1
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Zeng CW. Immune Cell-NSPC interactions: Friend or foe in CNS injury and repair? Differentiation 2025; 143:100855. [PMID: 40112742 DOI: 10.1016/j.diff.2025.100855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2025] [Revised: 03/07/2025] [Accepted: 03/10/2025] [Indexed: 03/22/2025]
Abstract
Neural stem/progenitor cells (NSPCs) play a crucial role in central nervous system (CNS) development, regeneration, and repair. However, their functionality and therapeutic potential are intricately modulated by interactions with immune cells, particularly macrophages and microglia. Microglia, as CNS-resident macrophages, are distinct from peripheral macrophages in their roles and characteristics, contributing to specialized functions within the CNS. Recent evidence suggests that microglia, as CNS-resident macrophages, contribute to the quality assurance of NSPCs by eliminating stressed or dysfunctional cells, yet the mechanisms underlying this process remain largely unexplored. Furthermore, macrophage polarization states, such as M1 and M2, appear to differentially influence NSPC quality, potentially impacting neurogenesis and regenerative outcomes. Identifying surface markers indicative of NSPC stress could provide a strategy for selecting optimal cells for transplantation therapies. Additionally, in vivo clonal labeling approaches may enable precise tracking of NSPC fate and their interactions with immune cells. Beyond macrophages and microglia, the roles of other immune cells, including T cells and neutrophils, particularly in injury and neurodegenerative disease contexts, in the context of CNS injury and disease are emerging areas of interest. Here, I discuss the emerging evidence supporting the interplay between the immune system and NSPCs, highlighting critical gaps in knowledge and proposing future research directions to harness immune-mediated mechanisms for optimizing neural regeneration and transplantation strategies.
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Affiliation(s)
- Chih-Wei Zeng
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
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2
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Tang Z, Zhang W, Shi P, Li S, Li X, Li Y, Xu Y, Shu Y, Hu Z, Xu J. MitoSort: Robust Demultiplexing of Pooled Single-cell Genomic Data Using Endogenous Mitochondrial Variants. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae073. [PMID: 39404807 DOI: 10.1093/gpbjnl/qzae073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 10/03/2024] [Accepted: 10/07/2024] [Indexed: 12/28/2024]
Abstract
Multiplexing across donors has emerged as a popular strategy to increase throughput, reduce costs, overcome technical batch effects, and improve doublet detection in single-cell genomic studies. To eliminate additional experimental steps, endogenous nuclear genome variants are used for demultiplexing pooled single-cell RNA sequencing (scRNA-seq) data by several computational tools. However, these tools have limitations when applied to single-cell sequencing methods that do not cover nuclear genomic regions well, such as single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq). Here, we demonstrate that mitochondrial germline variants are an alternative, robust, and computationally efficient endogenous barcode for sample demultiplexing. We propose MitoSort, a tool that uses mitochondrial germline variants to assign cells to their donor origins and identify cross-genotype doublets in single-cell genomic datasets. We evaluate its performance by using in silico pooled mitochondrial scATAC-seq (mtscATAC-seq) libraries and experimentally multiplexed data with cell hashtags. MitoSort achieves high accuracy and efficiency in genotype clustering and doublet detection for mtscATAC-seq data, addressing the limitations of current computational techniques tailored for scRNA-seq data. Moreover, MitoSort exhibits versatility, and can be applied to various single-cell sequencing approaches beyond mtscATAC-seq provided that the mitochondrial variants are reliably detected. Furthermore, we demonstrate the application of MitoSort in a case study where B cells from eight donors were pooled and assayed by single-cell multi-omics sequencing. Altogether, our results demonstrate the accuracy and efficiency of MitoSort, which enables reliable sample demultiplexing in various single-cell genomic applications. MitoSort is available at https://github.com/tangzhj/MitoSort.
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Affiliation(s)
- Zhongjie Tang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Weixing Zhang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Peiyu Shi
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Sijun Li
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Xinhui Li
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Yueming Li
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Yicong Xu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Yaqing Shu
- Department of Neurology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, 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 518055, China
| | - Jin Xu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
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3
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Penter L, Cieri N, Maurer K, Kwok M, Lyu H, Lu WS, Oliveira G, Gohil SH, Leshchiner I, Lareau CA, Ludwig LS, Neuberg DS, Kim HT, Li S, Bullinger L, Ritz J, Getz G, Garcia JS, Soiffer RJ, Livak KJ, Wu CJ. Tracking Rare Single Donor and Recipient Immune and Leukemia Cells after Allogeneic Hematopoietic Cell Transplantation Using Mitochondrial DNA Mutations. Blood Cancer Discov 2024; 5:442-459. [PMID: 39236287 PMCID: PMC11528187 DOI: 10.1158/2643-3230.bcd-23-0138] [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: 08/01/2023] [Revised: 06/30/2024] [Accepted: 09/03/2024] [Indexed: 09/07/2024] Open
Abstract
Combined tracking of clonal evolution and chimeric cell phenotypes could enable detection of the key cellular populations associated with response following therapy, including after allogeneic hematopoietic stem cell transplantation (HSCT). We demonstrate that mitochondrial DNA (mtDNA) mutations coevolve with somatic nuclear DNA mutations at relapse post-HSCT and provide a sensitive means to monitor these cellular populations. Furthermore, detection of mtDNA mutations via single-cell assay for transposase-accessible chromatin with select antigen profiling by sequencing (ASAP-seq) simultaneously determines not only donor and recipient cells but also their phenotype at frequencies of 0.1% to 1%. Finally, integration of mtDNA mutations, surface markers, and chromatin accessibility profiles enables the phenotypic resolution of leukemic populations from normal immune cells, thereby providing fresh insights into residual donor-derived engraftment and short-term clonal evolution following therapy for post-transplant leukemia relapse. As throughput evolves, we envision future development of single-cell sequencing-based post-transplant monitoring as a powerful approach for guiding clinical decision-making. Significance: mtDNA mutations enable single-cell tracking of leukemic clonal evolution and donor-recipient origin following allogeneic HSCT. This provides unprecedented insight into chimeric cellular phenotypes of early immune reconstitution, incipient relapse, and quality of donor engraftment with immediate translational potential for future clinical post-transplant monitoring and decision-making.
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Affiliation(s)
- Livius Penter
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Department of Hematology, Oncology, and Tumorimmunology, Campus Virchow Klinikum, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité–Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Digital Clinician Scientist Program, Berlin, Germany
| | - Nicoletta Cieri
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Katie Maurer
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Marwan Kwok
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Haoxiang Lyu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Wesley S. Lu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Giacomo Oliveira
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Satyen H. Gohil
- Department of Haematology, University College London Hospitals, London, United Kingdom
| | - Ignaty Leshchiner
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts
| | - Caleb A. Lareau
- Memorial Sloan Kettering Cancer Center, New York City, New York
| | - Leif S. Ludwig
- Department of Hematology, Oncology, and Tumorimmunology, Campus Virchow Klinikum, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Donna S. Neuberg
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Haesook T. Kim
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Shuqiang Li
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Lars Bullinger
- Department of Hematology, Oncology, and Tumorimmunology, Campus Virchow Klinikum, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jerome Ritz
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Gad Getz
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Jacqueline S. Garcia
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Robert J. Soiffer
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Kenneth J. Livak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Catherine J. Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts
- Harvard Medical School, Boston, Massachusetts
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4
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Zhang A, Liu W, Qiu S. Mitochondrial genetic variations in leukemia: a comprehensive overview. BLOOD SCIENCE 2024; 6:e00205. [PMID: 39247535 PMCID: PMC11379488 DOI: 10.1097/bs9.0000000000000205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 08/13/2024] [Indexed: 09/10/2024] Open
Abstract
Leukemias are a group of heterogeneous hematological malignancies driven by diverse genetic variations, and the advent of genomic sequencing technologies facilitates the investigation of genetic abnormalities in leukemia. However, these sequencing-based studies mainly focus on nuclear DNAs. Increasing evidence indicates that mitochondrial dysfunction is an important mechanism of leukemia pathogenesis, which is closely related to the mitochondrial genome variations. Here, we provide an overview of current research progress concerning mitochondrial genetic variations in leukemia, encompassing gene mutations and copy number variations. We also summarize currently accessible mitochondrial DNA (mtDNA) sequencing methods. Notably, somatic mtDNA mutations may serve as natural genetic barcodes for lineage tracing and longitudinal assessment of clonal dynamics. Collectively, these findings enhance our understanding of leukemia pathogenesis and foster the identification of novel therapeutic targets and interventions.
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Affiliation(s)
- Ao Zhang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Wenbing Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Shaowei Qiu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
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5
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Rathgeber AC, Ludwig LS, Penter L. Single-cell genomics-based immune and disease monitoring in blood malignancies. Clin Hematol Int 2024; 6:62-84. [PMID: 38884110 PMCID: PMC11180218 DOI: 10.46989/001c.117961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 12/25/2023] [Indexed: 06/18/2024] Open
Abstract
Achieving long-term disease control using therapeutic immunomodulation is a long-standing concept with a strong tradition in blood malignancies. Besides allogeneic hematopoietic stem cell transplantation that continues to provide potentially curative treatment for otherwise challenging diagnoses, recent years have seen impressive progress in immunotherapies for leukemias and lymphomas with immune checkpoint blockade, bispecific monoclonal antibodies, and CAR T cell therapies. Despite their success, non-response, relapse, and immune toxicities remain frequent, thus prioritizing the elucidation of the underlying mechanisms and identifying predictive biomarkers. The increasing availability of single-cell genomic tools now provides a system's immunology view to resolve the molecular and cellular mechanisms of immunotherapies at unprecedented resolution. Here, we review recent studies that leverage these technological advancements for tracking immune responses, the emergence of immune resistance, and toxicities. As single-cell immune monitoring tools evolve and become more accessible, we expect their wide adoption for routine clinical applications to catalyze more precise therapeutic steering of personal immune responses.
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Affiliation(s)
- Anja C. Rathgeber
- Berlin Institute for Medical Systems BiologyMax Delbrück Center for Molecular Medicine
- Department of Hematology, Oncology, and TumorimmunologyCharité - Universitätsmedizin Berlin
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin
| | - Leif S. Ludwig
- Berlin Institute for Medical Systems BiologyMax Delbrück Center for Molecular Medicine
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin
| | - Livius Penter
- Department of Hematology, Oncology, and TumorimmunologyCharité - Universitätsmedizin Berlin
- BIH Biomedical Innovation AcademyBerlin Institute of Health at Charité - Universitätsmedizin Berlin
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6
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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: 15] [Impact Index Per Article: 15.0] [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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7
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Xue Y, Su Z, Lin X, Ho MK, Yu KHO. Single-cell lineage tracing with endogenous markers. Biophys Rev 2024; 16:125-139. [PMID: 38495438 PMCID: PMC10937880 DOI: 10.1007/s12551-024-01179-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 01/18/2024] [Indexed: 03/19/2024] Open
Abstract
Resolving lineage relationships between cells in an organism provides key insights into the fate of individual cells and drives a fundamental understanding of the process of development and disease. A recent rapid increase in experimental and computational advances for detecting naturally occurring somatic nuclear and mitochondrial mutation at single-cell resolution has expanded lineage tracing from model organisms to humans. This review discusses the advantages and challenges of experimental and computational techniques for cell lineage tracing using somatic mutation as endogenous DNA barcodes to decipher the relationships between cells during development and tumour evolution. We outlook the advantages of spatial clonal evolution analysis and single-cell lineage tracing using endogenous genetic markers.
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Affiliation(s)
- Yan Xue
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Zezhuo Su
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
- Department of Orthopaedics and Traumatology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xinyi Lin
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Mun Kay Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Ken H. O. Yu
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
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8
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Penter L, Borji M, Nagler A, Lyu H, Lu WS, Cieri N, Maurer K, Oliveira G, Al'Khafaji AM, Garimella KV, Li S, Neuberg DS, Ritz J, Soiffer RJ, Garcia JS, Livak KJ, Wu CJ. Integrative genotyping of cancer and immune phenotypes by long-read sequencing. Nat Commun 2024; 15:32. [PMID: 38167262 PMCID: PMC10762175 DOI: 10.1038/s41467-023-44137-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
Single-cell transcriptomics has become the definitive method for classifying cell types and states, and can be augmented with genotype information to improve cell lineage identification. Due to constraints of short-read sequencing, current methods to detect natural genetic barcodes often require cumbersome primer panels and early commitment to targets. Here we devise a flexible long-read sequencing workflow and analysis pipeline, termed nanoranger, that starts from intermediate single-cell cDNA libraries to detect cell lineage-defining features, including single-nucleotide variants, fusion genes, isoforms, sequences of chimeric antigen and TCRs. Through systematic analysis of these classes of natural 'barcodes', we define the optimal targets for nanoranger, namely those loci close to the 5' end of highly expressed genes with transcript lengths shorter than 4 kB. As proof-of-concept, we apply nanoranger to longitudinal tracking of subclones of acute myeloid leukemia (AML) and describe the heterogeneous isoform landscape of thousands of marrow-infiltrating immune cells. We propose that enhanced cellular genotyping using nanoranger can improve the tracking of single-cell tumor and immune cell co-evolution.
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Grants
- P01 CA229092 NCI NIH HHS
- P50 CA101942 NCI NIH HHS
- UM1 CA186709 NCI NIH HHS
- U24 CA224316 NCI NIH HHS
- P01 CA066996 NCI NIH HHS
- U24 CA224331 NCI NIH HHS
- U24 CA224285 NCI NIH HHS
- R01 CA155010 NCI NIH HHS
- R50 CA251956 NCI NIH HHS
- U24 CA224309 NCI NIH HHS
- U24 CA224319 NCI NIH HHS
- K08 CA245209 NCI NIH HHS
- This work was supported by National Institutes of Health, National Cancer Institute grant P01CA229092 (CJW), UM1CA186709 (Principal Investigator: Geoffrey Shapiro), National Cancer Institute Cancer Therapy Evaluation Program, Bristol-Myers Squibb, and LLS Therapy Accelerator Program. L.P. was supported by a research fellowship from the German Research Foundation (DFG, PE 3127/1-1) and is a Scholar of the American Society of Hematology, participant in the BIH Charité Digital Clinician Scientist Program funded by the DFG, the Charité – Universitätsmedizin Berlin, and the Berlin Institute of Health at Charité (BIH) and is supported by the Max-Eder program of the German Cancer Aid. A.A. is supported by the Broad Institute IGNITE award. K.M. is suppored by the ASCO YIA award. G.O. was supported by the Claudia Adams Barr Program for Innovative Cancer Research and by DF/HCC Kidney Cancer SPORE P50 CA101942. S.L. is supported by the National Institutes of Health, National Cancer Institute Research Specialist Award (R50CA251956). JSG is supported by the Conquer Cancer Foundation Career Development Award, Leukemia and Lymphoma Society Translational Research Program Award, and NIH K08CA245209. NCI CTEP provided study drug (Ipilimumab) support. This work was further supported by the CIMAC-CIDC Network. Scientific and financial support for the CIMAC-CIDC Network is provided through National Institutes of Health, National Cancer Institute Cooperative Agreements U24CA224319 (to the Icahn School of Medicine at Mount Sinai CIMAC), U24CA224331 (to the Dana-Farber Cancer Institute CIMAC), U24CA224285 (to the MD Anderson Cancer Center CIMAC), U24CA224309 (to the Stanford University CIMAC), and U24CA224316 (to the CIDC at Dana-Farber Cancer Institute). The CIMAC-CIDC website is found at https://cimac-network.org/.
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Affiliation(s)
- Livius Penter
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Hematology, Oncology, and Tumorimmunology, Campus Virchow Klinikum, Berlin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Digital Clinician Scientist Program, Charitéplatz 1, 10117, Berlin, Germany
| | - Mehdi Borji
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Adi Nagler
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Haoxiang Lyu
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Wesley S Lu
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nicoletta Cieri
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Katie Maurer
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Giacomo Oliveira
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Aziz M Al'Khafaji
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Kiran V Garimella
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Shuqiang Li
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Donna S Neuberg
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jerome Ritz
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Robert J Soiffer
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Jacqueline S Garcia
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Kenneth J Livak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Catherine J Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
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9
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Pérez-González A, Bévant K, Blanpain C. Cancer cell plasticity during tumor progression, metastasis and response to therapy. NATURE CANCER 2023; 4:1063-1082. [PMID: 37537300 PMCID: PMC7615147 DOI: 10.1038/s43018-023-00595-y] [Citation(s) in RCA: 118] [Impact Index Per Article: 59.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 06/01/2023] [Indexed: 08/05/2023]
Abstract
Cell plasticity represents the ability of cells to be reprogrammed and to change their fate and identity, enabling homeostasis restoration and tissue regeneration following damage. Cell plasticity also contributes to pathological conditions, such as cancer, enabling cells to acquire new phenotypic and functional features by transiting across distinct cell states that contribute to tumor initiation, progression, metastasis and resistance to therapy. Here, we review the intrinsic and extrinsic mechanisms driving cell plasticity that promote tumor growth and proliferation as well as metastasis and drug tolerance. Finally, we discuss how cell plasticity could be exploited for anti-cancer therapy.
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Affiliation(s)
- Andrea Pérez-González
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Kevin Bévant
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Cédric Blanpain
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles (ULB), Brussels, Belgium.
- WELBIO, ULB, Bruxelles, Belgium.
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10
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Howland KK, Brock A. Cellular barcoding tracks heterogeneous clones through selective pressures and phenotypic transitions. Trends Cancer 2023; 9:591-601. [PMID: 37105856 PMCID: PMC10339273 DOI: 10.1016/j.trecan.2023.03.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/27/2023] [Accepted: 03/28/2023] [Indexed: 04/29/2023]
Abstract
Genomic DNA barcoding has emerged as a sensitive and flexible tool to measure the fates of clonal subpopulations within a heterogeneous cancer cell population. Coupling cellular barcoding with single-cell transcriptomics permits the longitudinal analysis of molecular mechanisms with detailed clone-level resolution. Numerous recent studies have employed these tools to track clonal cell states in cancer progression and treatment response. With these new technologies comes the opportunity to examine longstanding questions about the origins and contributions of tumor cell heterogeneity and the roles of selection and phenotypic plasticity in disease progression and treatment.
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Affiliation(s)
- Kennedy K Howland
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78734, USA
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78734, USA.
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11
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Abstract
Recent advances in cancer immunotherapy - ranging from immune-checkpoint blockade therapy to adoptive cellular therapy and vaccines - have revolutionized cancer treatment paradigms, yet the variability in clinical responses to these agents has motivated intense interest in understanding how the T cell landscape evolves with respect to response to immune intervention. Over the past decade, the advent of multidimensional single-cell technologies has provided the unprecedented ability to dissect the constellation of cell states of lymphocytes within a tumour microenvironment. In particular, the rapidly expanding capacity to definitively link intratumoural phenotypes with the antigen specificity of T cells provided by T cell receptors (TCRs) has now made it possible to focus on investigating the properties of T cells with tumour-specific reactivity. Moreover, the assessment of TCR clonality has enabled a molecular approach to track the trajectories, clonal dynamics and phenotypic changes of antitumour T cells over the course of immunotherapeutic intervention. Here, we review the current knowledge on the cellular states and antigen specificities of antitumour T cells and examine how fine characterization of T cell dynamics in patients has provided meaningful insights into the mechanisms underlying effective cancer immunotherapy. We highlight those T cell subsets associated with productive T cell responses and discuss how diverse immunotherapies might leverage the pre-existing tumour-reactive T cell pool or instruct de novo generation of antitumour specificities. Future studies aimed at elucidating the factors associated with the elicitation of productive antitumour T cell immunity are anticipated to instruct the design of more efficacious treatment strategies.
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Affiliation(s)
- Giacomo Oliveira
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Catherine J Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
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12
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Penter L, Liu Y, Wolff JO, Yang L, Taing L, Jhaveri A, Southard J, Patel M, Cullen NM, Pfaff KL, Cieri N, Oliveira G, Kim-Schulze S, Ranasinghe S, Leonard R, Robertson T, Morgan EA, Chen HX, Song MH, Thurin M, Li S, Rodig SJ, Cibulskis C, Gabriel S, Bachireddy P, Ritz J, Streicher H, Neuberg DS, Hodi FS, Davids MS, Gnjatic S, Livak KJ, Altreuter J, Michor F, Soiffer RJ, Garcia JS, Wu CJ. Mechanisms of response and resistance to combined decitabine and ipilimumab for advanced myeloid disease. Blood 2023; 141:1817-1830. [PMID: 36706355 PMCID: PMC10122106 DOI: 10.1182/blood.2022018246] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 01/10/2023] [Accepted: 01/11/2023] [Indexed: 01/29/2023] Open
Abstract
The challenge of eradicating leukemia in patients with acute myelogenous leukemia (AML) after initial cytoreduction has motivated modern efforts to combine synergistic active modalities including immunotherapy. Recently, the ETCTN/CTEP 10026 study tested the combination of the DNA methyltransferase inhibitor decitabine together with the immune checkpoint inhibitor ipilimumab for AML/myelodysplastic syndrome (MDS) either after allogeneic hematopoietic stem cell transplantation (HSCT) or in the HSCT-naïve setting. Integrative transcriptome-based analysis of 304 961 individual marrow-infiltrating cells for 18 of 48 subjects treated on study revealed the strong association of response with a high baseline ratio of T to AML cells. Clinical responses were predominantly driven by decitabine-induced cytoreduction. Evidence of immune activation was only apparent after ipilimumab exposure, which altered CD4+ T-cell gene expression, in line with ongoing T-cell differentiation and increased frequency of marrow-infiltrating regulatory T cells. For post-HSCT samples, relapse could be attributed to insufficient clearing of malignant clones in progenitor cell populations. In contrast to AML/MDS bone marrow, the transcriptomes of leukemia cutis samples from patients with durable remission after ipilimumab monotherapy showed evidence of increased infiltration with antigen-experienced resident memory T cells and higher expression of CTLA-4 and FOXP3. Altogether, activity of combined decitabine and ipilimumab is impacted by cellular expression states within the microenvironmental niche of leukemic cells. The inadequate elimination of leukemic progenitors mandates urgent development of novel approaches for targeting these cell populations to generate long-lasting responses. This trial was registered at www.clinicaltrials.gov as #NCT02890329.
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Affiliation(s)
- Livius Penter
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
- Harvard Medical School, Boston, MA
- Department of Hematology, Oncology, and Tumorimmunology, Campus Virchow Klinikum, Berlin, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Yang Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
| | | | - Lin Yang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
| | - Len Taing
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Aashna Jhaveri
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
| | - Jackson Southard
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA
| | - Manishkumar Patel
- Human Immune Monitoring Center at the Icahn School of Medicine at Mount Sinai, New York, NY
| | - Nicole M. Cullen
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Kathleen L. Pfaff
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Nicoletta Cieri
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
- Harvard Medical School, Boston, MA
| | - Giacomo Oliveira
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
- Harvard Medical School, Boston, MA
| | - Seunghee Kim-Schulze
- Human Immune Monitoring Center at the Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Rebecca Leonard
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Taylor Robertson
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Elizabeth A. Morgan
- Harvard Medical School, Boston, MA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA
| | - Helen X. Chen
- Cancer Therapy Evaluation Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD
| | - Minkyung H. Song
- Cancer Therapy Evaluation Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD
| | - Magdalena Thurin
- Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD
| | - Shuqiang Li
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA
| | - Scott J. Rodig
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA
| | - Carrie Cibulskis
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
| | - Stacey Gabriel
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
| | | | - Jerome Ritz
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Howard Streicher
- Cancer Therapy Evaluation Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD
| | - Donna S. Neuberg
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
| | - F. Stephen Hodi
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Matthew S. Davids
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Sacha Gnjatic
- Human Immune Monitoring Center at the Icahn School of Medicine at Mount Sinai, New York, NY
| | - Kenneth J. Livak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA
| | | | - Franziska Michor
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
| | - Robert J. Soiffer
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Jacqueline S. Garcia
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Catherine J. Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
- Harvard Medical School, Boston, MA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
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13
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Penter L, ten Hacken E, Southard J, Lareau CA, Ludwig LS, Li S, Neuberg DS, Livak KJ, Wu CJ. Mitochondrial DNA Mutations as Natural Barcodes for Lineage Tracing of Murine Tumor Models. Cancer Res 2023; 83:667-672. [PMID: 36469010 PMCID: PMC9988704 DOI: 10.1158/0008-5472.can-22-0275] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 05/13/2022] [Accepted: 06/07/2022] [Indexed: 12/13/2022]
Abstract
Murine models are indispensable tools for functional genomic studies and preclinical testing of novel therapeutic approaches. Mitochondrial single-cell assay for transposase-accessible chromatin using sequencing (mtscATAC-seq) enables the dissection of cellular heterogeneity and clonal dynamics by capturing chromatin accessibility, copy-number variations (CNV), and mitochondrial DNA (mtDNA) mutations, yet its applicability to murine studies remains unexplored. By leveraging mtscATAC-seq in novel chronic lymphocytic leukemia and Richter syndrome mouse models, we report the detection of mtDNA mutations, particularly in highly proliferative murine cells, alongside CNV and chromatin state changes indicative of clonal evolution upon secondary transplant. This study thus demonstrates the feasibility and utility of multi-modal single-cell and natural barcoding approaches to characterize murine cancer models. SIGNIFICANCE mtDNA mutations can serve as natural barcodes to enable lineage tracing in murine cancer models, which can be used to provide new insights into disease biology and to identify therapeutic vulnerabilities.
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Affiliation(s)
- Livius Penter
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Hematology, Oncology, and Tumorimmunology, Campus Virchow Klinikum, Berlin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Elisa ten Hacken
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Jackson Southard
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Caleb A. Lareau
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Leif S. Ludwig
- Department of Hematology, Oncology, and Tumorimmunology, Campus Virchow Klinikum, Berlin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 10115 Berlin, Germany
| | - Shuqiang Li
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts, USA
| | - Donna S. Neuberg
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Kenneth J. Livak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Catherine J. Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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14
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Nagler A, Wu CJ. The end of the beginning: application of single-cell sequencing to chronic lymphocytic leukemia. Blood 2023; 141:369-379. [PMID: 36095842 PMCID: PMC9936302 DOI: 10.1182/blood.2021014669] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/12/2022] [Accepted: 07/23/2022] [Indexed: 01/31/2023] Open
Abstract
Single-cell analysis has emerged over the past decade as a transformative technology informative for the systematic analysis of complex cell populations such as in cancers and the tumor immune microenvironment. The methodologic and analytical advancements in this realm have evolved rapidly, scaling from but a few cells at its outset to the current capabilities of processing and analyzing hundreds of thousands of individual cells at a time. The types of profiling attainable at individual cell resolution now range from genetic and transcriptomic characterization and extend to epigenomic and spatial analysis. Additionally, the increasing ability to achieve multiomic integration of these data layers now yields ever richer insights into diverse molecular disease subtypes and the patterns of cellular circuitry on a per-cancer basis. Over the years, chronic lymphocytic leukemia (CLL) consistently has been at the forefront of genomic investigation, given the ready accessibility of pure leukemia cells and immune cells from circulating blood of patients with this disease. Herein, we review the recent forays into the application of single-cell analysis to CLL, which are already revealing a new understanding of the natural progression of CLL, the impact of novel therapies, and the interactions with coevolving nonmalignant immune cell populations. As we emerge from the end of the beginning of this technologic revolution, CLL stands poised to reap the benefits of single-cell analysis from the standpoints of uncovering fresh fundamental biological knowledge and of providing a path to devising regimens of personalized diagnosis, treatment, and monitoring.
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Affiliation(s)
- Adi Nagler
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
| | - Catherine J. Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
- Harvard Medical School, Boston, MA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
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