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Wang M, Song WM, Ming C, Wang Q, Zhou X, Xu P, Krek A, Yoon Y, Ho L, Orr ME, Yuan GC, Zhang B. Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application. Mol Neurodegener 2022; 17:17. [PMID: 35236372 PMCID: PMC8889402 DOI: 10.1186/s13024-022-00517-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.
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Affiliation(s)
- Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Chen Ming
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Qian Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Yonejung Yoon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Lap Ho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Miranda E. Orr
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
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Nelson PT, Fardo DW, Katsumata Y. The MUC6/AP2A2 Locus and Its Relevance to Alzheimer's Disease: A Review. J Neuropathol Exp Neurol 2020; 79:568-584. [PMID: 32357373 PMCID: PMC7241941 DOI: 10.1093/jnen/nlaa024] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 01/10/2020] [Indexed: 12/11/2022] Open
Abstract
We recently reported evidence of Alzheimer's disease (AD)-linked genetic variation within the mucin 6 (MUC6) gene on chromosome 11p, nearby the adaptor-related protein complex 2 subunit alpha 2 (AP2A2) gene. This locus has interesting features related to human genomics and clinical research. MUC6 gene variants have been reported to potentially influence viral-including herpesvirus-immunity and the gut microbiome. Within the MUC6 gene is a unique variable number of tandem repeat (VNTR) region. We discovered an association between MUC6 VNTR repeat expansion and AD pathologic severity, particularly tau proteinopathy. Here, we review the relevant literature. The AD-linked VNTR polymorphism may also influence AP2A2 gene expression. AP2A2 encodes a polypeptide component of the adaptor protein complex, AP-2, which is involved in clathrin-coated vesicle function and was previously implicated in AD pathogenesis. To provide background information, we describe some key knowledge gaps in AD genetics research. The "missing/hidden heritability problem" of AD is highlighted. Extensive portions of the human genome, including the MUC6 VNTR, have not been thoroughly evaluated due to limitations of existing high-throughput sequencing technology. We present and discuss additional data, along with cautionary considerations, relevant to the hypothesis that MUC6 repeat expansion influences AD pathogenesis.
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Affiliation(s)
- Peter T Nelson
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky
- Department of Pathology, University of Kentucky, Lexington, Kentucky
| | - David W Fardo
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky
- Department of Biostatistics, University of Kentucky, Lexington, Kentucky
| | - Yuriko Katsumata
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky
- Department of Biostatistics, University of Kentucky, Lexington, Kentucky
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3
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Bagyinszky E, Giau VV, An SA. Transcriptomics in Alzheimer's Disease: Aspects and Challenges. Int J Mol Sci 2020; 21:E3517. [PMID: 32429229 PMCID: PMC7278930 DOI: 10.3390/ijms21103517] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 05/12/2020] [Accepted: 05/14/2020] [Indexed: 02/07/2023] Open
Abstract
Alzheimer's disease (AD) is the most common cause of dementia. Although the heritability of AD is high, the knowledge of the disease-associated genes, their expression, and their disease-related pathways remain limited. Hence, finding the association between gene dysfunctions and pathological mechanisms, such as neuronal transports, APP processing, calcium homeostasis, and impairment in mitochondria, should be crucial. Emerging studies have revealed that changes in gene expression and gene regulation may have a strong impact on neurodegeneration. The mRNA-transcription factor interactions, non-coding RNAs, alternative splicing, or copy number variants could also play a role in disease onset. These facts suggest that understanding the impact of transcriptomes in AD may improve the disease diagnosis and also the therapies. In this review, we highlight recent transcriptome investigations in multifactorial AD, with emphasis on the insights emerging at their interface.
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Affiliation(s)
- Eva Bagyinszky
- Department of Industrial and Environmental Engineering, Graduate School of Environment, Gachon University, Seongnam 13120, Korea;
- Department of Bionano Technology, Gachon University, Seongnam 13120, Korea
| | - Vo Van Giau
- Department of Industrial and Environmental Engineering, Graduate School of Environment, Gachon University, Seongnam 13120, Korea;
- Department of Bionano Technology, Gachon University, Seongnam 13120, Korea
| | - SeongSoo A. An
- Department of Bionano Technology, Gachon University, Seongnam 13120, Korea
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Huang X, Liu H, Li X, Guan L, Li J, Tellier LCAM, Yang H, Wang J, Zhang J. Revealing Alzheimer's disease genes spectrum in the whole-genome by machine learning. BMC Neurol 2018; 18:5. [PMID: 29320986 PMCID: PMC5763548 DOI: 10.1186/s12883-017-1010-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 12/21/2017] [Indexed: 11/23/2022] Open
Abstract
Background Alzheimer’s disease (AD) is an important, progressive neurodegenerative disease, with a complex genetic architecture. A key goal of biomedical research is to seek out disease risk genes, and to elucidate the function of these risk genes in the development of disease. For this purpose, expanding the AD-associated gene set is necessary. In past research, the prediction methods for AD related genes has been limited in their exploration of the target genome regions. We here present a genome-wide method for AD candidate genes predictions. Methods We present a machine learning approach (SVM), based upon integrating gene expression data with human brain-specific gene network data, to discover the full spectrum of AD genes across the whole genome. Results We classified AD candidate genes with an accuracy and the area under the receiver operating characteristic (ROC) curve of 84.56% and 94%. Our approach provides a supplement for the spectrum of AD-associated genes extracted from more than 20,000 genes in a genome wide scale. Conclusions In this study, we have elucidated the whole-genome spectrum of AD, using a machine learning approach. Through this method, we expect for the candidate gene catalogue to provide a more comprehensive annotation of AD for researchers. Electronic supplementary material The online version of this article (10.1186/s12883-017-1010-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xiaoyan Huang
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083, China.,BGI-Shenzhen, Shenzhen, 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, 518120, China
| | - Hankui Liu
- BGI-Shenzhen, Shenzhen, 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, 518120, China
| | - Xinming Li
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Liping Guan
- BGI-Shenzhen, Shenzhen, 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, 518120, China
| | - Jiankang Li
- BGI-Shenzhen, Shenzhen, 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, 518120, China
| | - Laurent Christian Asker M Tellier
- BGI-Shenzhen, Shenzhen, 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, 518120, China.,Department of Biology, Bioinformatics, University of Copenhagen, Copenhagen, Denmark
| | - Huanming Yang
- BGI-Shenzhen, Shenzhen, 518083, China.,James D. Watson Institute of Genome Sciences, Hangzhou, 310058, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen, 518083, China.,James D. Watson Institute of Genome Sciences, Hangzhou, 310058, China
| | - Jianguo Zhang
- BGI-Shenzhen, Shenzhen, 518083, China. .,China National GeneBank, BGI-Shenzhen, Shenzhen, 518120, China. .,Shenzhen Key Lab of Neurogenomics, BGI-Shenzhen, Shenzhen, 518120, China.
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5
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Castrillo JI, Lista S, Hampel H, Ritchie CW. Systems Biology Methods for Alzheimer’s Disease Research Toward Molecular Signatures, Subtypes, and Stages and Precision Medicine: Application in Cohort Studies and Trials. Methods Mol Biol 2018; 1750:31-66. [PMID: 29512064 DOI: 10.1007/978-1-4939-7704-8_3] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Juan I Castrillo
- Genetadi Biotech S.L. Parque Tecnológico de Bizkaia, Derio, Bizkaia, Spain.
| | - Simone Lista
- AXA Research Fund & UPMC Chair, F-75013, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l'hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l'hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l'hôpital, F-75013, Paris, France
| | - Harald Hampel
- AXA Research Fund & UPMC Chair, F-75013, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l'hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l'hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l'hôpital, F-75013, Paris, France
| | - Craig W Ritchie
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
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