Citation: | Lance M. O'Connor, Blake A. O'Connor, Su Bin Lim, Jialiu Zeng, Chih Hung Lo. Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective[J]. Journal of Pharmaceutical Analysis, 2023, 13(8): 836-850. doi: 10.1016/j.jpha.2023.06.011 |
J. Gauthier, A.T. Vincent, S.J. Charette, et al., A brief history of bioinformatics, Brief. Bioinform. 20 (2019) 1981-1996.
|
V. Svensson, R. Vento-Tormo, S.A. Teichmann, Exponential scaling of single-cell RNA-seq in the past decade, Nat. Protoc. 13 (2018) 599-604.
|
D.H. Geschwind, G. Konopka, Neuroscience in the era of functional genomics and systems biology, Nature 461 (2009) 908-915.
|
X. Dong, C. Liu, M. Dozmorov, Review of multi-omics data resources and integrative analysis for human brain disorders, Brief. Funct. Genomics 20 (2021) 223-234.
|
Y. Hasin, M. Seldin, A. Lusis, Multi-omics approaches to disease, Genome Biol. 18 (2017), 83.
|
B. Tasic, Single cell transcriptomics in neuroscience: Cell classification and beyond, Curr. Opin. Neurobiol. 50 (2018) 242-249.
|
E. Lein, L.E. Borm, S. Linnarsson, The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing, Science 358 (2017) 64-69.
|
W.X. Wang, J.L. Lefebvre, Morphological pseudotime ordering and fate mapping reveal diversification of cerebellar inhibitory interneurons, Nat. Commun. 13 (2022), 3433.
|
S.L. Wilson, G.P. Way, W. Bittremieux, et al., Sharing biological data: Why, when, and how, FEBS Lett. 595 (2021) 847-863.
|
G.M. Shepherd, J.S. Mirsky, M.D. Healy, et al., The Human Brain Project: Neuroinformatics tools for integrating, searching and modeling multidisciplinary neuroscience data, Trends Neurosci. 21 (1998) 460-468.
|
C. Villa, J.H. Yoon, Multi-omics for the understanding of brain diseases, Life 11 (2021), 1202.
|
C. Clark, M. Rabl, L. Dayon, et al., The promise of multi-omics approaches to discover biological alterations with clinical relevance in Alzheimer’s disease, Front. Aging Neurosci. 14 (2022), 1065904.
|
F.V. Greco, A. Pandi, T.J. Erb, et al., Harnessing the central dogma for stringent multi-level control of gene expression, Nat. Commun. 12 (2021), 1738.
|
A. Oulas, G. Minadakis, M. Zachariou, et al., Systems Bioinformatics: Increasing precision of computational diagnostics and therapeutics through network-based approaches, Brief. Bioinform. 20 (2019) 806-824.
|
S. Grillner, A. Kozlov, J.H. Kotaleski, Integrative neuroscience: Linking levels of analyses, Curr. Opin. Neurobiol. 15 (2005) 614-621.
|
T. Schneider-Poetsch, M. Yoshida, Along the central dogma-controlling gene expression with small molecules, Annu. Rev. Biochem. 87 (2018) 391-420.
|
G. Calabrese, C. Molzahn, T. Mayor, Protein interaction networks in neurodegenerative diseases: From physiological function to aggregation, J. Biol. Chem. 298 (2022), 102062.
|
C.H. Lo, J.N. Sachs, The role of wild-type tau in Alzheimer’s disease and related tauopathies, J. Life Sci. (Westlake Village) 2 (2020) 1-17.
|
C.H. Lo, Heterogeneous tau oligomers as molecular targets for Alzheimer’s disease and related tauopathies, Biophysica 2 (2022) 440-451.
|
C.H. Lo, Recent advances in cellular biosensor technology to investigate tau oligomerization, Bioeng. Transl. Med. 6 (2021), e10231.
|
Y. Hou, X. Dan, M. Babbar, et al., Ageing as a risk factor for neurodegenerative disease, Nat. Rev. Neurol. 15 (2019) 565-581.
|
L. Gan, M.R. Cookson, L. Petrucelli, et al., Converging pathways in neurodegeneration, from genetics to mechanisms, Nat. Neurosci. 21 (2018) 1300-1309.
|
R. Ghosh, S.J. Tabrizi, Gene suppression approaches to neurodegeneration, Alzheimer’s Res. Ther. 9 (2017), 82.
|
I.A. Qureshi, M.F. Mehler, Advances in epigenetics and epigenomics for neurodegenerative diseases, Curr. Neurol. Neurosci. Rep. 11 (2011) 464-473.
|
G. Yu, Q. Su, Y. Chen, et al., Epigenetics in neurodegenerative disorders induced by pesticides, Genes Environ. 43 (2021), 55.
|
P. Ghosh, A. Saadat, Neurodegeneration and epigenetics: A review, Neurologia (Engl Ed), 38 (2023) e62-e68.
|
F. Coppede, Targeting the epigenome to treat neurodegenerative diseases or delay their onset: A perspective, Neural Regen. Res. 17 (2022) 1745-1747.
|
J.Y. Hwang, K.A. Aromolaran, R.S. Zukin, The emerging field of epigenetics in neurodegeneration and neuroprotection, Nat. Rev. Neurosci. 18 (2017) 347-361.
|
A. Jowaed, I. Schmitt, O. Kaut, et al., Methylation regulates alpha-synuclein expression and is decreased in Parkinson’s disease patients’ brains, J. Neurosci. 30 (2010) 6355-6359.
|
L. Matsumoto, H. Takuma, A. Tamaoka, et al., CpG demethylation enhances alpha-synuclein expression and affects the pathogenesis of Parkinson’s disease, PLoS One 5 (2010), e15522.
|
F. Albrecht, M. List, C. Bock, et al., DeepBlue epigenomic data server: Programmatic data retrieval and analysis of epigenome region sets, Nucleic Acids Res. 44 (2016) W581-W586.
|
Z. Xiong, F. Yang, M. Li, et al., EWAS Open Platform: Integrated data, knowledge and toolkit for epigenome-wide association study, Nucleic Acids Res. 50 (2022) D1004-D1009.
|
Y. Kodama, J. Mashima, T. Kosuge, et al., DDBJ update: The Genomic Expression Archive (GEA) for functional genomics data, Nucleic Acids Res. 47 (2019) D69-D73.
|
E. Sollis, A. Mosaku, A. Abid, et al., The NHGRI-EBI GWAS Catalog: Knowledgebase and deposition resource, Nucleic Acids Res. 51 (2023) D977-D985.
|
D. Bujold, R. Gregoire, D. Brownlee, et al., IHEC data portal. I. Abugessaisa, T. Kasukawa, Practical Guide to Life Science Databases, first ed., Springer Nature, Singapore, 2011, pp. 77-94.
|
National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD), https://ncrad.iu.edu/. (Accessed 18 June 2023).
|
R.E. Consortium, A. Kundaje, W. Meuleman, et al., Integrative analysis of 111 reference human epigenomes, Nature 518 (2015) 317-330.
|
T. Barrett, S.E. Wilhite, P. Ledoux, et al., NCBI GEO: Archive for functional genomics data sets: Update, Nucleic Acids Res. 41 (2013) D991-D995.
|
C. Huttenhower, O. Hofmann, A quick guide to large-scale genomic data mining, PLoS Comput. Biol. 6 (2010), e1000779.
|
F. Shi, Y. He, Y. Chen, et al., Comparative analysis of multiple neurodegenerative diseases based on advanced epigenetic aging brain, Front. Genet. 12 (2021), 657636.
|
S. Mallik, Z. Zhao, Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise, Sci. Rep. 10 (2020), 22164.
|
C. Pellegrini, C. Pirazzini, C. Sala, et al., A meta-analysis of brain DNA methylation across sex, age, and Alzheimer’s disease points for accelerated epigenetic aging in neurodegeneration, Front. Aging Neurosci. 13 (2021), 639428.
|
H.U. Klein, C. McCabe, E. Gjoneska, et al., Epigenome-wide study uncovers large-scale changes in histone acetylation driven by tau pathology in aging and Alzheimer’s human brains, Nat. Neurosci. 22 (2019) 37-46.
|
P.L. De Jager, Deconstructing the epigenomic architecture of human neurodegeneration, Neurobiol. Dis. 153 (2021), 105331.
|
L.F. MacBean, A.R. Smith, K. Lunnon, Exploring beyond the DNA sequence: A review of epigenomic studies of DNA and histone modifications in dementia, Curr. Genet. Med. Rep. 8 (2020) 79-92.
|
T. Lu, C.E. Ang, X. Zhuang, Spatially resolved epigenomic profiling of single cells in complex tissues, Cell 185 (2022) 4448-4464.e17.
|
Y. Deng, M. Bartosovic, P. Kukanja, et al., Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level, Science 375 (2022) 681-686.
|
Y. Deng, M. Bartosovic, S. Ma, et al., Spatial profiling of chromatin accessibility in mouse and human tissues, Nature 609 (2022) 375-383.
|
R. Fan, D. Zhang, G. Su, Spatially resolved epigenome-transcriptome co-profiling of mammalian tissues at the cellular level, Res. Sq. (2022) 1-26.
|
I.A. Qureshi, M.F. Mehler, Understanding neurological disease mechanisms in the era of epigenetics, JAMA Neurol. 70 (2013) 703-710.
|
M.S. Rao, T.R. Van Vleet, R. Ciurlionis, et al., Comparison of RNA-seq and microarray gene expression platforms for the toxicogenomic evaluation of liver from short-term rat toxicity studies, Front. Genet. 9 (2018), 636.
|
J. Verheijen, K. Sleegers, Understanding Alzheimer disease at the interface between genetics and transcriptomics, Trends Genet. 34 (2018) 434-447.
|
S. Han, K. Nho, Y. Lee, Alternative splicing regulation of an Alzheimer’s risk variant in CLU, Int. J. Mol. Sci. 21 (2020), 7079.
|
M. Wang, N.D. Beckmann, P. Roussos, et al., The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease, Sci. Data 5 (2018), 180185.
|
D.A. Bennett, A.S. Buchman, P.A. Boyle, et al., Religious orders study and rush memory and aging project, J. Alzheimers Dis. 64 (2018) S161-S189.
|
A. Lachmann, D. Torre, A.B. Keenan, et al., Massive mining of publicly available RNA-seq data from human and mouse, Nat. Commun. 9 (2018), 1366.
|
Y. Nakamura, Y. Kodama, S. Saruhashi, et al., DDBJ sequence read archive/DDBJ omics archive, Nat. Preced. (2010). https://doi.org/10.1038/npre.2010.5085.1.
|
SYNAPSE. https://www.synapse.org/. (Accessed 18 June 2023).
|
J.I. Satoh, Y. Yamamoto, N. Asahina, et al., RNA-Seq data mining: Downregulation of NeuroD6 serves as a possible biomarker for Alzheimer’s disease brains, Dis. Markers 2014 (2014), 123165.
|
S. Mukherjee, L. Heath, C. Preuss, et al., Molecular estimation of neurodegeneration pseudotime in older brains, Nat. Commun. 11 (2020), 5781.
|
K. Hooshmand, G.M. Halliday, S.S. Pineda, et al., Overlap between central and peripheral transcriptomes in Parkinson’s disease but not Alzheimer’s disease, Int. J. Mol. Sci. 23 (2022), 5200.
|
M.B. Hossain, M.K. Islam, A. Adhikary, et al., Bioinformatics approach to identify significant biomarkers, drug targets shared between Parkinson’s disease and bipolar disorder: A pilot study, Bioinform. Biol. Insights 16 (2022), 11779322221079232.
|
S. Batchu, Progressive multiple sclerosis transcriptome deconvolution indicates increased M2 macrophages in inactive lesions, Eur. Neurol. 83 (2020) 433-435.
|
B. Hwang, J.H. Lee, D. Bang, Single-cell RNA sequencing technologies and bioinformatics pipelines, Exp. Mol. Med. 50 (2018) 1-14.
|
O.B. Poirion, X. Zhu, T. Ching, et al., Single-cell transcriptomics bioinformatics and computational challenges, Front. Genet. 7 (2016), 163.
|
S. Slovin, A. Carissimo, F. Panariello, et al., Single-cell RNA sequencing analysis: A step-by-step overview, Methods Mol. Biol. 2284 (2021) 343-365.
|
V. Svensson, E. da Veiga Beltrame, L. Pachter, A curated database reveals trends in single-cell transcriptomics, Database 2020 (2020), baaa073.
|
S. Ma, S.B. Lim, Single-cell RNA sequencing in Parkinson’s disease, Biomedicines 9 (2021), 368.
|
J. Jiang, C. Wang, R. Qi, et al., scREAD: A single-cell RNA-seq database for Alzheimer’s disease, iScience 23 (2020), 101769.
|
Q. Wang, S. Ding, Y. Li, et al., The Allen mouse brain common coordinate framework: A 3D reference atlas, Cell 181 (2020) 936-953.e20.
|
Y. Hu, S.G. Tattikota, Y. Liu, et al., DRscDB: A single-cell RNA-seq resource for data mining and data comparison across species, Comput. Struct. Biotechnol. J. 19 (2021) 2018-2026.
|
P.N. Pushparaj, G. Kalamegam, K.H. Wali Sait, et al., Decoding the role of astrocytes in the entorhinal cortex in Alzheimer’s disease using high-dimensional single-nucleus RNA sequencing data and next-generation knowledge discovery methodologies: Focus on drugs and natural product remedies for dementia, Front. Pharmacol. 12 (2021), 720170.
|
A.P. Tsai, C. Dong, P.B. Lin, et al., PLCG2 is associated with the inflammatory response and is induced by amyloid plaques in Alzheimer’s disease, Genome Med. 14 (2022), 17.
|
A. Grubman, G. Chew, J.F. Ouyang, et al., A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation, Nat. Neurosci. 22 (2019) 2087-2097.
|
S.F. Lau, H. Cao, A.K.Y. Fu, et al., Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer’s disease, Proc. Natl. Acad. Sci. U. S. A. 117 (2020) 25800-25809.
|
H. Mathys, J. Davila-Velderrain, Z. Peng, et al., Single-cell transcriptomic analysis of Alzheimer’s disease, Nature 570 (2019) 332-337.
|
X. Wang, L. Li, Cell type-specific potential pathogenic genes and functional pathways in Alzheimer’s Disease, BMC Neurol. 21 (2021), 381.
|
G. Pei, B. Fernandes, Y. Wang, et al., A single-cell atlas of the human brain in Alzheimer’s disease and its implications for personalized drug repositioning, bioRxiv. 2022. https://doi.org/10.1101/2022.06.14.496100
|
M. Wilhelm, J. Schlegl, H. Hahne, et al., Mass-spectrometry-based draft of the human proteome, Nature 509 (2014) 582-587.
|
X. Wang, Q. Liu, B. Zhang, Leveraging the complementary nature of RNA-Seq and shotgun proteomics data, Proteomics 14 (2014) 2676-2687.
|
T. Maier, M. Guell, L. Serrano, Correlation of mRNA and protein in complex biological samples, FEBS Lett. 583 (2009) 3966-3973.
|
E.M. Schoof, B. Furtwangler, N. Uresin, et al., Quantitative single-cell proteomics as a tool to characterize cellular hierarchies, Nat. Commun. 12 (2021), 3341.
|
K.M. Verrou, I. Tsamardinos, G. Papoutsoglou, Learning pathway dynamics from single-cell proteomic data: A comparative study, Cytometry A 97 (2020) 241-252.
|
V.S. Rao, K. Srinivas, G.N. Sujini, et al., Protein-protein interaction detection: Methods and analysis, Int. J. Proteom. 2014 (2014), 147648.
|
A. Zhang, Protein interaction networks: computational analysis. Cambridge University Press, 2009, pp. 1-292.
|
M.W. Gonzalez, M.G. Kann, Chapter 4: Protein interactions and disease, PLoS Comput. Biol. 8 (2012), e1002819.
|
R. Craig, J.P. Cortens, R.C. Beavis, Open source system for analyzing, validating, and storing protein identification data, J. Proteome Res. 3 (2004) 1234-1242.
|
S. Okuda, Y. Watanabe, Y. Moriya, et al., jPOSTrepo: An international standard data repository for proteomes, Nucleic Acids Res. 45 (2017) D1107-D1111.
|
M. Choi, J. Carver, C. Chiva, et al., MassIVE.quant: A community resource of quantitative mass spectrometry-based proteomics datasets, Nat. Meth. 17 (2020) 981-984.
|
Proteomic Data Commons, https://proteomic.datacommons.cancer.gov/pdc/. (Accessed 18 June 2023).
|
J.A. Vizcaino, E.W. Deutsch, R. Wang, et al., ProteomeXchange provides globally coordinated proteomics data submission and dissemination, Nat. Biotechnol. 32 (2014) 223-226.
|
L. Martens, H. Hermjakob, P. Jones, et al., PRIDE: The proteomics identifications database, Proteomics 5 (2005) 3537-3545.
|
P. Samaras, T. Schmidt, M. Frejno, et al., ProteomicsDB: A multi-omics and multi-organism resource for life science research, Nucleic Acids Res. 48 (2020) D1153-D1163.
|
A. Freitas, M. Aroso, S. Rocha, et al., Bioinformatic analysis of the human brain extracellular matrix proteome in neurodegenerative disorders, Eur. J. Neurosci. 53 (2021) 4016-4033.
|
S.C. Deolankar, A.H. Patil, D.A.B. Rex, et al., Mapping post-translational modifications in brain regions in Alzheimer’s disease using proteomics data mining, Omics 25 (2021) 525-536.
|
H. Haytural, R. Benfeitas, S. Schedin-Weiss, et al., Insights into the changes in the proteome of Alzheimer disease elucidated by a meta-analysis, Sci. Data 8 (2021), 312.
|
M.V. Kuleshov, M.R. Jones, A.D. Rouillard, et al., Enrichr: A comprehensive gene set enrichment analysis web server 2016 update, Nucleic Acids Res. 44 (2016) W90-W97.
|
Y. Kinoshita, T. Uo, S. Jayadev, et al., Potential applications and limitations of proteomics in the study of neurological disease, Arch. Neurol. 63 (2006) 1692-1696.
|
A.D. Brunner, M. Thielert, C. Vasilopoulou, et al., Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation, Mol. Syst. Biol. 18 (2022), e10798.
|
J.M. Perkel, Single-cell proteomics takes centre stage, Nature 597 (2021) 580-582.
|
I. Paul, C. White, I. Turcinovic, et al., Imaging the future: The emerging era of single-cell spatial proteomics, FEBS J. 288 (2021) 6990-7001.
|
H. Gallart-Ayala, T. Teav, J. Ivanisevic, Metabolomics meets lipidomics: Assessing the small molecule component of metabolism, BioEssays 42 (2020), e2000052.
|
R. Wang, B. Li, S.M. Lam, et al., Integration of lipidomics and metabolomics for in-depth understanding of cellular mechanism and disease progression, Yi Chuan Xue Bao 47 (2020) 69-83.
|
M.A. Alves, S. Lamichhane, A. Dickens, et al., Systems biology approaches to study lipidomes in health and disease, Biochim. Biophys. Acta Mol. Cell. Biol. Lipids. 1866 (2021), 158857.
|
D.B. Castellanos, C.A. Martin-Jimenez, F. Rojas-Rodriguez, et al., Brain lipidomics as a rising field in neurodegenerative contexts: Perspectives with Machine Learning approaches, Front. Neuroendocrinol. 61 (2021), 100899.
|
B.B. Misra, C. Langefeld, M. Olivier, et al., Integrated omics: Tools, advances and future approaches, J. Mol. Endocrinol. 62 (2019) R21-R45.
|
A. Schumacher-Schuh, A. Bieger, W.V. Borelli, et al., Advances in proteomic and metabolomic profiling of neurodegenerative diseases, Front. Neurol. 12 (2021), 792227.
|
Y. Shao, W. Le, Recent advances and perspectives of metabolomics-based investigations in Parkinson’s disease, Mol. Neurodegener. 14 (2019), 3.
|
P. Reveglia, C. Paolillo, G. Ferretti, et al., Challenges in LC-MS-based metabolomics for Alzheimer’s disease early detection: Targeted approaches versus untargeted approaches, Metabolomics 17 (2021), 78.
|
R.C. Petersen, P.S. Aisen, L.A. Beckett, et al., Alzheimer’s Disease Neuroimaging Initiative (ADNI): Clinical characterization, Neurology 74 (2010) 201-209.
|
D.S. Wishart, M.J. Lewis, J.A. Morrissey, et al., The human cerebrospinal fluid metabolome, J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 871 (2008) 164-173.
|
D.S. Wishart, D. Tzur, C. Knox, et al., HMDB: The human metabolome database, Nucleic Acids Res. 35 (2007) D521-D526.
|
D.S. Wishart, C. Knox, A.C. Guo, et al., HMDB: A knowledgebase for the human metabolome, Nucleic Acids Res. 37 (2009) D603-D610.
|
D.S. Wishart, T. Jewison, A.C. Guo, et al., HMDB 3.0-The human metabolome database in 2013, Nucleic Acids Res. 41 (2013) D801-D807.
|
D.S. Wishart, Y.D. Feunang, A. Marcu, et al., HMDB 4.0: The human metabolome database for 2018, Nucleic Acids Res. 46 (2018) D608-D617.
|
D.S. Wishart, A.C. Guo, E. Oler, et al., HMDB 5.0: The human metabolome database for 2022, Nucleic Acids Res. 50 (2022) D622-D631.
|
K. Watanabe, E. Yasugi, M. Oshima, How to search the glycolipid data in “LIPIDBANK for Web” the newly developed lipid database in Japan, Trends Glycosci. Glycotechnol. 12 (2000) 175-184.
|
T. Kind, K.H. Liu, D.Y. Lee, et al., LipidBlast in silico tandem mass spectrometry database for lipid identification, Nat. Meth. 10 (2013) 755-758.
|
D. Cotter, A. Maer, C. Guda, et al., LMPD: LIPID MAPS proteome database, Nucleic Acids Res. 34 (2006) D507-D510.
|
T.B. Mracica, A. Anghel, C.F. Ion, et al., MetaboAge DB: A repository of known ageing-related changes in the human metabolome, Biogerontology 21 (2020) 763-771.
|
K. Haug, R.M. Salek, P. Conesa, et al., MetaboLights: An open-access general-purpose repository for metabolomics studies and associated meta-data, Nucleic Acids Res. 41 (2013) D781-D786.
|
M. Sud, E. Fahy, D. Cotter, et al., Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools, Nucleic Acids Res. 44 (2016) D463-D470.
|
MetabolomeXchange, http://www.metabolomexchange.org/site/. (Accessed 18 June 2023)
|
N. Psychogios, D.D. Hau, J. Peng, et al., The human serum metabolome, PLoS One 6 (2011), e16957.
|
D.K. Barupal, R. Baillie, S. Fan, et al., Sets of coregulated serum lipids are associated with Alzheimer’s disease pathophysiology, Alzheimers Dement. (Amst) 11 (2019) 619-627.
|
Y. Tang, S. Shah, K.S. Cho, et al., Metabolomics in primary open angle glaucoma: A systematic review and meta-analysis, Front. Neurosci. 16 (2022), 835736.
|
Y. Perez-Riverol, M. Bai, F. da Veiga Leprevost, et al., Discovering and linking public omics data sets using the Omics Discovery Index, Nat. Biotechnol. 35 (2017) 406-409.
|
J. Pu, Y. Yu, Y. Liu, et al., MENDA: A comprehensive curated resource of metabolic characterization in depression, Brief Bioinform. 21 (2020) 1455-1464.
|
X. Han, R. Wang, Y. Zhou, et al., Mapping the mouse cell atlas by microwell-seq, Cell 172 (2018) 1091-1107.e17.
|
The Tabula Muris Consortium, Overall coordination, Logistical coordination, et al., Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris, Nature 562 (2018) 367-372.
|
J. Liao, X. Lu, X. Shao, et al., Uncovering an organ’s molecular architecture at single-cell resolution by spatially resolved transcriptomics, Trends Biotechnol. 39 (2021) 43-58.
|
J. Cheng, J. Liao, X. Shao, et al., Multiplexing methods for simultaneous large-scale transcriptomic profiling of samples at single-cell resolution, Adv. Sci. (Weinh) 8 (2021), e2101229.
|
K.H. Chen, A.N. Boettiger, J.R. Moffitt, et al., RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells, Science 348 (2015), aaa6090.
|
P.L. Stahl, F. Salmen, S. Vickovic, et al., Visualization and analysis of gene expression in tissue sections by spatial transcriptomics, Science 353 (2016) 78-82.
|
S.G. Rodriques, R.R. Stickels, A. Goeva, et al., Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution, Science 363 (2019) 1463-1467.
|
C.L. Eng, M. Lawson, Q. Zhu, et al., Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH, Nature 568 (2019) 235-239.
|
R. Satija, J.A. Farrell, D. Gennert, et al., Spatial reconstruction of single-cell gene expression data, Nat. Biotechnol. 33 (2015) 495-502.
|
K. Achim, J.B. Pettit, L.R. Saraiva, et al., High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin, Nat. Biotechnol. 33 (2015) 503-509.
|
D. Franjic, M. Skarica, S. Ma, et al., Transcriptomic taxonomy and neurogenic trajectories of adult human, macaque, and pig hippocampal and entorhinal cells, Neuron 110 (2022) 452-469.e14.
|
A.M. Femino, F.S. Fay, K. Fogarty, et al., Visualization of single RNA transcripts in situ, Science 280 (1998) 585-590.
|
C. Giesen, H.A.O. Wang, D. Schapiro, et al., Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry, Nat. Meth. 11 (2014) 417-422.
|
G. Gut, M.D. Herrmann, L. Pelkmans, Multiplexed protein maps link subcellular organization to cellular states, Science 361 (2018), eaar7042.
|
W. Chen, A. Lu, K. Craessaerts, et al., Spatial transcriptomics and in situ sequencing to study Alzheimer’s disease, Cell 182 (2020) 976-991.e19.
|
S. Prokop, K.R. Miller, S.R. Labra, et al., Impact of TREM2 risk variants on brain region-specific immune activation and plaque microenvironment in Alzheimer’s disease patient brain samples, Acta Neuropathol. 138 (2019) 613-630.
|
J.F. Navarro, D.L. Croteau, A. Jurek, et al., Spatial transcriptomics reveals genes associated with dysregulated mitochondrial functions and stress signaling in Alzheimer disease, iScience 23 (2020), 101556.
|
J. Aguila, S. Cheng, N. Kee, et al., Spatial RNA sequencing identifies robust markers of vulnerable and resistant human midbrain dopamine neurons and their expression in Parkinson’s disease, Front. Mol. Neurosci. 14 (2021), 699562.
|
T. Kamath, A. Abdulraouf, S.J. Burris, et al., Single-cell genomic profiling of human dopamine neurons identifies a population that selectively degenerates in Parkinson’s disease, Nat. Neurosci. 25 (2022) 588-595.
|
S. Smajic, C.A. Prada-Medina, Z. Landoulsi, et al., Single-cell sequencing of human midbrain reveals glial activation and a Parkinson-specific neuronal state, Brain 145 (2022) 964-978.
|
C.H. Lo, M. Skarica, M. Mansoor, et al., Astrocyte heterogeneity in multiple sclerosis: Current understanding and technical challenges, Front. Cell. Neurosci. 15 (2021), 726479.
|
L. Schirmer, D. Velmeshev, S. Holmqvist, et al., Neuronal vulnerability and multilineage diversity in multiple sclerosis, Nature 573 (2019) 75-82.
|
M. Kaufmann, H. Evans, A.L. Schaupp, et al., Identifying CNS-colonizing T cells as potential therapeutic targets to prevent progression of multiple sclerosis, Med 2 (2021) 296-312.e8.
|
M. Absinta, D. Maric, M. Gharagozloo, et al., A lymphocyte-microglia-astrocyte axis in chronic active multiple sclerosis, Nature 597 (2021) 709-714.
|
M. Kaufmann, A.L. Schaupp, R. Sun, et al., Identification of early neurodegenerative pathways in progressive multiple sclerosis, Nat. Neurosci. 25 (2022) 944-955.
|
B. Pardo, A. Spangler, L.M. Weber, et al., spatialLIBD: An R/Bioconductor package to visualize spatially-resolved transcriptomics data, BMC Genomics 23 (2022), 434.
|
D. Righelli, L.M. Weber, H.L. Crowell, et al., SpatialExperiment: Infrastructure for spatially-resolved transcriptomics data in R using Bioconductor, Bioinformatics 38 (2022) 3128-3131.
|
C.K. Mah, N. Ahmed, N. Lopez, et al., Bento: A toolkit for subcellular analysis of spatial transcriptomics data, bioRxiv. 2023. https://10.1101/2022.06.10.495510
|
L.M. Breckels, C.M. Mulvey, K.S. Lilley, et al., A Bioconductor workflow for processing and analysing spatial proteomics data, F1000Research 5 (2016), 2926.
|
G. Palla, H. Spitzer, M. Klein, et al., Squidpy: A scalable framework for spatial omics analysis, Nat. Meth. 19 (2022) 171-178.
|
S. Vickovic, B. Lotstedt, J. Klughammer, et al., SM-Omics is an automated platform for high-throughput spatial multi-omics, Nat. Commun. 13 (2022), 795.
|
A. Martinelli, M. Rapsomaniki, ATHENA: Analysis of tumor heterogeneity from spatial omics measurements, Bioinformatics 38 (2022) 3151-3153.
|
M.A. Kennedy, W.A. Hofstadter, I.M. Cristea, TRANSPIRE: A computational pipeline to elucidate intracellular protein movements from spatial proteomics data sets, J. Am. Soc. Mass Spectrom. 31 (2020) 1422-1439.
|
J.A. Christopher, A. Geladaki, C.S. Dawson, et al., Subcellular transcriptomics and proteomics: A comparative methods review, Mol. Cell. Proteom. 21 (2022), 100186.
|
A. Regev, S.A. Teichmann, E.S. Lander, et al., The human cell atlas, eLife 6 (2017), e27041.
|
HuBMAP Consortium, The human body at cellular resolution: The NIH Human Biomolecular Atlas Program, Nature 574 (2019) 187-192.
|
M. Frenkel-Morgenstern, A.A. Cohen, N. Geva-Zatorsky, et al., Dynamic proteomics: A database for dynamics and localizations of endogenous fluorescently-tagged proteins in living human cells, Nucleic Acids Res. 38 (2010) D508-D512.
|
R. Dries, Q. Zhu, R. Dong, et al., Giotto: A toolbox for integrative analysis and visualization of spatial expression data, Genome Biol. 22 (2021), 78.
|
Z. Xu, W. Wang, T. Yang, et al., STOmicsDB: A database of Spatial Transcriptomic data, bioRxiv. 2022. https://doi.org/10.1101/2022.03.11.481421.
|
Z. Fan, R. Chen, X. Chen, SpatialDB: A database for spatially resolved transcriptomes, Nucleic Acids Res. 48 (2020) D233-D237.
|
J. Qian, J. Liao, Z. Liu, et al., Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace, Nat. Commun. 14 (2023), 2484.
|
J. Liao, J. Qian, Y. Fang, et al., De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution, Nat. Commun. 13 (2022), 6498.
|
A. Zeisel, A.B. Munoz-Manchado, S. Codeluppi, et al., Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq, Science 347 (2015) 1138-1142.
|
F. Bao, Y. Deng, S. Wan, et al., Integrative spatial analysis of cell morphologies and transcriptional states with MUSE, Nat. Biotechnol. 40 (2022) 1200-1209.
|
V. Marx, Method of the year: Spatially resolved transcriptomics, Nat. Meth. 18 (2021) 9-14.
|
S.K. Longo, M.G. Guo, A.L. Ji, et al., Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics, Nat. Rev. Genet. 22 (2021) 627-644.
|
X. Shao, X. Lu, J. Liao, et al., New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data, Protein Cell 11 (2020) 866-880.
|
X. Shao, J. Liao, C. Li, et al., CellTalkDB: A manually curated database of ligand-receptor interactions in humans and mice, Brief. Bioinform. 22 (2021), bbaa269.
|
X. Shao, C. Li, H. Yang, et al., Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk, Nat. Commun. 13 (2022), 4429.
|
Y. Fangma, M. Liu, J. Liao, et al., Dissecting the brain with spatially resolved multi-omics, J. Pharm. Anal. 2023. https://doi.org/10.1016/j.jpha.2023.04.003.
|
T.J. Sejnowski, P.S. Churchland, J.A. Movshon, Putting big data to good use in neuroscience, Nat. Neurosci. 17 (2014) 1440-1441.
|
H. Lee, J.J. Lee, N.Y. Park, et al., Multi-omic analysis of selectively vulnerable motor neuron subtypes implicates altered lipid metabolism in ALS, Nat. Neurosci. 24 (2021) 1673-1685.
|
L. Xicota, F. Ichou, F.X. Lejeune, et al., Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer’s disease: The INSIGHT-preAD study, EBioMedicine 47 (2019) 518-528.
|
E. Puris, S. Kouril, L. Najdekr, et al., Metabolomic, lipidomic and proteomic characterisation of lipopolysaccharide-induced inflammation mouse model, Neuroscience 496 (2022) 165-178.
|
C. Clark, L. Dayon, M. Masoodi, et al., An integrative, hypothesis-free, multi-omics approach uncovers biological pathway alterations in Alzheimer’s disease, Alzheimers. Dement. 16 (2020), e038563.
|
S. Lee, N.A. Devanney, L.R. Golden, et al., APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge, Cell Rep. 42 (2023), 112196.
|
M.B. O’Rourke, S.E.L. Town, P.V. Dalla, et al., What is normalization? the strategies employed in top-down and bottom-up proteome analysis workflows, Proteomes 7 (2019), 29.
|
A. Conesa, P. Madrigal, S. Tarazona, et al., A survey of best practices for RNA-seq data analysis, Genome Biol. 17 (2016), 13.
|
P. Yang, H. Huang, C. Liu, Feature selection revisited in the single-cell era, Genome Biol. 22 (2021), 321.
|
A. Torres-Martos, M. Bustos-Aibar, A. Ramirez-Mena, et al., Omics data preprocessing for machine learning: A case study in childhood obesity, Genes 14 (2023), 248.
|
S. Nataf, M. Guillen, L. Pays, TGFB1-mediated gliosis in multiple sclerosis spinal cords is favored by the regionalized expression of HOXA5 and the age-dependent decline in androgen receptor ligands, Int. J. Mol. Sci. 20 (2019), 5934.
|
M.E. Garcia-Segura, B.R. Durainayagam, S. Liggi, et al., Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer’s disease mouse model and risk loci carriers, J. Neurochem. 164 (2023) 57-76.
|
G. Zhou, S. Li, J. Xia, Network-based approaches for multi-omics integration, Methods Mol. Biol. 2104 (2020) 469-487.
|
M. Kanehisa, S. Goto, KEGG: Kyoto encyclopedia of genes and genomes, Nucleic Acids Res. 28 (2000) 27-30.
|
M. Kanehisa, M. Furumichi, Y. Sato, et al., KEGG for taxonomy-based analysis of pathways and genomes, Nucleic Acids Res. 51 (2023) D587-D592.
|
M. Ashburner, C.A. Ball, J.A. Blake, et al., Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium Nat. Genet. 25 (2000) 25-29.
|
Gene Ontology Consortium, The Gene Ontology resource: Enriching a GOld mine., Nucleic Acids Res. 49 (2021) D325-D334.
|
G. Dennis Jr, B.T. Sherman, D.A. Hosack, et al., DAVID: Database for annotation, visualization, and integrated discovery, Genome Biol. 4 (2003), P3.
|
P.D. Thomas, D. Ebert, A. Muruganujan, et al., PANTHER: Making genome-scale phylogenetics accessible to all, Protein Sci. 31 (2022) 8-22.
|
H. Mi, A. Muruganujan, X. Huang, et al., Protocol Update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0), Nat. Protoc. 14 (2019) 703-721.
|
A. Subramanian, P. Tamayo, V.K. Mootha, et al., Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles, Proc. Natl. Acad. Sci. U. S. A. 102 (2005) 15545-15550.
|
A. Kramer, J. Green, J. Pollard Jr, et al., Causal analysis approaches in Ingenuity Pathway Analysis, Bioinformatics 30 (2014) 523-530.
|
D. Szklarczyk, A.L. Gable, D. Lyon, et al., STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets, Nucleic Acids Res. 47 (2019) D607-D613.
|
P. Shannon, A. Markiel, O. Ozier, et al., Cytoscape: A software environment for integrated models of biomolecular interaction networks, Genome Res. 13 (2003) 2498-2504.
|
F. Martin, T.M. Thomson, A. Sewer, et al., Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks, BMC Syst. Biol. 6 (2012), 54.
|
A.L. Tarca, S. Draghici, P. Khatri, et al., A novel signaling pathway impact analysis, Bioinformatics 25 (2009) 75-82.
|
L. Cottret, C. Frainay, M. Chazalviel, et al., MetExplore: Collaborative edition and exploration of metabolic networks, Nucleic Acids Res. 46 (2018) W495-W502.
|
T.C. Kuo, T.F. Tian, Y.J. Tseng, 3Omics: A web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data, BMC Syst. Biol. 7 (2013), 64.
|
T. Liu, P. Salguero, M. Petek, et al., PaintOmics 4: New tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases, Nucleic Acids Res. 50 (2022) W551-W559.
|
I. Subramanian, S. Verma, S. Kumar, et al., Multi-omics data integration, interpretation, and its application, Bioinform. Biol. Insights 14 (2020), 1177932219899051.
|
Y. Xu, R.P. McCord, Diagonal integration of multimodal single-cell data: Potential pitfalls and paths forward, Nat. Commun. 13 (2022), 3505.
|
C. Stark, B.J. Breitkreutz, T. Reguly, et al., BioGRID: A general repository for interaction datasets, Nucleic Acids Res. 34 (2006) D535-D539.
|
H. Fanaee-T, M. Thoresen, Multi-insight visualization of multi-omics data via ensemble dimension reduction and tensor factorization, Bioinformatics. 35 (2019) 1625-1633.
|
N. Vahabi, G. Michailidis, Unsupervised multi-omics data integration methods: A comprehensive review, Front. Genet. 13 (2022), 854752.
|
D.D. Lee, H.S. Seung, Algorithms for Non-negative Matrix Factorization, Proceedings of Adv. Neural Inf. Process, Dec 3 - 8, 2001, Vancouver, Canada, 2001.
|
M. Pierre-Jean, J.F. Deleuze, E. le Floch, et al., Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration, Brief. Bioinform. 21 (2020) 2011-2030.
|
Z. Yang, G. Michailidis, A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data, Bioinformatics 32 (2016) 1-8.
|
S. MacEachern, P. Muller, Efficient MCMC Schemes for Robust Model Extensions Using Encompassing Dirichlet Process Mixture Models - Robust Bayesian Analysis. P. Diggle, S. Zeger, Lecture Notes in Statistics, first ed., Springer, New York, 2000, pp. 295-315.
|
L. Cowen, T. Ideker, B.J. Raphael, et al., Network propagation: A universal amplifier of genetic associations, Nat. Rev. Genet. 18 (2017) 551-562.
|
W. Ye, G. Ji, P. Ye, et al., scNPF: An integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data, BMC Genomics 20 (2019), 347.
|
P. Langfelder, S. Horvath, WGCNA: An R package for weighted correlation network analysis, BMC Bioinformatics 9 (2008), 559.
|
S. Doledec, D. Chessel, Co-inertia analysis: an alternative method for studying species-environment relationships, Freshw. Biol. 31 (1994) 277-294.
|
K. Sankaran, S.P. Holmes, Multitable methods for microbiome data integration, Front. Genet. 10 (2019), 627.
|
R. Shen, A.B. Olshen, M. Ladanyi, Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis, Bioinformatics 25 (2009) 2906-2912.
|
Q. Mo, S. Wang, V.E. Seshan, et al., Pattern discovery and cancer gene identification in integrated cancer genomic data, Proc. Natl. Acad. Sci. U. S. A. 110 (2013) 4245-4250.
|
E.F. Lock, K.A. Hoadley, J.S. Marron, et al., Joint and individual variation explained (jive) for integrated analysis of multiple data types, Ann. Appl. Stat. 7 (2013) 523-542.
|
R. Louhimo, S. Hautaniemi, CNAmet: An R package for integrating copy number, methylation and expression data, Bioinformatics 27 (2011) 887-888.
|
C.J. Vaske, S.C. Benz, J.Z. Sanborn, et al., Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM, Bioinformatics 26 (2010) i237-i245.
|
J.-C. Park, N. Barahona-Torres, S.-Y. Jang, et al., Multi-omics-based autophagy-related untypical subtypes in patients with cerebral amyloid pathology, Adv. Sci (Weinh). 9 (2022), e2201212.
|
A. Catanese, S. Rajkumar, D. Sommer, et al., Multiomics and machine-learning identify novel transcriptional and mutational signatures in amyotrophic lateral sclerosis, Brain (2023), awad075.
|
S. Lovestone, P. Francis, I. Kloszewska, et al., AddNeuroMed: The European collaboration for the discovery of novel biomarkers for Alzheimer’s disease, Ann. N. Y. Acad. Sci. 1180 (2009) 36-46.
|
A. Hye, S. Lynham, M. Thambisetty, et al., Proteome-based plasma biomarkers for Alzheimer’s disease, Brain 129 (2006) 3042-3050.
|
J. Xu, G. Bankov, M. Kim, et al., Integrated lipidomics and proteomics network analysis highlights lipid and immunity pathways associated with Alzheimer’s disease, Transl. Neurodegener. 9 (2020), 36.
|
N.T. Seyfried, E.B. Dammer, V. Swarup, et al., A multi-network approach identifies protein-specific co-expression in asymptomatic and symptomatic Alzheimer’s disease, Cell Syst. 4 (2017) 60-72.e4.
|
J.B. Toledo, M. Arnold, G. Kastenmuller, et al., Metabolic network failures in Alzheimer’s disease: A biochemical roadmap, Alzheimers. Dement. 13 (2017) 965-984.
|
E. Bonnet, L. Calzone, T. Michoel, Integrative multi-omics module network inference with Lemon-Tree, PLoS Comput. Biol. 11 (2015), e1003983.
|
S. Jamal, S. Goyal, A. Shanker, et al., Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes, BMC Genomics 17 (2016), 807.
|
J. Menche, E. Guney, A. Sharma, et al., Integrating personalized gene expression profiles into predictive disease-associated gene pools, NPJ Syst. Biol. Appl. 3 (2017), 10.
|
P.T. Ram, J. Mendelsohn, G.B. Mills, Bioinformatics and systems biology, Mol. Oncol. 6 (2012) 147-154.
|
N.S. Buchan, D.K. Rajpal, Y. Webster, et al., The role of translational bioinformatics in drug discovery, Drug Discov. Today 16 (2011) 426-434.
|
J. Leipzig, A review of bioinformatic pipeline frameworks, Brief Bioinform. 18 (2017) 530-536.
|
A. Gupta, S. Gupta, S.K. Jatawa, et al., A simplest bioinformatics pipeline for whole transcriptome sequencing: Overview of the processing and steps from raw data to downstream analysis, bioRxiv. 2019. https://doi.org/10.1101/836973.
|
L. Siegwald, H. Touzet, Y. Lemoine, et al., Assessment of Common and Emerging Bioinformatics Pipelines for Targeted Metagenomics, PLoS One 12 (2017), e0169563.
|
C.P. Moritz, T. Muhlhaus, S. Tenzer, et al., Poor transcript-protein correlation in the brain: Negatively correlating gene products reveal neuronal polarity as a potential cause, J. Neurochem. 149 (2019) 582-604.
|
M. Jafari, Y. Guan, D.C. Wedge, et al., Re-evaluating experimental validation in the Big Data Era: a conceptual argument, Genome Biol. 22 (2021), 71.
|
C. Manzoni, D.A. Kia, J. Vandrovcova, et al., Genome, transcriptome and proteome: The rise of omics data and their integration in biomedical sciences, Brief Bioinform. 19 (2018) 286-302.
|
C.H. Lo, J. Zeng, Defective lysosomal acidification: A new prognostic marker and therapeutic target for neurodegenerative diseases, Transl. Neurodegener. 12 (2023), 29.
|
D. Pitt, C.H. Lo, S.A. Gauthier, et al., Toward precision phenotyping of multiple sclerosis, Neurol. Neuroimmunol. Neuroinflamm. 9 (2022), e200025.
|
K.S. Suh, S. Sarojini, M. Youssif, et al., Tissue banking, bioinformatics, and electronic medical records: The front-end requirements for personalized medicine, J. Oncol. 2013 (2013), 368751.
|
A. Kolodkin, E. Simeonidis, R. Balling, et al., Understanding complexity in neurodegenerative diseases: in silico reconstruction of emergence, Front. Physiol. 3 (2012), 291.
|
S. Golriz Khatami, S. Mubeen, M. Hofmann-Apitius, Data science in neurodegenerative disease: Its capabilities, limitations, and perspectives, Curr. Opin. Neurol. 33 (2020) 249-254.
|
M.A. Myszczynska, P.N. Ojamies, A.M.B. Lacoste, et al., Applications of machine learning to diagnosis and treatment of neurodegenerative diseases, Nat. Rev. Neurol. 16 (2020) 440-456.
|
A. Mammoliti, P. Smirnov, M. Nakano, et al., Orchestrating and sharing large multimodal data for transparent and reproducible research, Nat. Commun. 12 (2021), 5797.
|
S. Lam, A. Bayraktar, C. Zhang, et al., A systems biology approach for studying neurodegenerative diseases, Drug Discov. Today 25 (2020) 1146-1159.
|