1
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Yang F, Zhang Y, Huang T, Qin Z, Xu S, Weng L, Huang H, Li S, Zhang D. G-quadruplex embedded in semi-CHA reaction combined with invasive reaction for label-free detection of single nucleotide polymorphisms. Talanta 2024; 280:126686. [PMID: 39128314 DOI: 10.1016/j.talanta.2024.126686] [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: 06/30/2024] [Revised: 07/31/2024] [Accepted: 08/06/2024] [Indexed: 08/13/2024]
Abstract
G-quadruplex/thioflavin T (G4/THT) is one of the ideal label-free fluorescent light-emitting elements in the field of biosensors due to its good programmability and adaptability. However, the unsatisfactory luminous efficiency of single-molecule G4/THT limits its more practical applications. Here, we developed a G4 embedded semi-catalytic hairpin assembly (G4-SCHA) reaction by rationally modifying the traditional CHA reaction, and combined with the invasive reaction, supplemented by magnetic separation technology, for label-free sensitive detection of single nucleotide polymorphisms (SNPs). The invasive reaction enabled specific recognition of single-base mutations in DNA sequences as well as preliminary signal cycle amplification. Then, magnetic separation was used to shield the false positive signals. Finally, the G4-SCHA was created for secondary amplification and label-free output of the signal. This dual-signal amplified label-free biosensor has been shown to detect mutant targets as low as 78.54 fM. What's more, this biosensor could distinguish 0.01 % of the mutant targets from a mixed sample containing a large number of wild-type targets. In addition, the detection of real and complex biological samples also verified the practical application value of this biosensor in the field of molecular design breeding. Therefore, this study improves a label-free fluorescent light-emitting element, and then proposes a simple, efficient and universal label-free SNP biosensing strategy, which also provides an important reference for the development of other G4/THT based biosensors.
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Affiliation(s)
- Fang Yang
- Key Laboratory of Theoretical Organic Chemistry and Function Molecule, Ministry of Education, School of Chemistry and Chemical Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China; Research Center for Novel Computational Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou, 311121, China
| | - Yunshan Zhang
- Research Center for Novel Computational Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou, 311121, China
| | - Tuo Huang
- Key Laboratory of Theoretical Organic Chemistry and Function Molecule, Ministry of Education, School of Chemistry and Chemical Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China; Research Center for Novel Computational Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou, 311121, China
| | - Ziyue Qin
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China
| | - Shijie Xu
- Research Center for Novel Computational Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou, 311121, China
| | - Lin Weng
- Research Center for Intelligent Computing Platforms, Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
| | - Haowen Huang
- Key Laboratory of Theoretical Organic Chemistry and Function Molecule, Ministry of Education, School of Chemistry and Chemical Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China.
| | - Shuang Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China.
| | - Diming Zhang
- Research Center for Novel Computational Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou, 311121, China.
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2
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Ren P, Zhang J, Vijg J. Somatic mutations in aging and disease. GeroScience 2024; 46:5171-5189. [PMID: 38488948 PMCID: PMC11336144 DOI: 10.1007/s11357-024-01113-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 02/27/2024] [Indexed: 03/17/2024] Open
Abstract
Time always leaves its mark, and our genome is no exception. Mutations in the genome of somatic cells were first hypothesized to be the cause of aging in the 1950s, shortly after the molecular structure of DNA had been described. Somatic mutation theories of aging are based on the fact that mutations in DNA as the ultimate template for all cellular functions are irreversible. However, it took until the 1990s to develop the methods to test if DNA mutations accumulate with age in different organs and tissues and estimate the severity of the problem. By now, numerous studies have documented the accumulation of somatic mutations with age in normal cells and tissues of mice, humans, and other animals, showing clock-like mutational signatures that provide information on the underlying causes of the mutations. In this review, we will first briefly discuss the recent advances in next-generation sequencing that now allow quantitative analysis of somatic mutations. Second, we will provide evidence that the mutation rate differs between cell types, with a focus on differences between germline and somatic mutation rate. Third, we will discuss somatic mutational signatures as measures of aging, environmental exposure, and activities of DNA repair processes. Fourth, we will explain the concept of clonally amplified somatic mutations, with a focus on clonal hematopoiesis. Fifth, we will briefly discuss somatic mutations in the transcriptome and in our other genome, i.e., the genome of mitochondria. We will end with a brief discussion of a possible causal contribution of somatic mutations to the aging process.
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Affiliation(s)
- Peijun Ren
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Jie Zhang
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jan Vijg
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
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3
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Li H, Xu WX, Tan JC, Hong YM, He J, Zhao BP, Zhou JA, Zheng YM, Lei M, Zheng XQ, Ding J, Liu NN, Gao JJ, Zhang CQ, Wang H. Single-cell multi-omics identify novel regulators required for osteoclastogenesis during aging. iScience 2024; 27:110734. [PMID: 39280596 PMCID: PMC11401210 DOI: 10.1016/j.isci.2024.110734] [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: 11/16/2023] [Revised: 04/25/2024] [Accepted: 08/12/2024] [Indexed: 09/18/2024] Open
Abstract
Age-related osteoporosis manifests as a complex pathology that disrupts bone homeostasis and elevates fracture risk, yet the mechanisms facilitating age-related shifts in bone marrow macrophages/osteoclasts (BMMs/OCs) lineage are not fully understood. To decipher these mechanisms, we conducted an investigation into the determinants controlling BMMs/OCs differentiation. We performed single-cell multi-omics profiling on bone marrow samples from mice of different ages (1, 6, and 20 months) to gain a holistic understanding of cellular changes across time. Our analysis revealed that aging significantly instigates OC differentiation. Importantly, we identified Cebpd as a vital gene for osteoclastogenesis and bone resorption during the aging process. Counterbalancing the effects of Cebpd, we found Irf8, Sox4, and Klf4 to play crucial roles. By thoroughly examining the cellular dynamics underpinning bone aging, our study unveils novel insights into the mechanisms of age-related osteoporosis and presents potential therapeutic targets for future exploration.
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Affiliation(s)
- Hao Li
- Department of Orthopaedics, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wan-Xing Xu
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jing-Cong Tan
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yue-Mei Hong
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian He
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ben-Peng Zhao
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jin-An Zhou
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu-Min Zheng
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, Canada
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, QC, Canada
| | - Ming Lei
- Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiao-Qi Zheng
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Ding
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, Canada
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, QC, Canada
| | - Ning-Ning Liu
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun-Jie Gao
- Department of Orthopaedics, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Sixth People's Hospital Fujian, No. 16, Luoshan Section, Jinguang Road, Luoshan Street, Jinjiang City, Quanzhou, Fujian, China
| | - Chang-Qing Zhang
- Department of Orthopaedics, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hui Wang
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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4
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Wiens M, Farahani H, Scott RW, Underhill TM, Bashashati A. Benchmarking bulk and single-cell variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries. Genome Res 2024; 34:1196-1210. [PMID: 39147582 PMCID: PMC11444184 DOI: 10.1101/gr.277066.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 08/12/2024] [Indexed: 08/17/2024]
Abstract
Single-cell sequencing methodologies such as scRNA-seq and scATAC-seq have become widespread and effective tools to interrogate tissue composition. Increasingly, variant callers are being applied to these methodologies to resolve the genetic heterogeneity of a sample, especially in the case of detecting the clonal architecture of a tumor. Typically, traditional bulk DNA variant callers are applied to the pooled reads of a single-cell library to detect candidate mutations. Recently, multiple studies have applied such callers on reads from individual cells, with some citing the ability to detect rare variants with higher sensitivity. Many studies apply these two approaches to the Chromium (10x Genomics) scRNA-seq and scATAC-seq methodologies. However, Chromium-based libraries may offer additional challenges to variant calling compared with existing single-cell methodologies, raising questions regarding the validity of variants obtained from such a workflow. To determine the merits and challenges of various variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries, we use sample libraries with matched bulk whole-genome sequencing to evaluate the performance of callers. We review caller performance, finding that bulk callers applied on pooled reads significantly outperform individual-cell approaches. We also evaluate variants unique to scRNA-seq and scATAC-seq methodologies, finding patterns of noise but also potential capture of RNA-editing events. Finally, we review the notion that variant calling at the single-cell level can detect rare somatic variants, providing empirical results that suggest resolving such variants is infeasible in single-cell Chromium libraries.
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Affiliation(s)
- Matthew Wiens
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia V6T 2B9, Canada
| | - Hossein Farahani
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia V6T 2B9, Canada
| | - R Wilder Scott
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia V6T 2B9, Canada
| | - T Michael Underhill
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia V6T 2B9, Canada
- Department of Cellular & Physiological Sciences, University of British Columbia, Vancouver, British Columbia V6T 2A1, Canada
| | - Ali Bashashati
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia V6T 2B9, Canada;
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, British Columbia V6T 1Z7, Canada
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5
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Zhang Y, Yang F, Huang T, Xu S, Ye J, Weng L, Hu Y, Huang H, Li S, Zhang D. Entropy-Driven Catalytic G-Quadruple Cycle Amplification Integrated with Ligases for Label-Free Detection of Single Nucleotide Polymorphisms. Anal Chem 2024; 96:14971-14979. [PMID: 39213531 DOI: 10.1021/acs.analchem.4c03057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
G-Quadruplex/thioflavin (G4/THT) has become a very promising label-free fluorescent luminescent element for nucleic acid detection due to its good programmability and compatibility. However, the weak fluorescence efficiency of single-molecule G4/THT limits its potential applications. Here, we developed an entropy-driven catalytic (EDC) G4 (EDC-G4) cycle amplification technology as a universal label-free signal amplification and output system by properly programming classical EDC and G4 backbone sequences, preintegrated ligase chain reaction (LCR) for label-free sensitive detection of single nucleotide polymorphisms (SNPs). First, the positive strand LCR enabled specific transduction and preliminary signal amplification from single-base mutation information to single-strand information. Subsequently, the EDC-G4 cycle amplification reaction was activated, accompanied by the production of a large number of G4/THT luminophores to output fluorescent signals. The EDC-G4 system was proposed to address the weak fluorescence of G4/THT and obtain a label-free fluorescence signal amplification. The dual-signal amplification effect enabled the LCR-EDC-G4 detection system to accurately detect mutant target (MT) at concentrations as low as 22.39 fM and specifically identify 0.01% MT in a mixed detection pool. Moreover, the LCR-EDC-G4 system was further demonstrated for its potential application in real biological samples. Therefore, this study not only contributes ideas for the development of label-free fluorescent biosensing strategies but also provides a high-performance and practical SNP detection tool in parallel.
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Affiliation(s)
- Yunshan Zhang
- Research Center for Novel Computational Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou 311121, China
| | - Fang Yang
- Research Center for Novel Computational Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou 311121, China
- Key Laboratory of Theoretical Organic Chemistry and Function Molecule, Ministry of Education, School of Chemistry and Chemical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Tuo Huang
- Research Center for Novel Computational Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou 311121, China
- Key Laboratory of Theoretical Organic Chemistry and Function Molecule, Ministry of Education, School of Chemistry and Chemical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Shijie Xu
- Research Center for Novel Computational Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou 311121, China
| | - Jing Ye
- Research Center for Novel Computational Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou 311121, China
| | - Lin Weng
- Research Center for Intelligent Computing Platforms, Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China
| | - Ye Hu
- Nanjing Institute for Food and Drug Control, Nanjing, Jiangsu 211198, China
| | - Haowen Huang
- Key Laboratory of Theoretical Organic Chemistry and Function Molecule, Ministry of Education, School of Chemistry and Chemical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Shuang Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
| | - Diming Zhang
- Research Center for Novel Computational Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou 311121, China
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6
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Heid J, Cutler R, Lee M, Vijg J, Maslov AY. Concurrent analysis of genome and transcriptome in one single cell. BMC Res Notes 2024; 17:267. [PMID: 39285281 PMCID: PMC11403997 DOI: 10.1186/s13104-024-06927-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 08/29/2024] [Indexed: 09/22/2024] Open
Abstract
Thus far, multiple techniques for single cell analysis have been developed, yet we lack a relatively simple tool to assess DNA and RNA from the same cell at whole-transcriptome and whole-genome depths. Here we present an updated method for physical separation of cytoplasmic RNA from the nuclei, which allows for simultaneous studies of DNA and RNA from the same single cell. The method consists of three steps-(1) immobilization of a single cell on solid substrate, (2) hypotonic lysis of immobilized single cell, and (3) separation of cytosol containing aqueous phase and immobilized nucleus. We found that DNA and RNA extracted from single cell using our approach is suitable for downstream sequencing-based applications. We demonstrated that the coverage of transcriptome and genome sequencing data obtained after DNA/RNA separation is similar to that observed without separation. We also showed that the separation procedure does not create any noticeable bias in observed mutational load or mutation spectra. Thus, our method can serve as a tool for simultaneous complex analysis of the genome and transcriptome, providing necessary information on the relationship between somatic mutations and the regulation of gene expression.
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Affiliation(s)
- Johanna Heid
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
- Perlmutter Cancer Center, NYU Langone, New York, USA.
| | - Ronald Cutler
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Moonsook Lee
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Jan Vijg
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Alexander Y Maslov
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
- Laboratory of Applied Genomic Technologies, Voronezh State University of Engineering Technology, Voronezh, 394000, Russia.
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7
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Ren Y, Liu K, Yang H, Zhang Y, Deng S, Cao J, Xia X, Deng R. Multiplexing Imaging of Closely Located Single-Nucleotide Mutations in Single Cells via Encoded in situ PCR. ACS Sens 2024; 9:3549-3556. [PMID: 38982583 DOI: 10.1021/acssensors.4c00378] [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] [Indexed: 07/11/2024]
Abstract
Mutation accumulation in RNAs results in closely located single-nucleotide mutations (SNMs), which is highly associated with the drug resistance of pathogens. Imaging of SNMs in single cells has significance for understanding the heterogeneity of RNAs that are related to drug resistance, but the direct "see" closely located SNMs remains challenging. Herein, we designed an encoded ligation-mediated in situ polymerase chain reaction method (termed enPCR), which enabled the visualization of multiple closely located SNMs in bacterial RNAs. Unlike conventional ligation-based probes that can only discriminate a single SNM, this method can simultaneously image different SNMs at closely located sites with single-cell resolution using modular anchoring probes and encoded PCR primers. We tested the capacity of the method to detect closely located SNMs related to quinolone resistance in the gyrA gene of Salmonella enterica (S. enterica), and found that the simultaneous detection of the closely located SNMs can more precisely indicate the resistance of the S. enterica to quinolone compared to the detection of one SNM. The multiplexing imaging assay for SNMs can serve to reveal the relationship between complex cellular genotypes and phenotypes.
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Affiliation(s)
- Yao Ren
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Kerui Liu
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Hao Yang
- School of Chemical Engineering, Sichuan University, Chengdu 610065, China
| | - Yong Zhang
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Sha Deng
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Jijuan Cao
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian, Liaoning 116600, China
| | - Xuhan Xia
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Ruijie Deng
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
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8
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Heid J, Cutler R, Sun S, Lee M, Maslov AY, Dong X, Sidoli S, Vijg J. Negative selection allows human primary fibroblasts to tolerate high somatic mutation loads induced by N-ethyl-N-nitrosourea. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.07.588286. [PMID: 38617356 PMCID: PMC11014556 DOI: 10.1101/2024.04.07.588286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Single-cell sequencing has shown that thousands of mutations accumulate with age in most human tissues. While there is ample evidence that some mutations can clonally amplify and lead to disease, the total burden of mutations a cell tolerates without functional decline remains unknown. Here we addressed this question by exposing human primary fibroblasts to multiple, low doses of N-ethyl-N-nitrosourea (ENU) and analyzed somatic mutation burden using single-cell whole genome sequencing. The results indicate that individual cells can sustain ∼60,000 single-nucleotide variants (SNVs) with only a slight adverse effect on growth rate. We provide evidence that such high levels of mutations are only tolerated through negative selection against variants in gene coding regions, and in sequences associated with genetic pathways for maintaining basic cellular function and growth. Since most tissues in adults are non-dividing, these results suggest that somatic mutations in the absence of negative selection may have functionally adverse effects.
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9
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Zhang L, Lee M, Hao X, Ehlert J, Chi Z, Jin B, Maslov AY, Barabási AL, Hoeijmakers JHJ, Edelmann W, Vijg J, Dong X. Negative Selection Allows DNA Mismatch Repair-Deficient Mouse Fibroblasts In Vitro to Tolerate High Levels of Somatic Mutations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.04.592535. [PMID: 38766154 PMCID: PMC11100588 DOI: 10.1101/2024.05.04.592535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Substantial numbers of somatic mutations have been found to accumulate with age in different human tissues. Clonal cellular amplification of some of these mutations can cause cancer and other diseases. However, it is as yet unclear if and to what extent an increased burden of random mutations can affect cellular function without clonal amplification. We tested this in cell culture, which avoids the limitation that an increased mutation burden in vivo typically leads to cancer. We performed single-cell whole-genome sequencing of primary fibroblasts from DNA mismatch repair (MMR) deficient Msh2-/- mice and littermate control animals after long-term passaging. Apart from analyzing somatic mutation burden we analyzed clonality, mutational signatures, and hotspots in the genome, characterizing the complete landscape of somatic mutagenesis in normal and MMR-deficient mouse primary fibroblasts during passaging. While growth rate of Msh2-/- fibroblasts was not significantly different from the controls, the number of de novo single-nucleotide variants (SNVs) increased linearly up until at least 30,000 SNVs per cell, with the frequency of small insertions and deletions (INDELs) plateauing in the Msh2-/- fibroblasts to about 10,000 INDELS per cell. We provide evidence for negative selection and large-scale mutation-driven population changes, including significant clonal expansion of preexisting mutations and widespread cell-strain-specific hotspots. Overall, our results provide evidence that increased somatic mutation burden drives significant cell evolutionary changes in a dynamic cell culture system without significant effects on growth. Since similar selection processes against mutations preventing organ and tissue dysfunction during aging are difficult to envision, these results suggest that increased somatic mutation burden can play a causal role in aging and diseases other than cancer.
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Affiliation(s)
- Lei Zhang
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN 55455, USA
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Moonsook Lee
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Xiaoxiao Hao
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Current affiliation: the Big Data Center of Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510123, China
| | - Joseph Ehlert
- Network Science Institute, Northeastern University, Boston, MA, USA
| | - Zhongxuan Chi
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Bo Jin
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Alexander Y. Maslov
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA, USA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Network and Data Science, Central European University, Budapest, Hungary
| | - Jan H. J. Hoeijmakers
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
- University of Cologne, Faculty of Medicine, Cluster of Excellence for Aging Research, Institute for Genome Stability in Ageing and Disease, Cologne, Germany
- Princess Maxima Center for Pediatric Oncology, Oncode Institute, Utrecht, The Netherlands
| | - Winfried Edelmann
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Jan Vijg
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xiao Dong
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN 55455, USA
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN 55455, USA
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10
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Koptagel H, Jun SH, Hård J, Lagergren J. Scuphr: A probabilistic framework for cell lineage tree reconstruction. PLoS Comput Biol 2024; 20:e1012094. [PMID: 38723024 PMCID: PMC11125557 DOI: 10.1371/journal.pcbi.1012094] [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: 12/12/2022] [Revised: 05/24/2024] [Accepted: 04/20/2024] [Indexed: 05/25/2024] Open
Abstract
Cell lineage tree reconstruction methods are developed for various tasks, such as investigating the development, differentiation, and cancer progression. Single-cell sequencing technologies enable more thorough analysis with higher resolution. We present Scuphr, a distance-based cell lineage tree reconstruction method using bulk and single-cell DNA sequencing data from healthy tissues. Common challenges of single-cell DNA sequencing, such as allelic dropouts and amplification errors, are included in Scuphr. Scuphr computes the distance between cell pairs and reconstructs the lineage tree using the neighbor-joining algorithm. With its embarrassingly parallel design, Scuphr can do faster analysis than the state-of-the-art methods while obtaining better accuracy. The method's robustness is investigated using various synthetic datasets and a biological dataset of 18 cells.
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Affiliation(s)
- Hazal Koptagel
- School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
| | - Seong-Hwan Jun
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Joanna Hård
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Jens Lagergren
- School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
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11
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Liu X, Yang H, Liu J, Liu K, Jin L, Zhang Y, Khan MR, Zhong K, Cao J, He Q, Xia X, Deng R. In Situ Cas12a-Based Allele-Specific PCR for Imaging Single-Nucleotide Variations in Foodborne Pathogenic Bacteria. Anal Chem 2024; 96:2032-2040. [PMID: 38277772 DOI: 10.1021/acs.analchem.3c04532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Abstract
In situ profiling of single-nucleotide variations (SNVs) can elucidate drug-resistant genotypes with single-cell resolution. The capacity to directly "see" genetic information is crucial for investigating the relationship between mutated genes and phenotypes. Fluorescence in situ hybridization serves as a canonical tool for genetic imaging; however, it cannot detect subtle sequence alteration including SNVs. Herein, we develop an in situ Cas12a-based amplification refractory mutation system-PCR (ARMS-PCR) method that allows the visualization of SNVs related to quinolone resistance inside cells. The capacity of discriminating SNVs is enhanced by incorporating optimized mismatched bases in the allele-specific primers, thus allowing to specifically amplify quinolone-resistant related genes. After in situ ARMS-PCR, we employed a modified Cas12a/CRISPR RNA to tag the amplicon, thereby enabling specific binding of fluorophore-labeled DNA probes. The method allows to precisely quantify quinolone-resistant Salmonella enterica in the bacterial mixture. Utilizing this method, we investigated the survival competition capacity of quinolone-resistant and quinolone-sensitive bacteria toward antimicrobial peptides and indicated the enrichment of quinolone-resistant bacteria under colistin sulfate stress. The in situ Cas12a-based ARMS-PCR method holds the potential for profiling cellular phenotypes and gene regulation with single-nucleotide resolution at the single-cell level.
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Affiliation(s)
- Xinmiao Liu
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Hao Yang
- School of Chemical Engineering, Sichuan University, Chengdu 610065, China
| | - Jun Liu
- Chengdu Customs Technology Center, Chengdu 610041, China
| | - Kerui Liu
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Lulu Jin
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Yong Zhang
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Mohammad Rizwan Khan
- Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Kai Zhong
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Jijuan Cao
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian, Liaoning 116600, China
| | - Qiang He
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Xuhan Xia
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Ruijie Deng
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
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12
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Zhang L, Lee M, Maslov AY, Montagna C, Vijg J, Dong X. Analyzing somatic mutations by single-cell whole-genome sequencing. Nat Protoc 2024; 19:487-516. [PMID: 37996541 PMCID: PMC11406548 DOI: 10.1038/s41596-023-00914-8] [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: 12/14/2022] [Accepted: 09/18/2023] [Indexed: 11/25/2023]
Abstract
Somatic mutations are the cause of cancer and have been implicated in other, noncancerous diseases and aging. While clonally expanded mutations can be studied by deep sequencing of bulk DNA, very few somatic mutations expand clonally, and most are unique to each cell. We describe a detailed protocol for single-cell whole-genome sequencing to discover and analyze somatic mutations in tissues and organs. The protocol comprises single-cell multiple displacement amplification (SCMDA), which ensures efficiency and high fidelity in amplification, and the SCcaller software tool to call single-nucleotide variations and small insertions and deletions from the sequencing data by filtering out amplification artifacts. With SCMDA and SCcaller at its core, this protocol describes a complete procedure for the comprehensive analysis of somatic mutations in a single cell, covering (1) single-cell or nucleus isolation, (2) single-cell or nucleus whole-genome amplification, (3) library preparation and sequencing, and (4) computational analyses, including alignment, variant calling, and mutation burden estimation. Methods are also provided for mutation annotation, hotspot discovery and signature analysis. The protocol takes 12-15 h from single-cell isolation to library preparation and 3-7 d of data processing. Compared with other single-cell amplification methods or single-molecular sequencing, it provides high genomic coverage, high accuracy in single-nucleotide variation and small insertions and deletion calling from the same single-cell genome, and fewer processing steps. SCMDA and SCcaller require basic experience in molecular biology and bioinformatics. The protocol can be utilized for studying mutagenesis and genome mosaicism in normal and diseased human and animal tissues under various conditions.
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Affiliation(s)
- Lei Zhang
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN, USA.
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA.
| | - Moonsook Lee
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Alexander Y Maslov
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
- Laboratory of Applied Genomic Technologies, Voronezh State University of Engineering Technology, Voronezh, Russia
| | - Cristina Montagna
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Jan Vijg
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiao Dong
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN, USA.
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, USA.
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13
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Xue Y, Su Z, Lin X, Ho MK, Yu KHO. Single-cell lineage tracing with endogenous markers. Biophys Rev 2024; 16:125-139. [PMID: 38495438 PMCID: PMC10937880 DOI: 10.1007/s12551-024-01179-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 01/18/2024] [Indexed: 03/19/2024] Open
Abstract
Resolving lineage relationships between cells in an organism provides key insights into the fate of individual cells and drives a fundamental understanding of the process of development and disease. A recent rapid increase in experimental and computational advances for detecting naturally occurring somatic nuclear and mitochondrial mutation at single-cell resolution has expanded lineage tracing from model organisms to humans. This review discusses the advantages and challenges of experimental and computational techniques for cell lineage tracing using somatic mutation as endogenous DNA barcodes to decipher the relationships between cells during development and tumour evolution. We outlook the advantages of spatial clonal evolution analysis and single-cell lineage tracing using endogenous genetic markers.
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Affiliation(s)
- Yan Xue
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Zezhuo Su
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
- Department of Orthopaedics and Traumatology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xinyi Lin
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Mun Kay Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Ken H. O. Yu
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
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14
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LIU B. [Hypothesis of Genetic Diversity Selection in the Occurrence and Development of
Lung Cancer: Molecular Evolution and Clinical Significance]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2024; 26:943-949. [PMID: 38163980 PMCID: PMC10767663 DOI: 10.3779/j.issn.1009-3419.2023.101.34] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Indexed: 01/03/2024]
Abstract
So far, the monoclonal hypothesis of tumor occurrence and development cannot be justified. The genetic diversity selection hypothesis for the occurrence and development of lung cancer links Mendelian genetics with Darwin's theory of evolution, suggesting that the genetic diversity of tumor cell populations with polyclonal origins-monoclonal selection-subclonal expansion is the result of selection pressure. Normal cells acquire mutations in oncogenic driver genes and have a selective advantage over other cells, becoming tumor initiating cells; In the interaction with the tumor microenvironment (TME), the vast majority of initiating cells are recognized and killed by the human immune system. If immune escape occurs, the incidence of malignant tumors will greatly increase, and subclonal expansion, intratumour heterogeneity, etc. will occur. This article proposed the hypothesis of genetic diversity selection and analyzed its clinical significance.
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15
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Addala V, Newell F, Pearson JV, Redwood A, Robinson BW, Creaney J, Waddell N. Computational immunogenomic approaches to predict response to cancer immunotherapies. Nat Rev Clin Oncol 2024; 21:28-46. [PMID: 37907723 DOI: 10.1038/s41571-023-00830-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/03/2023] [Indexed: 11/02/2023]
Abstract
Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.
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Affiliation(s)
- Venkateswar Addala
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
- Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
| | - Felicity Newell
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - John V Pearson
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Alec Redwood
- National Centre for Asbestos Related Diseases, University of Western Australia, Perth, Western Australia, Australia
- Institute of Respiratory Health, Perth, Western Australia, Australia
- School of Biomedical Science, University of Western Australia, Perth, Western Australia, Australia
| | - Bruce W Robinson
- National Centre for Asbestos Related Diseases, University of Western Australia, Perth, Western Australia, Australia
- Institute of Respiratory Health, Perth, Western Australia, Australia
- Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
- Medical School, University of Western Australia, Perth, Western Australia, Australia
| | - Jenette Creaney
- National Centre for Asbestos Related Diseases, University of Western Australia, Perth, Western Australia, Australia
- Institute of Respiratory Health, Perth, Western Australia, Australia
- School of Biomedical Science, University of Western Australia, Perth, Western Australia, Australia
- Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
| | - Nicola Waddell
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
- Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
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16
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Rossi N, Gigante N, Vitacolonna N, Piazza C. Inferring Markov Chains to Describe Convergent Tumor Evolution With CIMICE. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:106-119. [PMID: 38015671 DOI: 10.1109/tcbb.2023.3337258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
The field of tumor phylogenetics focuses on studying the differences within cancer cell populations. Many efforts are done within the scientific community to build cancer progression models trying to understand the heterogeneity of such diseases. These models are highly dependent on the kind of data used for their construction, therefore, as the experimental technologies evolve, it is of major importance to exploit their peculiarities. In this work we describe a cancer progression model based on Single Cell DNA Sequencing data. When constructing the model, we focus on tailoring the formalism on the specificity of the data. We operate by defining a minimal set of assumptions needed to reconstruct a flexible DAG structured model, capable of identifying progression beyond the limitation of the infinite site assumption. Our proposal is conservative in the sense that we aim to neither discard nor infer knowledge which is not represented in the data. We provide simulations and analytical results to show the features of our model, test it on real data, show how it can be integrated with other approaches to cope with input noise. Moreover, our framework can be exploited to produce simulated data that follows our theoretical assumptions. Finally, we provide an open source R implementation of our approach, called CIMICE, that is publicly available on BioConductor.
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17
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Maslov AY, Vijg J. Somatic mutation burden in relation to aging and functional life span: implications for cellular reprogramming and rejuvenation. Curr Opin Genet Dev 2023; 83:102132. [PMID: 37931583 PMCID: PMC10841402 DOI: 10.1016/j.gde.2023.102132] [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: 07/05/2023] [Revised: 09/02/2023] [Accepted: 10/07/2023] [Indexed: 11/08/2023]
Abstract
The accrual of somatic mutations has been implicated as causal factors in aging since the 1950s. However, the quantitative analysis of somatic mutations has posed a major challenge due to the random nature of de novo mutations in normal tissues, which has limited analysis to tumors and other clonal lineages. Advances in single-cell and single-molecule next-generation sequencing now allow to obtain, for the first time, detailed insights into the landscape of somatic mutations in different human tissues and cell types as a function of age under various conditions. Here, we will briefly recapitulate progress in somatic mutation analysis and discuss the possible relationship between somatic mutation burden with functional life span, with a focus on differences between germ cells, stem cells, and differentiated cells.
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Affiliation(s)
- Alexander Y Maslov
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Laboratory of Applied Genomic Technologies, Voronezh State University of Engineering Technologies, Voronezh, Russia.
| | - Jan Vijg
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
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18
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Nulsen J, Hussain N, Al-Deka A, Yap J, Uddin K, Yau C, Ahmed AA. Completing a genomic characterisation of microscopic tumour samples with copy number. BMC Bioinformatics 2023; 24:453. [PMID: 38036971 PMCID: PMC10688092 DOI: 10.1186/s12859-023-05576-7] [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: 03/11/2023] [Accepted: 11/21/2023] [Indexed: 12/02/2023] Open
Abstract
BACKGROUND Genomic insights in settings where tumour sample sizes are limited to just hundreds or even tens of cells hold great clinical potential, but also present significant technical challenges. We previously developed the DigiPico sequencing platform to accurately identify somatic mutations from such samples. RESULTS Here, we complete this genomic characterisation with copy number. We present a novel protocol, PicoCNV, to call allele-specific somatic copy number alterations from picogram quantities of tumour DNA. We find that PicoCNV provides exactly accurate copy number in 84% of the genome for even the smallest samples, and demonstrate its clinical potential in maintenance therapy. CONCLUSIONS PicoCNV complements our existing platform, allowing for accurate and comprehensive genomic characterisations of cancers in settings where only microscopic samples are available.
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Affiliation(s)
- Joel Nulsen
- Weatherall Institute for Molecular Medicine, University of Oxford, Oxford, UK
- Nuffield Department for Women's and Reproductive Health, University of Oxford, Oxford, UK
- Singula Bio Ltd., Oxford, UK
| | - Nosheen Hussain
- Weatherall Institute for Molecular Medicine, University of Oxford, Oxford, UK
- Nuffield Department for Women's and Reproductive Health, University of Oxford, Oxford, UK
- Singula Bio Ltd., Oxford, UK
| | - Aws Al-Deka
- Weatherall Institute for Molecular Medicine, University of Oxford, Oxford, UK
- Nuffield Department for Women's and Reproductive Health, University of Oxford, Oxford, UK
- Singula Bio Ltd., Oxford, UK
| | - Jason Yap
- University of Birmingham, Birmingham, UK
| | | | - Christopher Yau
- Nuffield Department for Women's and Reproductive Health, University of Oxford, Oxford, UK
- Health Data Research UK, London, UK
| | - Ahmed Ashour Ahmed
- Weatherall Institute for Molecular Medicine, University of Oxford, Oxford, UK.
- Nuffield Department for Women's and Reproductive Health, University of Oxford, Oxford, UK.
- Singula Bio Ltd., Oxford, UK.
- Oxford Biomedical Research Centre, National Institute of Health Research, Oxford, UK.
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19
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You X, Cao Y, Suzuki T, Shao J, Zhu B, Masumura K, Xi J, Liu W, Zhang X, Luan Y. Genome-wide direct quantification of in vivo mutagenesis using high-accuracy paired-end and complementary consensus sequencing. Nucleic Acids Res 2023; 51:e109. [PMID: 37870450 PMCID: PMC10681716 DOI: 10.1093/nar/gkad909] [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: 06/05/2023] [Revised: 09/25/2023] [Accepted: 10/05/2023] [Indexed: 10/24/2023] Open
Abstract
Error-corrected next-generation sequencing (ecNGS) is an emerging technology for accurately measuring somatic mutations. Here, we report paired-end and complementary consensus sequencing (PECC-Seq), a high-accuracy ecNGS approach for genome-wide somatic mutation detection. We characterize a novel 2-aminoimidazolone lesion besides 7,8-dihydro-8-oxoguanine and the resulting end-repair artifacts originating from NGS library preparation that obscure the sequencing accuracy of NGS. We modify library preparation protocol for the enzymatic removal of end-repair artifacts and improve the accuracy of our previously developed duplex consensus sequencing method. Optimized PECC-Seq shows an error rate of <5 × 10-8 with consensus bases compressed from approximately 25 Gb of raw sequencing data, enabling the accurate detection of low-abundance somatic mutations. We apply PECC-Seq to the quantification of in vivo mutagenesis. Compared with the classic gpt gene mutation assay using gpt delta transgenic mice, PECC-Seq exhibits high sensitivity in quantitatively measuring dose-dependent mutagenesis induced by Aristolochic acid I (AAI). Moreover, PECC-Seq specifically characterizes the distinct genome-wide mutational signatures of AAI, Benzo[a]pyrene, N-Nitroso-N-ethylurea and N-nitrosodiethylamine and reveals the mutational signature of Quinoline in common mouse models. Overall, our findings demonstrate that high-accuracy PECC-Seq is a promising tool for genome-wide somatic mutagenesis quantification and for in vivo mutagenicity testing.
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Affiliation(s)
- Xinyue You
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yiyi Cao
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Takayoshi Suzuki
- Division of Genetics and Mutagenesis, National Institute of Health Sciences, Kawasaki 210-9501, Japan
| | - Jie Shao
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, The Chinese Academy of Sciences, Beijing 100085, China; The University of Chinese Academy of Sciences, Beijing 100049, China
| | - Benzhan Zhu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, The Chinese Academy of Sciences, Beijing 100085, China; The University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kenichi Masumura
- Division of Risk Assessment, National Institute of Health Sciences, Kawasaki 210-9501, Japan
| | - Jing Xi
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Weiying Liu
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xinyu Zhang
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yang Luan
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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20
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Derks LLM, van Boxtel R. Stem cell mutations, associated cancer risk, and consequences for regenerative medicine. Cell Stem Cell 2023; 30:1421-1433. [PMID: 37832550 PMCID: PMC10624213 DOI: 10.1016/j.stem.2023.09.008] [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: 07/17/2023] [Revised: 09/05/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023]
Abstract
Mutation accumulation in stem cells has been associated with cancer risk. However, the presence of numerous mutant clones in healthy tissues has raised the question of what limits cancer initiation. Here, we review recent developments in characterizing mutation accumulation in healthy tissues and compare mutation rates in stem cells during development and adult life with corresponding cancer risk. A certain level of mutagenesis within the stem cell pool might be beneficial to limit the size of malignant clones through competition. This knowledge impacts our understanding of carcinogenesis with potential consequences for the use of stem cells in regenerative medicine.
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Affiliation(s)
- Lucca L M Derks
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS Utrecht, the Netherlands; Oncode Institute, Jaarbeursplein 6, 3521 AL Utrecht, the Netherlands
| | - Ruben van Boxtel
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS Utrecht, the Netherlands; Oncode Institute, Jaarbeursplein 6, 3521 AL Utrecht, the Netherlands.
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21
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Borgsmüller N, Valecha M, Kuipers J, Beerenwinkel N, Posada D. Single-cell phylogenies reveal changes in the evolutionary rate within cancer and healthy tissues. CELL GENOMICS 2023; 3:100380. [PMID: 37719146 PMCID: PMC10504633 DOI: 10.1016/j.xgen.2023.100380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 05/03/2023] [Accepted: 07/18/2023] [Indexed: 09/19/2023]
Abstract
Cell lineages accumulate somatic mutations during organismal development, potentially leading to pathological states. The rate of somatic evolution within a cell population can vary due to multiple factors, including selection, a change in the mutation rate, or differences in the microenvironment. Here, we developed a statistical test called the Poisson Tree (PT) test to detect varying evolutionary rates among cell lineages, leveraging the phylogenetic signal of single-cell DNA sequencing (scDNA-seq) data. We applied the PT test to 24 healthy and cancer samples, rejecting a constant evolutionary rate in 11 out of 15 cancer and five out of nine healthy scDNA-seq datasets. In six cancer datasets, we identified subclonal mutations in known driver genes that could explain the rate accelerations of particular cancer lineages. Our findings demonstrate the efficacy of scDNA-seq for studying somatic evolution and suggest that cell lineages often evolve at different rates within cancer and healthy tissues.
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Affiliation(s)
- Nico Borgsmüller
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - Monica Valecha
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
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22
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Kim J, Kim S, Yeom H, Song SW, Shin K, Bae S, Ryu HS, Kim JY, Choi A, Lee S, Ryu T, Choi Y, Kim H, Kim O, Jung Y, Kim N, Han W, Lee HB, Lee AC, Kwon S. Barcoded multiple displacement amplification for high coverage sequencing in spatial genomics. Nat Commun 2023; 14:5261. [PMID: 37644058 PMCID: PMC10465490 DOI: 10.1038/s41467-023-41019-w] [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: 03/16/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023] Open
Abstract
Determining mutational landscapes in a spatial context is essential for understanding genetically heterogeneous cell microniches. Current approaches, such as Multiple Displacement Amplification (MDA), offer high genome coverage but limited multiplexing, which hinders large-scale spatial genomic studies. Here, we introduce barcoded MDA (bMDA), a technique that achieves high-coverage genomic analysis of low-input DNA while enhancing the multiplexing capabilities. By incorporating cell barcodes during MDA, bMDA streamlines library preparation in one pot, thereby overcoming a key bottleneck in spatial genomics. We apply bMDA to the integrative spatial analysis of triple-negative breast cancer tissues by examining copy number alterations, single nucleotide variations, structural variations, and kataegis signatures for each spatial microniche. This enables the assessment of subclonal evolutionary relationships within a spatial context. Therefore, bMDA has emerged as a scalable technology with the potential to advance the field of spatial genomics significantly.
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Affiliation(s)
- Jinhyun Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sungsik Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Huiran Yeom
- Division of Data Science, College of Information and Communication Technology, The University of Suwon, Hwaseong, 18323, Republic of Korea
| | - Seo Woo Song
- Basic Science and Engineering Initiative, Children's Heart Center, Stanford University, Stanford, CA, USA
| | - Kyoungseob Shin
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sangwook Bae
- Renal Division and Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Han Suk Ryu
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea
- Department of Pathology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Ji Young Kim
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Ahyoun Choi
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sumin Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Meteor Biotech, Co. Ltd., Seoul, 08826, Republic of Korea
| | - Taehoon Ryu
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Yeongjae Choi
- School of Materials Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea
| | - Hamin Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Okju Kim
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Yushin Jung
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Namphil Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Wonshik Han
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea
- Department of Surgery, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Han-Byoel Lee
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea.
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
- Department of Surgery, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
| | - Amos C Lee
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Meteor Biotech, Co. Ltd., Seoul, 08826, Republic of Korea.
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Inter-University Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea.
- Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul, 08826, Republic of Korea.
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23
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Hård J, Mold JE, Eisfeldt J, Tellgren-Roth C, Häggqvist S, Bunikis I, Contreras-Lopez O, Chin CS, Nordlund J, Rubin CJ, Feuk L, Michaëlsson J, Ameur A. Long-read whole-genome analysis of human single cells. Nat Commun 2023; 14:5164. [PMID: 37620373 PMCID: PMC10449900 DOI: 10.1038/s41467-023-40898-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023] Open
Abstract
Long-read sequencing has dramatically increased our understanding of human genome variation. Here, we demonstrate that long-read technology can give new insights into the genomic architecture of individual cells. Clonally expanded CD8+ T-cells from a human donor were subjected to droplet-based multiple displacement amplification (dMDA) to generate long molecules with reduced bias. PacBio sequencing generated up to 40% genome coverage per single-cell, enabling detection of single nucleotide variants (SNVs), structural variants (SVs), and tandem repeats, also in regions inaccessible by short reads. 28 somatic SNVs were detected, including one case of mitochondrial heteroplasmy. 5473 high-confidence SVs/cell were discovered, a sixteen-fold increase compared to Illumina-based results from clonally related cells. Single-cell de novo assembly generated a genome size of up to 598 Mb and 1762 (12.8%) complete gene models. In summary, our work shows the promise of long-read sequencing toward characterization of the full spectrum of genetic variation in single cells.
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Affiliation(s)
- Joanna Hård
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
- ETH AI Center, ETH Zurich, Zurich, Switzerland.
| | - Jeff E Mold
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Jesper Eisfeldt
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Christian Tellgren-Roth
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Susana Häggqvist
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Ignas Bunikis
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | | | | | - Jessica Nordlund
- Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Carl-Johan Rubin
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Lars Feuk
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Jakob Michaëlsson
- Center for Infectious Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Adam Ameur
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
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24
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Heid J, Cutler R, Lee M, Vijg J, Maslov AY. Concurrent analysis of genome and transcriptome in one single cell. RESEARCH SQUARE 2023:rs.3.rs-3186428. [PMID: 37577506 PMCID: PMC10418558 DOI: 10.21203/rs.3.rs-3186428/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Thus far, multiple techniques for single cell analysis have been developed, yet we lack a relatively simple tool to assess DNA and RNA from the same cell at whole-transcriptome and whole-genome depths. Here we present an updated method for physical separation of cytoplasmic RNA from the nuclei, which allows for simultaneous studies of DNA and RNA from the same single cell. The method consists of three steps - 1) immobilization of a single cell on solid substrate, 2) hypotonic lysis of immobilized single cell, and 3) separation of cytosol containing aqueous phase and immobilized nucleus. We found that DNA and RNA extracted from single cell using our approach is suitable for downstream sequencing-based applications. We demonstrated that the coverage of transcriptome and genome sequencing data obtained after DNA/RNA separation is similar to that observed without separation. We also showed that the separation procedure does not create any noticeable bias in observed mutational load or mutation spectra. Thus, our method can serve as a tool for simultaneous complex analysis of the genome and transcriptome, providing necessary information on the relationship between somatic mutations and the regulation of gene expression.
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Affiliation(s)
| | | | | | - Jan Vijg
- Albert Einstein College of Medicine
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25
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Van de Sande B, Lee JS, Mutasa-Gottgens E, Naughton B, Bacon W, Manning J, Wang Y, Pollard J, Mendez M, Hill J, Kumar N, Cao X, Chen X, Khaladkar M, Wen J, Leach A, Ferran E. Applications of single-cell RNA sequencing in drug discovery and development. Nat Rev Drug Discov 2023; 22:496-520. [PMID: 37117846 PMCID: PMC10141847 DOI: 10.1038/s41573-023-00688-4] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/10/2023] [Indexed: 04/30/2023]
Abstract
Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.
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Affiliation(s)
| | | | | | - Bart Naughton
- Computational Neurobiology, Eisai, Cambridge, MA, USA
| | - Wendi Bacon
- EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
- The Open University, Milton Keynes, UK
| | | | - Yong Wang
- Precision Bioinformatics, Prometheus Biosciences, San Diego, CA, USA
| | | | - Melissa Mendez
- Genomic Sciences, GlaxoSmithKline, Collegeville, PA, USA
| | - Jon Hill
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, USA
| | - Namit Kumar
- Informatics & Predictive Sciences, Bristol Myers Squibb, San Diego, CA, USA
| | - Xiaohong Cao
- Genomic Research Center, AbbVie Inc., Cambridge, MA, USA
| | - Xiao Chen
- Magnet Biomedicine, Cambridge, MA, USA
| | - Mugdha Khaladkar
- Human Genetics and Computational Biology, GlaxoSmithKline, Collegeville, PA, USA
| | - Ji Wen
- Oncology Research and Development Unit, Pfizer, La Jolla, CA, USA
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26
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Schneider BK, Sun S, Lee M, Li W, Skvir N, Neretti N, Vijg J, Secombe J. Expression of retrotransposons contributes to aging in Drosophila. Genetics 2023; 224:iyad073. [PMID: 37084379 PMCID: PMC10213499 DOI: 10.1093/genetics/iyad073] [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: 12/12/2022] [Revised: 12/12/2022] [Accepted: 04/11/2023] [Indexed: 04/23/2023] Open
Abstract
Retrotransposons are a class of transposable elements capable of self-replication and insertion into new genomic locations. Across species, the mobilization of retrotransposons in somatic cells has been suggested to contribute to the cell and tissue functional decline that occurs during aging. Retrotransposons are broadly expressed across cell types, and de novo insertions have been observed to correlate with tumorigenesis. However, the extent to which new retrotransposon insertions occur during normal aging and their effect on cellular and animal function remains understudied. Here, we use a single nucleus whole genome sequencing approach in Drosophila to directly test whether transposon insertions increase with age in somatic cells. Analyses of nuclei from thoraces and indirect flight muscles using a newly developed pipeline, Retrofind, revealed no significant increase in the number of transposon insertions with age. Despite this, reducing the expression of two different retrotransposons, 412 and Roo, extended lifespan, but did not alter indicators of health such as stress resistance. This suggests a key role for transposon expression and not insertion in regulating longevity. Transcriptomic analyses revealed similar changes to gene expression in 412 and Roo knockdown flies and highlighted changes to genes involved in proteolysis and immune function as potential contributors to the observed changes in longevity. Combined, our data show a clear link between retrotransposon expression and aging.
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Affiliation(s)
- Blair K Schneider
- Department of Genetics, Albert Einstein College of Medicine, 1300 Morris Park Ave., Ullmann 809 Bronx, NY 10461, USA
| | - Shixiang Sun
- Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Ave., Price 468 Bronx, NY 10461, USA
| | - Moonsook Lee
- Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Ave., Price 468 Bronx, NY 10461, USA
| | - Wenge Li
- Department of Cell Biology, Albert Einstein College of Medicine, 1300 Morris Park Ave., Ullmann 909 Bronx, NY 10461, USA
| | - Nicholas Skvir
- Department of Molecular biology, Cell biology and Biochemistry, Brown University, 70 Ship St., Providence 02903, USA
| | - Nicola Neretti
- Department of Molecular biology, Cell biology and Biochemistry, Brown University, 70 Ship St., Providence 02903, USA
| | - Jan Vijg
- Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Ave., Price 468 Bronx, NY 10461, USA
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Julie Secombe
- Department of Genetics, Albert Einstein College of Medicine, 1300 Morris Park Ave., Ullmann 809 Bronx, NY 10461, USA
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
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27
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Grody EI, Abraham A, Shukla V, Goyal Y. Toward a systems-level probing of tumor clonality. iScience 2023; 26:106574. [PMID: 37192968 PMCID: PMC10182304 DOI: 10.1016/j.isci.2023.106574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023] Open
Abstract
Cancer has been described as a genetic disease that clonally evolves in the face of selective pressures imposed by cell-intrinsic and extrinsic factors. Although classical models based on genetic data predominantly propose Darwinian mechanisms of cancer evolution, recent single-cell profiling of cancers has described unprecedented heterogeneity in tumors providing support for alternative models of branched and neutral evolution through both genetic and non-genetic mechanisms. Emerging evidence points to a complex interplay between genetic, non-genetic, and extrinsic environmental factors in shaping the evolution of tumors. In this perspective, we briefly discuss the role of cell-intrinsic and extrinsic factors that shape clonal behaviors during tumor progression, metastasis, and drug resistance. Taking examples of pre-malignant states associated with hematological malignancies and esophageal cancer, we discuss recent paradigms of tumor evolution and prospective approaches to further enhance our understanding of this spatiotemporally regulated process.
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Affiliation(s)
- Emanuelle I. Grody
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL 60208, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Ajay Abraham
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Human Immunobiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Vipul Shukla
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Human Immunobiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL 60208, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
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28
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Li Z, Min S, Alliey-Rodriguez N, Giase G, Cheng L, Craig DW, Faulkner GJ, Asif H, Liu C, Gershon ES. Single-neuron whole genome sequencing identifies increased somatic mutation burden in Alzheimer's disease related genes. Neurobiol Aging 2023; 123:222-232. [PMID: 36599749 DOI: 10.1016/j.neurobiolaging.2022.12.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 12/02/2022] [Accepted: 12/04/2022] [Indexed: 12/15/2022]
Abstract
Accumulation of somatic mutations in human neurons is associated with aging and neurodegeneration. To shed light on the somatic mutational burden in Alzheimer's disease (AD) neurons and get more insight into the role of somatic mutations in AD pathogenesis, we performed single-neuron whole genome sequencing to detect genome-wide somatic mutations (single nucleotide variants (SNVs) and Indels) in 96 single prefrontal cortex neurons from 8 AD patients and 8 elderly controls. We found that the mutational burden is ∼3000 somatic mutations per neuron genome in elderly subjects. AD patients have increased somatic mutation burden in AD-related annotation categories, including AD risk genes and differentially expressed genes in AD neurons. Mutational signature analysis showed somatic SNVs (sSNVs) primarily caused by aging and oxidative DNA damage processes but no significant difference was detected between AD and controls. Additionally, functional somatic mutations identified in AD patients showed significant enrichment in several AD-related pathways, including AD pathway, Notch-signaling pathway and Calcium-signaling pathway. These findings provide genetic insights into how somatic mutations may alter the function of single neurons and exert their potential roles in the pathogenesis of AD.
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Affiliation(s)
- Zongchang Li
- Department of Psychiatry, The Second Xiangya Hospital; Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China; Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL, USA
| | - Shishi Min
- Department of Psychiatry, The Second Xiangya Hospital; Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China; Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA; Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Ney Alliey-Rodriguez
- Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL, USA
| | - Gina Giase
- School of Public Health, University of Illinois at Chicago, Chicago, IL, USA
| | - Lijun Cheng
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
| | - David Wesley Craig
- Department of Translational Genomics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Geoffrey J Faulkner
- Mater Research Institute - University of Queensland, Woolloongabba, Queensland, Australia; Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Huma Asif
- Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL, USA.
| | - Chunyu Liu
- Department of Psychiatry, The Second Xiangya Hospital; Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China; Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA; School of Psychology, Shaanxi Normal University, Xi'an, China.
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL, USA; Department of Human Genetics, University of Chicago, Chicago, IL, USA.
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29
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Lu N, Qiao Y, Lu Z, Tu J. Chimera: The spoiler in multiple displacement amplification. Comput Struct Biotechnol J 2023; 21:1688-1696. [PMID: 36879882 PMCID: PMC9984789 DOI: 10.1016/j.csbj.2023.02.034] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 02/18/2023] [Accepted: 02/18/2023] [Indexed: 02/24/2023] Open
Abstract
Multiple displacement amplification (MDA) based on isothermal random priming and high fidelity phi29 DNA polymerase-mediated processive extension has revolutionized the field of whole genome amplification by enabling the amplification of minute amounts of DNA, such as from a single cell, generating vast amounts of DNA with high genome coverage. Despite its advantages, MDA has its own challenges, one of the grandest being the formation of chimeric sequences (chimeras), which presents in all MDA products and seriously disturbs the downstream analysis. In this review, we provide a comprehensive overview of current research on MDA chimeras. We first reviewed the mechanisms of chimera formation and chimera detection methods. We then systematically summarized the characteristics of chimeras, including overlap, chimeric distance, chimeric density, and chimeric rate, as found in independently published sequencing data. Finally, we reviewed the methods used to process chimeric sequences and their impacts on the improvement of data utilization efficiency. The information presented in this review will be useful for those interested in understanding the challenges with MDA and in improving its performance.
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Affiliation(s)
- Na Lu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Yi Qiao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Zuhong Lu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Jing Tu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
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30
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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: 12] [Impact Index Per Article: 6.0] [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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31
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Kang S, Borgsmüller N, Valecha M, Kuipers J, Alves JM, Prado-López S, Chantada D, Beerenwinkel N, Posada D, Szczurek E. SIEVE: joint inference of single-nucleotide variants and cell phylogeny from single-cell DNA sequencing data. Genome Biol 2022; 23:248. [PMID: 36451239 PMCID: PMC9714196 DOI: 10.1186/s13059-022-02813-9] [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: 04/23/2022] [Accepted: 11/08/2022] [Indexed: 12/02/2022] Open
Abstract
We present SIEVE, a statistical method for the joint inference of somatic variants and cell phylogeny under the finite-sites assumption from single-cell DNA sequencing. SIEVE leverages raw read counts for all nucleotides and corrects the acquisition bias of branch lengths. In our simulations, SIEVE outperforms other methods in phylogenetic reconstruction and variant calling accuracy, especially in the inference of homozygous variants. Applying SIEVE to three datasets, one for triple-negative breast (TNBC), and two for colorectal cancer (CRC), we find that double mutant genotypes are rare in CRC but unexpectedly frequent in the TNBC samples.
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Affiliation(s)
- Senbai Kang
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Nico Borgsmüller
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - Monica Valecha
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - Joao M. Alves
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Sonia Prado-López
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Institute of Solid State Electronics E362, Technische Universität Wien, Vienna, Austria
| | - Débora Chantada
- Department of Pathology, Hospital Álvaro Cunqueiro, Vigo, Spain
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
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32
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Mukherjee P, Park SH, Pathak N, Patino CA, Bao G, Espinosa HD. Integrating Micro and Nano Technologies for Cell Engineering and Analysis: Toward the Next Generation of Cell Therapy Workflows. ACS NANO 2022; 16:15653-15680. [PMID: 36154011 DOI: 10.1021/acsnano.2c05494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The emerging field of cell therapy offers the potential to treat and even cure a diverse array of diseases for which existing interventions are inadequate. Recent advances in micro and nanotechnology have added a multitude of single cell analysis methods to our research repertoire. At the same time, techniques have been developed for the precise engineering and manipulation of cells. Together, these methods have aided the understanding of disease pathophysiology, helped formulate corrective interventions at the cellular level, and expanded the spectrum of available cell therapeutic options. This review discusses how micro and nanotechnology have catalyzed the development of cell sorting, cellular engineering, and single cell analysis technologies, which have become essential workflow components in developing cell-based therapeutics. The review focuses on the technologies adopted in research studies and explores the opportunities and challenges in combining the various elements of cell engineering and single cell analysis into the next generation of integrated and automated platforms that can accelerate preclinical studies and translational research.
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Affiliation(s)
- Prithvijit Mukherjee
- Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Theoretical and Applied Mechanics Program, Northwestern University, Evanston, Illinois 60208, United States
| | - So Hyun Park
- Department of Bioengineering, Rice University, 6500 Main Street, Houston, Texas 77030, United States
| | - Nibir Pathak
- Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Theoretical and Applied Mechanics Program, Northwestern University, Evanston, Illinois 60208, United States
| | - Cesar A Patino
- Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Gang Bao
- Department of Bioengineering, Rice University, 6500 Main Street, Houston, Texas 77030, United States
| | - Horacio D Espinosa
- Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Theoretical and Applied Mechanics Program, Northwestern University, Evanston, Illinois 60208, United States
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33
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Lodato MA, Ziegenfuss JS. The two faces of DNA oxidation in genomic and functional mosaicism during aging in human neurons. FRONTIERS IN AGING 2022; 3:991460. [PMID: 36313183 PMCID: PMC9596766 DOI: 10.3389/fragi.2022.991460] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 09/26/2022] [Indexed: 11/29/2022]
Abstract
Maintaining genomic integrity in post-mitotic neurons in the human brain is paramount because these cells must survive for an individual's entire lifespan. Due to life-long synaptic plasticity and electrochemical transmission between cells, the brain engages in an exceptionally high level of mitochondrial metabolic activity. This activity results in the generation of reactive oxygen species with 8-oxo-7,8-dihydroguanine (8-oxoG) being one of the most prevalent oxidation products in the cell. 8-oxoG is important for the maintenance and transfer of genetic information into proper gene expression: a low basal level of 8-oxoG plays an important role in epigenetic modulation of neurodevelopment and synaptic plasticity, while a dysregulated increase in 8-oxoG damages the genome leading to somatic mutations and transcription errors. The slow yet persistent accumulation of DNA damage in the background of increasing cellular 8-oxoG is associated with normal aging as well as neurological disorders such as Alzheimer's disease and Parkinson's disease. This review explores the current understanding of how 8-oxoG plays a role in brain function and genomic instability, highlighting new methods being used to advance pathological hallmarks that differentiate normal healthy aging and neurodegenerative disease.
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Affiliation(s)
- Michael A. Lodato
- University of Massachusetts Chan Medical School, Worcester, MA, United States
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Kim J, Huang AY, Johnson SL, Lai J, Isacco L, Jeffries AM, Miller MB, Lodato MA, Walsh CA, Lee EA. Prevalence and mechanisms of somatic deletions in single human neurons during normal aging and in DNA repair disorders. Nat Commun 2022; 13:5918. [PMID: 36207339 PMCID: PMC9546902 DOI: 10.1038/s41467-022-33642-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 09/26/2022] [Indexed: 01/21/2023] Open
Abstract
Replication errors and various genotoxins cause DNA double-strand breaks (DSBs) where error-prone repair creates genomic mutations, most frequently focal deletions, and defective repair may lead to neurodegeneration. Despite its pathophysiological importance, the extent to which faulty DSB repair alters the genome, and the mechanisms by which mutations arise, have not been systematically examined reflecting ineffective methods. Here, we develop PhaseDel, a computational method to detect focal deletions and characterize underlying mechanisms in single-cell whole genome sequences (scWGS). We analyzed high-coverage scWGS of 107 single neurons from 18 neurotypical individuals of various ages, and found that somatic deletions increased with age and in highly expressed genes in human brain. Our analysis of 50 single neurons from DNA repair-deficient diseases with progressive neurodegeneration (Cockayne syndrome, Xeroderma pigmentosum, and Ataxia telangiectasia) reveals elevated somatic deletions compared to age-matched controls. Distinctive mechanistic signatures and transcriptional associations suggest roles for somatic deletions in neurodegeneration.
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Affiliation(s)
- Junho Kim
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
- Manton Center for Orphan Disease, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biological Sciences, Sungkyunkwan University, Suwon, South Korea
| | - August Yue Huang
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
- Manton Center for Orphan Disease, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Shelby L Johnson
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Jenny Lai
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
- Manton Center for Orphan Disease, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Laura Isacco
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
- Manton Center for Orphan Disease, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Ailsa M Jeffries
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Michael B Miller
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
- Manton Center for Orphan Disease, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael A Lodato
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
- Manton Center for Orphan Disease, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, MA, USA
- Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Christopher A Walsh
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA.
- Manton Center for Orphan Disease, Boston Children's Hospital, Boston, MA, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
| | - Eunjung Alice Lee
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA.
- Manton Center for Orphan Disease, Boston Children's Hospital, Boston, MA, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Luquette LJ, Miller MB, Zhou Z, Bohrson CL, Zhao Y, Jin H, Gulhan D, Ganz J, Bizzotto S, Kirkham S, Hochepied T, Libert C, Galor A, Kim J, Lodato MA, Garaycoechea JI, Gawad C, West J, Walsh CA, Park PJ. Single-cell genome sequencing of human neurons identifies somatic point mutation and indel enrichment in regulatory elements. Nat Genet 2022; 54:1564-1571. [PMID: 36163278 PMCID: PMC9833626 DOI: 10.1038/s41588-022-01180-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 08/04/2022] [Indexed: 01/13/2023]
Abstract
Accurate somatic mutation detection from single-cell DNA sequencing is challenging due to amplification-related artifacts. To reduce this artifact burden, an improved amplification technique, primary template-directed amplification (PTA), was recently introduced. We analyzed whole-genome sequencing data from 52 PTA-amplified single neurons using SCAN2, a new genotyper we developed to leverage mutation signatures and allele balance in identifying somatic single-nucleotide variants (SNVs) and small insertions and deletions (indels) in PTA data. Our analysis confirms an increase in nonclonal somatic mutation in single neurons with age, but revises the estimated rate of this accumulation to 16 SNVs per year. We also identify artifacts in other amplification methods. Most importantly, we show that somatic indels increase by at least three per year per neuron and are enriched in functional regions of the genome such as enhancers and promoters. Our data suggest that indels in gene-regulatory elements have a considerable effect on genome integrity in human neurons.
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Affiliation(s)
- Lovelace J Luquette
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Michael B Miller
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
- Manton Center for Orphan Disease, Boston Children's Hospital, Boston, MA, USA
- Division of Neuropathology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Departments of Neurology and Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Zinan Zhou
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
| | - Craig L Bohrson
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Yifan Zhao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Hu Jin
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Doga Gulhan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Javier Ganz
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
| | - Sara Bizzotto
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
| | - Samantha Kirkham
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
| | - Tino Hochepied
- Center for Inflammation Research, Flanders Institute for Biotechnolpogy (VIB), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Claude Libert
- Center for Inflammation Research, Flanders Institute for Biotechnolpogy (VIB), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Alon Galor
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Junho Kim
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
- Department of Biological Sciences, Sungkyunkwan University, Suwon, South Korea
| | - Michael A Lodato
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Juan I Garaycoechea
- Hubrecht Institute-KNAW, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Charles Gawad
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | | | - Christopher A Walsh
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA.
- Manton Center for Orphan Disease, Boston Children's Hospital, Boston, MA, USA.
- Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA, USA.
| | - Peter J Park
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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Ren P, Dong X, Vijg J. Age-related somatic mutation burden in human tissues. FRONTIERS IN AGING 2022; 3:1018119. [PMID: 36213345 PMCID: PMC9534562 DOI: 10.3389/fragi.2022.1018119] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 08/26/2022] [Indexed: 11/16/2022]
Abstract
The genome of multicellular organisms carries the hereditary information necessary for the development of all organs and tissues and to maintain function in adulthood. To ensure the genetic stability of the species, genomes are protected against changes in sequence information. However, genomes are not static. De novo mutations in germline cells are passed on to offspring and generate the variation needed in evolution. Moreover, postzygotic mutations occur in all somatic cells during development and aging. These somatic mutations remain limited to the individual, generating tissues that are genome mosaics. Insight into such mutations and their consequences has been limited due to their extremely low abundance, with most mutations unique for each cell. Recent advances in sequencing, including whole genome sequencing at the single-cell level, have now led to the first insights into somatic mutation burdens in human tissues. Here, we will first briefly describe the latest methodology for somatic mutation analysis, then review our current knowledge of somatic mutation burden in human tissues and, finally, briefly discuss the possible functional impact of somatic mutations on the aging process and age-related diseases, including cancer and diseases other than cancer.
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Affiliation(s)
- Peijun Ren
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China,*Correspondence: Peijun Ren, ; Xiao Dong, ; Jan Vijg, ,
| | - Xiao Dong
- Department of Genetics, Cell Biology and Development, Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN, United States,*Correspondence: Peijun Ren, ; Xiao Dong, ; Jan Vijg, ,
| | - Jan Vijg
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China,Department of Genetics, Albert Einstein College of Medicine, New York City, NY, United States,*Correspondence: Peijun Ren, ; Xiao Dong, ; Jan Vijg, ,
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Su X, Bai S, Xie G, Shi Y, Zhao L, Yang G, Tian F, He KY, Wang L, Li X, Long Q, Han ZG. Accurate tumor clonal structures require single-cell analysis. Ann N Y Acad Sci 2022; 1517:213-224. [PMID: 36081327 DOI: 10.1111/nyas.14897] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Tumor clonal structure is closely related to future progression, which has been mainly investigated as mutation abundance clustering in bulk samples. With relatively limited studies at single-cell resolution, a systematic comparison of the two approaches is still lacking. Here, using bulk and single-cell mutational data from the liver and colorectal cancers, we checked whether co-mutations determined by single-cell analysis had corresponding bulk variant allele frequency (VAF) peaks. While bulk analysis suggested the absence of subclonal peaks and, possibly, neutral evolution in some cases, the single-cell analysis identified coexisting subclones. The overlaps of bulk VAF ranges for co-mutations from different subclones made it difficult to separate them. Complex subclonal structures and dynamic evolution could be hidden under the seemingly clonal neutral pattern at the bulk level, suggesting single-cell analysis is necessary to avoid underestimation of tumor heterogeneity.
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Affiliation(s)
- Xianbin Su
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Shihao Bai
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Gangcai Xie
- Institute of Reproductive Medicine, Medical School of Nantong University, Nantong, China
| | - Yi Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, Shanghai, China
| | - Linan Zhao
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Guoliang Yang
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Futong Tian
- Department of Design, Politecnico di Milano, Milan, Italy
| | - Kun-Yan He
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lan Wang
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaolin Li
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qi Long
- Joint School of Life Sciences, Guangzhou Medical University & Guangzhou Institutes of Biomedicine and Health-Chinese Academy of Sciences, Guangzhou, China
| | - Ze-Guang Han
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
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Kuipers J, Singer J, Beerenwinkel N. Single-cell mutation calling and phylogenetic tree reconstruction with loss and recurrence. Bioinformatics 2022; 38:4713-4719. [PMID: 36000873 PMCID: PMC9563700 DOI: 10.1093/bioinformatics/btac577] [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: 02/01/2022] [Revised: 07/08/2022] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
Motivation Tumours evolve as heterogeneous populations of cells, which may be distinguished by different genomic aberrations. The resulting intra-tumour heterogeneity plays an important role in cancer patient relapse and treatment failure, so that obtaining a clear understanding of each patient’s tumour composition and evolutionary history is key for personalized therapies. Single-cell sequencing (SCS) now provides the possibility to resolve tumour heterogeneity at the highest resolution of individual tumour cells, but brings with it challenges related to the particular noise profiles of the sequencing protocols as well as the complexity of the underlying evolutionary process. Results By modelling the noise processes and allowing mutations to be lost or to reoccur during tumour evolution, we present a method to jointly call mutations in each cell, reconstruct the phylogenetic relationship between cells, and determine the locations of mutational losses and recurrences. Our Bayesian approach allows us to accurately call mutations as well as to quantify our certainty in such predictions. We show the advantages of allowing mutational loss or recurrence with simulated data and present its application to tumour SCS data. Availability and implementation SCIΦN is available at https://github.com/cbg-ethz/SCIPhIN. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Jochen Singer
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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39
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Rozhoňová H, Danciu D, Stark S, Rätsch G, Kahles A, Lehmann KV. SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing. Bioinformatics 2022; 38:4293-4300. [PMID: 35900151 PMCID: PMC9477524 DOI: 10.1093/bioinformatics/btac510] [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: 01/12/2022] [Revised: 07/04/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Several recently developed single-cell DNA sequencing technologies enable whole-genome sequencing of thousands of cells. However, the ultra-low coverage of the sequenced data (<0.05× per cell) mostly limits their usage to the identification of copy number alterations in multi-megabase segments. Many tumors are not copy number-driven, and thus single-nucleotide variant (SNV)-based subclone detection may contribute to a more comprehensive view on intra-tumor heterogeneity. Due to the low coverage of the data, the identification of SNVs is only possible when superimposing the sequenced genomes of hundreds of genetically similar cells. Thus, we have developed a new approach to efficiently cluster tumor cells based on a Bayesian filtering approach of relevant loci and exploiting read overlap and phasing. RESULTS We developed Single Cell Data Tumor Clusterer (SECEDO, lat. 'to separate'), a new method to cluster tumor cells based solely on SNVs, inferred on ultra-low coverage single-cell DNA sequencing data. We applied SECEDO to a synthetic dataset simulating 7250 cells and eight tumor subclones from a single patient and were able to accurately reconstruct the clonal composition, detecting 92.11% of the somatic SNVs, with the smallest clusters representing only 6.9% of the total population. When applied to five real single-cell sequencing datasets from a breast cancer patient, each consisting of ≈2000 cells, SECEDO was able to recover the major clonal composition in each dataset at the original coverage of 0.03×, achieving an Adjusted Rand Index (ARI) score of ≈0.6. The current state-of-the-art SNV-based clustering method achieved an ARI score of ≈0, even after merging cells to create higher coverage data (factor 10 increase), and was only able to match SECEDOs performance when pooling data from all five datasets, in addition to artificially increasing the sequencing coverage by a factor of 7. Variant calling on the resulting clusters recovered more than twice as many SNVs as would have been detected if calling on all cells together. Further, the allelic ratio of the called SNVs on each subcluster was more than double relative to the allelic ratio of the SNVs called without clustering, thus demonstrating that calling variants on subclones, in addition to both increasing sensitivity of SNV detection and attaching SNVs to subclones, significantly increases the confidence of the called variants. AVAILABILITY AND IMPLEMENTATION SECEDO is implemented in C++ and is publicly available at https://github.com/ratschlab/secedo. Instructions to download the data and the evaluation code to reproduce the findings in this paper are available at: https://github.com/ratschlab/secedo-evaluation. The code and data of the submitted version are archived at: https://doi.org/10.5281/zenodo.6516955. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Stefan Stark
- Biomedical Informatics Group, Department of Computer Science, ETH Zurich, Zurich, Switzerland,Swiss Institute of Bioinformatics, Lausanne, Switzerland,Biomedical Informatics Research, University Hospital Zurich, Zurich, Switzerland
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Huang AY, Lee EA. Identification of Somatic Mutations From Bulk and Single-Cell Sequencing Data. FRONTIERS IN AGING 2022; 2:800380. [PMID: 35822012 PMCID: PMC9261417 DOI: 10.3389/fragi.2021.800380] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 12/08/2021] [Indexed: 12/26/2022]
Abstract
Somatic mutations are DNA variants that occur after the fertilization of zygotes and accumulate during the developmental and aging processes in the human lifespan. Somatic mutations have long been known to cause cancer, and more recently have been implicated in a variety of non-cancer diseases. The patterns of somatic mutations, or mutational signatures, also shed light on the underlying mechanisms of the mutational process. Advances in next-generation sequencing over the decades have enabled genome-wide profiling of DNA variants in a high-throughput manner; however, unlike germline mutations, somatic mutations are carried only by a subset of the cell population. Thus, sensitive bioinformatic methods are required to distinguish mutant alleles from sequencing and base calling errors in bulk tissue samples. An alternative way to study somatic mutations, especially those present in an extremely small number of cells or even in a single cell, is to sequence single-cell genomes after whole-genome amplification (WGA); however, it is critical and technically challenging to exclude numerous technical artifacts arising during error-prone and uneven genome amplification in current WGA methods. To address these challenges, multiple bioinformatic tools have been developed. In this review, we summarize the latest progress in methods for identification of somatic mutations and the challenges that remain to be addressed in the future.
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Affiliation(s)
- August Yue Huang
- Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, United States, Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - Eunjung Alice Lee
- Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, United States, Department of Pediatrics, Harvard Medical School, Boston, MA, United States
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41
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Edrisi M, Valecha MV, Chowdary SBV, Robledo S, Ogilvie HA, Posada D, Zafar H, Nakhleh L. Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data. Bioinformatics 2022; 38:i195-i202. [PMID: 35758771 PMCID: PMC9235480 DOI: 10.1093/bioinformatics/btac254] [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] [Indexed: 11/30/2022] Open
Abstract
Motivation Single-nucleotide variants (SNVs) are the most common variations in the human genome. Recently developed methods for SNV detection from single-cell DNA sequencing data, such as SCIΦ and scVILP, leverage the evolutionary history of the cells to overcome the technical errors associated with single-cell sequencing protocols. Despite being accurate, these methods are not scalable to the extensive genomic breadth of single-cell whole-genome (scWGS) and whole-exome sequencing (scWES) data. Results Here, we report on a new scalable method, Phylovar, which extends the phylogeny-guided variant calling approach to sequencing datasets containing millions of loci. Through benchmarking on simulated datasets under different settings, we show that, Phylovar outperforms SCIΦ in terms of running time while being more accurate than Monovar (which is not phylogeny-aware) in terms of SNV detection. Furthermore, we applied Phylovar to two real biological datasets: an scWES triple-negative breast cancer data consisting of 32 cells and 3375 loci as well as an scWGS data of neuron cells from a normal human brain containing 16 cells and approximately 2.5 million loci. For the cancer data, Phylovar detected somatic SNVs with high or moderate functional impact that were also supported by bulk sequencing dataset and for the neuron dataset, Phylovar identified 5745 SNVs with non-synonymous effects some of which were associated with neurodegenerative diseases. Availability and implementation Phylovar is implemented in Python and is publicly available at https://github.com/NakhlehLab/Phylovar.
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Affiliation(s)
| | | | - Sunkara B V Chowdary
- Department of Computer Science & Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | | | - Huw A Ogilvie
- Department of Computer Science, Rice University, Houston, TX 77005, USA
| | - David Posada
- CINBIO, Universidade de Vigo, Vigo 36310, Spain.,Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain.,Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, Vigo 36310, Spain
| | - Hamim Zafar
- Department of Computer Science & Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India.,Department of Biological Sciences & Bioengineering, Institute of Technology Kanpur, Kanpur 208016, India.,Mehta Family Centre for Engineering in Medicine, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - Luay Nakhleh
- Department of Computer Science, Rice University, Houston, TX 77005, USA
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42
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Valecha M, Posada D. Somatic variant calling from single-cell DNA sequencing data. Comput Struct Biotechnol J 2022; 20:2978-2985. [PMID: 35782734 PMCID: PMC9218383 DOI: 10.1016/j.csbj.2022.06.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/06/2022] [Accepted: 06/06/2022] [Indexed: 11/03/2022] Open
Abstract
Single-cell sequencing has gained popularity in recent years. Despite its numerous applications, single-cell DNA sequencing data is highly error-prone due to technical biases arising from uneven sequencing coverage, allelic dropout, and amplification error. With these artifacts, the identification of somatic genomic variants becomes a challenging task, and over the years, several methods have been developed explicitly for this type of data. Single-cell variant callers implement distinct strategies, make different use of the data, and typically result in many discordant calls when applied to real data. Here, we review current approaches for single-cell variant calling, emphasizing single nucleotide variants. We highlight their potential benefits and shortcomings to help users choose a suitable tool for their data at hand.
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Key Words
- ADO, allelic dropout
- Allele dropout
- Amplification error
- CNV, copy number variant
- Indel, short insertion or deletion
- LDO, locus dropout
- SNV, single nucleotide variant
- SV, structural variant
- Single-cell genomics
- Somatic variants
- VAF, variant allele frequency
- Variant calling
- hSNP, heterozygous single-nucleotide polymorphism
- scATAC-seq, single-cell sequencing assay for transposase-accessible chromatin
- scDNA-seq, single-cell DNA sequencing
- scHi-C, single-cell Hi-C sequencing
- scMethyl-seq, single-cell Methylation sequencing
- scRNA-seq, single-cell RNA sequencing
- scWGA, single-cell whole-genome amplification
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Affiliation(s)
- Monica Valecha
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
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Alves JM, Prado-López S, Tomás L, Valecha M, Estévez-Gómez N, Alvariño P, Geisel D, Modest DP, Sauer IM, Pratschke J, Raschzok N, Sers C, Mamlouk S, Posada D. Clonality and timing of relapsing colorectal cancer metastasis revealed through whole-genome single-cell sequencing. Cancer Lett 2022; 543:215767. [PMID: 35688262 DOI: 10.1016/j.canlet.2022.215767] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/26/2022] [Accepted: 05/29/2022] [Indexed: 11/02/2022]
Abstract
Recurrence of tumor cells following local and systemic therapy is a significant hurdle in cancer. Most patients with metastatic colorectal cancer (mCRC) will relapse, despite resection of the metastatic lesions. A better understanding of the evolutionary history of recurrent lesions is required to identify the spatial and temporal patterns of metastatic progression and expose the genetic and evolutionary determinants of therapeutic resistance. With this goal in mind, here we leveraged a unique single-cell whole-genome sequencing dataset from recurrent hepatic lesions of an mCRC patient. Our phylogenetic analysis confirms that the treatment induced a severe demographic bottleneck in the liver metastasis but also that a previously diverged lineage survived this surgery, possibly after migration to a different site in the liver. This lineage evolved very slowly for two years under adjuvant drug therapy and diversified again in a very short period. We identified several non-silent mutations specific to this lineage and inferred a substantial contribution of chemotherapy to the overall, genome-wide mutational burden. All in all, our study suggests that mCRC subclones can migrate locally and evade resection, keep evolving despite rounds of chemotherapy, and re-expand explosively.
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Affiliation(s)
- Joao M Alves
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
| | - Sonia Prado-López
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
| | - Laura Tomás
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
| | - Monica Valecha
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
| | - Nuria Estévez-Gómez
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
| | - Pilar Alvariño
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
| | - Dominik Geisel
- Department of Radiology, Campus Virchow-Klinikum, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany
| | - Dominik Paul Modest
- Department of Hematology, Oncology, and Cancer Immunology, Campus Charité Mitte, Campus Virchow-Klinikum, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Igor M Sauer
- Department of Surgery, Campus Charité Mitte, Campus Virchow-Klinikum, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu, Berlin, Germany
| | - Johann Pratschke
- Department of Surgery, Campus Charité Mitte, Campus Virchow-Klinikum, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu, Berlin, Germany
| | - Nathanael Raschzok
- Department of Surgery, Campus Charité Mitte, Campus Virchow-Klinikum, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Academy, Clinician Scientist Program, Berlin, Germany
| | - Christine Sers
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Soulafa Mamlouk
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - David Posada
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain; Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310, Vigo, Spain.
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44
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Mo Y, Jiao Y. Advances and applications of single-cell omics technologies in plant research. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 110:1551-1563. [PMID: 35426954 DOI: 10.1111/tpj.15772] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 06/14/2023]
Abstract
Single-cell sequencing approaches reveal the intracellular dynamics of individual cells and answer biological questions with high-dimensional catalogs of millions of cells, including genomics, transcriptomics, chromatin accessibility, epigenomics, and proteomics data across species. These emerging yet thriving technologies have been fully embraced by the field of plant biology, with a constantly expanding portfolio of applications. Here, we introduce the current technical advances used for single-cell omics, especially single-cell genome and transcriptome sequencing. Firstly, we overview methods for protoplast and nucleus isolation and genome and transcriptome amplification. Subsequently, we use well-executed benchmarking studies to highlight advances made through the application of single-cell omics techniques. Looking forward, we offer a glimpse of additional hurdles and future opportunities that will introduce broad adoption of single-cell sequencing with revolutionary perspectives in plant biology.
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Affiliation(s)
- Yajin Mo
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, Center for Quantitative Biology, School of Life Sciences, Peking University, Beijing, 100871, China
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Yuling Jiao
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, Center for Quantitative Biology, School of Life Sciences, Peking University, Beijing, 100871, China
- State Key Laboratory of Plant Genomics and National Center for Plant Gene Research (Beijing), Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
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45
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Feng X, Chen L. SCSilicon: a tool for synthetic single-cell DNA sequencing data generation. BMC Genomics 2022; 23:359. [PMID: 35546390 PMCID: PMC9092674 DOI: 10.1186/s12864-022-08566-w] [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: 04/18/2022] [Accepted: 04/19/2022] [Indexed: 11/25/2022] Open
Abstract
Background Single-cell DNA sequencing is getting indispensable in the study of cell-specific cancer genomics. The performance of computational tools that tackle single-cell genome aberrations may be nevertheless undervalued or overvalued, owing to the insufficient size of benchmarking data. In silicon simulation is a cost-effective approach to generate as many single-cell genomes as possible in a controlled manner to make reliable and valid benchmarking. Results This study proposes a new tool, SCSilicon, which efficiently generates single-cell in silicon DNA reads with minimum manual intervention. SCSilicon automatically creates a set of genomic aberrations, including SNP, SNV, Indel, and CNV. Besides, SCSilicon yields the ground truth of CNV segmentation breakpoints and subclone cell labels. We have manually inspected a series of synthetic variations. We conducted a sanity check of the start-of-the-art single-cell CNV callers and found SCYN was the most robust one. Conclusions SCSilicon is a user-friendly software package for users to develop and benchmark single-cell CNV callers. Source code of SCSilicon is available at https://github.com/xikanfeng2/SCSilicon. Supplementary Information The online version contains supplementary material available at (10.1186/s12864-022-08566-w).
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Affiliation(s)
- Xikang Feng
- School of Software, Northwestern Polytechnical University, Xi'an, Shaanxi, 710072, China.
| | - Lingxi Chen
- Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
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46
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Jia Q, Chu H, Jin Z, Long H, Zhu B. High-throughput single-сell sequencing in cancer research. Signal Transduct Target Ther 2022; 7:145. [PMID: 35504878 PMCID: PMC9065032 DOI: 10.1038/s41392-022-00990-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/23/2022] [Accepted: 04/08/2022] [Indexed: 12/22/2022] Open
Abstract
With advances in sequencing and instrument technology, bioinformatics analysis is being applied to batches of massive cells at single-cell resolution. High-throughput single-cell sequencing can be utilized for multi-omics characterization of tumor cells, stromal cells or infiltrated immune cells to evaluate tumor progression, responses to environmental perturbations, heterogeneous composition of the tumor microenvironment, and complex intercellular interactions between these factors. Particularly, single-cell sequencing of T cell receptors, alone or in combination with single-cell RNA sequencing, is useful in the fields of tumor immunology and immunotherapy. Clinical insights obtained from single-cell analysis are critically important for exploring the biomarkers of disease progression or antitumor treatment, as well as for guiding precise clinical decision-making for patients with malignant tumors. In this review, we summarize the clinical applications of single-cell sequencing in the fields of tumor cell evolution, tumor immunology, and tumor immunotherapy. Additionally, we analyze the tumor cell response to antitumor treatment, heterogeneity of the tumor microenvironment, and response or resistance to immune checkpoint immunotherapy. The limitations of single-cell analysis in cancer research are also discussed.
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Affiliation(s)
- Qingzhu Jia
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China.,Chongqing Key Laboratory of Immunotherapy, Chongqing, 400037, China
| | - Han Chu
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China.,Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu, 610064, China
| | - Zheng Jin
- Research Institute, GloriousMed Clinical Laboratory Co., Ltd, Shanghai, 201318, China
| | - Haixia Long
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China. .,Chongqing Key Laboratory of Immunotherapy, Chongqing, 400037, China.
| | - Bo Zhu
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China. .,Chongqing Key Laboratory of Immunotherapy, Chongqing, 400037, China.
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47
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Maslov AY, Makhortov S, Sun S, Heid J, Dong X, Lee M, Vijg J. Single-molecule, quantitative detection of low-abundance somatic mutations by high-throughput sequencing. SCIENCE ADVANCES 2022; 8:eabm3259. [PMID: 35394831 PMCID: PMC8993124 DOI: 10.1126/sciadv.abm3259] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 02/18/2022] [Indexed: 06/14/2023]
Abstract
Postzygotic somatic mutations have been found associated with human disease, including diseases other than cancer. Most information on somatic mutations has come from studying clonally amplified mutant cells, based on a growth advantage or genetic drift. However, almost all somatic mutations are unique for each cell, and the quantitative analysis of these low-abundance mutations in normal tissues remains a major challenge in biology. Here, we introduce single-molecule mutation sequencing (SMM-seq), a novel approach for quantitative identification of point mutations in normal cells and tissues.
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Affiliation(s)
- Alexander Y. Maslov
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Laboratory of Applied Genomic Technologies, Voronezh State University of Engineering Technologies, Voronezh, Russia
| | - Sergey Makhortov
- Department of Programming and Information Technology, Voronezh State University, Voronezh, Russia
| | - Shixiang Sun
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Johanna Heid
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Xiao Dong
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Institute on the Biology of Aging and Metabolism and Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Moonsook Lee
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Jan Vijg
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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48
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Huang Z, Sun S, Lee M, Maslov AY, Shi M, Waldman S, Marsh A, Siddiqui T, Dong X, Peter Y, Sadoughi A, Shah C, Ye K, Spivack SD, Vijg J. Single-cell analysis of somatic mutations in human bronchial epithelial cells in relation to aging and smoking. Nat Genet 2022; 54:492-498. [PMID: 35410377 PMCID: PMC9844147 DOI: 10.1038/s41588-022-01035-w] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 02/16/2022] [Indexed: 01/19/2023]
Abstract
Although lung cancer risk among smokers is dependent on smoking dose, it remains unknown if this increased risk reflects an increased rate of somatic mutation accumulation in normal lung cells. Here, we applied single-cell whole-genome sequencing of proximal bronchial basal cells from 33 participants aged between 11 and 86 years with smoking histories varying from never-smoking to 116 pack-years. We found an increase in the frequency of single-nucleotide variants and small insertions and deletions with chronological age in never-smokers, with mutation frequencies significantly elevated among smokers. When plotted against smoking pack-years, mutations followed the linear increase in cancer risk until about 23 pack-years, after which no further increase in mutation frequency was observed, pointing toward individual selection for mutation avoidance. Known lung cancer-defined mutation signatures tracked with both age and smoking. No significant enrichment for somatic mutations in lung cancer driver genes was observed.
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Affiliation(s)
- Zhenqiu Huang
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA.
| | - Shixiang Sun
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Moonsook Lee
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Alexander Y Maslov
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
- Laboratory of Applied Genomic Technologies, Voronezh State University of Engineering Technologies, Voronezh, Russia
| | - Miao Shi
- Department of Pulmonary Medicine, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA
| | - Spencer Waldman
- Department of Pulmonary Medicine, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA
| | - Ava Marsh
- Department of Pulmonary Medicine, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA
| | - Taha Siddiqui
- Department of Pulmonary Medicine, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA
| | - Xiao Dong
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN, USA
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, USA
| | - Yakov Peter
- Department of Biology, Touro College, Queens, NY, USA
- Department of Pulmonary Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Ali Sadoughi
- Department of Pulmonary Medicine, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA
| | - Chirag Shah
- Department of Pulmonary Medicine, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kenny Ye
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Simon D Spivack
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA.
- Department of Pulmonary Medicine, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA.
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
| | - Jan Vijg
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA.
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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49
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Miller MB, Huang AY, Kim J, Zhou Z, Kirkham SL, Maury EA, Ziegenfuss JS, Reed HC, Neil JE, Rento L, Ryu SC, Ma CC, Luquette LJ, Ames HM, Oakley DH, Frosch MP, Hyman BT, Lodato MA, Lee EA, Walsh CA. Somatic genomic changes in single Alzheimer's disease neurons. Nature 2022; 604:714-722. [PMID: 35444284 PMCID: PMC9357465 DOI: 10.1038/s41586-022-04640-1] [Citation(s) in RCA: 90] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 03/14/2022] [Indexed: 02/02/2023]
Abstract
Dementia in Alzheimer's disease progresses alongside neurodegeneration1-4, but the specific events that cause neuronal dysfunction and death remain poorly understood. During normal ageing, neurons progressively accumulate somatic mutations5 at rates similar to those of dividing cells6,7 which suggests that genetic factors, environmental exposures or disease states might influence this accumulation5. Here we analysed single-cell whole-genome sequencing data from 319 neurons from the prefrontal cortex and hippocampus of individuals with Alzheimer's disease and neurotypical control individuals. We found that somatic DNA alterations increase in individuals with Alzheimer's disease, with distinct molecular patterns. Normal neurons accumulate mutations primarily in an age-related pattern (signature A), which closely resembles 'clock-like' mutational signatures that have been previously described in healthy and cancerous cells6-10. In neurons affected by Alzheimer's disease, additional DNA alterations are driven by distinct processes (signature C) that highlight C>A and other specific nucleotide changes. These changes potentially implicate nucleotide oxidation4,11, which we show is increased in Alzheimer's-disease-affected neurons in situ. Expressed genes exhibit signature-specific damage, and mutations show a transcriptional strand bias, which suggests that transcription-coupled nucleotide excision repair has a role in the generation of mutations. The alterations in Alzheimer's disease affect coding exons and are predicted to create dysfunctional genetic knockout cells and proteostatic stress. Our results suggest that known pathogenic mechanisms in Alzheimer's disease may lead to genomic damage to neurons that can progressively impair function. The aberrant accumulation of DNA alterations in neurodegeneration provides insight into the cascade of molecular and cellular events that occurs in the development of Alzheimer's disease.
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Affiliation(s)
- Michael B Miller
- Division of Neuropathology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - August Yue Huang
- Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Junho Kim
- Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Department of Biological Sciences, Sungkyunkwan University, Suwon, South Korea
| | - Zinan Zhou
- Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Samantha L Kirkham
- Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Eduardo A Maury
- Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Bioinformatics and Integrative Genomics Program, Harvard-MIT MD-PhD Program, Harvard Medical School, Boston, MA, USA
| | - Jennifer S Ziegenfuss
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Hannah C Reed
- Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Allegheny College, Meadville, PA, USA
| | - Jennifer E Neil
- Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Boston, MA, USA
| | - Lariza Rento
- Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Boston, MA, USA
| | - Steven C Ryu
- Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Chanthia C Ma
- Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Lovelace J Luquette
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Heather M Ames
- Department of Pathology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Derek H Oakley
- Department of Pathology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Matthew P Frosch
- Department of Pathology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Bradley T Hyman
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Michael A Lodato
- Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA.
| | - Eunjung Alice Lee
- Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
| | - Christopher A Walsh
- Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
- Howard Hughes Medical Institute, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
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50
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Abstract
Large differences in maximum longevity exist between different rodent species. Determination of the spontaneous and mutagen-induced mutation rates in these species by single (somatic) cell sequencing suggests more efficient DNA repair in long-lived species (including humans), but the data is too noisy to prove a strong correlation between longevity and preservation of DNA integrity.
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Affiliation(s)
- Bertrand Jordan
- Biologiste, généticien et immunologiste, président d'Aprogène (Association pour la promotion de la Génomique), 13007 Marseille, France
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