1
|
Bakulin A, Teyssier NB, Kampmann M, Khoroshkin M, Goodarzi H. pyPAGE: A framework for Addressing biases in gene-set enrichment analysis-A case study on Alzheimer's disease. PLoS Comput Biol 2024; 20:e1012346. [PMID: 39236079 DOI: 10.1371/journal.pcbi.1012346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 07/22/2024] [Indexed: 09/07/2024] Open
Abstract
Inferring the driving regulatory programs from comparative analysis of gene expression data is a cornerstone of systems biology. Many computational frameworks were developed to address this problem, including our iPAGE (information-theoretic Pathway Analysis of Gene Expression) toolset that uses information theory to detect non-random patterns of expression associated with given pathways or regulons. Our recent observations, however, indicate that existing approaches are susceptible to the technical biases that are inherent to most real world annotations. To address this, we have extended our information-theoretic framework to account for specific biases and artifacts in biological networks using the concept of conditional information. To showcase pyPAGE, we performed a comprehensive analysis of regulatory perturbations that underlie the molecular etiology of Alzheimer's disease (AD). pyPAGE successfully recapitulated several known AD-associated gene expression programs. We also discovered several additional regulons whose differential activity is significantly associated with AD. We further explored how these regulators relate to pathological processes in AD through cell-type specific analysis of single cell and spatial gene expression datasets. Our findings showcase the utility of pyPAGE as a precise and reliable biomarker discovery in complex diseases such as Alzheimer's disease.
Collapse
Affiliation(s)
- Artemy Bakulin
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
| | - Noam B Teyssier
- Institute for Neurodegenerative Diseases, University of California San Francisco, California, United States of America
| | - Martin Kampmann
- Institute for Neurodegenerative Diseases, University of California San Francisco, California, United States of America
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, United States of America
| | - Matvei Khoroshkin
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, United States of America
- Department of Urology, University of California San Francisco, San Francisco, California, United States of America
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, United States of America
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, United States of America
| | - Hani Goodarzi
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, United States of America
- Department of Urology, University of California San Francisco, San Francisco, California, United States of America
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, United States of America
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, United States of America
- Arc Institute, Palo Alto, California, United States of America
| |
Collapse
|
2
|
Chang Y, Liu J, Jiang Y, Ma A, Yeo YY, Guo Q, McNutt M, Krull JE, Rodig SJ, Barouch DH, Nolan GP, Xu D, Jiang S, Li Z, Liu B, Ma Q. Graph Fourier transform for spatial omics representation and analyses of complex organs. Nat Commun 2024; 15:7467. [PMID: 39209833 PMCID: PMC11362340 DOI: 10.1038/s41467-024-51590-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 08/08/2024] [Indexed: 09/04/2024] Open
Abstract
Spatial omics technologies decipher functional components of complex organs at cellular and subcellular resolutions. We introduce Spatial Graph Fourier Transform (SpaGFT) and apply graph signal processing to a wide range of spatial omics profiling platforms to generate their interpretable representations. This representation supports spatially variable gene identification and improves gene expression imputation, outperforming existing tools in analyzing human and mouse spatial transcriptomics data. SpaGFT can identify immunological regions for B cell maturation in human lymph nodes Visium data and characterize variations in secondary follicles using in-house human tonsil CODEX data. Furthermore, it can be integrated seamlessly into other machine learning frameworks, enhancing accuracy in spatial domain identification, cell type annotation, and subcellular feature inference by up to 40%. Notably, SpaGFT detects rare subcellular organelles, such as Cajal bodies and Set1/COMPASS complexes, in high-resolution spatial proteomics data. This approach provides an explainable graph representation method for exploring tissue biology and function.
Collapse
Affiliation(s)
- Yuzhou Chang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Jixin Liu
- School of Mathematics, Shandong University, 250100, Jinan, China
| | - Yi Jiang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA
- Program in Virology, Division of Medical Sciences, Harvard Medical School, Boston, MA, 20115, USA
| | - Qi Guo
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
| | - Megan McNutt
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
| | - Jordan E Krull
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Scott J Rodig
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, 02115, USA
- Department of Pathology, Brigham & Women's Hospital, Boston, MA, 02115, USA
| | - Dan H Barouch
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, 02139, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Sizun Jiang
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA
- Program in Virology, Division of Medical Sciences, Harvard Medical School, Boston, MA, 20115, USA
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, 02115, USA
| | - Zihai Li
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Bingqiang Liu
- School of Mathematics, Shandong University, 250100, Jinan, China.
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA.
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA.
| |
Collapse
|
3
|
Lv T, Chen Y, Hou X, Qin R, Yang Z, Hu Z, Bai F. Anterior-temporal hippocampal network mechanisms of left angular gyrus-navigated rTMS for memory improvement in aMCI: A sham-controlled study. Behav Brain Res 2024; 471:115117. [PMID: 38908485 DOI: 10.1016/j.bbr.2024.115117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 06/03/2024] [Accepted: 06/15/2024] [Indexed: 06/24/2024]
Abstract
INTRODUCTION Neuro-navigated repetitive transcranial magnetic stimulation (rTMS) of the left angular gyrus has been broadly investigated for the treatment of amnestic mild cognitive impairment (aMCI). Although abnormalities in two hippocampal networks, the anterior-temporal (AT) and posterior-medial (PM) networks, are consistent with aMCI and are potential therapeutic targets for rTMS, the underlying mechanisms of the therapeutic effects of rTMS on hippocampal network connections remain unknown. Here, we assessed the impact of left angular gyrus rTMS on activity in these networks and explored whether the treatment response was due to the distance between the clinically applied target (the group average optimal site) and the personalized target in patients with aMCI. METHODS Sixty subjects clinically diagnosed with aMCI participated in this study after 20 sessions of sham-controlled rTMS targeting the left angular gyrus. Resting-state functional magnetic resonance imaging and neuropsychological assessments were performed before and after rTMS. Functional connectivity alterations in the PM and AT networks were assessed using seed-based functional connectivity analysis and two-factor repeated measures analysis of variance (ANOVA). We then computed the correlations between the functional connectivity changes and clinical rating scales. Finally, we examined whether the Euclidean distance between the clinically applied and personalized targets predicted the subsequent treatment response. RESULTS Compared with the sham group, the active rTMS group showed rTMS-induced deactivation of functional connectivity within the medial temporal lobe-AT network, with a negative correlation with episodic memory score changes. Moreover, the active rTMS lowers the interdependency of changes in the PM and AT networks. Finally, the Euclidean distance between the clinically applied and personalized target distances could predict subsequent network lever responses in the active rTMS group. CONCLUSIONS Neuro-navigated rTMS selectively modulates widespread functional connectivity abnormalities in the PM and AT hippocampal networks in aMCI patients, and the modulation of hippocampal-AT network connectivity can efficiently reverse memory deficits. The results also highlight the necessity of personalized targets for fMRI.
Collapse
Affiliation(s)
- Tingyu Lv
- Geriatric Medicine Center, Taikang Xianlin Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210046, China; Institute of Geriatric Medicine, Medical School of Nanjing University, Nanjing 210046, China; Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China; Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Ya Chen
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Xinle Hou
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Ruomeng Qin
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Zhiyuan Yang
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Zheqi Hu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Feng Bai
- Geriatric Medicine Center, Taikang Xianlin Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210046, China; Institute of Geriatric Medicine, Medical School of Nanjing University, Nanjing 210046, China; Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China; Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China.
| |
Collapse
|
4
|
Dezem FS, Arjumand W, DuBose H, Morosini NS, Plummer J. Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives. Annu Rev Biomed Data Sci 2024; 7:131-153. [PMID: 38768396 DOI: 10.1146/annurev-biodatasci-102523-103640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative to the organization of the molecular microenvironment of tissue samples in normal and disease states. Spatial omics can be categorized into three major modalities: (a) next-generation sequencing-based assays, (b) imaging-based spatially resolved transcriptomics approaches including in situ hybridization/in situ sequencing, and (c) imaging-based spatial proteomics. These modalities allow assessment of transcripts and proteins at a cellular level, generating large and computationally challenging datasets. The lack of standardized computational pipelines to analyze and integrate these nonuniform structured data has made it necessary to apply artificial intelligence and machine learning strategies to best visualize and translate their complexity. In this review, we summarize the currently available techniques and computational strategies, highlight their advantages and limitations, and discuss their future prospects in the scientific field.
Collapse
Affiliation(s)
- Felipe Segato Dezem
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Wani Arjumand
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Hannah DuBose
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Natalia Silva Morosini
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Jasmine Plummer
- Department of Cellular and Molecular Biology and Comprehensive Cancer Center, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| |
Collapse
|
5
|
Guo J, You L, Zhou Y, Hu J, Li J, Yang W, Tang X, Sun Y, Gu Y, Dong Y, Chen X, Sato C, Zinman L, Rogaeva E, Wang J, Chen Y, Zhang M. Spatial enrichment and genomic analyses reveal the link of NOMO1 with amyotrophic lateral sclerosis. Brain 2024; 147:2826-2841. [PMID: 38643019 DOI: 10.1093/brain/awae123] [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: 10/21/2023] [Revised: 03/07/2024] [Accepted: 03/24/2024] [Indexed: 04/22/2024] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a severe motor neuron disease with uncertain genetic predisposition in most sporadic cases. The spatial architecture of cell types and gene expression are the basis of cell-cell interactions, biological function and disease pathology, but are not well investigated in the human motor cortex, a key ALS-relevant brain region. Recent studies indicated single nucleus transcriptomic features of motor neuron vulnerability in ALS motor cortex. However, the brain regional vulnerability of ALS-associated genes and the genetic link between region-specific genes and ALS risk remain largely unclear. Here, we developed an entropy-weighted differential gene expression matrix-based tool (SpatialE) to identify the spatial enrichment of gene sets in spatial transcriptomics. We benchmarked SpatialE against another enrichment tool (multimodal intersection analysis) using spatial transcriptomics data from both human and mouse brain tissues. To investigate regional vulnerability, we analysed three human motor cortex and two dorsolateral prefrontal cortex tissues for spatial enrichment of ALS-associated genes. We also used Cell2location to estimate the abundance of cell types in ALS-related cortex layers. To dissect the link of regionally expressed genes and ALS risk, we performed burden analyses of rare loss-of-function variants detected by whole-genome sequencing in ALS patients and controls, then analysed differential gene expression in the TargetALS RNA-sequencing dataset. SpatialE showed more accurate and specific spatial enrichment of regional cell type markers than multimodal intersection analysis in both mouse brain and human dorsolateral prefrontal cortex. Spatial transcriptomic analyses of human motor cortex showed heterogeneous cell types and spatial gene expression profiles. We found that 260 manually curated ALS-associated genes are significantly enriched in layer 5 of the motor cortex, with abundant expression of upper motor neurons and layer 5 excitatory neurons. Burden analyses of rare loss-of-function variants in Layer 5-associated genes nominated NOMO1 as a novel ALS-associated gene in a combined sample set of 6814 ALS patients and 3324 controls (P = 0.029). Gene expression analyses in CNS tissues revealed downregulation of NOMO1 in ALS, which is consistent with a loss-of-function disease mechanism. In conclusion, our integrated spatial transcriptomics and genomic analyses identified regional brain vulnerability in ALS and the association of a layer 5 gene (NOMO1) with ALS risk.
Collapse
Affiliation(s)
- Jingyan Guo
- The First Rehabilitation Hospital of Shanghai, Department of Medical Genetics, School of Medicine, Tongji University, 200090, Shanghai, China
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, 200120, Shanghai, China
| | - Linya You
- Department of Human Anatomy and Histoembryology, School of Basic Medical Sciences, Fudan University, 200032, Shanghai, China
- Key Laboratory of Medical Computing and Computer Assisted Intervention of Shanghai, 200032, Shanghai, China
| | - Yu Zhou
- The First Rehabilitation Hospital of Shanghai, Department of Medical Genetics, School of Medicine, Tongji University, 200090, Shanghai, China
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, 200120, Shanghai, China
| | - Jiali Hu
- The First Rehabilitation Hospital of Shanghai, Department of Medical Genetics, School of Medicine, Tongji University, 200090, Shanghai, China
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, 200120, Shanghai, China
| | - Jiahao Li
- The First Rehabilitation Hospital of Shanghai, Department of Medical Genetics, School of Medicine, Tongji University, 200090, Shanghai, China
| | - Wanli Yang
- The First Rehabilitation Hospital of Shanghai, Department of Medical Genetics, School of Medicine, Tongji University, 200090, Shanghai, China
| | - Xuelin Tang
- The First Rehabilitation Hospital of Shanghai, Department of Medical Genetics, School of Medicine, Tongji University, 200090, Shanghai, China
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, 200120, Shanghai, China
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON M5T 0S8, Canada
| | - Yimin Sun
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Fudan University, 200040, Shanghai, China
| | - Yuqi Gu
- The First Rehabilitation Hospital of Shanghai, Department of Medical Genetics, School of Medicine, Tongji University, 200090, Shanghai, China
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, 200120, Shanghai, China
| | - Yi Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Fudan University, 200040, Shanghai, China
| | - Xi Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Fudan University, 200040, Shanghai, China
| | - Christine Sato
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON M5T 0S8, Canada
| | - Lorne Zinman
- Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada
- Division of Neurology, University of Toronto, Toronto, ON M5S 3H2, Canada
| | - Ekaterina Rogaeva
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON M5T 0S8, Canada
- Division of Neurology, University of Toronto, Toronto, ON M5S 3H2, Canada
| | - Jian Wang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Fudan University, 200040, Shanghai, China
| | - Yan Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Fudan University, 200040, Shanghai, China
| | - Ming Zhang
- The First Rehabilitation Hospital of Shanghai, Department of Medical Genetics, School of Medicine, Tongji University, 200090, Shanghai, China
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, 200120, Shanghai, China
- Clinical Center for Brain and Spinal Cord Research, School of Medicine, Tongji University, 200331, Shanghai, China
- Institute for Advanced Study, Tongji University, 200092, Shanghai, China
| |
Collapse
|
6
|
Totty M, Hicks SC, Guo B. SpotSweeper: spatially-aware quality control for spatial transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597765. [PMID: 38895212 PMCID: PMC11185656 DOI: 10.1101/2024.06.06.597765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Quality control (QC) is a crucial step to ensure the reliability and accuracy of the data obtained from RNA sequencing experiments, including spatially-resolved transcriptomics (SRT). Existing QC approaches for SRT that have been adopted from single-nucleus RNA sequencing (snRNA-seq) methods are confounded by spatial biology and are inappropriate for SRT data. In addition, no methods currently exist for identifying histological tissue artifacts unique to SRT. Here, we introduce SpotSweeper, spatially-aware QC methods for identifying local outliers and regional artifacts in SRT. SpotSweeper evaluates the quality of individual spots relative to their local neighborhood, thus minimizing bias due to biological heterogeneity, and uses multiscale methods to detect regional artifacts. Using SpotSweeper on publicly available data, we identified a consistent set of Visium barcodes/spots as systematically low quality and demonstrate that SpotSweeper accurately identifies two distinct types of regional artifacts, resulting in improved downstream clustering and marker gene detection for spatial domains.
Collapse
Affiliation(s)
- Michael Totty
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
| | - Boyi Guo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| |
Collapse
|
7
|
Wang C, Acosta D, McNutt M, Bian J, Ma A, Fu H, Ma Q. A single-cell and spatial RNA-seq database for Alzheimer's disease (ssREAD). Nat Commun 2024; 15:4710. [PMID: 38844475 PMCID: PMC11156951 DOI: 10.1038/s41467-024-49133-z] [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: 09/04/2023] [Accepted: 05/21/2024] [Indexed: 06/09/2024] Open
Abstract
Alzheimer's Disease (AD) pathology has been increasingly explored through single-cell and single-nucleus RNA-sequencing (scRNA-seq & snRNA-seq) and spatial transcriptomics (ST). However, the surge in data demands a comprehensive, user-friendly repository. Addressing this, we introduce a single-cell and spatial RNA-seq database for Alzheimer's disease (ssREAD). It offers a broader spectrum of AD-related datasets, an optimized analytical pipeline, and improved usability. The database encompasses 1,053 samples (277 integrated datasets) from 67 AD-related scRNA-seq & snRNA-seq studies, totaling 7,332,202 cells. Additionally, it archives 381 ST datasets from 18 human and mouse brain studies. Each dataset is annotated with details such as species, gender, brain region, disease/control status, age, and AD Braak stages. ssREAD also provides an analysis suite for cell clustering, identification of differentially expressed and spatially variable genes, cell-type-specific marker genes and regulons, and spot deconvolution for integrative analysis. ssREAD is freely available at https://bmblx.bmi.osumc.edu/ssread/ .
Collapse
Affiliation(s)
- Cankun Wang
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Diana Acosta
- Department of Neuroscience, The Ohio State University, Columbus, OH, 43210, USA
| | - Megan McNutt
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, 32606, USA
| | - Anjun Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Hongjun Fu
- Department of Neuroscience, The Ohio State University, Columbus, OH, 43210, USA.
- Chronic Brain Injury Program, The Ohio State University, Columbus, OH, 43210, USA.
| | - Qin Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA.
| |
Collapse
|
8
|
Gong Y, Haeri M, Zhang X, Li Y, Liu A, Wu D, Zhang Q, Jazwinski SM, Zhou X, Wang X, Jiang L, Chen YP, Yan X, Swerdlow RH, Shen H, Deng HW. Spatial Dissection of the Distinct Cellular Responses to Normal Aging and Alzheimer's Disease in Human Prefrontal Cortex at Single-Nucleus Resolution. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.21.24306783. [PMID: 38826275 PMCID: PMC11142279 DOI: 10.1101/2024.05.21.24306783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Aging significantly elevates the risk for Alzheimer's disease (AD), contributing to the accumulation of AD pathologies, such as amyloid-β (Aβ), inflammation, and oxidative stress. The human prefrontal cortex (PFC) is highly vulnerable to the impacts of both aging and AD. Unveiling and understanding the molecular alterations in PFC associated with normal aging (NA) and AD is essential for elucidating the mechanisms of AD progression and developing novel therapeutics for this devastating disease. In this study, for the first time, we employed a cutting-edge spatial transcriptome platform, STOmics® SpaTial Enhanced Resolution Omics-sequencing (Stereo-seq), to generate the first comprehensive, subcellular resolution spatial transcriptome atlas of the human PFC from six AD cases at various neuropathological stages and six age, sex, and ethnicity matched controls. Our analyses revealed distinct transcriptional alterations across six neocortex layers, highlighted the AD-associated disruptions in laminar architecture, and identified changes in layer-to-layer interactions as AD progresses. Further, throughout the progression from NA to various stages of AD, we discovered specific genes that were significantly upregulated in neurons experiencing high stress and in nearby non-neuronal cells, compared to cells distant from the source of stress. Notably, the cell-cell interactions between the neurons under the high stress and adjacent glial cells that promote Aβ clearance and neuroprotection were diminished in AD in response to stressors compared to NA. Through cell-type specific gene co-expression analysis, we identified three modules in excitatory and inhibitory neurons associated with neuronal protection, protein dephosphorylation, and negative regulation of Aβ plaque formation. These modules negatively correlated with AD progression, indicating a reduced capacity for toxic substance clearance in AD subject samples. Moreover, we have discovered a novel transcription factor, ZNF460, that regulates all three modules, establishing it as a potential new therapeutic target for AD. Overall, utilizing the latest spatial transcriptome platform, our study developed the first transcriptome-wide atlas with subcellular resolution for assessing the molecular alterations in the human PFC due to AD. This atlas sheds light on the potential mechanisms underlying the progression from NA to AD.
Collapse
Affiliation(s)
- Yun Gong
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Mohammad Haeri
- Department of Pathology & Laboratory Medicine, University of Kansas Medical Center, Kansas City, MO, 66160, USA
| | - Xiao Zhang
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Yisu Li
- Department of Cell and Molecular Biology, School of Science of Engineering, Tulane University, New Orleans, LA, 70118, USA
| | - Anqi Liu
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Di Wu
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Qilei Zhang
- School of Basic Medical Sciences, Central South University, Changsha, Hunan, 410008, China
| | - S. Michal Jazwinski
- Tulane Center for Aging, Deming Department of Medicine, Tulane University School of Medicne, New Orleans, LA 70112, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Xiaoying Wang
- Clinical Neuroscience Research Center, Departments of Neurosurgery and Neurology, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Lindong Jiang
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Yi-Ping Chen
- Department of Cell and Molecular Biology, School of Science of Engineering, Tulane University, New Orleans, LA, 70118, USA
| | - Xiaoxin Yan
- School of Basic Medical Sciences, Central South University, Changsha, Hunan, 410008, China
| | - Russell H. Swerdlow
- Department of Neurology, University of Kansas Medical Center, Kansas City, MO, 66160, USA
| | - Hui Shen
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112, USA
| |
Collapse
|
9
|
Ma Y, Shi W, Dong Y, Sun Y, Jin Q. Spatial Multi-Omics in Alzheimer's Disease: A Multi-Dimensional Approach to Understanding Pathology and Progression. Curr Issues Mol Biol 2024; 46:4968-4990. [PMID: 38785566 PMCID: PMC11119029 DOI: 10.3390/cimb46050298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024] Open
Abstract
Alzheimer's Disease (AD) presents a complex neuropathological landscape characterized by hallmark amyloid plaques and neurofibrillary tangles, leading to progressive cognitive decline. Despite extensive research, the molecular intricacies contributing to AD pathogenesis are inadequately understood. While single-cell omics technology holds great promise for application in AD, particularly in deciphering the understanding of different cell types and analyzing rare cell types and transcriptomic expression changes, it is unable to provide spatial distribution information, which is crucial for understanding the pathological processes of AD. In contrast, spatial multi-omics research emerges as a promising and comprehensive approach to analyzing tissue cells, potentially better suited for addressing these issues in AD. This article focuses on the latest advancements in spatial multi-omics technology and compares various techniques. Additionally, we provide an overview of current spatial omics-based research results in AD. These technologies play a crucial role in facilitating new discoveries and advancing translational AD research in the future. Despite challenges such as balancing resolution, increasing throughput, and data analysis, the application of spatial multi-omics holds immense potential in revolutionizing our understanding of human disease processes and identifying new biomarkers and therapeutic targets, thereby potentially contributing to the advancement of AD research.
Collapse
Affiliation(s)
| | | | | | | | - Qiguan Jin
- College of Physical Education, Yangzhou University, Yangzhou 225127, China; (Y.M.); (W.S.); (Y.D.); (Y.S.)
| |
Collapse
|
10
|
Gouveia Roque C, Phatnani H, Hengst U. The broken Alzheimer's disease genome. CELL GENOMICS 2024; 4:100555. [PMID: 38697121 PMCID: PMC11099344 DOI: 10.1016/j.xgen.2024.100555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/25/2024] [Accepted: 04/07/2024] [Indexed: 05/04/2024]
Abstract
The complex pathobiology of late-onset Alzheimer's disease (AD) poses significant challenges to therapeutic and preventative interventions. Despite these difficulties, genomics and related disciplines are allowing fundamental mechanistic insights to emerge with clarity, particularly with the introduction of high-resolution sequencing technologies. After all, the disrupted processes at the interface between DNA and gene expression, which we call the broken AD genome, offer detailed quantitative evidence unrestrained by preconceived notions about the disease. In addition to highlighting biological pathways beyond the classical pathology hallmarks, these advances have revitalized drug discovery efforts and are driving improvements in clinical tools. We review genetic, epigenomic, and gene expression findings related to AD pathogenesis and explore how their integration enables a better understanding of the multicellular imbalances contributing to this heterogeneous condition. The frontiers opening on the back of these research milestones promise a future of AD care that is both more personalized and predictive.
Collapse
Affiliation(s)
- Cláudio Gouveia Roque
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY 10013, USA; The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA.
| | - Hemali Phatnani
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY 10013, USA; Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University, New York, NY 10032, USA
| | - Ulrich Hengst
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA; Department of Pathology & Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA.
| |
Collapse
|
11
|
Li J, Wang Y, Raina MA, Xu C, Su L, Guo Q, Ma Q, Wang J, Xu D. scBSP: A fast and accurate tool for identifying spatially variable genes from spatial transcriptomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.06.592851. [PMID: 38765956 PMCID: PMC11100755 DOI: 10.1101/2024.05.06.592851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Spatially resolved transcriptomics have enabled the inference of gene expression patterns within two and three-dimensional space, while introducing computational challenges due to growing spatial resolutions and sparse expressions. Here, we introduce scBSP, an open-source, versatile, and user-friendly package designed for identifying spatially variable genes in large-scale spatial transcriptomics. scBSP implements sparse matrix operation to significantly increase the computational efficiency in both computational time and memory usage, processing the high-definition spatial transcriptomics data for 19,950 genes on 181,367 spots within 10 seconds. Applied to diverse sequencing data and simulations, scBSP efficiently identifies spatially variable genes, demonstrating fast computational speed and consistency across various sequencing techniques and spatial resolutions for both two and three-dimensional data with up to millions of cells. On a sample with hundreds of thousands of sports, scBSP identifies SVGs accurately in seconds to on a typical desktop computer.
Collapse
|
12
|
Wang C, Acosta D, McNutt M, Bian J, Ma A, Fu H, Ma Q. A Single-cell and Spatial RNA-seq Database for Alzheimer's Disease (ssREAD). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.08.556944. [PMID: 37745592 PMCID: PMC10515769 DOI: 10.1101/2023.09.08.556944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Alzheimer's Disease (AD) pathology has been increasingly explored through single-cell and single-nucleus RNA-sequencing (scRNA-seq & snRNA-seq) and spatial transcriptomics (ST). However, the surge in data demands a comprehensive, user-friendly repository. Addressing this, we introduce a single-cell and spatial RNA-seq database for Alzheimer's disease (ssREAD). It offers a broader spectrum of AD-related datasets, an optimized analytical pipeline, and improved usability. The database encompasses 1,053 samples (277 integrated datasets) from 67 AD-related scRNA-seq & snRNA-seq studies, totaling 7,332,202 cells. Additionally, it archives 381 ST datasets from 18 human and mouse brain studies. Each dataset is annotated with details such as species, gender, brain region, disease/control status, age, and AD Braak stages. ssREAD also provides an analysis suite for cell clustering, identification of differentially expressed and spatially variable genes, cell-type-specific marker genes and regulons, and spot deconvolution for integrative analysis. ssREAD is freely available at https://bmblx.bmi.osumc.edu/ssread/.
Collapse
Affiliation(s)
- Cankun Wang
- Department of Biomedical Informatics, The Ohio State University, OH 43210, USA
| | - Diana Acosta
- Department of Neuroscience, The Ohio State University, OH 43210, USA
| | - Megan McNutt
- Department of Biomedical Informatics, The Ohio State University, OH 43210, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, University of Florida, FL 32606, USA
| | - Anjun Ma
- Department of Biomedical Informatics, The Ohio State University, OH 43210, USA
| | - Hongjun Fu
- Department of Neuroscience, The Ohio State University, OH 43210, USA
- Chronic Brain Injury Program, The Ohio State University, OH 43210, USA
| | - Qin Ma
- Department of Biomedical Informatics, The Ohio State University, OH 43210, USA
| |
Collapse
|
13
|
Rush A, Weil C, Siminoff L, Griffin C, Paul CL, Mahadevan A, Sutherland G. The Experts Speak: Challenges in Banking Brain Tissue for Research. Biopreserv Biobank 2024; 22:179-184. [PMID: 38621226 PMCID: PMC11265615 DOI: 10.1089/bio.2024.29135.ajr] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024] Open
Affiliation(s)
- A Rush
- Menzies Centre for Health Policy and Economics, The University of Sydney, Sydney, Australia
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - C Weil
- Independent Consultant, Human Research Protections and Bioethics, Bethesda, USA
| | - L Siminoff
- College of Public Health, Department of Social and Behavioral Sciences, Temple University, Pennsylvania, USA
| | - C Griffin
- College of Health, Medicine and Wellbeing University of Newcastle, Newcastle, Australia
- Hunter Medical Research Institute, Newcastle, Australia
- Mark Hughes Foundation Centre for Brain Cancer Research, The University of Newcastle, Newcastle, Australia
| | - C L Paul
- College of Health, Medicine and Wellbeing University of Newcastle, Newcastle, Australia
- Hunter Medical Research Institute, Newcastle, Australia
- Mark Hughes Foundation Centre for Brain Cancer Research, The University of Newcastle, Newcastle, Australia
| | - A Mahadevan
- Department of Neuropathology, National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India
| | - G Sutherland
- Charles Perkins Centre and School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| |
Collapse
|
14
|
Zhang Z, Liu X, Zhang S, Song Z, Lu K, Yang W. A review and analysis of key biomarkers in Alzheimer's disease. Front Neurosci 2024; 18:1358998. [PMID: 38445255 PMCID: PMC10912539 DOI: 10.3389/fnins.2024.1358998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/02/2024] [Indexed: 03/07/2024] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.
Collapse
Affiliation(s)
- Zhihao Zhang
- School of Computer Science and Technology, Xinjiang University, Ürümqi, China
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
| | - Xiangtao Liu
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
| | - Suixia Zhang
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- State Key Laboratory of Pathogenesis, Prevention, Treatment of Central Asian High Incidence Diseases, First Affiliated Hospital of Xinjiang Medical University, Ürümqi, China
| | - Zhixin Song
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
| | - Ke Lu
- School of Computer Science and Technology, Xinjiang University, Ürümqi, China
| | - Wenzhong Yang
- School of Computer Science and Technology, Xinjiang University, Ürümqi, China
| |
Collapse
|
15
|
Chang Y, Liu J, Jiang Y, Ma A, Yeo YY, Guo Q, McNutt M, Krull J, Rodig SJ, Barouch DH, Nolan G, Xu D, Jiang S, Li Z, Liu B, Ma Q. Graph Fourier transform for spatial omics representation and analyses of complex organs. RESEARCH SQUARE 2024:rs.3.rs-3952048. [PMID: 38410424 PMCID: PMC10896409 DOI: 10.21203/rs.3.rs-3952048/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Spatial omics technologies are capable of deciphering detailed components of complex organs or tissue in cellular and subcellular resolution. A robust, interpretable, and unbiased representation method for spatial omics is necessary to illuminate novel investigations into biological functions, whereas a mathematical theory deficiency still exists. We present SpaGFT (Spatial Graph Fourier Transform), which provides a unique analytical feature representation of spatial omics data and elucidates molecular signatures linked to critical biological processes within tissues and cells. It outperformed existing tools in spatially variable gene prediction and gene expression imputation across human/mouse Visium data. Integrating SpaGFT representation into existing machine learning frameworks can enhance up to 40% accuracy of spatial domain identification, cell type annotation, cell-to-spot alignment, and subcellular hallmark inference. SpaGFT identified immunological regions for B cell maturation in human lymph node Visium data, characterized secondary follicle variations from in-house human tonsil CODEX data, and detected extremely rare subcellular organelles such as Cajal body and Set1/COMPASS. This new method lays the groundwork for a new theoretical model in explainable AI, advancing our understanding of tissue organization and function.
Collapse
Affiliation(s)
- Yuzhou Chang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Jixin Liu
- School of Mathematics, Shandong University, Jinan 250100, China
| | - Yi Jiang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA
| | - Qi Guo
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
| | - Megan McNutt
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
| | - Jordan Krull
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Scott J. Rodig
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA 02115 USA
- Department of Pathology, Brigham & Women’s Hospital, Boston, MA 02115, USA
| | - Dan H. Barouch
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA
- William Bosworth Castle Professor of Medicine, Harvard Medical School
- Ragon Institute of MGH, MIT, and Harvard
| | - Garry Nolan
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Sizun Jiang
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA 02115 USA
- Department of Pathology, Brigham & Women’s Hospital, Boston, MA 02115, USA
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Department of Microbiology, Harvard Medical School, Boston, MA 02115, USA
| | - Zihai Li
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan 250100, China
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| |
Collapse
|
16
|
Hao M, Luo E, Chen Y, Wu Y, Li C, Chen S, Gao H, Bian H, Gu J, Wei L, Zhang X. STEM enables mapping of single-cell and spatial transcriptomics data with transfer learning. Commun Biol 2024; 7:56. [PMID: 38184694 PMCID: PMC10771471 DOI: 10.1038/s42003-023-05640-1] [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: 07/20/2023] [Accepted: 11/27/2023] [Indexed: 01/08/2024] Open
Abstract
Profiling spatial variations of cellular composition and transcriptomic characteristics is important for understanding the physiology and pathology of tissues. Spatial transcriptomics (ST) data depict spatial gene expression but the currently dominating high-throughput technology is yet not at single-cell resolution. Single-cell RNA-sequencing (SC) data provide high-throughput transcriptomic information at the single-cell level but lack spatial information. Integrating these two types of data would be ideal for revealing transcriptomic landscapes at single-cell resolution. We develop the method STEM (SpaTially aware EMbedding) for this purpose. It uses deep transfer learning to encode both ST and SC data into a unified spatially aware embedding space, and then uses the embeddings to infer SC-ST mapping and predict pseudo-spatial adjacency between cells in SC data. Semi-simulation and real data experiments verify that the embeddings preserved spatial information and eliminated technical biases between SC and ST data. We apply STEM to human squamous cell carcinoma and hepatic lobule datasets to uncover the localization of rare cell types and reveal cell-type-specific gene expression variation along a spatial axis. STEM is powerful for mapping SC and ST data to build single-cell level spatial transcriptomic landscapes, and can provide mechanistic insights into the spatial heterogeneity and microenvironments of tissues.
Collapse
Affiliation(s)
- Minsheng Hao
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Erpai Luo
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Yixin Chen
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Yanhong Wu
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Chen Li
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Sijie Chen
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Haoxiang Gao
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Haiyang Bian
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Jin Gu
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Lei Wei
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Xuegong Zhang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China.
- School of Life Sciences and School of Medicine, Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China.
| |
Collapse
|
17
|
Das S. Omics Approaches in Alzheimer's Disease Research. J Alzheimers Dis 2024; 99:S183-S185. [PMID: 38640162 DOI: 10.3233/jad-240272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2024]
Affiliation(s)
- Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| |
Collapse
|
18
|
Noori A, Jayakumar R, Moturi V, Li Z, Liu R, Serrano-Pozo A, Hyman BT, Das S. Alzheimer DataLENS: An Open Data Analytics Portal for Alzheimer's Disease Research. J Alzheimers Dis 2024; 99:S397-S407. [PMID: 38306039 DOI: 10.3233/jad-230884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Background Recent Alzheimer's disease (AD) discoveries are increasingly based on studies from a variety of omics technologies on large cohorts. Currently, there is no easily accessible resource for neuroscientists to browse, query, and visualize these complex datasets in a harmonized manner. Objective Create an online portal of public omics datasets for AD research. Methods We developed Alzheimer DataLENS, a web-based portal, using the R Shiny platform to query and visualize publicly available transcriptomics and genetics studies of AD on human cohorts. To ensure consistent representation of AD findings, all datasets were processed through a uniform bioinformatics pipeline. Results Alzheimer DataLENS currently houses 2 single-nucleus RNA sequencing datasets, over 30 bulk RNA sequencing datasets from 19 brain regions and 3 cohorts, and 2 genome-wide association studies (GWAS). Available visualizations for single-nucleus data include bubble plots, heatmaps, and UMAP plots; for bulk expression data include box plots and heatmaps; for pathways include protein-protein interaction network plots; and for GWAS results include Manhattan plots. Alzheimer DataLENS also links to two other knowledge resources: the AD Progression Atlas and the Astrocyte Atlas. Conclusions Alzheimer DataLENS is a valuable resource for investigators to quickly and systematically explore omics datasets and is freely accessible at https://alzdatalens.partners.org.
Collapse
Affiliation(s)
- Ayush Noori
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Vaishnavi Moturi
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Zhaozhi Li
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Rongxin Liu
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Alberto Serrano-Pozo
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Bradley T Hyman
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| |
Collapse
|
19
|
Muñoz-Castro C, Serrano-Pozo A. Astrocyte-Neuron Interactions in Alzheimer's Disease. ADVANCES IN NEUROBIOLOGY 2024; 39:345-382. [PMID: 39190082 DOI: 10.1007/978-3-031-64839-7_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/28/2024]
Abstract
Besides its two defining misfolded proteinopathies-Aβ plaques and tau neurofibrillary tangles-Alzheimer's disease (AD) is an exemplar of a neurodegenerative disease with prominent reactive astrogliosis, defined as the set of morphological, molecular, and functional changes that astrocytes suffer as the result of a toxic exposure. Reactive astrocytes can be observed in the vicinity of plaques and tangles, and the relationship between astrocytes and these AD neuropathological lesions is bidirectional so that each AD neuropathological hallmark causes specific changes in astrocytes, and astrocytes modulate the severity of each neuropathological feature in a specific manner. Here, we will review both how astrocytes change as a result of their chronic exposure to AD neuropathology and how those astrocytic changes impact each AD neuropathological feature. We will emphasize the repercussions that AD-associated reactive astrogliosis has for the astrocyte-neuron interaction and highlight areas of uncertainty and priorities for future research.
Collapse
Affiliation(s)
- Clara Muñoz-Castro
- Instituto de Biomedicina de Sevilla, IBiS/Hospital Universitario Virgen del Rocío/CSIC/Departamento de Bioquímica y Biología Molecular, Universidad de Sevilla, Seville, Spain
| | - Alberto Serrano-Pozo
- Massachusetts General Hospital Neurology Department, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
20
|
Fergany A, Zong C, Ekuban FA, Wu B, Ueha S, Shichino S, Matsushima K, Iwakura Y, Ichihara S, Ichihara G. Transcriptome analysis of the cerebral cortex of acrylamide-exposed wild-type and IL-1β-knockout mice. Arch Toxicol 2024; 98:181-205. [PMID: 37971544 PMCID: PMC10761544 DOI: 10.1007/s00204-023-03627-9] [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: 09/09/2023] [Accepted: 10/12/2023] [Indexed: 11/19/2023]
Abstract
Acrylamide is an environmental electrophile that has been produced in large amounts for many years. There is concern about the adverse health effects of acrylamide exposure due to its widespread industrial use and also presence in commonly consumed foods and others. IL-1β is a key cytokine that protects the brain from inflammatory insults, but its role in acrylamide-induced neurotoxicity remains unknown. We reported recently that deletion of IL-1β gene exacerbates ACR-induced neurotoxicity in mice. The aim of this study was to identify genes or signaling pathway(s) involved in enhancement of ACR-induced neurotoxicity by IL-1β gene deletion or ACR-induced neurotoxicity to generate a hypothesis mechanism explaining ACR-induced neurotoxicity. C57BL/6 J wild-type and IL-1β KO mice were exposed to ACR at 0, 12.5, 25 mg/kg by oral gavage for 7 days/week for 4 weeks, followed by extraction of mRNA from mice cerebral cortex for RNA sequence analysis. IL-1β deletion altered the expression of genes involved in extracellular region, including upregulation of PFN1 gene related to amyotrophic lateral sclerosis and increased the expression of the opposite strand of IL-1β. Acrylamide exposure enhanced mitochondria oxidative phosphorylation, synapse and ribosome pathways, and activated various pathways of different neurodegenerative diseases, such as Alzheimer disease, Parkinson disease, Huntington disease, and prion disease. Protein network analysis suggested the involvement of different proteins in related to learning and cognitive function, such as Egr1, Egr2, Fos, Nr4a1, and Btg2. Our results identified possible pathways involved in IL-1β deletion-potentiated and ACR-induced neurotoxicity in mice.
Collapse
Affiliation(s)
- Alzahraa Fergany
- Department of Occupational and Environmental Health, Faculty of Pharmaceutical Sciences, Tokyo University of Science, Building No. 15, 2641 Yamazaki, Noda, Chiba, 278-8510, Japan
- Laboratory of Genetics and Genetic Engineering in Department of Animal Husbandry and Animal Wealth Development, Faculty of Veterinary Medicine, Alexandria University, Alexandria, Egypt
| | - Cai Zong
- Department of Occupational and Environmental Health, Faculty of Pharmaceutical Sciences, Tokyo University of Science, Building No. 15, 2641 Yamazaki, Noda, Chiba, 278-8510, Japan
| | - Frederick Adams Ekuban
- Department of Occupational and Environmental Health, Faculty of Pharmaceutical Sciences, Tokyo University of Science, Building No. 15, 2641 Yamazaki, Noda, Chiba, 278-8510, Japan
| | - Bin Wu
- Division of Molecular Regulation of Inflammatory and Immune Diseases, Research Institute for Biomedical Sciences, Tokyo University of Science, Noda, Japan
| | - Satoshi Ueha
- Division of Molecular Regulation of Inflammatory and Immune Diseases, Research Institute for Biomedical Sciences, Tokyo University of Science, Noda, Japan
| | - Shigeyuki Shichino
- Division of Molecular Regulation of Inflammatory and Immune Diseases, Research Institute for Biomedical Sciences, Tokyo University of Science, Noda, Japan
| | - Kouji Matsushima
- Division of Molecular Regulation of Inflammatory and Immune Diseases, Research Institute for Biomedical Sciences, Tokyo University of Science, Noda, Japan
| | - Yoichiro Iwakura
- Division of Experimental Animal Immunology, Research Institute for Biomedical Sciences, Tokyo University of Science, Noda, Japan
| | - Sahoko Ichihara
- Department of Environmental and Preventive Medicine, Jichi Medical University, Shimotsuke, Japan
| | - Gaku Ichihara
- Department of Occupational and Environmental Health, Faculty of Pharmaceutical Sciences, Tokyo University of Science, Building No. 15, 2641 Yamazaki, Noda, Chiba, 278-8510, Japan.
| |
Collapse
|
21
|
Wang J, Li J, Kramer ST, Su L, Chang Y, Xu C, Eadon MT, Kiryluk K, Ma Q, Xu D. Dimension-agnostic and granularity-based spatially variable gene identification using BSP. Nat Commun 2023; 14:7367. [PMID: 37963892 PMCID: PMC10645821 DOI: 10.1038/s41467-023-43256-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 11/03/2023] [Indexed: 11/16/2023] Open
Abstract
Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a non-parametric model by comparing gene expression pattens at two spatial granularities to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.
Collapse
Affiliation(s)
- Juexin Wang
- Department of BioHealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, Indianapolis, IN, 46202, USA.
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.
| | - Jinpu Li
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Skyler T Kramer
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Li Su
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Yuzhou Chang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Chunhui Xu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Michael T Eadon
- Department of Medicine, Indiana University, Indianapolis, IN, 46202, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA.
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA.
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA.
| |
Collapse
|
22
|
Chen H, Fan L, Guo Q, Wong MY, Yu F, Foxe N, Wang W, Nessim A, Carling G, Liu B, Lopez-Lee C, Huang Y, Amin S, Patel T, Mok SA, Song WM, Zhang B, Ma Q, Fu H, Gan L, Luo W. DAP12 deficiency alters microglia-oligodendrocyte communication and enhances resilience against tau toxicity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.26.563970. [PMID: 37961594 PMCID: PMC10634844 DOI: 10.1101/2023.10.26.563970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Pathogenic tau accumulation fuels neurodegeneration in Alzheimer's disease (AD). Enhancing aging brain's resilience to tau pathology would lead to novel therapeutic strategies. DAP12 (DNAX-activation protein 12) is critically involved in microglial immune responses. Previous studies have showed that mice lacking DAP12 in tauopathy mice exhibit higher tau pathology but are protected from tau-induced cognitive deficits. However, the exact mechanism remains elusive. Our current study uncovers a novel resilience mechanism via microglial interaction with oligodendrocytes. Despite higher tau inclusions, Dap12 deletion curbs tau-induced brain inflammation and ameliorates myelin and synapse loss. Specifically, removal of Dap12 abolished tau-induced disease-associated clusters in microglia (MG) and intermediate oligodendrocytes (iOli), which are spatially correlated with tau pathology in AD brains. Our study highlights the critical role of interactions between microglia and oligodendrocytes in tau toxicity and DAP12 signaling as a promising target for enhancing resilience in AD.
Collapse
Affiliation(s)
- Hao Chen
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Li Fan
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Qi Guo
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Man Ying Wong
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Fangmin Yu
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Nessa Foxe
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | | | - Aviram Nessim
- The State University of New York at Stony Brook, Long Island, New York, USA
| | - Gillian Carling
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Program of Neuroscience, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Bangyan Liu
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Program of Neuroscience, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Chloe Lopez-Lee
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Program of Neuroscience, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Yige Huang
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Program of Neuroscience, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Sadaf Amin
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Tark Patel
- Department of Biochemistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB Canada
| | - Sue-Ann Mok
- Department of Biochemistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB Canada
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Hongjun Fu
- Department of Neuroscience, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Li Gan
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Millburn High School, New Jersey, NJ, USA
| | - Wenjie Luo
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| |
Collapse
|
23
|
Chen H, Fan L, Guo Q, Wong MY, Yu F, Foxe N, Wang W, Nessim A, Carling G, Liu B, Lopez-Lee C, Huang Y, Amin S, Mok SA, Song WM, Zhang B, Ma Q, Fu H, Gan L, Luo W. DAP12 deficiency alters microglia-oligodendrocyte communication and enhances resilience against tau toxicity. RESEARCH SQUARE 2023:rs.3.rs-3454358. [PMID: 37961627 PMCID: PMC10635319 DOI: 10.21203/rs.3.rs-3454358/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Pathogenic tau accumulation fuels neurodegeneration in Alzheimer's disease (AD). Enhancing aging brain's resilience to tau pathology would lead to novel therapeutic strategies. DAP12 (DNAX-activation protein 12) is critically involved in microglial immune responses. Previous studies have showed that mice lacking DAP12 in tauopathy mice exhibit higher tau pathology but are protected from tau-induced cognitive deficits. However, the exact mechanism remains elusive. Our current study uncovers a novel resilience mechanism via microglial interaction with oligodendrocytes. Despite higher tau inclusions, Dap12 deletion curbs tau-induced brain inflammation and ameliorates myelin and synapse loss. Specifically, removal of Dap12 abolished tau-induced disease-associated clusters in microglia (MG) and intermediate oligodendrocytes (iOli), which are spatially correlated with tau pathology in AD brains. Our study highlights the critical role of interactions between microglia and oligodendrocytes in tau toxicity and DAP12 signaling as a promising target for enhancing resilience in AD.
Collapse
Affiliation(s)
- Hao Chen
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Li Fan
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Qi Guo
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Man Ying Wong
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Fangmin Yu
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Nessa Foxe
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | | | - Aviram Nessim
- The State University of New York at Stony Brook, Long Island, New York, USA
| | - Gillian Carling
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Program of Neuroscience, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Bangyan Liu
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Program of Neuroscience, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Chloe Lopez-Lee
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Program of Neuroscience, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Yige Huang
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Program of Neuroscience, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Sadaf Amin
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Sue-Ann Mok
- Department of Biochemistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB Canada
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Hongjun Fu
- Department of Neuroscience, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Li Gan
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Millburn High School, New Jersey, NJ, USA
| | - Wenjie Luo
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| |
Collapse
|
24
|
Signal B, Pérez Suárez TG, Taberlay PC, Woodhouse A. Cellular specificity is key to deciphering epigenetic changes underlying Alzheimer's disease. Neurobiol Dis 2023; 186:106284. [PMID: 37683959 DOI: 10.1016/j.nbd.2023.106284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 08/23/2023] [Accepted: 09/05/2023] [Indexed: 09/10/2023] Open
Abstract
Different cell types in the brain play distinct roles in Alzheimer's disease (AD) progression. Late onset AD (LOAD) is a complex disease, with a large genetic component, but many risk loci fall in non-coding genome regions. Epigenetics implicates the non-coding genome with control of gene expression. The epigenome is highly cell-type specific and dynamically responds to the environment. Therefore, epigenetic mechanisms are well placed to explain genetic and environmental factors that are associated with AD. However, given this cellular specificity, purified cell populations or single cells need to be profiled to avoid effect masking. Here we review the current state of cell-type specific genome-wide profiling in LOAD, covering DNA methylation (CpG, CpH, and hydroxymethylation), histone modifications, and chromatin changes. To date, these data reveal that distinct cell types contribute and react differently to AD progression through epigenetic alterations. This review addresses the current gap in prior bulk-tissue derived work by spotlighting cell-specific changes that govern the complex interplay of cells throughout disease progression and are critical in understanding and developing effective treatments for AD.
Collapse
Affiliation(s)
- Brandon Signal
- School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia.
| | | | - Phillippa C Taberlay
- School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia
| | - Adele Woodhouse
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, TAS, Australia
| |
Collapse
|
25
|
Zhou X, Dong K, Zhang S. Integrating spatial transcriptomics data across different conditions, technologies and developmental stages. NATURE COMPUTATIONAL SCIENCE 2023; 3:894-906. [PMID: 38177758 DOI: 10.1038/s43588-023-00528-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 09/05/2023] [Indexed: 01/06/2024]
Abstract
With the rapid generation of spatial transcriptomics (ST) data, integrative analysis of multiple ST datasets from different conditions, technologies and developmental stages is becoming increasingly important. Here we present a graph attention neural network called STAligner for integrating and aligning ST datasets, enabling spatially aware data integration, simultaneous spatial domain identification and downstream comparative analysis. We apply STAligner to ST datasets of the human cortex slices from different samples, the mouse olfactory bulb slices generated by two profiling technologies, the mouse hippocampus tissue slices under normal and Alzheimer's disease conditions, and the spatiotemporal atlases of mouse organogenesis. STAligner efficiently captures the shared tissue structures across different slices, the disease-related substructures and the dynamical changes during mouse embryonic development. In addition, the shared spatial domain and nearest-neighbor pairs identified by STAligner can be further considered as corresponding pairs to guide the three-dimensional reconstruction of consecutive slices, achieving more accurate local structure-guided registration than the existing method.
Collapse
Affiliation(s)
- Xiang Zhou
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Kangning Dong
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Shihua Zhang
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China.
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China.
| |
Collapse
|
26
|
STAligner enables the integration and alignment of multiple spatial transcriptomics datasets. NATURE COMPUTATIONAL SCIENCE 2023; 3:831-832. [PMID: 38177765 DOI: 10.1038/s43588-023-00543-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
|
27
|
Ma W, Su Y, Zhang P, Wan G, Cheng X, Lu C, Gu X. Identification of mitochondrial-related genes as potential biomarkers for the subtyping and prediction of Alzheimer's disease. Front Mol Neurosci 2023; 16:1205541. [PMID: 37470054 PMCID: PMC10352499 DOI: 10.3389/fnmol.2023.1205541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 06/19/2023] [Indexed: 07/21/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is a progressive and debilitating neurodegenerative disorder prevalent among older adults. Although AD symptoms can be managed through certain treatments, advancing the understanding of underlying disease mechanisms and developing effective therapies is critical. Methods In this study, we systematically analyzed transcriptome data from temporal lobes of healthy individuals and patients with AD to investigate the relationship between AD and mitochondrial autophagy. Machine learning algorithms were used to identify six genes-FUNDC1, MAP1LC3A, CSNK2A1, VDAC1, CSNK2B, and ATG5-for the construction of an AD prediction model. Furthermore, AD was categorized into three subtypes through consensus clustering analysis. Results The identified genes are closely linked to the onset and progression of AD and can serve as reliable biomarkers. The differences in gene expression, clinical features, immune infiltration, and pathway enrichment were examined among the three AD subtypes. Potential drugs for the treatment of each subtype were also identified. Discussion The findings observed in the present study can help to deepen the understanding of the underlying disease mechanisms of AD and enable the development of precision medicine and personalized treatment approaches.
Collapse
Affiliation(s)
- Wenhao Ma
- School of Pharmacy, Shanghai University of Medicine and Health Sciences, Shanghai, China
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yuelin Su
- Department of Ultrasound Medicine, Huashan Hospital Affiliated to Fudan University, Shanghai, China
| | - Peng Zhang
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Guoqing Wan
- School of Pharmacy, Shanghai University of Medicine and Health Sciences, Shanghai, China
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Xiaoqin Cheng
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Changlian Lu
- School of Pharmacy, Shanghai University of Medicine and Health Sciences, Shanghai, China
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Xuefeng Gu
- School of Pharmacy, Shanghai University of Medicine and Health Sciences, Shanghai, China
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| |
Collapse
|
28
|
Sancha-Velasco A, Uceda-Heras A, García-Cabezas MÁ. Cortical type: a conceptual tool for meaningful biological interpretation of high-throughput gene expression data in the human cerebral cortex. Front Neuroanat 2023; 17:1187280. [PMID: 37426901 PMCID: PMC10323436 DOI: 10.3389/fnana.2023.1187280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/31/2023] [Indexed: 07/11/2023] Open
Abstract
The interpretation of massive high-throughput gene expression data requires computational and biological analyses to identify statistically and biologically significant differences, respectively. There are abundant sources that describe computational tools for statistical analysis of massive gene expression data but few address data analysis for biological significance. In the present article we exemplify the importance of selecting the proper biological context in the human brain for gene expression data analysis and interpretation. For this purpose, we use cortical type as conceptual tool to make predictions about gene expression in areas of the human temporal cortex. We predict that the expression of genes related to glutamatergic transmission would be higher in areas of simpler cortical type, the expression of genes related to GABAergic transmission would be higher in areas of more complex cortical type, and the expression of genes related to epigenetic regulation would be higher in areas of simpler cortical type. Then, we test these predictions with gene expression data from several regions of the human temporal cortex obtained from the Allen Human Brain Atlas. We find that the expression of several genes shows statistically significant differences in agreement with the predicted gradual expression along the laminar complexity gradient of the human cortex, suggesting that simpler cortical types may have greater glutamatergic excitability and epigenetic turnover compared to more complex types; on the other hand, complex cortical types seem to have greater GABAergic inhibitory control compared to simpler types. Our results show that cortical type is a good predictor of synaptic plasticity, epigenetic turnover, and selective vulnerability in human cortical areas. Thus, cortical type can provide a meaningful context for interpreting high-throughput gene expression data in the human cerebral cortex.
Collapse
Affiliation(s)
- Ariadna Sancha-Velasco
- Department of Anatomy, Histology and Neuroscience, School of Medicine, Autonomous University of Madrid, Madrid, Spain
- Master Program in Neuroscience, Autonomous University of Madrid, Madrid, Spain
| | - Alicia Uceda-Heras
- Master Program in Neuroscience, Autonomous University of Madrid, Madrid, Spain
- Ph.D. Program in Neuroscience UAM-Cajal, Autonomous University of Madrid, Madrid, Spain
| | - Miguel Ángel García-Cabezas
- Department of Anatomy, Histology and Neuroscience, School of Medicine, Autonomous University of Madrid, Madrid, Spain
- Master Program in Neuroscience, Autonomous University of Madrid, Madrid, Spain
- Ph.D. Program in Neuroscience UAM-Cajal, Autonomous University of Madrid, Madrid, Spain
- Neural Systems Laboratory, Department of Health Sciences, Boston University, Boston, MA, United States
| |
Collapse
|
29
|
Gabitto MI, Travaglini KJ, Rachleff VM, Kaplan ES, Long B, Ariza J, Ding Y, Mahoney JT, Dee N, Goldy J, Melief EJ, Brouner K, Campos J, Carr AJ, Casper T, Chakrabarty R, Clark M, Compos J, Cool J, Valera Cuevas NJ, Dalley R, Darvas M, Ding SL, Dolbeare T, Mac Donald CL, Egdorf T, Esposito L, Ferrer R, Gala R, Gary A, Gloe J, Guilford N, Guzman J, Ho W, Jarksy T, Johansen N, Kalmbach BE, Keene LM, Khawand S, Kilgore M, Kirkland A, Kunst M, Lee BR, Malone J, Maltzer Z, Martin N, McCue R, McMillen D, Meyerdierks E, Meyers KP, Mollenkopf T, Montine M, Nolan AL, Nyhus J, Olsen PA, Pacleb M, Pham T, Pom CA, Postupna N, Ruiz A, Schantz AM, Sorensen SA, Staats B, Sullivan M, Sunkin SM, Thompson C, Tieu M, Ting J, Torkelson A, Tran T, Wang MQ, Waters J, Wilson AM, Haynor D, Gatto N, Jayadev S, Mufti S, Ng L, Mukherjee S, Crane PK, Latimer CS, Levi BP, Smith K, Close JL, Miller JA, Hodge RD, Larson EB, Grabowski TJ, Hawrylycz M, Keene CD, Lein ES. Integrated multimodal cell atlas of Alzheimer's disease. RESEARCH SQUARE 2023:rs.3.rs-2921860. [PMID: 37292694 PMCID: PMC10246227 DOI: 10.21203/rs.3.rs-2921860/v1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Alzheimer's disease (AD) is the most common cause of dementia in older adults. Neuropathological and imaging studies have demonstrated a progressive and stereotyped accumulation of protein aggregates, but the underlying molecular and cellular mechanisms driving AD progression and vulnerable cell populations affected by disease remain coarsely understood. The current study harnesses single cell and spatial genomics tools and knowledge from the BRAIN Initiative Cell Census Network to understand the impact of disease progression on middle temporal gyrus cell types. We used image-based quantitative neuropathology to place 84 donors spanning the spectrum of AD pathology along a continuous disease pseudoprogression score and multiomic technologies to profile single nuclei from each donor, mapping their transcriptomes, epigenomes, and spatial coordinates to a common cell type reference with unprecedented resolution. Temporal analysis of cell-type proportions indicated an early reduction of Somatostatin-expressing neuronal subtypes and a late decrease of supragranular intratelencephalic-projecting excitatory and Parvalbumin-expressing neurons, with increases in disease-associated microglial and astrocytic states. We found complex gene expression differences, ranging from global to cell type-specific effects. These effects showed different temporal patterns indicating diverse cellular perturbations as a function of disease progression. A subset of donors showed a particularly severe cellular and molecular phenotype, which correlated with steeper cognitive decline. We have created a freely available public resource to explore these data and to accelerate progress in AD research at SEA-AD.org.
Collapse
Affiliation(s)
| | | | - Victoria M. Rachleff
- Allen Institute for Brain Science, Seattle, WA, 98109
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | | | - Brian Long
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Jeanelle Ariza
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Yi Ding
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Erica J. Melief
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | | | - John Campos
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | | | - Tamara Casper
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | - Michael Clark
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Jazmin Compos
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Jonah Cool
- Chan Zuckerberg Initiative, Redwood City, CA 94063
| | | | - Rachel Dalley
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Martin Darvas
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Tim Dolbeare
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Luke Esposito
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | - Rohan Gala
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Amanda Gary
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Jessica Gloe
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | | | - Windy Ho
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Tim Jarksy
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | | | - Lisa M. Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Sarah Khawand
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Mitch Kilgore
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Amanda Kirkland
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Michael Kunst
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Brian R. Lee
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | - Zoe Maltzer
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Naomi Martin
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Rachel McCue
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | | | - Kelly P. Meyers
- Kaiser Permanente Washington Research Institute, Seattle, WA, 98101
| | | | - Mark Montine
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Amber L. Nolan
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Julie Nyhus
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Paul A. Olsen
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Maiya Pacleb
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Thanh Pham
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | - Nadia Postupna
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Augustin Ruiz
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Aimee M. Schantz
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | | | - Brian Staats
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Matt Sullivan
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | | | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Jonathan Ting
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Amy Torkelson
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Tracy Tran
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | - Jack Waters
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Angela M. Wilson
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - David Haynor
- Department of Radiology, University of Washington, Seattle, WA 98014
| | - Nicole Gatto
- Kaiser Permanente Washington Research Institute, Seattle, WA, 98101
| | - Suman Jayadev
- Department of Neurology, University of Washington, Seattle, WA 98104
| | - Shoaib Mufti
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | - Paul K. Crane
- Department of Medicine, University of Washington, Seattle, WA 98104
| | - Caitlin S. Latimer
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Boaz P. Levi
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | | | | | | | - Eric B. Larson
- Department of Medicine, University of Washington, Seattle, WA 98104
| | | | | | - C. Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Ed S. Lein
- Allen Institute for Brain Science, Seattle, WA, 98109
| |
Collapse
|
30
|
Nwadiugwu M, Shen H, Deng HW. Potential Molecular Mechanisms of Alzheimer's Disease from Genetic Studies. BIOLOGY 2023; 12:biology12040602. [PMID: 37106802 PMCID: PMC10136191 DOI: 10.3390/biology12040602] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/09/2023] [Accepted: 04/12/2023] [Indexed: 04/29/2023]
Abstract
The devastating effects of Alzheimer's disease (AD) are yet to be ameliorated due to the absence of curative treatment options. AD is an aging-related disease that affects cognition, and molecular imbalance is one of its hallmarks. There is a need to identify common causes of molecular imbalance in AD and their potential mechanisms for continuing research. A narrative synthesis of molecular mechanisms in AD from primary studies that employed single-cell sequencing (scRNA-seq) or spatial genomics was conducted using Embase and PubMed databases. We found that differences in molecular mechanisms in AD could be grouped into four key categories: sex-specific features, early-onset features, aging, and immune system pathways. The reported causes of molecular imbalance were alterations in bile acid (BA) synthesis, PITRM1, TREM2, olfactory mucosa (OM) cells, cholesterol catabolism, NFkB, double-strand break (DSB) neuronal damage, P65KD silencing, tau and APOE expression. What changed from previous findings in contrast to results obtained were explored to find potential factors for AD-modifying investigations.
Collapse
Affiliation(s)
- Martin Nwadiugwu
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Hui Shen
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA 70112, USA
| |
Collapse
|
31
|
Wang J, Li J, Kramer ST, Su L, Chang Y, Xu C, Ma Q, Xu D. Dimension-agnostic and granularity-based spatially variable gene identification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.21.533713. [PMID: 36993544 PMCID: PMC10055351 DOI: 10.1101/2023.03.21.533713] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a spatial granularity-guided and non-parametric model to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This new method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.
Collapse
Affiliation(s)
- Juexin Wang
- Department of BioHealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Jinpu Li
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Skyler T Kramer
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Li Su
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Yuzhou Chang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Chunhui Xu
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| |
Collapse
|
32
|
Wang J, Li J, Kramer S, Su L, Chang Y, Xu C, Ma Q, Xu D. Dimension-agnostic and granularity-based spatially variable gene identification. RESEARCH SQUARE 2023:rs.3.rs-2687726. [PMID: 36993309 PMCID: PMC10055640 DOI: 10.21203/rs.3.rs-2687726/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a spatial granularity-guided and non-parametric model to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This new method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.
Collapse
Affiliation(s)
- Juexin Wang
- Department of BioHealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Jinpu Li
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Skyler Kramer
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Li Su
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Yuzhou Chang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Chunhui Xu
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| |
Collapse
|
33
|
Das S, Li Z, Wachter A, Alla S, Noori A, Abdourahman A, Tamm JA, Woodbury ME, Talanian RV, Biber K, Karran EH, Hyman BT, Serrano-Pozo A. Distinct Transcriptomic Responses to Aβ plaques, Neurofibrillary Tangles, and APOE in Alzheimer's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.20.533303. [PMID: 36993332 PMCID: PMC10055287 DOI: 10.1101/2023.03.20.533303] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
INTRODUCTION Omics studies have revealed that various brain cell types undergo profound molecular changes in Alzheimer's disease (AD) but the spatial relationships with plaques and tangles and APOE -linked differences remain unclear. METHODS We performed laser capture microdissection of Aβ plaques, the 50μm halo around them, tangles with the 50μm halo around them, and areas distant (>50μm) from plaques and tangles in the temporal cortex of AD and control donors, followed by RNA-sequencing. RESULTS Aβ plaques exhibited upregulated microglial (neuroinflammation/phagocytosis) and downregulated neuronal (neurotransmission/energy metabolism) genes, whereas tangles had mostly downregulated neuronal genes. Aβ plaques had more differentially expressed genes than tangles. We identified a gradient Aβ plaque>peri-plaque>tangle>distant for these changes. AD APOE ε4 homozygotes had greater changes than APOE ε3 across locations, especially within Aβ plaques. DISCUSSION Transcriptomic changes in AD consist primarily of neuroinflammation and neuronal dysfunction, are spatially associated mainly with Aβ plaques, and are exacerbated by the APOE ε4 allele.
Collapse
Affiliation(s)
- Sudeshna Das
- Massachusetts General Hospital, Neurology Dept. Boston, MA 02114
- Massachusetts Alzheimer’s Disease Research Center, Charlestown, MA 02129
- Harvard Medical School, Boston, MA 02115
| | - Zhaozhi Li
- Massachusetts General Hospital, Neurology Dept. Boston, MA 02114
- Massachusetts Alzheimer’s Disease Research Center, Charlestown, MA 02129
| | - Astrid Wachter
- AbbVie Deutschland GmbH & Co. KG, Genomics Research Center, Knollstrasse, 67061 Ludwigshafen
| | - Srinija Alla
- Massachusetts General Hospital, Neurology Dept. Boston, MA 02114
| | - Ayush Noori
- Massachusetts General Hospital, Neurology Dept. Boston, MA 02114
| | - Aicha Abdourahman
- AbbVie, Cambridge Research Center, 200 Sidney Street, Cambridge, MA 02139
| | - Joseph A. Tamm
- AbbVie, Cambridge Research Center, 200 Sidney Street, Cambridge, MA 02139
| | - Maya E. Woodbury
- AbbVie, Cambridge Research Center, 200 Sidney Street, Cambridge, MA 02139
| | - Robert V. Talanian
- AbbVie, Cambridge Research Center, 200 Sidney Street, Cambridge, MA 02139
| | - Knut Biber
- AbbVie Deutschland GmbH & Co. KG, Neuroscience Research Center, Knollstrasse, 67061 Ludwigshafen
| | - Eric H. Karran
- AbbVie, Cambridge Research Center, 200 Sidney Street, Cambridge, MA 02139
| | - Bradley T. Hyman
- Massachusetts General Hospital, Neurology Dept. Boston, MA 02114
- Massachusetts Alzheimer’s Disease Research Center, Charlestown, MA 02129
- Harvard Medical School, Boston, MA 02115
| | - Alberto Serrano-Pozo
- Massachusetts General Hospital, Neurology Dept. Boston, MA 02114
- Massachusetts Alzheimer’s Disease Research Center, Charlestown, MA 02129
- Harvard Medical School, Boston, MA 02115
| |
Collapse
|
34
|
Li J, Zeng Q, Luo X, Li K, Liu X, Hong L, Zhang X, Zhong S, Qiu T, Liu Z, Chen Y, Huang P, Zhang M. Decoupling of Regional Cerebral Blood Flow and Brain Function Along the Alzheimer's Disease Continuum. J Alzheimers Dis 2023; 95:287-298. [PMID: 37483006 DOI: 10.3233/jad-230503] [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] [Indexed: 07/25/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is accompanied with impaired neurovascular coupling. However, its early alteration remains elusive along the AD continuum. OBJECTIVE This study aimed to investigate the early disruption of neurovascular coupling in cognitively normal (CN) and mild cognitive impairment (MCI) elderly and its association with cognition and AD pathologies. METHODS We included 43 amyloid-β-negative CN participants and 38 amyloid-β-positive individuals (18 CN and 20 MCI) from the Alzheimer's Disease Neuroimaging Initiative dataset. Regional homogeneity (ReHo) map was used to represent neuronal activity and cerebral blood flow (CBF) map was used to represent cerebral blood perfusion. Neurovascular coupling was assessed by CBF/ReHo ratio at the voxel level. Analyses of covariance to detect the between-group differences and to further investigate the relations between CBF/ReHo ratio and AD biomarkers or cognition. In addition, the correlation of cerebral small vessel disease (SVD) burden and neurovascular coupling was assessed as well. RESULTS Related to amyloid-β-negative CN group, amyloid-β-positive groups showed decreased CBF/ReHo ratio mainly in the left medial and inferior temporal gyrus. Furthermore, lower CBF/ReHo ratio was associated with a lower Mini-Mental State Examination score as well as higher AD pathological burden. No association between CBF/ReHo ratio and SVD burden was observed. CONCLUSION AD pathology is a major correlate of the disturbed neurovascular coupling along the AD continuum, independent of SVD pathology. The CBF/ReHo ratio may be an index for detecting neurovascular coupling abnormalities, which could be used for early diagnosis in the future.
Collapse
Affiliation(s)
- Jixuan Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qingze Zeng
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao Luo
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Kaicheng Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Luwei Hong
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xinyi Zhang
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Siyan Zhong
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Tiantian Qiu
- Department of Radiology, Linyi People's Hospital, Linyi, China
| | - Zhirong Liu
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yanxing Chen
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| |
Collapse
|
35
|
Ya D, Zhang Y, Cui Q, Jiang Y, Yang J, Tian N, Xiang W, Lin X, Li Q, Liao R. Application of spatial transcriptome technologies to neurological diseases. Front Cell Dev Biol 2023; 11:1142923. [PMID: 36936681 PMCID: PMC10020196 DOI: 10.3389/fcell.2023.1142923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023] Open
Abstract
Spatial transcriptome technology acquires gene expression profiles while retaining spatial location information, it displays the gene expression properties of cells in situ. Through the investigation of cell heterogeneity, microenvironment, function, and cellular interactions, spatial transcriptome technology can deeply explore the pathogenic mechanisms of cell-type-specific responses and spatial localization in neurological diseases. The present article overviews spatial transcriptome technologies based on microdissection, in situ hybridization, in situ sequencing, in situ capture, and live cell labeling. Each technology is described along with its methods, detection throughput, spatial resolution, benefits, and drawbacks. Furthermore, their applications in neurodegenerative disease, neuropsychiatric illness, stroke and epilepsy are outlined. This information can be used to understand disease mechanisms, pick therapeutic targets, and establish biomarkers.
Collapse
Affiliation(s)
- Dongshan Ya
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Yingmei Zhang
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Qi Cui
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Yanlin Jiang
- Department of Pharmacology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Jiaxin Yang
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Ning Tian
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Wenjing Xiang
- Department of Neurology ward 2, Guilin People’s Hospital, Guilin, China
| | - Xiaohui Lin
- Department of Geriatrics, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Qinghua Li
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Rujia Liao
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- *Correspondence: Rujia Liao,
| |
Collapse
|