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Lin Y, Liang Y, Wang D, Chang Y, Ma Q, Wang Y, He F, Xu D. A contrastive learning approach to integrate spatial transcriptomics and histological images. Comput Struct Biotechnol J 2024; 23:1786-1795. [PMID: 38707535 PMCID: PMC11068546 DOI: 10.1016/j.csbj.2024.04.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 04/14/2024] [Accepted: 04/15/2024] [Indexed: 05/07/2024] Open
Abstract
The rapid growth of spatially resolved transcriptomics technology provides new perspectives on spatial tissue architecture. Deep learning has been widely applied to derive useful representations for spatial transcriptome analysis. However, effectively integrating spatial multi-modal data remains challenging. Here, we present ConGcR, a contrastive learning-based model for integrating gene expression, spatial location, and tissue morphology for data representation and spatial tissue architecture identification. Graph convolution and ResNet were used as encoders for gene expression with spatial location and histological image inputs, respectively. We further enhanced ConGcR with a graph auto-encoder as ConGaR to better model spatially embedded representations. We validated our models using 16 human brains, four chicken hearts, eight breast tumors, and 30 human lung spatial transcriptomics samples. The results showed that our models generated more effective embeddings for obtaining tissue architectures closer to the ground truth than other methods. Overall, our models not only can improve tissue architecture identification's accuracy but also may provide valuable insights and effective data representation for other tasks in spatial transcriptome analyses.
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Affiliation(s)
- Yu Lin
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
- Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Yanchun Liang
- School of Computer Science, Zhuhai College of Science and Technology, Zhuhai 519041, China
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Duolin Wang
- Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Yuzhou Chang
- Department of Biomedical Informatics, Ohio State University, Columbus, OH 43210, United States
| | - Qin Ma
- Department of Biomedical Informatics, Ohio State University, Columbus, OH 43210, United States
| | - Yan Wang
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Fei He
- Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Dong Xu
- Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
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2
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Wang S, Greenbaum J, Qiu C, Swerdlow RH, Haeri M, Gong Y, Shen H, Xiao H, Deng H. Gene interactions analysis of brain spatial transcriptome for Alzheimer's disease. Genes Dis 2024; 11:101337. [PMID: 39281834 PMCID: PMC11402150 DOI: 10.1016/j.gendis.2024.101337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/25/2023] [Accepted: 02/21/2024] [Indexed: 09/18/2024] Open
Abstract
Recent studies have explored the spatial transcriptomics patterns of Alzheimer's disease (AD) brain by spatial sequencing in mouse models, enabling the identification of unique genome-wide transcriptomic features associated with different spatial regions and pathological status. However, the dynamics of gene interactions that occur during amyloid-β accumulation remain largely unknown. In this study, we performed analyses on ligand-receptor communication, transcription factor regulatory network, and spot-specific network to reveal the dependence and the dynamics of gene associations/interactions on spatial regions and pathological status with mouse and human brains. We first used a spatial transcriptomics dataset of the App NL-G-F knock-in AD and wild-type mouse model. We revealed 17 ligand-receptor pairs with opposite tendencies throughout the amyloid-β accumulation process and showed the specific ligand-receptor interactions across the hippocampus layers at different extents of pathological changes. We then identified nerve function related transcription factors in the hippocampus and entorhinal cortex, as well as genes with different transcriptomic association degrees in AD versus wild-type mice. Finally, another independent spatial transcriptomics dataset from different AD mouse models and human single-nuclei RNA-seq data/AlzData database were used for validation. This is the first study to identify various gene associations throughout amyloid-β accumulation based on spatial transcriptomics, establishing the foundations to reveal advanced and in-depth AD etiology from a novel perspective based on the comprehensive analyses of gene interactions that are spatio-temporal dependent.
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Affiliation(s)
- Shengran Wang
- Reproductive Medicine Center, The Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, China
- Center for System Biology, Data Sciences and Reproductive Health, School of Basic Medical Science, Central South University, Changsha, Hunan 410008, China
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Jonathan Greenbaum
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Chuan Qiu
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Russell H Swerdlow
- Department of Pathology and KU Alzheimer's Disease Research Center, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Mohammad Haeri
- Department of Pathology and KU Alzheimer's Disease Research Center, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Yun Gong
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Hui Shen
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Hongmei Xiao
- Institute of Reproductive & Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan 410008, China
- Center of Reproductive Health, School of Basic Medical Science, Central South University, Changsha, Hunan 410008, China
| | - Hongwen Deng
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA 70112, USA
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3
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Wang Y, Thottappillil N, Gomez-Salazar M, Tower RJ, Qin Q, Del Rosario Alvia IC, Xu M, Cherief M, Cheng R, Archer M, Arondekar S, Reddy S, Broderick K, Péault B, James AW. Integrated transcriptomics of human blood vessels defines a spatially controlled niche for early mesenchymal progenitor cells. Dev Cell 2024; 59:2687-2703.e6. [PMID: 39025061 PMCID: PMC11496018 DOI: 10.1016/j.devcel.2024.06.015] [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: 11/01/2023] [Revised: 03/28/2024] [Accepted: 06/19/2024] [Indexed: 07/20/2024]
Abstract
Human blood vessel walls show concentric layers, with the outermost tunica adventitia harboring mesenchymal progenitor cells. These progenitor cells maintain vessel homeostasis and provide a robust cell source for cell-based therapies. However, human adventitial stem cell niche has not been studied in detail. Here, using spatial and single-cell transcriptomics, we characterized the phenotype, potential, and microanatomic distribution of human perivascular progenitors. Initially, spatial transcriptomics identified heterogeneity between perivascular layers of arteries and veins and delineated the tunica adventitia into inner and outer layers. From this spatial atlas, we inferred a hierarchy of mesenchymal progenitors dictated by a more primitive cell with a high surface expression of CD201 (PROCR). When isolated from humans and mice, CD201Low expression typified a mesodermal committed subset with higher osteogenesis and less proliferation than CD201High cells, with a downstream effect on canonical Wnt signaling through DACT2. CD201Low cells also displayed high translational potential for bone tissue generation.
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Affiliation(s)
- Yiyun Wang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21205, USA
| | | | | | - Robert J Tower
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Qizhi Qin
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21205, USA
| | | | - Mingxin Xu
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Masnsen Cherief
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Ray Cheng
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Mary Archer
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Shreya Arondekar
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Sashank Reddy
- Department of Plastic and Reconstructive Surgery, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Kristen Broderick
- Department of Plastic and Reconstructive Surgery, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Bruno Péault
- Department of Orthopedic Surgery and Orthopedic Hospital Research Center, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Aaron W James
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21205, USA.
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4
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Marmolejo-Garza A, Chatre L, Croteau DL, Herron-Bedoya A, Luu MDA, Bernay B, Pontin J, Bohr VA, Boddeke E, Dolga AM. Nicotinamide riboside modulates the reactive species interactome, bioenergetic status and proteomic landscape in a brain-region-specific manner. Neurobiol Dis 2024; 200:106645. [PMID: 39179121 DOI: 10.1016/j.nbd.2024.106645] [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: 07/01/2024] [Revised: 08/17/2024] [Accepted: 08/19/2024] [Indexed: 08/26/2024] Open
Abstract
Nicotinamide riboside (NR), a precursor of nicotinamide adenine dinucleotide (NAD+), has robust cognitive benefits and alleviates neuroinflammation in Alzheimer's Disease (AD) mouse models without decreasing beta-amyloid plaque pathology. Such effects may be mediated by the reactive species interactome (RSI), at the metabolome level. In this study, we employed in vitro and in vivo models of oxidative stress, aging and AD to profile the effects of NR on neuronal survival, RSI, and the whole proteome characterization of cortex and hippocampus. RSI analysis yielded a complex modulation upon NR treatment. We constructed protein co-expression networks and correlated them to NR treatment and all measured reactive species. We observed brain-area specific effects of NR on co-expressed protein modules of oxidative phosphorylation, fatty acid oxidation, and neurotransmitter regulation pathways, which correlated with RSI components. The current study contributes to the understanding of modulation of the metabolome, specifically after NR treatment in AD and how it may play disease-modifying roles.
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Affiliation(s)
- Alejandro Marmolejo-Garza
- Faculty of Science and Engineering, Department of Molecular Pharmacology, Groningen Research Institute of Pharmacy (GRIP), University of Groningen, 9713, AV, Groningen, the Netherlands; Department of Biomedical Sciences of Cells & Systems, section Molecular Neurobiology, Faculty of Medical Sciences, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Laurent Chatre
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT, UMR6030, GIP CYCERON, F-14000 Caen, France
| | - Deborah L Croteau
- Section on DNA repair, National Institute on Aging, 251 Bayview Blvd, Baltimore, MD, USA; Laboratory of Genetics and Genomics, Computational Biology and Genomics Core, National Institute on Aging, 251 Bayview Blvd, Baltimore, USA
| | - Alejandro Herron-Bedoya
- Faculty of Science and Engineering, Department of Molecular Pharmacology, Groningen Research Institute of Pharmacy (GRIP), University of Groningen, 9713, AV, Groningen, the Netherlands
| | - Minh Danh Anh Luu
- Faculty of Science and Engineering, Department of Molecular Pharmacology, Groningen Research Institute of Pharmacy (GRIP), University of Groningen, 9713, AV, Groningen, the Netherlands
| | - Benoit Bernay
- Université de Caen Normandie, US EMerode, Plateform Proteogen, F-14000 Caen, France
| | - Julien Pontin
- Université de Caen Normandie, US EMerode, Plateform Proteogen, F-14000 Caen, France
| | - Vilhelm A Bohr
- Section on DNA repair, National Institute on Aging, 251 Bayview Blvd, Baltimore, MD, USA; Center for Healthy Aging, Department of Cellular and Molecular Medicine, SUND, University of Copenhagen, 2200, Copenhagen N, Denmark; Department of Cellular and Molecular Medicine, Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark
| | - Erik Boddeke
- Department of Biomedical Sciences of Cells & Systems, section Molecular Neurobiology, Faculty of Medical Sciences, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Cellular and Molecular Medicine, Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark
| | - Amalia M Dolga
- Faculty of Science and Engineering, Department of Molecular Pharmacology, Groningen Research Institute of Pharmacy (GRIP), University of Groningen, 9713, AV, Groningen, the Netherlands.
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Zhang L, Sagan A, Qin B, Kim E, Hu B, Osmanbeyoglu HU. STAN, a computational framework for inferring spatially informed transcription factor activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.26.600782. [PMID: 38979296 PMCID: PMC11230390 DOI: 10.1101/2024.06.26.600782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Transcription factors (TFs) drive significant cellular changes in response to environmental cues and intercellular signaling. Neighboring cells influence TF activity and, consequently, cellular fate and function. Spatial transcriptomics (ST) captures mRNA expression patterns across tissue samples, enabling characterization of the local microenvironment. However, these datasets have not been fully leveraged to systematically estimate TF activity governing cell identity. Here, we present STAN ( S patially informed T ranscription factor A ctivity N etwork), a linear mixed-effects computational method that predicts spot-specific, spatially informed TF activities by integrating curated TF-target gene priors, mRNA expression, spatial coordinates, and morphological features from corresponding imaging data. We tested STAN using lymph node, breast cancer, and glioblastoma ST datasets to demonstrate its applicability by identifying TFs associated with specific cell types, spatial domains, pathological regions, and ligand‒receptor pairs. STAN augments the utility of STs to reveal the intricate interplay between TFs and spatial organization across a spectrum of cellular contexts.
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Qian J, Bao H, Shao X, Fang Y, Liao J, Chen Z, Li C, Guo W, Hu Y, Li A, Yao Y, Fan X, Cheng Y. Simulating multiple variability in spatially resolved transcriptomics with scCube. Nat Commun 2024; 15:5021. [PMID: 38866768 PMCID: PMC11169532 DOI: 10.1038/s41467-024-49445-0] [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: 09/28/2023] [Accepted: 06/03/2024] [Indexed: 06/14/2024] Open
Abstract
A pressing challenge in spatially resolved transcriptomics (SRT) is to benchmark the computational methods. A widely-used approach involves utilizing simulated data. However, biases exist in terms of the currently available simulated SRT data, which seriously affects the accuracy of method evaluation and validation. Herein, we present scCube ( https://github.com/ZJUFanLab/scCube ), a Python package for independent, reproducible, and technology-diverse simulation of SRT data. scCube not only enables the preservation of spatial expression patterns of genes in reference-based simulations, but also generates simulated data with different spatial variability (covering the spatial pattern type, the resolution, the spot arrangement, the targeted gene type, and the tissue slice dimension, etc.) in reference-free simulations. We comprehensively benchmark scCube with existing single-cell or SRT simulators, and demonstrate the utility of scCube in benchmarking spot deconvolution, gene imputation, and resolution enhancement methods in detail through three applications.
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Affiliation(s)
- Jingyang Qian
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Hudong Bao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Xin Shao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Yin Fang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, 310013, China
| | - Jie Liao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Zhuo Chen
- College of Computer Science and Technology, Zhejiang University, Hangzhou, 310013, China
| | - Chengyu Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Wenbo Guo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Yining Hu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Anyao Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Yue Yao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Xiaohui Fan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Yiyu Cheng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China.
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7
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Hou Y, Chu X, Park J, Zhu Q, Hussain M, Li Z, Madsen HB, Yang B, Wei Y, Wang Y, Fang EF, Croteau DL, Bohr VA. Urolithin A improves Alzheimer's disease cognition and restores mitophagy and lysosomal functions. Alzheimers Dement 2024; 20:4212-4233. [PMID: 38753870 PMCID: PMC11180933 DOI: 10.1002/alz.13847] [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: 11/04/2023] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Compromised autophagy, including impaired mitophagy and lysosomal function, plays pivotal roles in Alzheimer's disease (AD). Urolithin A (UA) is a gut microbial metabolite of ellagic acid that stimulates mitophagy. The effects of UA's long-term treatment of AD and mechanisms of action are unknown. METHODS We addressed these questions in three mouse models of AD with behavioral, electrophysiological, biochemical, and bioinformatic approaches. RESULTS Long-term UA treatment significantly improved learning, memory, and olfactory function in different AD transgenic mice. UA also reduced amyloid beta (Aβ) and tau pathologies and enhanced long-term potentiation. UA induced mitophagy via increasing lysosomal functions. UA improved cellular lysosomal function and normalized lysosomal cathepsins, primarily cathepsin Z, to restore lysosomal function in AD, indicating the critical role of cathepsins in UA-induced therapeutic effects on AD. CONCLUSIONS Our study highlights the importance of lysosomal dysfunction in AD etiology and points to the high translational potential of UA. HIGHLIGHTS Long-term urolithin A (UA) treatment improved learning, memory, and olfactory function in Alzheimer's disease (AD) mice. UA restored lysosomal functions in part by regulating cathepsin Z (Ctsz) protein. UA modulates immune responses and AD-specific pathophysiological pathways.
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Affiliation(s)
- Yujun Hou
- Institute for Regenerative MedicineState Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji UniversityShanghaiChina
- DNA Repair SectionNational Institute on AgingBaltimoreMarylandUSA
| | - Xixia Chu
- DNA Repair SectionNational Institute on AgingBaltimoreMarylandUSA
| | - Jae‐Hyeon Park
- DNA Repair SectionNational Institute on AgingBaltimoreMarylandUSA
| | - Qing Zhu
- Institute for Regenerative MedicineState Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji UniversityShanghaiChina
| | - Mansoor Hussain
- DNA Repair SectionNational Institute on AgingBaltimoreMarylandUSA
| | - Zhiquan Li
- Danish Center for Healthy Aging, ICMMUniversity of CopenhagenCopenhagenDenmark
| | | | - Beimeng Yang
- DNA Repair SectionNational Institute on AgingBaltimoreMarylandUSA
| | - Yong Wei
- DNA Repair SectionNational Institute on AgingBaltimoreMarylandUSA
| | - Yue Wang
- DNA Repair SectionNational Institute on AgingBaltimoreMarylandUSA
| | - Evandro F. Fang
- Department of Clinical Molecular BiologyUniversity of Oslo and Akershus University HospitalLørenskogNorway
- The Norwegian Centre on Healthy Ageing (NO‐Age)OsloAkershusNorway
| | - Deborah L. Croteau
- DNA Repair SectionNational Institute on AgingBaltimoreMarylandUSA
- Computational Biology & Genomics Core, LGGNational Institute on AgingBaltimoreMarylandUSA
| | - Vilhelm A. Bohr
- DNA Repair SectionNational Institute on AgingBaltimoreMarylandUSA
- Danish Center for Healthy Aging, ICMMUniversity of CopenhagenCopenhagenDenmark
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8
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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.
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Affiliation(s)
| | | | | | | | - Qiguan Jin
- College of Physical Education, Yangzhou University, Yangzhou 225127, China; (Y.M.); (W.S.); (Y.D.); (Y.S.)
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9
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Cao J, Li C, Cui Z, Deng S, Lei T, Liu W, Yang H, Chen P. Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery. Theranostics 2024; 14:2946-2968. [PMID: 38773973 PMCID: PMC11103497 DOI: 10.7150/thno.95908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/25/2024] [Indexed: 05/24/2024] Open
Abstract
Recent advancements in modern science have provided robust tools for drug discovery. The rapid development of transcriptome sequencing technologies has given rise to single-cell transcriptomics and single-nucleus transcriptomics, increasing the accuracy of sequencing and accelerating the drug discovery process. With the evolution of single-cell transcriptomics, spatial transcriptomics (ST) technology has emerged as a derivative approach. Spatial transcriptomics has emerged as a hot topic in the field of omics research in recent years; it not only provides information on gene expression levels but also offers spatial information on gene expression. This technology has shown tremendous potential in research on disease understanding and drug discovery. In this article, we introduce the analytical strategies of spatial transcriptomics and review its applications in novel target discovery and drug mechanism unravelling. Moreover, we discuss the current challenges and issues in this research field that need to be addressed. In conclusion, spatial transcriptomics offers a new perspective for drug discovery.
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Affiliation(s)
- Junxian Cao
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Caifeng Li
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Zhao Cui
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Shiwen Deng
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Tong Lei
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Wei Liu
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Hongjun Yang
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Peng Chen
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
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10
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Yan H, Lu C, Lan C, Wang S, Zhang W, He Z, Hu J, Ai J, Liu GH, Ma S, Zhou Y, Qu J. Degeneration Directory: a multi-omics web resource for degenerative diseases. Protein Cell 2024; 15:385-392. [PMID: 38153694 PMCID: PMC11074994 DOI: 10.1093/procel/pwad066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 12/07/2023] [Indexed: 12/29/2023] Open
Affiliation(s)
- Haoteng Yan
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu HospitalCapital Medical University, Beijing 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Beijing Municipal Geriatric Medical Research Center, XuanwuHospital, Capital Medical University, Beijing 100053, China
| | - Changfa Lu
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
| | - Chenyang Lan
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
| | - Si Wang
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu HospitalCapital Medical University, Beijing 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Beijing Municipal Geriatric Medical Research Center, XuanwuHospital, Capital Medical University, Beijing 100053, China
- The Fifth People’s Hospital of Chongqing, Chongqing 400062, China
- Aging Biomarker Consortium, Beijing 100101, China
| | - Weiqi Zhang
- Aging Biomarker Consortium, Beijing 100101, China
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China NationalCenter for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Zan He
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu HospitalCapital Medical University, Beijing 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Beijing Municipal Geriatric Medical Research Center, XuanwuHospital, Capital Medical University, Beijing 100053, China
| | - Jinghao Hu
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu HospitalCapital Medical University, Beijing 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Beijing Municipal Geriatric Medical Research Center, XuanwuHospital, Capital Medical University, Beijing 100053, China
| | - Jiaqi Ai
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu HospitalCapital Medical University, Beijing 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Beijing Municipal Geriatric Medical Research Center, XuanwuHospital, Capital Medical University, Beijing 100053, China
| | - Guang-Hui Liu
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu HospitalCapital Medical University, Beijing 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Beijing Municipal Geriatric Medical Research Center, XuanwuHospital, Capital Medical University, Beijing 100053, China
- Aging Biomarker Consortium, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Shuai Ma
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- Aging Biomarker Consortium, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Yuanchun Zhou
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- Aging Biomarker Consortium, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
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11
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Wu C, Tu T, Xie M, Wang Y, Yan B, Gong Y, Zhang J, Zhou X, Xie Z. Spatially resolved transcriptome of the aging mouse brain. Aging Cell 2024; 23:e14109. [PMID: 38372175 PMCID: PMC11113349 DOI: 10.1111/acel.14109] [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: 09/28/2023] [Revised: 01/22/2024] [Accepted: 01/29/2024] [Indexed: 02/20/2024] Open
Abstract
Brain aging is associated with cognitive decline, memory loss and many neurodegenerative disorders. The mammalian brain has distinct structural regions that perform specific functions. However, our understanding in gene expression and cell types within the context of the spatial organization of the mammalian aging brain is limited. Here we generated spatial transcriptomic maps of young and old mouse brains. We identified 27 distinguished brain spatial domains, including layer-specific subregions that are difficult to dissect individually. We comprehensively characterized spatial-specific changes in gene expression in the aging brain, particularly for isocortex, the hippocampal formation, brainstem and fiber tracts, and validated some gene expression differences by qPCR and immunohistochemistry. We identified aging-related genes and pathways that vary in a coordinated manner across spatial regions and parsed the spatial features of aging-related signals, providing important clues to understand genes with specific functions in different brain regions during aging. Combined with single-cell transcriptomics data, we characterized the spatial distribution of brain cell types. The proportion of immature neurons decreased in the DG region with aging, indicating that the formation of new neurons is blocked. Finally, we detected changes in information interactions between regions and found specific pathways were deregulated with aging, including classic signaling WNT and layer-specific signaling COLLAGEN. In summary, we established a spatial molecular atlas of the aging mouse brain (http://sysbio.gzzoc.com/Mouse-Brain-Aging/), which provides important resources and novel insights into the molecular mechanism of brain aging.
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Affiliation(s)
- Cheng Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic CenterSun Yat‐sen UniversityGuangzhouChina
| | - Tianxiang Tu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic CenterSun Yat‐sen UniversityGuangzhouChina
| | - Mingzhe Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic CenterSun Yat‐sen UniversityGuangzhouChina
| | - Yiting Wang
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, MOE Innovative Center for New Drug Development of Immune Inflammatory DiseasesInstitutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology, Eye & ENT Hospital, Fudan UniversityShanghaiChina
| | - Biao Yan
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, MOE Innovative Center for New Drug Development of Immune Inflammatory DiseasesInstitutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology, Eye & ENT Hospital, Fudan UniversityShanghaiChina
| | - Yajun Gong
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic CenterSun Yat‐sen UniversityGuangzhouChina
| | - Jiayi Zhang
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, MOE Innovative Center for New Drug Development of Immune Inflammatory DiseasesInstitutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology, Eye & ENT Hospital, Fudan UniversityShanghaiChina
| | - Xiaolai Zhou
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic CenterSun Yat‐sen UniversityGuangzhouChina
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic CenterSun Yat‐sen UniversityGuangzhouChina
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12
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Jiang W, Caruana DL, Back J, Lee FY. Unique Spatial Transcriptomic Profiling of the Murine Femoral Fracture Callus: A Preliminary Report. Cells 2024; 13:522. [PMID: 38534368 DOI: 10.3390/cells13060522] [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: 02/03/2024] [Revised: 03/07/2024] [Accepted: 03/14/2024] [Indexed: 03/28/2024] Open
Abstract
Fracture callus formation is a dynamic stage of bone activity and repair with precise, spatially localized gene expression. Metastatic breast cancer impairs fracture healing by disrupting bone homeostasis and imparting an altered genomic profile. Previous sequencing techniques such as single-cell RNA and in situ hybridization are limited by missing spatial context and low throughput, respectively. We present a preliminary approach using the Visium CytAssist spatial transcriptomics platform to provide the first spatially intact characterization of genetic expression changes within an orthopedic model of impaired fracture healing. Tissue slides prepared from BALB/c mice with or without MDA-MB-231 metastatic breast cancer cells were used. Both unsupervised clustering and histology-based annotations were performed to identify the hard callus, soft callus, and interzone for differential gene expression between the wild-type and pathological fracture model. The spatial transcriptomics platform successfully localized validated genes of the hard (Dmp1, Sost) and soft callus (Acan, Col2a1). The fibrous interzone was identified as a region of extensive genomic heterogeneity. MDA-MB-231 samples demonstrated downregulation of the critical bone matrix and structural regulators that may explain the weakened bone structure of pathological fractures. Spatial transcriptomics may represent a valuable tool in orthopedic research by providing temporal and spatial context.
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Affiliation(s)
- Will Jiang
- Department of Orthopaedics & Rehabilitation, Yale School of Medicine, 47 College Place, New Haven, CT 06510, USA
| | - Dennis L Caruana
- Department of Orthopaedics & Rehabilitation, Yale School of Medicine, 47 College Place, New Haven, CT 06510, USA
| | - Jungho Back
- Department of Orthopaedics & Rehabilitation, Yale School of Medicine, 47 College Place, New Haven, CT 06510, USA
| | - Francis Y Lee
- Department of Orthopaedics & Rehabilitation, Yale School of Medicine, 47 College Place, New Haven, CT 06510, USA
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13
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Danishuddin, Khan S, Kim JJ. Spatial transcriptomics data and analytical methods: An updated perspective. Drug Discov Today 2024; 29:103889. [PMID: 38244672 DOI: 10.1016/j.drudis.2024.103889] [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: 10/31/2023] [Revised: 01/01/2024] [Accepted: 01/15/2024] [Indexed: 01/22/2024]
Abstract
Spatial transcriptomics (ST) is a newly emerging field that integrates high-resolution imaging and transcriptomic data to enable the high-throughput analysis of the spatial localization of transcripts in diverse biological systems. The rapid progress in this field necessitates the development of innovative computational methods to effectively tackle the distinct challenges posed by the analysis of ST data. These platforms, integrating AI techniques, offer a promising avenue for understanding disease mechanisms and expediting drug discovery. Despite significant advances in the development of ST data analysis techniques, there is an ongoing need to enhance these models for increased biological relevance. In this review, we briefly discuss the ST-related databases and current deep-learning-based models for spatial transcriptome data analyses and highlight their roles and future perspectives in biomedical applications.
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Affiliation(s)
- Danishuddin
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea.
| | - Shawez Khan
- National Center for Cancer Immune Therapy (CCIT-DK), Department of Oncology, Copenhagen University Hospital, Herlev, Denmark
| | - Jong Joo Kim
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea.
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14
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Zou D, Huang X, Lan Y, Pan M, Xie J, Huang Q, Zeng J, Zou C, Pei Z, Zou C, Mao Y, Luo J. Single-cell and spatial transcriptomics reveals that PTPRG activates the m 6A methyltransferase VIRMA to block mitophagy-mediated neuronal death in Alzheimer's disease. Pharmacol Res 2024; 201:107098. [PMID: 38325728 DOI: 10.1016/j.phrs.2024.107098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/31/2024] [Accepted: 02/02/2024] [Indexed: 02/09/2024]
Abstract
Neuronal death is one of the key pathologies in Alzheimer's disease (AD). How neuronal death begins in AD is far from clear, so clarifying this process may help develop effective therapies. This study collected single-cell RNA sequencing data of 85 AD samples and 83 control samples, covering the prefrontal cortex, internal olfactory cortex, superior parietal lobe, superior frontal gyrus, caudal internal olfactory cortex, somatosensory cortex, hippocampus, superior frontal cortex and peripheral blood mononuclear cells. Additionally, spatial transcriptomic data of coronal sections from 6 AppNL-G-F AD mice and 6 control C57Bl/6 J mice were acquired. The main single-cell and spatial transcriptomics results were experimentally validated in wild type and 5 × FAD mice. We found that the microglia subpopulation Mic_PTPRG can communicate with specific types of neurons (especially excitatory ExNeu_PRKN_VIRMA and inhibitory InNeu_PRKN_VIRMA neuronal subpopulations) and cause them to express PTPRG during AD progression. Within neurons, PTPRG binds and upregulates the m6A methyltransferase VIRMA, thus inhibiting translation of PRKN mRNA to prevent the clearance of damaged mitochondria in neurons through suppressing mitophagy. As the disease progresses, the energy and nutrient metabolic pathways in neurons are reprogrammed, leading to their death. Consistently, we determined that PTPTRG can physically interact with VIRMA in mouse brains and PRKN is significantly upregulated in 5 × FAD mouse brain. Altogether, our findings demonstrate that PTPRG activates the m6A methyltransferase VIRMA to block mitophagy-mediated neuronal death in AD, which is a potential pathway, through which microglia and neuronal PTPRG modify neuronal connections in the brain during AD progression.
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Affiliation(s)
- Donghua Zou
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China.
| | - Xiaohua Huang
- Department of Neurology, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China
| | - Yating Lan
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Mika Pan
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Jieqiong Xie
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Qi Huang
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Jingyi Zeng
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Chun Zou
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Zifei Pei
- Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Cuihua Zou
- Guangxi Medical University Cancer Hospital, Nanning 530022, Guangxi, China.
| | - Yingwei Mao
- Department of Biology, Pennsylvania State University, University Park, PA 16802, USA.
| | - Jiefeng Luo
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China.
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15
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Anton PE, Rutt LN, Kaufman ML, Busquet N, Kovacs EJ, McCullough RL. Binge ethanol exposure in advanced age elevates neuroinflammation and early indicators of neurodegeneration and cognitive impairment in female mice. Brain Behav Immun 2024; 116:303-316. [PMID: 38151165 PMCID: PMC11446185 DOI: 10.1016/j.bbi.2023.12.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 12/29/2023] Open
Abstract
Binge drinking is rising among aged adults (>65 years of age), however the contribution of alcohol misuse to neurodegenerative disease development is not well understood. Both advanced age and repeated binge ethanol exposure increase neuroinflammation, which is an important component of neurodegeneration and cognitive dysfunction. Surprisingly, the distinct effects of binge ethanol exposure on neuroinflammation and associated degeneration in the aged brain have not been well characterized. Here, we establish a model of intermittent binge ethanol exposure in young and aged female mice to investigate the effects of advanced age and binge ethanol on these outcomes. Following intermittent binge ethanol exposure, expression of pro-inflammatory mediators (tnf-α, il-1β, ccl2) was distinctly increased in isolated hippocampal tissue by the combination of advanced age and ethanol. Binge ethanol exposure also increased measures of senescence, the nod like receptor pyrin domain containing 3 (NLRP3) inflammasome, and microglia reactivity in the brains of aged mice compared to young. Binge ethanol exposure also promoted neuropathology in the hippocampus of aged mice, including tau hyperphosphorylation and neuronal death. We further identified advanced age-related deficits in contextual memory that were further negatively impacted by ethanol exposure. These data suggest binge drinking superimposed with advanced age promotes early markers of neurodegenerative disease development and cognitive decline, which may be driven by heightened neuroinflammatory responses to ethanol. Taken together, we propose this novel exposure model of intermittent binge ethanol can be used to identify therapeutic targets to prevent advanced age- and ethanol-related neurodegeneration.
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Affiliation(s)
- Paige E Anton
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, United States; Alcohol Research Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Lauren N Rutt
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, United States; Alcohol Research Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Michael L Kaufman
- RNA Bioscience Initiative, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Nicolas Busquet
- Animal Behavior and In Vivo Neurophysiology Core, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Elizabeth J Kovacs
- GI and Liver Innate Immune Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States; Division of GI Trauma and Endocrine Surgery, Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States; Alcohol Research Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Rebecca L McCullough
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, United States; GI and Liver Innate Immune Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States; Alcohol Research Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
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16
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Chen C, Kim HJ, Yang P. Evaluating spatially variable gene detection methods for spatial transcriptomics data. Genome Biol 2024; 25:18. [PMID: 38225676 PMCID: PMC10789051 DOI: 10.1186/s13059-023-03145-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 12/14/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND The identification of genes that vary across spatial domains in tissues and cells is an essential step for spatial transcriptomics data analysis. Given the critical role it serves for downstream data interpretations, various methods for detecting spatially variable genes (SVGs) have been proposed. However, the lack of benchmarking complicates the selection of a suitable method. RESULTS Here we systematically evaluate a panel of popular SVG detection methods on a large collection of spatial transcriptomics datasets, covering various tissue types, biotechnologies, and spatial resolutions. We address questions including whether different methods select a similar set of SVGs, how reliable is the reported statistical significance from each method, how accurate and robust is each method in terms of SVG detection, and how well the selected SVGs perform in downstream applications such as clustering of spatial domains. Besides these, practical considerations such as computational time and memory usage are also crucial for deciding which method to use. CONCLUSIONS Our study evaluates the performance of each method from multiple aspects and highlights the discrepancy among different methods when calling statistically significant SVGs across diverse datasets. Overall, our work provides useful considerations for choosing methods for identifying SVGs and serves as a key reference for the future development of related methods.
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Affiliation(s)
- Carissa Chen
- Computational Systems Biology Group, Faculty of Medicine and Health, Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia
| | - Hani Jieun Kim
- Computational Systems Biology Group, Faculty of Medicine and Health, Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia.
| | - Pengyi Yang
- Computational Systems Biology Group, Faculty of Medicine and Health, Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia.
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia.
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
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17
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Chen Y, Yang S, Yu K, Zhang J, Wu M, Zheng Y, Zhu Y, Dai J, Wang C, Zhu X, Dai Y, Sun Y, Wu T, Wang S. Spatial omics: An innovative frontier in aging research. Ageing Res Rev 2024; 93:102158. [PMID: 38056503 DOI: 10.1016/j.arr.2023.102158] [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: 08/28/2023] [Revised: 11/25/2023] [Accepted: 12/01/2023] [Indexed: 12/08/2023]
Abstract
Disentangling the impact of aging on health and disease has become critical as population aging progresses rapidly. Studying aging at the molecular level is complicated by the diverse aging profiles and dynamics. However, the examination of cellular states within aging tissues in situ is hampered by the lack of high-resolution spatial data. Emerging spatial omics technologies facilitate molecular and spatial analysis of tissues, providing direct access to precise information on various functional regions and serving as a favorable tool for unraveling the heterogeneity of aging. In this review, we summarize the recent advances in spatial omics application in multi-organ aging research, which has enhanced the understanding of aging mechanisms from multiple standpoints. We also discuss the main challenges in spatial omics research to date, the opportunities for further developing the technology, and the potential applications of spatial omics in aging and aging-related diseases.
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Affiliation(s)
- Ying Chen
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Shuhao Yang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Kaixu Yu
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jinjin Zhang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Meng Wu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Yongqiang Zheng
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Centre, Sun Yat-sen University, Guangzhou, China
| | - Yun Zhu
- Department of Internal Medicine, Southern Illinois University School of Medicine, 801 N. Rutledge, P.O. Box 19628, Springfield, IL 62702, USA
| | - Jun Dai
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Chunyan Wang
- College of Science & Engineering Jinan University, Guangzhou, China
| | - Xiaoran Zhu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Yun Dai
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Yunhong Sun
- Hubei Key Laboratory of Food Nutrition and Safety, MOE Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tong Wu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China.
| | - Shixuan Wang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China.
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18
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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.
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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
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19
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Curry AR, Ooi L, Matosin N. How spatial omics approaches can be used to map the biological impacts of stress in psychiatric disorders: a perspective, overview and technical guide. Stress 2024; 27:2351394. [PMID: 38752853 DOI: 10.1080/10253890.2024.2351394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 04/29/2024] [Indexed: 05/21/2024] Open
Abstract
Exposure to significant levels of stress and trauma throughout life is a leading risk factor for the development of major psychiatric disorders. Despite this, we do not have a comprehensive understanding of the mechanisms that explain how stress raises psychiatric disorder risk. Stress in humans is complex and produces variable molecular outcomes depending on the stress type, timing, and duration. Deciphering how stress increases disorder risk has consequently been challenging to address with the traditional single-target experimental approaches primarily utilized to date. Importantly, the molecular processes that occur following stress are not fully understood but are needed to find novel treatment targets. Sequencing-based omics technologies, allowing for an unbiased investigation of physiological changes induced by stress, are rapidly accelerating our knowledge of the molecular sequelae of stress at a single-cell resolution. Spatial multi-omics technologies are now also emerging, allowing for simultaneous analysis of functional molecular layers, from epigenome to proteome, with anatomical context. The technology has immense potential to transform our understanding of how disorders develop, which we believe will significantly propel our understanding of how specific risk factors, such as stress, contribute to disease course. Here, we provide our perspective of how we believe these technologies will transform our understanding of the neurobiology of stress, and also provided a technical guide to assist molecular psychiatry and stress researchers who wish to implement spatial omics approaches in their own research. Finally, we identify potential future directions using multi-omics technology in stress research.
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Affiliation(s)
- Amber R Curry
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia
- Molecular Horizons, School of Chemistry and Molecular Bioscience, Faculty of Science Medicine and Health, University of Wollongong, Wollongong, NSW, Australia
| | - Lezanne Ooi
- Molecular Horizons, School of Chemistry and Molecular Bioscience, Faculty of Science Medicine and Health, University of Wollongong, Wollongong, NSW, Australia
| | - Natalie Matosin
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia
- Molecular Horizons, School of Chemistry and Molecular Bioscience, Faculty of Science Medicine and Health, University of Wollongong, Wollongong, NSW, Australia
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20
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Wang T, Song Z, Zhao X, Wu Y, Wu L, Haghparast A, Wu H. Spatial transcriptomic analysis of the mouse brain following chronic social defeat stress. EXPLORATION (BEIJING, CHINA) 2023; 3:20220133. [PMID: 38264685 PMCID: PMC10742195 DOI: 10.1002/exp.20220133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 09/03/2023] [Indexed: 01/25/2024]
Abstract
Depression is a highly prevalent and disabling mental disorder, involving numerous genetic changes that are associated with abnormal functions in multiple regions of the brain. However, there is little transcriptomic-wide characterization of chronic social defeat stress (CSDS) to comprehensively compare the transcriptional changes in multiple brain regions. Spatial transcriptomics (ST) was used to reveal the spatial difference of gene expression in the control, resilient (RES) and susceptible (SUS) mouse brains, and annotated eight anatomical brain regions and six cell types. The gene expression profiles uncovered that CSDS leads to gene synchrony changes in different brain regions. Then it was identified that inhibitory neurons and synaptic functions in multiple regions were primarily affected by CSDS. The brain regions Hippocampus (HIP), Isocortex, and Amygdala (AMY) present more pronounced transcriptional changes in genes associated with depressive psychiatric disorders than other regions. Signalling communication between these three brain regions may play a critical role in susceptibility to CSDS. Taken together, this study provides important new insights into CSDS susceptibility at the ST level, which offers a new approach for understanding and treating depression.
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Affiliation(s)
- Ting Wang
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Zhihong Song
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Xin Zhao
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Yan Wu
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Liying Wu
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Abbas Haghparast
- Neuroscience Research Center, School of MedicineShahid Beheshti University of Medical SciencesTehranIran
| | - Haitao Wu
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
- Key Laboratory of Neuroregeneration, Co‐innovation Center of NeuroregenerationNantong UniversityNantongChina
- Chinese Institute for Brain ResearchBeijingChina
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21
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Zhu S, Shi J, Jin Q, Zhang Y, Zhang R, Chen X, Wang C, Shi T, Li L. Mitochondrial dysfunction following repeated administration of alprazolam causes attenuation of hippocampus-dependent memory consolidation in mice. Aging (Albany NY) 2023; 15:10428-10452. [PMID: 37801512 PMCID: PMC10599724 DOI: 10.18632/aging.205087] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 09/12/2023] [Indexed: 10/08/2023]
Abstract
The frequently repeated administration of alprazolam (Alp), a highly effective benzodiazepine sedative-hypnotic agent, in anxiety, insomnia, and other diseases is closely related to many negative adverse reactions that are mainly manifested as memory impairment. However, the exact molecular mechanisms underlying these events are poorly understood. Therefore, we conducted a proteomic analysis on the hippocampus in mice that received repeated administration of Alp for 24 days. A total of 439 significantly differentially expressed proteins (DEPs) were identified in mice with repeated administration of Alp compared to the control group, and the GO and KEGG analysis revealed the enrichment of terms related to mitochondrial function, cycle, mitophagy and cognition. In vitro experiments have shown that Alp may affect the cell cycle, reduce the mitochondrial membrane potential (MMP) to induce apoptosis in HT22 cells, and affect the progress of mitochondrial energy metabolism and morphology in the hippocampal neurons. Furthermore, in vivo behavioral experiments including IntelliCage System (ICS) and nover object recognition (NOR), hippocampal neuronal pathological changes with HE staining, and the expression levels of brain-deprived neuron factor (BDNF) with immunohistochemistry showed a significant decrease in memory consolidation in mice with repeated administration of Alp, which could be rescued by the co-administration of the mitochondrial protector NSI-189. To the best of our knowledge, this is the first study to identify a link between repeated administration of Alp and mitochondrial dysfunction and that mitochondrial impairment directly causes the attenuation of memory consolidation in mice.
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Affiliation(s)
- Siqing Zhu
- State Key Laboratory of NBC Protection for Civilian, Changping, Beijing 102205, China
| | - Jingjing Shi
- State Key Laboratory of NBC Protection for Civilian, Changping, Beijing 102205, China
| | - Qian Jin
- State Key Laboratory of NBC Protection for Civilian, Changping, Beijing 102205, China
| | - Yi Zhang
- State Key Laboratory of NBC Protection for Civilian, Changping, Beijing 102205, China
| | - Ruihua Zhang
- State Key Laboratory of NBC Protection for Civilian, Changping, Beijing 102205, China
| | - Xuejun Chen
- State Key Laboratory of NBC Protection for Civilian, Changping, Beijing 102205, China
| | - Chen Wang
- State Key Laboratory of NBC Protection for Civilian, Changping, Beijing 102205, China
| | - Tong Shi
- State Key Laboratory of NBC Protection for Civilian, Changping, Beijing 102205, China
| | - Liqin Li
- State Key Laboratory of NBC Protection for Civilian, Changping, Beijing 102205, China
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22
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Seal S, Bitler BG, Ghosh D. SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data. PLoS Genet 2023; 19:e1010983. [PMID: 37862362 PMCID: PMC10619839 DOI: 10.1371/journal.pgen.1010983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 11/01/2023] [Accepted: 09/19/2023] [Indexed: 10/22/2023] Open
Abstract
In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms uncovering interesting biological insights.
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Affiliation(s)
- Souvik Seal
- Department of Public Health Sciences, School of Medicine, Medical University of South Carolina, Charleston, South Carolina, United States of America
| | - Benjamin G. Bitler
- Department of Obstetrics and Gynecology, School of Medicine, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado, United States of America
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23
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Kwon SH, Parthiban S, Tippani M, Divecha HR, Eagles NJ, Lobana JS, Williams SR, Mak M, Bharadwaj RA, Kleinman JE, Hyde TM, Page SC, Hicks SC, Martinowich K, Maynard KR, Collado-Torres L. Influence of Alzheimer's disease related neuropathology on local microenvironment gene expression in the human inferior temporal cortex. GEN BIOTECHNOLOGY 2023; 2:399-417. [PMID: 39329069 PMCID: PMC11426291 DOI: 10.1089/genbio.2023.0019] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/28/2024]
Abstract
Neuropathological lesions in the brains of individuals affected with neurodegenerative disorders are hypothesized to trigger molecular and cellular processes that disturb homeostasis of local microenvironments. Here, we applied the 10x Genomics Visium Spatial Proteogenomics (Visium-SPG) platform, which couples spatial gene expression with immunofluorescence protein co-detection, to evaluate its ability to quantify changes in spatial gene expression with respect to amyloid-β (Aβ) and hyperphosphorylated tau (pTau) pathology in post-mortem human brain tissue from individuals with Alzheimer's disease (AD). We identified transcriptomic signatures associated with proximity to Aβ in the human inferior temporal cortex (ITC) during late-stage AD, which we further investigated at cellular resolution with combined immunofluorescence and single molecule fluorescent in situ hybridization (smFISH). The study provides a data analysis workflow for Visium-SPG, and the data represent a proof-of-principal for the power of multi-omic profiling in identifying changes in molecular dynamics that are spatially-associated with pathology in the human brain. We provide the scientific community with web-based, interactive resources to access the datasets of the spatially resolved AD-related transcriptomes at https://research.libd.org/Visium_SPG_AD/.
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Affiliation(s)
- Sang Ho Kwon
- The Biochemistry, Cellular, and Molecular Biology Graduate Program, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Sowmya Parthiban
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Madhavi Tippani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Heena R. Divecha
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Nicholas J. Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Jashandeep S. Lobana
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | | | | | - Rahul A. Bharadwaj
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Stephanie C. Page
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- The Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Kristen R. Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
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24
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Charitakis N, Salim A, Piers AT, Watt KI, Porrello ER, Elliott DA, Ramialison M. Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods. Genome Biol 2023; 24:209. [PMID: 37723583 PMCID: PMC10506280 DOI: 10.1186/s13059-023-03045-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 08/21/2023] [Indexed: 09/20/2023] Open
Abstract
Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing number of published tools designed to define SVG sets currently lack benchmarking methods to accurately assess performance. This study compares results of 6 purpose-built packages for SVG identification across 9 public and 5 simulated datasets and highlights discrepancies between results. Additional tools for generation of simulated data and development of benchmarking methods are required to improve methods for identifying SVGs.
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Affiliation(s)
- Natalie Charitakis
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
| | - Agus Salim
- Melbourne School of Population and Global Health, University of Melbourne, Bouverie St, Carlton, VIC, 3053, Australia
- School of Mathematics and Statistics, University of Melbourne, Swanston Street, Parkville, VIC, 3010, Australia
| | - Adam T Piers
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Melbourne Centre for Cardiovascular Genomics and Regenerative Medicine, The Royal Children's Hospital, Melbourne, VIC, 3052, Australia
| | - Kevin I Watt
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Department of Anatomy and Physiology, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia
- Department of Diabetes, Monash University, Alfred Centre, Commercial Road, Melbourne, VIC, 3004, Australia
| | - Enzo R Porrello
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Anatomy and Physiology, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
| | - David A Elliott
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
| | - Mirana Ramialison
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
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25
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O'Connor LM, O'Connor BA, Lim SB, Zeng J, Lo CH. Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective. J Pharm Anal 2023; 13:836-850. [PMID: 37719197 PMCID: PMC10499660 DOI: 10.1016/j.jpha.2023.06.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 09/19/2023] Open
Abstract
Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.
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Affiliation(s)
- Lance M. O'Connor
- College of Biological Sciences, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Blake A. O'Connor
- School of Pharmacy, University of Wisconsin, Madison, WI, 53705, USA
| | - Su Bin Lim
- Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon, 16499, South Korea
| | - Jialiu Zeng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Chih Hung Lo
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
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26
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Fangma Y, Liu M, Liao J, Chen Z, Zheng Y. Dissecting the brain with spatially resolved multi-omics. J Pharm Anal 2023; 13:694-710. [PMID: 37577383 PMCID: PMC10422112 DOI: 10.1016/j.jpha.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 08/15/2023] Open
Abstract
Recent studies have highlighted spatially resolved multi-omics technologies, including spatial genomics, transcriptomics, proteomics, and metabolomics, as powerful tools to decipher the spatial heterogeneity of the brain. Here, we focus on two major approaches in spatial transcriptomics (next-generation sequencing-based technologies and image-based technologies), and mass spectrometry imaging technologies used in spatial proteomics and spatial metabolomics. Furthermore, we discuss their applications in neuroscience, including building the brain atlas, uncovering gene expression patterns of neurons for special behaviors, deciphering the molecular basis of neuronal communication, and providing a more comprehensive explanation of the molecular mechanisms underlying central nervous system disorders. However, further efforts are still needed toward the integrative application of multi-omics technologies, including the real-time spatial multi-omics analysis in living cells, the detailed gene profile in a whole-brain view, and the combination of functional verification.
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Affiliation(s)
- Yijia Fangma
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Mengting Liu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Jie Liao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhong Chen
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yanrong Zheng
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
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27
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Park HE, Jo SH, Lee RH, Macks CP, Ku T, Park J, Lee CW, Hur JK, Sohn CH. Spatial Transcriptomics: Technical Aspects of Recent Developments and Their Applications in Neuroscience and Cancer Research. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2206939. [PMID: 37026425 PMCID: PMC10238226 DOI: 10.1002/advs.202206939] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 03/10/2023] [Indexed: 06/04/2023]
Abstract
Spatial transcriptomics is a newly emerging field that enables high-throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems. By transitioning from conventional biological studies to "in situ" biology, spatial transcriptomics can provide transcriptome-scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and discussions regarding future directions of spatial multi-omics and their expanding roles in biomedical applications are emphasized.
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Affiliation(s)
- Han-Eol Park
- Center for Nanomedicine, Institute for Basic Science, Yonsei University, Seoul, 03722, Republic of Korea
- Graduate Program in Nanobiomedical Engineering, Advanced Science Institute, Yonsei University, Seoul, 03722, Republic of Korea
- School of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Song Hyun Jo
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Rosalind H Lee
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea
| | - Christian P Macks
- Center for Nanomedicine, Institute for Basic Science, Yonsei University, Seoul, 03722, Republic of Korea
- Graduate Program in Nanobiomedical Engineering, Advanced Science Institute, Yonsei University, Seoul, 03722, Republic of Korea
| | - Taeyun Ku
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Jihwan Park
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea
| | - Chung Whan Lee
- Department of Chemistry, Gachon University, Seongnam, Gyeonggi-do, 13120, Republic of Korea
| | - Junho K Hur
- Department of Genetics, College of Medicine, Hanyang University, Seoul, 04763, Republic of Korea
| | - Chang Ho Sohn
- Center for Nanomedicine, Institute for Basic Science, Yonsei University, Seoul, 03722, Republic of Korea
- Graduate Program in Nanobiomedical Engineering, Advanced Science Institute, Yonsei University, Seoul, 03722, Republic of Korea
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28
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Paul BD, Pieper AA. Protective Roles of Hydrogen Sulfide in Alzheimer's Disease and Traumatic Brain Injury. Antioxidants (Basel) 2023; 12:antiox12051095. [PMID: 37237961 DOI: 10.3390/antiox12051095] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 05/06/2023] [Accepted: 05/09/2023] [Indexed: 05/28/2023] Open
Abstract
The gaseous signaling molecule hydrogen sulfide (H2S) critically modulates a plethora of physiological processes across evolutionary boundaries. These include responses to stress and other neuromodulatory effects that are typically dysregulated in aging, disease, and injury. H2S has a particularly prominent role in modulating neuronal health and survival under both normal and pathologic conditions. Although toxic and even fatal at very high concentrations, emerging evidence has also revealed a pronounced neuroprotective role for lower doses of endogenously generated or exogenously administered H2S. Unlike traditional neurotransmitters, H2S is a gas and, therefore, is unable to be stored in vesicles for targeted delivery. Instead, it exerts its physiologic effects through the persulfidation/sulfhydration of target proteins on reactive cysteine residues. Here, we review the latest discoveries on the neuroprotective roles of H2S in Alzheimer's disease (AD) and traumatic brain injury, which is one the greatest risk factors for AD.
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Affiliation(s)
- Bindu D Paul
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Lieber Institute for Brain Development, Baltimore, MD 21205, USA
| | - Andrew A Pieper
- Brain Health Medicines Center, Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
- Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA
- Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA
- Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
- Department of Pathology, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
- Department of Neuroscience, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
- Translational Therapeutics Core, Cleveland Alzheimer's Disease Research Center, Cleveland, OH 44106, USA
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29
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Mohammadi E, Chojnowska K, Bieńkowski M, Kostecka A, Koczkowska M, Żmijewski MA, Jąkalski M, Ingelsson M, Filipowicz N, Olszewski P, Davies H, Wierzbicka JM, Hyman BT, Dumanski JP, Piotrowski A, Mieczkowski J. Size matters: the impact of nucleus size on results from spatial transcriptomics. J Transl Med 2023; 21:270. [PMID: 37081484 PMCID: PMC10120157 DOI: 10.1186/s12967-023-04129-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 04/12/2023] [Indexed: 04/22/2023] Open
Abstract
BACKGROUND Visium Spatial Gene Expression (ST) is a method combining histological spatial information with transcriptomics profiles directly from tissue sections. The use of spatial information has made it possible to discover new modes of gene expression regulations. However, in the ST experiment, the nucleus size of cells may exceed the thickness of a tissue slice. This may, in turn, negatively affect comprehensive capturing the transcriptomics profile in a single slice, especially for tissues having large differences in the size of nuclei. METHODS Here, we defined the effect of Consecutive Slices Data Integration (CSDI) on unveiling accurate spot clustering and deconvolution of spatial transcriptomic spots in human postmortem brains. By considering the histological information as reference, we assessed the improvement of unsupervised clustering and single nuclei RNA-seq and ST data integration before and after CSDI. RESULTS Apart from the escalated number of defined clusters representing neuronal layers, the pattern of clusters in consecutive sections was concordant only after CSDI. Besides, the assigned cell labels to spots matches the histological pattern of tissue sections after CSDI. CONCLUSION CSDI can be applied to investigate consecutive sections studied with ST in the human cerebral cortex, avoiding misinterpretation of spot clustering and annotation, increasing accuracy of cell recognition as well as improvement in uncovering the layers of grey matter in the human brain.
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Affiliation(s)
- Elyas Mohammadi
- 3P-Medicine Laboratory, Medical University of Gdańsk, 80 210, Gdańsk, Poland
| | | | - Michał Bieńkowski
- Department of Pathomorphology, Medical University of Gdańsk, 80 210, Gdańsk, Poland
| | - Anna Kostecka
- 3P-Medicine Laboratory, Medical University of Gdańsk, 80 210, Gdańsk, Poland
| | | | - Michał A Żmijewski
- Department of Histology, Medical University of Gdańsk, 80 210, Gdańsk, Poland
| | - Marcin Jąkalski
- 3P-Medicine Laboratory, Medical University of Gdańsk, 80 210, Gdańsk, Poland
| | - Martin Ingelsson
- Department of Public Health and Caring Sciences, Geriatrics, Rudbeck Laboratory, Uppsala University, 751 85, Uppsala, Sweden
- Krembil Brain Institute, University Health Network, Toronto, ON, M5G 2C4, Canada
- Department of Medicine and Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Natalia Filipowicz
- 3P-Medicine Laboratory, Medical University of Gdańsk, 80 210, Gdańsk, Poland
| | - Paweł Olszewski
- 3P-Medicine Laboratory, Medical University of Gdańsk, 80 210, Gdańsk, Poland
| | - Hanna Davies
- Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, 751 85, Uppsala, Sweden
| | | | - Bradley T Hyman
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA
- Massachusetts Alzheimer's Disease Research Center, Charlestown, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Jan P Dumanski
- 3P-Medicine Laboratory, Medical University of Gdańsk, 80 210, Gdańsk, Poland
- Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, 751 85, Uppsala, Sweden
| | | | - Jakub Mieczkowski
- 3P-Medicine Laboratory, Medical University of Gdańsk, 80 210, Gdańsk, Poland.
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30
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Seal S, Bitler BG, Ghosh D. SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.23.533980. [PMID: 36993287 PMCID: PMC10055313 DOI: 10.1101/2023.03.23.533980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms revealing interesting biological insights.
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Affiliation(s)
- Souvik Seal
- Department of Public Health Sciences, School of Medicine, Medical University of South Carolina, Charleston, USA
| | - Benjamin G. Bitler
- Department of Obstetrics and Gynecology, School of Medicine, University of Colorado Denver - Anschutz Medical Campus, Aurora, USA
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver - Anschutz Medical Campus, Aurora, USA
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31
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Lee S, Devanney NA, Golden LR, Smith CT, Schwartz JL, Walsh AE, Clarke HA, Goulding DS, Allenger EJ, Morillo-Segovia G, Friday CM, Gorman AA, Hawkinson TR, MacLean SM, Williams HC, Sun RC, Morganti JM, Johnson LA. APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge. Cell Rep 2023; 42:112196. [PMID: 36871219 PMCID: PMC10117631 DOI: 10.1016/j.celrep.2023.112196] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 11/29/2022] [Accepted: 02/14/2023] [Indexed: 03/06/2023] Open
Abstract
The E4 allele of Apolipoprotein E (APOE) is associated with both metabolic dysfunction and a heightened pro-inflammatory response: two findings that may be intrinsically linked through the concept of immunometabolism. Here, we combined bulk, single-cell, and spatial transcriptomics with cell-specific and spatially resolved metabolic analyses in mice expressing human APOE to systematically address the role of APOE across age, neuroinflammation, and AD pathology. RNA sequencing (RNA-seq) highlighted immunometabolic changes across the APOE4 glial transcriptome, specifically in subsets of metabolically distinct microglia enriched in the E4 brain during aging or following an inflammatory challenge. E4 microglia display increased Hif1α expression and a disrupted tricarboxylic acid (TCA) cycle and are inherently pro-glycolytic, while spatial transcriptomics and mass spectrometry imaging highlight an E4-specific response to amyloid that is characterized by widespread alterations in lipid metabolism. Taken together, our findings emphasize a central role for APOE in regulating microglial immunometabolism and provide valuable, interactive resources for discovery and validation research.
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Affiliation(s)
- Sangderk Lee
- Sanders Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA
| | - Nicholas A Devanney
- Department of Physiology, University of Kentucky, Lexington, KY 40536, USA; Sanders Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA
| | - Lesley R Golden
- Department of Physiology, University of Kentucky, Lexington, KY 40536, USA
| | - Cathryn T Smith
- Department of Physiology, University of Kentucky, Lexington, KY 40536, USA
| | - James L Schwartz
- Sanders Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA
| | - Adeline E Walsh
- Department of Physiology, University of Kentucky, Lexington, KY 40536, USA
| | - Harrison A Clarke
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536, USA; Department of Biochemistry & Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA; Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, FL, USA
| | - Danielle S Goulding
- Sanders Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA
| | | | | | - Cassi M Friday
- Department of Physiology, University of Kentucky, Lexington, KY 40536, USA
| | - Amy A Gorman
- Sanders Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA
| | - Tara R Hawkinson
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536, USA; Department of Biochemistry & Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA; Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, FL, USA
| | - Steven M MacLean
- Department of Physiology, University of Kentucky, Lexington, KY 40536, USA
| | - Holden C Williams
- Department of Physiology, University of Kentucky, Lexington, KY 40536, USA
| | - Ramon C Sun
- Sanders Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA; Department of Neuroscience, University of Kentucky, Lexington, KY 40536, USA; Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA; Department of Biochemistry & Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA; Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, FL, USA
| | - Josh M Morganti
- Sanders Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA; Department of Neuroscience, University of Kentucky, Lexington, KY 40536, USA.
| | - Lance A Johnson
- Department of Physiology, University of Kentucky, Lexington, KY 40536, USA; Sanders Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA.
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32
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Piol D, Robberechts T, Da Cruz S. Lost in local translation: TDP-43 and FUS in axonal/neuromuscular junction maintenance and dysregulation in amyotrophic lateral sclerosis. Neuron 2023; 111:1355-1380. [PMID: 36963381 DOI: 10.1016/j.neuron.2023.02.028] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/21/2022] [Accepted: 02/16/2023] [Indexed: 03/26/2023]
Abstract
Key early features of amyotrophic lateral sclerosis (ALS) are denervation of neuromuscular junctions and axonal degeneration. Motor neuron homeostasis relies on local translation through controlled regulation of axonal mRNA localization, transport, and stability. Yet the composition of the local transcriptome, translatome (mRNAs locally translated), and proteome during health and disease remains largely unexplored. This review covers recent discoveries on axonal translation as a critical mechanism for neuronal maintenance/survival. We focus on two RNA binding proteins, transactive response DNA binding protein-43 (TDP-43) and fused in sarcoma (FUS), whose mutations cause ALS and frontotemporal dementia (FTD). Emerging evidence points to their essential role in the maintenance of axons and synapses, including mRNA localization, transport, and local translation, and whose dysfunction may contribute to ALS. Finally, we describe recent advances in omics-based approaches mapping compartment-specific local RNA and protein compositions, which will be invaluable to elucidate fundamental local processes and identify key targets for therapy development.
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Affiliation(s)
- Diana Piol
- VIB-KU Leuven Center for Brain and Disease Research, Department of Neurosciences, KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Tessa Robberechts
- VIB-KU Leuven Center for Brain and Disease Research, Department of Neurosciences, KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Sandrine Da Cruz
- VIB-KU Leuven Center for Brain and Disease Research, Department of Neurosciences, KU Leuven, Leuven Brain Institute, Leuven, Belgium.
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33
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Fan Z, Luo Y, Lu H, Wang T, Feng Y, Zhao W, Kim P, Zhou X. SPASCER: spatial transcriptomics annotation at single-cell resolution. Nucleic Acids Res 2023; 51:D1138-D1149. [PMID: 36243975 PMCID: PMC9825565 DOI: 10.1093/nar/gkac889] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/21/2022] [Accepted: 10/13/2022] [Indexed: 01/30/2023] Open
Abstract
In recent years, the explosive growth of spatial technologies has enabled the characterization of spatial heterogeneity of tissue architectures. Compared to traditional sequencing, spatial transcriptomics reserves the spatial information of each captured location and provides novel insights into diverse spatially related biological contexts. Even though two spatial transcriptomics databases exist, they provide limited analytical information. Information such as spatial heterogeneity of genes and cells, cell-cell communication activities in space, and the cell type compositions in the microenvironment are critical clues to unveil the mechanism of tumorigenesis and embryo differentiation. Therefore, we constructed a new spatial transcriptomics database, named SPASCER (https://ccsm.uth.edu/SPASCER), designed to help understand the heterogeneity of tissue organizations, region-specific microenvironment, and intercellular interactions across tissue architectures at multiple levels. SPASCER contains datasets from 43 studies, including 1082 sub-datasets from 16 organ types across four species. scRNA-seq was integrated to deconvolve/map spatial transcriptomics, and processed with spatial cell-cell interaction, gene pattern and pathway enrichment analysis. Cell-cell interactions and gene regulation network of scRNA-seq from matched spatial transcriptomics were performed as well. The application of SPASCER will provide new insights into tissue architecture and a solid foundation for the mechanistic understanding of many biological processes in healthy and diseased tissues.
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Affiliation(s)
- Zhiwei Fan
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Yangyang Luo
- West China Hospital, Sichuan University, Chengdu 610041, China
| | - Huifen Lu
- West China Hospital, Sichuan University, Chengdu 610041, China
| | - Tiangang Wang
- School of Life Science and Technology, Xidian University, Xi’an 710126, China
| | - YuZhou Feng
- West China Hospital, Sichuan University, Chengdu 610041, China
| | - Weiling Zhao
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Pora Kim
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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34
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Molecular subtypes of ALS are associated with differences in patient prognosis. Nat Commun 2023; 14:95. [PMID: 36609402 PMCID: PMC9822908 DOI: 10.1038/s41467-022-35494-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 12/06/2022] [Indexed: 01/09/2023] Open
Abstract
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease with poorly understood clinical heterogeneity, underscored by significant differences in patient age at onset, symptom progression, therapeutic response, disease duration, and comorbidity presentation. We perform a patient stratification analysis to better understand the variability in ALS pathology, utilizing postmortem frontal and motor cortex transcriptomes derived from 208 patients. Building on the emerging role of transposable element (TE) expression in ALS, we consider locus-specific TEs as distinct molecular features during stratification. Here, we identify three unique molecular subtypes in this ALS cohort, with significant differences in patient survival. These results suggest independent disease mechanisms drive some of the clinical heterogeneity in ALS.
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35
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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.
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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,
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36
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Farvadi F, Hashemi F, Amini A, Alsadat Vakilinezhad M, Raee MJ. Early Diagnosis of Alzheimer's Disease with Blood Test; Tempting but Challenging. INTERNATIONAL JOURNAL OF MOLECULAR AND CELLULAR MEDICINE 2023; 12:172-210. [PMID: 38313372 PMCID: PMC10837916 DOI: 10.22088/ijmcm.bums.12.2.172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 11/25/2023] [Accepted: 12/13/2023] [Indexed: 02/06/2024]
Abstract
The increasing prevalence of Alzheimer's disease (AD) has led to a health crisis. According to official statistics, more than 55 million people globally have AD or other types of dementia, making it the sixth leading cause of death. It is still difficult to diagnose AD and there is no definitive diagnosis yet; post-mortem autopsy is still the only definite method. Moreover, clinical manifestations occur very late in the course of disease progression; therefore, profound irreversible changes have already occurred when the disease manifests. Studies have shown that in the preclinical stage of AD, changes in some biomarkers are measurable prior to any neurological damage or other symptoms. Hence, creating a reliable, fast, and affordable method capable of detecting AD in early stage has attracted the most attention. Seeking clinically applicable, inexpensive, less invasive, and much more easily accessible biomarkers for early diagnosis of AD, blood-based biomarkers (BBBs) seem to be an ideal option. This review is an inclusive report of BBBs that have been shown to be altered in the course of AD progression. The aim of this report is to provide comprehensive insight into the research status of early detection of AD based on BBBs.
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Affiliation(s)
- Fakhrossadat Farvadi
- Center for Nanotechnology in Drug Delivery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Fatemeh Hashemi
- School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, the University of Newcastle, Newcastle, Australia
| | - Azadeh Amini
- Department of Pharmaceutical Biomaterials and Medical Biomaterials Research Center, Faculty of Pharmacy, Tehran University of Medical sciences, Tehran, Iran
| | | | - Mohammad Javad Raee
- Center for Nanotechnology in Drug Delivery, Shiraz University of Medical Sciences, Shiraz, Iran
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37
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Chen S, Chang Y, Li L, Acosta D, Li Y, Guo Q, Wang C, Turkes E, Morrison C, Julian D, Hester ME, Scharre DW, Santiskulvong C, Song SX, Plummer JT, Serrano GE, Beach TG, Duff KE, Ma Q, Fu H. Spatially resolved transcriptomics reveals genes associated with the vulnerability of middle temporal gyrus in Alzheimer's disease. Acta Neuropathol Commun 2022; 10:188. [PMID: 36544231 PMCID: PMC9773466 DOI: 10.1186/s40478-022-01494-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 12/11/2022] [Indexed: 12/24/2022] Open
Abstract
Human middle temporal gyrus (MTG) is a vulnerable brain region in early Alzheimer's disease (AD), but little is known about the molecular mechanisms underlying this regional vulnerability. Here we utilize the 10 × Visium platform to define the spatial transcriptomic profile in both AD and control (CT) MTG. We identify unique marker genes for cortical layers and the white matter, and layer-specific differentially expressed genes (DEGs) in human AD compared to CT. Deconvolution of the Visium spots showcases the significant difference in particular cell types among cortical layers and the white matter. Gene co-expression analyses reveal eight gene modules, four of which have significantly altered co-expression patterns in the presence of AD pathology. The co-expression patterns of hub genes and enriched pathways in the presence of AD pathology indicate an important role of cell-cell-communications among microglia, oligodendrocytes, astrocytes, and neurons, which may contribute to the cellular and regional vulnerability in early AD. Using single-molecule fluorescent in situ hybridization, we validated the cell-type-specific expression of three novel DEGs (e.g., KIF5A, PAQR6, and SLC1A3) and eleven previously reported DEGs associated with AD pathology (i.e., amyloid beta plaques and intraneuronal neurofibrillary tangles or neuropil threads) at the single cell level. Our results may contribute to the understanding of the complex architecture and neuronal and glial response to AD pathology of this vulnerable brain region.
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Affiliation(s)
- Shuo Chen
- grid.261331.40000 0001 2285 7943Department of Neuroscience, College of Medicine, Ohio State University, Columbus, OH 43210 USA ,grid.261331.40000 0001 2285 7943Biomedical Sciences Graduate Program, Ohio State University, Columbus, OH 43210 USA
| | - Yuzhou Chang
- grid.261331.40000 0001 2285 7943Biomedical Sciences Graduate Program, Ohio State University, Columbus, OH 43210 USA ,grid.261331.40000 0001 2285 7943Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Liangping Li
- grid.261331.40000 0001 2285 7943Department of Neuroscience, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Diana Acosta
- grid.261331.40000 0001 2285 7943Department of Neuroscience, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Yang Li
- grid.261331.40000 0001 2285 7943Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Qi Guo
- grid.261331.40000 0001 2285 7943Biomedical Sciences Graduate Program, Ohio State University, Columbus, OH 43210 USA ,grid.261331.40000 0001 2285 7943Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Cankun Wang
- grid.261331.40000 0001 2285 7943Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Emir Turkes
- grid.83440.3b0000000121901201UK Dementia Research Institute, UCL Queen Square Institute of Neurology, London, UK
| | - Cody Morrison
- grid.261331.40000 0001 2285 7943Department of Neuroscience, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Dominic Julian
- grid.240344.50000 0004 0392 3476The Steve and Cindy Rasmussen Institute for Genomic Medicine, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205 USA
| | - Mark E. Hester
- grid.240344.50000 0004 0392 3476The Steve and Cindy Rasmussen Institute for Genomic Medicine, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205 USA
| | - Douglas W. Scharre
- grid.261331.40000 0001 2285 7943Department of Neurology, Center for Cognitive and Memory Disorders, Center for Neuromodulation, Ohio State University, Columbus, OH 43210 USA
| | - Chintda Santiskulvong
- grid.50956.3f0000 0001 2152 9905Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048 USA
| | - Sarah XueYing Song
- grid.50956.3f0000 0001 2152 9905Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048 USA
| | - Jasmine T. Plummer
- grid.50956.3f0000 0001 2152 9905Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048 USA
| | - Geidy E. Serrano
- grid.414208.b0000 0004 0619 8759Banner Sun Health Research Institute, Sun City, AZ 85351 USA
| | - Thomas G. Beach
- grid.414208.b0000 0004 0619 8759Banner Sun Health Research Institute, Sun City, AZ 85351 USA
| | - Karen E. Duff
- grid.83440.3b0000000121901201UK Dementia Research Institute, UCL Queen Square Institute of Neurology, London, UK
| | - Qin Ma
- grid.261331.40000 0001 2285 7943Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Hongjun Fu
- grid.261331.40000 0001 2285 7943Department of Neuroscience, College of Medicine, Ohio State University, Columbus, OH 43210 USA
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Acharyya S, Zhou X, Baladandayuthapani V. SpaceX: gene co-expression network estimation for spatial transcriptomics. Bioinformatics 2022; 38:5033-5041. [PMID: 36179087 PMCID: PMC9665869 DOI: 10.1093/bioinformatics/btac645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 08/27/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION The analysis of spatially resolved transcriptome enables the understanding of the spatial interactions between the cellular environment and transcriptional regulation. In particular, the characterization of the gene-gene co-expression at distinct spatial locations or cell types in the tissue enables delineation of spatial co-regulatory patterns as opposed to standard differential single gene analyses. To enhance the ability and potential of spatial transcriptomics technologies to drive biological discovery, we develop a statistical framework to detect gene co-expression patterns in a spatially structured tissue consisting of different clusters in the form of cell classes or tissue domains. RESULTS We develop SpaceX (spatially dependent gene co-expression network), a Bayesian methodology to identify both shared and cluster-specific co-expression network across genes. SpaceX uses an over-dispersed spatial Poisson model coupled with a high-dimensional factor model which is based on a dimension reduction technique for computational efficiency. We show via simulations, accuracy gains in co-expression network estimation and structure by accounting for (increasing) spatial correlation and appropriate noise distributions. In-depth analysis of two spatial transcriptomics datasets in mouse hypothalamus and human breast cancer using SpaceX, detected multiple hub genes which are related to cognitive abilities for the hypothalamus data and multiple cancer genes (e.g. collagen family) from the tumor region for the breast cancer data. AVAILABILITY AND IMPLEMENTATION The SpaceX R-package is available at github.com/bayesrx/SpaceX. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Satwik Acharyya
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
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Savelieff MG, Chen KS, Elzinga SE, Feldman EL. Diabetes and dementia: Clinical perspective, innovation, knowledge gaps. J Diabetes Complications 2022; 36:108333. [PMID: 36240668 PMCID: PMC10076101 DOI: 10.1016/j.jdiacomp.2022.108333] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/30/2022] [Indexed: 10/31/2022]
Abstract
The world faces a pandemic-level prevalence of type 2 diabetes. In parallel with this massive burden of metabolic disease is the growing prevalence of dementia as the population ages. The two health issues are intertwined. The Lancet Commission on dementia prevention, intervention, and care was convened to tackle the growing global concern of dementia by identifying risk factors. It concluded, along with other studies, that diabetes as well as obesity and the metabolic syndrome more broadly, which are frequently comorbid, raise the risk of developing dementia. Type 2 diabetes is a modifiable risk factor; however, it is uncertain whether anti-diabetic drugs mitigate risk of developing dementia. Reasons are manifold but constitute a critical knowledge gap in the field. This review outlines studies of type 2 diabetes on risk of dementia, illustrating key concepts. Moreover, it identifies knowledge gaps, reviews strategies to help fill these gaps, and concludes with a series of recommendations to mitigate risk and advance understanding of type 2 diabetes and dementia.
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Affiliation(s)
- Masha G Savelieff
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kevin S Chen
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI 48109, USA; Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Sarah E Elzinga
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI 48109, USA; Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Eva L Feldman
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI 48109, USA; Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA.
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Marmolejo-Garza A, Medeiros-Furquim T, Rao R, Eggen BJL, Boddeke E, Dolga AM. Transcriptomic and epigenomic landscapes of Alzheimer's disease evidence mitochondrial-related pathways. BIOCHIMICA ET BIOPHYSICA ACTA. MOLECULAR CELL RESEARCH 2022; 1869:119326. [PMID: 35839870 DOI: 10.1016/j.bbamcr.2022.119326] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 02/06/2023]
Abstract
Alzheimers disease (AD) is the main cause of dementia and it is defined by cognitive decline coupled to extracellular deposit of amyloid-beta protein and intracellular hyperphosphorylation of tau protein. Historically, efforts to target such hallmarks have failed in numerous clinical trials. In addition to these hallmark-targeted approaches, several clinical trials focus on other AD pathological processes, such as inflammation, mitochondrial dysfunction, and oxidative stress. Mitochondria and mitochondrial-related mechanisms have become an attractive target for disease-modifying strategies, as mitochondrial dysfunction prior to clinical onset has been widely described in AD patients and AD animal models. Mitochondrial function relies on both the nuclear and mitochondrial genome. Findings from omics technologies have shed light on AD pathophysiology at different levels (e.g., epigenome, transcriptome and proteome). Most of these studies have focused on the nuclear-encoded components. The first part of this review provides an updated overview of the mechanisms that regulate mitochondrial gene expression and function. The second part of this review focuses on evidence of mitochondrial dysfunction in AD. We have focused on published findings and datasets that study AD. We analyzed published data and provide examples for mitochondrial-related pathways. These pathways are strikingly dysregulated in AD neurons and glia in sex-, cell- and disease stage-specific manners. Analysis of mitochondrial omics data highlights the involvement of mitochondria in AD, providing a rationale for further disease modeling and drug targeting.
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Affiliation(s)
- Alejandro Marmolejo-Garza
- Department of Molecular Pharmacology, Faculty of Science and Engineering, Groningen Research Institute of Pharmacy (GRIP), University of Groningen, the Netherlands; Department of Biomedical Sciences of Cells & Systems, Section Molecular Neurobiology, Faculty of Medical Sciences, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Tiago Medeiros-Furquim
- Department of Molecular Pharmacology, Faculty of Science and Engineering, Groningen Research Institute of Pharmacy (GRIP), University of Groningen, the Netherlands; Department of Biomedical Sciences of Cells & Systems, Section Molecular Neurobiology, Faculty of Medical Sciences, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ramya Rao
- Department of Molecular Pharmacology, Faculty of Science and Engineering, Groningen Research Institute of Pharmacy (GRIP), University of Groningen, the Netherlands
| | - Bart J L Eggen
- Department of Biomedical Sciences of Cells & Systems, Section Molecular Neurobiology, Faculty of Medical Sciences, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Erik Boddeke
- Department of Biomedical Sciences of Cells & Systems, Section Molecular Neurobiology, Faculty of Medical Sciences, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen N, Denmark.
| | - Amalia M Dolga
- Department of Molecular Pharmacology, Faculty of Science and Engineering, Groningen Research Institute of Pharmacy (GRIP), University of Groningen, the Netherlands.
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Microenvironment components and spatially resolved single-cell transcriptome atlas of breast cancer metastatic axillary lymph nodes. Acta Biochim Biophys Sin (Shanghai) 2022; 54:1336-1348. [PMID: 36148946 PMCID: PMC9828062 DOI: 10.3724/abbs.2022131] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
As an indicator of clinical prognosis, lymph node metastasis of breast cancer has drawn great attention. Many reports have revealed the characteristics of metastatic breast cancer cells, however, the effect of breast cancer cells on the microenvironment components of lymph nodes and spatial transcriptome atlas remains unclear. In this study, by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics, we investigate the transcriptional profiling of six surgically excised lymph node samples and the spatial organization of one positive lymph node. We identify the existence of osteoclast-like giant cells (OGC) which have high expressions of CD68 and CD163, the biomarkers of tumor-associated macrophages (TAMs). Through a spatially resolved transcriptomic method, we find that OGCs are scattered among metastatic breast cancer cells. In the lymph node microenvironment with breast cancer cell infiltration, TAMs are enriched in protumoral pathways including NF-κB signaling pathways and NOD-like receptor signaling pathways. Further subclustering demonstrates the potential differentiation trajectory in which macrophages develop from a state of active chemokine production to a state of active lymphocyte activation. This study is the first to integrate scRNA-seq and spatial transcriptomics in the tumor microenvironment of axillary lymph nodes, offering a systematic approach to delve into breast cancer lymph node metastasis.
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Paron F, Barattucci S, Cappelli S, Romano M, Berlingieri C, Stuani C, Laurents D, Mompeán M, Buratti E. Unravelling the toxic effects mediated by the neurodegenerative disease-associated S375G mutation of TDP-43 and its S375E phosphomimetic variant. J Biol Chem 2022; 298:102252. [PMID: 35835219 PMCID: PMC9364110 DOI: 10.1016/j.jbc.2022.102252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 06/23/2022] [Accepted: 06/25/2022] [Indexed: 12/05/2022] Open
Abstract
TAR DNA-binding protein 43 (TDP-43) is a nucleic acid–binding protein found in the nucleus that accumulates in the cytoplasm under pathological conditions, leading to proteinopathies, such as frontotemporal dementia and ALS. An emerging area of TDP-43 research is represented by the study of its post-translational modifications, the way they are connected to disease-associated mutations, and what this means for pathological processes. Recently, we described a novel mutation in TDP-43 in an early onset ALS case that was affecting a potential phosphorylation site in position 375 (S375G). A preliminary characterization showed that both the S375G mutation and its phosphomimetic variant, S375E, displayed altered nuclear–cytoplasmic distribution and cellular toxicity. To better investigate these effects, here we established cell lines expressing inducible WT, S375G, and S375E TDP-43 variants. Interestingly, we found that these mutants do not seem to affect well-studied aspects of TDP-43, such as RNA splicing or autoregulation, or protein conformation, dynamics, or aggregation, although they do display dysmorphic nuclear shape and cell cycle alterations. In addition, RNA-Seq analysis of these cell lines showed that although the disease-associated S375G mutation and its phosphomimetic S375E variant regulate distinct sets of genes, they have a common target in mitochondrial apoptotic genes. Taken together, our data strongly support the growing evidence that alterations in TDP-43 post-translational modifications can play a potentially important role in disease pathogenesis and provide a further link between TDP-43 pathology and mitochondrial health.
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Affiliation(s)
- Francesca Paron
- Molecular Pathology, International Centre for Genetic and Engineering Biotechnology (ICGEB), Trieste, Italy
| | - Simone Barattucci
- Molecular Pathology, International Centre for Genetic and Engineering Biotechnology (ICGEB), Trieste, Italy
| | - Sara Cappelli
- Molecular Pathology, International Centre for Genetic and Engineering Biotechnology (ICGEB), Trieste, Italy
| | - Maurizio Romano
- Department of Life Sciences, University of Trieste, Trieste, Italy
| | - Christian Berlingieri
- Molecular Pathology, International Centre for Genetic and Engineering Biotechnology (ICGEB), Trieste, Italy
| | - Cristiana Stuani
- Molecular Pathology, International Centre for Genetic and Engineering Biotechnology (ICGEB), Trieste, Italy
| | - Douglas Laurents
- "Rocasolano" Institute for Physical Chemistry, Spanish National Research Council, Serrano 119, 28006, Madrid, Spain
| | - Miguel Mompeán
- "Rocasolano" Institute for Physical Chemistry, Spanish National Research Council, Serrano 119, 28006, Madrid, Spain
| | - Emanuele Buratti
- Molecular Pathology, International Centre for Genetic and Engineering Biotechnology (ICGEB), Trieste, Italy.
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Baratta AM, Brandner AJ, Plasil SL, Rice RC, Farris SP. Advancements in Genomic and Behavioral Neuroscience Analysis for the Study of Normal and Pathological Brain Function. Front Mol Neurosci 2022; 15:905328. [PMID: 35813067 PMCID: PMC9259865 DOI: 10.3389/fnmol.2022.905328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022] Open
Abstract
Psychiatric and neurological disorders are influenced by an undetermined number of genes and molecular pathways that may differ among afflicted individuals. Functionally testing and characterizing biological systems is essential to discovering the interrelationship among candidate genes and understanding the neurobiology of behavior. Recent advancements in genetic, genomic, and behavioral approaches are revolutionizing modern neuroscience. Although these tools are often used separately for independent experiments, combining these areas of research will provide a viable avenue for multidimensional studies on the brain. Herein we will briefly review some of the available tools that have been developed for characterizing novel cellular and animal models of human disease. A major challenge will be openly sharing resources and datasets to effectively integrate seemingly disparate types of information and how these systems impact human disorders. However, as these emerging technologies continue to be developed and adopted by the scientific community, they will bring about unprecedented opportunities in our understanding of molecular neuroscience and behavior.
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Affiliation(s)
- Annalisa M. Baratta
- Center for Neuroscience, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Adam J. Brandner
- Center for Neuroscience, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Sonja L. Plasil
- Department of Pharmacology & Chemical Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Rachel C. Rice
- Center for Neuroscience, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Sean P. Farris
- Center for Neuroscience, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Anesthesiology and Perioperative Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- *Correspondence: Sean P. Farris,
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Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nat Methods 2022; 19:662-670. [PMID: 35577954 DOI: 10.1038/s41592-022-01480-9] [Citation(s) in RCA: 143] [Impact Index Per Article: 71.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 03/30/2022] [Indexed: 01/07/2023]
Abstract
Spatial transcriptomics approaches have substantially advanced our capacity to detect the spatial distribution of RNA transcripts in tissues, yet it remains challenging to characterize whole-transcriptome-level data for single cells in space. Addressing this need, researchers have developed integration methods to combine spatial transcriptomic data with single-cell RNA-seq data to predict the spatial distribution of undetected transcripts and/or perform cell type deconvolution of spots in histological sections. However, to date, no independent studies have comparatively analyzed these integration methods to benchmark their performance. Here we present benchmarking of 16 integration methods using 45 paired datasets (comprising both spatial transcriptomics and scRNA-seq data) and 32 simulated datasets. We found that Tangram, gimVI, and SpaGE outperformed other integration methods for predicting the spatial distribution of RNA transcripts, whereas Cell2location, SpatialDWLS, and RCTD are the top-performing methods for the cell type deconvolution of spots. We provide a benchmark pipeline to help researchers select optimal integration methods to process their datasets.
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45
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Marshall JL, Noel T, Wang QS, Chen H, Murray E, Subramanian A, Vernon KA, Bazua-Valenti S, Liguori K, Keller K, Stickels RR, McBean B, Heneghan RM, Weins A, Macosko EZ, Chen F, Greka A. High-resolution Slide-seqV2 spatial transcriptomics enables discovery of disease-specific cell neighborhoods and pathways. iScience 2022; 25:104097. [PMID: 35372810 PMCID: PMC8971939 DOI: 10.1016/j.isci.2022.104097] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/15/2022] [Accepted: 03/11/2022] [Indexed: 12/21/2022] Open
Abstract
High-resolution spatial transcriptomics enables mapping of RNA expression directly from intact tissue sections; however, its utility for the elucidation of disease processes and therapeutically actionable pathways remains unexplored. We applied Slide-seqV2 to mouse and human kidneys, in healthy and distinct disease paradigms. First, we established the feasibility of Slide-seqV2 in tissue from nine distinct human kidneys, which revealed a cell neighborhood centered around a population of LYVE1+ macrophages. Second, in a mouse model of diabetic kidney disease, we detected changes in the cellular organization of the spatially restricted kidney filter and blood-flow-regulating apparatus. Third, in a mouse model of a toxic proteinopathy, we identified previously unknown, disease-specific cell neighborhoods centered around macrophages. In a spatially restricted subpopulation of epithelial cells, we discovered perturbations in 77 genes associated with the unfolded protein response. Our studies illustrate and experimentally validate the utility of Slide-seqV2 for the discovery of disease-specific cell neighborhoods.
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Affiliation(s)
- Jamie L. Marshall
- Kidney Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Teia Noel
- Kidney Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Qingbo S. Wang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA 02115, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Statistical Genetics, Graduate School of Medicine, Osaka University, Osaka 565-0871, Japan
| | - Haiqi Chen
- Program in Cell Circuits and Epigenetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Evan Murray
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ayshwarya Subramanian
- Kidney Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Katherine A. Vernon
- Kidney Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Silvana Bazua-Valenti
- Kidney Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Katie Liguori
- Kidney Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Keith Keller
- Kidney Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Robert R. Stickels
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Graduate School of Arts and Sciences, Harvard University, Cambridge, MA 02115, USA
- Division of Medical Science, Harvard University, Boston, MA 02115, USA
| | - Breanna McBean
- Broad Summer Research Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Rowan M. Heneghan
- Kidney Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Astrid Weins
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Evan Z. Macosko
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Fei Chen
- Program in Cell Circuits and Epigenetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Anna Greka
- Kidney Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
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Zhang L, Chen D, Song D, Liu X, Zhang Y, Xu X, Wang X. Clinical and translational values of spatial transcriptomics. Signal Transduct Target Ther 2022; 7:111. [PMID: 35365599 PMCID: PMC8972902 DOI: 10.1038/s41392-022-00960-w] [Citation(s) in RCA: 62] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 03/04/2022] [Accepted: 03/09/2022] [Indexed: 02/06/2023] Open
Abstract
The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-seq) acts as a pivotal component to bridge the pathological phenomes of human tissues with molecular alterations, defining in situ intercellular molecular communications and knowledge on spatiotemporal molecular medicine. The present article overviews the development of ST and aims to evaluate clinical and translational values for understanding molecular pathogenesis and uncovering disease-specific biomarkers. We compare the advantages and disadvantages of sequencing- and imaging-based technologies and highlight opportunities and challenges of ST. We also describe the bioinformatics tools necessary on dissecting spatial patterns of gene expression and cellular interactions and the potential applications of ST in human diseases for clinical practice as one of important issues in clinical and translational medicine, including neurology, embryo development, oncology, and inflammation. Thus, clear clinical objectives, designs, optimizations of sampling procedure and protocol, repeatability of ST, as well as simplifications of analysis and interpretation are the key to translate ST from bench to clinic.
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Affiliation(s)
- Linlin Zhang
- Zhongshan Hospital, Department of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Shanghai, 200000, China
| | - Dongsheng Chen
- Suzhou Institute of Systems Medicine, Suzhou, 215123, Jiangsu, China
| | - Dongli Song
- Zhongshan Hospital, Department of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Shanghai, 200000, China
| | - Xiaoxia Liu
- Zhongshan Hospital, Department of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Shanghai, 200000, China
| | - Yanan Zhang
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen, 518055, China
| | - Xun Xu
- BGI-Shenzhen, Shenzhen, 518083, China.
| | - Xiangdong Wang
- Zhongshan Hospital, Department of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Shanghai, 200000, China.
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Li K, Yan C, Li C, Chen L, Zhao J, Zhang Z, Bao S, Sun J, Zhou M. Computational elucidation of spatial gene expression variation from spatially resolved transcriptomics data. MOLECULAR THERAPY - NUCLEIC ACIDS 2022; 27:404-411. [PMID: 35036053 PMCID: PMC8728308 DOI: 10.1016/j.omtn.2021.12.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Recent advances in spatially resolved transcriptomics (SRT) have revolutionized biological and medical research and enabled unprecedented insight into the functional organization and cell communication of tissues and organs in situ. Identifying and elucidating gene spatial expression variation (SE analysis) is fundamental to elucidate the SRT landscape. There is an urgent need for public repositories and computational techniques of SRT data in SE analysis alongside technological breakthroughs and large-scale data generation. Increasing efforts to use in silico techniques in SE analysis have been made. However, these attempts are widely scattered among a large number of studies that are not easily accessible or comprehensible by both medical and life scientists. This study provides a survey and a summary of public resources on SE analysis in SRT studies. An updated systematic overview of state-of-the-art computational approaches and tools currently available in SE analysis are presented herein, emphasizing recent advances. Finally, the present study explores the future perspectives and challenges of in silico techniques in SE analysis. This study guides medical and life scientists to look for dedicated resources and more competent tools for characterizing spatial patterns of gene expression.
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Affiliation(s)
- Ke Li
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China
| | - Congcong Yan
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China
| | - Chenghao Li
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China
| | - Lu Chen
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China
| | - Jingting Zhao
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China
| | - Zicheng Zhang
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China
| | - Siqi Bao
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China
| | - Jie Sun
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China
- Corresponding author Jie Sun, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China.
| | - Meng Zhou
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China
- Corresponding author Meng Zhou, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China.
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48
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Onoda N, Kawabata A, Hasegawa K, Sakakura M, Urakawa I, Seki M, Zenkoh J, Suzuki A, Suzuki Y. Spatial and single-cell transcriptome analysis reveals changes in gene expression in response to drug perturbation in rat kidney. DNA Res 2022; 29:dsac007. [PMID: 35325072 PMCID: PMC9014450 DOI: 10.1093/dnares/dsac007] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 03/22/2022] [Indexed: 12/25/2022] Open
Abstract
The kidney is a complex organ that consists of various types of cells. It is occasionally difficult to resolve molecular alterations and possible perturbations that the kidney experiences due to drug-induced damage. In this study, we performed spatial and single-cell transcriptome analysis of rat kidneys and constructed a precise rat renal cell atlas with spatial information. Using the constructed catalogue, we were able to characterize cells of several minor populations, such as macula densa or juxtaglomerular cells. Further inspection of the spatial gene expression data allowed us to identify the upregulation of genes involved in the renin regulating pathway in losartan-treated populations. Losartan is an angiotensin II receptor antagonist drug, and the observed upregulation of the renin pathway-related genes could be due to feedback from the hypotensive action of the drug. Furthermore, we found spatial heterogeneity in the response to losartan among the glomeruli. These results collectively indicate that integrated single-cell and spatial gene expression analysis is a powerful approach to reveal the detailed associations between the different cell types spanning the complicated renal compartments.
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Affiliation(s)
- Naoki Onoda
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa-shi, Chiba 277-0882, Japan
- Research Core Function Laboratories, Research Unit, R&D Division, Kyowa Kirin Co., Ltd., Machida-shi, Tokyo 194-8533, Japan
| | - Ayako Kawabata
- Research Core Function Laboratories, Research Unit, R&D Division, Kyowa Kirin Co., Ltd., Machida-shi, Tokyo 194-8533, Japan
| | - Kumi Hasegawa
- Biomedical Science Research Laboratories 1, Research Unit, R&D Division, Kyowa Kirin Co., Ltd., Machida-shi, Tokyo 194-8533, Japan
| | - Megumi Sakakura
- Research Core Function Laboratories, Research Unit, R&D Division, Kyowa Kirin Co., Ltd., Machida-shi, Tokyo 194-8533, Japan
| | - Itaru Urakawa
- Research Core Function Laboratories, Research Unit, R&D Division, Kyowa Kirin Co., Ltd., Machida-shi, Tokyo 194-8533, Japan
| | - Masahide Seki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa-shi, Chiba 277-0882, Japan
| | - Junko Zenkoh
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa-shi, Chiba 277-0882, Japan
| | - Ayako Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa-shi, Chiba 277-0882, Japan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa-shi, Chiba 277-0882, Japan
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49
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Rowlands CF, Taylor A, Rice G, Whiffin N, Hall HN, Newman WG, Black GCM, O'Keefe RT, Hubbard S, Douglas AGL, Baralle D, Briggs TA, Ellingford JM. MRSD: A quantitative approach for assessing suitability of RNA-seq in the investigation of mis-splicing in Mendelian disease. Am J Hum Genet 2022; 109:210-222. [PMID: 35065709 PMCID: PMC8874219 DOI: 10.1016/j.ajhg.2021.12.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 12/12/2021] [Indexed: 12/16/2022] Open
Abstract
Variable levels of gene expression between tissues complicates the use of RNA sequencing of patient biosamples to delineate the impact of genomic variants. Here, we describe a gene- and tissue-specific metric to inform the feasibility of RNA sequencing. This overcomes limitations of using expression values alone as a metric to predict RNA-sequencing utility. We have derived a metric, minimum required sequencing depth (MRSD), that estimates the depth of sequencing required from RNA sequencing to achieve user-specified sequencing coverage of a gene, transcript, or group of genes. We applied MRSD across four human biosamples: whole blood, lymphoblastoid cell lines (LCLs), skeletal muscle, and cultured fibroblasts. MRSD has high precision (90.1%-98.2%) and overcomes transcript region-specific sequencing biases. Applying MRSD scoring to established disease gene panels shows that fibroblasts, of these four biosamples, are the optimum source of RNA for 63.1% of gene panels. Using this approach, up to 67.8% of the variants of uncertain significance in ClinVar that are predicted to impact splicing could be assayed by RNA sequencing in at least one of the biosamples. We demonstrate the utility and benefits of MRSD as a metric to inform functional assessment of splicing aberrations, in particular in the context of Mendelian genetic disorders to improve diagnostic yield.
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Affiliation(s)
- Charlie F Rowlands
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK; Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Algy Taylor
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Gillian Rice
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK
| | - Nicola Whiffin
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Hildegard Nikki Hall
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - William G Newman
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK; Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Graeme C M Black
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK; Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Raymond T O'Keefe
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK
| | - Simon Hubbard
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK
| | - Andrew G L Douglas
- Wessex Clinical Genetics Service, Princess Anne Hospital, University Hospital Southampton NHS Foundation Trust, Coxford Rd, Southampton SO16 5YA, UK; Faculty of Medicine, University of Southampton, Duthie Building, Southampton General Hospital, Tremona Road, Southampton SO16 6YD, UK
| | - Diana Baralle
- Wessex Clinical Genetics Service, Princess Anne Hospital, University Hospital Southampton NHS Foundation Trust, Coxford Rd, Southampton SO16 5YA, UK; Faculty of Medicine, University of Southampton, Duthie Building, Southampton General Hospital, Tremona Road, Southampton SO16 6YD, UK
| | - Tracy A Briggs
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK; Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Jamie M Ellingford
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK; Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK.
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50
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Melo Ferreira R, Freije BJ, Eadon MT. Deconvolution Tactics and Normalization in Renal Spatial Transcriptomics. Front Physiol 2022; 12:812947. [PMID: 35095570 PMCID: PMC8793484 DOI: 10.3389/fphys.2021.812947] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 12/02/2021] [Indexed: 01/16/2023] Open
Abstract
The kidney is composed of heterogeneous groups of epithelial, endothelial, immune, and stromal cells, all in close anatomic proximity. Spatial transcriptomic technologies allow the interrogation of in situ expression signatures in health and disease, overlaid upon a histologic image. However, some spatial gene expression platforms have not yet reached single-cell resolution. As such, deconvolution of spatial transcriptomic spots is important to understand the proportion of cell signature arising from these varied cell types in each spot. This article reviews the various deconvolution strategies discussed in the 2021 Indiana O'Brien Center for Microscopy workshop. The unique features of Seurat transfer score methodology, SPOTlight, Robust Cell Type Decomposition, and BayesSpace are reviewed. The application of normalization and batch effect correction across spatial transcriptomic samples is also discussed.
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Affiliation(s)
| | | | - Michael T. Eadon
- Division of Nephrology, Indiana University School of Medicine, Indianapolis, ID, United States
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