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Deng S, Gong H, Zhang D, Zhang M, He X. A statistical method for quantifying progenitor cells reveals incipient cell fate commitments. Nat Methods 2024; 21:597-608. [PMID: 38379073 DOI: 10.1038/s41592-024-02189-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 01/19/2024] [Indexed: 02/22/2024]
Abstract
Quantifying the number of progenitor cells that found an organ, tissue or cell population is of fundamental importance for understanding the development and homeostasis of a multicellular organism. Previous efforts rely on marker genes that are specifically expressed in progenitors. This strategy is, however, often hindered by the lack of ideal markers. Here we propose a general statistical method to quantify the progenitors of any tissues or cell populations in an organism, even in the absence of progenitor-specific markers, by exploring the cell phylogenetic tree that records the cell division history during development. The method, termed targeting coalescent analysis (TarCA), computes the probability that two randomly sampled cells of a tissue coalesce within the tissue-specific monophyletic clades. The inverse of this probability then serves as a measure of the progenitor number of the tissue. Both mathematic modeling and computer simulations demonstrated the high accuracy of TarCA, which was then validated using real data from nematode, fruit fly and mouse, all with related cell phylogenetic trees. We further showed that TarCA can be used to identify lineage-specific upregulated genes during embryogenesis, revealing incipient cell fate commitments in mouse embryos.
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Affiliation(s)
- Shanjun Deng
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Han Gong
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Di Zhang
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Mengdong Zhang
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 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.
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2
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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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3
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Deng S, He X. Tree of life at two levels: from species to cell. Sci Bull (Beijing) 2023; 68:2515-2518. [PMID: 37778944 DOI: 10.1016/j.scib.2023.09.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Affiliation(s)
- Shanjun Deng
- 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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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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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: 13] [Impact Index Per Article: 4.3] [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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