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scAEGAN: Unification of single-cell genomics data by adversarial learning of latent space correspondences. PLoS One 2023; 18:e0281315. [PMID: 36735690 PMCID: PMC9897517 DOI: 10.1371/journal.pone.0281315] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 01/19/2023] [Indexed: 02/04/2023] Open
Abstract
Recent progress in Single-Cell Genomics has produced different library protocols and techniques for molecular profiling. We formulate a unifying, data-driven, integrative, and predictive methodology for different libraries, samples, and paired-unpaired data modalities. Our design of scAEGAN includes an autoencoder (AE) network integrated with adversarial learning by a cycleGAN (cGAN) network. The AE learns a low-dimensional embedding of each condition, whereas the cGAN learns a non-linear mapping between the AE representations. We evaluate scAEGAN using simulated data and real scRNA-seq datasets, different library preparations (Fluidigm C1, CelSeq, CelSeq2, SmartSeq), and several data modalities as paired scRNA-seq and scATAC-seq. The scAEGAN outperforms Seurat3 in library integration, is more robust against data sparsity, and beats Seurat 4 in integrating paired data from the same cell. Furthermore, in predicting one data modality from another, scAEGAN outperforms Babel. We conclude that scAEGAN surpasses current state-of-the-art methods and unifies integration and prediction challenges.
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Decoding Susceptibility to Respiratory Viral Infections and Asthma Inception in Children. Int J Mol Sci 2020; 21:ijms21176372. [PMID: 32887352 PMCID: PMC7503410 DOI: 10.3390/ijms21176372] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 08/31/2020] [Accepted: 09/01/2020] [Indexed: 01/19/2023] Open
Abstract
Human Respiratory Syncytial Virus and Human Rhinovirus are the most frequent cause of respiratory tract infections in infants and children and are major triggers of acute viral bronchiolitis, wheezing and asthma exacerbations. Here, we will discuss the application of the powerful tools of systems biology to decode the molecular mechanisms that determine risk for infection and subsequent asthma. An important conceptual advance is the understanding that the innate immune system is governed by a Bow-tie architecture, where diverse input signals converge onto a few core pathways (e.g., IRF7), which in turn generate diverse outputs that orchestrate effector and regulatory functions. Molecular profiling studies in children with severe exacerbations of asthma/wheeze have identified two major immunological phenotypes. The IRF7hi phenotype is characterised by robust upregulation of antiviral response networks, and the IRF7lo phenotype is characterised by upregulation of markers of TGFβ signalling and type 2 inflammation. Similar phenotypes have been identified in infants and children with severe viral bronchiolitis. Notably, genome-wide association studies supported by experimental validation have identified key pathways that increase susceptibility to HRV infection (ORMDL3 and CHDR3) and modulate TGFβ signalling (GSDMB, TGFBR1, and SMAD3). Moreover, functional deficiencies in the activation of type I and III interferon responses are already evident at birth in children at risk of developing febrile lower respiratory tract infections and persistent asthma/wheeze, suggesting that the trajectory to asthma begins at birth or in utero. Finally, exposure to microbes and their products reprograms innate immunity and provides protection from the development of allergies and asthma in children, and therefore microbial products are logical candidates for the primary prevention of asthma.
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Gomez-Cabrero D, Marabita F, Tarazona S, Cano I, Roca J, Conesa A, Sabatier P, Tegnér J. Guidelines for Developing Successful Short Advanced Courses in Systems Medicine and Systems Biology. Cell Syst 2017; 5:168-175. [PMID: 28843483 DOI: 10.1016/j.cels.2017.05.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 02/21/2017] [Accepted: 05/31/2017] [Indexed: 12/15/2022]
Abstract
Systems medicine and systems biology have inherent educational challenges. These have largely been addressed either by providing new masters programs or by redesigning undergraduate programs. In contrast, short courses can respond to a different need: they can provide condensed updates for professionals across academia, the clinic, and industry. These courses have received less attention. Here, we share our experiences in developing and providing such courses to current and future leaders in systems biology and systems medicine. We present guidelines for how to reproduce our courses, and we offer suggestions for how to select students who will nurture an interdisciplinary learning environment and thrive there.
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Affiliation(s)
- David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Center for Molecular Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital, L8, 17176 Stockholm, Sweden; Science for Life Laboratory, 17121 Solna, Sweden; Mucosal and Salivary Biology Division, King's College London Dental Institute, London SE1 9RT, UK.
| | - Francesco Marabita
- Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Center for Molecular Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital, L8, 17176 Stockholm, Sweden; Science for Life Laboratory, 17121 Solna, Sweden
| | - Sonia Tarazona
- Centro de Investigacion Principe Felipe, 46012 Valencia, Spain; Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Camí de Vera, 46022 Valencia, Spain
| | - Isaac Cano
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, 08007 Barcelona, Spain; Center for Biomedical Network Research in Respiratory Diseases (CIBERES), 28029 Madrid, Spain
| | - Josep Roca
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, 08007 Barcelona, Spain; Center for Biomedical Network Research in Respiratory Diseases (CIBERES), 28029 Madrid, Spain
| | - Ana Conesa
- Centro de Investigacion Principe Felipe, 46012 Valencia, Spain; Microbiology and Cell Science Department, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, FL 32603, USA
| | - Philippe Sabatier
- TIMC-IMAG Laboratory, UMR 5525, Centre National de la Recherche Scientifique, Vetagro Sup, Université Grenoble-Alpes, 38400 Saint-Martin-d'Hères, France
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Center for Molecular Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital, L8, 17176 Stockholm, Sweden; Science for Life Laboratory, 17121 Solna, Sweden; Biological and Environmental Sciences and Engineering Division (BESE), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.
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