1
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Boughter CT, Chatterjee B, Ohta Y, Gorga K, Blair C, Hill EM, Fasana Z, Adebamowo A, Ammar F, Kosik I, Murugan V, Chen WH, Singh NJ, Meier-Schellersheim M. CountASAP: A Lightweight, Easy to Use Python Package for Processing ASAPseq Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.20.595042. [PMID: 38903111 PMCID: PMC11188107 DOI: 10.1101/2024.05.20.595042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Declining sequencing costs coupled with the increasing availability of easy-to-use kits for the isolation of DNA and RNA transcripts from single cells have driven a rapid proliferation of studies centered around genomic and transcriptomic data. Simultaneously, a wealth of new techniques have been developed that utilize single cell technologies to interrogate a broad range of cell-biological processes. One recently developed technique, transposase-accessible chromatin with sequencing (ATAC) with select antigen profiling by sequencing (ASAPseq), provides a combination of chromatin accessibility assessments with measurements of cell-surface marker expression levels. While software exists for the characterization of these datasets, there currently exists no tool explicitly designed to reformat ASAP surface marker FASTQ data into a count matrix which can then be used for these downstream analyses. To address this, we created CountASAP, an easy-to-use Python package purposefully designed to transform FASTQ files from ASAP experiments into count matrices compatible with commonly-used downstream bioinformatic analysis packages. CountASAP takes advantage of the independence of the relevant data structures to perform fully parallelized matches of each sequenced read to user-supplied input ASAP oligos and unique cell-identifier sequences.
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Affiliation(s)
- Christopher T. Boughter
- Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892
| | - Budhaditya Chatterjee
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Yuko Ohta
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Katrina Gorga
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Carly Blair
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Elizabeth M. Hill
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Zachary Fasana
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Adedola Adebamowo
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Farah Ammar
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Ivan Kosik
- Cellular Biology Section, Laboratory of Viral Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892
| | - Vel Murugan
- Virginia G. Piper Center for Personalized Diagnostics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287
| | - Wilbur H. Chen
- Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Nevil J. Singh
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Martin Meier-Schellersheim
- Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892
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Salcher S, Heidegger I, Untergasser G, Fotakis G, Scheiber A, Martowicz A, Noureen A, Krogsdam A, Schatz C, Schäfer G, Trajanoski Z, Wolf D, Sopper S, Pircher A. Comparative analysis of 10X Chromium vs. BD Rhapsody whole transcriptome single-cell sequencing technologies in complex human tissues. Heliyon 2024; 10:e28358. [PMID: 38689972 PMCID: PMC11059509 DOI: 10.1016/j.heliyon.2024.e28358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 03/14/2024] [Accepted: 03/18/2024] [Indexed: 05/02/2024] Open
Abstract
The development of single-cell omics tools has enabled scientists to study the tumor microenvironment (TME) in unprecedented detail. However, each of the different techniques may have its unique strengths and limitations. Here we directly compared two commercially available high-throughput single-cell RNA sequencing (scRNA-seq) technologies - droplet-based 10X Chromium vs. microwell-based BD Rhapsody - using paired samples from patients with localized prostate cancer (PCa) undergoing a radical prostatectomy. Although high technical consistency was observed in unraveling the whole transcriptome, the relative abundance of cell populations differed. Cells with low mRNA content such as T cells were underrepresented in the droplet-based system, at least partly due to lower RNA capture rates. In contrast, microwell-based scRNA-seq recovered less cells of epithelial origin. Moreover, we discovered platform-dependent variabilities in mRNA quantification and cell-type marker annotation. Overall, our study provides important information for selection of the appropriate scRNA-seq platform and for the interpretation of published results.
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Affiliation(s)
- Stefan Salcher
- Department of Internal Medicine V, Haematology & Oncology, Comprehensive Cancer Center Innsbruck (CCCI) and Tyrolean Cancer Research Institute (TKFI), Medical University of Innsbruck (MUI), Innsbruck, Austria
| | - Isabel Heidegger
- Department of Urology, Medical University of Innsbruck, Innsbruck, Austria
| | - Gerold Untergasser
- Department of Internal Medicine V, Haematology & Oncology, Comprehensive Cancer Center Innsbruck (CCCI) and Tyrolean Cancer Research Institute (TKFI), Medical University of Innsbruck (MUI), Innsbruck, Austria
| | - Georgios Fotakis
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Austria
| | - Alexandra Scheiber
- Department of Internal Medicine V, Haematology & Oncology, Comprehensive Cancer Center Innsbruck (CCCI) and Tyrolean Cancer Research Institute (TKFI), Medical University of Innsbruck (MUI), Innsbruck, Austria
| | - Agnieszka Martowicz
- Department of Internal Medicine V, Haematology & Oncology, Comprehensive Cancer Center Innsbruck (CCCI) and Tyrolean Cancer Research Institute (TKFI), Medical University of Innsbruck (MUI), Innsbruck, Austria
| | - Asma Noureen
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Austria
| | - Anne Krogsdam
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Austria
| | - Christoph Schatz
- Department of Pathology, Medical University Innsbruck, Innsbruck, Austria
| | - Georg Schäfer
- Department of Pathology, Medical University Innsbruck, Innsbruck, Austria
| | - Zlatko Trajanoski
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Austria
| | - Dominik Wolf
- Department of Internal Medicine V, Haematology & Oncology, Comprehensive Cancer Center Innsbruck (CCCI) and Tyrolean Cancer Research Institute (TKFI), Medical University of Innsbruck (MUI), Innsbruck, Austria
| | - Sieghart Sopper
- Department of Internal Medicine V, Haematology & Oncology, Comprehensive Cancer Center Innsbruck (CCCI) and Tyrolean Cancer Research Institute (TKFI), Medical University of Innsbruck (MUI), Innsbruck, Austria
| | - Andreas Pircher
- Department of Internal Medicine V, Haematology & Oncology, Comprehensive Cancer Center Innsbruck (CCCI) and Tyrolean Cancer Research Institute (TKFI), Medical University of Innsbruck (MUI), Innsbruck, Austria
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3
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Imodoye SO, Adedokun KA, Bello IO. From complexity to clarity: unravelling tumor heterogeneity through the lens of tumor microenvironment for innovative cancer therapy. Histochem Cell Biol 2024; 161:299-323. [PMID: 38189822 DOI: 10.1007/s00418-023-02258-6] [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] [Accepted: 12/06/2023] [Indexed: 01/09/2024]
Abstract
Despite the tremendous clinical successes recorded in the landscape of cancer therapy, tumor heterogeneity remains a formidable challenge to successful cancer treatment. In recent years, the emergence of high-throughput technologies has advanced our understanding of the variables influencing tumor heterogeneity beyond intrinsic tumor characteristics. Emerging knowledge shows that drivers of tumor heterogeneity are not only intrinsic to cancer cells but can also emanate from their microenvironment, which significantly favors tumor progression and impairs therapeutic response. Although much has been explored to understand the fundamentals of the influence of innate tumor factors on cancer diversity, the roles of the tumor microenvironment (TME) are often undervalued. It is therefore imperative that a clear understanding of the interactions between the TME and other tumor intrinsic factors underlying the plastic molecular behaviors of cancers be identified to develop patient-specific treatment strategies. This review highlights the roles of the TME as an emerging factor in tumor heterogeneity. More particularly, we discuss the role of the TME in the context of tumor heterogeneity and explore the cutting-edge diagnostic and therapeutic approaches that could be used to resolve this recurring clinical conundrum. We conclude by speculating on exciting research questions that can advance our understanding of tumor heterogeneity with the goal of developing customized therapeutic solutions.
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Affiliation(s)
- Sikiru O Imodoye
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
| | - Kamoru A Adedokun
- Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
| | - Ibrahim O Bello
- Department of Oral Medicine and Diagnostic Sciences, College of Dentistry, King Saud University, Riyadh, Saudi Arabia.
- Department of Pathology, University of Helsinki, Haartmaninkatu 3, 00014, Helsinki, Finland.
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4
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Xining Z, Sai L. The Evolving Function of Vasculature and Pro-angiogenic Therapy in Fat Grafting. Cell Transplant 2024; 33:9636897241264976. [PMID: 39056562 PMCID: PMC11282510 DOI: 10.1177/09636897241264976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/31/2024] [Accepted: 06/05/2024] [Indexed: 07/28/2024] Open
Abstract
Autologous fat grating is a widely-accepted method to correct soft tissue deficiency. Although fat transplantation shows excellent biocompatibility and simple applicability, the relatively low retention rate caused by fat necrosis is still a challenge. The vasculature is integral after fat grafting, serving multiple crucial functions. Rapid and effective angiogenesis within grafts is essential for supplying oxygen necessary for adipocytes' survival. It facilitates the influx of inflammatory cells to remove necrotic adipocytes and aids in the delivery of regenerative cells for adipose tissue regeneration in fat grafts. The vasculature also provides a niche for interaction between adipose progenitor cells and vascular progenitor cells, enhancing angiogenesis and adipogenesis in grafts. Various methods, such as enriching grafts with diverse pro-angiogenic cells or utilizing cell-free approaches, have been employed to enhance angiogenesis. Beige and dedifferentiated adipocytes in grafts could increase vessel density. This review aims to outline the function of vasculature in fat grafting and discuss different cell or cell-free approaches that can enhance angiogenesis following fat grafting.
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Affiliation(s)
- Zhang Xining
- The Plastic and Aesthetic Center, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Luo Sai
- The Plastic and Aesthetic Center, The First Affiliated Hospital of Harbin Medical University, Harbin, China
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Amini P, Hajihosseini M, Pyne S, Dinu I. Geographically weighted linear combination test for gene-set analysis of a continuous spatial phenotype as applied to intratumor heterogeneity. Front Cell Dev Biol 2023; 11:1065586. [PMID: 36998245 PMCID: PMC10044624 DOI: 10.3389/fcell.2023.1065586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
Background: The impact of gene-sets on a spatial phenotype is not necessarily uniform across different locations of cancer tissue. This study introduces a computational platform, GWLCT, for combining gene set analysis with spatial data modeling to provide a new statistical test for location-specific association of phenotypes and molecular pathways in spatial single-cell RNA-seq data collected from an input tumor sample.Methods: The main advantage of GWLCT consists of an analysis beyond global significance, allowing the association between the gene-set and the phenotype to vary across the tumor space. At each location, the most significant linear combination is found using a geographically weighted shrunken covariance matrix and kernel function. Whether a fixed or adaptive bandwidth is determined based on a cross-validation cross procedure. Our proposed method is compared to the global version of linear combination test (LCT), bulk and random-forest based gene-set enrichment analyses using data created by the Visium Spatial Gene Expression technique on an invasive breast cancer tissue sample, as well as 144 different simulation scenarios.Results: In an illustrative example, the new geographically weighted linear combination test, GWLCT, identifies the cancer hallmark gene-sets that are significantly associated at each location with the five spatially continuous phenotypic contexts in the tumors defined by different well-known markers of cancer-associated fibroblasts. Scan statistics revealed clustering in the number of significant gene-sets. A spatial heatmap of combined significance over all selected gene-sets is also produced. Extensive simulation studies demonstrate that our proposed approach outperforms other methods in the considered scenarios, especially when the spatial association increases.Conclusion: Our proposed approach considers the spatial covariance of gene expression to detect the most significant gene-sets affecting a continuous phenotype. It reveals spatially detailed information in tissue space and can thus play a key role in understanding the contextual heterogeneity of cancer cells.
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Affiliation(s)
- Payam Amini
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
- School of Medicine, Keele University, Keele, Staffordshire, United Kingdom
| | - Morteza Hajihosseini
- School of Public Health, University of Alberta, Edmonton, AB, Canada
- Stanford Department of Urology, Center for Academic Medicine, Palo Alto, CA, United States
| | - Saumyadipta Pyne
- Health Analytics Network, Pittsburgh, PA, United States
- University of California, Santa Barbara, Santa Barbara, CA, United States
- *Correspondence: Saumyadipta Pyne, ; Irina Dinu,
| | - Irina Dinu
- School of Public Health, University of Alberta, Edmonton, AB, Canada
- *Correspondence: Saumyadipta Pyne, ; Irina Dinu,
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6
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Li W, Shao C, Zhou H, Du H, Chen H, Wan H, He Y. Multi-omics research strategies in ischemic stroke: A multidimensional perspective. Ageing Res Rev 2022; 81:101730. [PMID: 36087702 DOI: 10.1016/j.arr.2022.101730] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 08/23/2022] [Accepted: 09/03/2022] [Indexed: 01/31/2023]
Abstract
Ischemic stroke (IS) is a multifactorial and heterogeneous neurological disorder with high rate of death and long-term impairment. Despite years of studies, there are still no stroke biomarkers for clinical practice, and the molecular mechanisms of stroke remain largely unclear. The high-throughput omics approach provides new avenues for discovering biomarkers of IS and explaining its pathological mechanisms. However, single-omics approaches only provide a limited understanding of the biological pathways of diseases. The integration of multiple omics data means the simultaneous analysis of thousands of genes, RNAs, proteins and metabolites, revealing networks of interactions between multiple molecular levels. Integrated analysis of multi-omics approaches will provide helpful insights into stroke pathogenesis, therapeutic target identification and biomarker discovery. Here, we consider advances in genomics, transcriptomics, proteomics and metabolomics and outline their use in discovering the biomarkers and pathological mechanisms of IS. We then delineate strategies for achieving integration at the multi-omics level and discuss how integrative omics and systems biology can contribute to our understanding and management of IS.
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Affiliation(s)
- Wentao Li
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
| | - Chongyu Shao
- School of Life Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
| | - Huifen Zhou
- School of Life Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
| | - Haixia Du
- School of Life Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
| | - Haiyang Chen
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
| | - Haitong Wan
- School of Life Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
| | - Yu He
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
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7
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Oldoni E, Saunders G, Bietrix F, Garcia Bermejo ML, Niehues A, ’t Hoen PAC, Nordlund J, Hajduch M, Scherer A, Kivinen K, Pitkänen E, Mäkela TP, Gut I, Scollen S, Kozera Ł, Esteller M, Shi L, Ussi A, Andreu AL, van Gool AJ. Tackling the translational challenges of multi-omics research in the realm of European personalised medicine: A workshop report. Front Mol Biosci 2022; 9:974799. [PMID: 36310597 PMCID: PMC9608444 DOI: 10.3389/fmolb.2022.974799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/08/2022] [Indexed: 11/13/2022] Open
Abstract
Personalised medicine (PM) presents a great opportunity to improve the future of individualised healthcare. Recent advances in -omics technologies have led to unprecedented efforts characterising the biology and molecular mechanisms that underlie the development and progression of a wide array of complex human diseases, supporting further development of PM. This article reflects the outcome of the 2021 EATRIS-Plus Multi-omics Stakeholder Group workshop organised to 1) outline a global overview of common promises and challenges that key European stakeholders are facing in the field of multi-omics research, 2) assess the potential of new technologies, such as artificial intelligence (AI), and 3) establish an initial dialogue between key initiatives in this space. Our focus is on the alignment of agendas of European initiatives in multi-omics research and the centrality of patients in designing solutions that have the potential to advance PM in long-term healthcare strategies.
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Affiliation(s)
- Emanuela Oldoni
- European Infrastructure for Translational Medicine (EATRIS), Amsterdam, Netherlands
- *Correspondence: Gary Saunders, ; Emanuela Oldoni,
| | - Gary Saunders
- European Infrastructure for Translational Medicine (EATRIS), Amsterdam, Netherlands
- *Correspondence: Gary Saunders, ; Emanuela Oldoni,
| | - Florence Bietrix
- European Infrastructure for Translational Medicine (EATRIS), Amsterdam, Netherlands
| | - Maria Laura Garcia Bermejo
- Biomarkers and Therapeutic Targets Group, Ramon and Cajal Health Research Institute (IRYCIS), Madrid, Spain
| | - Anna Niehues
- Translational Metabolomic Laboratory, Department of Laboratory Medicine, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Peter A. C. ’t Hoen
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Jessica Nordlund
- Department of Medical Sciences, Molecular Precision Medicine and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Marian Hajduch
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University and University Hospital in Olomouc, Olomouc, Czechia
| | - Andreas Scherer
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Katja Kivinen
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
- HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Esa Pitkänen
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
- HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Tomi Pekka Mäkela
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
- HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Ivo Gut
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | | | - Łukasz Kozera
- Biobanking and BioMolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC), Graz, Austria
| | - Manel Esteller
- Josep Carreras Leukemia Research Institute (IJC), Badalona, Spain
- Centro de Investigacion Biomedica en Red Cancer (CIBERONC), Madrid, Spain
- Institucio Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Physiological Sciences Department, School of Medicine and Health Sciences, University of Barcelona (UB), Barcelona, Spain
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China
| | - Anton Ussi
- European Infrastructure for Translational Medicine (EATRIS), Amsterdam, Netherlands
| | - Antonio L. Andreu
- European Infrastructure for Translational Medicine (EATRIS), Amsterdam, Netherlands
| | - Alain J. van Gool
- Translational Metabolomic Laboratory, Department of Laboratory Medicine, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
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Qi J, Sheng Q, Zhou Y, Hua J, Xiao S, Jin S. scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information. Cell Biosci 2022; 12:142. [PMID: 36056412 PMCID: PMC9440561 DOI: 10.1186/s13578-022-00886-4] [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: 05/30/2022] [Accepted: 08/17/2022] [Indexed: 11/17/2022] Open
Abstract
Background Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to capture transcriptomes at single-cell resolution. However, dropout events distort the gene expression levels and underlying biological signals, misleading the downstream analysis of scRNA-seq data. Results We develop a statistical model-based multidimensional imputation algorithm, scMTD, that identifies local cell neighbors and specific gene co-expression networks based on the pseudo-time of cells, leveraging information on cell-level, gene-level, and transcriptome dynamic to recover scRNA-seq data. Compared with the state-of-the-art imputation methods through several real-data-based analytical experiments, scMTD effectively recovers biological signals of transcriptomes and consistently outperforms the other algorithms in improving FISH validation, trajectory inference, differential expression analysis, clustering analysis, and identification of cell types. Conclusions scMTD maintains the gene expression characteristics, enhances the clustering of cell subpopulations, assists the study of gene expression dynamics, contributes to the discovery of rare cell types, and applies to both UMI-based and non-UMI-based data. Overall, scMTD’s reliability, applicability, and scalability make it a promising imputation approach for scRNA-seq data. Supplementary Information The online version contains supplementary material available at 10.1186/s13578-022-00886-4.
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9
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Xiang Z, Li J, Lu D, Wei X, Xu X. Advances in multi-omics research on viral hepatitis. Front Microbiol 2022; 13:987324. [PMID: 36118247 PMCID: PMC9478034 DOI: 10.3389/fmicb.2022.987324] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 08/11/2022] [Indexed: 11/13/2022] Open
Abstract
Viral hepatitis is a major global public health problem that affects hundreds of millions of people and is associated with significant morbidity and mortality. Five biologically unrelated hepatotropic viruses account for the majority of the global burden of viral hepatitis, including hepatitis A virus (HAV), hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis D virus (HDV), and hepatitis E virus (HEV). Omics is defined as the comprehensive study of the functions, relationships and roles of various types of molecules in biological cells. The multi-omics analysis has been proposed and considered key to advancing clinical precision medicine, mainly including genomics, transcriptomics and proteomics, metabolomics. Overall, the applications of multi-omics can show the origin of hepatitis viruses, explore the diagnostic and prognostics biomarkers and screen out the therapeutic targets for viral hepatitis and related diseases. To better understand the pathogenesis of viral hepatitis and related diseases, comprehensive multi-omics analysis has been widely carried out. This review mainly summarizes the applications of multi-omics in different types of viral hepatitis and related diseases, aiming to provide new insight into these diseases.
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Affiliation(s)
- Ze Xiang
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiayuan Li
- Zhejiang University School of Medicine, Hangzhou, China
| | - Di Lu
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- NHC Key Laboratory of Combined Multi-Organ Transplantation, Hangzhou, China
- Institute of Organ Transplantation, Zhejiang University, Hangzhou, China
| | - Xuyong Wei
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- NHC Key Laboratory of Combined Multi-Organ Transplantation, Hangzhou, China
- Institute of Organ Transplantation, Zhejiang University, Hangzhou, China
- Xuyong Wei,
| | - Xiao Xu
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- NHC Key Laboratory of Combined Multi-Organ Transplantation, Hangzhou, China
- Institute of Organ Transplantation, Zhejiang University, Hangzhou, China
- *Correspondence: Xiao Xu,
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10
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Xu L, Xu Y, Xue T, Zhang X, Li J. AdImpute: An Imputation Method for Single-Cell RNA-Seq Data Based on Semi-Supervised Autoencoders. Front Genet 2021; 12:739677. [PMID: 34567089 PMCID: PMC8456123 DOI: 10.3389/fgene.2021.739677] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 08/18/2021] [Indexed: 01/04/2023] Open
Abstract
Motivation: The emergence of single-cell RNA sequencing (scRNA-seq) technology has paved the way for measuring RNA levels at single-cell resolution to study precise biological functions. However, the presence of a large number of missing values in its data will affect downstream analysis. This paper presents AdImpute: an imputation method based on semi-supervised autoencoders. The method uses another imputation method (DrImpute is used as an example) to fill the results as imputation weights of the autoencoder, and applies the cost function with imputation weights to learn the latent information in the data to achieve more accurate imputation. Results: As shown in clustering experiments with the simulated data sets and the real data sets, AdImpute is more accurate than other four publicly available scRNA-seq imputation methods, and minimally modifies the biologically silent genes. Overall, AdImpute is an accurate and robust imputation method.
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Affiliation(s)
- Li Xu
- College of Computer Science and Technology, Harbin Engineering University, Harbin, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Yin Xu
- School of Mathematics, Harbin Institute of Technology, Harbin, China
| | - Tong Xue
- College of Computer Science and Technology, Harbin Engineering University, Harbin, China
| | - Xinyu Zhang
- College of Computer Science and Technology, Harbin Engineering University, Harbin, China
| | - Jin Li
- College of Computer Science and Technology, Harbin Engineering University, Harbin, China
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11
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Bedoya-Reina OC, Li W, Arceo M, Plescher M, Bullova P, Pui H, Kaucka M, Kharchenko P, Martinsson T, Holmberg J, Adameyko I, Deng Q, Larsson C, Juhlin CC, Kogner P, Schlisio S. Single-nuclei transcriptomes from human adrenal gland reveal distinct cellular identities of low and high-risk neuroblastoma tumors. Nat Commun 2021; 12:5309. [PMID: 34493726 PMCID: PMC8423786 DOI: 10.1038/s41467-021-24870-7] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 07/08/2021] [Indexed: 12/23/2022] Open
Abstract
Childhood neuroblastoma has a remarkable variability in outcome. Age at diagnosis is one of the most important prognostic factors, with children less than 1 year old having favorable outcomes. Here we study single-cell and single-nuclei transcriptomes of neuroblastoma with different clinical risk groups and stages, including healthy adrenal gland. We compare tumor cell populations with embryonic mouse sympatho-adrenal derivatives, and post-natal human adrenal gland. We provide evidence that low and high-risk neuroblastoma have different cell identities, representing two disease entities. Low-risk neuroblastoma presents a transcriptome that resembles sympatho- and chromaffin cells, whereas malignant cells enriched in high-risk neuroblastoma resembles a subtype of TRKB+ cholinergic progenitor population identified in human post-natal gland. Analyses of these populations reveal different gene expression programs for worst and better survival in correlation with age at diagnosis. Our findings reveal two cellular identities and a composition of human neuroblastoma tumors reflecting clinical heterogeneity and outcome.
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Affiliation(s)
- O C Bedoya-Reina
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.
| | - W Li
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - M Arceo
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - M Plescher
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - P Bullova
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - H Pui
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - M Kaucka
- Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - P Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Harvard Stem Cell Institute, Cambridge, MA, USA
| | - T Martinsson
- Department of Pathology and Genetics, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - J Holmberg
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - I Adameyko
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroimmunology, Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Q Deng
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - C Larsson
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - C C Juhlin
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - P Kogner
- Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - S Schlisio
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.
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12
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Zhang W, Xue X, Zheng X, Fan Z. NMFLRR: Clustering scRNA-seq data by integrating non-negative matrix factorization with low rank representation. IEEE J Biomed Health Inform 2021; 26:1394-1405. [PMID: 34310328 DOI: 10.1109/jbhi.2021.3099127] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Fast-developing single-cell technologies create unprecedented opportunities to reveal cell heterogeneity and diversity. Accurate classification of single cells is a critical prerequisite for recovering the mechanisms of heterogeneity. However, the scRNA-seq profiles we obtained at present have high dimensionality, sparsity, and noise, which pose challenges for existing clustering methods in grouping cells that belong to the same subpopulation based on transcriptomic profiles. Although many computational methods have been proposed developing novel and effective computational methods to accurately identify cell types remains a considerable challenge. We present a new computational framework to identify cell types by integrating low-rank representation (LRR) and nonnegative matrix factorization (NMF); this framework is named NMFLRR. The LRR captures the global properties of original data by using nuclear norms, and a locality constrained graph regularization term is introduced to characterize the data's local geometric information. The similarity matrix and low-dimensional features of data can be simultaneously obtained by applying the alternating direction method of multipliers (ADMM) algorithm to handle each variable alternatively in an iterative way. We finally obtained the predicted cell types by using a spectral algorithm based on the optimized similarity matrix. Nine real scRNA-seq datasets were used to test the performance of NMFLRR and fifteen other competitive methods, and the accuracy and robustness of the simulation results suggest the NMFLRR is a promising algorithm for the classification of single cells. The simulation code is freely available at: https://github.com/wzhangwhu/NMFLRR_code.
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13
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Qi J, Zhou Y, Zhao Z, Jin S. SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data. PLoS Comput Biol 2021; 17:e1009118. [PMID: 34138847 PMCID: PMC8266063 DOI: 10.1371/journal.pcbi.1009118] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 07/08/2021] [Accepted: 05/27/2021] [Indexed: 11/19/2022] Open
Abstract
The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis. Single-cell RNA sequencing (scRNA-seq) allows researchers to analyze gene expression of thousands of single cells simultaneously. However, the low amount of extracted mRNA leads to a large number of dropout events, which introduce computational challenges and hinder downstream analysis of data. To address this problem, we developed SDImpute, a novel statistical method to recover the scRNA-seq data based on cell-level and gene-level information in this manuscript. The goal of our algorithm is to denoise the scRNA-seq data while maintaining the biological nature of gene expression. Combining SDImpute with the downstream analysis tools, we considered the matched bulk expression data and known cell labels of the scRNA-seq data as criteria to design experiments to validate the performance of our method in both simulated and real datasets. Moreover, we offer an R package with detailed instructions and an example input dataset. We hope that SDImpute will be beneficial to researchers to identify mechanisms underlying some biological processes by analysis of the scRNA-seq data.
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Affiliation(s)
- Jing Qi
- School of Mathematics, Harbin Institute of Technology, Harbin, P.R, China
| | - Yang Zhou
- School of Mathematics, Harbin Institute of Technology, Harbin, P.R, China
| | - Zicen Zhao
- School of Mathematics, Harbin Institute of Technology, Harbin, P.R, China
| | - Shuilin Jin
- School of Mathematics, Harbin Institute of Technology, Harbin, P.R, China
- * E-mail:
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14
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Zhou M, Varol A, Efferth T. Multi-omics approaches to improve malaria therapy. Pharmacol Res 2021; 167:105570. [PMID: 33766628 DOI: 10.1016/j.phrs.2021.105570] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 03/02/2021] [Accepted: 03/16/2021] [Indexed: 01/07/2023]
Abstract
Malaria contributes to the most widespread infectious diseases worldwide. Even though current drugs are commercially available, the ever-increasing drug resistance problem by malaria parasites poses new challenges in malaria therapy. Hence, searching for efficient therapeutic strategies is of high priority in malaria control. In recent years, multi-omics technologies have been extensively applied to provide a more holistic view of functional principles and dynamics of biological mechanisms. We briefly review multi-omics technologies and focus on recent malaria progress conducted with the help of various omics methods. Then, we present up-to-date advances for multi-omics approaches in malaria. Next, we describe resistance phenomena to established antimalarial drugs and underlying mechanisms. Finally, we provide insight into novel multi-omics approaches, new drugs and vaccine developments and analyze current gaps in multi-omics research. Although multi-omics approaches have been successfully used in malaria studies, they are still limited. Many gaps need to be filled to bridge the gap between basic research and treatment of malaria patients. Multi-omics approaches will foster a better understanding of the molecular mechanisms of Plasmodium that are essential for the development of novel drugs and vaccines to fight this disastrous disease.
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Affiliation(s)
- Min Zhou
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany
| | - Ayşegül Varol
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany
| | - Thomas Efferth
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany.
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15
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Qi J, Zhou Y, Hua J, Zhang L, Bian J, Liu B, Zhao Z, Jin S. The scRNA-seq Expression Profiling of the Receptor ACE2 and the Cellular Protease TMPRSS2 Reveals Human Organs Susceptible to SARS-CoV-2 Infection. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:E284. [PMID: 33401657 PMCID: PMC7794913 DOI: 10.3390/ijerph18010284] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 12/21/2020] [Accepted: 12/22/2020] [Indexed: 01/08/2023]
Abstract
COVID-19 patients always develop multiple organ dysfunction syndromes other than lungs, suggesting the novel virus SARS-CoV-2 also invades other organs. Therefore, studying the viral susceptibility of other organs is important for a deeper understanding of viral pathogenesis. Angiotensin-converting enzyme II (ACE2) is the receptor protein of SARS-CoV-2, and TMPRSS2 promotes virus proliferation and transmission. We investigated the ACE2 and TMPRSS2 expression levels of cell types from 31 organs to evaluate the risk of viral infection using single-cell RNA sequencing (scRNA-seq) data. For the first time, we found that the gall bladder and fallopian tube are vulnerable to SARS-CoV-2 infection. Besides, the nose, heart, small intestine, large intestine, esophagus, brain, testis, and kidney are also identified to be high-risk organs with high expression levels of ACE2 and TMPRSS2. Moreover, the susceptible organs are grouped into three risk levels based on the ACE2 and TMPRSS2 expression. As a result, the respiratory system, digestive system, and urinary system are at the top-risk level for SARS-CoV-2 infection. This study provides evidence for SARS-CoV-2 infection in the human nervous system, digestive system, reproductive system, respiratory system, circulatory system, and urinary system using scRNA-seq data, which helps in the clinical diagnosis and treatment of patients.
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Affiliation(s)
| | | | | | | | | | | | | | - Shuilin Jin
- School of Mathematics, Harbin Institute of Technology, Harbin 150001, China; (J.Q.); (Y.Z.); (J.H.); (L.Z.); (J.B.); (B.L.); (Z.Z.)
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16
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Rejection-associated Mitochondrial Impairment After Heart Transplantation. Transplant Direct 2020; 6:e616. [PMID: 33134492 PMCID: PMC7575170 DOI: 10.1097/txd.0000000000001065] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 08/12/2020] [Accepted: 09/01/2020] [Indexed: 01/14/2023] Open
Abstract
Background. Mitochondrial dysfunction is associated with poor allograft prognosis. Mitochondrial-related gene expression (GE) in endomyocardial biopsies (EMBs) could be useful as a nonimmune functional marker of rejection. We hypothesize that acute cardiac allograft rejection is associated with decreased mitochondrial-related GE in EMBs. Methods. We collected 64 routines or clinically indicated EMB from 47 patients after heart transplant. The EMBs were subjected to mRNA sequencing. We conducted weighted gene coexpression network analysis to construct module-derived eigengenes. The modules were assessed by gene ontology enrichment and hub gene analysis. Modules were correlated with the EMBs following the International Society of Heart and Lung Transplantation histology-based criteria and a classification based on GE alone; we also correlated with clinical parameters. Results. The modules enriched with mitochondria-related and immune-response genes showed the strongest correlation to the clinical traits. Compared with the no-rejection samples, rejection samples had a decreased activity of mitochondrial-related genes and an increased activity of immune-response genes. Biologic processes and hub genes in the mitochondria-related modules were primarily involved with energy generation, substrate metabolism, and regulation of oxidative stress. Compared with International Society of Heart and Lung Transplantation criteria, GE-based classification had stronger correlation to the weighted gene coexpression network analysis–derived functional modules. The brain natriuretic peptide level, ImmuKnow, and Allomap scores had negative relationships with the expression of mitochondria-related modules and positive relationships with immune-response modules. Conclusions. During acute cardiac allograft rejection, there was a decreased activity of mitochondrial-related genes, related to an increased activity of immune-response genes, and depressed allograft function manifested by brain natriuretic peptide elevation. This suggests a rejection-associated mitochondrial impairment.
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17
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Single cell approaches to address adipose tissue stromal cell heterogeneity. Biochem J 2020; 477:583-600. [PMID: 32026949 DOI: 10.1042/bcj20190467] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 01/15/2020] [Accepted: 01/20/2020] [Indexed: 12/21/2022]
Abstract
A central function of adipose tissue is in the management of systemic energy homeostasis that is achieved through the co-ordinated regulation of energy storage and mobilization, adipokine release, and immune functions. With the dramatic increase in the prevalence of obesity and obesity-related metabolic disease over the past 30 years, there has been extensive interest in targeting adipose tissue for therapeutic benefit. However, in order for this goal to be achieved it is essential to establish a comprehensive atlas of adipose tissue cellular composition and define mechanisms of intercellular communication that mediate pathologic and therapeutic responses. While traditional methods, such as fluorescence-activated cell sorting (FACS) and genetic lineage tracing, have greatly advanced the field, these approaches are inherently limited by the choice of markers and the ability to comprehensively identify and characterize dynamic interactions among stromal cells within the tissue microenvironment. Single cell RNA sequencing (scRNAseq) has emerged as a powerful tool for deconvolving cellular heterogeneity and holds promise for understanding the development and plasticity of adipose tissue under normal and pathological conditions. scRNAseq has recently been used to characterize adipose stem cell (ASC) populations and has provided new insights into subpopulations of macrophages that arise during anabolic and catabolic remodeling in white adipose tissue. The current review summarizes recent findings that use this technology to explore adipose tissue heterogeneity and plasticity.
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18
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Bondareva O, Sheikh BN. Vascular Homeostasis and Inflammation in Health and Disease-Lessons from Single Cell Technologies. Int J Mol Sci 2020; 21:E4688. [PMID: 32630148 PMCID: PMC7369864 DOI: 10.3390/ijms21134688] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 06/23/2020] [Accepted: 06/30/2020] [Indexed: 02/07/2023] Open
Abstract
The vascular system is critical infrastructure that transports oxygen and nutrients around the body, and dynamically adapts its function to an array of environmental changes. To fulfil the demands of diverse organs, each with unique functions and requirements, the vascular system displays vast regional heterogeneity as well as specialized cell types. Our understanding of the heterogeneity of vascular cells and the molecular mechanisms that regulate their function is beginning to benefit greatly from the rapid development of single cell technologies. Recent studies have started to analyze and map vascular beds in a range of organs in healthy and diseased states at single cell resolution. The current review focuses on recent biological insights on the vascular system garnered from single cell analyses. We cover the themes of vascular heterogeneity, phenotypic plasticity of vascular cells in pathologies such as atherosclerosis and cardiovascular disease, as well as the contribution of defective microvasculature to the development of neurodegenerative disorders such as Alzheimer's disease. Further adaptation of single cell technologies to study the vascular system will be pivotal in uncovering the mechanisms that drive the array of diseases underpinned by vascular dysfunction.
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Affiliation(s)
- Olga Bondareva
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Philipp-Rosenthal-Str. 27, 04103 Leipzig, Germany
| | - Bilal N. Sheikh
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Philipp-Rosenthal-Str. 27, 04103 Leipzig, Germany
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19
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Choi JR, Yong KW, Choi JY, Cowie AC. Single-Cell RNA Sequencing and Its Combination with Protein and DNA Analyses. Cells 2020; 9:cells9051130. [PMID: 32375335 PMCID: PMC7291268 DOI: 10.3390/cells9051130] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 04/28/2020] [Accepted: 05/01/2020] [Indexed: 12/14/2022] Open
Abstract
Heterogeneity in cell populations poses a significant challenge for understanding complex cell biological processes. The analysis of cells at the single-cell level, especially single-cell RNA sequencing (scRNA-seq), has made it possible to comprehensively dissect cellular heterogeneity and access unobtainable biological information from bulk analysis. Recent efforts have combined scRNA-seq profiles with genomic or proteomic data, and show added value in describing complex cellular heterogeneity than transcriptome measurements alone. With the rising demand for scRNA-seq for biomedical and clinical applications, there is a strong need for a timely and comprehensive review on the scRNA-seq technologies and their potential biomedical applications. In this review, we first discuss the latest state of development by detailing each scRNA-seq technology, including both conventional and microfluidic technologies. We then summarize their advantages and limitations along with their biomedical applications. The efforts of integrating the transcriptome profile with highly multiplexed proteomic and genomic data are thoroughly reviewed with results showing the integrated data being more informative than transcriptome data alone. Lastly, the latest progress toward commercialization, the remaining challenges, and future perspectives on the development of scRNA-seq technologies are briefly discussed.
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Affiliation(s)
- Jane Ru Choi
- Centre for Blood Research, Life Sciences Centre, University of British Columbia, 2350 Health Sciences Mall, Vancouver, BV V6T 1Z3, Canada
- Department of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC V6T 1Z4, Canada
- Correspondence: (J.R.C.); (K.W.Y.)
| | - Kar Wey Yong
- Department of Surgery, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB T6G 2R3, Canada
- Correspondence: (J.R.C.); (K.W.Y.)
| | - Jean Yu Choi
- Ninewells Hospital & Medical School, Faculty of Medicine, University of Dundee, Dow Street, Dundee DD1 5EH, UK; (J.Y.C.); (A.C.C.)
| | - Alistair C. Cowie
- Ninewells Hospital & Medical School, Faculty of Medicine, University of Dundee, Dow Street, Dundee DD1 5EH, UK; (J.Y.C.); (A.C.C.)
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20
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Papili Gao N, Hartmann T, Fang T, Gunawan R. CALISTA: Clustering and LINEAGE Inference in Single-Cell Transcriptional Analysis. Front Bioeng Biotechnol 2020; 8:18. [PMID: 32117910 PMCID: PMC7010602 DOI: 10.3389/fbioe.2020.00018] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 01/10/2020] [Indexed: 12/11/2022] Open
Abstract
We present Clustering and Lineage Inference in Single-Cell Transcriptional Analysis (CALISTA), a numerically efficient and highly scalable toolbox for an end-to-end analysis of single-cell transcriptomic profiles. CALISTA includes four essential single-cell analyses for cell differentiation studies, including single-cell clustering, reconstruction of cell lineage specification, transition gene identification, and cell pseudotime ordering, which can be applied individually or in a pipeline. In these analyses, we employ a likelihood-based approach where single-cell mRNA counts are described by a probabilistic distribution function associated with stochastic gene transcriptional bursts and random technical dropout events. We illustrate the efficacy of CALISTA using single-cell gene expression datasets from different single-cell transcriptional profiling technologies and from a few hundreds to tens of thousands of cells. CALISTA is freely available on https://www.cabselab.com/calista.
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Affiliation(s)
- Nan Papili Gao
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Thomas Hartmann
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Tao Fang
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Rudiyanto Gunawan
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland.,Department of Chemical and Biological Engineering, University at Buffalo, Buffalo, NY, United States
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21
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Townes FW, Hicks SC, Aryee MJ, Irizarry RA. Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. Genome Biol 2019; 20:295. [PMID: 31870412 PMCID: PMC6927135 DOI: 10.1186/s13059-019-1861-6] [Citation(s) in RCA: 206] [Impact Index Per Article: 41.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 10/15/2019] [Indexed: 12/23/2022] Open
Abstract
Single-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero inflation. Current normalization procedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We propose simple multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection using deviance. These methods outperform the current practice in a downstream clustering assessment using ground truth datasets.
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Affiliation(s)
- F. William Townes
- Department of Biostatistics, Harvard University, Cambridge, MA USA
- Present Address: Department of Computer Science, Princeton University, Princeton, NJ USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD USA
| | - Martin J. Aryee
- Department of Biostatistics, Harvard University, Cambridge, MA USA
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA USA
- Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA USA
- Department of Pathology, Harvard Medical School, Boston, MA USA
| | - Rafael A. Irizarry
- Department of Biostatistics, Harvard University, Cambridge, MA USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA USA
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22
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Townes FW, Hicks SC, Aryee MJ, Irizarry RA. Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. Genome Biol 2019; 20:295. [PMID: 31870412 DOI: 10.1101/574574] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 10/15/2019] [Indexed: 05/24/2023] Open
Abstract
Single-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero inflation. Current normalization procedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We propose simple multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection using deviance. These methods outperform the current practice in a downstream clustering assessment using ground truth datasets.
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Affiliation(s)
- F William Townes
- Department of Biostatistics, Harvard University, Cambridge, MA, USA
- Present Address: Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
| | - Martin J Aryee
- Department of Biostatistics, Harvard University, Cambridge, MA, USA
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Rafael A Irizarry
- Department of Biostatistics, Harvard University, Cambridge, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
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23
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Olivier M, Asmis R, Hawkins GA, Howard TD, Cox LA. The Need for Multi-Omics Biomarker Signatures in Precision Medicine. Int J Mol Sci 2019; 20:ijms20194781. [PMID: 31561483 PMCID: PMC6801754 DOI: 10.3390/ijms20194781] [Citation(s) in RCA: 242] [Impact Index Per Article: 48.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 09/11/2019] [Accepted: 09/25/2019] [Indexed: 12/12/2022] Open
Abstract
Recent advances in omics technologies have led to unprecedented efforts characterizing the molecular changes that underlie the development and progression of a wide array of complex human diseases, including cancer. As a result, multi-omics analyses—which take advantage of these technologies in genomics, transcriptomics, epigenomics, proteomics, metabolomics, and other omics areas—have been proposed and heralded as the key to advancing precision medicine in the clinic. In the field of precision oncology, genomics approaches, and, more recently, other omics analyses have helped reveal several key mechanisms in cancer development, treatment resistance, and recurrence risk, and several of these findings have been implemented in clinical oncology to help guide treatment decisions. However, truly integrated multi-omics analyses have not been applied widely, preventing further advances in precision medicine. Additional efforts are needed to develop the analytical infrastructure necessary to generate, analyze, and annotate multi-omics data effectively to inform precision medicine-based decision-making.
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Affiliation(s)
- Michael Olivier
- Center for Precision Medicine, Department of Internal Medicine, Wake Forest Baptist Health Comprehensive Cancer Center, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.
| | - Reto Asmis
- Center for Precision Medicine, Department of Internal Medicine, Wake Forest Baptist Health Comprehensive Cancer Center, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.
| | - Gregory A Hawkins
- Center for Precision Medicine, Department of Biochemistry, Wake Forest Baptist Health Comprehensive Cancer Center, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.
| | - Timothy D Howard
- Center for Precision Medicine, Department of Biochemistry, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.
| | - Laura A Cox
- Center for Precision Medicine, Department of Internal Medicine, Wake Forest Baptist Health Comprehensive Cancer Center, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.
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24
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Accurate estimation of cell-type composition from gene expression data. Nat Commun 2019; 10:2975. [PMID: 31278265 PMCID: PMC6611906 DOI: 10.1038/s41467-019-10802-z] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 05/24/2019] [Indexed: 01/20/2023] Open
Abstract
The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly, our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations. Bulk RNA-seq data harbors valuable information about gene expression levels from different cell types in tissue samples. Here, the authors develop DWLS, a computational method for estimating cell-type composition of bulk data by leveraging single-cell RNA-seq-derived cell-type signatures.
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Abstract
A microfluidic device as a pivotal research tool in chemistry and life science is now widely recognized. Indeed, microfluidic techniques have made significant advancements in fundamental research, such as the inherent heterogeneity of single-cells studies in cell populations, which would be helpful in understanding cellular molecular mechanisms and clinical diagnosis of major diseases. Single-cell analyses on microdevices have shown great potential for precise fluid control, cell manipulation, and signal output with rapid and high throughput. Moreover, miniaturized devices also have open functions such as integrating with traditional detection methods, for example, optical, electrochemical or mass spectrometry for single-cell analysis. In this review, we summarized recent advances of single-cell analysis based on various microfluidic approaches from different dimensions, such as in vitro, ex vivo, and in vivo analysis of single cells.
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Affiliation(s)
- Xiaowen Ou
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology
| | - Peng Chen
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology
| | - Bi-Feng Liu
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology
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Tumour heterogeneity and metastasis at single-cell resolution. Nat Cell Biol 2018; 20:1349-1360. [PMID: 30482943 DOI: 10.1038/s41556-018-0236-7] [Citation(s) in RCA: 343] [Impact Index Per Article: 57.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Accepted: 10/24/2018] [Indexed: 02/07/2023]
Abstract
Tumours comprise a heterogeneous collection of cells with distinct genetic and phenotypic properties that can differentially promote progression, metastasis and drug resistance. Emerging single-cell technologies provide a new opportunity to profile individual cells within tumours and investigate what roles they play in these processes. This Review discusses key technological considerations for single-cell studies in cancer, new findings using single-cell technologies and critical open questions for future applications.
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AutoImpute: Autoencoder based imputation of single-cell RNA-seq data. Sci Rep 2018; 8:16329. [PMID: 30397240 PMCID: PMC6218547 DOI: 10.1038/s41598-018-34688-x] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 10/15/2018] [Indexed: 12/22/2022] Open
Abstract
The emergence of single-cell RNA sequencing (scRNA-seq) technologies has enabled us to measure the expression levels of thousands of genes at single-cell resolution. However, insufficient quantities of starting RNA in the individual cells cause significant dropout events, introducing a large number of zero counts in the expression matrix. To circumvent this, we developed an autoencoder-based sparse gene expression matrix imputation method. AutoImpute, which learns the inherent distribution of the input scRNA-seq data and imputes the missing values accordingly with minimal modification to the biologically silent genes. When tested on real scRNA-seq datasets, AutoImpute performed competitively wrt., the existing single-cell imputation methods, on the grounds of expression recovery from subsampled data, cell-clustering accuracy, variance stabilization and cell-type separability.
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Sommarin MNE, Warfvinge R, Safi F, Karlsson G. A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations. J Vis Exp 2018. [PMID: 30417863 DOI: 10.3791/57831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is inherently a single-cell assay, however prior to molecular analysis, the target cells are often prospectively isolated in bulk, thereby losing single-cell resolution. Single-cell gene expression analysis provides a means to understand molecular differences between individual cells in heterogeneous cell populations. In bulk cell analysis an overrepresentation of a distinct cell type results in biases and occlusions of signals from rare cells with biological importance. By utilizing FACS index sorting coupled to single-cell gene expression analysis, populations can be investigated without the loss of single-cell resolution while cells with intermediate cell surface marker expression are also captured, enabling evaluation of the relevance of continuous surface marker expression. Here, we describe an approach that combines single-cell reverse transcription quantitative PCR (RT-qPCR) and FACS index sorting to simultaneously characterize the molecular and immunophenotypic heterogeneity within cell populations. In contrast to single-cell RNA sequencing methods, the use of qPCR with specific target amplification allows for robust measurements of low-abundance transcripts with fewer dropouts, while it is not confounded by issues related to cell-to-cell variations in read depth. Moreover, by directly index-sorting single-cells into lysis buffer this method, allows for cDNA synthesis and specific target pre-amplification to be performed in one step as well as for correlation of subsequently derived molecular signatures with cell surface marker expression. The described approach has been developed to investigate hematopoietic single-cells, but have also been used successfully on other cell types. In conclusion, the approach described herein allows for sensitive measurement of mRNA expression for a panel of pre-selected genes with the possibility to develop protocols for subsequent prospective isolation of molecularly distinct subpopulations.
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Affiliation(s)
| | - Rebecca Warfvinge
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University
| | - Fatemeh Safi
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University
| | - Göran Karlsson
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University;
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29
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Vora NL, Hui L. Next-generation sequencing and prenatal 'omics: advanced diagnostics and new insights into human development. Genet Med 2018; 20:791-799. [PMID: 30032162 PMCID: PMC6123255 DOI: 10.1038/s41436-018-0087-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 06/01/2018] [Indexed: 12/16/2022] Open
Abstract
Prenatal genetics has evolved over the last decade to include application of new 'omics technologies to improve perinatal care. The clinical utility of these technologies when applied to direct fetal specimens from amniocentesis or chorionic villus sampling is being explored. In this review, we provide an overview of use of prenatal exome sequencing and role in evaluation of the structurally abnormal fetus, potential applications of genome sequencing, and finally, use of transcriptomics to assess placental and fetal well-being.
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Affiliation(s)
- Neeta L Vora
- Department of Obstetrics & Gynecology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
| | - Lisa Hui
- Department of Obstetrics & Gynaecology, University of Melbourne, Heidelberg, Victoria, Australia
- Department of Perinatal Medicine, Mercy Hospital for Women, Heidelberg, Victoria, Australia
- Murdoch Children's Research Institute, Public Health Genetics Group, Parkville, Victoria, Australia
- Department of Obstetrics and Gynaecology, The Northern Hospital, Epping, Victoria, Australia
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Dasgupta S, Bader GD, Goyal S. Single-Cell RNA Sequencing: A New Window into Cell Scale Dynamics. Biophys J 2018; 115:429-435. [PMID: 30033145 DOI: 10.1016/j.bpj.2018.07.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Revised: 06/29/2018] [Accepted: 07/03/2018] [Indexed: 01/04/2023] Open
Abstract
Single-cell genomics has recently emerged as a powerful tool for observing multicellular systems at a much higher level of resolution and depth than previously possible. High-throughput single-cell RNA sequencing techniques are able to simultaneously quantify expression levels of several thousands of genes within individual cells for tens of thousands of cells within a complex tissue. This has led to development of novel computational methods to analyze this high-dimensional data, investigating longstanding and fundamental questions regarding the granularity of cell types, the definition of cell states, and transitions from one cell type to another along developmental trajectories. In this perspective, we outline this emerging field starting from the "input data" (e.g., quantifying transcription levels in single cells), which are analyzed to define "identities" (e.g., cell types, states, and key genes) and to build "interactions" using models that can infer relations and transitions between cells.
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Affiliation(s)
| | - Gary D Bader
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
| | - Sidhartha Goyal
- Department of Physics, University of Toronto, Toronto, Ontario, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
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Abstract
Single-cell analysis has become an established method to study cell heterogeneity and for rare cell characterization. Despite the high cost and technical constraints, applications are increasing every year in all fields of biology. Following the trend, there is a tremendous development of tools for single-cell analysis, especially in the RNA sequencing field. Every improvement increases sensitivity and throughput. Collecting a large amount of data also stimulates the development of new approaches for bioinformatic analysis and interpretation. However, the essential requirement for any analysis is the collection of single cells of high quality. The single-cell isolation must be fast, effective, and gentle to maintain the native expression profiles. Classical methods for single-cell isolation are micromanipulation, microdissection, and fluorescence-activated cell sorting (FACS). In the last decade several new and highly efficient approaches have been developed, which not just supplement but may fully replace the traditional ones. These new techniques are based on microfluidic chips, droplets, micro-well plates, and automatic collection of cells using capillaries, magnets, an electric field, or a punching probe. In this review we summarize the current methods and developments in this field. We discuss the advantages of the different commercially available platforms and their applicability, and also provide remarks on future developments.
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Yuan Y, Lee H, Hu H, Scheben A, Edwards D. Single-Cell Genomic Analysis in Plants. Genes (Basel) 2018; 9:genes9010050. [PMID: 29361790 PMCID: PMC5793201 DOI: 10.3390/genes9010050] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 01/05/2018] [Accepted: 01/10/2018] [Indexed: 12/26/2022] Open
Abstract
Individual cells in an organism are variable, which strongly impacts cellular processes. Advances in sequencing technologies have enabled single-cell genomic analysis to become widespread, addressing shortcomings of analyses conducted on populations of bulk cells. While the field of single-cell plant genomics is in its infancy, there is great potential to gain insights into cell lineage and functional cell types to help understand complex cellular interactions in plants. In this review, we discuss current approaches for single-cell plant genomic analysis, with a focus on single-cell isolation, DNA amplification, next-generation sequencing, and bioinformatics analysis. We outline the technical challenges of analysing material from a single plant cell, and then examine applications of single-cell genomics and the integration of this approach with genome editing. Finally, we indicate future directions we expect in the rapidly developing field of plant single-cell genomic analysis.
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Affiliation(s)
- Yuxuan Yuan
- School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia.
| | - HueyTyng Lee
- School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia.
- School of Agriculture and Food Science, The University of Queensland, Brisbane, QLD 4072, Australia.
| | - Haifei Hu
- School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia.
| | - Armin Scheben
- School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia.
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia.
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