1
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Liu F, Zhang X, Yang Y. Simulation of CRISPR-Cas9 editing on evolving barcode and accuracy of lineage tracing. Sci Rep 2024; 14:19213. [PMID: 39160220 PMCID: PMC11333585 DOI: 10.1038/s41598-024-70154-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 08/13/2024] [Indexed: 08/21/2024] Open
Abstract
We designed a simulation program that mimics the CRISPR-Cas9 editing on evolving barcode and double strand break repair procedure along with cell divisions. Emerging barcode mutations tend to build upon previously existing mutations, occurring sequentially with each generation. This process results in a unique mutation profile in each cell. We sample the barcodes in leaf cells and reconstruct the lineage, comparing it to the original lineage tree to test algorithm accuracy under different parameter settings. Our computational simulations validate the reasonable assumptions deduced from experimental observations, emphasizing that factors such as sampling size, barcode length, multiple barcodes, indel probabilities, and Cas9 activity are critical for accurate and successful lineage tracing. Among the many factors we found that sampling size and indel probabilities are two major ones that affect lineage tracing accuracy. Large segment deletions in early generations could greatly impact lineage accuracy. These simulation results offer insightful recommendations for enhancing the design and analysis of Cas9-mediated molecular barcodes in actual experiments.
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Affiliation(s)
- Fengshuo Liu
- Graduate Program in Cancer and Cell Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Xiang Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Yipeng Yang
- Department of Mathematics and Statistics, University of Houston - Clear Lake, 2700 Bay Area Blvd, Houston, TX, 77058, USA.
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2
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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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3
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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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4
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Guan G, Luo C, Tang LH, Tang C. Modulating cell proliferation by asymmetric division: A conserved pattern in the early embryogenesis of nematode species. MICROPUBLICATION BIOLOGY 2024; 2024:10.17912/micropub.biology.001006. [PMID: 38505394 PMCID: PMC10949086 DOI: 10.17912/micropub.biology.001006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 11/24/2023] [Accepted: 02/22/2024] [Indexed: 03/21/2024]
Abstract
In the early stage of the nematode Caenorhabditis elegans embryogenesis, the zygote divides asymmetrically into a symmetric fast lineage and an asymmetric slow lineage, producing 16 and 8 cells respectively almost at the same time, followed by the onset of gastrulation. It was recently reported that this cell division pattern is optimal for rapid cell proliferation. In this work, we compare the cell lineages of 9 nematode species, revealing that this pattern is conserved for >60 million years. It further suggests that such lineage design has an important functional role and it might speed up embryonic development in the nematode kingdom, not limited to C. elegans , and independent of the maternal-zygotic transition dynamics.
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Affiliation(s)
- Guoye Guan
- Center for Quantitative Biology, Peking University
- South Bay Interdisciplinary Science Center, Songshan Lake Materials Laboratory
- Department of Physics, Hong Kong Baptist University
- Current Address: Department of Systems Biology, Harvard Medical School
- Current Address: Department of Data Science, Dana-Farber Cancer Institute
| | - Ce Luo
- Center for Quantitative Biology, Peking University
| | - Lei-Han Tang
- South Bay Interdisciplinary Science Center, Songshan Lake Materials Laboratory
- Department of Physics, Hong Kong Baptist University
- Institute of Computational and Theoretical Studies, Hong Kong Baptist University
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University
| | - Chao Tang
- Center for Quantitative Biology, Peking University
- Peking-Tsinghua Center for Life Sciences, Peking University
- School of Physics, Peking University
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5
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Pan X, Zhang X. Studying temporal dynamics of single cells: expression, lineage and regulatory networks. Biophys Rev 2024; 16:57-67. [PMID: 38495440 PMCID: PMC10937865 DOI: 10.1007/s12551-023-01090-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/27/2023] [Indexed: 03/19/2024] Open
Abstract
Learning how multicellular organs are developed from single cells to different cell types is a fundamental problem in biology. With the high-throughput scRNA-seq technology, computational methods have been developed to reveal the temporal dynamics of single cells from transcriptomic data, from phenomena on cell trajectories to the underlying mechanism that formed the trajectory. There are several distinct families of computational methods including Trajectory Inference (TI), Lineage Tracing (LT), and Gene Regulatory Network (GRN) Inference which are involved in such studies. This review summarizes these computational approaches which use scRNA-seq data to study cell differentiation and cell fate specification as well as the advantages and limitations of different methods. We further discuss how GRNs can potentially affect cell fate decisions and trajectory structures. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-023-01090-5.
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Affiliation(s)
- Xinhai Pan
- 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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6
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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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7
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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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8
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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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9
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Prusokiene A, Prusokas A, Retkute R. Machine learning based lineage tree reconstruction improved with knowledge of higher level relationships between cells and genomic barcodes. NAR Genom Bioinform 2023; 5:lqad077. [PMID: 37608801 PMCID: PMC10440785 DOI: 10.1093/nargab/lqad077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 06/26/2023] [Accepted: 08/11/2023] [Indexed: 08/24/2023] Open
Abstract
Tracking cells as they divide and progress through differentiation is a fundamental step in understanding many biological processes, such as the development of organisms and progression of diseases. In this study, we investigate a machine learning approach to reconstruct lineage trees in experimental systems based on mutating synthetic genomic barcodes. We refine previously proposed methodology by embedding information of higher level relationships between cells and single-cell barcode values into a feature space. We test performance of the algorithm on shallow trees (up to 100 cells) and deep trees (up to 10 000 cells). Our proposed algorithm can improve tree reconstruction accuracy in comparison to reconstructions based on a maximum parsimony method, but this comes at a higher computational time requirement.
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Affiliation(s)
- Alisa Prusokiene
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | | | - Renata Retkute
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
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10
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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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11
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Crowell HL, Morillo Leonardo SX, Soneson C, Robinson MD. The shaky foundations of simulating single-cell RNA sequencing data. Genome Biol 2023; 24:62. [PMID: 36991470 PMCID: PMC10061781 DOI: 10.1186/s13059-023-02904-1] [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: 11/23/2021] [Accepted: 03/20/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND With the emergence of hundreds of single-cell RNA-sequencing (scRNA-seq) datasets, the number of computational tools to analyze aspects of the generated data has grown rapidly. As a result, there is a recurring need to demonstrate whether newly developed methods are truly performant-on their own as well as in comparison to existing tools. Benchmark studies aim to consolidate the space of available methods for a given task and often use simulated data that provide a ground truth for evaluations, thus demanding a high quality standard results credible and transferable to real data. RESULTS Here, we evaluated methods for synthetic scRNA-seq data generation in their ability to mimic experimental data. Besides comparing gene- and cell-level quality control summaries in both one- and two-dimensional settings, we further quantified these at the batch- and cluster-level. Secondly, we investigate the effect of simulators on clustering and batch correction method comparisons, and, thirdly, which and to what extent quality control summaries can capture reference-simulation similarity. CONCLUSIONS Our results suggest that most simulators are unable to accommodate complex designs without introducing artificial effects, they yield over-optimistic performance of integration and potentially unreliable ranking of clustering methods, and it is generally unknown which summaries are important to ensure effective simulation-based method comparisons.
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Affiliation(s)
- Helena L Crowell
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | | | - Charlotte Soneson
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
- Current address: Friedrich Miescher Institute for Biomedical Research and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Mark D Robinson
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
- SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.
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12
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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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13
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Fang W, Bell CM, Sapirstein A, Asami S, Leeper K, Zack DJ, Ji H, Kalhor R. Quantitative fate mapping: A general framework for analyzing progenitor state dynamics via retrospective lineage barcoding. Cell 2022; 185:4604-4620.e32. [PMID: 36423582 PMCID: PMC9708097 DOI: 10.1016/j.cell.2022.10.028] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 08/23/2022] [Accepted: 10/26/2022] [Indexed: 11/24/2022]
Abstract
Natural and induced somatic mutations that accumulate in the genome during development record the phylogenetic relationships of cells; whether these lineage barcodes capture the complex dynamics of progenitor states remains unclear. We introduce quantitative fate mapping, an approach to reconstruct the hierarchy, commitment times, population sizes, and commitment biases of intermediate progenitor states during development based on a time-scaled phylogeny of their descendants. To reconstruct time-scaled phylogenies from lineage barcodes, we introduce Phylotime, a scalable maximum likelihood clustering approach based on a general barcoding mutagenesis model. We validate these approaches using realistic in silico and in vitro barcoding experiments. We further establish criteria for the number of cells that must be analyzed for robust quantitative fate mapping and a progenitor state coverage statistic to assess the robustness. This work demonstrates how lineage barcodes, natural or synthetic, enable analyzing progenitor fate and dynamics long after embryonic development in any organism.
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Affiliation(s)
- Weixiang Fang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Claire M Bell
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Ophthalmology, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Abel Sapirstein
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Soichiro Asami
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Kathleen Leeper
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Donald J Zack
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Ophthalmology, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Hongkai Ji
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
| | - Reza Kalhor
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
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14
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Chen C, Liao Y, Peng G. Connecting past and present: single-cell lineage tracing. Protein Cell 2022; 13:790-807. [PMID: 35441356 PMCID: PMC9237189 DOI: 10.1007/s13238-022-00913-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 03/06/2022] [Indexed: 01/16/2023] Open
Abstract
Central to the core principle of cell theory, depicting cells' history, state and fate is a fundamental goal in modern biology. By leveraging clonal analysis and single-cell RNA-seq technologies, single-cell lineage tracing provides new opportunities to interrogate both cell states and lineage histories. During the past few years, many strategies to achieve lineage tracing at single-cell resolution have been developed, and three of them (integration barcodes, polylox barcodes, and CRISPR barcodes) are noteworthy as they are amenable in experimentally tractable systems. Although the above strategies have been demonstrated in animal development and stem cell research, much care and effort are still required to implement these methods. Here we review the development of single-cell lineage tracing, major characteristics of the cell barcoding strategies, applications, as well as technical considerations and limitations, providing a guide to choose or improve the single-cell barcoding lineage tracing.
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Affiliation(s)
- Cheng Chen
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Yuanxin Liao
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Guangdun Peng
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China.
- Center for Cell Lineage and Atlas, Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
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15
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Sankaran VG, Weissman JS, Zon LI. Cellular barcoding to decipher clonal dynamics in disease. Science 2022; 378:eabm5874. [PMID: 36227997 PMCID: PMC10111813 DOI: 10.1126/science.abm5874] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Cellular barcodes are distinct DNA sequences that enable one to track specific cells across time or space. Recent advances in our ability to detect natural or synthetic cellular barcodes, paired with single-cell readouts of cell state, have markedly increased our knowledge of clonal dynamics and genealogies of the cells that compose a variety of tissues and organs. These advances hold promise to redefine our view of human disease. Here, we provide an overview of cellular barcoding approaches, discuss applications to gain new insights into disease mechanisms, and provide an outlook on future applications. We discuss unanticipated insights gained through barcoding in studies of cancer and blood cell production and describe how barcoding can be applied to a growing array of medical fields, particularly with the increasing recognition of clonal contributions in human diseases.
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Affiliation(s)
- Vijay G Sankaran
- Division of Hematology and Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Harvard Stem Cell Institute, Cambridge, MA 02138, USA
| | - Jonathan S Weissman
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.,David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.,Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Leonard I Zon
- Division of Hematology and Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Harvard Stem Cell Institute, Cambridge, MA 02138, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.,Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Harvard Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
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16
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Zirin J, Bosch J, Viswanatha R, Mohr SE, Perrimon N. State-of-the-art CRISPR for in vivo and cell-based studies in Drosophila. Trends Genet 2022; 38:437-453. [PMID: 34933779 PMCID: PMC9007876 DOI: 10.1016/j.tig.2021.11.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/22/2021] [Accepted: 11/23/2021] [Indexed: 12/31/2022]
Abstract
For more than 100 years, the fruit fly, Drosophila melanogaster, has served as a powerful model organism for biological and biomedical research due to its many genetic and physiological similarities to humans and the availability of sophisticated technologies used to manipulate its genome and genes. The Drosophila research community quickly adopted CRISPR technologies and, in the 8 years since the first clustered regularly interspaced short palindromic repeats (CRISPR) publications in flies, has explored and innovated methods for mutagenesis, precise genome engineering, and beyond. Moreover, the short lifespan and ease of genetics have made Drosophila an ideal testing ground for in vivo applications and refinements of the rapidly evolving set of CRISPR-associated (CRISPR-Cas) tools. Here, we review innovations in delivery of CRISPR reagents, increased efficiency of cutting and homology-directed repair (HDR), and alternatives to standard Cas9-based approaches. While the focus is primarily on in vivo systems, we also describe the role of Drosophila cultured cells as both an indispensable first step in the process of assessing new CRISPR technologies and a platform for genome-wide CRISPR pooled screens.
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Affiliation(s)
- Jonathan Zirin
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA.
| | - Justin Bosch
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Raghuvir Viswanatha
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Stephanie E Mohr
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Norbert Perrimon
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA.
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17
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Yao M, Ren T, Pan Y, Xue X, Li R, Zhang L, Li Y, Huang K. A New Generation of Lineage Tracing Dynamically Records Cell Fate Choices. Int J Mol Sci 2022; 23:ijms23095021. [PMID: 35563412 PMCID: PMC9105840 DOI: 10.3390/ijms23095021] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/26/2022] [Accepted: 04/28/2022] [Indexed: 11/16/2022] Open
Abstract
Reconstructing the development of lineage relationships and cell fate mapping has been a fundamental problem in biology. Using advanced molecular biology and single-cell RNA sequencing, we have profiled transcriptomes at the single-cell level and mapped cell fates during development. Recently, CRISPR/Cas9 barcode editing for large-scale lineage tracing has been used to reconstruct the pseudotime trajectory of cells and improve lineage tracing accuracy. This review presents the progress of the latest CbLT (CRISPR-based Lineage Tracing) and discusses the current limitations and potential technical pitfalls in their application and other emerging concepts.
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18
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Pan X, Li H, Zhang X. TedSim: temporal dynamics simulation of single-cell RNA sequencing data and cell division history. Nucleic Acids Res 2022; 50:4272-4288. [PMID: 35412632 PMCID: PMC9071466 DOI: 10.1093/nar/gkac235] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 03/23/2022] [Accepted: 03/31/2022] [Indexed: 11/18/2022] Open
Abstract
Recently, lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes, which allows for the reconstruction of the cell division tree and makes it possible to reconstruct ancestral cell types and trace the origin of each cell type. Meanwhile, trajectory inference methods are widely used to infer cell trajectories and pseudotime in a dynamic process using gene expression data of present-day cells. Here, we present TedSim (single-cell temporal dynamics simulator), which simulates the cell division events from the root cell to present-day cells, simultaneously generating two data modalities for each single cell: the lineage barcode and gene expression data. TedSim is a framework that connects the two problems: lineage tracing and trajectory inference. Using TedSim, we conducted analysis to show that (i) TedSim generates realistic gene expression and barcode data, as well as realistic relationships between these two data modalities; (ii) trajectory inference methods can recover the underlying cell state transition mechanism with balanced cell type compositions; and (iii) integrating gene expression and barcode data can provide more insights into the temporal dynamics in cell differentiation compared to using only one type of data, but better integration methods need to be developed.
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Affiliation(s)
- Xinhai Pan
- School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Hechen Li
- School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Xiuwei Zhang
- School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
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19
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Konno N, Kijima Y, Watano K, Ishiguro S, Ono K, Tanaka M, Mori H, Masuyama N, Pratt D, Ideker T, Iwasaki W, Yachie N. Deep distributed computing to reconstruct extremely large lineage trees. Nat Biotechnol 2022; 40:566-575. [PMID: 34992246 PMCID: PMC9934975 DOI: 10.1038/s41587-021-01111-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 10/01/2021] [Indexed: 02/07/2023]
Abstract
Phylogeny estimation (the reconstruction of evolutionary trees) has recently been applied to CRISPR-based cell lineage tracing, allowing the developmental history of an individual tissue or organism to be inferred from a large number of mutated sequences in somatic cells. However, current computational methods are not able to construct phylogenetic trees from extremely large numbers of input sequences. Here, we present a deep distributed computing framework to comprehensively trace accurate large lineages (FRACTAL) that substantially enhances the scalability of current lineage estimation software tools. FRACTAL first reconstructs only an upstream lineage of the input sequences and recursively iterates the same produce for its downstream lineages using independent computing nodes. We demonstrate the utility of FRACTAL by reconstructing lineages from >235 million simulated sequences and from >16 million cells from a simulated experiment with a CRISPR system that accumulates mutations during cell proliferation. We also successfully applied FRACTAL to evolutionary tree reconstructions and to an experiment using error-prone PCR (EP-PCR) for large-scale sequence diversification.
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Affiliation(s)
- Naoki Konno
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.,Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan.,Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Yusuke Kijima
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.,Department of Aquatic Bioscience, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.,School of Biomedical Engineering, Faculty of Applied Science and Faculty of Medicine, The University of British Columbia, Vancouver, British Columbia, Canada.,These authors contributed equally: Yusuke Kijima, Keito Watano, Soh Ishiguro
| | - Keito Watano
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.,Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan.,Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.,These authors contributed equally: Yusuke Kijima, Keito Watano, Soh Ishiguro
| | - Soh Ishiguro
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.,School of Biomedical Engineering, Faculty of Applied Science and Faculty of Medicine, The University of British Columbia, Vancouver, British Columbia, Canada.,These authors contributed equally: Yusuke Kijima, Keito Watano, Soh Ishiguro
| | - Keiichiro Ono
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Mamoru Tanaka
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Hideto Mori
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.,Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Graduate School of Media and Governance, Keio University, Fujisawa, Japan
| | - Nanami Masuyama
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.,School of Biomedical Engineering, Faculty of Applied Science and Faculty of Medicine, The University of British Columbia, Vancouver, British Columbia, Canada.,Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Graduate School of Media and Governance, Keio University, Fujisawa, Japan
| | - Dexter Pratt
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Trey Ideker
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA.,Departments of Bioengineering and Computer Science, University of California San Diego, La Jolla, CA, USA
| | - Wataru Iwasaki
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan.,Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Nozomu Yachie
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan. .,School of Biomedical Engineering, Faculty of Applied Science and Faculty of Medicine, The University of British Columbia, Vancouver, British Columbia, Canada. .,Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan. .,Graduate School of Media and Governance, Keio University, Fujisawa, Japan.
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20
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Takasugi PR, Wang S, Truong KT, Drage EP, Kanishka SN, Higbee MA, Bamidele N, Ojelabi O, Sontheimer EJ, Gagnon JA. Orthogonal CRISPR-Cas tools for genome editing, inhibition, and CRISPR recording in zebrafish embryos. Genetics 2022; 220:iyab196. [PMID: 34735006 PMCID: PMC8733422 DOI: 10.1093/genetics/iyab196] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 10/26/2021] [Indexed: 11/15/2022] Open
Abstract
The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas universe continues to expand. The type II CRISPR-Cas system from Streptococcus pyogenes (SpyCas9) is the most widely used for genome editing due to its high efficiency in cells and organisms. However, concentrating on a single CRISPR-Cas system imposes limits on target selection and multiplexed genome engineering. We hypothesized that CRISPR-Cas systems originating from different bacterial species could operate simultaneously and independently due to their distinct single-guide RNAs (sgRNAs) or CRISPR-RNAs (crRNAs), and protospacer adjacent motifs (PAMs). Additionally, we hypothesized that CRISPR-Cas activity in zebrafish could be regulated through the expression of inhibitory anti-CRISPR (Acr) proteins. Here, we use a simple mutagenesis approach to demonstrate that CRISPR-Cas systems from S. pyogenes (SpyCas9), Streptococcus aureus (SauCas9), Lachnospiraceae bacterium (LbaCas12a, previously known as LbCpf1) are orthogonal systems capable of operating simultaneously in zebrafish. CRISPR systems from Acidaminococcus sp. (AspCas12a, previously known as AsCpf1) and Neisseria meningitidis (Nme2Cas9) were also active in embryos. We implemented multichannel CRISPR recording using three CRISPR systems and show that LbaCas12a may provide superior information density compared with previous methods. We also demonstrate that type II Acrs (anti-CRISPRs) are effective inhibitors of SpyCas9 in zebrafish. Our results indicate that at least five CRISPR-Cas systems and two anti-CRISPR proteins are functional in zebrafish embryos. These orthogonal CRISPR-Cas systems and Acr proteins will enable combinatorial and intersectional strategies for spatiotemporal control of genome editing and genetic recording in animals.
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Affiliation(s)
- Paige R Takasugi
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Shengzhou Wang
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Kimberly T Truong
- Department of Mathematics, University of Utah, Salt Lake City, UT 84112, USA
| | - Evan P Drage
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Sahar N Kanishka
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Marissa A Higbee
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Nathan Bamidele
- RNA Therapeutics Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Ogooluwa Ojelabi
- RNA Therapeutics Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Erik J Sontheimer
- RNA Therapeutics Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
- Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - James A Gagnon
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
- Henry Eyring Center for Cell & Genome Science, University of Utah, Salt Lake City, UT 84112, USA
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21
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Mapping single-cell-resolution cell phylogeny reveals cell population dynamics during organ development. Nat Methods 2021; 18:1506-1514. [PMID: 34857936 DOI: 10.1038/s41592-021-01325-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 10/18/2021] [Indexed: 12/20/2022]
Abstract
Mapping the cell phylogeny of a complex multicellular organism relies on somatic mutations accumulated from zygote to adult. Available cell barcoding methods can record about three mutations per barcode, enabling only low-resolution mapping of the cell phylogeny of complex organisms. Here we developed SMALT, a substitution mutation-aided lineage-tracing system that outperforms the available cell barcoding methods in mapping cell phylogeny. We applied SMALT to Drosophila melanogaster and obtained on average more than 20 mutations on a three-kilobase-pair barcoding sequence in early-adult cells. Using the barcoding mutations, we obtained high-quality cell phylogenetic trees, each comprising several thousand internal nodes with 84-93% median bootstrap support. The obtained cell phylogenies enabled a population genetic analysis that estimates the longitudinal dynamics of the number of actively dividing parental cells (Np) in each organ through development. The Np dynamics revealed the trajectory of cell births and provided insight into the balance of symmetric and asymmetric cell division.
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22
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Abstract
Nuclei are central hubs for information processing in eukaryotic cells. The need to fit large genomes into small nuclei imposes severe restrictions on genome organization and the mechanisms that drive genome-wide regulatory processes. How a disordered polymer such as chromatin, which has vast heterogeneity in its DNA and histone modification profiles, folds into discernibly consistent patterns is a fundamental question in biology. Outstanding questions include how genomes are spatially and temporally organized to regulate cellular processes with high precision and whether genome organization is causally linked to transcription regulation. The advent of next-generation sequencing, super-resolution imaging, multiplexed fluorescent in situ hybridization, and single-molecule imaging in individual living cells has caused a resurgence in efforts to understand the spatiotemporal organization of the genome. In this review, we discuss structural and mechanistic properties of genome organization at different length scales and examine changes in higher-order chromatin organization during important developmental transitions.
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Affiliation(s)
- Rajarshi P Ghosh
- Department of Molecular and Cell Biology and Howard Hughes Medical Institute, University of California, Berkeley, California 94720, USA; ,
| | - Barbara J Meyer
- Department of Molecular and Cell Biology and Howard Hughes Medical Institute, University of California, Berkeley, California 94720, USA; ,
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23
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Wolf S, Wan Y, McDole K. Current approaches to fate mapping and lineage tracing using image data. Development 2021; 148:dev198994. [PMID: 34498046 DOI: 10.1242/dev.198994] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Visualizing, tracking and reconstructing cell lineages in developing embryos has been an ongoing effort for well over a century. Recent advances in light microscopy, labelling strategies and computational methods to analyse complex image datasets have enabled detailed investigations into the fates of cells. Combined with powerful new advances in genomics and single-cell transcriptomics, the field of developmental biology is able to describe the formation of the embryo like never before. In this Review, we discuss some of the different strategies and applications to lineage tracing in live-imaging data and outline software methodologies that can be applied to various cell-tracking challenges.
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Affiliation(s)
- Steffen Wolf
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK
| | - Yinan Wan
- Biozentrum, University of Basel, Basel, 4056, Switzerland
| | - Katie McDole
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK
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24
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Gong W, Granados AA, Hu J, Jones MG, Raz O, Salvador-Martínez I, Zhang H, Chow KHK, Kwak IY, Retkute R, Prusokiene A, Prusokas A, Khodaverdian A, Zhang R, Rao S, Wang R, Rennert P, Saipradeep VG, Sivadasan N, Rao A, Joseph T, Srinivasan R, Peng J, Han L, Shang X, Garry DJ, Yu T, Chung V, Mason M, Liu Z, Guan Y, Yosef N, Shendure J, Telford MJ, Shapiro E, Elowitz MB, Meyer P. Benchmarked approaches for reconstruction of in vitro cell lineages and in silico models of C. elegans and M. musculus developmental trees. Cell Syst 2021; 12:810-826.e4. [PMID: 34146472 DOI: 10.1016/j.cels.2021.05.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 02/01/2021] [Accepted: 05/11/2021] [Indexed: 12/20/2022]
Abstract
The recent advent of CRISPR and other molecular tools enabled the reconstruction of cell lineages based on induced DNA mutations and promises to solve the ones of more complex organisms. To date, no lineage reconstruction algorithms have been rigorously examined for their performance and robustness across dataset types and number of cells. To benchmark such methods, we decided to organize a DREAM challenge using in vitro experimental intMEMOIR recordings and in silico data for a C. elegans lineage tree of about 1,000 cells and a Mus musculus tree of 10,000 cells. Some of the 22 approaches submitted had excellent performance, but structural features of the trees prevented optimal reconstructions. Using smaller sub-trees as training sets proved to be a good approach for tuning algorithms to reconstruct larger trees. The simulation and reconstruction methods here generated delineate a potential way forward for solving larger cell lineage trees such as in mouse.
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Affiliation(s)
- Wuming Gong
- Lillehei Heart Institute, University of Minnesota, 2231 6th St S.E, 4-165 CCRB, Minneapolis, MN 55114, USA
| | | | - Jingyuan Hu
- Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Matthew G Jones
- Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkeley, CA, USA; Integrative Program of Quantitative Biology, University of California, San Francisco, San Francisco, CA, USA
| | - Ofir Raz
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Irepan Salvador-Martínez
- Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, Gower Street, London WC1E 6BT, UK
| | - Hanrui Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ke-Huan K Chow
- California Institute of Technology, Pasadena, CA 91125, USA
| | - Il-Youp Kwak
- Department of Applied Statistics, College of Business & Economics, Chung-Ang University, 84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea
| | - Renata Retkute
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
| | - Alisa Prusokiene
- School of Natural and Environmental Sciences, Newcastle University, Newcastle NE1 7RU, UK
| | | | - Alex Khodaverdian
- Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkeley, CA, USA
| | - Richard Zhang
- Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkeley, CA, USA
| | - Suhas Rao
- Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkeley, CA, USA
| | - Robert Wang
- Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkeley, CA, USA
| | - Phil Rennert
- EC Wise Inc., 1299 4th St #505, San Rafael, CA 94901, USA
| | | | - Naveen Sivadasan
- TCS Research and Innovation, Tata Consultancy Services, Hyderabad 500019, India
| | - Aditya Rao
- TCS Research and Innovation, Tata Consultancy Services, Hyderabad 500019, India
| | - Thomas Joseph
- TCS Research and Innovation, Tata Consultancy Services, Hyderabad 500019, India
| | - Rajgopal Srinivasan
- TCS Research and Innovation, Tata Consultancy Services, Hyderabad 500019, India
| | - Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Lu Han
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Daniel J Garry
- Lillehei Heart Institute, University of Minnesota, 2231 6th St S.E, 4-165 CCRB, Minneapolis, MN 55114, USA
| | - Thomas Yu
- Sage Bionetworks, 2901 3rd Ave #330, Seattle, WA 98121, USA
| | - Verena Chung
- Sage Bionetworks, 2901 3rd Ave #330, Seattle, WA 98121, USA
| | - Michael Mason
- Sage Bionetworks, 2901 3rd Ave #330, Seattle, WA 98121, USA
| | - Zhandong Liu
- Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Nir Yosef
- Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkeley, CA, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA; Brotman Baty Institute for Precision Medicine, Seattle, WA, USA; Howard Hughes Medical Institute, Seattle, WA, USA
| | - Maximilian J Telford
- Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, Gower Street, London WC1E 6BT, UK
| | - Ehud Shapiro
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel
| | | | - Pablo Meyer
- T.J. Watson Research Center, IBM, Healthcare & Life Sciences, 1101 Kitchawan Rd 10598, Yorktown Heights, NY 10598, USA.
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25
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Forrow A, Schiebinger G. LineageOT is a unified framework for lineage tracing and trajectory inference. Nat Commun 2021; 12:4940. [PMID: 34400634 PMCID: PMC8367995 DOI: 10.1038/s41467-021-25133-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 07/08/2021] [Indexed: 12/20/2022] Open
Abstract
Understanding the genetic and epigenetic programs that control differentiation during development is a fundamental challenge, with broad impacts across biology and medicine. Measurement technologies like single-cell RNA-sequencing and CRISPR-based lineage tracing have opened new windows on these processes, through computational trajectory inference and lineage reconstruction. While these two mathematical problems are deeply related, methods for trajectory inference are not typically designed to leverage information from lineage tracing and vice versa. Here, we present LineageOT, a unified framework for lineage tracing and trajectory inference. Specifically, we leverage mathematical tools from graphical models and optimal transport to reconstruct developmental trajectories from time courses with snapshots of both cell states and lineages. We find that lineage data helps disentangle complex state transitions with increased accuracy using fewer measured time points. Moreover, integrating lineage tracing with trajectory inference in this way could enable accurate reconstruction of developmental pathways that are impossible to recover with state-based methods alone.
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Affiliation(s)
- Aden Forrow
- Mathematical Institute, University of Oxford, Oxford, UK.
| | - Geoffrey Schiebinger
- Department of Mathematics, University of British Columbia, Vancouver, BC, Canada.
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26
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Salvador-Martínez I, Grillo M, Averof M, Telford MJ. CeLaVi: an interactive cell lineage visualization tool. Nucleic Acids Res 2021; 49:W80-W85. [PMID: 33956141 PMCID: PMC8265160 DOI: 10.1093/nar/gkab325] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 04/10/2021] [Accepted: 04/20/2021] [Indexed: 12/17/2022] Open
Abstract
Recent innovations in genetics and imaging are providing the means to reconstruct cell lineages, either by tracking cell divisions using live microscopy, or by deducing the history of cells using molecular recorders. A cell lineage on its own, however, is simply a description of cell divisions as branching events. A major goal of current research is to integrate this description of cell relationships with information about the spatial distribution and identities of the cells those divisions produce. Visualizing, interpreting and exploring these complex data in an intuitive manner requires the development of new tools. Here we present CeLaVi, a web-based visualization tool that allows users to navigate and interact with a representation of cell lineages, whilst simultaneously visualizing the spatial distribution, identities and properties of cells. CeLaVi’s principal functions include the ability to explore and manipulate the cell lineage tree; to visualise the spatial distribution of cell clones at different depths of the tree; to colour cells in the 3D viewer based on lineage relationships; to visualise various cell qualities on the 3D viewer (e.g. gene expression, cell type) and to annotate selected cells/clones. All these capabilities are demonstrated with four different example data sets. CeLaVi is available at http://www.celavi.pro.
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Affiliation(s)
- Irepan Salvador-Martínez
- Centre for Life's Origins and Evolution, Department of Genetics Evolution and Environment, University College London, Gower Street, London, WC1E 6BT, UK
| | - Marco Grillo
- Institut de Génomique Fonctionnelle de Lyon (IGFL), École Normale Supérieure de Lyon, 32 avenue Tony Garnier, 69007 Lyon, France.,Centre National de la Recherche Scientifique (CNRS), France
| | - Michalis Averof
- Institut de Génomique Fonctionnelle de Lyon (IGFL), École Normale Supérieure de Lyon, 32 avenue Tony Garnier, 69007 Lyon, France.,Centre National de la Recherche Scientifique (CNRS), France
| | - Maximilian J Telford
- Centre for Life's Origins and Evolution, Department of Genetics Evolution and Environment, University College London, Gower Street, London, WC1E 6BT, UK
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27
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Chow KHK, Budde MW, Granados AA, Cabrera M, Yoon S, Cho S, Huang TH, Koulena N, Frieda KL, Cai L, Lois C, Elowitz MB. Imaging cell lineage with a synthetic digital recording system. Science 2021; 372:eabb3099. [PMID: 33833095 DOI: 10.1126/science.abb3099] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 02/25/2021] [Indexed: 12/13/2022]
Abstract
During multicellular development, spatial position and lineage history play powerful roles in controlling cell fate decisions. Using a serine integrase-based recording system, we engineered cells to record lineage information in a format that can be read out in situ. The system, termed integrase-editable memory by engineered mutagenesis with optical in situ readout (intMEMOIR), allowed in situ reconstruction of lineage relationships in cultured mouse cells and flies. intMEMOIR uses an array of independent three-state genetic memory elements that can recombine stochastically and irreversibly, allowing up to 59,049 distinct digital states. It reconstructed lineage trees in stem cells and enabled simultaneous analysis of single-cell clonal history, spatial position, and gene expression in Drosophila brain sections. These results establish a foundation for microscopy-readable lineage recording and analysis in diverse systems.
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Affiliation(s)
- Ke-Huan K Chow
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Mark W Budde
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Alejandro A Granados
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Maria Cabrera
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Shinae Yoon
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Soomin Cho
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Ting-Hao Huang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Noushin Koulena
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | | | - Long Cai
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Carlos Lois
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
| | - Michael B Elowitz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
- Howard Hughes Medical Institute, California Institute of Technology, Pasadena, CA 91125, USA
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28
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FENG JEAN, DEWITT WILLIAMS, MCKENNA AARON, SIMON NOAH, WILLIS AMYD, MATSEN FREDERICKA. ESTIMATION OF CELL LINEAGE TREES BY MAXIMUM-LIKELIHOOD PHYLOGENETICS. Ann Appl Stat 2021; 15:343-362. [PMID: 35990087 PMCID: PMC9387344 DOI: 10.1214/20-aoas1400] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
CRISPR technology has enabled cell lineage tracing for complex multicellular organisms through insertion-deletion mutations of synthetic genomic barcodes during organismal development. To reconstruct the cell lineage tree from the mutated barcodes, current approaches apply general-purpose computational tools that are agnostic to the mutation process and are unable to take full advantage of the data's structure. We propose a statistical model for the CRISPR mutation process and develop a procedure to estimate the resulting tree topology, branch lengths, and mutation parameters by iteratively applying penalized maximum likelihood estimation. By assuming the barcode evolves according to a molecular clock, our method infers relative ordering across parallel lineages, whereas existing techniques only infer ordering for nodes along the same lineage. When analyzing transgenic zebrafish data from McKenna, Findlay and Gagnon et al. (2016), we find that our method recapitulates known aspects of zebrafish development and the results are consistent across samples.
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Affiliation(s)
- JEAN FENG
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | | | - AARON MCKENNA
- Department of Molecular and Systems Biology, Dartmouth College
| | - NOAH SIMON
- Department of Biostatistics, University of Washington
| | - AMY D. WILLIS
- Department of Biostatistics, University of Washington
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29
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Quinn JJ, Jones MG, Okimoto RA, Nanjo S, Chan MM, Yosef N, Bivona TG, Weissman JS. Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts. Science 2021; 371:eabc1944. [PMID: 33479121 PMCID: PMC7983364 DOI: 10.1126/science.abc1944] [Citation(s) in RCA: 138] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 09/23/2020] [Accepted: 12/17/2020] [Indexed: 12/11/2022]
Abstract
Detailed phylogenies of tumor populations can recount the history and chronology of critical events during cancer progression, such as metastatic dissemination. We applied a Cas9-based, single-cell lineage tracer to study the rates, routes, and drivers of metastasis in a lung cancer xenograft mouse model. We report deeply resolved phylogenies for tens of thousands of cancer cells traced over months of growth and dissemination. This revealed stark heterogeneity in metastatic capacity, arising from preexisting and heritable differences in gene expression. We demonstrate that these identified genes can drive invasiveness and uncovered an unanticipated suppressive role for KRT17 We also show that metastases disseminated via multidirectional tissue routes and complex seeding topologies. Overall, we demonstrate the power of tracing cancer progression at subclonal resolution and vast scale.
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Affiliation(s)
- Jeffrey J Quinn
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
- Inscripta, Inc., Boulder, CO, USA
| | - Matthew G Jones
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA
- Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA, USA
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Ross A Okimoto
- UCSF Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Shigeki Nanjo
- UCSF Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Michelle M Chan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Nir Yosef
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA.
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
- Chan Zuckerberg Biohub Investigator, San Francisco, CA, USA
- Ragon Institute of Massachusetts General Hospital, MIT and Harvard University, Cambridge, MA, USA
| | - Trever G Bivona
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
- UCSF Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Jonathan S Weissman
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
- Whitehead Institute, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
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30
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Stadler T, Pybus OG, Stumpf MPH. Phylodynamics for cell biologists. Science 2021; 371:371/6526/eaah6266. [PMID: 33446527 DOI: 10.1126/science.aah6266] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 08/13/2020] [Indexed: 12/12/2022]
Abstract
Multicellular organisms are composed of cells connected by ancestry and descent from progenitor cells. The dynamics of cell birth, death, and inheritance within an organism give rise to the fundamental processes of development, differentiation, and cancer. Technical advances in molecular biology now allow us to study cellular composition, ancestry, and evolution at the resolution of individual cells within an organism or tissue. Here, we take a phylogenetic and phylodynamic approach to single-cell biology. We explain how "tree thinking" is important to the interpretation of the growing body of cell-level data and how ecological null models can benefit statistical hypothesis testing. Experimental progress in cell biology should be accompanied by theoretical developments if we are to exploit fully the dynamical information in single-cell data.
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Affiliation(s)
- T Stadler
- Department of Biosystems Science and Engineering, ETH Zürich, Switzerland. .,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - O G Pybus
- Department of Zoology, University of Oxford, Oxford, UK.
| | - M P H Stumpf
- Melbourne Integrative Genomics, School of BioSciences and School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.
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31
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Next-Generation Lineage Tracing and Fate Mapping to Interrogate Development. Dev Cell 2020; 56:7-21. [PMID: 33217333 DOI: 10.1016/j.devcel.2020.10.021] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 09/15/2020] [Accepted: 10/27/2020] [Indexed: 12/20/2022]
Abstract
Lineage tracing and fate mapping, overlapping yet distinct disciplines to follow cells and their progeny, have evolved rapidly over the last century. Lineage tracing aims to identify all progeny arising from an individual cell, placing them within a lineage hierarchy. The recent emergence of genomic technologies, such as single-cell and spatial transcriptomics, has fostered sophisticated new methods to reconstruct lineage relationships at high resolution. In contrast, fate maps, schematics showing which parts of the embryo will develop into which tissue, have remained relatively static since the 1970s. However, fate maps provide spatial information, often lost in lineage reconstruction, that can offer fundamental mechanistic insight into development. Here, we broadly review the origins of fate mapping and lineage tracing approaches. We focus on the most recent developments in lineage tracing, permitted by advances in single-cell genomics. Finally, we explore the current potential to leverage these new technologies to synthesize high-resolution fate maps and discuss their potential for interrogating development at new depths.
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32
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Wagner DE, Klein AM. Lineage tracing meets single-cell omics: opportunities and challenges. Nat Rev Genet 2020; 21:410-427. [PMID: 32235876 PMCID: PMC7307462 DOI: 10.1038/s41576-020-0223-2] [Citation(s) in RCA: 304] [Impact Index Per Article: 76.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/03/2020] [Indexed: 12/20/2022]
Abstract
A fundamental goal of developmental and stem cell biology is to map the developmental history (ontogeny) of differentiated cell types. Recent advances in high-throughput single-cell sequencing technologies have enabled the construction of comprehensive transcriptional atlases of adult tissues and of developing embryos from measurements of up to millions of individual cells. Parallel advances in sequencing-based lineage-tracing methods now facilitate the mapping of clonal relationships onto these landscapes and enable detailed comparisons between molecular and mitotic histories. Here we review recent progress and challenges, as well as the opportunities that emerge when these two complementary representations of cellular history are synthesized into integrated models of cell differentiation.
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Affiliation(s)
- Daniel E Wagner
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Obstetrics, Gynecology and Reproductive Science, Center for Reproductive Sciences, Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA.
| | - Allon M Klein
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
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33
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Zafar H, Lin C, Bar-Joseph Z. Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data. Nat Commun 2020; 11:3055. [PMID: 32546686 PMCID: PMC7298005 DOI: 10.1038/s41467-020-16821-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 05/25/2020] [Indexed: 02/07/2023] Open
Abstract
Recent studies combine two novel technologies, single-cell RNA-sequencing and CRISPR-Cas9 barcode editing for elucidating developmental lineages at the whole organism level. While these studies provided several insights, they face several computational challenges. First, lineages are reconstructed based on noisy and often saturated random mutation data. Additionally, due to the randomness of the mutations, lineages from multiple experiments cannot be combined to reconstruct a species-invariant lineage tree. To address these issues we developed a statistical method, LinTIMaT, which reconstructs cell lineages using a maximum-likelihood framework by integrating mutation and expression data. Our analysis shows that expression data helps resolve the ambiguities arising in when lineages are inferred based on mutations alone, while also enabling the integration of different individual lineages for the reconstruction of an invariant lineage tree. LinTIMaT lineages have better cell type coherence, improve the functional significance of gene sets and provide new insights on progenitors and differentiation pathways.
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Affiliation(s)
- Hamim Zafar
- Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India
- Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, India
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Chieh Lin
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
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34
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Sakata RC, Ishiguro S, Mori H, Tanaka M, Tatsuno K, Ueda H, Yamamoto S, Seki M, Masuyama N, Nishida K, Nishimasu H, Arakawa K, Kondo A, Nureki O, Tomita M, Aburatani H, Yachie N. Base editors for simultaneous introduction of C-to-T and A-to-G mutations. Nat Biotechnol 2020; 38:865-869. [PMID: 32483365 DOI: 10.1038/s41587-020-0509-0] [Citation(s) in RCA: 117] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 04/03/2020] [Indexed: 12/20/2022]
Abstract
We describe base editors that combine both cytosine and adenine base-editing functions. A codon-optimized fusion of the cytosine deaminase PmCDA1, the adenosine deaminase TadA and a Cas9 nickase (Target-ACEmax) showed a high median simultaneous C-to-T and A-to-G editing activity at 47 genomic targets. On-target as well as DNA and RNA off-target activities of Target-ACEmax were similar to those of existing single-function base editors.
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Affiliation(s)
- Rina C Sakata
- Synthetic Biology Division, Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan.,College of Arts and Sciences, University of Tokyo, Tokyo, Japan
| | - Soh Ishiguro
- Synthetic Biology Division, Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan.,Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Graduate School of Media and Governance, Keio University, Fujisawa, Japan
| | - Hideto Mori
- Synthetic Biology Division, Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan.,Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Graduate School of Media and Governance, Keio University, Fujisawa, Japan
| | - Mamoru Tanaka
- Synthetic Biology Division, Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan
| | - Kenji Tatsuno
- Genome Science Division, Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan
| | - Hiroki Ueda
- Biological Data Science Division, Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan
| | - Shogo Yamamoto
- Genome Science Division, Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan
| | - Motoaki Seki
- Synthetic Biology Division, Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan
| | - Nanami Masuyama
- Synthetic Biology Division, Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan.,Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Graduate School of Media and Governance, Keio University, Fujisawa, Japan
| | - Keiji Nishida
- Engineering Biology Research Center, Kobe University, Kobe, Japan.,Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan
| | - Hiroshi Nishimasu
- Department of Biological Sciences, School of Science, University of Tokyo, Tokyo, Japan
| | - Kazuharu Arakawa
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Graduate School of Media and Governance, Keio University, Fujisawa, Japan.,Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan
| | - Akihiko Kondo
- Engineering Biology Research Center, Kobe University, Kobe, Japan.,Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan.,Department of Chemical Science and Engineering, Graduate School of Engineering, Kobe University, Kobe, Japan
| | - Osamu Nureki
- Department of Biological Sciences, School of Science, University of Tokyo, Tokyo, Japan
| | - Masaru Tomita
- Synthetic Biology Division, Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan.,Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Graduate School of Media and Governance, Keio University, Fujisawa, Japan.,Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan
| | - Hiroyuki Aburatani
- Genome Science Division, Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan
| | - Nozomu Yachie
- Synthetic Biology Division, Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan. .,College of Arts and Sciences, University of Tokyo, Tokyo, Japan. .,Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan. .,Graduate School of Media and Governance, Keio University, Fujisawa, Japan. .,Department of Biological Sciences, School of Science, University of Tokyo, Tokyo, Japan. .,PRESTO, Japan Science and Technology Agency (JST), Tokyo, Japan.
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35
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Cotterell J, Vila-Cejudo M, Batlle-Morera L, Sharpe J. Endogenous CRISPR/Cas9 arrays for scalable whole-organism lineage tracing. Development 2020; 147:147/9/dev184481. [DOI: 10.1242/dev.184481] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 03/06/2020] [Indexed: 12/26/2022]
Abstract
ABSTRACT
The past decade has seen a renewed appreciation of the central importance of cellular lineages to many questions in biology (especially organogenesis, stem cells and tumor biology). This has been driven in part by a renaissance in genetic clonal-labeling techniques. Recent approaches are based on accelerated mutation of DNA sequences, which can then be sequenced from individual cells to re-create a ‘phylogenetic’ tree of cell lineage. However, current approaches depend on making transgenic alterations to the genome in question, which limit their application. Here, we introduce a new method that completely avoids the need for prior genetic engineering, by identifying endogenous CRISPR/Cas9 target arrays suitable for lineage analysis. In both mouse and zebrafish, we identify the highest quality compact arrays as judged by equal base composition, 5′ G sequence, minimal likelihood of residing in the functional genome, minimal off targets and ease of amplification. We validate multiple high-quality endogenous CRISPR/Cas9 arrays, demonstrating their utility for lineage tracing. Our pragmatically scalable technique thus can produce deep and broad lineages in vivo, while removing the dependence on genetic engineering.
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Affiliation(s)
- James Cotterell
- European Molecular Biology Laboratory (EMBL) Barcelona, 08003 Barcelona, Spain
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08003 Barcelona, Spain
| | - Marta Vila-Cejudo
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08003 Barcelona, Spain
| | - Laura Batlle-Morera
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08003 Barcelona, Spain
| | - James Sharpe
- European Molecular Biology Laboratory (EMBL) Barcelona, 08003 Barcelona, Spain
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08003 Barcelona, Spain
- Institucio' Catalana de Recerca i Estudis Avancats (ICREA), 08010 Barcelona, Spain
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36
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Jones MG, Khodaverdian A, Quinn JJ, Chan MM, Hussmann JA, Wang R, Xu C, Weissman JS, Yosef N. Inference of single-cell phylogenies from lineage tracing data using Cassiopeia. Genome Biol 2020; 21:92. [PMID: 32290857 PMCID: PMC7155257 DOI: 10.1186/s13059-020-02000-8] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 03/13/2020] [Indexed: 12/14/2022] Open
Abstract
The pairing of CRISPR/Cas9-based gene editing with massively parallel single-cell readouts now enables large-scale lineage tracing. However, the rapid growth in complexity of data from these assays has outpaced our ability to accurately infer phylogenetic relationships. First, we introduce Cassiopeia-a suite of scalable maximum parsimony approaches for tree reconstruction. Second, we provide a simulation framework for evaluating algorithms and exploring lineage tracer design principles. Finally, we generate the most complex experimental lineage tracing dataset to date, 34,557 human cells continuously traced over 15 generations, and use it for benchmarking phylogenetic inference approaches. We show that Cassiopeia outperforms traditional methods by several metrics and under a wide variety of parameter regimes, and provide insight into the principles for the design of improved Cas9-enabled recorders. Together, these should broadly enable large-scale mammalian lineage tracing efforts. Cassiopeia and its benchmarking resources are publicly available at www.github.com/YosefLab/Cassiopeia.
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Affiliation(s)
- Matthew G Jones
- Biological and Medical Informatics Graduate Program, University of California San Francisco, San Francisco, CA, USA
- Center for Computational Biology, University of California Berkeley, Berkeley, CA, USA
- Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA
| | - Alex Khodaverdian
- Department of Electrical Engineering and Computer Science and Center for Computational Biology, University of California Berkeley, Berkeley, CA, USA
| | - Jeffrey J Quinn
- Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA
- Center for RNA Systems Biology, University of California San Francisco, San Francisco, CA, USA
| | - Michelle M Chan
- Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA
- Center for RNA Systems Biology, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey A Hussmann
- Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA
- Center for RNA Systems Biology, University of California San Francisco, San Francisco, CA, USA
- University of California, San Francisco, Department of Microbiology and Immunology, San Francisco, California, USA
| | - Robert Wang
- Center for Computational Biology, University of California Berkeley, Berkeley, CA, USA
| | - Chenling Xu
- Center for Computational Biology, University of California Berkeley, Berkeley, CA, USA
| | - Jonathan S Weissman
- Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, USA.
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.
- Center for RNA Systems Biology, University of California San Francisco, San Francisco, CA, USA.
| | - Nir Yosef
- Center for Computational Biology, University of California Berkeley, Berkeley, CA, USA.
- Department of Electrical Engineering and Computer Science and Center for Computational Biology, University of California Berkeley, Berkeley, CA, USA.
- Ragon Institute of Massachusetts General Hospital - MIT and Harvard, Cambridge, MA, USA.
- Chan Zuckerberg Biohub Investigator, San Francisco, CA, USA.
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37
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Olivares-Chauvet P, Junker JP. Inclusion of temporal information in single cell transcriptomics. Int J Biochem Cell Biol 2020; 122:105745. [PMID: 32283227 DOI: 10.1016/j.biocel.2020.105745] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 03/25/2020] [Accepted: 04/06/2020] [Indexed: 10/24/2022]
Abstract
Single cell transcriptomics has emerged as a powerful method for dissecting cell type diversity and for understanding mechanisms of cell fate decisions. However, inclusion of temporal information remains challenging, since each cell can be measured only once by sequencing analysis. Here, we discuss recent progress and current efforts towards inclusion of temporal information in single cell transcriptomics. Even from snapshot data, temporal dynamics can be computationally inferred via pseudo-temporal ordering of single cell transcriptomes. Temporal information can also come from analysis of intronic reads or from RNA metabolic labeling, which can provide additional evidence for pseudo-time trajectories and enable more fine-grained analysis of gene regulatory interactions. These approaches measure dynamics on short timescales of hours. Emerging methods for high-throughput lineage tracing now enable information storage over long timescales by using CRISPR/Cas9 to record information in the genome, which can later be read out by sequencing.
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Affiliation(s)
- Pedro Olivares-Chauvet
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany.
| | - Jan Philipp Junker
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany.
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38
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Ye C, Chen Z, Liu Z, Wang F, He X. Defining endogenous barcoding sites for CRISPR/Cas9-based cell lineage tracing in zebrafish. J Genet Genomics 2020; 47:85-91. [PMID: 32173285 DOI: 10.1016/j.jgg.2019.11.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 11/14/2019] [Accepted: 11/25/2019] [Indexed: 12/26/2022]
Abstract
There is a growing interest in developing experimental methods for tracking the developmental cell lineages of a complex organism. The recently developed CRISPR/Cas9-based barcoding method is, although highly promising, difficult to scale up because it relies on exogenous barcoding sequences that are engineered into the genome. In this study, we characterized 78 high-quality endogenous sites in the zebrafish genome that can be used as CRISPR/Cas9-based barcoding sites. The 78 sites are all highly expressed in most of the cell types according to single-cell RNA sequencing (scRNA-seq) data. Hence, the barcoding information of the 78 endogenous sites is recovered by the available scRNA-seq platforms, enabling simultaneous characterization of cell type and cell lineage information.
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Affiliation(s)
- Chang Ye
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, China
| | - Zhuoxin Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, China.
| | - Zhan Liu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, China
| | - Feng Wang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, China
| | - Xionglei He
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, China.
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39
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Teets EM, Gregory C, Shaffer J, Blachly JS, Blaser BW. Quantifying Hematopoietic Stem Cell Clonal Diversity by Selecting Informative Amplicon Barcodes. Sci Rep 2020; 10:2153. [PMID: 32034234 PMCID: PMC7005852 DOI: 10.1038/s41598-020-59119-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 01/24/2020] [Indexed: 11/09/2022] Open
Abstract
Hematopoietic stem cells (HSCs) are functionally and genetically diverse and this diversity decreases with age and disease. Numerous systems have been developed to quantify HSC diversity by genetic barcoding, but no framework has been established to empirically validate barcode sequences. Here we have developed an analytical framework, Selection of informative Amplicon Barcodes from Experimental Replicates (SABER), that identifies barcodes that are unique among a large set of experimental replicates. Amplicon barcodes were sequenced from the blood of 56 adult zebrafish divided into training and validation sets. Informative barcodes were identified and samples with a high fraction of informative barcodes were chosen by bootstrapping. There were 4.2 ± 1.8 barcoded HSC clones per sample in the training set and 3.5 ± 2.1 in the validation set (p = 0.3). SABER reproducibly quantifies functional HSCs and can accommodate a wide range of experimental group sizes. Future large-scale studies aiming to understand the mechanisms of HSC clonal evolution will benefit from this new approach to identifying informative amplicon barcodes.
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Affiliation(s)
- Emily M Teets
- The Ohio State University College of Medicine, Department of Medicine, Division of Hematology, The Ohio State University Comprehensive Cancer Center, Ohio, USA
| | - Charles Gregory
- The Ohio State University College of Medicine, Department of Medicine, Division of Hematology, The Ohio State University Comprehensive Cancer Center, Ohio, USA
| | - Jami Shaffer
- The Ohio State University College of Medicine, Department of Medicine, Division of Hematology, The Ohio State University Comprehensive Cancer Center, Ohio, USA
| | - James S Blachly
- The Ohio State University College of Medicine, Department of Medicine, Division of Hematology, The Ohio State University Comprehensive Cancer Center, Ohio, USA
- The Ohio State University College of Medicine, Department of Biomedical Informatics, Ohio, USA
| | - Bradley W Blaser
- The Ohio State University College of Medicine, Department of Medicine, Division of Hematology, The Ohio State University Comprehensive Cancer Center, Ohio, USA.
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40
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Espinosa-Medina I, Garcia-Marques J, Cepko C, Lee T. High-throughput dense reconstruction of cell lineages. Open Biol 2019; 9:190229. [PMID: 31822210 PMCID: PMC6936253 DOI: 10.1098/rsob.190229] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 11/12/2019] [Indexed: 12/11/2022] Open
Abstract
The first meeting exclusively dedicated to the 'High-throughput dense reconstruction of cell lineages' took place at Janelia Research Campus (Howard Hughes Medical Institute) from 14 to 18 April 2019. Organized by Tzumin Lee, Connie Cepko, Jorge Garcia-Marques and Isabel Espinosa-Medina, this meeting echoed the recent eruption of new tools that allow the reconstruction of lineages based on the phylogenetic analysis of DNA mutations induced during development. Combined with single-cell RNA sequencing, these tools promise to solve the lineage of complex model organisms at single-cell resolution. Here, we compile the conference consensus on the technological and computational challenges emerging from the use of the new strategies, as well as potential solutions.
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Affiliation(s)
- Isabel Espinosa-Medina
- Howard Hughes Medical Institute, Janelia Research Campus, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Jorge Garcia-Marques
- Howard Hughes Medical Institute, Janelia Research Campus, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Connie Cepko
- Department of Genetics and Ophthalmology, Harvard Medical School, Boston, MA 02115, USA
| | - Tzumin Lee
- Howard Hughes Medical Institute, Janelia Research Campus, 19700 Helix Drive, Ashburn, VA 20147, USA
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41
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Masuyama N, Mori H, Yachie N. DNA barcodes evolve for high-resolution cell lineage tracing. Curr Opin Chem Biol 2019; 52:63-71. [DOI: 10.1016/j.cbpa.2019.05.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Revised: 04/26/2019] [Accepted: 05/10/2019] [Indexed: 10/26/2022]
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42
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Katayama K, Mitsunobu H, Nishida K. Mammalian synthetic biology by CRISPRs engineering and applications. Curr Opin Chem Biol 2019; 52:79-84. [DOI: 10.1016/j.cbpa.2019.05.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 05/21/2019] [Accepted: 05/23/2019] [Indexed: 10/26/2022]
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Abstract
Every animal grows from a single fertilized egg into an intricate network of cell types and organ systems. This process is captured in a lineage tree: a diagram of every cell's ancestry back to the founding zygote. Biologists have long sought to trace this cell lineage tree in individual organisms and have developed a variety of technologies to map the progeny of specific cells. However, there are billions to trillions of cells in complex organisms, and conventional approaches can only map a limited number of clonal populations per experiment. A new generation of tools that use molecular recording methods integrated with single cell profiling technologies may provide a solution. Here, we summarize recent breakthroughs in these technologies, outline experimental and computational challenges, and discuss biological questions that can be addressed using single cell dynamic lineage tracing.
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Affiliation(s)
- Aaron McKenna
- Department of Molecular and Systems Biology, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA
| | - James A Gagnon
- Center for Cell and Genome Science, University of Utah, Salt Lake City, UT 84112, USA
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
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44
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Salvador-Martínez I, Grillo M, Averof M, Telford MJ. Is it possible to reconstruct an accurate cell lineage using CRISPR recorders? eLife 2019; 8:e40292. [PMID: 30688650 PMCID: PMC6349403 DOI: 10.7554/elife.40292] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 01/11/2019] [Indexed: 02/02/2023] Open
Abstract
Cell lineages provide the framework for understanding how cell fates are decided during development. Describing cell lineages in most organisms is challenging; even a fruit fly larva has ~50,000 cells and a small mammal has >1 billion cells. Recently, the idea of applying CRISPR to induce mutations during development, to be used as heritable markers for lineage reconstruction, has been proposed by several groups. While an attractive idea, its practical value depends on the accuracy of the cell lineages that can be generated. Here, we use computer simulations to estimate the performance of these approaches under different conditions. We incorporate empirical data on CRISPR-induced mutation frequencies in Drosophila. We show significant impacts from multiple biological and technical parameters - variable cell division rates, skewed mutational outcomes, target dropouts and different sequencing strategies. Our approach reveals the limitations of published CRISPR recorders, and indicates how future implementations can be optimised. Editorial note This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
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Affiliation(s)
- Irepan Salvador-Martínez
- Centre for Life’s Origins and Evolution, Department of Genetics Evolution and EnvironmentUniversity College LondonLondonUnited Kingdom
| | - Marco Grillo
- Institut de Génomique Fonctionnelle de Lyon (IGFL)École Normale Supérieure de LyonLyonFrance
- Centre National de la Recherche Scientifique (CNRS)ParisFrance
| | - Michalis Averof
- Institut de Génomique Fonctionnelle de Lyon (IGFL)École Normale Supérieure de LyonLyonFrance
- Centre National de la Recherche Scientifique (CNRS)ParisFrance
| | - Maximilian J Telford
- Centre for Life’s Origins and Evolution, Department of Genetics Evolution and EnvironmentUniversity College LondonLondonUnited Kingdom
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