1
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Qiao Y, Cheng T, Miao Z, Cui Y, Tu J. Recent Innovations and Technical Advances in High-Throughput Parallel Single-Cell Whole-Genome Sequencing Methods. SMALL METHODS 2024:e2400789. [PMID: 38979872 DOI: 10.1002/smtd.202400789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2024] [Indexed: 07/10/2024]
Abstract
Single-cell whole-genome sequencing (scWGS) detects cell heterogeneity at the aspect of genomic variations, which are inheritable and play an important role in life processes such as aging and cancer progression. The recent explosive development of high-throughput single-cell sequencing methods has enabled high-performance heterogeneity detection through a vast number of novel strategies. Despite the limitation on total cost, technical advances in high-throughput single-cell whole-genome sequencing methods are made for higher genome coverage, parallel throughput, and level of integration. This review highlights the technical advancements in high-throughput scWGS in the aspects of strategies design, data efficiency, parallel handling platforms, and their applications on human genome. The experimental innovations, remaining challenges, and perspectives are summarized and discussed.
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Affiliation(s)
- Yi Qiao
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Tianguang Cheng
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Zikun Miao
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Yue Cui
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Jing Tu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
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2
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Ruan X, Cheng Y, Ye Y, Wang Y, Chen X, Yang Y, Liu T, Yan F. PIPET: predicting relevant subpopulations in single-cell data using phenotypic information from bulk data. Brief Bioinform 2024; 25:bbae260. [PMID: 38819254 PMCID: PMC11141296 DOI: 10.1093/bib/bbae260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/11/2024] [Accepted: 05/15/2024] [Indexed: 06/01/2024] Open
Abstract
Single-cell RNA sequencing has revealed cellular heterogeneity in complex tissues, notably benefiting research on diseases such as cancer. However, the integration of single-cell data from small samples with extensive clinical features in bulk data remains underexplored. In this study, we introduce PIPET, an algorithmic method for predicting relevant subpopulations in single-cell data based on multivariate phenotypic information from bulk data. PIPET generates feature vectors for each phenotype from differentially expressed genes in bulk data and then identifies relevant cellular subpopulations by assessing the similarity between single-cell data and these vectors. Subsequently, phenotype-related cell states can be analyzed based on these subpopulations. In simulated datasets, PIPET showed robust performance in predicting multiclassification cellular subpopulations. Application of PIPET to lung adenocarcinoma single-cell RNA sequencing data revealed cellular subpopulations with poor survival and associations with TP53 mutations. Similarly, in breast cancer single-cell data, PIPET identified cellular subpopulations associated with the PAM50 clinical subtypes and triple-negative breast cancer subtypes. Overall, PIPET effectively identified relevant cellular subpopulations in single-cell data, guided by phenotypic information from bulk data. This approach comprehensively delineates the molecular characteristics of each cellular subpopulation, offering insights into disease-related subpopulations and guiding personalized treatment strategies.
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Affiliation(s)
- Xinjia Ruan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Yu Cheng
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Yuqing Ye
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Yuhang Wang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Xinyi Chen
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Yuqing Yang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Tiantian Liu
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
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3
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Shahrouzi P, Forouz F, Mathelier A, Kristensen VN, Duijf PHG. Copy number alterations: a catastrophic orchestration of the breast cancer genome. Trends Mol Med 2024:S1471-4914(24)00120-5. [PMID: 38772764 DOI: 10.1016/j.molmed.2024.04.017] [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: 02/26/2024] [Revised: 04/12/2024] [Accepted: 04/26/2024] [Indexed: 05/23/2024]
Abstract
Breast cancer (BCa) is a prevalent malignancy that predominantly affects women around the world. Somatic copy number alterations (CNAs) are tumor-specific amplifications or deletions of DNA segments that often drive BCa development and therapy resistance. Hence, the complex patterns of CNAs complement BCa classification systems. In addition, understanding the precise contributions of CNAs is essential for tailoring personalized treatment approaches. This review highlights how tumor evolution drives the acquisition of CNAs, which in turn shape the genomic landscapes of BCas. It also discusses advanced methodologies for identifying recurrent CNAs, studying CNAs in BCa and their clinical impact.
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Affiliation(s)
- Parastoo Shahrouzi
- Department of Medical Genetics, Institute of Basic Medical Science, Faculty of Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway.
| | - Farzaneh Forouz
- School of Pharmacy, University of Queensland, Woolloongabba, Brisbane, Australia
| | - Anthony Mathelier
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway; Center for Bioinformatics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway; Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Vessela N Kristensen
- Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway; Division of Medicine, Department of Clinical Molecular Biology and Laboratory Science (EpiGen), Akershus University Hospital, Lørenskog, Norway; Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Pascal H G Duijf
- Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway; Centre for Cancer Biology, UniSA Clinical and Health Sciences, University of South Australia and SA Pathology, Adelaide, Australia.
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4
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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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5
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Gondal MN, Cieslik M, Chinnaiyan AM. Integrated cancer cell-specific single-cell RNA-seq datasets of immune checkpoint blockade-treated patients. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.17.576110. [PMID: 38328153 PMCID: PMC10849474 DOI: 10.1101/2024.01.17.576110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Immune checkpoint blockade (ICB) therapies have emerged as a promising avenue for the treatment of various cancers. Despite their success, the efficacy of these treatments is variable across patients and cancer types. Numerous single-cell RNA-sequencing (scRNA-seq) studies have been conducted to unravel cell-specific responses to ICB treatment. However, these studies are limited in their sample sizes and require advanced coding skills for exploration. Here, we have compiled eight scRNA-seq datasets from nine cancer types, encompassing 174 patients, and 90,270 cancer cells. This compilation forms a unique resource tailored for investigating how cancer cells respond to ICB treatment across cancer types. We meticulously curated, quality-checked, pre-processed, and analyzed the data, ensuring easy access for researchers. Moreover, we designed a user-friendly interface for seamless exploration. By sharing the code and data for creating these interfaces, we aim to assist fellow researchers. These resources offer valuable support to those interested in leveraging and exploring single-cell datasets across diverse cancer types, facilitating a comprehensive understanding of ICB responses.
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Affiliation(s)
- Mahnoor N. Gondal
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI USA
| | - Marcin Cieslik
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI USA
- Department of Pathology, University of Michigan, Ann Arbor, MI USA
- University of Michigan Rogel Cancer Center, Ann Arbor, MI USA
| | - Arul M. Chinnaiyan
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI USA
- Department of Pathology, University of Michigan, Ann Arbor, MI USA
- Department of Urology, University of Michigan, Ann Arbor, MI USA
- Howard Hughes Medical Institute, Ann Arbor, MI USA
- University of Michigan Rogel Cancer Center, Ann Arbor, MI USA
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6
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Cao W, Wang X, Luo K, Li Y, Sun J, Fu R, Zhang Q, Hong N, Cheung E, Jin W. Single cell analyses of cancer cells identified two regulatorily and functionally distinct categories in differentially expressed genes among tumor subclones. Heliyon 2024; 10:e28071. [PMID: 38524605 PMCID: PMC10958426 DOI: 10.1016/j.heliyon.2024.e28071] [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: 10/12/2023] [Revised: 03/10/2024] [Accepted: 03/11/2024] [Indexed: 03/26/2024] Open
Abstract
To explore the feature of cancer cells and tumor subclones, we analyzed 101,065 single-cell transcriptomes from 12 colorectal cancer (CRC) patients and 92 single cell genomes from one of these patients. We found cancer cells, endothelial cells and stromal cells in tumor tissue expressed much more genes and had stronger cell-cell interactions than their counterparts in normal tissue. We identified copy number variations (CNVs) in each cancer cell and found correlation between gene copy number and expression level in cancer cells at single cell resolution. Analysis of tumor subclones inferred by CNVs showed accumulation of mutations in each tumor subclone along lineage trajectories. We found differentially expressed genes (DEGs) between tumor subclones had two populations: DEGCNV and DEGreg. DEGCNV, showing high CNV-expression correlation and whose expression differences depend on the differences of CNV level, enriched in housekeeping genes and cell adhesion associated genes. DEGreg, showing low CNV-expression correlation and mainly in low CNV variation regions and regions without CNVs, enriched in cytokine signaling genes. Furthermore, cell-cell communication analyses showed that DEGCNV tends to involve in cell-cell contact while DEGreg tends to involve in secreted signaling, which further support that DEGCNV and DEGreg are two regulatorily and functionally distinct categories.
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Affiliation(s)
- Wei Cao
- School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
- Cancer Centre, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR
| | - Xuefei Wang
- School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Kaiwen Luo
- School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Yang Li
- Shenzhen People's Hospital, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Jiahong Sun
- School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Ruqing Fu
- School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Qi Zhang
- School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Ni Hong
- School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Edwin Cheung
- Cancer Centre, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR
| | - Wenfei Jin
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
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7
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Cai X, Zhang W, Zheng X, Xu Y, Li Y. scEM: A New Ensemble Framework for Predicting Cell Type Composition Based on scRNA-Seq Data. Interdiscip Sci 2024:10.1007/s12539-023-00601-y. [PMID: 38368575 DOI: 10.1007/s12539-023-00601-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 12/22/2023] [Accepted: 12/24/2023] [Indexed: 02/19/2024]
Abstract
With the advent of single-cell RNA sequencing (scRNA-seq) technology, many scRNA-seq data have become available, providing an unprecedented opportunity to explore cellular composition and heterogeneity. Recently, many computational algorithms for predicting cell type composition have been developed, and these methods are typically evaluated on different datasets and performance metrics using diverse techniques. Consequently, the lack of comprehensive and standardized comparative analysis makes it difficult to gain a clear understanding of the strengths and weaknesses of these methods. To address this gap, we reviewed 20 cutting-edge unsupervised cell type identification methods and evaluated these methods comprehensively using 24 real scRNA-seq datasets of varying scales. In addition, we proposed a new ensemble cell-type identification method, named scEM, which learns the consensus similarity matrix by applying the entropy weight method to the four representative methods are selected. The Louvain algorithm is adopted to obtain the final classification of individual cells based on the consensus matrix. Extensive evaluation and comparison with 11 other similarity-based methods under real scRNA-seq datasets demonstrate that the newly developed ensemble algorithm scEM is effective in predicting cellular type composition.
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Affiliation(s)
- Xianxian Cai
- School of Sciences, East China Jiaotong University, Nanchang, 330013, China
| | - Wei Zhang
- School of Sciences, East China Jiaotong University, Nanchang, 330013, China.
| | - Xiaoying Zheng
- Operations research and planning department, Naval University of Engineering, Wuhan, 430033, China
| | - Yaxin Xu
- School of Sciences, East China Jiaotong University, Nanchang, 330013, China
| | - Yuanyuan Li
- School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan, China
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8
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Lynch A, Bradford S, Burkard ME. The reckoning of chromosomal instability: past, present, future. Chromosome Res 2024; 32:2. [PMID: 38367036 DOI: 10.1007/s10577-024-09746-y] [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: 01/11/2024] [Revised: 01/11/2024] [Accepted: 01/27/2024] [Indexed: 02/19/2024]
Abstract
Quantitative measures of CIN are crucial to our understanding of its role in cancer. Technological advances have changed the way CIN is quantified, offering increased accuracy and insight. Here, we review measures of CIN through its rise as a field, discuss considerations for its measurement, and look forward to future quantification of CIN.
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Affiliation(s)
- Andrew Lynch
- UW Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- McArdle Laboratory for Cancer Research, University of Wisconsin, Madison, WI, USA
- Division of Hematology/Oncology, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Shermineh Bradford
- UW Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- McArdle Laboratory for Cancer Research, University of Wisconsin, Madison, WI, USA
- Division of Hematology/Oncology, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Mark E Burkard
- UW Carbone Cancer Center, University of Wisconsin, Madison, WI, USA.
- McArdle Laboratory for Cancer Research, University of Wisconsin, Madison, WI, USA.
- Division of Hematology/Oncology, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.
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9
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Khan SU, Fatima K, Aisha S, Malik F. Unveiling the mechanisms and challenges of cancer drug resistance. Cell Commun Signal 2024; 22:109. [PMID: 38347575 PMCID: PMC10860306 DOI: 10.1186/s12964-023-01302-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 08/30/2023] [Indexed: 02/15/2024] Open
Abstract
Cancer treatment faces many hurdles and resistance is one among them. Anti-cancer treatment strategies are evolving due to innate and acquired resistance capacity, governed by genetic, epigenetic, proteomic, metabolic, or microenvironmental cues that ultimately enable selected cancer cells to survive and progress under unfavorable conditions. Although the mechanism of drug resistance is being widely studied to generate new target-based drugs with better potency than existing ones. However, due to the broader flexibility in acquired drug resistance, advanced therapeutic options with better efficacy need to be explored. Combination therapy is an alternative with a better success rate though the risk of amplified side effects is commonplace. Moreover, recent groundbreaking precision immune therapy is one of the ways to overcome drug resistance and has revolutionized anticancer therapy to a greater extent with the only limitation of being individual-specific and needs further attention. This review will focus on the challenges and strategies opted by cancer cells to withstand the current therapies at the molecular level and also highlights the emerging therapeutic options -like immunological, and stem cell-based options that may prove to have better potential to challenge the existing problem of therapy resistance. Video Abstract.
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Affiliation(s)
- Sameer Ullah Khan
- Division of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Holcombe Blvd, Houston, TX, 77030, USA.
- Division of Cancer Pharmacology, CSIR-Indian Institute of Integrative Medicine, Srinagar-190005, Jammu and Kashmir, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India.
| | - Kaneez Fatima
- Division of Cancer Pharmacology, CSIR-Indian Institute of Integrative Medicine, Srinagar-190005, Jammu and Kashmir, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India
| | - Shariqa Aisha
- Division of Cancer Pharmacology, CSIR-Indian Institute of Integrative Medicine, Srinagar-190005, Jammu and Kashmir, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India
| | - Fayaz Malik
- Division of Cancer Pharmacology, CSIR-Indian Institute of Integrative Medicine, Srinagar-190005, Jammu and Kashmir, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India.
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10
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He KJ, Gong G, Liang E, Lv Y, Lin S, Xu J. Pan-cancer analysis of 60S Ribosomal Protein L7-Like 1 (RPL7L1) and validation in liver hepatocellular carcinoma. Transl Oncol 2024; 40:101844. [PMID: 38042135 PMCID: PMC10701367 DOI: 10.1016/j.tranon.2023.101844] [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: 09/27/2023] [Revised: 11/04/2023] [Accepted: 11/21/2023] [Indexed: 12/04/2023] Open
Abstract
BACKGROUND AND AIMS There is an association between cancer and increased ribosome biogenesis. At present, the RPL7L1 (60S Ribosomal Protein L7-Like 1) were less reported by literature search. Study reports that RPL7L1 is associated with mouse embryonic and skeletal muscle. The study of RPL7L1 on tumors has not been reported. METHODS Our team downloaded the pan-cancer dataset that is uniformly normalized from the UCSC database (N=19131). Our study examined the relationship between RPL7L1 expression level and clinical prognosis with methylation, anti-tumour immunity, functional states, MSI, TMB, DNSss, LOH and chemotherapeutic responses in 43 cancer types and subtypes. RESULTS AND CONCLUSIONS RPL7L1 was overexpressed in nine tumor types. Gene mutation, tumor microenvironment and methylation modification of RPL7L1 plays a key role in patient prognosis. And the high expression of RPL7L1 was associated with TMB, MSI, LOH especially LIHC and HNSC. We experimentally verified that genes can promote the proliferation and migration of tumor cells. Our study suggested that RPL7L1 biomarker can be used for treating cancer, detecting it, and predicting its prognosis.
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Affiliation(s)
- Ke-Jie He
- The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou city, Zhejiang Province, China.
| | - Guoyu Gong
- School of Medicine, Xiamen University, Xiamen China
| | - E Liang
- Xiamen Xianyue Hospital, Xiamen China
| | - Yangbo Lv
- The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou city, Zhejiang Province, China
| | - Shuiquan Lin
- The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou city, Zhejiang Province, China
| | - Jianguang Xu
- The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou city, Zhejiang Province, China.
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11
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Pozniak J, Pedri D, Landeloos E, Van Herck Y, Antoranz A, Vanwynsberghe L, Nowosad A, Roda N, Makhzami S, Bervoets G, Maciel LF, Pulido-Vicuña CA, Pollaris L, Seurinck R, Zhao F, Flem-Karlsen K, Damsky W, Chen L, Karagianni D, Cinque S, Kint S, Vandereyken K, Rombaut B, Voet T, Vernaillen F, Annaert W, Lambrechts D, Boecxstaens V, Saeys Y, van den Oord J, Bosisio F, Karras P, Shain AH, Bosenberg M, Leucci E, Paschen A, Rambow F, Bechter O, Marine JC. A TCF4-dependent gene regulatory network confers resistance to immunotherapy in melanoma. Cell 2024; 187:166-183.e25. [PMID: 38181739 DOI: 10.1016/j.cell.2023.11.037] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 08/23/2023] [Accepted: 11/29/2023] [Indexed: 01/07/2024]
Abstract
To better understand intrinsic resistance to immune checkpoint blockade (ICB), we established a comprehensive view of the cellular architecture of the treatment-naive melanoma ecosystem and studied its evolution under ICB. Using single-cell, spatial multi-omics, we showed that the tumor microenvironment promotes the emergence of a complex melanoma transcriptomic landscape. Melanoma cells harboring a mesenchymal-like (MES) state, a population known to confer resistance to targeted therapy, were significantly enriched in early on-treatment biopsies from non-responders to ICB. TCF4 serves as the hub of this landscape by being a master regulator of the MES signature and a suppressor of the melanocytic and antigen presentation transcriptional programs. Targeting TCF4 genetically or pharmacologically, using a bromodomain inhibitor, increased immunogenicity and sensitivity of MES cells to ICB and targeted therapy. We thereby uncovered a TCF4-dependent regulatory network that orchestrates multiple transcriptional programs and contributes to resistance to both targeted therapy and ICB in melanoma.
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Affiliation(s)
- Joanna Pozniak
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium.
| | - Dennis Pedri
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium; Laboratory for Membrane Trafficking, Center for Brain and Disease Research, VIB, Leuven, Belgium
| | - Ewout Landeloos
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium; Department of General Medical Oncology, UZ Leuven, Leuven, Belgium
| | | | - Asier Antoranz
- Laboratory of Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven and UZ Leuven, Leuven, Belgium
| | - Lukas Vanwynsberghe
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Ada Nowosad
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Niccolò Roda
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Samira Makhzami
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Greet Bervoets
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Lucas Ferreira Maciel
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Carlos Ariel Pulido-Vicuña
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Lotte Pollaris
- Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Ruth Seurinck
- Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Fang Zhao
- Laboratory of Molecular Tumor Immunology, Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; German Cancer Consortium (DKTK), Partner Site Essen, Essen, Germany
| | - Karine Flem-Karlsen
- Department of Dermatology, Yale University, 15 York Street, New Haven, CT 05610, USA
| | - William Damsky
- Departments of Dermatology and Pathology, Yale University, 15 York Street, New Haven, CT 05610, USA
| | - Limin Chen
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Despoina Karagianni
- Immune Regulation and Tumor Immunotherapy Group, Cancer Immunology Unit, Research Department of Haematology, UCL Cancer Institute, London WC1E 6DD, UK
| | - Sonia Cinque
- Laboratory for RNA Cancer Biology, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Sam Kint
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven, Belgium; KU Leuven Institute for Single Cell Omics (LISCO), KU Leuven, Leuven, Belgium
| | - Katy Vandereyken
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven, Belgium; KU Leuven Institute for Single Cell Omics (LISCO), KU Leuven, Leuven, Belgium
| | - Benjamin Rombaut
- Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Thierry Voet
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven, Belgium; KU Leuven Institute for Single Cell Omics (LISCO), KU Leuven, Leuven, Belgium
| | | | - Wim Annaert
- Laboratory for Membrane Trafficking, Center for Brain and Disease Research, VIB, Leuven, Belgium
| | - Diether Lambrechts
- Laboratory of Translational Genetics, Center for Cancer Biology, VIB, Leuven, Belgium; Center for Human Genetics, KU Leuven, Leuven, Belgium
| | | | - Yvan Saeys
- Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Joost van den Oord
- Laboratory of Translational Cell and Tissue Research, Department of Pathology, UZ Leuven, Leuven, Belgium
| | - Francesca Bosisio
- Laboratory of Translational Cell and Tissue Research, Department of Pathology, UZ Leuven, Leuven, Belgium
| | - Panagiotis Karras
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - A Hunter Shain
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Marcus Bosenberg
- Departments of Dermatology, Pathology and Immunobiology, Yale University, New Haven, CT 05610, USA
| | - Eleonora Leucci
- Laboratory for RNA Cancer Biology, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Annette Paschen
- Laboratory of Molecular Tumor Immunology, Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; German Cancer Consortium (DKTK), Partner Site Essen, Essen, Germany
| | - Florian Rambow
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium; Department of Applied Computational Cancer Research, Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany; University Duisburg-Essen, Essen, Germany.
| | - Oliver Bechter
- Department of General Medical Oncology, UZ Leuven, Leuven, Belgium.
| | - Jean-Christophe Marine
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium.
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12
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Wang K, Zhang X, Cheng H, Ma W, Bao G, Dong L, Gou Y, Yang J, Cai H. SingleScan: a comprehensive resource for single-cell sequencing data processing and mining. BMC Bioinformatics 2023; 24:463. [PMID: 38062357 PMCID: PMC10704760 DOI: 10.1186/s12859-023-05590-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 11/30/2023] [Indexed: 12/18/2023] Open
Abstract
Single-cell sequencing has shed light on previously inaccessible biological questions from different fields of research, including organism development, immune function, and disease progression. The number of single-cell-based studies increased dramatically over the past decade. Several new methods and tools have been continuously developed, making it extremely tricky to navigate this research landscape and develop an up-to-date workflow to analyze single-cell sequencing data, particularly for researchers seeking to enter this field without computational experience. Moreover, choosing appropriate tools and optimal parameters to meet the demands of researchers represents a major challenge in processing single-cell sequencing data. However, a specific resource for easy access to detailed information on single-cell sequencing methods and data processing pipelines is still lacking. In the present study, an online resource called SingleScan was developed to curate all up-to-date single-cell transcriptome/genome analyzing tools and pipelines. All the available tools were categorized according to their main tasks, and several typical workflows for single-cell data analysis were summarized. In addition, spatial transcriptomics, which is a breakthrough molecular analysis method that enables researchers to measure all gene activity in tissue samples and map the site of activity, was included along with a portion of single-cell and spatial analysis solutions. For each processing step, the available tools and specific parameters used in published articles are provided and how these parameters affect the results is shown in the resource. All information used in the resource was manually extracted from related literature. An interactive website was designed for data retrieval, visualization, and download. By analyzing the included tools and literature, users can gain insights into the trends of single-cell studies and easily grasp the specific usage of a specific tool. SingleScan will facilitate the analysis of single-cell sequencing data and promote the development of new tools to meet the growing and diverse needs of the research community. The SingleScan database is publicly accessible via the website at http://cailab.labshare.cn/SingleScan .
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Affiliation(s)
- Kun Wang
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, China
| | - Xiao Zhang
- Department of Breast Surgery, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, 610072, China
| | - Hansen Cheng
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, China
| | - Wenhao Ma
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, China
| | - Guangchao Bao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, China
| | - Liting Dong
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, China
| | - Yixiong Gou
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, China
| | - Jian Yang
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, China.
| | - Haoyang Cai
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, China.
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13
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Paas-Oliveros E, Hernández-Lemus E, de Anda-Jáuregui G. Computational single cell oncology: state of the art. Front Genet 2023; 14:1256991. [PMID: 38028624 PMCID: PMC10663273 DOI: 10.3389/fgene.2023.1256991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.
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Affiliation(s)
- Ernesto Paas-Oliveros
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Guillermo de Anda-Jáuregui
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Investigadores por Mexico, Conahcyt, Mexico City, Mexico
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14
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Xin Y, Jiang Q, Liu C, Qiu J. Plumbagin has an inhibitory effect on the growth of TSCC PDX model and it enhances the anticancer efficacy of cisplatin. Aging (Albany NY) 2023; 15:12225-12250. [PMID: 37925175 PMCID: PMC10683608 DOI: 10.18632/aging.205175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 10/02/2023] [Indexed: 11/06/2023]
Abstract
BACKGROUND Head and neck squamous cell carcinomas are the sixth most common malignant tumors worldwide. Tongue squamous cell carcinoma is a common malignant tumor of this type, and it is associated with poor prognosis, a high rate of recurrence and a low survival rate. Plumbagin is derived from Plumbago zeylanica L, several studies report that plumbagin could inhibit cell, tumor metastasis, induce apoptosis in various cancer cells. Patient-derived xenograft (PDX) model can maintain the heterogeneity and microenvironment of human tumors, is a powerful research tool for developing potentially effective therapies for TSCC. METHODS Tumor tissues obtained from TSCC patients were implanted into immunodeficient mice to establish TSCC PDX models. Subsequently, the PDX models were used to evaluate the anti-tumor effects of plumbagin on TSCC. Furthermore, we conducted next-generation sequencing (NGS) and explored the mRNA expression profiles between the treatment and control groups. We selected eight mRNAs related to the characteristics and prognosis of TSCC patients for further analysis. RESULTS Plumbagin could inhibit the growth of TSCC PDX models and inhibit expression of Akt/mTOR pathway. In addition, plumbagin was shown to increase drug sensitivity to cisplatin. The eight mRNAs selected for further analysis, AXL, SCG5, VOPP1, DCBLD2 and DRAM1 are cancer-promoting genes, DUSP1, AQP5 and BLNK are cancer suppressor genes. And they were related to the diagnosis, growth, prognosis, and immune cell infiltration in TSCC patients. CONCLUSION Plumbagin exhibits an inhibitory effect on the growth of the PDX model of TSCC. Moreover, plumbagin enhances the inhibitory effects of cisplatin.
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Affiliation(s)
- Yuqi Xin
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
- Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Qingkun Jiang
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
- Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Chenshu Liu
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
- Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Jiaxuan Qiu
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
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15
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Kwon J, Kang J, Jo A, Seo K, An D, Baykan MY, Lee JH, Kim N, Eum HH, Hwang S, Lee JM, Park WY, An HJ, Lee HO, Park JE, Choi JK. Single-cell mapping of combinatorial target antigens for CAR switches using logic gates. Nat Biotechnol 2023; 41:1593-1605. [PMID: 36797491 DOI: 10.1038/s41587-023-01686-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 01/20/2023] [Indexed: 02/18/2023]
Abstract
Identification of optimal target antigens that distinguish cancer cells from normal surrounding tissue cells remains a key challenge in chimeric antigen receptor (CAR) cell therapy for tumors with intratumoral heterogeneity. In this study, we dissected tissue complexity to the level of individual cells through the construction of a single-cell expression atlas that integrates ~1.4 million tumor, tumor-infiltrating normal and reference normal cells from 412 tumors and 12 normal organs. We used a two-step screening method using random forest and convolutional neural networks to select gene pairs that contribute most to discrimination between individual malignant and normal cells. Tumor coverage and specificity are evaluated for the AND, OR and NOT logic gates based on the combinatorial expression pattern of the pairing genes across individual single cells. Single-cell transcriptome-coupled epitope profiling validates the AND, OR and NOT switch targets identified in ovarian cancer and colorectal cancer.
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Affiliation(s)
- Joonha Kwon
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Junho Kang
- Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Areum Jo
- Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Biomedicine and Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea
| | - Kayoung Seo
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Dohyeon An
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Mert Yakup Baykan
- Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Jun Hyeong Lee
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Nayoung Kim
- Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Biomedicine and Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hye Hyeon Eum
- Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Biomedicine and Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sohyun Hwang
- Department of Pathology, CHA Bundang Medical Center, CHA University, Seongnam-si, Republic of Korea
- Department of Biomedical Science, CHA University, Pocheon-si, Republic of Korea
| | - Ji Min Lee
- CHA Advanced Research Institute, CHA Bundang Medical Center, Seongnam-si, Republic of Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Hee Jung An
- Department of Pathology, CHA Bundang Medical Center, CHA University, Seongnam-si, Republic of Korea.
| | - Hae-Ock Lee
- Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
- Department of Biomedicine and Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea.
| | - Jong-Eun Park
- Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea.
| | - Jung Kyoon Choi
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.
- Penta Medix Co., Ltd., Seongnam-si, Republic of Korea.
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16
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Gao T, Kastriti ME, Ljungström V, Heinzel A, Tischler AS, Oberbauer R, Loh PR, Adameyko I, Park PJ, Kharchenko PV. A pan-tissue survey of mosaic chromosomal alterations in 948 individuals. Nat Genet 2023; 55:1901-1911. [PMID: 37904053 PMCID: PMC10838621 DOI: 10.1038/s41588-023-01537-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 09/18/2023] [Indexed: 11/01/2023]
Abstract
Genetic mutations accumulate in an organism's body throughout its lifetime. While somatic single-nucleotide variants have been well characterized in the human body, the patterns and consequences of large chromosomal alterations in normal tissues remain largely unknown. Here, we present a pan-tissue survey of mosaic chromosomal alterations (mCAs) in 948 healthy individuals from the Genotype-Tissue Expression project, augmenting RNA-based allelic imbalance estimation with haplotype phasing. We found that approximately a quarter of the individuals carry a clonally-expanded mCA in at least one tissue, with incidence strongly correlated with age. The prevalence and genome-wide patterns of mCAs vary considerably across tissue types, suggesting tissue-specific mutagenic exposure and selection pressures. The mCA landscapes in normal adrenal and pituitary glands resemble those in tumors arising from these tissues, whereas the same is not true for the esophagus and skin. Together, our findings show a widespread age-dependent emergence of mCAs across normal human tissues with intricate connections to tumorigenesis.
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Affiliation(s)
- Teng Gao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Maria Eleni Kastriti
- Department of Neuroimmunology, Center for Brain Research, Medical University of Vienna, Vienna, Austria
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Sweden
| | - Viktor Ljungström
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andreas Heinzel
- Department of Nephrology, Internal Medicine III, Medical University of Vienna, Vienna, Austria
| | - Arthur S Tischler
- Department of Pathology and Laboratory Medicine, Tufts Medical Center, Boston, MA, USA
| | - Rainer Oberbauer
- Department of Nephrology, Internal Medicine III, Medical University of Vienna, Vienna, Austria
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Igor Adameyko
- Department of Neuroimmunology, Center for Brain Research, Medical University of Vienna, Vienna, Austria
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Sweden
| | - Peter J Park
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA.
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17
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Du J, Gu XR, Yu XX, Cao YJ, Hou J. Essential procedures of single-cell RNA sequencing in multiple myeloma and its translational value. BLOOD SCIENCE 2023; 5:221-236. [PMID: 37941914 PMCID: PMC10629747 DOI: 10.1097/bs9.0000000000000172] [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: 05/17/2023] [Accepted: 09/18/2023] [Indexed: 11/10/2023] Open
Abstract
Multiple myeloma (MM) is a malignant neoplasm characterized by clonal proliferation of abnormal plasma cells. In many countries, it ranks as the second most prevalent malignant neoplasm of the hematopoietic system. Although treatment methods for MM have been continuously improved and the survival of patients has been dramatically prolonged, MM remains an incurable disease with a high probability of recurrence. As such, there are still many challenges to be addressed. One promising approach is single-cell RNA sequencing (scRNA-seq), which can elucidate the transcriptome heterogeneity of individual cells and reveal previously unknown cell types or states in complex tissues. In this review, we outlined the experimental workflow of scRNA-seq in MM, listed some commonly used scRNA-seq platforms and analytical tools. In addition, with the advent of scRNA-seq, many studies have made new progress in the key molecular mechanisms during MM clonal evolution, cell interactions and molecular regulation in the microenvironment, and drug resistance mechanisms in target therapy. We summarized the main findings and sequencing platforms for applying scRNA-seq to MM research and proposed broad directions for targeted therapies based on these findings.
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Affiliation(s)
- Jun Du
- Department of Hematology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Xiao-Ran Gu
- School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China
| | - Xiao-Xiao Yu
- School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China
| | - Yang-Jia Cao
- Department of Hematology, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shanxi 710000, China
| | - Jian Hou
- Department of Hematology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
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18
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Watkins TBK, Colliver EC, Huska MR, Kaufmann TL, Lim EL, Duncan CB, Haase K, Van Loo P, Swanton C, McGranahan N, Schwarz RF. Refphase: Multi-sample phasing reveals haplotype-specific copy number heterogeneity. PLoS Comput Biol 2023; 19:e1011379. [PMID: 37871126 PMCID: PMC10621967 DOI: 10.1371/journal.pcbi.1011379] [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: 12/13/2022] [Revised: 11/02/2023] [Accepted: 07/22/2023] [Indexed: 10/25/2023] Open
Abstract
Most computational methods that infer somatic copy number alterations (SCNAs) from bulk sequencing of DNA analyse tumour samples individually. However, the sequencing of multiple tumour samples from a patient's disease is an increasingly common practice. We introduce Refphase, an algorithm that leverages this multi-sampling approach to infer haplotype-specific copy numbers through multi-sample phasing. We demonstrate Refphase's ability to infer haplotype-specific SCNAs and characterise their intra-tumour heterogeneity, to uncover previously undetected allelic imbalance in low purity samples, and to identify parallel evolution in the context of whole genome doubling in a pan-cancer cohort of 336 samples from 99 tumours.
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Affiliation(s)
- Thomas B. K. Watkins
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, United Kingdom
- The Francis Crick Institute, London, United Kingdom
| | | | - Matthew R. Huska
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin, Germany
| | - Tom L. Kaufmann
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin, Germany
- Department of Electrical Engineering & Computer Science, Technische Universität Berlin, Berlin, Germany
- BIFOLD—Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Emilia L. Lim
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, United Kingdom
- The Francis Crick Institute, London, United Kingdom
| | - Cody B. Duncan
- Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Kerstin Haase
- German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Experimental and Clinical Research Center (ECRC) of the MDC and Charité Berlin, Berlin, Germany
- Department of Pediatric Oncology and Hematology, Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Peter Van Loo
- The Francis Crick Institute, London, United Kingdom
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, United Kingdom
- The Francis Crick Institute, London, United Kingdom
- Department of Medical Oncology, University College London Hospitals, London, United Kingdom
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, United Kingdom
- Cancer Genome Evolution Research Group, University College London Cancer Institute, London, United Kingdom
| | - Roland F. Schwarz
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin, Germany
- BIFOLD—Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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19
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Dileep V, Boix CA, Mathys H, Marco A, Welch GM, Meharena HS, Loon A, Jeloka R, Peng Z, Bennett DA, Kellis M, Tsai LH. Neuronal DNA double-strand breaks lead to genome structural variations and 3D genome disruption in neurodegeneration. Cell 2023; 186:4404-4421.e20. [PMID: 37774679 PMCID: PMC10697236 DOI: 10.1016/j.cell.2023.08.038] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 04/02/2023] [Accepted: 08/29/2023] [Indexed: 10/01/2023]
Abstract
Persistent DNA double-strand breaks (DSBs) in neurons are an early pathological hallmark of neurodegenerative diseases including Alzheimer's disease (AD), with the potential to disrupt genome integrity. We used single-nucleus RNA-seq in human postmortem prefrontal cortex samples and found that excitatory neurons in AD were enriched for somatic mosaic gene fusions. Gene fusions were particularly enriched in excitatory neurons with DNA damage repair and senescence gene signatures. In addition, somatic genome structural variations and gene fusions were enriched in neurons burdened with DSBs in the CK-p25 mouse model of neurodegeneration. Neurons enriched for DSBs also had elevated levels of cohesin along with progressive multiscale disruption of the 3D genome organization aligned with transcriptional changes in synaptic, neuronal development, and histone genes. Overall, this study demonstrates the disruption of genome stability and the 3D genome organization by DSBs in neurons as pathological steps in the progression of neurodegenerative diseases.
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Affiliation(s)
- Vishnu Dileep
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Carles A Boix
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hansruedi Mathys
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Asaf Marco
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Gwyneth M Welch
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Hiruy S Meharena
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Anjanet Loon
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ritika Jeloka
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Zhuyu Peng
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Manolis Kellis
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Li-Huei Tsai
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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20
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Yi D, Nam JW, Jeong H. Toward the functional interpretation of somatic structural variations: bulk- and single-cell approaches. Brief Bioinform 2023; 24:bbad297. [PMID: 37587831 PMCID: PMC10516374 DOI: 10.1093/bib/bbad297] [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: 05/10/2023] [Revised: 07/05/2023] [Accepted: 07/23/2023] [Indexed: 08/18/2023] Open
Abstract
Structural variants (SVs) are genomic rearrangements that can take many different forms such as copy number alterations, inversions and translocations. During cell development and aging, somatic SVs accumulate in the genome with potentially neutral, deleterious or pathological effects. Generation of somatic SVs is a key mutational process in cancer development and progression. Despite their importance, the detection of somatic SVs is challenging, making them less studied than somatic single-nucleotide variants. In this review, we summarize recent advances in whole-genome sequencing (WGS)-based approaches for detecting somatic SVs at the tissue and single-cell levels and discuss their advantages and limitations. First, we describe the state-of-the-art computational algorithms for somatic SV calling using bulk WGS data and compare the performance of somatic SV detectors in the presence or absence of a matched-normal control. We then discuss the unique features of cutting-edge single-cell-based techniques for analyzing somatic SVs. The advantages and disadvantages of bulk and single-cell approaches are highlighted, along with a discussion of their sensitivity to copy-neutral SVs, usefulness for functional inferences and experimental and computational costs. Finally, computational approaches for linking somatic SVs to their functional readouts, such as those obtained from single-cell transcriptome and epigenome analyses, are illustrated, with a discussion of the promise of these approaches in health and diseases.
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Affiliation(s)
- Dohun Yi
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Jin-Wu Nam
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Research Institute for Convergence of Basic Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Hyobin Jeong
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
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21
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Ramakrishnan A, Symeonidi A, Hanel P, Schmid KT, Richter ML, Schubert M, Colomé-Tatché M. epiAneufinder identifies copy number alterations from single-cell ATAC-seq data. Nat Commun 2023; 14:5846. [PMID: 37730813 PMCID: PMC10511508 DOI: 10.1038/s41467-023-41076-1] [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: 04/26/2022] [Accepted: 08/23/2023] [Indexed: 09/22/2023] Open
Abstract
Single-cell open chromatin profiling via scATAC-seq has become a mainstream measurement of open chromatin in single-cells. Here we present epiAneufinder, an algorithm that exploits the read count information from scATAC-seq data to extract genome-wide copy number alterations (CNAs) for individual cells, allowing the study of CNA heterogeneity present in a sample at the single-cell level. Using different cancer scATAC-seq datasets, we show that epiAneufinder can identify intratumor clonal heterogeneity in populations of single cells based on their CNA profiles. We demonstrate that these profiles are concordant with the ones inferred from single-cell whole genome sequencing data for the same samples. EpiAneufinder allows the inference of single-cell CNA information from scATAC-seq data, without the need of additional experiments, unlocking a layer of genomic variation which is otherwise unexplored.
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Affiliation(s)
- Akshaya Ramakrishnan
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Aikaterini Symeonidi
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
- Biomedical Center (BMC), Physiological Chemistry, Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany.
| | - Patrick Hanel
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Biomedical Center (BMC), Physiological Chemistry, Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany
| | - Katharina T Schmid
- Biomedical Center (BMC), Physiological Chemistry, Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany
| | - Maria L Richter
- Biomedical Center (BMC), Physiological Chemistry, Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany
| | - Michael Schubert
- Oncode Institute, Division of Cell Biology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, the Netherlands
| | - Maria Colomé-Tatché
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
- Biomedical Center (BMC), Physiological Chemistry, Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany.
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22
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Fagin JA, Krishnamoorthy GP, Landa I. Pathogenesis of cancers derived from thyroid follicular cells. Nat Rev Cancer 2023; 23:631-650. [PMID: 37438605 PMCID: PMC10763075 DOI: 10.1038/s41568-023-00598-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/08/2023] [Indexed: 07/14/2023]
Abstract
The genomic simplicity of differentiated cancers derived from thyroid follicular cells offers unique insights into how oncogenic drivers impact tumour phenotype. Essentially, the main oncoproteins in thyroid cancer activate nodes in the receptor tyrosine kinase-RAS-BRAF pathway, which constitutively induces MAPK signalling to varying degrees consistent with their specific biochemical mechanisms of action. The magnitude of the flux through the MAPK signalling pathway determines key elements of thyroid cancer biology, including differentiation state, invasive properties and the cellular composition of the tumour microenvironment. Progression of disease results from genomic lesions that drive immortalization, disrupt chromatin accessibility and cause cell cycle checkpoint dysfunction, in conjunction with a tumour microenvironment characterized by progressive immunosuppression. This Review charts the genomic trajectories of these common endocrine tumours, while connecting them to the biological states that they confer.
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Affiliation(s)
- James A Fagin
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Gnana P Krishnamoorthy
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Iñigo Landa
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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23
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Chen G, Zhu W, Liu Y, Zhang L, Xie L, Song X, Song X. The clonal heterogeneity of colon cancer with liver metastases. J Gastroenterol 2023; 58:642-655. [PMID: 37042990 PMCID: PMC10307713 DOI: 10.1007/s00535-023-01989-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 03/29/2023] [Indexed: 04/13/2023]
Abstract
BACKGROUND Colon cancer with liver metastases (CCLM) characterized by genetic heterogeneity is an evolutionary process leading to variations in response to selective pressure, but the underlying evolutionary models still remains unclear. METHODS Total of 30 samples, including primary tumor and two to four matched liver metastases from 8 treatment-naïve patients with CCLM were collected, and subjected to whole-exome DNA sequencing. PyClone was used to calculate intra and inter-tumor heterogeneity, LICHeE was used to reconstruct the cancer phylogeny trees and investigate the subclonal composition. RESULTS The genetic differences were observed between primary and metastatic lesions, as well as among multiple metastases in all patients. The natural history models of colorectal cancer in each case were identified, including parallel, linear, and branching evolution. Liver metastases could originate from primary lesions or other metastases. Pathway and process enrichment analysis also showed obvious heterogeneity and enhancement of several molecular functions. CONCLUSIONS Our data reveal the genetic and heterogeneity between primary and metastatic lesions, as well as among multiple metastases and provide genomic evidence for clonal heterogeneity for CCLM.
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Affiliation(s)
- Guanxuan Chen
- Department of Intensive Care Unit, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Wanqi Zhu
- Department of Research and Education, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Yang Liu
- Shanghai OrigiMed Co., Ltd, Shanghai, People's Republic of China
| | - Liwen Zhang
- Shanghai OrigiMed Co., Ltd, Shanghai, People's Republic of China
| | - Li Xie
- Department of Clinical Laboratory, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
- Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Xingguo Song
- Department of Clinical Laboratory, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China.
| | - Xianrang Song
- Department of Clinical Laboratory, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China.
- Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China.
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24
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Jiang H, Zhan S, Ching WK, Chen L. Robust joint clustering of multi-omics single-cell data via multi-modal high-order neighborhood Laplacian matrix optimization. Bioinformatics 2023; 39:btad414. [PMID: 37382572 PMCID: PMC10329495 DOI: 10.1093/bioinformatics/btad414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/03/2023] [Accepted: 06/28/2023] [Indexed: 06/30/2023] Open
Abstract
MOTIVATION Simultaneous profiling of multi-omics single-cell data represents exciting technological advancements for understanding cellular states and heterogeneity. Cellular indexing of transcriptomes and epitopes by sequencing allowed for parallel quantification of cell-surface protein expression and transcriptome profiling in the same cells; methylome and transcriptome sequencing from single cells allows for analysis of transcriptomic and epigenomic profiling in the same individual cells. However, effective integration method for mining the heterogeneity of cells over the noisy, sparse, and complex multi-modal data is in growing need. RESULTS In this article, we propose a multi-modal high-order neighborhood Laplacian matrix optimization framework for integrating the multi-omics single-cell data: scHoML. Hierarchical clustering method was presented for analyzing the optimal embedding representation and identifying cell clusters in a robust manner. This novel method by integrating high-order and multi-modal Laplacian matrices would robustly represent the complex data structures and allow for systematic analysis at the multi-omics single-cell level, thus promoting further biological discoveries. AVAILABILITY AND IMPLEMENTATION Matlab code is available at https://github.com/jianghruc/scHoML.
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Affiliation(s)
- Hao Jiang
- School of Mathematics, Renmin University of China, Beijing 100872, China
| | - Senwen Zhan
- School of Mathematics, Renmin University of China, Beijing 100872, China
| | - Wai-Ki Ching
- Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
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25
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Gao T, Soldatov R, Sarkar H, Kurkiewicz A, Biederstedt E, Loh PR, Kharchenko PV. Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes. Nat Biotechnol 2023; 41:417-426. [PMID: 36163550 PMCID: PMC10289836 DOI: 10.1038/s41587-022-01468-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 08/11/2022] [Indexed: 11/09/2022]
Abstract
Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and nongenetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of copy number variations from scRNA-seq. Numbat exploits the evolutionary relationships between subclones to iteratively infer single-cell copy number profiles and tumor clonal phylogeny. Analysis of 22 tumor samples, including multiple myeloma, gastric, breast and thyroid cancers, shows that Numbat can reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. Numbat requires neither sample-matched DNA data nor a priori genotyping, and is applicable to a wide range of experimental settings and cancer types.
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Affiliation(s)
- Teng Gao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Ruslan Soldatov
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Hirak Sarkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Adam Kurkiewicz
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Evan Biederstedt
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Stem Cell Institute, Cambridge, MA, USA.
- Altos Labs, San Diego, CA, USA.
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26
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De Falco A, Caruso F, Su XD, Iavarone A, Ceccarelli M. A variational algorithm to detect the clonal copy number substructure of tumors from scRNA-seq data. Nat Commun 2023; 14:1074. [PMID: 36841879 PMCID: PMC9968345 DOI: 10.1038/s41467-023-36790-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 02/16/2023] [Indexed: 02/27/2023] Open
Abstract
Single-cell RNA sequencing is the reference technology to characterize the composition of the tumor microenvironment and to study tumor heterogeneity at high resolution. Here we report Single CEll Variational ANeuploidy analysis (SCEVAN), a fast variational algorithm for the deconvolution of the clonal substructure of tumors from single-cell RNA-seq data. It uses a multichannel segmentation algorithm exploiting the assumption that all the cells in a given copy number clone share the same breakpoints. Thus, the smoothed expression profile of every individual cell constitutes part of the evidence of the copy number profile in each subclone. SCEVAN can automatically and accurately discriminate between malignant and non-malignant cells, resulting in a practical framework to analyze tumors and their microenvironment. We apply SCEVAN to datasets encompassing 106 samples and 93,322 cells from different tumor types and technologies. We demonstrate its application to characterize the intratumor heterogeneity and geographic evolution of malignant brain tumors.
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Affiliation(s)
- Antonio De Falco
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples 'Federico II', 80128, Naples, Italy.,BIOGEM Institute of Molecular Biology and Genetics, 83031, Ariano Irpino, Italy
| | - Francesca Caruso
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples 'Federico II', 80128, Naples, Italy.,BIOGEM Institute of Molecular Biology and Genetics, 83031, Ariano Irpino, Italy
| | - Xiao-Dong Su
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, 5 Yiheyuan Road, Haidian District, 100871, Beijing, China
| | - Antonio Iavarone
- Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL, USA.,Department of Neurological Surgery, University of Miami, Miller School of Medicine, Miami, FL, USA
| | - Michele Ceccarelli
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples 'Federico II', 80128, Naples, Italy. .,BIOGEM Institute of Molecular Biology and Genetics, 83031, Ariano Irpino, Italy.
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27
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Jun SH, Toosi H, Mold J, Engblom C, Chen X, O'Flanagan C, Hagemann-Jensen M, Sandberg R, Aparicio S, Hartman J, Roth A, Lagergren J. Reconstructing clonal tree for phylo-phenotypic characterization of cancer using single-cell transcriptomics. Nat Commun 2023; 14:982. [PMID: 36813776 PMCID: PMC9946941 DOI: 10.1038/s41467-023-36202-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 01/20/2023] [Indexed: 02/24/2023] Open
Abstract
Functional characterization of the cancer clones can shed light on the evolutionary mechanisms driving cancer's proliferation and relapse mechanisms. Single-cell RNA sequencing data provide grounds for understanding the functional state of cancer as a whole; however, much research remains to identify and reconstruct clonal relationships toward characterizing the changes in functions of individual clones. We present PhylEx that integrates bulk genomics data with co-occurrences of mutations from single-cell RNA sequencing data to reconstruct high-fidelity clonal trees. We evaluate PhylEx on synthetic and well-characterized high-grade serous ovarian cancer cell line datasets. PhylEx outperforms the state-of-the-art methods both when comparing capacity for clonal tree reconstruction and for identifying clones. We analyze high-grade serous ovarian cancer and breast cancer data to show that PhylEx exploits clonal expression profiles beyond what is possible with expression-based clustering methods and clear the way for accurate inference of clonal trees and robust phylo-phenotypic analysis of cancer.
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Affiliation(s)
- Seong-Hwan Jun
- SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden.,Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, USA
| | - Hosein Toosi
- SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jeff Mold
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Camilla Engblom
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Xinsong Chen
- Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden
| | - Ciara O'Flanagan
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | | | - Rickard Sandberg
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Samuel Aparicio
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada.,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
| | - Johan Hartman
- Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden.,Department of Clinical Pathology and Cytology, Karolinska University Laboratory, Stockholm, Sweden
| | - Andrew Roth
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada. .,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada. .,Department of Computer Science, University of British Columbia, Vancouver, Canada.
| | - Jens Lagergren
- SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden.
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28
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Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy. J Clin Med 2023; 12:jcm12041279. [PMID: 36835813 PMCID: PMC9968102 DOI: 10.3390/jcm12041279] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 01/28/2023] [Accepted: 02/01/2023] [Indexed: 02/08/2023] Open
Abstract
The emergence of immunotherapy has dramatically changed the cancer treatment paradigm and generated tremendous promise in precision medicine. However, cancer immunotherapy is greatly limited by its low response rates and immune-related adverse events. Transcriptomics technology is a promising tool for deciphering the molecular underpinnings of immunotherapy response and therapeutic toxicity. In particular, applying single-cell RNA-seq (scRNA-seq) has deepened our understanding of tumor heterogeneity and the microenvironment, providing powerful help for developing new immunotherapy strategies. Artificial intelligence (AI) technology in transcriptome analysis meets the need for efficient handling and robust results. Specifically, it further extends the application scope of transcriptomic technologies in cancer research. AI-assisted transcriptomic analysis has performed well in exploring the underlying mechanisms of drug resistance and immunotherapy toxicity and predicting therapeutic response, with profound significance in cancer treatment. In this review, we summarized emerging AI-assisted transcriptomic technologies. We then highlighted new insights into cancer immunotherapy based on AI-assisted transcriptomic analysis, focusing on tumor heterogeneity, the tumor microenvironment, immune-related adverse event pathogenesis, drug resistance, and new target discovery. This review summarizes solid evidence for immunotherapy research, which might help the cancer research community overcome the challenges faced by immunotherapy.
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29
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Khozyainova AA, Valyaeva AA, Arbatsky MS, Isaev SV, Iamshchikov PS, Volchkov EV, Sabirov MS, Zainullina VR, Chechekhin VI, Vorobev RS, Menyailo ME, Tyurin-Kuzmin PA, Denisov EV. Complex Analysis of Single-Cell RNA Sequencing Data. BIOCHEMISTRY (MOSCOW) 2023; 88:231-252. [PMID: 37072324 PMCID: PMC10000364 DOI: 10.1134/s0006297923020074] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool for studying the physiology of normal and pathologically altered tissues. This approach provides information about molecular features (gene expression, mutations, chromatin accessibility, etc.) of cells, opens up the possibility to analyze the trajectories/phylogeny of cell differentiation and cell-cell interactions, and helps in discovery of new cell types and previously unexplored processes. From a clinical point of view, scRNA-seq facilitates deeper and more detailed analysis of molecular mechanisms of diseases and serves as a basis for the development of new preventive, diagnostic, and therapeutic strategies. The review describes different approaches to the analysis of scRNA-seq data, discusses the advantages and disadvantages of bioinformatics tools, provides recommendations and examples of their successful use, and suggests potential directions for improvement. We also emphasize the need for creating new protocols, including multiomics ones, for the preparation of DNA/RNA libraries of single cells with the purpose of more complete understanding of individual cells.
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Affiliation(s)
- Anna A Khozyainova
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia.
| | - Anna A Valyaeva
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119991, Russia
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Mikhail S Arbatsky
- Laboratory of Artificial Intelligence and Bioinformatics, The Russian Clinical Research Center for Gerontology, Pirogov Russian National Medical University, Moscow, 129226, Russia
- School of Public Administration, Lomonosov Moscow State University, Moscow, 119991, Russia
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Sergey V Isaev
- Research Institute of Personalized Medicine, National Center for Personalized Medicine of Endocrine Diseases, National Medical Research Center for Endocrinology, Moscow, 117036, Russia
| | - Pavel S Iamshchikov
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
- Laboratory of Complex Analysis of Big Bioimage Data, National Research Tomsk State University, Tomsk, 634050, Russia
| | - Egor V Volchkov
- Department of Oncohematology, Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, 117198, Russia
| | - Marat S Sabirov
- Laboratory of Bioinformatics and Molecular Genetics, Koltzov Institute of Developmental Biology of the Russian Academy of Sciences, Moscow, 119334, Russia
| | - Viktoria R Zainullina
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Vadim I Chechekhin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Rostislav S Vorobev
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Maxim E Menyailo
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Pyotr A Tyurin-Kuzmin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Evgeny V Denisov
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
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30
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Shou Y, Zhu R, Tang Z, Man XY. A prediction model identifying glycolysis signature as therapeutic target for psoriasis. Front Immunol 2023; 14:1188745. [PMID: 37205116 PMCID: PMC10185821 DOI: 10.3389/fimmu.2023.1188745] [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: 03/17/2023] [Accepted: 04/20/2023] [Indexed: 05/21/2023] Open
Abstract
Background The hyperproliferation featured with upregulated glycolysis is a hallmark of psoriasis. However, molecular difference of keratinocyte glycolysis amongst varied pathologic states in psoriasis remain elusive. Objectives To characterize glycolysis status of psoriatic skin and assess the potential of glycolysis score for therapeutic decision. Methods We analyzed 345414 cells collected from different cohorts of single-cell RNA seq database. A new method, Scissor, was used to integrate the phenotypes in GSE11903 to guide single-cell data analysis, allowing identification of responder subpopulations. AUCell algorithm was performed to evaluate the glycolysis status of single cell. Glycolysis signature was used for further ordering in trajectory analysis. The signature model was built with logistic regression analysis and validated using external datasets. Results Keratinocytes (KCs) expressing SLC2A1 and LDH1 were identified as a novel glycolysis-related subpopulation. Scissor+ cells and Scissor- cells were defined as response and non-response phenotypes. In Scissor+ SLC2A1+ LDH1+ KCs, ATP synthesis pathway was activated, especially, the glycolysis pathway being intriguing. Based on the glycolysis signature, keratinocyte differentiation was decomposed into a three-phase trajectory of normal, non-lesional, and lesional psoriatic cells. The area under the curve (AUC) and Brier score (BS) were used to estimate the performance of the glycolysis signature in distinguishing response and non-response samples in GSE69967 (AUC =0.786, BS =17.7) and GSE85034 (AUC=0.849, BS=11.1). Furthermore, Decision Curve Analysis suggested that the glycolysis score was clinically practicable. Conclusion We demonstrated a novel glycolysis-related subpopulation of KCs, identified 12-glycolysis signature, and validated its promising predictive efficacy of treatment effectiveness.
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Affiliation(s)
- Yanhong Shou
- Department of Dermatology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ronghui Zhu
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhenwei Tang
- Department of Dermatology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao-Yong Man
- Department of Dermatology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Xiao-Yong Man,
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31
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Martin A, Mercader A, Dominguez F, Quiñonero A, Perez M, Gonzalez-Martin R, Delgado A, Mifsud A, Pellicer A, De Los Santos MJ. Mosaic results after preimplantation genetic testing for aneuploidy may be accompanied by changes in global gene expression. Front Mol Biosci 2023; 10:1180689. [PMID: 37122560 PMCID: PMC10140421 DOI: 10.3389/fmolb.2023.1180689] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 04/04/2023] [Indexed: 05/02/2023] Open
Abstract
Aneuploidy in preimplantation embryos is a major cause of human reproductive failure. Unlike uniformly aneuploid embryos, embryos diagnosed as diploid-aneuploid mosaics after preimplantation genetic testing for aneuploidy (PGT-A) can develop into healthy infants. However, the reason why these embryos achieve full reproductive competence needs further research. Current RNA sequencing techniques allow for the investigation of the human preimplantation transcriptome, providing new insights into the molecular mechanisms of embryo development. In this prospective study, using euploid embryo gene expression as a control, we compared the transcriptome profiles of inner cell mass and trophectoderm samples from blastocysts with different levels of chromosomal mosaicism. A total of 25 samples were analyzed from 14 blastocysts with previous PGT-A diagnosis, including five low-level mosaic embryos and four high-level mosaic embryos. Global gene expression profiles visualized in cluster heatmaps were correlated with the original PGT-A diagnosis. In addition, gene expression distance based on the number of differentially expressed genes increased with the mosaic level, compared to euploid controls. Pathways involving apoptosis, mitosis, protein degradation, metabolism, and mitochondrial energy production were among the most deregulated within mosaic embryos. Retrospective analysis of the duration of blastomere cell cycles in mosaic embryos revealed several mitotic delays compared to euploid controls, providing additional evidence of the mosaic status. Overall, these findings suggest that embryos with mosaic results are not simply a misdiagnosis by-product, but may also have a genuine molecular identity that is compatible with their reproductive potential.
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Affiliation(s)
- A. Martin
- IVI-RMA Foundation, Health Research Institute La Fe, Valencia, Spain
| | - A. Mercader
- IVI-RMA Foundation, Health Research Institute La Fe, Valencia, Spain
- IVI-RMA Valencia, Valencia, Spain
| | - F. Dominguez
- IVI-RMA Foundation, Health Research Institute La Fe, Valencia, Spain
| | - A. Quiñonero
- IVI-RMA Foundation, Health Research Institute La Fe, Valencia, Spain
| | - M. Perez
- IVI-RMA Foundation, Health Research Institute La Fe, Valencia, Spain
| | | | | | | | - A. Pellicer
- IVI-RMA Foundation, Health Research Institute La Fe, Valencia, Spain
- IVI-RMA Rome, Rome, Italy
| | - M. J. De Los Santos
- IVI-RMA Foundation, Health Research Institute La Fe, Valencia, Spain
- IVI-RMA Valencia, Valencia, Spain
- *Correspondence: M. J. De Los Santos,
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32
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Lv G, Zhang L, Gao L, Cui J, Liu Z, Sun B, Wang G, Tang Q. The application of single-cell sequencing in pancreatic neoplasm: analysis, diagnosis and treatment. Br J Cancer 2023; 128:206-218. [PMID: 36307645 PMCID: PMC9902442 DOI: 10.1038/s41416-022-02023-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 10/11/2022] [Accepted: 10/12/2022] [Indexed: 02/08/2023] Open
Abstract
Pancreatic neoplasms, including pancreatic ductal adenocarcinoma (PDAC), intraductal papillary mucinous neoplasm (IPMN) and pancreatic cystic neoplasms (PCNs), are the most puzzling diseases. Numerous studies have not brought significant improvements in prognosis and diagnosis, especially in PDAC. One important reason is that previous studies only focused on differences between patients and healthy individuals but ignored intratumoral heterogeneity. In recent years, single-cell sequencing techniques, represented by single-cell RNA sequencing (scRNA-seq), have emerged by which researchers can analyse each cell in tumours instead of their average levels. Herein, we summarise the new current knowledge of single-cell sequencing in pancreatic neoplasms with respect to techniques, tumour heterogeneities and treatments.
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Affiliation(s)
- Gaoyuan Lv
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, 150000, Harbin, Heilongjiang Province, China
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Liang Zhang
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, 150000, Harbin, Heilongjiang Province, China
| | - Lei Gao
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, 150000, Harbin, Heilongjiang Province, China
| | - Jitao Cui
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, 150000, Harbin, Heilongjiang Province, China
| | - Ziying Liu
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, 150000, Harbin, Heilongjiang Province, China
| | - Bei Sun
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, 150000, Harbin, Heilongjiang Province, China
| | - Gang Wang
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, 150000, Harbin, Heilongjiang Province, China.
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
| | - Qiushi Tang
- Chinese Journal of Practical Surgery, Chinese Medical University, Shenyang, China.
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33
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Usman OH, Zhang L, Xie G, Kocher HM, Hwang CI, Wang YJ, Mallory X, Irianto J. Genomic heterogeneity in pancreatic cancer organoids and its stability with culture. NPJ Genom Med 2022; 7:71. [PMID: 36535941 PMCID: PMC9763422 DOI: 10.1038/s41525-022-00342-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
The establishment of patient-derived pancreatic cancer organoid culture in recent years creates an exciting opportunity for researchers to perform a wide range of in vitro studies on a model that closely recapitulates the tumor. One of the outstanding question in pancreatic cancer biology is the causes and consequences of genomic heterogeneity observed in the disease. However, to use pancreatic cancer organoids as a model to study genomic variations, we need to first understand the degree of genomic heterogeneity and its stability within organoids. Here, we used single-cell whole-genome sequencing to investigate the genomic heterogeneity of two independent pancreatic cancer organoid lines, as well as their genomic stability with extended culture. Clonal populations with similar copy number profiles were observed within the organoids, and the proportion of these clones was shifted with extended culture, suggesting the growth advantage of some clones. However, sub-clonal genomic heterogeneity was also observed within each clonal population, indicating the genomic instability of the pancreatic cancer cells themselves. Furthermore, our transcriptomic analysis also revealed a positive correlation between copy number alterations and gene expression regulation, suggesting the "gene dosage" effect of these copy number alterations that translates to gene expression regulation.
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Affiliation(s)
- Olalekan H Usman
- Department of Biomedical Sciences, College of Medicine, Florida State University, Tallahassee, FL, 32306, USA
| | - Liting Zhang
- Department of Computer Science, Florida State University, Tallahassee, FL, 32306, USA
| | - Gengqiang Xie
- Department of Biomedical Sciences, College of Medicine, Florida State University, Tallahassee, FL, 32306, USA
| | - Hemant M Kocher
- Barts Cancer Institute, Queen Mary, John Vane Science Centre, University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Chang-Il Hwang
- Department of Microbiology and Molecular Genetics, University of California Davis, Davis, CA, 95616, USA
| | - Yue Julia Wang
- Department of Biomedical Sciences, College of Medicine, Florida State University, Tallahassee, FL, 32306, USA
| | - Xian Mallory
- Department of Computer Science, Florida State University, Tallahassee, FL, 32306, USA
| | - Jerome Irianto
- Department of Biomedical Sciences, College of Medicine, Florida State University, Tallahassee, FL, 32306, USA.
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34
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Jeong H, Grimes K, Rauwolf KK, Bruch PM, Rausch T, Hasenfeld P, Benito E, Roider T, Sabarinathan R, Porubsky D, Herbst SA, Erarslan-Uysal B, Jann JC, Marschall T, Nowak D, Bourquin JP, Kulozik AE, Dietrich S, Bornhauser B, Sanders AD, Korbel JO. Functional analysis of structural variants in single cells using Strand-seq. Nat Biotechnol 2022:10.1038/s41587-022-01551-4. [PMID: 36424487 DOI: 10.1038/s41587-022-01551-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 10/07/2022] [Indexed: 11/27/2022]
Abstract
Somatic structural variants (SVs) are widespread in cancer, but their impact on disease evolution is understudied due to a lack of methods to directly characterize their functional consequences. We present a computational method, scNOVA, which uses Strand-seq to perform haplotype-aware integration of SV discovery and molecular phenotyping in single cells by using nucleosome occupancy to infer gene expression as a readout. Application to leukemias and cell lines identifies local effects of copy-balanced rearrangements on gene deregulation, and consequences of SVs on aberrant signaling pathways in subclones. We discovered distinct SV subclones with dysregulated Wnt signaling in a chronic lymphocytic leukemia patient. We further uncovered the consequences of subclonal chromothripsis in T cell acute lymphoblastic leukemia, which revealed c-Myb activation, enrichment of a primitive cell state and informed successful targeting of the subclone in cell culture, using a Notch inhibitor. By directly linking SVs to their functional effects, scNOVA enables systematic single-cell multiomic studies of structural variation in heterogeneous cell populations.
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Affiliation(s)
- Hyobin Jeong
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.,Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Seoul, Republic of Korea
| | - Karen Grimes
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.,Faculty of Biosciences, EMBL and Heidelberg University, Heidelberg, Germany
| | - Kerstin K Rauwolf
- Division of Pediatric Oncology, University Children's Hospital, Zürich, Switzerland
| | - Peter-Martin Bruch
- Department of Hematology, Oncology and Rheumatology, Heidelberg University Hospital, Heidelberg, Germany.,Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany.,Department of Hematology and Oncology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Tobias Rausch
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.,Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany
| | - Patrick Hasenfeld
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Eva Benito
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Tobias Roider
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.,Department of Hematology, Oncology and Rheumatology, Heidelberg University Hospital, Heidelberg, Germany.,Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany
| | | | - David Porubsky
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany.,Max Planck Institute for Informatics, Saarbrücken, Germany.,Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Sophie A Herbst
- Department of Hematology, Oncology and Rheumatology, Heidelberg University Hospital, Heidelberg, Germany.,Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany
| | - Büşra Erarslan-Uysal
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany.,Department of Pediatric Oncology, Hematology, and Immunology, University of Heidelberg and Hopp Children's Cancer Center, Heidelberg, Germany
| | - Johann-Christoph Jann
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Heidelberg, Germany
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Daniel Nowak
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Heidelberg, Germany
| | - Jean-Pierre Bourquin
- Division of Pediatric Oncology, University Children's Hospital, Zürich, Switzerland
| | - Andreas E Kulozik
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany.,Department of Pediatric Oncology, Hematology, and Immunology, University of Heidelberg and Hopp Children's Cancer Center, Heidelberg, Germany
| | - Sascha Dietrich
- Department of Hematology, Oncology and Rheumatology, Heidelberg University Hospital, Heidelberg, Germany.,Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany.,Department of Hematology and Oncology, University Hospital Düsseldorf, Düsseldorf, Germany.,Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Beat Bornhauser
- Division of Pediatric Oncology, University Children's Hospital, Zürich, Switzerland
| | - Ashley D Sanders
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany. .,Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany. .,Berlin Institute of Health (BIH), Berlin, Germany. .,Charité-Universitätsmedizin, Berlin, Germany.
| | - Jan O Korbel
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany. .,Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany. .,Bridging Research Division on Mechanisms of Genomic Variation and Data Science, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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35
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Srivastava A, Bencomo T, Das I, Lee CS. Unravelling the landscape of skin cancer through single-cell transcriptomics. Transl Oncol 2022; 27:101557. [PMID: 36257209 PMCID: PMC9576539 DOI: 10.1016/j.tranon.2022.101557] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/12/2022] [Accepted: 09/15/2022] [Indexed: 11/15/2022] Open
Abstract
The human skin is a complex organ that forms the first line of defense against pathogens and external injury. It is composed of a wide variety of cells that work together to maintain homeostasis and prevent disease, such as skin cancer. The exponentially rising incidence of skin malignancies poses a growing public health challenge, particularly when the disease course is complicated by metastasis and therapeutic resistance. Recent advances in single-cell transcriptomics have provided a high-resolution view of gene expression heterogeneity that can be applied to skin cancers to define cell types and states, understand disease evolution, and develop new therapeutic concepts. This approach has been particularly valuable in characterizing the contribution of immune cells in skin cancer, an area of great clinical importance given the increasing use of immunotherapy in this setting. In this review, we highlight recent skin cancer studies utilizing bulk RNA sequencing, introduce various single-cell transcriptomics approaches, and summarize key findings obtained by applying single-cell transcriptomics to skin cancer.
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Affiliation(s)
- Ankit Srivastava
- Stanford Program in Epithelial Biology, Stanford University, Stanford, CA 94305 United States of America,Department of Microbiology, Tumor and Cell Biology, Science for Life Laboratory, Karolinska Institute, Stockholm 17177, Sweden
| | - Tomas Bencomo
- Stanford Program in Epithelial Biology, Stanford University, Stanford, CA 94305 United States of America
| | - Ishani Das
- Division of Oncology, School of Medicine, Stanford University, Stanford, CA 94305 United States of America
| | - Carolyn S. Lee
- Stanford Program in Epithelial Biology, Stanford University, Stanford, CA 94305 United States of America,Stanford Cancer Institute, Stanford University, Stanford, CA 94305 United States of America,Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA 94304 United States of America,Corresponding author at: 269 Campus Drive, Room 2160, Stanford, CA 94305.
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36
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Gasper W, Rossi F, Ligorio M, Ghersi D. Variant calling enhances the identification of cancer cells in single-cell RNA sequencing data. PLoS Comput Biol 2022; 18:e1010576. [PMID: 36191033 PMCID: PMC9560611 DOI: 10.1371/journal.pcbi.1010576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/13/2022] [Accepted: 09/15/2022] [Indexed: 12/14/2022] Open
Abstract
Single-cell RNA-sequencing is an invaluable research tool that allows for the investigation of gene expression in heterogeneous cancer cell populations in ways that bulk RNA-seq cannot. However, normal (i.e., non tumor) cells in cancer samples have the potential to confound the downstream analysis of single-cell RNA-seq data. Existing methods for identifying cancer and normal cells include copy number variation inference, marker-gene expression analysis, and expression-based clustering. This work aims to extend the existing approaches for identifying cancer cells in single-cell RNA-seq samples by incorporating variant calling and the identification of putative driver alterations. We found that putative driver alterations can be detected in single-cell RNA-seq data obtained with full-length transcript technologies and noticed that a subset of cells in tumor samples are enriched for putative driver alterations as compared to normal cells. Furthermore, we show that the number of putative driver alterations and inferred copy number variation are not correlated in all samples. Taken together, our findings suggest that augmenting existing cancer-cell filtering methods with variant calling and analysis can increase the number of tumor cells that can be confidently included in downstream analyses of single-cell full-length transcript RNA-seq datasets.
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Affiliation(s)
- William Gasper
- School of Interdisciplinary Informatics, University of Nebraska at Omaha, Omaha, Nebraska, United States of America
| | - Francesca Rossi
- Department of Surgery, University of Texas Southwestern, Dallas, Texas, United States of America
| | - Matteo Ligorio
- Department of Surgery, University of Texas Southwestern, Dallas, Texas, United States of America
- * E-mail: (ML); (DG)
| | - Dario Ghersi
- School of Interdisciplinary Informatics, University of Nebraska at Omaha, Omaha, Nebraska, United States of America
- * E-mail: (ML); (DG)
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37
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Carangelo G, Magi A, Semeraro R. From multitude to singularity: An up-to-date overview of scRNA-seq data generation and analysis. Front Genet 2022; 13:994069. [PMID: 36263428 PMCID: PMC9575985 DOI: 10.3389/fgene.2022.994069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/15/2022] [Indexed: 11/23/2022] Open
Abstract
Single cell RNA sequencing (scRNA-seq) is today a common and powerful technology in biomedical research settings, allowing to profile the whole transcriptome of a very large number of individual cells and reveal the heterogeneity of complex clinical samples. Traditionally, cells have been classified by their morphology or by expression of certain proteins in functionally distinct settings. The advent of next generation sequencing (NGS) technologies paved the way for the detection and quantitative analysis of cellular content. In this context, transcriptome quantification techniques made their advent, starting from the bulk RNA sequencing, unable to dissect the heterogeneity of a sample, and moving to the first single cell techniques capable of analyzing a small number of cells (1–100), arriving at the current single cell techniques able to generate hundreds of thousands of cells. As experimental protocols have improved rapidly, computational workflows for processing the data have also been refined, opening up to novel methods capable of scaling computational times more favorably with the dataset size and making scRNA-seq much better suited for biomedical research. In this perspective, we will highlight the key technological and computational developments which have enabled the analysis of this growing data, making the scRNA-seq a handy tool in clinical applications.
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Affiliation(s)
- Giulia Carangelo
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, Florence, Italy
| | - Alberto Magi
- Department of Information Engineering, University of Florence, Florence, Italy
| | - Roberto Semeraro
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
- *Correspondence: Roberto Semeraro,
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38
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Karras P, Bordeu I, Pozniak J, Nowosad A, Pazzi C, Van Raemdonck N, Landeloos E, Van Herck Y, Pedri D, Bervoets G, Makhzami S, Khoo JH, Pavie B, Lamote J, Marin-Bejar O, Dewaele M, Liang H, Zhang X, Hua Y, Wouters J, Browaeys R, Bergers G, Saeys Y, Bosisio F, van den Oord J, Lambrechts D, Rustgi AK, Bechter O, Blanpain C, Simons BD, Rambow F, Marine JC. A cellular hierarchy in melanoma uncouples growth and metastasis. Nature 2022; 610:190-198. [PMID: 36131018 PMCID: PMC10439739 DOI: 10.1038/s41586-022-05242-7] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 08/17/2022] [Indexed: 12/29/2022]
Abstract
Although melanoma is notorious for its high degree of heterogeneity and plasticity1,2, the origin and magnitude of cell-state diversity remains poorly understood. Equally, it is unclear whether growth and metastatic dissemination are supported by overlapping or distinct melanoma subpopulations. Here, by combining mouse genetics, single-cell and spatial transcriptomics, lineage tracing and quantitative modelling, we provide evidence of a hierarchical model of tumour growth that mirrors the cellular and molecular logic underlying the cell-fate specification and differentiation of the embryonic neural crest. We show that tumorigenic competence is associated with a spatially localized perivascular niche, a phenotype acquired through an intercellular communication pathway established by endothelial cells. Consistent with a model in which only a fraction of cells are fated to fuel growth, temporal single-cell tracing of a population of melanoma cells with a mesenchymal-like state revealed that these cells do not contribute to primary tumour growth but, instead, constitute a pool of metastatic initiating cells that switch cell identity while disseminating to secondary organs. Our data provide a spatially and temporally resolved map of the diversity and trajectories of melanoma cell states and suggest that the ability to support growth and metastasis are limited to distinct pools of cells. The observation that these phenotypic competencies can be dynamically acquired after exposure to specific niche signals warrant the development of therapeutic strategies that interfere with the cancer cell reprogramming activity of such microenvironmental cues.
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Affiliation(s)
- Panagiotis Karras
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Ignacio Bordeu
- Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, University of Cambridge, Cambridge, UK
- The Wellcome Trust/CRUK Gurdon Institute, University of Cambridge, Cambridge, UK
- Departamento de Física, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Santiago, Chile
| | - Joanna Pozniak
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Ada Nowosad
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Cecilia Pazzi
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Nina Van Raemdonck
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Ewout Landeloos
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | | | - Dennis Pedri
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Greet Bervoets
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Samira Makhzami
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | | | - Benjamin Pavie
- VIB BioImaging Core, VIB Center for Brain and Disease Research, Leuven, Belgium
- VIB Bioimaging Core, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Jochen Lamote
- FACS Expertise Center, Center for Cancer Biology, VIB, Leuven, Belgium
| | - Oskar Marin-Bejar
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Michael Dewaele
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | | | | | - Yichao Hua
- Department of Oncology, KU Leuven, Leuven, Belgium
- Laboratory of Tumor Microenvironment and Therapeutic Resistance, Center for Cancer Biology, VIB, Leuven, Belgium
| | - Jasper Wouters
- Center for Brain & Disease Research, VIB-KU Leuven, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Robin Browaeys
- Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Gabriele Bergers
- Department of Oncology, KU Leuven, Leuven, Belgium
- Laboratory of Tumor Microenvironment and Therapeutic Resistance, Center for Cancer Biology, VIB, Leuven, Belgium
| | - Yvan Saeys
- Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Francesca Bosisio
- Laboratory for Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Joost van den Oord
- Laboratory for Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Diether Lambrechts
- Laboratory of Translational Genetics, Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Translational Genetics, Center for Human Genetics, KU Leuven, Leuven, Belgium
| | - Anil K Rustgi
- Herbert Irving Comprehensive Center, Columbia University Irving Medical Center, New York, USA
| | - Oliver Bechter
- Department of General Medical Oncology, UZ Leuven, Leuven, Belgium
| | - Cedric Blanpain
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Benjamin D Simons
- Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, University of Cambridge, Cambridge, UK
- The Wellcome Trust/CRUK Gurdon Institute, University of Cambridge, Cambridge, UK
- Wellcome Trust-MRC Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Florian Rambow
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium.
- Department of Oncology, KU Leuven, Leuven, Belgium.
- Department of Applied Computational Cancer Research, Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany.
- University Duisburg-Essen, Essen, Germany.
- German Cancer Consortium (DKTK), partner site Essen, Essen, Germany.
| | - Jean-Christophe Marine
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium.
- Department of Oncology, KU Leuven, Leuven, Belgium.
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39
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Li PH, Kong XY, He YZ, Liu Y, Peng X, Li ZH, Xu H, Luo H, Park J. Recent developments in application of single-cell RNA sequencing in the tumour immune microenvironment and cancer therapy. Mil Med Res 2022; 9:52. [PMID: 36154923 PMCID: PMC9511789 DOI: 10.1186/s40779-022-00414-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 08/20/2022] [Indexed: 11/10/2022] Open
Abstract
The advent of single-cell RNA sequencing (scRNA-seq) has provided insight into the tumour immune microenvironment (TIME). This review focuses on the application of scRNA-seq in investigation of the TIME. Over time, scRNA-seq methods have evolved, and components of the TIME have been deciphered with high resolution. In this review, we first introduced the principle of scRNA-seq and compared different sequencing approaches. Novel cell types in the TIME, a continuous transitional state, and mutual intercommunication among TIME components present potential targets for prognosis prediction and treatment in cancer. Thus, we concluded novel cell clusters of cancer-associated fibroblasts (CAFs), T cells, tumour-associated macrophages (TAMs) and dendritic cells (DCs) discovered after the application of scRNA-seq in TIME. We also proposed the development of TAMs and exhausted T cells, as well as the possible targets to interrupt the process. In addition, the therapeutic interventions based on cellular interactions in TIME were also summarized. For decades, quantification of the TIME components has been adopted in clinical practice to predict patient survival and response to therapy and is expected to play an important role in the precise treatment of cancer. Summarizing the current findings, we believe that advances in technology and wide application of single-cell analysis can lead to the discovery of novel perspectives on cancer therapy, which can subsequently be implemented in the clinic. Finally, we propose some future directions in the field of TIME studies that can be aided by scRNA-seq technology.
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Affiliation(s)
- Pei-Heng Li
- Department of Thyroid and Parathyroid Surgery, Laboratory of Thyroid and Parathyroid Disease, Frontiers Science Centre for Disease-Related Molecular Network, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, 610044, China
| | - Xiang-Yu Kong
- Department of Thyroid and Parathyroid Surgery, Laboratory of Thyroid and Parathyroid Disease, Frontiers Science Centre for Disease-Related Molecular Network, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, 610044, China
| | - Ya-Zhou He
- Department of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610044, China
| | - Yi Liu
- Department of Rheumatology and Immunology, Rare Diseases Centre, West China Hospital, Sichuan University, Chengdu, 610044, China
| | - Xi Peng
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Zhi-Hui Li
- Department of Thyroid and Parathyroid Surgery, Laboratory of Thyroid and Parathyroid Disease, Frontiers Science Centre for Disease-Related Molecular Network, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, 610044, China
| | - Heng Xu
- State Key Laboratory of Biotherapy and Cancer Centre, West China Hospital, Sichuan University and Collaborative Innovation Centre, Chengdu, 610044, China
| | - Han Luo
- Department of Thyroid and Parathyroid Surgery, Laboratory of Thyroid and Parathyroid Disease, Frontiers Science Centre for Disease-Related Molecular Network, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, 610044, China.
| | - Jihwan Park
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea.
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40
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Precise identification of cancer cells from allelic imbalances in single cell transcriptomes. Commun Biol 2022; 5:884. [PMID: 36071103 PMCID: PMC9452529 DOI: 10.1038/s42003-022-03808-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 08/05/2022] [Indexed: 01/08/2023] Open
Abstract
A fundamental step of tumour single cell mRNA analysis is separating cancer and non-cancer cells. We show that the common approach to separation, using shifts in average expression, can lead to erroneous biological conclusions. By contrast, allelic imbalances representing copy number changes directly detect the cancer genotype and accurately separate cancer from non-cancer cells. Our findings provide a definitive approach to identifying cancer cells from single cell mRNA sequencing data. The identification of cancer cells from single cell transcriptomes can be improved by detecting allelic imbalances due to copy number changes.
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41
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Unified K-means coupled self-representation and neighborhood kernel learning for clustering single-cell RNA-sequencing data. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.06.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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42
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Liu HP, Wang D, Lai HM. Can we infer tumor presence of single cell transcriptomes and their tumor of origin from bulk transcriptomes by machine learning? Comput Struct Biotechnol J 2022; 20:2672-2679. [PMID: 35685355 PMCID: PMC9162953 DOI: 10.1016/j.csbj.2022.05.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/09/2022] [Accepted: 05/19/2022] [Indexed: 11/17/2022] Open
Abstract
There is a growing need to build a model that uses single cell RNA-seq (scRNA-seq) to separate malignant cells from nonmalignant cells and to identify tumor of origin of single cells and/or circulating tumor cells (CTCs). Currently, it is infeasible to build a tumor of origin model learnt from scRNA-seq by machine learning (ML). We then wondered if an ML model learnt from bulk transcriptomes is applicable to scRNA-seq to infer single cells’ tumor presence and further indicate their tumor of origin. We used k-nearest neighbors, one-versus-all support vector machine, one-versus-one support vector machine, random forest and introduced scTumorTrace to conduct a pioneering experiment containing leukocytes and seven major cancer types where bulk RNA-seq and scRNA-seq data were available. 13 ML models learnt from bulk RNA-seq were all reliable to use (F-score > 96%) shown by a validation set of bulk transcriptomes, but none of them was applicable to scRNA-seq except scTumorTrace. Making inferences from bulk RNA-seq to scRNA-seq was impaired by feature selection and improved by log2-transformed TPM units. scTumorTrace with transcriptome-wide 2-tuples showed F-score beyond 98.74 and 94.29% in inferring tumor presence and tumor of origin at single-cell resolution and correctly identified 45 single candidate prostate CTCs but lineage-confirmed non-CTCs as leukocytes. We concluded that modern ML techniques are quantitative and could hardly address the raised questions. scTumorTrace with transcriptome-wide 2-tuples is qualitative, standardization-free and not subject to log2-transformed quantities, enabling us to infer tumor presence of single cell transcriptomes and their tumor of origin from bulk transcriptomes.
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Affiliation(s)
- Hua-Ping Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - Dongwen Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - Hung-Ming Lai
- Aiphaqua Genomics Research Unit, Taipei 111, Taiwan
- Corresponding author.
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43
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Anderson DJ, Pauler FM, McKenna A, Shendure J, Hippenmeyer S, Horwitz MS. Simultaneous brain cell type and lineage determined by scRNA-seq reveals stereotyped cortical development. Cell Syst 2022; 13:438-453.e5. [PMID: 35452605 DOI: 10.1016/j.cels.2022.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 01/21/2022] [Accepted: 03/30/2022] [Indexed: 11/30/2022]
Abstract
Mutations are acquired frequently, such that each cell's genome inscribes its history of cell divisions. Common genomic alterations involve loss of heterozygosity (LOH). LOH accumulates throughout the genome, offering large encoding capacity for inferring cell lineage. Using only single-cell RNA sequencing (scRNA-seq) of mouse brain cells, we found that LOH events spanning multiple genes are revealed as tracts of monoallelically expressed, constitutionally heterozygous single-nucleotide variants (SNVs). We simultaneously inferred cell lineage and marked developmental time points based on X chromosome inactivation and the total number of LOH events while identifying cell types from gene expression patterns. Our results are consistent with progenitor cells giving rise to multiple cortical cell types through stereotyped expansion and distinct waves of neurogenesis. This type of retrospective analysis could be incorporated into scRNA-seq pipelines and, compared with experimental approaches for determining lineage in model organisms, is applicable where genetic engineering is prohibited, such as humans.
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Affiliation(s)
- Donovan J Anderson
- Allen Discovery Center for Lineage Tracing and Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA 98109, USA
| | - Florian M Pauler
- Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria
| | | | - Jay Shendure
- Allen Discovery Center for Lineage Tracing, Department of Genome Sciences, and Howard Hughes Medical Institute, University of Washington, Seattle, WA 98109, USA
| | - Simon Hippenmeyer
- Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria
| | - Marshall S Horwitz
- Allen Discovery Center for Lineage Tracing and Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA 98109, USA.
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44
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Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data. Nat Biotechnol 2022; 40:527-538. [PMID: 34764492 PMCID: PMC9010342 DOI: 10.1038/s41587-021-01091-3] [Citation(s) in RCA: 120] [Impact Index Per Article: 60.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 09/10/2021] [Indexed: 02/07/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) distinguishes cell types, states and lineages within the context of heterogeneous tissues. However, current single-cell data cannot directly link cell clusters with specific phenotypes. Here we present Scissor, a method that identifies cell subpopulations from single-cell data that are associated with a given phenotype. Scissor integrates phenotype-associated bulk expression data and single-cell data by first quantifying the similarity between each single cell and each bulk sample. It then optimizes a regression model on the correlation matrix with the sample phenotype to identify relevant subpopulations. Applied to a lung cancer scRNA-seq dataset, Scissor identified subsets of cells associated with worse survival and with TP53 mutations. In melanoma, Scissor discerned a T cell subpopulation with low PDCD1/CTLA4 and high TCF7 expression associated with an immunotherapy response. Beyond cancer, Scissor was effective in interpreting facioscapulohumeral muscular dystrophy and Alzheimer's disease datasets. Scissor identifies biologically and clinically relevant cell subpopulations from single-cell assays by leveraging phenotype and bulk-omics datasets.
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45
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Milite S, Bergamin R, Patruno L, Calonaci N, Caravagna G. A Bayesian method to cluster single-cell RNA sequencing data using Copy Number Alterations. Bioinformatics 2022; 38:2512-2518. [PMID: 35298589 DOI: 10.1093/bioinformatics/btac143] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 01/31/2022] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Cancers are composed by several heterogeneous subpopulations, each one harbouring different genetic and epigenetic somatic alterations that contribute to disease onset and therapy response. In recent years, copy number alterations leading to tumour aneuploidy have been identified as potential key drivers of such populations, but the definition of the precise makeup of cancer subclones from sequencing assays remains challenging. In the end, little is known about the mapping between complex copy number alterations and their effect on cancer phenotypes. RESULTS We introduce CONGAS, a Bayesian probabilistic method to phase bulk DNA and single-cell RNA measurements from independent assays. CONGAS jointly identifies clusters of single cells with subclonal copy number alterations, and differences in RNA expression. The model builds statistical priors leveraging bulk DNA sequencing data, does not require a normal reference and scales fast thanks to a GPU backend and variational inference. We test CONGAS on both simulated and real data, and find that it can determine the tumour subclonal composition at the single-cell level together with clone-specific RNA phenotypes in tumour data generated from both 10x and Smart-Seq assays. AVAILABILITY CONGAS is available as 2 packages: CONGAS (https://github.com/caravagnalab/congas), which implements the model in Python, and RCONGAS (https://caravagnalab.github.io/rcongas/), which provides R functions to process inputs, outputs, and run CONGAS fits. The analysis of real data and scripts to generate figures of this paper are available via RCONGAS; code associated to simulations is available at https://github.com/caravagnalab/rcongas_test. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Salvatore Milite
- Department of Mathematics and Geosciences, University of Trieste, Italy
| | - Riccardo Bergamin
- Department of Mathematics and Geosciences, University of Trieste, Italy
| | - Lucrezia Patruno
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Italy
| | - Nicola Calonaci
- Department of Mathematics and Geosciences, University of Trieste, Italy
| | - Giulio Caravagna
- Department of Mathematics and Geosciences, University of Trieste, Italy
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46
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Ozcan Z, San Lucas FA, Wong JW, Chang K, Stopsack KH, Fowler J, Jakubek YA, Scheet P. Chromosomal imbalances detected via RNA-sequencing in 28 cancers. Bioinformatics 2022; 38:1483-1490. [PMID: 34999743 PMCID: PMC8896613 DOI: 10.1093/bioinformatics/btab861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 11/05/2021] [Accepted: 01/03/2022] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION RNA-sequencing (RNA-seq) of tumor tissue is typically only used to measure gene expression. Here, we present a statistical approach that leverages existing RNA-seq data to also detect somatic copy number alterations (SCNAs), a pervasive phenomenon in human cancers, without a need to sequence the corresponding DNA. RESULTS We present an analysis of 4942 participant samples from 28 cancers in The Cancer Genome Atlas (TCGA), demonstrating robust detection of SCNAs from RNA-seq. Using genotype imputation and haplotype information, our RNA-based method had a median sensitivity of 85% to detect SCNAs defined by DNA analysis, at high specificity (∼95%). As an example of translational potential, we successfully replicated SCNA features associated with breast cancer subtypes. Our results credential haplotype-based inference based on RNA-seq to detect SCNAs in clinical and population-based settings. AVAILABILITY AND IMPLEMENTATION The analyses presented use the data publicly available from TCGA Research Network (http://cancergenome.nih.gov/). See Methods for details regarding data downloads. hapLOHseq software is freely available under The MIT license and can be downloaded from http://scheet.org/software.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zuhal Ozcan
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Francis A San Lucas
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Justin W Wong
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Kyle Chang
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Konrad H Stopsack
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jerry Fowler
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yasminka A Jakubek
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Paul Scheet
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
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47
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Melnekoff DT, Laganà A. Single-Cell Sequencing Technologies in Precision Oncology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:269-282. [PMID: 35230694 DOI: 10.1007/978-3-030-91836-1_15] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Single-cell sequencing technologies are revolutionizing cancer research and are poised to become the standard for translational cancer studies. Rapidly decreasing costs and increasing throughput and resolution are paving the way for the adoption of single-cell technologies in clinical settings for personalized medicine applications. In this chapter, we review the state of the art of single-cell DNA and RNA sequencing technologies, the computational tools to analyze the data, and their potential application to precision oncology. We also discuss the advantages of single-cell over bulk sequencing for the dissection of intra-tumor heterogeneity and the characterization of subclonal cell populations, the implementation of targeted drug repurposing approaches, and describe advanced methodologies for multi-omics data integration and to assess cell signaling at single-cell resolution.
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Affiliation(s)
- David T Melnekoff
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alessandro Laganà
- Department of Genetics and Genomic Sciences, Department of Oncological Sciences, Mount Sinai Icahn School of Medicine, New York, NY, USA.
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48
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Wang M, Song WM, Ming C, Wang Q, Zhou X, Xu P, Krek A, Yoon Y, Ho L, Orr ME, Yuan GC, Zhang B. Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application. Mol Neurodegener 2022; 17:17. [PMID: 35236372 PMCID: PMC8889402 DOI: 10.1186/s13024-022-00517-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.
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Affiliation(s)
- Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Chen Ming
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Qian Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Yonejung Yoon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Lap Ho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Miranda E. Orr
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
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Jin Z, Huang W, Shen N, Li J, Wang X, Dong J, Park PJ, Xi R. Single-cell gene fusion detection by scFusion. Nat Commun 2022; 13:1084. [PMID: 35228538 PMCID: PMC8885711 DOI: 10.1038/s41467-022-28661-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 02/03/2022] [Indexed: 11/09/2022] Open
Abstract
Gene fusions can play important roles in tumor initiation and progression. While fusion detection so far has been from bulk samples, full-length single-cell RNA sequencing (scRNA-seq) offers the possibility of detecting gene fusions at the single-cell level. However, scRNA-seq data have a high noise level and contain various technical artifacts that can lead to spurious fusion discoveries. Here, we present a computational tool, scFusion, for gene fusion detection based on scRNA-seq. We evaluate the performance of scFusion using simulated and five real scRNA-seq datasets and find that scFusion can efficiently and sensitively detect fusions with a low false discovery rate. In a T cell dataset, scFusion detects the invariant TCR gene recombinations in mucosal-associated invariant T cells that many methods developed for bulk data fail to detect; in a multiple myeloma dataset, scFusion detects the known recurrent fusion IgH-WHSC1, which is associated with overexpression of the WHSC1 oncogene. Our results demonstrate that scFusion can be used to investigate cellular heterogeneity of gene fusions and their transcriptional impact at the single-cell level. Gene fusions are an important class of mutations in tumor genomes. Here, the authors develop a single-cell gene fusion detection method scFusion and demonstrate its applications in cancer single-cell studies.
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50
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Caprioli C, Nazari I, Milovanovic S, Pelicci PG. Single-Cell Technologies to Decipher the Immune Microenvironment in Myeloid Neoplasms: Perspectives and Opportunities. Front Oncol 2022; 11:796477. [PMID: 35186713 PMCID: PMC8847379 DOI: 10.3389/fonc.2021.796477] [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: 10/16/2021] [Accepted: 12/31/2021] [Indexed: 11/26/2022] Open
Abstract
Myeloid neoplasms (MN) are heterogeneous clonal disorders arising from the expansion of hematopoietic stem and progenitor cells. In parallel with genetic and epigenetic dynamics, the immune system plays a critical role in modulating tumorigenesis, evolution and therapeutic resistance at the various stages of disease progression. Single-cell technologies represent powerful tools to assess the cellular composition of the complex tumor ecosystem and its immune environment, to dissect interactions between neoplastic and non-neoplastic components, and to decipher their functional heterogeneity and plasticity. In addition, recent progress in multi-omics approaches provide an unprecedented opportunity to study multiple molecular layers (DNA, RNA, proteins) at the level of single-cell or single cellular clones during disease evolution or in response to therapy. Applying single-cell technologies to MN holds the promise to uncover novel cell subsets or phenotypic states and highlight the connections between clonal evolution and immune escape, which is crucial to fully understand disease progression and therapeutic resistance. This review provides a perspective on the various opportunities and challenges in the field, focusing on key questions in MN research and discussing their translational value, particularly for the development of more efficient immunotherapies.
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Affiliation(s)
- Chiara Caprioli
- Department of Experimental Oncology, Istituto Europeo di Oncologia, Milan, Italy.,Scuola Europea di Medicina Molecolare (SEMM) European School of Molecular Medicine, Milan, Italy.,Hematology and Bone Marrow Transplant Unit, Papa Giovanni XXIII Hospital, Bergamo, Italy
| | - Iman Nazari
- Department of Experimental Oncology, Istituto Europeo di Oncologia, Milan, Italy.,Scuola Europea di Medicina Molecolare (SEMM) European School of Molecular Medicine, Milan, Italy
| | - Sara Milovanovic
- Department of Experimental Oncology, Istituto Europeo di Oncologia, Milan, Italy.,Scuola Europea di Medicina Molecolare (SEMM) European School of Molecular Medicine, Milan, Italy
| | - Pier Giuseppe Pelicci
- Department of Experimental Oncology, Istituto Europeo di Oncologia, Milan, Italy.,Scuola Europea di Medicina Molecolare (SEMM) European School of Molecular Medicine, Milan, Italy
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