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Johnson AG, Dammer EB, Webster JA, Duong DM, Seyfried NT, Hales CM. Proteomic networks of gray and white matter reveal tissue-specific changes in human tauopathy. Ann Clin Transl Neurol 2024. [PMID: 38924699 DOI: 10.1002/acn3.52134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 05/06/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
OBJECTIVE To define tauopathy-associated changes in the human gray and white matter proteome. METHOD We applied tandem mass tagged labeling and mass spectrometry, consensus, and ratio weighted gene correlation network analysis (WGCNA) to gray and white matter sampled from postmortem human dorsolateral prefrontal cortex. The sampled tissues included control as well as Alzheimer's disease, corticobasal degeneration, progressive supranuclear palsy, frontotemporal degeneration with tau pathology, and chronic traumatic encephalopathy. RESULTS Only eight proteins were unique to gray matter while six were unique to white matter. Comparison of the gray and white matter proteome revealed an enrichment of microglial proteins in the white matter. Consensus WGCNA sorted over 6700 protein isoforms into 46 consensus modules across the gray and white matter proteomic networks. Consensus network modules demonstrated unique and shared disease-associated microglial and endothelial protein changes. Ratio WGCNA sorted over 6500 protein ratios (white:gray) into 33 modules. Modules associated with mitochondrial proteins and processes demonstrated higher white:gray ratios in diseased tissues relative to control, driven by mitochondrial protein downregulation in gray and upregulation in white. INTERPRETATION The dataset is a valuable resource for understanding proteomic changes in human tauopathy gray and white matter. The identification of unique and shared disease-associated changes across gray and white matter emphasizes the utility of examining both tissue types. Future studies of microglial, endothelial, and mitochondrial changes in white matter may provide novel insights into tauopathy-associated changes in human brain.
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Affiliation(s)
- Ashlyn G Johnson
- Neuroscience Graduate Program, Laney Graduate School, Emory University, Atlanta, Georgia, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, Georgia, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Eric B Dammer
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, Georgia, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, Georgia, USA
| | - James A Webster
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, Georgia, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Duc M Duong
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, Georgia, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Nicholas T Seyfried
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, Georgia, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Chadwick M Hales
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, Georgia, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, USA
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2
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Wojtas AM, Dammer EB, Guo Q, Ping L, Shantaraman A, Duong DM, Yin L, Fox EJ, Seifar F, Lee EB, Johnson ECB, Lah JJ, Levey AI, Levites Y, Rangaraju S, Golde TE, Seyfried NT. Proteomic changes in the human cerebrovasculature in Alzheimer's disease and related tauopathies linked to peripheral biomarkers in plasma and cerebrospinal fluid. Alzheimers Dement 2024; 20:4043-4065. [PMID: 38713744 PMCID: PMC11180878 DOI: 10.1002/alz.13821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 02/21/2024] [Accepted: 03/02/2024] [Indexed: 05/09/2024]
Abstract
INTRODUCTION Cerebrovascular dysfunction is a pathological hallmark of Alzheimer's disease (AD). Nevertheless, detecting cerebrovascular changes within bulk tissues has limited our ability to characterize proteomic alterations from less abundant cell types. METHODS We conducted quantitative proteomics on bulk brain tissues and isolated cerebrovasculature from the same individuals, encompassing control (N = 28), progressive supranuclear palsy (PSP) (N = 18), and AD (N = 21) cases. RESULTS Protein co-expression network analysis identified unique cerebrovascular modules significantly correlated with amyloid plaques, cerebrovascular amyloid angiopathy (CAA), and/or tau pathology. The protein products within AD genetic risk loci were concentrated within cerebrovascular modules. The overlap between differentially abundant proteins in AD cerebrospinal fluid (CSF) and plasma with cerebrovascular network highlighted a significant increase of matrisome proteins, SMOC1 and SMOC2, in CSF, plasma, and brain. DISCUSSION These findings enhance our understanding of cerebrovascular deficits in AD, shedding light on potential biomarkers associated with CAA and vascular dysfunction in neurodegenerative diseases.
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Affiliation(s)
- Aleksandra M. Wojtas
- Department of BiochemistryEmory University School of MedicineAtlantaGeorgiaUSA
- Center for Neurodegenerative DiseaseEmory University School of MedicineAtlantaGeorgiaUSA
| | - Eric B. Dammer
- Department of BiochemistryEmory University School of MedicineAtlantaGeorgiaUSA
- Center for Neurodegenerative DiseaseEmory University School of MedicineAtlantaGeorgiaUSA
| | - Qi Guo
- Department of BiochemistryEmory University School of MedicineAtlantaGeorgiaUSA
- Center for Neurodegenerative DiseaseEmory University School of MedicineAtlantaGeorgiaUSA
| | - Lingyan Ping
- Department of BiochemistryEmory University School of MedicineAtlantaGeorgiaUSA
- Center for Neurodegenerative DiseaseEmory University School of MedicineAtlantaGeorgiaUSA
| | - Ananth Shantaraman
- Department of BiochemistryEmory University School of MedicineAtlantaGeorgiaUSA
- Center for Neurodegenerative DiseaseEmory University School of MedicineAtlantaGeorgiaUSA
| | - Duc M. Duong
- Department of BiochemistryEmory University School of MedicineAtlantaGeorgiaUSA
- Center for Neurodegenerative DiseaseEmory University School of MedicineAtlantaGeorgiaUSA
| | - Luming Yin
- Department of BiochemistryEmory University School of MedicineAtlantaGeorgiaUSA
- Center for Neurodegenerative DiseaseEmory University School of MedicineAtlantaGeorgiaUSA
| | - Edward J. Fox
- Department of BiochemistryEmory University School of MedicineAtlantaGeorgiaUSA
- Center for Neurodegenerative DiseaseEmory University School of MedicineAtlantaGeorgiaUSA
| | - Fatemeh Seifar
- Department of BiochemistryEmory University School of MedicineAtlantaGeorgiaUSA
- Center for Neurodegenerative DiseaseEmory University School of MedicineAtlantaGeorgiaUSA
| | - Edward B. Lee
- Department of Pathology and Laboratory MedicineUniversity of PennsylvaniaPennsylvaniaUSA
| | - Erik C. B. Johnson
- Center for Neurodegenerative DiseaseEmory University School of MedicineAtlantaGeorgiaUSA
- Department of NeurologyEmory University School of MedicineAtlantaGeorgiaUSA
| | - James J. Lah
- Center for Neurodegenerative DiseaseEmory University School of MedicineAtlantaGeorgiaUSA
- Department of NeurologyEmory University School of MedicineAtlantaGeorgiaUSA
| | - Allan I. Levey
- Department of BiochemistryEmory University School of MedicineAtlantaGeorgiaUSA
- Center for Neurodegenerative DiseaseEmory University School of MedicineAtlantaGeorgiaUSA
- Department of NeurologyEmory University School of MedicineAtlantaGeorgiaUSA
| | - Yona Levites
- Center for Neurodegenerative DiseaseEmory University School of MedicineAtlantaGeorgiaUSA
- Department of Pharmacology and Chemical BiologyEmory University School of MedicineAtlantaGeorgiaUSA
| | - Srikant Rangaraju
- Center for Neurodegenerative DiseaseEmory University School of MedicineAtlantaGeorgiaUSA
- Department of NeurologyEmory University School of MedicineAtlantaGeorgiaUSA
| | - Todd E. Golde
- Center for Neurodegenerative DiseaseEmory University School of MedicineAtlantaGeorgiaUSA
- Department of NeurologyEmory University School of MedicineAtlantaGeorgiaUSA
- Department of Pharmacology and Chemical BiologyEmory University School of MedicineAtlantaGeorgiaUSA
| | - Nicholas T. Seyfried
- Department of BiochemistryEmory University School of MedicineAtlantaGeorgiaUSA
- Center for Neurodegenerative DiseaseEmory University School of MedicineAtlantaGeorgiaUSA
- Department of NeurologyEmory University School of MedicineAtlantaGeorgiaUSA
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3
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Kumar P, Goettemoeller AM, Espinosa-Garcia C, Tobin BR, Tfaily A, Nelson RS, Natu A, Dammer EB, Santiago JV, Malepati S, Cheng L, Xiao H, Duong DD, Seyfried NT, Wood LB, Rowan MJM, Rangaraju S. Native-state proteomics of Parvalbumin interneurons identifies unique molecular signatures and vulnerabilities to early Alzheimer's pathology. Nat Commun 2024; 15:2823. [PMID: 38561349 PMCID: PMC10985119 DOI: 10.1038/s41467-024-47028-7] [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: 08/09/2023] [Accepted: 03/18/2024] [Indexed: 04/04/2024] Open
Abstract
Dysfunction in fast-spiking parvalbumin interneurons (PV-INs) may represent an early pathophysiological perturbation in Alzheimer's Disease (AD). Defining early proteomic alterations in PV-INs can provide key biological and translationally-relevant insights. We used cell-type-specific in-vivo biotinylation of proteins (CIBOP) coupled with mass spectrometry to obtain native-state PV-IN proteomes. PV-IN proteomic signatures include high metabolic and translational activity, with over-representation of AD-risk and cognitive resilience-related proteins. In bulk proteomes, PV-IN proteins were associated with cognitive decline in humans, and with progressive neuropathology in humans and the 5xFAD mouse model of Aβ pathology. PV-IN CIBOP in early stages of Aβ pathology revealed signatures of increased mitochondria and metabolism, synaptic and cytoskeletal disruption and decreased mTOR signaling, not apparent in whole-brain proteomes. Furthermore, we demonstrated pre-synaptic defects in PV-to-excitatory neurotransmission, validating our proteomic findings. Overall, in this study we present native-state proteomes of PV-INs, revealing molecular insights into their unique roles in cognitive resiliency and AD pathogenesis.
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Affiliation(s)
- Prateek Kumar
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, USA
- 3 Department of Neurology, Yale University School of Medicine, New Haven, CT, 06510, USA
| | - Annie M Goettemoeller
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, USA
- Neuroscience Graduate Program, Laney Graduate School, Emory University, Atlanta, USA
| | - Claudia Espinosa-Garcia
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, 30322, USA
- 3 Department of Neurology, Yale University School of Medicine, New Haven, CT, 06510, USA
| | - Brendan R Tobin
- Georgia W. Woodruff School of Mechanical Engineering, Parker H. Petit Institute for Bioengineering and Bioscience, and Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Ali Tfaily
- 3 Department of Neurology, Yale University School of Medicine, New Haven, CT, 06510, USA
| | - Ruth S Nelson
- 3 Department of Neurology, Yale University School of Medicine, New Haven, CT, 06510, USA
| | - Aditya Natu
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Eric B Dammer
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, USA
- Department of Biochemistry, Emory University, Atlanta, GA, 30322, USA
| | - Juliet V Santiago
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, USA
- Neuroscience Graduate Program, Laney Graduate School, Emory University, Atlanta, USA
| | - Sneha Malepati
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, USA
- Department of Cell Biology, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Lihong Cheng
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, USA
| | - Hailian Xiao
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, USA
| | - Duc D Duong
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, USA
- Department of Biochemistry, Emory University, Atlanta, GA, 30322, USA
| | - Nicholas T Seyfried
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, USA
- Department of Biochemistry, Emory University, Atlanta, GA, 30322, USA
| | - Levi B Wood
- Georgia W. Woodruff School of Mechanical Engineering, Parker H. Petit Institute for Bioengineering and Bioscience, and Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30322, USA
- School of Chemical and Biological Engineering, GeoInsrgia titute of Technology, Atlanta, GA, 30322, USA
| | - Matthew J M Rowan
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, USA.
- Department of Cell Biology, Emory University School of Medicine, Atlanta, GA, 30322, USA.
| | - Srikant Rangaraju
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, 30322, USA.
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, USA.
- 3 Department of Neurology, Yale University School of Medicine, New Haven, CT, 06510, USA.
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Garmire LX, Li Y, Huang Q, Xu C, Teichmann SA, Kaminski N, Pellegrini M, Nguyen Q, Teschendorff AE. Challenges and perspectives in computational deconvolution of genomics data. Nat Methods 2024; 21:391-400. [PMID: 38374264 DOI: 10.1038/s41592-023-02166-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 12/26/2023] [Indexed: 02/21/2024]
Abstract
Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.
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Affiliation(s)
- Lana X Garmire
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
| | - Yijun Li
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Qianhui Huang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Chuan Xu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Naftali Kaminski
- Pulmonary, Critical Care & Sleep Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Matteo Pellegrini
- Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland and QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- UCL Cancer Institute, University College London, London, UK
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5
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Wojtas AM, Dammer EB, Guo Q, Ping L, Shantaraman A, Duong DM, Yin L, Fox EJ, Seifar F, Lee EB, Johnson ECB, Lah JJ, Levey AI, Levites Y, Rangaraju S, Golde TE, Seyfried NT. Proteomic Changes in the Human Cerebrovasculature in Alzheimer's Disease and Related Tauopathies Linked to Peripheral Biomarkers in Plasma and Cerebrospinal Fluid. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.10.24301099. [PMID: 38260316 PMCID: PMC10802758 DOI: 10.1101/2024.01.10.24301099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Dysfunction of the neurovascular unit stands as a significant pathological hallmark of Alzheimer's disease (AD) and age-related neurodegenerative diseases. Nevertheless, detecting vascular changes in the brain within bulk tissues has proven challenging, limiting our ability to characterize proteomic alterations from less abundant cell types. To address this challenge, we conducted quantitative proteomic analyses on both bulk brain tissues and cerebrovascular-enriched fractions from the same individuals, encompassing cognitively unimpaired control, progressive supranuclear palsy (PSP), and AD cases. Protein co-expression network analysis identified modules unique to the cerebrovascular fractions, specifically enriched with pericytes, endothelial cells, and smooth muscle cells. Many of these modules also exhibited significant correlations with amyloid plaques, cerebral amyloid angiopathy (CAA), and/or tau pathology in the brain. Notably, the protein products within AD genetic risk loci were found concentrated within modules unique to the vascular fractions, consistent with a role of cerebrovascular deficits in the etiology of AD. To prioritize peripheral AD biomarkers associated with vascular dysfunction, we assessed the overlap between differentially abundant proteins in AD cerebrospinal fluid (CSF) and plasma with a vascular-enriched network modules in the brain. This analysis highlighted matrisome proteins, SMOC1 and SMOC2, as being increased in CSF, plasma, and brain. Immunohistochemical analysis revealed SMOC1 deposition in both parenchymal plaques and CAA in the AD brain, whereas SMOC2 was predominantly localized to CAA. Collectively, these findings significantly enhance our understanding of the involvement of cerebrovascular abnormalities in AD, shedding light on potential biomarkers and molecular pathways associated with CAA and vascular dysfunction in neurodegenerative diseases.
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Affiliation(s)
- Aleksandra M. Wojtas
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
| | - Eric B. Dammer
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
| | - Qi Guo
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
| | - Lingyan Ping
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
| | - Ananth Shantaraman
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
| | - Duc M. Duong
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
| | - Luming Yin
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
| | - Edward J. Fox
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
- Department of Pharmacology and Chemical Biology, Emory University School of Medicine, Atlanta, GA, USA
| | - Fatemeh Seifar
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
- Department of Pharmacology and Chemical Biology, Emory University School of Medicine, Atlanta, GA, USA
| | - Edward B. Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, PA, USA
| | - Erik C. B. Johnson
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
| | - James J. Lah
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
| | - Allan I. Levey
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
| | - Yona Levites
- Department of Pharmacology and Chemical Biology, Emory University School of Medicine, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
| | - Srikant Rangaraju
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
| | - Todd E. Golde
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Department of Pharmacology and Chemical Biology, Emory University School of Medicine, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
| | - Nicholas T. Seyfried
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
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Cai M, Zhou J, McKennan C, Wang J. scMD facilitates cell type deconvolution using single-cell DNA methylation references. Commun Biol 2024; 7:1. [PMID: 38168620 PMCID: PMC10762261 DOI: 10.1038/s42003-023-05690-5] [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: 08/14/2023] [Accepted: 12/08/2023] [Indexed: 01/05/2024] Open
Abstract
The proliferation of single-cell RNA-sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell-type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent developments in single-cell DNA methylation (scDNAm), there are emerging opportunities for deconvolving bulk DNAm data, particularly for solid tissues like brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create precise cell-type signature matrixes that surpass state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD's superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer's disease.
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Affiliation(s)
- Manqi Cai
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, CA, USA
| | - Chris McKennan
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.
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7
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Cai M, Zhou J, McKennan C, Wang J. scMD: cell type deconvolution using single-cell DNA methylation references. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.03.551733. [PMID: 37577715 PMCID: PMC10418231 DOI: 10.1101/2023.08.03.551733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
The proliferation of single-cell RNA sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent development in single-cell DNA methylation (scDNAm), new avenues have been opened for deconvolving bulk DNAm data, particularly for solid tissues like the brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create a precise cell-type signature matrix that surpasses state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD's superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer's disease.
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Affiliation(s)
- Manqi Cai
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA
| | - Chris McKennan
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA
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8
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Kumar P, Goettemoeller AM, Espinosa-Garcia C, Tobin BR, Tfaily A, Nelson RS, Natu A, Dammer EB, Santiago JV, Malepati S, Cheng L, Xiao H, Duong D, Seyfried NT, Wood LB, Rowan MJ, Rangaraju S. Native-state proteomics of Parvalbumin interneurons identifies novel molecular signatures and metabolic vulnerabilities to early Alzheimer's disease pathology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.17.541038. [PMID: 37292756 PMCID: PMC10245729 DOI: 10.1101/2023.05.17.541038] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
One of the earliest pathophysiological perturbations in Alzheimer's Disease (AD) may arise from dysfunction of fast-spiking parvalbumin (PV) interneurons (PV-INs). Defining early protein-level (proteomic) alterations in PV-INs can provide key biological and translationally relevant insights. Here, we use cell-type-specific in vivo biotinylation of proteins (CIBOP) coupled with mass spectrometry to obtain native-state proteomes of PV interneurons. PV-INs exhibited proteomic signatures of high metabolic, mitochondrial, and translational activity, with over-representation of causally linked AD genetic risk factors. Analyses of bulk brain proteomes indicated strong correlations between PV-IN proteins with cognitive decline in humans, and with progressive neuropathology in humans and mouse models of Aβ pathology. Furthermore, PV-IN-specific proteomes revealed unique signatures of increased mitochondrial and metabolic proteins, but decreased synaptic and mTOR signaling proteins in response to early Aβ pathology. PV-specific changes were not apparent in whole-brain proteomes. These findings showcase the first native state PV-IN proteomes in mammalian brain, revealing a molecular basis for their unique vulnerabilities in AD.
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9
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Chen Y, Hunter E, Arbabi K, Guet-McCreight A, Consens M, Felsky D, Sibille E, Tripathy SJ. Robust differences in cortical cell type proportions across healthy human aging inferred through cross-dataset transcriptome analyses. Neurobiol Aging 2023; 125:49-61. [PMID: 36841202 DOI: 10.1016/j.neurobiolaging.2023.01.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 01/22/2023] [Accepted: 01/24/2023] [Indexed: 02/01/2023]
Abstract
Age-related declines in cognitive function are driven by cell type-specific changes in the brain. However, it remains challenging to study cellular differences associated with healthy aging as traditional approaches scale poorly to the sample sizes needed to capture aging and cellular heterogeneity. Here, we employed cellular deconvolution to estimate relative cell type proportions using frontal cortex bulk gene expression from individuals without psychiatric conditions or brain pathologies. Our analyses comprised 8 datasets and 6 cohorts (1142 subjects and 1429 samples) with ages of death spanning 15-90 years. We found aging associated with profound differences in cellular proportions, with the largest changes reflecting fewer somatostatin- and vasoactive intestinal peptide-expressing interneurons, more astrocytes and other non-neuronal cells, and a suggestive "U-shaped" quadratic relationship for microglia. Cell type associations with age were markedly robust across bulk-and single nucleus datasets. Altogether, we present a comprehensive account of proportional differences in cortical cell types associated with healthy aging.
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Affiliation(s)
- Yuxiao Chen
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Emma Hunter
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Keon Arbabi
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Alex Guet-McCreight
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Micaela Consens
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Daniel Felsky
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Etienne Sibille
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
| | - Shreejoy J Tripathy
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Physiology, University of Toronto, Toronto, Ontario, Canada.
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10
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Nie X, Qin D, Zhou X, Duo H, Hao Y, Li B, Liang G. Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges. Comput Biol Med 2023; 159:106939. [PMID: 37075602 DOI: 10.1016/j.compbiomed.2023.106939] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/31/2023] [Accepted: 04/14/2023] [Indexed: 04/21/2023]
Abstract
With the rapid development of single-cell RNA-sequencing techniques, various computational methods and tools were proposed to analyze these high-throughput data, which led to an accelerated reveal of potential biological information. As one of the core steps of single-cell transcriptome data analysis, clustering plays a crucial role in identifying cell types and interpreting cellular heterogeneity. However, the results generated by different clustering methods showed distinguishing, and those unstable partitions can affect the accuracy of the analysis to a certain extent. To overcome this challenge and obtain more accurate results, currently clustering ensemble is frequently applied to cluster analysis of single-cell transcriptome datasets, and the results generated by all clustering ensembles are nearly more reliable than those from most of the single clustering partitions. In this review, we summarize applications and challenges of the clustering ensemble method in single-cell transcriptome data analysis, and provide constructive thoughts and references for researchers in this field.
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Affiliation(s)
- Xiner Nie
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, China; College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Dan Qin
- Department of Biology, College of Science, Northeastern University, Boston, MA, 02115, USA
| | - Xinyi Zhou
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China.
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, China.
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11
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Huang P, Cai M, Lu X, McKennan C, Wang J. Accurate estimation of rare cell type fractions from tissue omics data via hierarchical deconvolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.15.532820. [PMID: 36993280 PMCID: PMC10055056 DOI: 10.1101/2023.03.15.532820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Bulk transcriptomics in tissue samples reflects the average expression levels across different cell types and is highly influenced by cellular fractions. As such, it is critical to estimate cellular fractions to both deconfound differential expression analyses and infer cell type-specific differential expression. Since experimentally counting cells is infeasible in most tissues and studies, in silico cellular deconvolution methods have been developed as an alternative. However, existing methods are designed for tissues consisting of clearly distinguishable cell types and have difficulties estimating highly correlated or rare cell types. To address this challenge, we propose Hierarchical Deconvolution (HiDecon) that uses single-cell RNA sequencing references and a hierarchical cell type tree, which models the similarities among cell types and cell differentiation relationships, to estimate cellular fractions in bulk data. By coordinating cell fractions across layers of the hierarchical tree, cellular fraction information is passed up and down the tree, which helps correct estimation biases by pooling information across related cell types. The flexible hierarchical tree structure also enables estimating rare cell fractions by splitting the tree to higher resolutions. Through simulations and real data applications with the ground truth of measured cellular fractions, we demonstrate that HiDecon significantly outperforms existing methods and accurately estimates cellular fractions.
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Affiliation(s)
- Penghui Huang
- Deparment of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Manqi Cai
- Deparment of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xinghua Lu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chris McKennan
- Deparment of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jiebiao Wang
- Deparment of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
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