our ability to generate large quantities of scRNA-seq data increases, along with a growing number of scRNA-seq protocols and data analysis methods, new challenges related to the contextualisation and downstream applications of insights are arising. Here, we review the fundamental concept of cellular identity from the perspective of single-cell transcriptomics in the respiratory context, drawing attention to the need to generate reference annotations and to standardise the terminology used in literature. Findings about airway epithelial cell types, states and fates obtained from scRNA-seq experiments are compared and contrasted with information accumulated through the use of conventional methods. This review attempts to discuss major opportunities and to outline some of the key limitations of the modern-day scRNA-seq that need to be addressed to enable efficient and meaningful integration of scRNA-seq data from different platforms and studies, with each other as well as with data from other high-throughput sequencing-based genomic, transcriptomic and epigenetic analyses.
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Affiliation(s)
- Oleksandr Dudchenko
- Department of Infection, Immunity and Cardiovascular Disease, The Medical School, University of Sheffield, Sheffield, South Yorkshire, U.K
| | - Jose Ordovas-Montanes
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, MA, U.S.A
- Programme in Immunology, Harvard Medical School, Boston, MA, U.S.A
| | - Colin D. Bingle
- Department of Infection, Immunity and Cardiovascular Disease, The Medical School, University of Sheffield, Sheffield, South Yorkshire, U.K
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35
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Wang Y, Zhang X, Wang Z. Cellular barcoding: From developmental tracing to anti-tumor drug discovery. Cancer Lett 2023:216281. [PMID: 37336285 DOI: 10.1016/j.canlet.2023.216281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/31/2023] [Accepted: 06/10/2023] [Indexed: 06/21/2023]
Abstract
Clonal evolution has gained immense attention in explaining cancer cell status, history, and fate during cancer progression. Current single-cell or spatial transcriptome technologies have broadened our understanding of various mechanisms underlying cancer initiation, relapse, and drug resistance. However, technical challenges still hinder a better understanding of the dynamics of distinctive phenotypic states and abnormal trajectories from normal physiological transition to malignant stages. Cellular barcoding enabled lineage tracing on parallelly massive cells at single-cell resolution through different mechanisms lately, enabling new insights into exploring developmental trajectories, cancer progression, and targeted therapies. This review summarizes the latest noteworthy and robust strategies for different types of cellular barcodes. To introduce the major characteristics, advantages and limitations of these different strategies, this review will further guide in choosing or improving cellular barcoding technologies and their applications in cancer research.
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Affiliation(s)
- Yuqing Wang
- Medical Center of Hematology, The Second Affiliated Hospital, Army Medical University, Chongqing, 40037, China; State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 40037, China
| | - Xi Zhang
- Medical Center of Hematology, The Second Affiliated Hospital, Army Medical University, Chongqing, 40037, China; State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 40037, China; Jinfeng Laboratory, Chongqing, 401329, China.
| | - Zheng Wang
- Medical Center of Hematology, The Second Affiliated Hospital, Army Medical University, Chongqing, 40037, China; State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 40037, China; Bio-Med Informatics Research Center & Clinical Research Center, The Second Affiliated Hospital, Army Medical University, Chongqing, 400037, China; Jinfeng Laboratory, Chongqing, 401329, China.
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36
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Tyser RCV. Formation of the Heart: Defining Cardiomyocyte Progenitors at Single-Cell Resolution. Curr Cardiol Rep 2023; 25:495-503. [PMID: 37119451 PMCID: PMC10188409 DOI: 10.1007/s11886-023-01880-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/04/2023] [Indexed: 05/01/2023]
Abstract
PURPOSE OF REVIEW Formation of the heart requires the coordinated addition of multiple progenitor sources which have undergone different pathways of specification and differentiation. In this review, I aim to put into context how recent studies defining cardiac progenitor heterogeneity build on our understanding of early heart development and also discuss the questions raised by this new insight. RECENT FINDINGS With the development of sequencing technologies and imaging approaches, it has been possible to define, at high temporal resolution, the molecular profile and anatomical location of cardiac progenitors at the single-cell level, during the formation of the mammalian heart. Given the recent progress in our understanding of early heart development and technical advances in high-resolution time-lapse imaging and lineage analysis, we are now in a position of great potential, allowing us to resolve heart formation at previously impossible levels of detail. Understanding how this essential organ forms not only addresses questions of fundamental biological significance but also provides a blueprint for strategies to both treat and model heart disease.
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Affiliation(s)
- Richard C V Tyser
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Jeffrey Cheah Biomedical Centre, Cambridge, CB2 0AW, UK.
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37
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Johnson MS, Venkataram S, Kryazhimskiy S. Best Practices in Designing, Sequencing, and Identifying Random DNA Barcodes. J Mol Evol 2023; 91:263-280. [PMID: 36651964 PMCID: PMC10276077 DOI: 10.1007/s00239-022-10083-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 12/15/2022] [Indexed: 01/19/2023]
Abstract
Random DNA barcodes are a versatile tool for tracking cell lineages, with applications ranging from development to cancer to evolution. Here, we review and critically evaluate barcode designs as well as methods of barcode sequencing and initial processing of barcode data. We first demonstrate how various barcode design decisions affect data quality and propose a new design that balances all considerations that we are currently aware of. We then discuss various options for the preparation of barcode sequencing libraries, including inline indices and Unique Molecular Identifiers (UMIs). Finally, we test the performance of several established and new bioinformatic pipelines for the extraction of barcodes from raw sequencing reads and for error correction. We find that both alignment and regular expression-based approaches work well for barcode extraction, and that error-correction pipelines designed specifically for barcode data are superior to generic ones. Overall, this review will help researchers to approach their barcoding experiments in a deliberate and systematic way.
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Affiliation(s)
- Milo S Johnson
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, 94720, USA
| | - Sandeep Venkataram
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA, 92093, USA
| | - Sergey Kryazhimskiy
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA, 92093, USA.
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Grody EI, Abraham A, Shukla V, Goyal Y. Toward a systems-level probing of tumor clonality. iScience 2023; 26:106574. [PMID: 37192968 PMCID: PMC10182304 DOI: 10.1016/j.isci.2023.106574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023] Open
Abstract
Cancer has been described as a genetic disease that clonally evolves in the face of selective pressures imposed by cell-intrinsic and extrinsic factors. Although classical models based on genetic data predominantly propose Darwinian mechanisms of cancer evolution, recent single-cell profiling of cancers has described unprecedented heterogeneity in tumors providing support for alternative models of branched and neutral evolution through both genetic and non-genetic mechanisms. Emerging evidence points to a complex interplay between genetic, non-genetic, and extrinsic environmental factors in shaping the evolution of tumors. In this perspective, we briefly discuss the role of cell-intrinsic and extrinsic factors that shape clonal behaviors during tumor progression, metastasis, and drug resistance. Taking examples of pre-malignant states associated with hematological malignancies and esophageal cancer, we discuss recent paradigms of tumor evolution and prospective approaches to further enhance our understanding of this spatiotemporally regulated process.
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Affiliation(s)
- Emanuelle I. Grody
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL 60208, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Ajay Abraham
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Human Immunobiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Vipul Shukla
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Human Immunobiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Corresponding author
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL 60208, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Corresponding author
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Li J, Qin Y, Shen C, Zhang J, Tu S, Yang J, Wang Y, Zhou R, Zhang K, Chen J, Yang W. A new miniMOS tool kit capable of visualizing single copy insertion in C. elegans. PeerJ 2023; 11:e15433. [PMID: 37214099 PMCID: PMC10199674 DOI: 10.7717/peerj.15433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 04/26/2023] [Indexed: 05/24/2023] Open
Abstract
The miniMOS technique has been widely used in the C. elegans community to generate single copy insertions. A worm is considered as a potential insertion candidate if it is resistant to G418 antibiotics and does not express a co-injected fluorescence marker. If the expression of the extrachromosomal array is very low, it is possible for a worm to be mistakenly identified as a miniMOS candidate, as this low expression level can still confer resistance to G418 without producing a detectable fluorescence signal from the co-injection marker. This may increase the workload for identifying the insertion locus in the subsequent steps. In the present study, we modified the plasmid platform for miniMOS insertion by incorporating a myo-2 promoter-driven TagRFP or a ubiquitous H2B::GFP expression cassette into the targeting vector and introducing two loxP sites flanking the selection cassettes. Based on this new miniMOS tool kit, the removable fluorescence reporters can be used to visualize the single copy insertions, greatly reducing insertion locus identification efforts. In our experience, this new platform greatly facilitates the isolation of the miniMOS mutants.
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Affiliation(s)
- Jiangyun Li
- Department of Physiology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Yuang Qin
- Department of Physiology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Chengchen Shen
- Department of Physiology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Jun Zhang
- Department of Forensic Pathology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Shasha Tu
- Department of Physiology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Jingxuan Yang
- Department of Physiology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Yu Wang
- Department of Physiology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Ruyun Zhou
- Department of Anatomy, Bioimaging and Neuro-cell Science, Jichi Medical University, Tochigi, Japan
| | - Kui Zhang
- Department of Forensic Pathology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Jianping Chen
- Department of Pathogenic Biology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Wenxing Yang
- Department of Physiology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China
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40
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Pan X, Li H, Putta P, Zhang X. LinRace: single cell lineage reconstruction using paired lineage barcode and gene expression data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.12.536601. [PMID: 37090498 PMCID: PMC10120693 DOI: 10.1101/2023.04.12.536601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Understanding how single cells divide and differentiate into different cell types in developed organs is one of the major tasks of developmental and stem cell biology. Recently, lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes in single cells, which allows for the reconstruction of the cell division tree, and even the detection of cell types and differentiation trajectories at the whole organism level. While most state-of-the-art methods for lineage reconstruction utilize only the lineage barcode data, methods that incorporate gene expression data are emerging, aiming to improve the accuracy of lineage reconstruction. However, effectively incorporating the gene expression data requires a reasonable model on how gene expression data changes along generations of divisions. Here, we present LinRace (Lineage Reconstruction with asymmetric cell division model), a method that integrates the lineage barcode and gene expression data using the asymmetric cell division model and infers cell lineage under a framework combining Neighbor Joining and maximum-likelihood heuristics. On both simulated and real data, LinRace outputs more accurate cell division trees than existing methods. Moreover, LinRace can output the cell states (cell types) of ancestral cells, which is rarely performed with existing lineage reconstruction methods. The information on ancestral cells can be used to analyze how a progenitor cell generates a large population of cells with various functionalities. LinRace is available at: https://github.com/ZhangLabGT/LinRace.
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Affiliation(s)
- Xinhai Pan
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta GA 30332, USA
| | - Hechen Li
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta GA 30332, USA
| | - Pranav Putta
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta GA 30332, USA
| | - Xiuwei Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta GA 30332, USA
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Mowlaei ME, Shi X. FSF-GA: A Feature Selection Framework for Phenotype Prediction Using Genetic Algorithms. Genes (Basel) 2023; 14:genes14051059. [PMID: 37239419 DOI: 10.3390/genes14051059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/04/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023] Open
Abstract
(1) Background: Phenotype prediction is a pivotal task in genetics in order to identify how genetic factors contribute to phenotypic differences. This field has seen extensive research, with numerous methods proposed for predicting phenotypes. Nevertheless, the intricate relationship between genotypes and complex phenotypes, including common diseases, has resulted in an ongoing challenge to accurately decipher the genetic contribution. (2) Results: In this study, we propose a novel feature selection framework for phenotype prediction utilizing a genetic algorithm (FSF-GA) that effectively reduces the feature space to identify genotypes contributing to phenotype prediction. We provide a comprehensive vignette of our method and conduct extensive experiments using a widely used yeast dataset. (3) Conclusions: Our experimental results show that our proposed FSF-GA method delivers comparable phenotype prediction performance as compared to baseline methods, while providing features selected for predicting phenotypes. These selected feature sets can be used to interpret the underlying genetic architecture that contributes to phenotypic variation.
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Affiliation(s)
- Mohammad Erfan Mowlaei
- Department of Computer and Information Sciences, Temple University, 925 N. 12th Street, Philadelphia, PA 19122, USA
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, 925 N. 12th Street, Philadelphia, PA 19122, USA
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42
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Peterman E, Quitevis EJA, Black EC, Horton EC, Aelmore RL, White E, Sagasti A, Rasmussen JP. Zebrafish cutaneous injury models reveal that Langerhans cells engulf axonal debris in adult epidermis. Dis Model Mech 2023; 16:dmm049911. [PMID: 36876992 PMCID: PMC10110399 DOI: 10.1242/dmm.049911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 02/28/2023] [Indexed: 03/07/2023] Open
Abstract
Somatosensory neurons extend enormous peripheral axons to the skin, where they detect diverse environmental stimuli. Somatosensory peripheral axons are easily damaged due to their small caliber and superficial location. Axonal damage results in Wallerian degeneration, creating vast quantities of cellular debris that phagocytes must remove to maintain organ homeostasis. The cellular mechanisms that ensure efficient clearance of axon debris from stratified adult skin are unknown. Here, we established zebrafish scales as a tractable model to study axon degeneration in the adult epidermis. Using this system, we demonstrated that skin-resident immune cells known as Langerhans cells engulf the majority of axon debris. In contrast to immature skin, adult keratinocytes did not significantly contribute to debris removal, even in animals lacking Langerhans cells. Our study establishes a powerful new model for studying Wallerian degeneration and identifies a new function for Langerhans cells in maintenance of adult skin homeostasis following injury. These findings have important implications for pathologies that trigger somatosensory axon degeneration.
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Affiliation(s)
- Eric Peterman
- Department of Biology, University of Washington, Seattle, WA 98195, USA
| | | | - Erik C. Black
- Department of Biology, University of Washington, Seattle, WA 98195, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA 98195, USA
| | - Emma C. Horton
- Department of Biology, University of Washington, Seattle, WA 98195, USA
| | - Rune L. Aelmore
- Department of Biology, University of Washington, Seattle, WA 98195, USA
| | - Ethan White
- Department of Biology, University of Washington, Seattle, WA 98195, USA
| | - Alvaro Sagasti
- Molecular, Cell and Developmental Biology Department, University of California, Los Angeles, CA 90095, USA
- Molecular Biology Institute, University of California, Los Angeles, CA 90095, USA
| | - Jeffrey P. Rasmussen
- Department of Biology, University of Washington, Seattle, WA 98195, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA
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43
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Sommer ER, Napoli GC, Chau CH, Price DK, Figg WD. Targeting the metastatic niche: Single-cell lineage tracing in prime time. iScience 2023; 26:106174. [PMID: 36895653 PMCID: PMC9988656 DOI: 10.1016/j.isci.2023.106174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
Abstract
Identification of actionable drug targets remains a rate-limiting step of, and one of the most prominent barriers to successful drug development for metastatic cancers. CRISPR-Cas9, a tool for making targeted genomic edits, has given rise to various novel applications that have greatly accelerated discovery in developmental biology. Recent work has coupled a CRISPR-Cas9-based lineage tracing platform with single-cell transcriptomics in the unexplored context of cancer metastasis. In this perspective, we briefly reflect on the development of these distinct technological advances and the process by which they have become integrated. We also highlight the importance of single-cell lineage tracing in oncology drug development and suggest the profound capacity of a high-resolution, computational approach to reshape cancer drug discovery by enabling identification of novel metastasis-specific drug targets and mechanisms of resistance.
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Affiliation(s)
- Elijah R Sommer
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Giulia C Napoli
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Cindy H Chau
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Douglas K Price
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - William D Figg
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
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44
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Amini P, Hajihosseini M, Pyne S, Dinu I. Geographically weighted linear combination test for gene-set analysis of a continuous spatial phenotype as applied to intratumor heterogeneity. Front Cell Dev Biol 2023; 11:1065586. [PMID: 36998245 PMCID: PMC10044624 DOI: 10.3389/fcell.2023.1065586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
Background: The impact of gene-sets on a spatial phenotype is not necessarily uniform across different locations of cancer tissue. This study introduces a computational platform, GWLCT, for combining gene set analysis with spatial data modeling to provide a new statistical test for location-specific association of phenotypes and molecular pathways in spatial single-cell RNA-seq data collected from an input tumor sample.Methods: The main advantage of GWLCT consists of an analysis beyond global significance, allowing the association between the gene-set and the phenotype to vary across the tumor space. At each location, the most significant linear combination is found using a geographically weighted shrunken covariance matrix and kernel function. Whether a fixed or adaptive bandwidth is determined based on a cross-validation cross procedure. Our proposed method is compared to the global version of linear combination test (LCT), bulk and random-forest based gene-set enrichment analyses using data created by the Visium Spatial Gene Expression technique on an invasive breast cancer tissue sample, as well as 144 different simulation scenarios.Results: In an illustrative example, the new geographically weighted linear combination test, GWLCT, identifies the cancer hallmark gene-sets that are significantly associated at each location with the five spatially continuous phenotypic contexts in the tumors defined by different well-known markers of cancer-associated fibroblasts. Scan statistics revealed clustering in the number of significant gene-sets. A spatial heatmap of combined significance over all selected gene-sets is also produced. Extensive simulation studies demonstrate that our proposed approach outperforms other methods in the considered scenarios, especially when the spatial association increases.Conclusion: Our proposed approach considers the spatial covariance of gene expression to detect the most significant gene-sets affecting a continuous phenotype. It reveals spatially detailed information in tissue space and can thus play a key role in understanding the contextual heterogeneity of cancer cells.
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Affiliation(s)
- Payam Amini
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
- School of Medicine, Keele University, Keele, Staffordshire, United Kingdom
| | - Morteza Hajihosseini
- School of Public Health, University of Alberta, Edmonton, AB, Canada
- Stanford Department of Urology, Center for Academic Medicine, Palo Alto, CA, United States
| | - Saumyadipta Pyne
- Health Analytics Network, Pittsburgh, PA, United States
- University of California, Santa Barbara, Santa Barbara, CA, United States
- *Correspondence: Saumyadipta Pyne, ; Irina Dinu,
| | - Irina Dinu
- School of Public Health, University of Alberta, Edmonton, AB, Canada
- *Correspondence: Saumyadipta Pyne, ; Irina Dinu,
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45
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Shlyakhtina Y, Bloechl B, Portal MM. BdLT-Seq as a barcode decay-based method to unravel lineage-linked transcriptome plasticity. Nat Commun 2023; 14:1085. [PMID: 36841849 PMCID: PMC9968323 DOI: 10.1038/s41467-023-36744-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 02/14/2023] [Indexed: 02/26/2023] Open
Abstract
Cell plasticity is a core biological process underlying a myriad of molecular and cellular events taking place throughout organismal development and evolution. It has been postulated that cellular systems thrive to balance the organization of meta-stable states underlying this phenomenon, thereby maintaining a degree of populational homeostasis compatible with an ever-changing environment and, thus, life. Notably, albeit circumstantial evidence has been gathered in favour of the latter conceptual framework, a direct observation of meta-state dynamics and the biological consequences of such a process in generating non-genetic clonal diversity and divergent phenotypic output remains largely unexplored. To fill this void, here we develop a lineage-tracing technology termed Barcode decay Lineage Tracing-Seq. BdLT-Seq is based on episome-encoded molecular identifiers that, supported by the dynamic decay of the tracing information upon cell division, ascribe directionality to a cell lineage tree whilst directly coupling non-genetic molecular features to phenotypes in comparable genomic landscapes. We show that cell transcriptome states are both inherited, and dynamically reshaped following constrained rules encoded within the cell lineage in basal growth conditions, upon oncogene activation and throughout the process of reversible resistance to therapeutic cues thus adjusting phenotypic output leading to intra-clonal non-genetic diversity.
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Affiliation(s)
- Yelyzaveta Shlyakhtina
- Cell Plasticity & Epigenetics Lab, Cancer Research UK - Manchester Institute, The University of Manchester, SK10 4TG, Manchester, UK
| | - Bianca Bloechl
- Cell Plasticity & Epigenetics Lab, Cancer Research UK - Manchester Institute, The University of Manchester, SK10 4TG, Manchester, UK
| | - Maximiliano M Portal
- Cell Plasticity & Epigenetics Lab, Cancer Research UK - Manchester Institute, The University of Manchester, SK10 4TG, Manchester, UK.
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46
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Cotner M, Meng S, Jost T, Gardner A, De Santiago C, Brock A. Integration of quantitative methods and mathematical approaches for the modeling of cancer cell proliferation dynamics. Am J Physiol Cell Physiol 2023; 324:C247-C262. [PMID: 36503241 PMCID: PMC9886359 DOI: 10.1152/ajpcell.00185.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 11/21/2022] [Accepted: 11/21/2022] [Indexed: 12/15/2022]
Abstract
Physiological processes rely on the control of cell proliferation, and the dysregulation of these processes underlies various pathological conditions, including cancer. Mathematical modeling can provide new insights into the complex regulation of cell proliferation dynamics. In this review, we first examine quantitative experimental approaches for measuring cell proliferation dynamics in vitro and compare the various types of data that can be obtained in these settings. We then explore the toolbox of common mathematical modeling frameworks that can describe cell behavior, dynamics, and interactions of proliferation. We discuss how these wet-laboratory studies may be integrated with different mathematical modeling approaches to aid the interpretation of the results and to enable the prediction of cell behaviors, specifically in the context of cancer.
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Affiliation(s)
- Michael Cotner
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas
| | - Sarah Meng
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas
| | - Tyler Jost
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas
| | - Andrea Gardner
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas
| | - Carolina De Santiago
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas
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47
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Espinosa-Medina I, Feliciano D, Belmonte-Mateos C, Linda Miyares R, Garcia-Marques J, Foster B, Lindo S, Pujades C, Koyama M, Lee T. TEMPO enables sequential genetic labeling and manipulation of vertebrate cell lineages. Neuron 2023; 111:345-361.e10. [PMID: 36417906 DOI: 10.1016/j.neuron.2022.10.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 08/15/2022] [Accepted: 10/26/2022] [Indexed: 11/24/2022]
Abstract
During development, regulatory factors appear in a precise order to determine cell fates over time. Consequently, to investigate complex tissue development, it is necessary to visualize and manipulate cell lineages with temporal control. Current strategies for tracing vertebrate cell lineages lack genetic access to sequentially produced cells. Here, we present TEMPO (Temporal Encoding and Manipulation in a Predefined Order), an imaging-readable genetic tool allowing differential labeling and manipulation of consecutive cell generations in vertebrates. TEMPO is based on CRISPR and powered by a cascade of gRNAs that drive orderly activation and inactivation of reporters and/or effectors. Using TEMPO to visualize zebrafish and mouse neurogenesis, we recapitulated birth-order-dependent neuronal fates. Temporally manipulating cell-cycle regulators in mouse cortex progenitors altered the proportion and distribution of neurons and glia, revealing the effects of temporal gene perturbation on serial cell fates. Thus, TEMPO enables sequential manipulation of molecular factors, crucial to study cell-type specification.
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Affiliation(s)
| | - Daniel Feliciano
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Carla Belmonte-Mateos
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, PRBB, Barcelona 08003, Spain
| | - Rosa Linda Miyares
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Jorge Garcia-Marques
- Centro Nacional de Biotecnologia, Consejo Superior de Investigaciones Cientificas, Madrid 28049, Spain
| | - Benjamin Foster
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Sarah Lindo
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Cristina Pujades
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, PRBB, Barcelona 08003, Spain
| | - Minoru Koyama
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON M1C 1A4, Canada
| | - Tzumin Lee
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
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48
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Henke K, Farmer DT, Niu X, Kraus JM, Galloway JL, Youngstrom DW. Genetically engineered zebrafish as models of skeletal development and regeneration. Bone 2023; 167:116611. [PMID: 36395960 PMCID: PMC11080330 DOI: 10.1016/j.bone.2022.116611] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/01/2022] [Accepted: 11/08/2022] [Indexed: 11/16/2022]
Abstract
Zebrafish (Danio rerio) are aquatic vertebrates with significant homology to their terrestrial counterparts. While zebrafish have a centuries-long track record in developmental and regenerative biology, their utility has grown exponentially with the onset of modern genetics. This is exemplified in studies focused on skeletal development and repair. Herein, the numerous contributions of zebrafish to our understanding of the basic science of cartilage, bone, tendon/ligament, and other skeletal tissues are described, with a particular focus on applications to development and regeneration. We summarize the genetic strengths that have made the zebrafish a powerful model to understand skeletal biology. We also highlight the large body of existing tools and techniques available to understand skeletal development and repair in the zebrafish and introduce emerging methods that will aid in novel discoveries in skeletal biology. Finally, we review the unique contributions of zebrafish to our understanding of regeneration and highlight diverse routes of repair in different contexts of injury. We conclude that zebrafish will continue to fill a niche of increasing breadth and depth in the study of basic cellular mechanisms of skeletal biology.
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Affiliation(s)
- Katrin Henke
- Department of Orthopaedics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA.
| | - D'Juan T Farmer
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA 90095, USA; Department of Orthopaedic Surgery, University of California, Los Angeles, CA 90095, USA.
| | - Xubo Niu
- Center for Regenerative Medicine, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
| | - Jessica M Kraus
- Department of Orthopaedic Surgery, University of Connecticut Health Center, Farmington, CT 06030, USA.
| | - Jenna L Galloway
- Center for Regenerative Medicine, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
| | - Daniel W Youngstrom
- Department of Orthopaedic Surgery, University of Connecticut Health Center, Farmington, CT 06030, USA.
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49
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Sellahewa SG, Li JY, Xiao Q. Updated Perspectives on Direct Vascular Cellular Reprogramming and Their Potential Applications in Tissue Engineered Vascular Grafts. J Funct Biomater 2022; 14:21. [PMID: 36662068 PMCID: PMC9866165 DOI: 10.3390/jfb14010021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/25/2022] [Accepted: 12/27/2022] [Indexed: 01/03/2023] Open
Abstract
Cardiovascular disease is a globally prevalent disease with far-reaching medical and socio-economic consequences. Although improvements in treatment pathways and revascularisation therapies have slowed disease progression, contemporary management fails to modulate the underlying atherosclerotic process and sustainably replace damaged arterial tissue. Direct cellular reprogramming is a rapidly evolving and innovative tissue regenerative approach that holds promise to restore functional vasculature and restore blood perfusion. The approach utilises cell plasticity to directly convert somatic cells to another cell fate without a pluripotent stage. In this narrative literature review, we comprehensively analyse and compare direct reprogramming protocols to generate endothelial cells, vascular smooth muscle cells and vascular progenitors. Specifically, we carefully examine the reprogramming factors, their molecular mechanisms, conversion efficacies and therapeutic benefits for each induced vascular cell. Attention is given to the application of these novel approaches with tissue engineered vascular grafts as a therapeutic and disease-modelling platform for cardiovascular diseases. We conclude with a discussion on the ethics of direct reprogramming, its current challenges, and future perspectives.
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Affiliation(s)
- Saneth Gavishka Sellahewa
- William Harvey Research Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Jojo Yijiao Li
- William Harvey Research Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Qingzhong Xiao
- William Harvey Research Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
- Key Laboratory of Cardiovascular Diseases, School of Basic Medical Sciences, Guangzhou Institute of Cardiovascular Disease, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou 511436, China
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A Device for Isolation of Selected Single Adherent Cells. BIOMED RESEARCH INTERNATIONAL 2022; 2022:4303586. [PMID: 36567910 PMCID: PMC9780011 DOI: 10.1155/2022/4303586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 10/29/2022] [Accepted: 11/12/2022] [Indexed: 12/23/2022]
Abstract
In recent years, extensive research has been focused on the field of single cell analysis. The isolation of single cells is the first step in this type of research. However, the techniques used for direct isolation and acquisition of single adherent cells are limited. Here, we present a method of obtaining selected single adherent cells using a separation device. Compared with other single cell isolation methods, this method has the advantages of simple operation, low cost, minimal cell damage, and preservation of cell morphology. Our methodology is, therefore, suitable for the collection of selected single adherent cells.
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