1
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Liu S, Obert C, Yu YP, Zhao J, Ren BG, Liu JJ, Wiseman K, Krajacich BJ, Wang W, Metcalfe K, Smith M, Ben-Yehezkel T, Luo JH. Utility analyses of AVITI sequencing chemistry. BMC Genomics 2024; 25:778. [PMID: 39127634 DOI: 10.1186/s12864-024-10686-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 08/02/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND DNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist. RESULTS Here, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences' AVITI and Illumina's NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina's NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms' respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate. CONCLUSION These results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.
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Affiliation(s)
- Silvia Liu
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15261, USA.
- High Throughput Genome Center, University of Pittsburgh School of Medicine, Pittsburgh, USA.
- Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, Pittsburgh, USA.
| | - Caroline Obert
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA, 92121, USA
| | - Yan-Ping Yu
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15261, USA
- High Throughput Genome Center, University of Pittsburgh School of Medicine, Pittsburgh, USA
- Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Junhua Zhao
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA, 92121, USA
| | - Bao-Guo Ren
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15261, USA
- High Throughput Genome Center, University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Jia-Jun Liu
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15261, USA
- High Throughput Genome Center, University of Pittsburgh School of Medicine, Pittsburgh, USA
- Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Kelly Wiseman
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA, 92121, USA
| | - Benjamin J Krajacich
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA, 92121, USA
| | - Wenjia Wang
- Department of Biostatistics, University of Pittsburgh School of Public Health, Pittsburgh, USA
| | - Kyle Metcalfe
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA, 92121, USA
| | - Mat Smith
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA, 92121, USA
| | - Tuval Ben-Yehezkel
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA, 92121, USA
| | - Jian-Hua Luo
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15261, USA.
- High Throughput Genome Center, University of Pittsburgh School of Medicine, Pittsburgh, USA.
- Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, Pittsburgh, USA.
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2
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Yu PS, Tu CC, Wara-Aswapati N, Wang CY, Tu YK, Hou HH, Ueno T, Chen IH, Fu KL, Li HY, Chen YW. Microbiome of periodontitis and peri-implantitis before and after therapy: Long-read 16S rRNA gene amplicon sequencing. J Periodontal Res 2024; 59:657-668. [PMID: 38718089 DOI: 10.1111/jre.13269] [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: 12/15/2023] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 07/16/2024]
Abstract
AIMS The microbial profiles of peri-implantitis and periodontitis (PT) are inconclusive. The controversies mainly arise from the differences in sampling sites, targeted gene fragment, and microbiome analysis techniques. The objective of this study was to explore the microbiomes of peri-implantitis (PI), control implants (CI), PT and control teeth (CT), and the microbial change of PI after nonsurgical treatment (PIAT). METHODS Twenty-two patients diagnosed with both PT and peri-implantitis were recruited. Clinical periodontal parameters and radiographic bone levels were recorded. In each patient, the subgingival and submucosal plaque samples were collected from sites with PI, CI, PT, CT, and PIAT. Microbiome diversity was analyzed by high-throughput amplicon sequencing using full-length of 16S rRNA gene by next generation sequencing. RESULTS The 16S rRNA gene sequencing analysis revealed 512 OTUs in oral microbiome and 377 OTUs reached strain levels. The PI and PT groups possessed their own unique core microbiome. Treponema denticola was predominant in PI with probing depth of 8-10 mm. Interestingly, Thermovirga lienii DSM 17291 and Dialister invisus DSM 15470 were found to associate with PI. Nonsurgical treatment for peri-implantitis did not significantly alter the microbiome, except Rothia aeria. CONCLUSION Our study suggests Treponemas species may play a pivotal role in peri-implantitis. Nonsurgical treatment did not exert a major influence on the peri-implantitis microbiome in short-term follow-up. PT and peri-implantitis possess the unique microbiome profiles, and different therapeutic strategies may be suggested in the future.
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Affiliation(s)
- Pei-Shiuan Yu
- Department of Dentistry, National Taiwan University Hospital and Graduate Institute of Clinical Dentistry, National Taiwan University, Taipei, Taiwan
| | - Che-Chang Tu
- Department of Dentistry, National Taiwan University Hospital and Graduate Institute of Clinical Dentistry, National Taiwan University, Taipei, Taiwan
| | - Nawarat Wara-Aswapati
- Department of Periodontology, Faculty of Dentistry, Khon Kaen University, Khon Kaen, Thailand
| | - Chen-Ying Wang
- Department of Dentistry, National Taiwan University Hospital and Graduate Institute of Clinical Dentistry, National Taiwan University, Taipei, Taiwan
| | - Yu-Kang Tu
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Hsin-Han Hou
- Graduate Institute of Oral Biology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Takaaki Ueno
- Department of Dentistry and Oral Surgery, Faculty of Medicine, Osaka Medical and Pharmaceutical University, Osaka, Japan
| | - I-Hui Chen
- Department of Dentistry, National Taiwan University Hospital and Graduate Institute of Clinical Dentistry, National Taiwan University, Taipei, Taiwan
- Division of Periodontology, Department of Dentistry, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Kuan-Lun Fu
- Department of Dentistry, National Taiwan University Hospital and Graduate Institute of Clinical Dentistry, National Taiwan University, Taipei, Taiwan
| | - Huei-Ying Li
- Medical Microbiota Center of the First Core Laboratory, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Yi-Wen Chen
- Department of Dentistry, National Taiwan University Hospital and Graduate Institute of Clinical Dentistry, National Taiwan University, Taipei, Taiwan
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3
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Liu S, Obert C, Yu YP, Zhao J, Ren BG, Liu JJ, Wiseman K, Krajacich BJ, Wang W, Metcalfe K, Smith M, Ben-Yehezkel T, Luo JH. Utility Analyses of AVITI Sequencing Chemistry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.590136. [PMID: 38712138 PMCID: PMC11071311 DOI: 10.1101/2024.04.18.590136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Background DNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist. Results Here, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences' AVITI and Illumina's NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina's NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms' respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate. Conclusion These results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.
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Affiliation(s)
- Silvia Liu
- Department of Pathology, University of Pittsburgh School of Medicine, United States
- High Throughput Genome Center, University of Pittsburgh School of Medicine, United States
- Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, United States
| | - Caroline Obert
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121, United States
| | - Yan-Ping Yu
- Department of Pathology, University of Pittsburgh School of Medicine, United States
- High Throughput Genome Center, University of Pittsburgh School of Medicine, United States
- Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, United States
| | - Junhua Zhao
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121, United States
| | - Bao-Guo Ren
- Department of Pathology, University of Pittsburgh School of Medicine, United States
- High Throughput Genome Center, University of Pittsburgh School of Medicine, United States
| | - Jia-Jun Liu
- Department of Pathology, University of Pittsburgh School of Medicine, United States
- High Throughput Genome Center, University of Pittsburgh School of Medicine, United States
- Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, United States
| | - Kelly Wiseman
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121, United States
| | - Benjamin J Krajacich
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121, United States
| | - Wenjia Wang
- Department of Biostatistics, University of Pittsburgh School of Public Health, United States
| | - Kyle Metcalfe
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121, United States
| | - Mat Smith
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121, United States
| | - Tuval Ben-Yehezkel
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121, United States
| | - Jian-Hua Luo
- Department of Pathology, University of Pittsburgh School of Medicine, United States
- High Throughput Genome Center, University of Pittsburgh School of Medicine, United States
- Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, United States
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4
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Gupta P, O’Neill H, Wolvetang E, Chatterjee A, Gupta I. Advances in single-cell long-read sequencing technologies. NAR Genom Bioinform 2024; 6:lqae047. [PMID: 38774511 PMCID: PMC11106032 DOI: 10.1093/nargab/lqae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/18/2024] [Accepted: 04/29/2024] [Indexed: 05/24/2024] Open
Abstract
With an increase in accuracy and throughput of long-read sequencing technologies, they are rapidly being assimilated into the single-cell sequencing pipelines. For transcriptome sequencing, these techniques provide RNA isoform-level information in addition to the gene expression profiles. Long-read sequencing technologies not only help in uncovering complex patterns of cell-type specific splicing, but also offer unprecedented insights into the origin of cellular complexity and thus potentially new avenues for drug development. Additionally, single-cell long-read DNA sequencing enables high-quality assemblies, structural variant detection, haplotype phasing, resolving high-complexity regions, and characterization of epigenetic modifications. Given that significant progress has primarily occurred in single-cell RNA isoform sequencing (scRiso-seq), this review will delve into these advancements in depth and highlight the practical considerations and operational challenges, particularly pertaining to downstream analysis. We also aim to offer a concise introduction to complementary technologies for single-cell sequencing of the genome, epigenome and epitranscriptome. We conclude by identifying certain key areas of innovation that may drive these technologies further and foster more widespread application in biomedical science.
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Affiliation(s)
- Pallavi Gupta
- University of Queensland – IIT Delhi Research Academy, Hauz Khas, New Delhi 110016, India
- Australian Institute of Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia, QLD 4072, Australia
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Hannah O’Neill
- Department of Pathology, Dunedin School of Medicine, University of Otago, 58 Hanover Street, Dunedin 9054, New Zealand
| | - Ernst J Wolvetang
- Australian Institute of Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia, QLD 4072, Australia
| | - Aniruddha Chatterjee
- Department of Pathology, Dunedin School of Medicine, University of Otago, 58 Hanover Street, Dunedin 9054, New Zealand
| | - Ishaan Gupta
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
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5
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Wang W, Li Y, Ko S, Feng N, Zhang M, Liu JJ, Zheng S, Ren B, Yu YP, Luo JH, Tseng GC, Liu S. IFDlong: an isoform and fusion detector for accurate annotation and quantification of long-read RNA-seq data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.11.593690. [PMID: 38798496 PMCID: PMC11118288 DOI: 10.1101/2024.05.11.593690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Advancements in long-read transcriptome sequencing (long-RNA-seq) technology have revolutionized the study of isoform diversity. These full-length transcripts enhance the detection of various transcriptome structural variations, including novel isoforms, alternative splicing events, and fusion transcripts. By shifting the open reading frame or altering gene expressions, studies have proved that these transcript alterations can serve as crucial biomarkers for disease diagnosis and therapeutic targets. In this project, we proposed IFDlong, a bioinformatics and biostatistics tool to detect isoform and fusion transcripts using bulk or single-cell long-RNA-seq data. Specifically, the software performed gene and isoform annotation for each long-read, defined novel isoforms, quantified isoform expression by a novel expectation-maximization algorithm, and profiled the fusion transcripts. For evaluation, IFDlong pipeline achieved overall the best performance when compared with several existing tools in large-scale simulation studies. In both isoform and fusion transcript quantification, IFDlong is able to reach more than 0.8 Spearman's correlation with the truth, and more than 0.9 cosine similarity when distinguishing multiple alternative splicing events. In novel isoform simulation, IFDlong can successfully balance the sensitivity (higher than 90%) and specificity (higher than 90%). Furthermore, IFDlong has proved its accuracy and robustness in diverse in-house and public datasets on healthy tissues, cell lines and multiple types of diseases. Besides bulk long-RNA-seq, IFDlong pipeline has proved its compatibility to single-cell long-RNA-seq data. This new software may hold promise for significant impact on long-read transcriptome analysis. The IFDlong software is available at https://github.com/wenjiaking/IFDlong.
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Affiliation(s)
- Wenjia Wang
- Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Yuzhen Li
- Department of Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, PA
| | - Sungjin Ko
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA
| | - Ning Feng
- Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA
| | - Manling Zhang
- Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA
| | - Jia-Jun Liu
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA
| | - Songyang Zheng
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA
| | - Baoguo Ren
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA
| | - Yan P. Yu
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA
| | - Jian-Hua Luo
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA
| | - George C. Tseng
- Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Silvia Liu
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA
- Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA
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6
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Yuan CU, Quah FX, Hemberg M. Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing. Mol Aspects Med 2024; 96:101255. [PMID: 38368637 DOI: 10.1016/j.mam.2024.101255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 02/20/2024]
Abstract
Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.
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Affiliation(s)
- Chengwei Ulrika Yuan
- Department of Biochemistry, University of Cambridge, Cambridge, UK; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Fu Xiang Quah
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Martin Hemberg
- Gene Lay Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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7
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Liu S, Yu YP, Ren BG, Ben-Yehezkel T, Obert C, Smith M, Wang W, Ostrowska A, Soto-Gutierrez A, Luo JH. Long-read single-cell sequencing reveals expressions of hypermutation clusters of isoforms in human liver cancer cells. eLife 2024; 12:RP87607. [PMID: 38206124 PMCID: PMC10945587 DOI: 10.7554/elife.87607] [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] [Indexed: 01/12/2024] Open
Abstract
The protein diversity of mammalian cells is determined by arrays of isoforms from genes. Genetic mutation is essential in species evolution and cancer development. Accurate long-read transcriptome sequencing at single-cell level is required to decipher the spectrum of protein expressions in mammalian organisms. In this report, we developed a synthetic long-read single-cell sequencing technology based on LOOPSeq technique. We applied this technology to analyze 447 transcriptomes of hepatocellular carcinoma (HCC) and benign liver from an individual. Through Uniform Manifold Approximation and Projection analysis, we identified a panel of mutation mRNA isoforms highly specific to HCC cells. The evolution pathways that led to the hyper-mutation clusters in single human leukocyte antigen molecules were identified. Novel fusion transcripts were detected. The combination of gene expressions, fusion gene transcripts, and mutation gene expressions significantly improved the classification of liver cancer cells versus benign hepatocytes. In conclusion, LOOPSeq single-cell technology may hold promise to provide a new level of precision analysis on the mammalian transcriptome.
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Affiliation(s)
- Silvia Liu
- Department of Pathology, University of PittsburghPittsburghUnited States
- High Throughput Genome Center, University of PittsburghPittsburghUnited States
- Pittsburgh Liver Research Center, University of PittsburghPittsburghUnited States
| | - Yan-Ping Yu
- Department of Pathology, University of PittsburghPittsburghUnited States
- High Throughput Genome Center, University of PittsburghPittsburghUnited States
- Pittsburgh Liver Research Center, University of PittsburghPittsburghUnited States
| | - Bao-Guo Ren
- Department of Pathology, University of PittsburghPittsburghUnited States
- High Throughput Genome Center, University of PittsburghPittsburghUnited States
- Pittsburgh Liver Research Center, University of PittsburghPittsburghUnited States
| | | | | | - Mat Smith
- Element Biosciences IncSan DiegoUnited States
| | - Wenjia Wang
- Biostatistics, University of PittsburghPittsburghUnited States
| | - Alina Ostrowska
- Department of Pathology, University of PittsburghPittsburghUnited States
- Pittsburgh Liver Research Center, University of PittsburghPittsburghUnited States
| | - Alejandro Soto-Gutierrez
- Department of Pathology, University of PittsburghPittsburghUnited States
- Pittsburgh Liver Research Center, University of PittsburghPittsburghUnited States
| | - Jian-Hua Luo
- Department of Pathology, University of PittsburghPittsburghUnited States
- High Throughput Genome Center, University of PittsburghPittsburghUnited States
- Pittsburgh Liver Research Center, University of PittsburghPittsburghUnited States
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8
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Liu S, Yu YP, Ren BG, Ben-Yehezkel T, Obert C, Smith M, Wang W, Ostrowska A, Soto-Gutierrez A, Luo JH. Long-read single-cell sequencing reveals expressions of hypermutation clusters of isoforms in human liver cancer cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.16.532991. [PMID: 36993628 PMCID: PMC10055174 DOI: 10.1101/2023.03.16.532991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
The protein diversity of mammalian cells is determined by arrays of isoforms from genes. Genetic mutation is essential in species evolution and cancer development. Accurate Long-read transcriptome sequencing at single-cell level is required to decipher the spectrum of protein expressions in mammalian organisms. In this report, we developed a synthetic long-read single-cell sequencing technology based on LOOPseq technique. We applied this technology to analyze 447 transcriptomes of hepatocellular carcinoma (HCC) and benign liver from an individual. Through Uniform Manifold Approximation and Projection (UMAP) analysis, we identified a panel of mutation mRNA isoforms highly specific to HCC cells. The evolution pathways that led to the hyper-mutation clusters in single human leukocyte antigen (HLA) molecules were identified. Novel fusion transcripts were detected. The combination of gene expressions, fusion gene transcripts, and mutation gene expressions significantly improved the classification of liver cancer cells versus benign hepatocytes. In conclusion, LOOPseq single-cell technology may hold promise to provide a new level of precision analysis on the mammalian transcriptome.
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Affiliation(s)
- Silvia Liu
- Department of Pathology, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
- High Throughput Genome Center, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
- Pittsburgh Liver Research Center, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
| | - Yan-Ping Yu
- Department of Pathology, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
- High Throughput Genome Center, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
- Pittsburgh Liver Research Center, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
| | - Bao-Guo Ren
- Department of Pathology, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
- High Throughput Genome Center, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
- Pittsburgh Liver Research Center, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
| | - Tuval Ben-Yehezkel
- Element Biosciences, Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121
| | - Caroline Obert
- Element Biosciences, Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121
| | - Mat Smith
- Element Biosciences, Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121
| | - Wenjia Wang
- Biostatistics, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
| | - Alina Ostrowska
- Department of Pathology, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
- Pittsburgh Liver Research Center, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
| | - Alejandro Soto-Gutierrez
- Department of Pathology, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
- Pittsburgh Liver Research Center, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
| | - Jian-Hua Luo
- Department of Pathology, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
- High Throughput Genome Center, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
- Pittsburgh Liver Research Center, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261
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9
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Epigenetic and transcriptional activation of the secretory kinase FAM20C as an oncogene in glioma. J Genet Genomics 2023:S1673-8527(23)00023-1. [PMID: 36708808 DOI: 10.1016/j.jgg.2023.01.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/03/2023] [Accepted: 01/14/2023] [Indexed: 01/26/2023]
Abstract
Gliomas are the most prevalent and aggressive malignancies of the nervous system. Previous bioinformatic studies have revealed the crucial role of the secretory pathway kinase FAM20C in the prediction of glioma invasion and malignancy. However, little is known about the pathogenesis of FAM20C in the regulation of glioma. Here, we construct the full-length transcriptome atlas in paired gliomas and observe that 22 genes are upregulated by full-length transcriptome and differential APA analysis. Analysis of ATAC-seq data reveals that both FAM20C and NPTN are the hub genes with chromatin openness and differential expression. Further, in vitro and in vivo studies suggest that FAM20C stimulates the proliferation and metastasis of glioma cells. Meanwhile, NPTN, a novel cancer suppressor gene, counteracts the function of FAM20C by inhibiting both the proliferation and migration of glioma. The blockade of FAM20C by neutralizing antibodies results in the regression of xenograft tumors. Moreover, MAX, BRD4, MYC, and REST are found to be the potential trans-active factors for the regulation of FAM20C. Taken together, our results uncover the oncogenic role of FAM20C in glioma and shed new light on the treatment of glioma by abolishing FAM20C.
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10
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Liu Y, Fan F, Drmanac R, Peters BA, Wang O. Large-Scale Complete Sequencing and Haplotyping of 1-10 kb DNA Molecules Using Short Massively Parallel Reads. Methods Mol Biol 2023; 2590:59-70. [PMID: 36335492 DOI: 10.1007/978-1-0716-2819-5_4] [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: 06/16/2023]
Abstract
In this chapter, we describe a simple, low-cost method for making many copies of a single DNA molecule (1-10 kb in length) as a concatemer on a long DNA strand. This can enable applications requiring high-quality contiguous sequence and haplotype data from long single DNA molecules at large scale.
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Affiliation(s)
- Youtao Liu
- MGI, Shenzhen, Guangdong Province, PR China
| | - Fei Fan
- BGI-Shenzhen, Shenzhen, Guangdong Province, PR China
| | - Radoje Drmanac
- Advanced Genomics Technology Laboratory, Complete Genomics/MGI, San Jose, CA, USA
| | - Brock A Peters
- Advanced Genomics Technology Laboratory, Complete Genomics/MGI, San Jose, CA, USA
| | - Ou Wang
- BGI-Shenzhen, Shenzhen, Guangdong Province, PR China.
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11
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Dorney R, Dhungel BP, Rasko JEJ, Hebbard L, Schmitz U. Recent advances in cancer fusion transcript detection. Brief Bioinform 2022; 24:6918739. [PMID: 36527429 PMCID: PMC9851307 DOI: 10.1093/bib/bbac519] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/11/2022] [Accepted: 10/31/2022] [Indexed: 12/23/2022] Open
Abstract
Extensive investigation of gene fusions in cancer has led to the discovery of novel biomarkers and therapeutic targets. To date, most studies have neglected chromosomal rearrangement-independent fusion transcripts and complex fusion structures such as double or triple-hop fusions, and fusion-circRNAs. In this review, we untangle fusion-related terminology and propose a classification system involving both gene and transcript fusions. We highlight the importance of RNA-level fusions and how long-read sequencing approaches can improve detection and characterization. Moreover, we discuss novel bioinformatic tools to identify fusions in long-read sequencing data and strategies to experimentally validate and functionally characterize fusion transcripts.
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Affiliation(s)
- Ryley Dorney
- epartment of Molecular & Cell Biology, College of Public Health, Medical & Vet Sciences, James Cook University, Douglas, QLD 4811, Australia,Centre for Tropical Bioinformatics and Molecular Biology, Australian Institute of Tropical Health and Medicine, James Cook University, Cairns 4878, Australia
| | - Bijay P Dhungel
- Gene and Stem Cell Therapy Program Centenary Institute, The University of Sydney, Camperdown, NSW 2050, Australia,Faculty of Medicine & Health, The University of Sydney, Camperdown, NSW 2006, Australia,Centre for Tropical Bioinformatics and Molecular Biology, Australian Institute of Tropical Health and Medicine, James Cook University, Cairns 4878, Australia
| | - John E J Rasko
- Gene and Stem Cell Therapy Program Centenary Institute, The University of Sydney, Camperdown, NSW 2050, Australia,Faculty of Medicine & Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Lionel Hebbard
- epartment of Molecular & Cell Biology, College of Public Health, Medical & Vet Sciences, James Cook University, Douglas, QLD 4811, Australia,Storr Liver Centre, Westmead Institute for Medical Research, Westmead Hospital and University of Sydney, Sydney, New South Wales, Australia
| | - Ulf Schmitz
- Corresponding author. Ulf Schmitz, Department of Molecular and Cell Biology, College of Public Health, Medical and Vet Sciences, James Cook University, Douglas, QLD 4811, Australia. E-mail:
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Srinivas M, O’Sullivan O, Cotter PD, van Sinderen D, Kenny JG. The Application of Metagenomics to Study Microbial Communities and Develop Desirable Traits in Fermented Foods. Foods 2022; 11:3297. [PMID: 37431045 PMCID: PMC9601669 DOI: 10.3390/foods11203297] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 10/11/2022] [Accepted: 10/19/2022] [Indexed: 11/18/2022] Open
Abstract
The microbial communities present within fermented foods are diverse and dynamic, producing a variety of metabolites responsible for the fermentation processes, imparting characteristic organoleptic qualities and health-promoting traits, and maintaining microbiological safety of fermented foods. In this context, it is crucial to study these microbial communities to characterise fermented foods and the production processes involved. High Throughput Sequencing (HTS)-based methods such as metagenomics enable microbial community studies through amplicon and shotgun sequencing approaches. As the field constantly develops, sequencing technologies are becoming more accessible, affordable and accurate with a further shift from short read to long read sequencing being observed. Metagenomics is enjoying wide-spread application in fermented food studies and in recent years is also being employed in concert with synthetic biology techniques to help tackle problems with the large amounts of waste generated in the food sector. This review presents an introduction to current sequencing technologies and the benefits of their application in fermented foods.
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Affiliation(s)
- Meghana Srinivas
- Food Biosciences Department, Teagasc Food Research Centre, Moorepark, P61 C996 Cork, Ireland
- APC Microbiome Ireland, University College Cork, T12 CY82 Cork, Ireland
- School of Microbiology, University College Cork, T12 CY82 Cork, Ireland
| | - Orla O’Sullivan
- Food Biosciences Department, Teagasc Food Research Centre, Moorepark, P61 C996 Cork, Ireland
- APC Microbiome Ireland, University College Cork, T12 CY82 Cork, Ireland
- VistaMilk SFI Research Centre, Fermoy, P61 C996 Cork, Ireland
| | - Paul D. Cotter
- Food Biosciences Department, Teagasc Food Research Centre, Moorepark, P61 C996 Cork, Ireland
- APC Microbiome Ireland, University College Cork, T12 CY82 Cork, Ireland
- VistaMilk SFI Research Centre, Fermoy, P61 C996 Cork, Ireland
| | - Douwe van Sinderen
- APC Microbiome Ireland, University College Cork, T12 CY82 Cork, Ireland
- School of Microbiology, University College Cork, T12 CY82 Cork, Ireland
| | - John G. Kenny
- Food Biosciences Department, Teagasc Food Research Centre, Moorepark, P61 C996 Cork, Ireland
- APC Microbiome Ireland, University College Cork, T12 CY82 Cork, Ireland
- VistaMilk SFI Research Centre, Fermoy, P61 C996 Cork, Ireland
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13
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Castaldi PJ, Abood A, Farber CR, Sheynkman GM. Bridging the splicing gap in human genetics with long-read RNA sequencing: finding the protein isoform drivers of disease. Hum Mol Genet 2022; 31:R123-R136. [PMID: 35960994 PMCID: PMC9585682 DOI: 10.1093/hmg/ddac196] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 08/08/2022] [Accepted: 08/09/2022] [Indexed: 02/04/2023] Open
Abstract
Aberrant splicing underlies many human diseases, including cancer, cardiovascular diseases and neurological disorders. Genome-wide mapping of splicing quantitative trait loci (sQTLs) has shown that genetic regulation of alternative splicing is widespread. However, identification of the corresponding isoform or protein products associated with disease-associated sQTLs is challenging with short-read RNA-seq, which cannot precisely characterize full-length transcript isoforms. Furthermore, contemporary sQTL interpretation often relies on reference transcript annotations, which are incomplete. Solutions to these issues may be found through integration of newly emerging long-read sequencing technologies. Long-read sequencing offers the capability to sequence full-length mRNA transcripts and, in some cases, to link sQTLs to transcript isoforms containing disease-relevant protein alterations. Here, we provide an overview of sQTL mapping approaches, the use of long-read sequencing to characterize sQTL effects on isoforms, the linkage of RNA isoforms to protein-level functions and comment on future directions in the field. Based on recent progress, long-read RNA sequencing promises to be part of the human disease genetics toolkit to discover and treat protein isoforms causing rare and complex diseases.
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Affiliation(s)
- Peter J Castaldi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Division of General Medicine and Primary Care, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Abdullah Abood
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
| | - Charles R Farber
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
| | - Gloria M Sheynkman
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22903, USA
- UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA 22903, USA
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Zhang K, Lin R, Chang Y, Zhou Q, Zhang Z. 16S-FASAS: an integrated pipeline for synthetic full-length 16S rRNA gene sequencing data analysis. PeerJ 2022; 10:e14043. [PMID: 36172503 PMCID: PMC9511998 DOI: 10.7717/peerj.14043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 08/21/2022] [Indexed: 01/19/2023] Open
Abstract
Background The full-length 16S rRNA sequencing can better improve the taxonomic and phylogenetic resolution compared to the partial 16S rRNA gene sequencing. The 16S-FAS-NGS (16S rRNA full-length amplicon sequencing based on a next-generation sequencing platform) technology can generate high-quality, full-length 16S rRNA gene sequences using short-read sequencers, together with assembly procedures. However there is a lack of a data analysis suite that can help process and analyze the synthetic long read data. Results Herein, we developed software named 16S-FASAS (16S full-length amplicon sequencing data analysis software) for 16S-FAS-NGS data analysis, which provided high-fidelity species-level microbiome data. 16S-FASAS consists of data quality control, de novo assembly, annotation, and visualization modules. We verified the performance of 16S-FASAS on both mock and fecal samples. In mock communities, we proved that taxonomy assignment by MegaBLAST had fewer misclassifications and tended to find more low abundance species than the USEARCH-UNOISE3-based classifier, resulting in species-level classification of 85.71% (6/7), 85.71% (6/7), 72.72% (8/11), and 70% (7/10) of the target bacteria. When applied to fecal samples, we found that the 16S-FAS-NGS datasets generated contigs grouped into 60 and 56 species, from which 71.62% (43/60) and 76.79% (43/56) were shared with the Pacbio datasets. Conclusions 16S-FASAS is a valuable tool that helps researchers process and interpret the results of full-length 16S rRNA gene sequencing. Depending on the full-length amplicon sequencing technology, the 16S-FASAS pipeline enables a more accurate report on the bacterial complexity of microbiome samples. 16S-FASAS is freely available for use at https://github.com/capitalbio-bioinfo/FASAS.
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Affiliation(s)
- Ke Zhang
- CapitalBio Corporation, Beijing, China,National Engineering Research Center for Beijing Biochip Technology, Beijing, China
| | - Rongnan Lin
- CapitalBio Corporation, Beijing, China,National Engineering Research Center for Beijing Biochip Technology, Beijing, China
| | - Yujun Chang
- CapitalBio Corporation, Beijing, China,National Engineering Research Center for Beijing Biochip Technology, Beijing, China
| | - Qing Zhou
- CapitalBio Corporation, Beijing, China,National Engineering Research Center for Beijing Biochip Technology, Beijing, China
| | - Zhi Zhang
- CapitalBio Corporation, Beijing, China,National Engineering Research Center for Beijing Biochip Technology, Beijing, China
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