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Chen Y, Liu Z, Zhang B, Wu H, Lv X, Zhang Y, Lin Y. Biomedical Utility of Non-Enzymatic DNA Amplification Reaction: From Material Design to Diagnosis and Treatment. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2404641. [PMID: 39152925 DOI: 10.1002/smll.202404641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 08/04/2024] [Indexed: 08/19/2024]
Abstract
Nucleic acid nanotechnology has become a promising strategy for disease diagnosis and treatment, owing to remarkable programmability, precision, and biocompatibility. However, current biosensing and biotherapy approaches by nucleic acids exhibit limitations in sensitivity, specificity, versatility, and real-time monitoring. DNA amplification reactions present an advantageous strategy to enhance the performance of biosensing and biotherapy platforms. Non-enzymatic DNA amplification reaction (NEDAR), such as hybridization chain reaction and catalytic hairpin assembly, operate via strand displacement. NEDAR presents distinct advantages over traditional enzymatic DNA amplification reactions, including simplified procedures, milder reaction conditions, higher specificity, enhanced controllability, and excellent versatility. Consequently, research focusing on NEDAR-based biosensing and biotherapy has garnered significant attention. NEDAR demonstrates high efficacy in detecting multiple types of biomarkers, including nucleic acids, small molecules, and proteins, with high sensitivity and specificity, enabling the parallel detection of multiple targets. Besides, NEDAR can strengthen drug therapy, cellular behavior control, and cell encapsulation. Moreover, NEDAR holds promise for constructing assembled diagnosis-treatment nanoplatforms in the forms of pure DNA nanostructures and hybrid nanomaterials, which offer utility in disease monitoring and precise treatment. Thus, this paper aims to comprehensively elucidate the reaction mechanism of NEDAR and review the substantial advancements in NEDAR-based diagnosis and treatment over the past five years, encompassing NEDAR-based design strategies, applications, and prospects.
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Affiliation(s)
- Ye Chen
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Zhiqiang Liu
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Bowen Zhang
- Department of Prosthodontics, Tianjin Stomatological Hospital, School of Medicine, Nankai University, Tianjin, 300041, P. R. China
- Tianjin Key Laboratory of Oral and Maxillofacial Function Reconstruction, Tianjin, 300041, P. R. China
| | - Haoyan Wu
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Xiaoying Lv
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Yuxin Zhang
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Yunfeng Lin
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
- Sichuan Provincial Engineering Research Center of Oral Biomaterials, Chengdu, Sichuan, 610041, P. R. China
- National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
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2
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Qi G, Battle A. Computational methods for allele-specific expression in single cells. Trends Genet 2024:S0168-9525(24)00169-0. [PMID: 39127549 DOI: 10.1016/j.tig.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 07/16/2024] [Accepted: 07/17/2024] [Indexed: 08/12/2024]
Abstract
Allele-specific expression (ASE) is a powerful signal that can be used to investigate multiple molecular mechanisms, such as cis-regulatory effects and imprinting. Single-cell RNA-sequencing (scRNA-seq) enables ASE characterization at the resolution of individual cells. In this review, we highlight the computational methods for processing and analyzing single-cell ASE data. We first describe a bioinformatics pipeline to obtain ASE counts from raw reads synthesized from previous literature. We then discuss statistical methods for detecting allelic imbalance and its variability across conditions using scRNA-seq data. In addition, we describe other methods that use single-cell ASE to address specific biological questions. Finally, we discuss future directions and emphasize the need for an integrated, optimized bioinformatics pipeline, and further development of statistical methods for different technologies.
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Affiliation(s)
- Guanghao Qi
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
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3
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Yang C, Liu Y, Lv C, Xu M, Xu K, Shi J, Tan T, Zhou W, Lv D, Li Y, Xu J, Shao T. CanCellVar: A database for single-cell variants map in human cancer. Am J Hum Genet 2024; 111:1420-1430. [PMID: 38838674 PMCID: PMC11267512 DOI: 10.1016/j.ajhg.2024.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 05/15/2024] [Accepted: 05/15/2024] [Indexed: 06/07/2024] Open
Abstract
Numerous variants, including both single-nucleotide variants (SNVs) in DNA and A>G RNA edits in mRNA as essential drivers of cellular proliferation and tumorigenesis, are commonly associated with cancer progression and growth. Thus, mining and summarizing single-cell variants will provide a refined and higher-resolution view of cancer and further contribute to precision medicine. Here, we established a database, CanCellVar, which aims to provide and visualize the comprehensive atlas of single-cell variants in tumor microenvironment. The current CanCellVar identified ∼3 million variants (∼1.4 million SNVs and ∼1.4 million A>G RNA edits) involved in 2,754,531 cells of 5 major cell types across 37 cancer types. CanCellVar provides the basic annotation information as well as cellular and molecular function properties of variants. In addition, the clinical relevance of variants can be obtained including tumor grade, treatment, metastasis, and others. Several flexible tools were also developed to aid retrieval and to analyze cell-cell interactions, gene expression, cell-development trajectories, regulation, and molecular structure affected by variants. Collectively, CanCellVar will serve as a valuable resource for investigating the functions and characteristics of single-cell variations and their roles in human tumor evolution and treatment.
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Affiliation(s)
- Changbo Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China
| | - Yujie Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China
| | - Chongwen Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China
| | - Mengjia Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China
| | - Kang Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China
| | - Jingyi Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China
| | - Tingting Tan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China
| | - Weiwei Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China
| | - Dezhong Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China
| | - Yongsheng Li
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China.
| | - Tingting Shao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150001, China.
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4
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Kim A, Martinez S, Edwards N, Horvath A. ScSNViz: a user-friendly toolset for visualization and analysis of Cell-Specific Expressed SNVs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.31.596816. [PMID: 38895293 PMCID: PMC11185531 DOI: 10.1101/2024.05.31.596816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Motivation Understanding genetic variation at the single-cell level is crucial for insights into cellular heterogeneity, clonal evolution, and gene expression regulation, but there is a scarcity of tools for visualizing and analyzing cell-level genetic variants. Results We introduce scSNViz, a comprehensive R-based toolset for visualization and analysis of cell-specific expressed Single Nucleotide Variants (sceSNVs) within cell-barcoded single-cell RNA-sequencing (scRNA-seq) data. ScSNViz offers 3D sceSNV visualization capabilities for dimensionally reduced scRNA-seq gene expression data, compatibility with popular scRNA-seq processing tools like Seurat, cell-type classification tools such as SingleR and scType, and trajectory inference computation using Slingshot. Furthermore, scSNViz conducts estimation, summary, and graphical representation of statistical metrics pertaining to sceSNVs distribution and expression across individual cells. It also provides support for the analysis of individual sceSNVs as well as sets comprising multiple expressed sceSNVs of interest. Availability ScSNViz is implemented as user-friendly R-scripts, freely available on https://horvathlab.github.io/NGS/scSNViz , supported by help utilities, and requiring no specialized bioinformatics skills for use.
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5
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Muyas F, Sauer CM, Valle-Inclán JE, Li R, Rahbari R, Mitchell TJ, Hormoz S, Cortés-Ciriano I. De novo detection of somatic mutations in high-throughput single-cell profiling data sets. Nat Biotechnol 2024; 42:758-767. [PMID: 37414936 PMCID: PMC11098751 DOI: 10.1038/s41587-023-01863-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 06/07/2023] [Indexed: 07/08/2023]
Abstract
Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism and cell plasticity. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq (assay for transposase-accessible chromatin sequence) data sets directly without requiring matched bulk or single-cell DNA sequencing data. SComatic distinguishes somatic mutations from polymorphisms, RNA-editing events and artefacts using filters and statistical tests parameterized on non-neoplastic samples. Using >2.6 million single cells from 688 single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) data sets spanning cancer and non-neoplastic samples, we show that SComatic detects mutations in single cells accurately, even in differentiated cells from polyclonal tissues that are not amenable to mutation detection using existing methods. Validated against matched genome sequencing and scRNA-seq data, SComatic achieves F1 scores between 0.6 and 0.7 across diverse data sets, in comparison to 0.2-0.4 for the second-best performing method. In summary, SComatic permits de novo mutational signature analysis, and the study of clonal heterogeneity and mutational burdens at single-cell resolution.
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Affiliation(s)
- Francesc Muyas
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Carolin M Sauer
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Jose Espejo Valle-Inclán
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Ruoyan Li
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Raheleh Rahbari
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Thomas J Mitchell
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Department of Surgery, University of Cambridge, Cambridge, UK
| | - Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Isidro Cortés-Ciriano
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.
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6
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Roehrig A, Hirsch TZ, Pire A, Morcrette G, Gupta B, Marcaillou C, Imbeaud S, Chardot C, Gonzales E, Jacquemin E, Sekiguchi M, Takita J, Nagae G, Hiyama E, Guérin F, Fabre M, Aerts I, Taque S, Laithier V, Branchereau S, Guettier C, Brugières L, Fresneau B, Zucman-Rossi J, Letouzé E. Single-cell multiomics reveals the interplay of clonal evolution and cellular plasticity in hepatoblastoma. Nat Commun 2024; 15:3031. [PMID: 38589411 PMCID: PMC11001886 DOI: 10.1038/s41467-024-47280-x] [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: 04/06/2023] [Accepted: 03/21/2024] [Indexed: 04/10/2024] Open
Abstract
Hepatoblastomas (HB) display heterogeneous cellular phenotypes that influence the clinical outcome, but the underlying mechanisms are poorly understood. Here, we use a single-cell multiomic strategy to unravel the molecular determinants of this plasticity. We identify a continuum of HB cell states between hepatocytic (scH), liver progenitor (scLP) and mesenchymal (scM) differentiation poles, with an intermediate scH/LP population bordering scLP and scH areas in spatial transcriptomics. Chromatin accessibility landscapes reveal the gene regulatory networks of each differentiation pole, and the sequence of transcription factor activations underlying cell state transitions. Single-cell mapping of somatic alterations reveals the clonal architecture of each tumor, showing that each genetic subclone displays its own range of cellular plasticity across differentiation states. The most scLP subclones, overexpressing stem cell and DNA repair genes, proliferate faster after neo-adjuvant chemotherapy. These results highlight how the interplay of clonal evolution and epigenetic plasticity shapes the potential of HB subclones to respond to chemotherapy.
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Affiliation(s)
- Amélie Roehrig
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
| | - Theo Z Hirsch
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
| | - Aurore Pire
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
| | - Guillaume Morcrette
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
- Department of Pathology, Robert Debré and Necker-Enfants Malades Hospitals, APHP, Paris, France
| | - Barkha Gupta
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
| | | | - Sandrine Imbeaud
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
| | | | - Emmanuel Gonzales
- Pediatric Hepatology and Liver Transplantation Unit, National Reference Centre for Rare Pediatric Liver Diseases, FILFOIE, ERN RARE LIVER, APHP, Bicêtre University Hospital, University of Paris-Saclay, Le Kremlin Bicêtre, and INSERM UMR_S 1193, Hepatinov, University of Paris-Saclay, Orsay, France
| | - Emmanuel Jacquemin
- Pediatric Hepatology and Liver Transplantation Unit, National Reference Centre for Rare Pediatric Liver Diseases, FILFOIE, ERN RARE LIVER, APHP, Bicêtre University Hospital, University of Paris-Saclay, Le Kremlin Bicêtre, and INSERM UMR_S 1193, Hepatinov, University of Paris-Saclay, Orsay, France
| | - Masahiro Sekiguchi
- Department of Pediatrics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Junko Takita
- Department of Pediatrics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Pediatrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Genta Nagae
- Genome Science Laboratory, Research Center for Advanced Science and Technology (RCAST), the University of Tokyo, Tokyo, Japan
| | - Eiso Hiyama
- Department of Pediatric Surgery, Hiroshima University Hospital, Hiroshima, Japan
- Department of Biomedical Science, Natural Science Center for Basic Research and Development, Hiroshima University, Hiroshima, Japan
| | - Florent Guérin
- Department of Pediatric Surgery, Bicêtre Hospital, APHP, Paris-Saclay University, Orsay, France
| | - Monique Fabre
- Department of Pathology, Hôpital Universitaire Necker-Enfants malades, AP-HP, Paris, France
| | - Isabelle Aerts
- Oncology Center SIREDO, Institut Curie, PSL Research University, Paris, France
| | - Sophie Taque
- Département de Pédiatrie, CHU Fontenoy, Rennes, France
| | - Véronique Laithier
- Department of Children Oncology, Centre Hospitalier Universitaire Besançon, Besançon, France
| | - Sophie Branchereau
- Department of Pediatric Surgery, Bicêtre Hospital, APHP, Paris-Saclay University, Orsay, France
| | - Catherine Guettier
- Department of Pathology Hôpital Bicêtre-AP-HP, INSERM U1193, Paris-Saclay University, Orsay, France
| | - Laurence Brugières
- Gustave Roussy, Université Paris-Saclay, Department of Children and Adolescents Oncology, Villejuif, France
| | - Brice Fresneau
- Gustave Roussy, Université Paris-Saclay, Department of Children and Adolescents Oncology, Villejuif, France
| | - Jessica Zucman-Rossi
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France.
- Hôpital Européen Georges Pompidou, Assistance Publique Hôpitaux de Paris, Paris, France.
| | - Eric Letouzé
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France.
- CRCI2NA, Nantes Université, INSERM, CNRS, Nantes, France.
- University Hospital Hôtel-Dieu, Nantes, France.
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7
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Chen L, Chang D, Tandukar B, Deivendran D, Pozniak J, Cruz-Pacheco N, Cho RJ, Cheng J, Yeh I, Marine C, Bastian BC, Ji AL, Shain AH. STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer. Genome Biol 2023; 24:273. [PMID: 38037084 PMCID: PMC10688493 DOI: 10.1186/s13059-023-03121-6] [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: 01/08/2023] [Accepted: 11/22/2023] [Indexed: 12/02/2023] Open
Abstract
Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.
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Affiliation(s)
- Limin Chen
- Department of Dermatology, University of California, San Francisco, San Francisco, USA
| | - Darwin Chang
- Department of Immunology, H. Lee Moffitt Cancer Center, Tampa, USA
| | - Bishal Tandukar
- Department of Dermatology, University of California, San Francisco, San Francisco, USA
| | - Delahny Deivendran
- Department of Dermatology, University of California, San Francisco, San Francisco, USA
| | - Joanna Pozniak
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Louvain, Belgium
- Laboratory for Molecular Cancer Biology, Department of Oncology, KU Leuven, Louvain, Belgium
| | - Noel Cruz-Pacheco
- Department of Dermatology, University of California, San Francisco, San Francisco, USA
| | - Raymond J Cho
- Department of Dermatology, University of California, San Francisco, San Francisco, USA
| | - Jeffrey Cheng
- Department of Dermatology, University of California, San Francisco, San Francisco, USA
| | - Iwei Yeh
- Department of Dermatology, University of California, San Francisco, San Francisco, USA
- Department of Pathology, University of California, San Francisco, San Francisco, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, USA
| | - Chris Marine
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Louvain, Belgium
- Laboratory for Molecular Cancer Biology, Department of Oncology, KU Leuven, Louvain, Belgium
| | - Boris C Bastian
- Department of Dermatology, University of California, San Francisco, San Francisco, USA
- Department of Pathology, University of California, San Francisco, San Francisco, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, USA
| | - Andrew L Ji
- Department of Dermatology, Department of Oncological Sciences, Black Family Stem Cell Institute, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York City, USA
| | - A Hunter Shain
- Department of Dermatology, University of California, San Francisco, San Francisco, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, USA.
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8
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Pilet J, Hirsch TZ, Gupta B, Roehrig A, Morcrette G, Pire A, Letouzé E, Fresneau B, Taque S, Brugières L, Branchereau S, Chardot C, Aerts I, Sarnacki S, Fabre M, Guettier C, Rebouissou S, Zucman-Rossi J. Preneoplastic liver colonization by 11p15.5 altered mosaic cells in young children with hepatoblastoma. Nat Commun 2023; 14:7122. [PMID: 37932266 PMCID: PMC10628292 DOI: 10.1038/s41467-023-42418-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 10/11/2023] [Indexed: 11/08/2023] Open
Abstract
Pediatric liver tumors are very rare tumors with the most common diagnosis being hepatoblastoma. While hepatoblastomas are predominantly sporadic, around 15% of cases develop as part of predisposition syndromes such as Beckwith-Wiedemann (11p15.5 locus altered). Here, we identify mosaic genetic alterations of 11p15.5 locus in the liver of hepatoblastoma patients without a clinical diagnosis of Beckwith-Wiedemann syndrome. We do not retrieve these alterations in children with other types of pediatric liver tumors. We show that mosaic 11p15.5 alterations in liver FFPE sections of hepatoblastoma patients display IGF2 overexpression and H19 downregulation together with an alteration of the liver zonation. Moreover, mosaic livers' microenvironment is enriched in extracellular matrix and angiogenesis. Spatial transcriptomics and single-nucleus RNAseq analyses identify a 60-gene signature in 11p15.5 altered hepatocytes. These data provide insights for 11p15.5 mosaicism detection and its functional consequences during the early steps of carcinogenesis.
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Grants
- FunGeST team (FUNctional GEnomics of Solid Tumors) is supported by Ligue contre le cancer (équipe labellisée), SFCE (Société Française de Lutte Contre les Cancers et les Leucémies de l’Enfant), the SIRIC CARPEM, PeLiCan.Resist InCa (Pediatric LIver CANcer database to combat RESISTance to treatment, Institut National du Cancer), France Génomique, association Etoile de Martin, Fédération Enfants et Santé, association Hubert Gouin “Enfance et Cancer,” INSERM Plan Cancer, CisMutHep InCa High-Risk High_Gain (Institut National du Cancer, grant number PEDIAHR22-009). This work was also supported by the Fondation pour la Recherche Médicale, grant number ECO201906008977 to AR and grant number ECO20170637540 to JP. AP received a funding from Fondation Nuovo-Soldati.
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Affiliation(s)
- Jill Pilet
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, Inserm, F-75006, Paris, France
| | - Theo Z Hirsch
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, Inserm, F-75006, Paris, France
| | - Barkha Gupta
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, Inserm, F-75006, Paris, France
| | - Amélie Roehrig
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, Inserm, F-75006, Paris, France
| | - Guillaume Morcrette
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, Inserm, F-75006, Paris, France
| | - Aurore Pire
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, Inserm, F-75006, Paris, France
| | - Eric Letouzé
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, Inserm, F-75006, Paris, France
| | - Brice Fresneau
- Gustave Roussy, Université Paris-Saclay, Department of Children and Adolescents Oncology, Villejuif, France
| | - Sophie Taque
- Department of Paediatrics, CHU Rennes, Rennes, France
| | - Laurence Brugières
- Gustave Roussy, Université Paris-Saclay, Department of Children and Adolescents Oncology, Villejuif, France
| | - Sophie Branchereau
- Department of Pediatric Surgery, Bicêtre Hospital, AP-HP, Paris-Saclay University, Le Kremlin-Bicêtre, France
| | - Christophe Chardot
- Department of Pediatric Surgery, Hôpital Necker-Enfants Malades, AP-HP, Université Paris Cité, Paris, France
| | - Isabelle Aerts
- Institut Curie, PSL Research University, Oncology Center SIREDO, Paris, France
| | - Sabine Sarnacki
- Department of Pediatric Surgery, Hôpital Necker-Enfants Malades, AP-HP, Université Paris Cité, Paris, France
| | - Monique Fabre
- Pathology Department, Necker Enfants Malades Hospital, Université Paris Cité, AP-HP, Paris, France
| | - Catherine Guettier
- Department of Pathology Hôpital Bicêtre-AP-HP, INSERM U1193, Paris-Saclay University, Le Kremlin-Bicêtre, France
| | - Sandra Rebouissou
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, Inserm, F-75006, Paris, France
| | - Jessica Zucman-Rossi
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, Inserm, F-75006, Paris, France.
- Institut du Cancer Paris CARPEM, AP-HP, Department of Oncology, Hopital Européen Georges Pompidou, F-75015, Paris, France.
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9
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Derrien J, Gastineau S, Frigout A, Giordano N, Cherkaoui M, Gaborit V, Boinon R, Douillard E, Devic M, Magrangeas F, Moreau P, Minvielle S, Touzeau C, Letouzé E. Acquired resistance to a GPRC5D-directed T-cell engager in multiple myeloma is mediated by genetic or epigenetic target inactivation. NATURE CANCER 2023; 4:1536-1543. [PMID: 37653140 DOI: 10.1038/s43018-023-00625-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 07/28/2023] [Indexed: 09/02/2023]
Abstract
Bispecific antibodies targeting GPRC5D demonstrated promising efficacy in multiple myeloma, but acquired resistance usually occurs within a few months. Using a single-nucleus multi-omic strategy in three patients from the MYRACLE cohort (ClinicalTrials.gov registration: NCT03807128 ), we identified two resistance mechanisms, by bi-allelic genetic inactivation of GPRC5D or by long-range epigenetic silencing of its promoter and enhancer regions. Molecular profiling of target genes may help to guide the choice of immunotherapy and early detection of resistance in multiple myeloma.
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Affiliation(s)
- Jennifer Derrien
- Nantes Université, INSERM, CNRS, Université d'Angers, CRCI2NA, Nantes, France
| | - Sarah Gastineau
- Nantes Université, INSERM, CNRS, Université d'Angers, CRCI2NA, Nantes, France
| | - Antoine Frigout
- Nantes Université, INSERM, CNRS, Université d'Angers, CRCI2NA, Nantes, France
| | - Nils Giordano
- Nantes Université, INSERM, CNRS, Université d'Angers, CRCI2NA, Nantes, France
| | - Mia Cherkaoui
- Nantes Université, INSERM, CNRS, Université d'Angers, CRCI2NA, Nantes, France
| | - Victor Gaborit
- Nantes Université, INSERM, CNRS, Université d'Angers, CRCI2NA, Nantes, France
- University Hospital Hôtel-Dieu, Nantes, France
| | - Rémi Boinon
- Nantes Université, INSERM, CNRS, Université d'Angers, CRCI2NA, Nantes, France
| | - Elise Douillard
- Nantes Université, INSERM, CNRS, Université d'Angers, CRCI2NA, Nantes, France
- University Hospital Hôtel-Dieu, Nantes, France
| | - Magali Devic
- Nantes Université, INSERM, CNRS, Université d'Angers, CRCI2NA, Nantes, France
- University Hospital Hôtel-Dieu, Nantes, France
| | - Florence Magrangeas
- Nantes Université, INSERM, CNRS, Université d'Angers, CRCI2NA, Nantes, France
- University Hospital Hôtel-Dieu, Nantes, France
| | - Philippe Moreau
- Nantes Université, INSERM, CNRS, Université d'Angers, CRCI2NA, Nantes, France
- Hematology Department, University Hospital Hôtel-Dieu, Nantes, France
| | - Stéphane Minvielle
- Nantes Université, INSERM, CNRS, Université d'Angers, CRCI2NA, Nantes, France
- University Hospital Hôtel-Dieu, Nantes, France
| | - Cyrille Touzeau
- Nantes Université, INSERM, CNRS, Université d'Angers, CRCI2NA, Nantes, France
- Hematology Department, University Hospital Hôtel-Dieu, Nantes, France
| | - Eric Letouzé
- Nantes Université, INSERM, CNRS, Université d'Angers, CRCI2NA, Nantes, France.
- University Hospital Hôtel-Dieu, Nantes, France.
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10
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Van Horebeek L, David M, Dedoncker N, Mallants K, Bijnens B, Goris A, Dubois B. A targeted sequencing extension for transcript genotyping in single-cell transcriptomics. Life Sci Alliance 2023; 6:e202301971. [PMID: 37696578 PMCID: PMC10494938 DOI: 10.26508/lsa.202301971] [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: 02/05/2023] [Revised: 08/17/2023] [Accepted: 08/29/2023] [Indexed: 09/13/2023] Open
Abstract
As no existing methods within the single-cell RNA sequencing repertoire combine genotyping of specific genomic loci with high throughput, we evaluated a straightforward, targeted sequencing approach as an extension to high-throughput droplet-based single-cell RNA sequencing. Overlaying standard gene expression data with transcript level genotype information provides a strategy to study the impact of genetic variants. Here, we describe this targeted sequencing extension, explain how to process the data and evaluate how technical parameters such as amount of input cDNA, number of amplification rounds, and sequencing depth influence the number of transcripts detected. Finally, we demonstrate how targeted sequencing can be used in two contexts: (1) simultaneous investigation of the presence of a somatic variant and its potential impact on the transcriptome of affected cells and (2) evaluation of allele-specific expression of a germline variant in ad hoc cell subsets. Through these and other comparable applications, our targeted sequencing extension has the potential to improve our understanding of functional effects caused by genetic variation.
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Affiliation(s)
- Lies Van Horebeek
- https://ror.org/05f950310 Laboratory for Neuroimmunology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Margaux David
- https://ror.org/05f950310 Laboratory for Neuroimmunology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Nina Dedoncker
- https://ror.org/05f950310 Laboratory for Neuroimmunology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Klara Mallants
- https://ror.org/05f950310 Laboratory for Neuroimmunology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Baukje Bijnens
- https://ror.org/05f950310 Laboratory for Neuroimmunology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - An Goris
- https://ror.org/05f950310 Laboratory for Neuroimmunology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Bénédicte Dubois
- https://ror.org/05f950310 Laboratory for Neuroimmunology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
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11
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Townsend SE, Westfall JJ, Navarro JB, Koboldt DC, Mardis ER, Miller KE, Bedrosian TA. Single-nuclei transcriptomics enable detection of somatic variants in patient brain tissue. Sci Rep 2023; 13:527. [PMID: 36631516 PMCID: PMC9834227 DOI: 10.1038/s41598-023-27700-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 01/06/2023] [Indexed: 01/12/2023] Open
Abstract
Somatic variants are a major cause of human disease, including neurological disorders like focal epilepsies, but can be challenging to study due to their mosaicism in bulk tissue biopsies. Coupling single-cell genotype and transcriptomic data has potential to provide insight into the role somatic variants play in disease etiology, such as by determining what cell types are affected or how the mutations affect gene expression. Here, we asked whether commonly used single-nucleus 3'- or 5'-RNA-sequencing assays can be used to derive single-nucleus genotype data for a priori known variants that are located near to either end of a transcript. To that end, we compared performance of commercially available single-nuclei 3'- and 5'- gene expression kits using resected brain samples from three pediatric patients with focal epilepsy. We quantified the ability to detect genetic variants in single-nucleus datasets depending on distance from the transcript end. Finally, we demonstrated the ability to identify affected cell types in a patient with a RHEB somatic variant causing an epilepsy-associated cortical malformation. Our results demonstrate that single-nuclei 3' or 5'-RNA-sequencing data can be used to identify known somatic variants in single-nuclei when they are expressed within proximity to a transcript end.
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Affiliation(s)
- Sydney E. Townsend
- grid.240344.50000 0004 0392 3476Institute for Genomic Medicine, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43215 USA ,grid.261331.40000 0001 2285 7943Biomedical Sciences Graduate Program, College of Medicine, The Ohio State University, Columbus, OH 43210 USA
| | - Jesse J. Westfall
- grid.240344.50000 0004 0392 3476Institute for Genomic Medicine, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43215 USA
| | - Jason B. Navarro
- grid.240344.50000 0004 0392 3476Institute for Genomic Medicine, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43215 USA
| | - Daniel C. Koboldt
- grid.240344.50000 0004 0392 3476Institute for Genomic Medicine, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43215 USA ,grid.261331.40000 0001 2285 7943Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, OH 43210 USA
| | - Elaine R. Mardis
- grid.240344.50000 0004 0392 3476Institute for Genomic Medicine, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43215 USA ,grid.261331.40000 0001 2285 7943Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, OH 43210 USA ,grid.261331.40000 0001 2285 7943Department of Neurosurgery, College of Medicine, The Ohio State University, Columbus, OH 43210 USA
| | - Katherine E. Miller
- grid.240344.50000 0004 0392 3476Institute for Genomic Medicine, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43215 USA ,grid.261331.40000 0001 2285 7943Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, OH 43210 USA
| | - Tracy A. Bedrosian
- grid.240344.50000 0004 0392 3476Institute for Genomic Medicine, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43215 USA ,grid.261331.40000 0001 2285 7943Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, OH 43210 USA
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12
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Edwards N, Dillard C, Prashant NM, Hongyu L, Yang M, Ulianova E, Horvath A. SCExecute: custom cell barcode-stratified analyses of scRNA-seq data. Bioinformatics 2022; 39:6854977. [PMID: 36448703 PMCID: PMC9825775 DOI: 10.1093/bioinformatics/btac768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 11/11/2022] [Accepted: 11/29/2022] [Indexed: 12/05/2022] Open
Abstract
MOTIVATION In single-cell RNA-sequencing (scRNA-seq) data, stratification of sequencing reads by cellular barcode is necessary to study cell-specific features. However, apart from gene expression, the analyses of cell-specific features are not sufficiently supported by available tools designed for high-throughput sequencing data. RESULTS We introduce SCExecute, which executes a user-provided command on barcode-stratified, extracted on-the-fly, single-cell binary alignment map (scBAM) files. SCExecute extracts the alignments with each cell barcode from aligned, pooled single-cell sequencing data. Simple commands, monolithic programs, multi-command shell scripts or complex shell-based pipelines are then executed on each scBAM file. scBAM files can be restricted to specific barcodes and/or genomic regions of interest. We demonstrate SCExecute with two popular variant callers-GATK and Strelka2-executed in shell-scripts together with commands for BAM file manipulation and variant filtering, to detect single-cell-specific expressed single nucleotide variants from droplet scRNA-seq data (10X Genomics Chromium System).In conclusion, SCExecute facilitates custom cell-level analyses on barcoded scRNA-seq data using currently available tools and provides an effective solution for studying low (cellular) frequency transcriptome features. AVAILABILITY AND IMPLEMENTATION SCExecute is implemented in Python3 using the Pysam package and distributed for Linux, MacOS and Python environments from https://horvathlab.github.io/NGS/SCExecute. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Christian Dillard
- Department of Biochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA
| | - N M Prashant
- Department of Biochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA,Departments of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Liu Hongyu
- Department of Biochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA,Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA
| | - Mia Yang
- Department of Biochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA
| | - Evgenia Ulianova
- Department of Biochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA
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13
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RDAClone: Deciphering Tumor Heterozygosity through Single-Cell Genomics Data Analysis with Robust Deep Autoencoder. Genes (Basel) 2021; 12:genes12121847. [PMID: 34946794 PMCID: PMC8701080 DOI: 10.3390/genes12121847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 12/27/2022] Open
Abstract
Rapid advances in single-cell genomics sequencing (SCGS) have allowed researchers to characterize tumor heterozygosity with unprecedented resolution and reveal the phylogenetic relationships between tumor cells or clones. However, high sequencing error rates of current SCGS data, i.e., false positives, false negatives, and missing bases, severely limit its application. Here, we present a deep learning framework, RDAClone, to recover genotype matrices from noisy data with an extended robust deep autoencoder, cluster cells into subclones by the Louvain-Jaccard method, and further infer evolutionary relationships between subclones by the minimum spanning tree. Studies on both simulated and real datasets demonstrate its robustness and superiority in data denoising, cell clustering, and evolutionary tree reconstruction, particularly for large datasets.
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14
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Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments. Genes (Basel) 2021; 12:genes12101558. [PMID: 34680953 PMCID: PMC8535975 DOI: 10.3390/genes12101558] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/25/2021] [Accepted: 09/28/2021] [Indexed: 11/17/2022] Open
Abstract
Currently, the detection of single nucleotide variants (SNVs) from 10 x Genomics single-cell RNA sequencing data (scRNA-seq) is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gaining of information regarding SNV assessments from individual cell scRNA-seq data, wherein the alignments are split by cellular barcode prior to the variant call. We also reanalyze publicly available data on the MCF7 cell line during anticancer treatment. We assessed SNV calls by three variant callers—GATK, Strelka2, and Mutect2, in combination with a method for the cell-level tabulation of the sequencing read counts bearing variant alleles–SCReadCounts (single-cell read counts). Our analysis shows that variant calls on individual cell alignments identify at least a two-fold higher number of SNVs as compared to the pooled scRNA-seq; these SNVs are enriched in novel variants and in stop-codon and missense substitutions. Our study indicates an immense potential of SNV calls from individual cell scRNA-seq data and emphasizes the need for cell-level variant detection approaches and tools, which can contribute to the understanding of the cellular heterogeneity and the relationships to phenotypes, and help elucidate somatic mutation evolution and functionality.
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