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Mahajani S, Salahi A, Gonzalez B, Nelson C, Hsiung F. Decoding complexity: the need to enhance precision and streamline spatial understanding in neuroscience. Neural Regen Res 2025; 20:801-802. [PMID: 38886946 DOI: 10.4103/nrr.nrr-d-23-02067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/21/2024] [Indexed: 06/20/2024] Open
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2
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Lozinski BM, Ta K, Dong Y. Emerging role of galectin 3 in neuroinflammation and neurodegeneration. Neural Regen Res 2024; 19:2004-2009. [PMID: 38227529 DOI: 10.4103/1673-5374.391181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 11/15/2023] [Indexed: 01/17/2024] Open
Abstract
Neuroinflammation and neurodegeneration are key processes that mediate the development and progression of neurological diseases. However, the mechanisms modulating these processes in different diseases remain incompletely understood. Advances in single cell based multi-omic analyses have helped to identify distinct molecular signatures such as Lgals3 that is associated with neuroinflammation and neurodegeneration in the central nervous system (CNS). Lgals3 encodes galectin-3 (Gal3), a β-galactoside and glycan binding glycoprotein that is frequently upregulated by reactive microglia/macrophages in the CNS during various neurological diseases. While Gal3 has previously been associated with non-CNS inflammatory and fibrotic diseases, recent studies highlight Gal3 as a prominent regulator of inflammation and neuroaxonal damage in the CNS during diseases such as multiple sclerosis, Alzheimer's disease, and Parkinson's disease. In this review, we summarize the pleiotropic functions of Gal3 and discuss evidence that demonstrates its detrimental role in neuroinflammation and neurodegeneration during different neurological diseases. We also consider the challenges of translating preclinical observations into targeting Gal3 in the human CNS.
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Affiliation(s)
- Brian M Lozinski
- Department of Clinical Neuroscience, University of Calgary, Calgary, AB, Canada
| | - Khanh Ta
- Deparment of Biochemistry, Microbiology & Immunology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Yifei Dong
- Deparment of Biochemistry, Microbiology & Immunology, University of Saskatchewan, Saskatoon, SK, Canada
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3
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Lin S, Cui Y, Zhao F, Yang Z, Song J, Yao J, Zhao Y, Qian BZ, Zhao Y, Yuan Z. Complete spatially resolved gene expression is not necessary for identifying spatial domains. CELL GENOMICS 2024; 4:100565. [PMID: 38781966 DOI: 10.1016/j.xgen.2024.100565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/29/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024]
Abstract
Spatially resolved transcriptomics (SRT) technologies have revolutionized the study of tissue organization. We introduce a graph convolutional network with an attention and positive emphasis mechanism, termed BINARY, relying exclusively on binarized SRT data to accurately delineate spatial domains. BINARY outperforms existing methods across various SRT data types while using significantly less input information. Our study suggests that precise gene expression quantification may not always be essential, inspiring further exploration of the broader applications of spatially resolved binarized gene expression data.
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Affiliation(s)
- Senlin Lin
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Yan Cui
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China
| | - Fangyuan Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Zhidong Yang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800, Australia
| | | | - Yu Zhao
- AI Lab, Tencent, Shenzhen, China
| | - Bin-Zhi Qian
- Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, The Human Phenome Institute, Zhangjiang-Fudan International Innovation Center, Fudan University, Shanghai, China
| | - Yi Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
| | - Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China.
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4
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Huang R, Kratka CE, Pea J, McCann C, Nelson J, Bryan JP, Zhou LT, Russo DD, Zaniker EJ, Gandhi AH, Shalek AK, Cleary B, Farhi SL, Duncan FE, Goods BA. Single-cell and spatiotemporal profile of ovulation in the mouse ovary. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.20.594719. [PMID: 38826447 PMCID: PMC11142086 DOI: 10.1101/2024.05.20.594719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Ovulation is a spatiotemporally coordinated process that involves several tightly controlled events, including oocyte meiotic maturation, cumulus expansion, follicle wall rupture and repair, and ovarian stroma remodeling. To date, no studies have detailed the precise window of ovulation at single-cell resolution. Here, we performed parallel single-cell RNA-seq and spatial transcriptomics on paired mouse ovaries across an ovulation time course to map the spatiotemporal profile of ovarian cell types. We show that major ovarian cell types exhibit time-dependent transcriptional states enriched for distinct functions and have specific localization profiles within the ovary. We also identified gene markers for ovulation-dependent cell states and validated these using orthogonal methods. Finally, we performed cell-cell interaction analyses to identify ligand-receptor pairs that may drive ovulation, revealing previously unappreciated interactions. Taken together, our data provides a rich and comprehensive resource of murine ovulation that can be mined for discovery by the scientific community.
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5
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Tran KM, Kwang N, Gomez-Arboledas A, Kawauchi S, Mar C, Chao D, Da Cunha C, Wang S, Collins S, Walker A, Shi KX, Alcantara JA, Neumann J, Tenner AJ, LaFerla FM, Hohsfield LA, Swarup V, MacGregor GR, Green KN. APOE Christchurch enhances a disease-associated microglial response to plaque but suppresses response to tau pathology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.03.597211. [PMID: 38895362 PMCID: PMC11185750 DOI: 10.1101/2024.06.03.597211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Background Apolipoprotein E ε4 (APOE4) is the strongest genetic risk factor for late-onset Alzheimer's disease (LOAD). A recent case report identified a rare variant in APOE, APOE3-R136S (Christchurch), proposed to confer resistance to autosomal dominant Alzheimer's Disease (AD). However, it remains unclear whether and how this variant exerts its protective effects. Methods We introduced the R136S variant into mouse Apoe ( ApoeCh ) and investigated its effect on the development of AD-related pathology using the 5xFAD model of amyloidosis and the PS19 model of tauopathy. We used immunohistochemical and biochemical analysis along with single-cell spatial transcriptomics and proteomics to explore the impact of the ApoeCh variant on AD pathological development and the brain's response to plaques and tau. Results In 5xFAD mice, ApoeCh enhances a Disease-Associated Microglia (DAM) phenotype in microglia surrounding plaques, and reduces plaque load, dystrophic neurites, and plasma neurofilament light chain. By contrast, in PS19 mice, ApoeCh suppresses the microglial and astrocytic responses to tau-laden neurons and does not reduce tau accumulation or phosphorylation, but partially rescues tau-induced synaptic and myelin loss. We compared how microglia responses differ between the two mouse models to elucidate the distinct DAM signatures induced by ApoeCh . We identified upregulation of antigen presentation-related genes in the DAM response in a PS19 compared to a 5xFAD background, suggesting a differential response to amyloid versus tau pathology that is modulated by the presence of ApoeCh . Conclusions These findings highlight the ability of the ApoeCh variant to modulate microglial responses based on the type of pathology, enhancing DAM reactivity in amyloid models and dampening neuroinflammation to promote protection in tau models. This suggests that the Christchurch variant's protective effects likely involve multiple mechanisms, including changes in receptor binding and microglial programming.
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6
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Puska G, Szendi V, Dobolyi A. Lateral septum as a possible regulatory center of maternal behaviors. Neurosci Biobehav Rev 2024; 161:105683. [PMID: 38649125 DOI: 10.1016/j.neubiorev.2024.105683] [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: 09/29/2023] [Revised: 04/09/2024] [Accepted: 04/17/2024] [Indexed: 04/25/2024]
Abstract
The lateral septum (LS) is involved in controlling anxiety, aggression, feeding, and other motivated behaviors. Lesion studies have also implicated the LS in various forms of caring behaviors. Recently, novel experimental tools have provided a more detailed insight into the function of the LS, including the specific role of distinct cell types and their neuronal connections in behavioral regulations, in which the LS participates. This article discusses the regulation of different types of maternal behavioral alterations using the distributions of established maternal hormones such as prolactin, estrogens, and the neuropeptide oxytocin. It also considers the distribution of neurons activated in mothers in response to pups and other maternal activities, as well as gene expressional alterations in the maternal LS. Finally, this paper proposes further research directions to keep up with the rapidly developing knowledge on maternal behavioral control in other maternal brain regions.
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Affiliation(s)
- Gina Puska
- Laboratory of Molecular and Systems Neurobiology, Department of Physiology and Neurobiology, Eötvös Loránd University, Budapest, Hungary; Department of Zoology, University of Veterinary Medicine Budapest, Budapest, Hungary
| | - Vivien Szendi
- Laboratory of Molecular and Systems Neurobiology, Department of Physiology and Neurobiology, Eötvös Loránd University, Budapest, Hungary
| | - Arpád Dobolyi
- Laboratory of Molecular and Systems Neurobiology, Department of Physiology and Neurobiology, Eötvös Loránd University, Budapest, Hungary; Laboratory of Neuromorphology, Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary.
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7
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Nakashima A, Takeuchi H. Shaping the olfactory map: cell type-specific activity patterns guide circuit formation. Front Neural Circuits 2024; 18:1409680. [PMID: 38860141 PMCID: PMC11163119 DOI: 10.3389/fncir.2024.1409680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 04/30/2024] [Indexed: 06/12/2024] Open
Abstract
The brain constructs spatially organized sensory maps to represent sensory information. The formation of sensory maps has traditionally been thought to depend on synchronous neuronal activity. However, recent evidence from the olfactory system suggests that cell type-specific temporal patterns of spontaneous activity play an instructive role in shaping the olfactory glomerular map. These findings challenge traditional views and highlight the importance of investigating the spatiotemporal dynamics of neural activity to understand the development of complex neural circuits. This review discusses the implications of new findings in the olfactory system and outlines future research directions.
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Affiliation(s)
- Ai Nakashima
- Laboratory of Chemical Pharmacology, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan
| | - Haruki Takeuchi
- Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, Tokyo, Japan
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8
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Moll T, Harvey C, Alhathli E, Gornall S, O'Brien D, Cooper-Knock J. Non-coding genome contribution to ALS. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2024; 176:75-86. [PMID: 38802183 DOI: 10.1016/bs.irn.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The majority of amyotrophic lateral sclerosis (ALS) is caused by a complex gene-environment interaction. Despite high estimates of heritability, the genetic basis of disease in the majority of ALS patients are unknown. This limits the development of targeted genetic therapies which require an understanding of patient-specific genetic drivers. There is good evidence that the majority of these missing genetic risk factors are likely to be found within the non-coding genome. However, a major challenge in the discovery of non-coding risk variants is determining which variants are functional in which specific CNS cell type. We summarise current discoveries of ALS-associated genetic drivers within the non-coding genome and we make the case that improved cell-specific annotation of genomic function is required to advance this field, particularly via single-cell epigenetic profiling and spatial transcriptomics. We highlight the example of TBK1 where an apparent paradox exists between pathogenic coding variants which cause loss of protein function, and protective non-coding variants which cause reduced gene expression; the paradox is resolved when it is understood that the non-coding variants are acting primarily via change in gene expression within microglia, and the effect of coding variants is most prominent in neurons. We propose that cell-specific functional annotation of ALS-associated genetic variants will accelerate discovery of the genetic architecture underpinning disease in the vast majority of patients.
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Affiliation(s)
- Tobias Moll
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, United Kingdom
| | - Calum Harvey
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, United Kingdom
| | - Elham Alhathli
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, United Kingdom
| | - Sarah Gornall
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, United Kingdom
| | - David O'Brien
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, United Kingdom
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, United Kingdom.
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9
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Zhang S, Shu H, Zhou J, Rubin-Sigler J, Yang X, Liu Y, Cooper-Knock J, Monte E, Zhu C, Tu S, Li H, Tong M, Ecker JR, Ichida JK, Shen Y, Zeng J, Tsao PS, Snyder MP. Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594252. [PMID: 38798507 PMCID: PMC11118500 DOI: 10.1101/2024.05.14.594252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.
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Affiliation(s)
- Sai Zhang
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
- Departments of Biostatistics & Biomedical Engineering, Genetics Institute, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Hantao Shu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jingtian Zhou
- Arc Institute, Palo Alto, CA, USA
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jasper Rubin-Sigler
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Xiaoyu Yang
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Yuxi Liu
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Emma Monte
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Chenchen Zhu
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sharon Tu
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Han Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Mingming Tong
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Justin K. Ichida
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Yin Shen
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Jianyang Zeng
- School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Philip S. Tsao
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael P. Snyder
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
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Vornholt E, Liharska LE, Cheng E, Hashemi A, Park YJ, Ziafat K, Wilkins L, Silk H, Linares LM, Thompson RC, Sullivan B, Moya E, Nadkarni GN, Sebra R, Schadt EE, Kopell BH, Charney AW, Beckmann ND. Characterizing cell type specific transcriptional differences between the living and postmortem human brain. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.01.24306590. [PMID: 38746297 PMCID: PMC11092720 DOI: 10.1101/2024.05.01.24306590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Single-nucleus RNA sequencing (snRNA-seq) is often used to define gene expression patterns characteristic of brain cell types as well as to identify cell type specific gene expression signatures of neurological and mental illnesses in postmortem human brains. As methods to obtain brain tissue from living individuals emerge, it is essential to characterize gene expression differences associated with tissue originating from either living or postmortem subjects using snRNA-seq, and to assess whether and how such differences may impact snRNA-seq studies of brain tissue. To address this, human prefrontal cortex single nuclei gene expression was generated and compared between 31 samples from living individuals and 21 postmortem samples. The same cell types were consistently identified in living and postmortem nuclei, though for each cell type, a large proportion of genes were differentially expressed between samples from postmortem and living individuals. Notably, estimation of cell type proportions by cell type deconvolution of pseudo-bulk data was found to be more accurate in samples from living individuals. To allow for future integration of living and postmortem brain gene expression, a model was developed that quantifies from gene expression data the probability a human brain tissue sample was obtained postmortem. These probabilities are established as a means to statistically account for the gene expression differences between samples from living and postmortem individuals. Together, the results presented here provide a deep characterization of both differences between snRNA-seq derived from samples from living and postmortem individuals, as well as qualify and account for their effect on common analyses performed on this type of data.
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11
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Sampaio C. Huntington disease - Update on ongoing therapeutic developments and a look toward the future. Parkinsonism Relat Disord 2024; 122:106049. [PMID: 38418319 DOI: 10.1016/j.parkreldis.2024.106049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Accepted: 02/12/2024] [Indexed: 03/01/2024]
Affiliation(s)
- Cristina Sampaio
- CHDI Management, Inc. Advisors to CHDI Foundation, Princeton, USA; Faculdade Medicina da Universidade de Lisboa (FMUL), Portugal.
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12
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Rey F, Esposito L, Maghraby E, Mauri A, Berardo C, Bonaventura E, Tonduti D, Carelli S, Cereda C. Role of epigenetics and alterations in RNA metabolism in leukodystrophies. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1854. [PMID: 38831585 DOI: 10.1002/wrna.1854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 06/05/2024]
Abstract
Leukodystrophies are a class of rare heterogeneous disorders which affect the white matter of the brain, ultimately leading to a disruption in brain development and a damaging effect on cognitive, motor and social-communicative development. These disorders present a great clinical heterogeneity, along with a phenotypic overlap and this could be partially due to contributions from environmental stimuli. It is in this context that there is a great need to investigate what other factors may contribute to both disease insurgence and phenotypical heterogeneity, and novel evidence are raising the attention toward the study of epigenetics and transcription mechanisms that can influence the disease phenotype beyond genetics. Modulation in the epigenetics machinery including histone modifications, DNA methylation and non-coding RNAs dysregulation, could be crucial players in the development of these disorders, and moreover an aberrant RNA maturation process has been linked to leukodystrophies. Here, we provide an overview of these mechanisms hoping to supply a closer step toward the analysis of leukodystrophies not only as genetically determined but also with an added level of complexity where epigenetic dysregulation is of key relevance. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNA RNA in Disease and Development > RNA in Disease RNA in Disease and Development > RNA in Development.
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Affiliation(s)
- Federica Rey
- Pediatric Clinical Research Center "Romeo ed Enrica Invernizzi," Department of Biomedical and Clinical Sciences, University of Milano, Milan, Italy
- Center of Functional Genomics and Rare Diseases, Department of Pediatrics, Buzzi Children's Hospital, Milan, Italy
| | - Letizia Esposito
- Pediatric Clinical Research Center "Romeo ed Enrica Invernizzi," Department of Biomedical and Clinical Sciences, University of Milano, Milan, Italy
- Center of Functional Genomics and Rare Diseases, Department of Pediatrics, Buzzi Children's Hospital, Milan, Italy
| | - Erika Maghraby
- Center of Functional Genomics and Rare Diseases, Department of Pediatrics, Buzzi Children's Hospital, Milan, Italy
- Department of Biology and Biotechnology "L. Spallanzani" (DBB), University of Pavia, Pavia, Italy
| | - Alessia Mauri
- Pediatric Clinical Research Center "Romeo ed Enrica Invernizzi," Department of Biomedical and Clinical Sciences, University of Milano, Milan, Italy
- Center of Functional Genomics and Rare Diseases, Department of Pediatrics, Buzzi Children's Hospital, Milan, Italy
| | - Clarissa Berardo
- Pediatric Clinical Research Center "Romeo ed Enrica Invernizzi," Department of Biomedical and Clinical Sciences, University of Milano, Milan, Italy
- Center of Functional Genomics and Rare Diseases, Department of Pediatrics, Buzzi Children's Hospital, Milan, Italy
| | - Eleonora Bonaventura
- Unit of Pediatric Neurology, COALA Center for Diagnosis and Treatment of Leukodystrophies, V. Buzzi Children's Hospital, Milan, Italy
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Davide Tonduti
- Unit of Pediatric Neurology, COALA Center for Diagnosis and Treatment of Leukodystrophies, V. Buzzi Children's Hospital, Milan, Italy
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Stephana Carelli
- Pediatric Clinical Research Center "Romeo ed Enrica Invernizzi," Department of Biomedical and Clinical Sciences, University of Milano, Milan, Italy
- Center of Functional Genomics and Rare Diseases, Department of Pediatrics, Buzzi Children's Hospital, Milan, Italy
| | - Cristina Cereda
- Center of Functional Genomics and Rare Diseases, Department of Pediatrics, Buzzi Children's Hospital, Milan, Italy
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Xu J, Huang D, Zhang X. scmFormer Integrates Large-Scale Single-Cell Proteomics and Transcriptomics Data by Multi-Task Transformer. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307835. [PMID: 38483032 PMCID: PMC11109621 DOI: 10.1002/advs.202307835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/24/2024] [Indexed: 05/23/2024]
Abstract
Transformer-based models have revolutionized single cell RNA-seq (scRNA-seq) data analysis. However, their applicability is challenged by the complexity and scale of single-cell multi-omics data. Here a novel single-cell multi-modal/multi-task transformer (scmFormer) is proposed to fill up the existing blank of integrating single-cell proteomics with other omics data. Through systematic benchmarking, it is demonstrated that scmFormer excels in integrating large-scale single-cell multimodal data and heterogeneous multi-batch paired multi-omics data, while preserving shared information across batchs and distinct biological information. scmFormer achieves 54.5% higher average F1 score compared to the second method in transferring cell-type labels from single-cell transcriptomics to proteomics data. Using COVID-19 datasets, it is presented that scmFormer successfully integrates over 1.48 million cells on a personal computer. Moreover, it is also proved that scmFormer performs better than existing methods on generating the unmeasured modality and is well-suited for spatial multi-omic data. Thus, scmFormer is a powerful and comprehensive tool for analyzing single-cell multi-omics data.
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Affiliation(s)
- Jing Xu
- Key Laboratory of Plant Germplasm Enhancement and Specialty AgricultureWuhan Botanical GardenChinese Academy of SciencesWuhan430074China
- University of Chinese Academy of SciencesBeijing100049China
| | - De‐Shuang Huang
- Eastern Institute for Advanced StudyEastern Institute of TechnologyNingbo315200China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty AgricultureWuhan Botanical GardenChinese Academy of SciencesWuhan430074China
- Center of Economic BotanyCore Botanical GardensChinese Academy of SciencesWuhan430074China
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14
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Ohn J, Seo MK, Park J, Lee D, Choi H. SpatialSPM: statistical parametric mapping for the comparison of gene expression pattern images in multiple spatial transcriptomic datasets. Nucleic Acids Res 2024:gkae293. [PMID: 38676948 DOI: 10.1093/nar/gkae293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 03/19/2024] [Accepted: 04/05/2024] [Indexed: 04/29/2024] Open
Abstract
Spatial transcriptomic (ST) techniques help us understand the gene expression levels in specific parts of tissues and organs, providing insights into their biological functions. Even though ST dataset provides information on the gene expression and its location for each sample, it is challenging to compare spatial gene expression patterns across tissue samples with different shapes and coordinates. Here, we propose a method, SpatialSPM, that reconstructs ST data into multi-dimensional image matrices to ensure comparability across different samples through spatial registration process. We demonstrated the applicability of this method by kidney and mouse olfactory bulb datasets as well as mouse brain ST datasets to investigate and directly compare gene expression in a specific anatomical region of interest, pixel by pixel, across various biological statuses. Beyond traditional analyses, SpatialSPM is capable of generating statistical parametric maps, including T-scores and Pearson correlation coefficients. This feature enables the identification of specific regions exhibiting differentially expressed genes across tissue samples, enhancing the depth and specificity of ST studies. Our approach provides an efficient way to analyze ST datasets and may offer detailed insights into various biological conditions.
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Affiliation(s)
| | | | | | | | - Hongyoon Choi
- Portrai, Inc., Seoul 03136, Republic of Korea
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
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15
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Casey C, Fullard JF, Sleator RD. Unravelling the genetic basis of Schizophrenia. Gene 2024; 902:148198. [PMID: 38266791 DOI: 10.1016/j.gene.2024.148198] [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: 09/01/2023] [Revised: 12/07/2023] [Accepted: 01/19/2024] [Indexed: 01/26/2024]
Abstract
Neuronal development is a highly regulated mechanism that is central to organismal function in animals. In humans, disruptions to this process can lead to a range of neurodevelopmental phenotypes, including Schizophrenia (SCZ). SCZ has a significant genetic component, whereby an individual with an SCZ affected family member is eight times more likely to develop the disease than someone with no family history of SCZ. By examining a combination of genomic, transcriptomic and epigenomic datasets, large-scale 'omics' studies aim to delineate the relationship between genetic variation and abnormal cellular activity in the SCZ brain. Herein, we provide a brief overview of some of the key omics methods currently being used in SCZ research, including RNA-seq, the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and high-throughput chromosome conformation capture (3C) approaches (e.g., Hi-C), as well as single-cell/nuclei iterations of these methods. We also discuss how these techniques are being employed to further our understanding of the genetic basis of SCZ, and to identify associated molecular pathways, biomarkers, and candidate drug targets.
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Affiliation(s)
- Clara Casey
- Department of Biological Sciences, Munster Technological University, Bishopstown, Cork, Ireland; Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Roy D Sleator
- Department of Biological Sciences, Munster Technological University, Bishopstown, Cork, Ireland.
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16
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Vásquez CE, Knak Guerra KT, Renner J, Rasia-Filho AA. Morphological heterogeneity of neurons in the human central amygdaloid nucleus. J Neurosci Res 2024; 102:e25319. [PMID: 38629777 DOI: 10.1002/jnr.25319] [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: 11/26/2023] [Revised: 02/23/2024] [Accepted: 03/03/2024] [Indexed: 04/19/2024]
Abstract
The central amygdaloid nucleus (CeA) has an ancient phylogenetic development and functions relevant for animal survival. Local cells receive intrinsic amygdaloidal information that codes emotional stimuli of fear, integrate them, and send cortical and subcortical output projections that prompt rapid visceral and social behavior responses. We aimed to describe the morphology of the neurons that compose the human CeA (N = 8 adult men). Cells within CeA coronal borders were identified using the thionine staining and were further analyzed using the "single-section" Golgi method followed by open-source software procedures for two-dimensional and three-dimensional image reconstructions. Our results evidenced varied neuronal cell body features, number and thickness of primary shafts, dendritic branching patterns, and density and shape of dendritic spines. Based on these criteria, we propose the existence of 12 morphologically different spiny neurons in the human CeA and discuss the variability in the dendritic architecture within cellular types, including likely interneurons. Some dendritic shafts were long and straight, displayed few collaterals, and had planar radiation within the coronal neuropil volume. Most of the sampled neurons showed a few to moderate density of small stubby/wide spines. Long spines (thin and mushroom) were observed occasionally. These novel data address the synaptic processing and plasticity in the human CeA. Our morphological description can be combined with further transcriptomic, immunohistochemical, and electrophysiological/connectional approaches. It serves also to investigate how neurons are altered in neurological and psychiatric disorders with hindered emotional perception, in anxiety, following atrophy in schizophrenia, and along different stages of Alzheimer's disease.
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Affiliation(s)
- Carlos E Vásquez
- Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Kétlyn T Knak Guerra
- Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Josué Renner
- Department of Basic Sciences/Physiology and Graduate Program in Biosciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil
| | - Alberto A Rasia-Filho
- Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Department of Basic Sciences/Physiology and Graduate Program in Biosciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil
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17
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Spathopoulou A, Podlesnic M, De Gaetano L, Kirsch EM, Tisch M, Finotello F, Aigner L, Günther K, Edenhofer F. Single-cell Profiling of Reprogrammed Human Neural Stem Cells Unveils High Similarity to Neural Progenitors in the Developing Central Nervous System. Stem Cell Rev Rep 2024:10.1007/s12015-024-10698-3. [PMID: 38519702 DOI: 10.1007/s12015-024-10698-3] [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] [Accepted: 02/14/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Similar to induced pluripotent cells (iPSCs), induced neural stem cells (iNSCs) can be directly converted from human somatic cells such as dermal fibroblasts and peripheral blood monocytes. While previous studies have demonstrated the resemblance of iNSCs to neural stem cells derived from primary sources and embryonic stem cells, respectively, a comprehensive analysis of the correlation between iNSCs and their physiological counterparts remained to be investigated. METHODS Nowadays, single-cell sequencing technologies provide unique opportunities for in-depth cellular benchmarking of complex cell populations. Our study involves the comprehensive profiling of converted human iNSCs at a single-cell transcriptomic level, alongside conventional methods, like flow cytometry and immunofluorescence stainings. RESULTS Our results show that the iNSC conversion yields a homogeneous cell population expressing bona fide neural stem cell markers. Extracting transcriptomic signatures from published single cell transcriptomic atlas data and comparison to the iNSC transcriptome reveals resemblance to embryonic neuroepithelial cells of early neurodevelopmental stages observed in vivo at 5 weeks of development. CONCLUSION Our data underscore the physiological relevance of directly converted iNSCs, making them a valuable in vitro system for modeling human central nervous system development and establishing translational applications in cell therapy and compound screening.
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Affiliation(s)
- Angeliki Spathopoulou
- Department of Molecular Biology & CMBI, Genomics, Stem Cell & Regenerative Medicine Group, University of Innsbruck, Technikerstraße 25, 6020, Innsbruck, Austria
| | - Martina Podlesnic
- Department of Molecular Biology & CMBI, Genomics, Stem Cell & Regenerative Medicine Group, University of Innsbruck, Technikerstraße 25, 6020, Innsbruck, Austria
| | - Laura De Gaetano
- Department of Molecular Biology & CMBI, Genomics, Stem Cell & Regenerative Medicine Group, University of Innsbruck, Technikerstraße 25, 6020, Innsbruck, Austria
| | - Elena Marie Kirsch
- Institute of Molecular Regenerative Medicine, Paracelsus Medical University, Salzburg, Austria
- Center for Stroke Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Department of Experimental Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Marcel Tisch
- Department of Molecular Biology & CMBI, Genomics, Stem Cell & Regenerative Medicine Group, University of Innsbruck, Technikerstraße 25, 6020, Innsbruck, Austria
| | - Francesca Finotello
- Department of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, Innsbruck, Austria
| | - Ludwig Aigner
- Institute of Molecular Regenerative Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Katharina Günther
- Department of Molecular Biology & CMBI, Genomics, Stem Cell & Regenerative Medicine Group, University of Innsbruck, Technikerstraße 25, 6020, Innsbruck, Austria
- Institute of Molecular Regenerative Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Frank Edenhofer
- Department of Molecular Biology & CMBI, Genomics, Stem Cell & Regenerative Medicine Group, University of Innsbruck, Technikerstraße 25, 6020, Innsbruck, Austria.
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18
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Amartumur S, Nguyen H, Huynh T, Kim TS, Woo RS, Oh E, Kim KK, Lee LP, Heo C. Neuropathogenesis-on-chips for neurodegenerative diseases. Nat Commun 2024; 15:2219. [PMID: 38472255 PMCID: PMC10933492 DOI: 10.1038/s41467-024-46554-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
Developing diagnostics and treatments for neurodegenerative diseases (NDs) is challenging due to multifactorial pathogenesis that progresses gradually. Advanced in vitro systems that recapitulate patient-like pathophysiology are emerging as alternatives to conventional animal-based models. In this review, we explore the interconnected pathogenic features of different types of ND, discuss the general strategy to modelling NDs using a microfluidic chip, and introduce the organoid-on-a-chip as the next advanced relevant model. Lastly, we overview how these models are being applied in academic and industrial drug development. The integration of microfluidic chips, stem cells, and biotechnological devices promises to provide valuable insights for biomedical research and developing diagnostic and therapeutic solutions for NDs.
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Affiliation(s)
- Sarnai Amartumur
- Department of Biophysics, Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Korea
| | - Huong Nguyen
- Department of Biophysics, Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Korea
| | - Thuy Huynh
- Department of Biophysics, Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Korea
| | - Testaverde S Kim
- Center for Integrated Nanostructure Physics (CINAP), Institute for Basic Science (IBS), Suwon, 16419, Korea
| | - Ran-Sook Woo
- Department of Anatomy and Neuroscience, College of Medicine, Eulji University, Daejeon, 34824, Korea
| | - Eungseok Oh
- Department of Neurology, Chungnam National University Hospital, Daejeon, 35015, Korea
| | - Kyeong Kyu Kim
- Department of Precision Medicine, Graduate School of Basic Medical Science (GSBMS), Institute for Anti-microbial Resistance Research and Therapeutics, Sungkyunkwan University School of Medicine, Suwon, 16419, Korea
| | - Luke P Lee
- Department of Biophysics, Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Korea.
- Harvard Medical School, Division of Engineering in Medicine and Renal Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA.
- Department of Bioengineering, Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, 94720, USA.
| | - Chaejeong Heo
- Department of Biophysics, Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Korea.
- Center for Integrated Nanostructure Physics (CINAP), Institute for Basic Science (IBS), Suwon, 16419, Korea.
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19
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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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20
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Welsh N, Disano K, Linzey M, Pike SC, Smith AD, Pachner AR, Gilli F. CXCL10/IgG1 Axis in Multiple Sclerosis as a Potential Predictive Biomarker of Disease Activity. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2024; 11:e200200. [PMID: 38346270 DOI: 10.1212/nxi.0000000000200200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/16/2023] [Indexed: 02/15/2024]
Abstract
BACKGROUND AND OBJECTIVES Multiple sclerosis (MS) is a heterogeneous disease, and its course is difficult to predict. Prediction models can be established by measuring intrathecally synthesized proteins involved in inflammation, glial activation, and CNS injury. METHODS To determine how these intrathecal proteins relate to the short-term, i.e., 12 months, disease activity in relapsing-remitting MS (RRMS), we measured the intrathecal synthesis of 46 inflammatory mediators and 14 CNS injury or glial activation markers in matched serum and CSF samples from 47 patients with MS (pwMS), i.e., 23 RRMS and 24 clinically isolated syndrome (CIS), undergoing diagnostic lumbar puncture. Subsequently, all pwMS were followed for ≥12 months in a retrospective follow-up study and ultimately classified into "active", i.e., developing clinical and/or radiologic disease activity, n = 18) or "nonactive", i.e., not having disease activity, n = 29. Disease activity in patients with CIS corresponded to conversion to RRMS. Thus, patients with CIS were subclassified as "converters" or "nonconverters" based on their conversion status at the end of a 12-month follow-up. Twenty-seven patients with noninflammatory neurologic diseases were included as negative controls. Data were subjected to differential expression analysis and modeling techniques to define the connectivity arrangement (network) between neuroinflammation and CNS injury relevant to short-term disease activity in RRMS. RESULTS Lower age and/or higher CXCL13 levels positively distinguished active/converting vs nonactive/nonconverting patients. Network analysis significantly improved the prediction of short-term disease activity because active/converting patients featured a stronger positive connection between IgG1 and CXCL10. Accordingly, analysis of disease activity-free survival demonstrated that pwMS, both RRMS and CIS, with a lower or negative IgG1-CXCL10 correlation, have a higher probability of activity-free survival than the patients with a significant correlation (p < 0.0001, HR ≥ 2.87). DISCUSSION Findings indicate that a significant IgG1-CXCL10 positive correlation predicts the risk of short-term disease activity in patients with RRMS and CIS. Thus, the present results can be used to develop a predictive model for MS activity and conversion to RRMS.
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Affiliation(s)
- Nora Welsh
- From the Integrative Neuroscience (N.W., M.L., S.C.P.), Dartmouth College, Hanover, NH; Neurology (N.W., K.D., S.C.P., A.D.S., A.R.P., F.G.), Dartmouth Hitchcock Medical Center, Lebanon, NH; and Veteran Affairs Medical Center (K.D.), White River Junction, VT
| | - Krista Disano
- From the Integrative Neuroscience (N.W., M.L., S.C.P.), Dartmouth College, Hanover, NH; Neurology (N.W., K.D., S.C.P., A.D.S., A.R.P., F.G.), Dartmouth Hitchcock Medical Center, Lebanon, NH; and Veteran Affairs Medical Center (K.D.), White River Junction, VT
| | - Michael Linzey
- From the Integrative Neuroscience (N.W., M.L., S.C.P.), Dartmouth College, Hanover, NH; Neurology (N.W., K.D., S.C.P., A.D.S., A.R.P., F.G.), Dartmouth Hitchcock Medical Center, Lebanon, NH; and Veteran Affairs Medical Center (K.D.), White River Junction, VT
| | - Steven C Pike
- From the Integrative Neuroscience (N.W., M.L., S.C.P.), Dartmouth College, Hanover, NH; Neurology (N.W., K.D., S.C.P., A.D.S., A.R.P., F.G.), Dartmouth Hitchcock Medical Center, Lebanon, NH; and Veteran Affairs Medical Center (K.D.), White River Junction, VT
| | - Andrew D Smith
- From the Integrative Neuroscience (N.W., M.L., S.C.P.), Dartmouth College, Hanover, NH; Neurology (N.W., K.D., S.C.P., A.D.S., A.R.P., F.G.), Dartmouth Hitchcock Medical Center, Lebanon, NH; and Veteran Affairs Medical Center (K.D.), White River Junction, VT
| | - Andrew R Pachner
- From the Integrative Neuroscience (N.W., M.L., S.C.P.), Dartmouth College, Hanover, NH; Neurology (N.W., K.D., S.C.P., A.D.S., A.R.P., F.G.), Dartmouth Hitchcock Medical Center, Lebanon, NH; and Veteran Affairs Medical Center (K.D.), White River Junction, VT
| | - Francesca Gilli
- From the Integrative Neuroscience (N.W., M.L., S.C.P.), Dartmouth College, Hanover, NH; Neurology (N.W., K.D., S.C.P., A.D.S., A.R.P., F.G.), Dartmouth Hitchcock Medical Center, Lebanon, NH; and Veteran Affairs Medical Center (K.D.), White River Junction, VT
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21
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Singhal V, Chou N, Lee J, Yue Y, Liu J, Chock WK, Lin L, Chang YC, Teo EML, Aow J, Lee HK, Chen KH, Prabhakar S. BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis. Nat Genet 2024; 56:431-441. [PMID: 38413725 PMCID: PMC10937399 DOI: 10.1038/s41588-024-01664-3] [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: 04/03/2023] [Accepted: 01/16/2024] [Indexed: 02/29/2024]
Abstract
Spatial omics data are clustered to define both cell types and tissue domains. We present Building Aggregates with a Neighborhood Kernel and Spatial Yardstick (BANKSY), an algorithm that unifies these two spatial clustering problems by embedding cells in a product space of their own and the local neighborhood transcriptome, representing cell state and microenvironment, respectively. BANKSY's spatial feature augmentation strategy improved performance on both tasks when tested on diverse RNA (imaging, sequencing) and protein (imaging) datasets. BANKSY revealed unexpected niche-dependent cell states in the mouse brain and outperformed competing methods on domain segmentation and cell typing benchmarks. BANKSY can also be used for quality control of spatial transcriptomics data and for spatially aware batch effect correction. Importantly, it is substantially faster and more scalable than existing methods, enabling the processing of millions of cell datasets. In summary, BANKSY provides an accurate, biologically motivated, scalable and versatile framework for analyzing spatially resolved omics data.
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Affiliation(s)
- Vipul Singhal
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Nigel Chou
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Joseph Lee
- Faculty of Science, National University of Singapore, Singapore, Republic of Singapore
| | - Yifei Yue
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Republic of Singapore
| | - Jinyue Liu
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Wan Kee Chock
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Li Lin
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | | | | | - Jonathan Aow
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Hwee Kuan Lee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- School of Computing, National University of Singapore, Singapore, Republic of Singapore
- Singapore Eye Research Institute, Singapore, Republic of Singapore
- International Research Laboratory on Artificial Intelligence, Singapore, Republic of Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Republic of Singapore
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Republic of Singapore
| | - Kok Hao Chen
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
| | - Shyam Prabhakar
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
- Population and Global Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Republic of Singapore.
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Republic of Singapore.
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22
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Zito A, Lee JT. Variable expression of MECP2, CDKL5, and FMR1 in the human brain: Implications for gene restorative therapies. Proc Natl Acad Sci U S A 2024; 121:e2312757121. [PMID: 38386709 PMCID: PMC10907246 DOI: 10.1073/pnas.2312757121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 12/28/2023] [Indexed: 02/24/2024] Open
Abstract
MECP2, CDKL5, and FMR1 are three X-linked neurodevelopmental genes associated with Rett, CDKL5-, and fragile-X syndrome, respectively. These syndromes are characterized by distinct constellations of severe cognitive and neurobehavioral anomalies, reflecting the broad but unique expression patterns of each of the genes in the brain. As these disorders are not thought to be neurodegenerative and may be reversible, a major goal has been to restore expression of the functional proteins in the patient's brain. Strategies have included gene therapy, gene editing, and selective Xi-reactivation methodologies. However, tissue penetration and overall delivery to various regions of the brain remain challenging for each strategy. Thus, gaining insights into how much restoration would be required and what regions/cell types in the brain must be targeted for meaningful physiological improvement would be valuable. As a step toward addressing these questions, here we perform a meta-analysis of single-cell transcriptomics data from the human brain across multiple developmental stages, in various brain regions, and in multiple donors. We observe a substantial degree of expression variability for MECP2, CDKL5, and FMR1 not only across cell types but also between donors. The wide range of expression may help define a therapeutic window, with the low end delineating a minimum level required to restore physiological function and the high end informing toxicology margin. Finally, the inter-cellular and inter-individual variability enable identification of co-varying genes and will facilitate future identification of biomarkers.
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Affiliation(s)
- Antonino Zito
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA02114
- Department of Genetics, The Blavatnik Institute, Harvard Medical School, Boston, MA02114
| | - Jeannie T. Lee
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA02114
- Department of Genetics, The Blavatnik Institute, Harvard Medical School, Boston, MA02114
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23
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He Z, Chen Q, Wang K, Lin J, Peng Y, Zhang J, Yan X, Jie Y. Single-cell transcriptomics analysis of cellular heterogeneity and immune mechanisms in neurodegenerative diseases. Eur J Neurosci 2024; 59:333-357. [PMID: 38221677 DOI: 10.1111/ejn.16242] [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: 05/17/2023] [Revised: 12/04/2023] [Accepted: 12/12/2023] [Indexed: 01/16/2024]
Abstract
Single-cell transcriptomics analysis is an advanced technology that can describe the intracellular transcriptome in complex tissues. It profiles and analyses datasets by single-cell RNA sequencing. Neurodegenerative diseases are identified by the abnormal apoptosis of neurons in the brain with few or no effective therapy strategies at present, which has been a growing healthcare concern and brought a great burden to society. The transcriptome of individual cells provides deep insights into previously unforeseen cellular heterogeneity and gene expression differences in neurodegenerative disorders. It detects multiple cell subsets and functional changes during pathological progression, which deepens the understanding of the molecular underpinnings and cellular basis of neurodegenerative diseases. Furthermore, the transcriptome analysis of immune cells shows the regulation of immune response. Different subtypes of immune cells and their interaction are found to contribute to disease progression. This finding enables the discovery of novel targets and biomarkers for early diagnosis. In this review, we emphasize the principles of the technology, and its recent progress in the study of cellular heterogeneity and immune mechanisms in neurodegenerative diseases. The application of single-cell transcriptomics analysis in neurodegenerative disorders would help explore the pathogenesis of these diseases and develop novel therapeutic methods.
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Affiliation(s)
- Ziping He
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
- Clinical Medicine Eight-Year Program, Xiangya School of Medicine, Central South University, Changsha, China
| | - Qianqian Chen
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
| | - Kaiyue Wang
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
- Clinical Medicine Eight-Year Program, Xiangya School of Medicine, Central South University, Changsha, China
| | - Jiang Lin
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
| | - Yilin Peng
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
| | - Jinlong Zhang
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
- Department of Forensic Science, School of Basic Medical Science, Xinjiang Medical University, Urumqi, China
| | - Xisheng Yan
- Department of Cardiovascular Medicine, Wuhan Third Hospital & Tongren Hospital of Wuhan University, Wuhan, China
| | - Yan Jie
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
- Department of Forensic Science, School of Basic Medical Science, Xinjiang Medical University, Urumqi, China
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24
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Tripathi S, Nathan CL, Tate MC, Horbinski CM, Templer JW, Rosenow JM, Sita TL, James CD, Deneen B, Miller SD, Heimberger AB. The immune system and metabolic products in epilepsy and glioma-associated epilepsy: emerging therapeutic directions. JCI Insight 2024; 9:e174753. [PMID: 38193532 PMCID: PMC10906461 DOI: 10.1172/jci.insight.174753] [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] [Indexed: 01/10/2024] Open
Abstract
Epilepsy has a profound impact on quality of life. Despite the development of new antiseizure medications (ASMs), approximately one-third of affected patients have drug-refractory epilepsy and are nonresponsive to medical treatment. Nearly all currently approved ASMs target neuronal activity through ion channel modulation. Recent human and animal model studies have implicated new immunotherapeutic and metabolomic approaches that may benefit patients with epilepsy. In this Review, we detail the proinflammatory immune landscape of epilepsy and contrast this with the immunosuppressive microenvironment in patients with glioma-related epilepsy. In the tumor setting, excessive neuronal activity facilitates immunosuppression, thereby contributing to subsequent glioma progression. Metabolic modulation of the IDH1-mutant pathway provides a dual pathway for reversing immune suppression and dampening seizure activity. Elucidating the relationship between neurons and immunoreactivity is an area for the prioritization and development of the next era of ASMs.
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Affiliation(s)
- Shashwat Tripathi
- Department of Neurological Surgery
- Malnati Brain Tumor Institute of the Robert H. Lurie Comprehensive Cancer Center
| | | | | | - Craig M. Horbinski
- Malnati Brain Tumor Institute of the Robert H. Lurie Comprehensive Cancer Center
- Department of Pathology, and
| | | | | | - Timothy L. Sita
- Department of Neurological Surgery
- Department of Radiation Oncology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Charles D. James
- Department of Neurological Surgery
- Malnati Brain Tumor Institute of the Robert H. Lurie Comprehensive Cancer Center
| | - Benjamin Deneen
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - Stephen D. Miller
- Department of Microbiology-Immunology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Amy B. Heimberger
- Department of Neurological Surgery
- Malnati Brain Tumor Institute of the Robert H. Lurie Comprehensive Cancer Center
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25
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Cartas-Cejudo P, Cortés A, Lachén-Montes M, Anaya-Cubero E, Peral E, Ausín K, Díaz-Peña R, Fernández-Irigoyen J, Santamaría E. Mapping the human brain proteome: opportunities, challenges, and clinical potential. Expert Rev Proteomics 2024; 21:55-63. [PMID: 38299555 DOI: 10.1080/14789450.2024.2313073] [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: 09/25/2023] [Accepted: 01/24/2024] [Indexed: 02/02/2024]
Abstract
INTRODUCTION Due to the segmented functions and complexity of the human brain, the characterization of molecular profiles within specific areas such as brain structures and biofluids is essential to unveil the molecular basis for structure specialization as well as the molecular imbalance associated with neurodegenerative and psychiatric diseases. AREAS COVERED Much of our knowledge about brain functionality derives from neurophysiological, anatomical, and transcriptomic approaches. More recently, laser capture and imaging proteomics, technological and computational developments in LC-MS/MS, as well as antibody/aptamer-based platforms have allowed the generation of novel cellular, spatial, and posttranslational dimensions as well as innovative facets in biomarker validation and druggable target identification. EXPERT OPINION Proteomics is a powerful toolbox to functionally characterize, quantify, and localize the extensive protein catalog of the human brain across physiological and pathological states. Brain function depends on multi-dimensional protein homeostasis, and its elucidation will help us to characterize biological pathways that are essential to properly maintain cognitive functions. In addition, comprehensive human brain pathological proteomes may be the basis in computational drug-repositioning methods as a strategy for unveiling potential new therapies in neurodegenerative and psychiatric disorders.
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Affiliation(s)
- Paz Cartas-Cejudo
- Clinical Neuroproteomics Unit, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Adriana Cortés
- Clinical Neuroproteomics Unit, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Mercedes Lachén-Montes
- Clinical Neuroproteomics Unit, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Elena Anaya-Cubero
- Clinical Neuroproteomics Unit, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Erika Peral
- Proteomics Platform, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Karina Ausín
- Proteomics Platform, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Ramón Díaz-Peña
- Proteomics Platform, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Joaquín Fernández-Irigoyen
- Proteomics Platform, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Enrique Santamaría
- Clinical Neuroproteomics Unit, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
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26
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Bormann D, Knoflach M, Poreba E, Riedl CJ, Testa G, Orset C, Levilly A, Cottereau A, Jauk P, Hametner S, Golabi B, Copic D, Klas K, Direder M, Kühtreiber H, Salek M, zur Nedden S, Baier-Bitterlich G, Kiechl S, Haider C, Endmayr V, Höftberger R, Ankersmit HJ, Mildner M. Single nucleus RNA sequencing reveals glial cell type-specific responses to ischemic stroke. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.26.573302. [PMID: 38234821 PMCID: PMC10793395 DOI: 10.1101/2023.12.26.573302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Reactive neuroglia critically shape the braińs response to ischemic stroke. However, their phenotypic heterogeneity impedes a holistic understanding of the cellular composition and microenvironment of the early ischemic lesion. Here we generated a single cell resolution transcriptomics dataset of the injured brain during the acute recovery from permanent middle cerebral artery occlusion. This approach unveiled infarction and subtype specific molecular signatures in oligodendrocyte lineage cells and astrocytes, which ranged among the most transcriptionally perturbed cell types in our dataset. Specifically, we characterized and compared infarction restricted proliferating oligodendrocyte precursor cells (OPCs), mature oligodendrocytes and heterogeneous reactive astrocyte populations. Our analyses unveiled unexpected commonalities in the transcriptional response of oligodendrocyte lineage cells and astrocytes to ischemic injury. Moreover, OPCs and reactive astrocytes were involved in a shared immuno-glial cross talk with stroke specific myeloid cells. In situ, osteopontin positive myeloid cells accumulated in close proximity to proliferating OPCs and reactive astrocytes, which expressed the osteopontin receptor CD44, within the perilesional zone specifically. In vitro, osteopontin increased the migratory capacity of OPCs. Collectively, our study highlights molecular cross talk events which might govern the cellular composition and microenvironment of infarcted brain tissue in the early stages of recovery.
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Affiliation(s)
- Daniel Bormann
- Applied Immunology Laboratory, Department of Thoracic Surgery, Medical University of Vienna, 1090 Vienna, Austria
- Aposcience AG, 1200 Vienna, Austria
| | - Michael Knoflach
- Department of Neurology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
- VASCage, Research Centre on Vascular Ageing and Stroke, 6020 Innsbruck, Austria
| | - Emilia Poreba
- Department of Dermatology, Medical University of Vienna, 1090 Vienna, Austria
| | - Christian J. Riedl
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
| | - Giulia Testa
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
| | - Cyrille Orset
- Normandie University, UNICAEN, ESR3P, INSERM UMR-S U1237, Physiopathology and Imaging of Neurological Disorders (PhIND), GIP Cyceron, Institut Blood and Brain @ Caen-Normandie (BB@C), Caen, France
- Department of Clinical Research, Caen-Normandie University Hospital, Caen, France
| | - Anthony Levilly
- Normandie University, UNICAEN, ESR3P, INSERM UMR-S U1237, Physiopathology and Imaging of Neurological Disorders (PhIND), GIP Cyceron, Institut Blood and Brain @ Caen-Normandie (BB@C), Caen, France
- Department of Clinical Research, Caen-Normandie University Hospital, Caen, France
| | - Andreá Cottereau
- Normandie University, UNICAEN, ESR3P, INSERM UMR-S U1237, Physiopathology and Imaging of Neurological Disorders (PhIND), GIP Cyceron, Institut Blood and Brain @ Caen-Normandie (BB@C), Caen, France
- Department of Clinical Research, Caen-Normandie University Hospital, Caen, France
| | - Philipp Jauk
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria
| | - Simon Hametner
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
| | - Bahar Golabi
- Department of Dermatology, Medical University of Vienna, 1090 Vienna, Austria
| | - Dragan Copic
- Applied Immunology Laboratory, Department of Thoracic Surgery, Medical University of Vienna, 1090 Vienna, Austria
- Aposcience AG, 1200 Vienna, Austria
- Division of Nephrology and Dialysis, Department of Internal Medicine III, Medical University of Vienna, 1090 Vienna, Austria
| | - Katharina Klas
- Applied Immunology Laboratory, Department of Thoracic Surgery, Medical University of Vienna, 1090 Vienna, Austria
- Aposcience AG, 1200 Vienna, Austria
| | - Martin Direder
- Applied Immunology Laboratory, Department of Thoracic Surgery, Medical University of Vienna, 1090 Vienna, Austria
- Aposcience AG, 1200 Vienna, Austria
- Department of Orthopedics and Trauma Surgery, Medical University of Vienna, 1090 Vienna, Austria
| | - Hannes Kühtreiber
- Applied Immunology Laboratory, Department of Thoracic Surgery, Medical University of Vienna, 1090 Vienna, Austria
- Aposcience AG, 1200 Vienna, Austria
| | - Melanie Salek
- Applied Immunology Laboratory, Department of Thoracic Surgery, Medical University of Vienna, 1090 Vienna, Austria
- Aposcience AG, 1200 Vienna, Austria
| | - Stephanie zur Nedden
- Institute of Neurobiochemistry, CCB-Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Gabriele Baier-Bitterlich
- Institute of Neurobiochemistry, CCB-Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Stefan Kiechl
- Department of Neurology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
- VASCage, Research Centre on Vascular Ageing and Stroke, 6020 Innsbruck, Austria
| | - Carmen Haider
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
| | - Verena Endmayr
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
| | - Romana Höftberger
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
| | - Hendrik J. Ankersmit
- Applied Immunology Laboratory, Department of Thoracic Surgery, Medical University of Vienna, 1090 Vienna, Austria
- Aposcience AG, 1200 Vienna, Austria
| | - Michael Mildner
- Department of Dermatology, Medical University of Vienna, 1090 Vienna, Austria
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27
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Li E, Kampmann M. Toward a CRISPR understanding of gene function in human brain development. Cell Stem Cell 2023; 30:1561-1562. [PMID: 38065064 DOI: 10.1016/j.stem.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 11/08/2023] [Accepted: 11/08/2023] [Indexed: 12/18/2023]
Abstract
Genome-wide association studies pinpoint genetic risk factors for neurodevelopmental disorders (NDDs), but the next challenge is to understand the mechanisms through which these genes affect brain development. Two recent CRISPR screens in human brain organoids1,2 interrogate the function of risk genes for autism spectrum disorder and other NDDs.
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Affiliation(s)
- Emmy Li
- Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA, USA; Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA.
| | - Martin Kampmann
- Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA, USA; Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA.
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28
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Awuah WA, Ahluwalia A, Ghosh S, Roy S, Tan JK, Adebusoye FT, Ferreira T, Bharadwaj HR, Shet V, Kundu M, Yee ALW, Abdul-Rahman T, Atallah O. The molecular landscape of neurological disorders: insights from single-cell RNA sequencing in neurology and neurosurgery. Eur J Med Res 2023; 28:529. [PMID: 37974227 PMCID: PMC10652629 DOI: 10.1186/s40001-023-01504-w] [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: 08/10/2023] [Accepted: 11/03/2023] [Indexed: 11/19/2023] Open
Abstract
Single-cell ribonucleic acid sequencing (scRNA-seq) has emerged as a transformative technology in neurological and neurosurgical research, revolutionising our comprehension of complex neurological disorders. In brain tumours, scRNA-seq has provided valuable insights into cancer heterogeneity, the tumour microenvironment, treatment resistance, and invasion patterns. It has also elucidated the brain tri-lineage cancer hierarchy and addressed limitations of current models. Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis have been molecularly subtyped, dysregulated pathways have been identified, and potential therapeutic targets have been revealed using scRNA-seq. In epilepsy, scRNA-seq has explored the cellular and molecular heterogeneity underlying the condition, uncovering unique glial subpopulations and dysregulation of the immune system. ScRNA-seq has characterised distinct cellular constituents and responses to spinal cord injury in spinal cord diseases, as well as provided molecular signatures of various cell types and identified interactions involved in vascular remodelling. Furthermore, scRNA-seq has shed light on the molecular complexities of cerebrovascular diseases, such as stroke, providing insights into specific genes, cell-specific expression patterns, and potential therapeutic interventions. This review highlights the potential of scRNA-seq in guiding precision medicine approaches, identifying clinical biomarkers, and facilitating therapeutic discovery. However, challenges related to data analysis, standardisation, sample acquisition, scalability, and cost-effectiveness need to be addressed. Despite these challenges, scRNA-seq has the potential to transform clinical practice in neurological and neurosurgical research by providing personalised insights and improving patient outcomes.
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Affiliation(s)
- Wireko Andrew Awuah
- Faculty of Medicine, Sumy State University, Zamonstanksya 7, Sumy, 40007, Ukraine
| | | | - Shankaneel Ghosh
- Institute of Medical Sciences and SUM Hospital, Bhubaneswar, India
| | - Sakshi Roy
- School of Medicine, Queen's University Belfast, Belfast, UK
| | | | | | - Tomas Ferreira
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | | | - Vallabh Shet
- Faculty of Medicine, Bangalore Medical College and Research Institute, Bangalore, Karnataka, India
| | - Mrinmoy Kundu
- Institute of Medical Sciences and SUM Hospital, Bhubaneswar, India
| | | | - Toufik Abdul-Rahman
- Faculty of Medicine, Sumy State University, Zamonstanksya 7, Sumy, 40007, Ukraine
| | - Oday Atallah
- Department of Neurosurgery, Hannover Medical School, Carl-Neuberg-Strasse 1, 30625, Hannover, Germany
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29
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Rumker L, Sakaue S, Reshef Y, Kang JB, Yazar S, Alquicira-Hernandez J, Valencia C, Lagattuta KA, Mah-Som A, Nathan A, Powell JE, Loh PR, Raychaudhuri S. Identifying genetic variants that influence the abundance of cell states in single-cell data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.13.566919. [PMID: 38014313 PMCID: PMC10680752 DOI: 10.1101/2023.11.13.566919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Introductory ParagraphTo understand genetic mechanisms driving disease, it is essential but difficult to map how risk alleles affect the composition of cells present in the body. Single-cell profiling quantifies granular information about tissues, but variant-associated cell states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce GeNA (Genotype-Neighborhood Associations), a statistical tool to identify cell state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of scRNA-seq peripheral blood profiling from 969 individuals,1GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (p=1.96×10-11) associates with increased abundance of NK cells expressing TNF-α response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-TNF treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.
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Affiliation(s)
- Laurie Rumker
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Saori Sakaue
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yakir Reshef
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joyce B. Kang
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seyhan Yazar
- Translational Genomics, Garvan Institute of Medical Research, Sydney, Australia
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, Australia
| | - Jose Alquicira-Hernandez
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Cristian Valencia
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kaitlyn A Lagattuta
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Annelise Mah-Som
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joseph E. Powell
- Translational Genomics, Garvan Institute of Medical Research, Sydney, Australia
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, Australia
| | - Po-Ru Loh
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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30
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Bormann D, Copic D, Klas K, Direder M, Riedl CJ, Testa G, Kühtreiber H, Poreba E, Hametner S, Golabi B, Salek M, Haider C, Endmayr V, Shaw LE, Höftberger R, Ankersmit HJ, Mildner M. Exploring the heterogeneous transcriptional response of the CNS to systemic LPS and Poly(I:C). Neurobiol Dis 2023; 188:106339. [PMID: 37913832 DOI: 10.1016/j.nbd.2023.106339] [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: 07/19/2023] [Revised: 10/25/2023] [Accepted: 10/29/2023] [Indexed: 11/03/2023] Open
Abstract
Peripheral contact to pathogen-associated molecular patterns (PAMPs) evokes a systemic innate immune response which is rapidly relayed to the central nervous system (CNS). The remarkable cellular heterogeneity of the CNS poses a significant challenge to the study of cell type and stimulus dependent responses of neural cells during acute inflammation. Here we utilized single nuclei RNA sequencing (snRNAseq), serum proteome profiling and primary cell culture methods to systematically compare the acute response of the mammalian brain to the bacterial PAMP lipopolysaccharide (LPS) and the viral PAMP polyinosinic:polycytidylic acid (Poly(I:C)), at single cell resolution. Our study unveiled convergent transcriptional cytokine and cellular stress responses in brain vascular and ependymal cells and a downregulation of several key mediators of directed blood brain barrier (BBB) transport. In contrast the neuronal response to PAMPs was limited in acute neuroinflammation. Moreover, our study highlighted the dominant role of IFN signalling upon Poly(I:C) challenge, particularly in cells of the oligodendrocyte lineage. Collectively our study unveils heterogeneous, shared and distinct cell type and stimulus dependent acute responses of the CNS to bacterial and viral PAMP challenges. Our findings highlight inflammation induced dysregulations of BBB-transporter gene expression, suggesting potential translational implications on drug pharmacokinetics variability during acute neuroinflammation. The pronounced dependency of oligodendrocytes on IFN stimulation during viral PAMP challenges, emphasizes their limited molecular viral response repertoire.
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Affiliation(s)
- Daniel Bormann
- Department of Thoracic Surgery, Applied Immunology Laboratory, Medical University of Vienna, Vienna, Austria; Aposcience AG, 1200 Vienna, Austria
| | - Dragan Copic
- Department of Thoracic Surgery, Applied Immunology Laboratory, Medical University of Vienna, Vienna, Austria; Aposcience AG, 1200 Vienna, Austria; Division of Nephrology and Dialysis, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
| | - Katharina Klas
- Department of Thoracic Surgery, Applied Immunology Laboratory, Medical University of Vienna, Vienna, Austria; Aposcience AG, 1200 Vienna, Austria
| | - Martin Direder
- Department of Thoracic Surgery, Applied Immunology Laboratory, Medical University of Vienna, Vienna, Austria; Aposcience AG, 1200 Vienna, Austria; Department of Orthopedics and Trauma Surgery, Medical University of Vienna, Vienna, Austria
| | - Christian J Riedl
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Giulia Testa
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Hannes Kühtreiber
- Department of Thoracic Surgery, Applied Immunology Laboratory, Medical University of Vienna, Vienna, Austria; Aposcience AG, 1200 Vienna, Austria
| | - Emilia Poreba
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Simon Hametner
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Bahar Golabi
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Melanie Salek
- Department of Thoracic Surgery, Applied Immunology Laboratory, Medical University of Vienna, Vienna, Austria; Aposcience AG, 1200 Vienna, Austria
| | - Carmen Haider
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Verena Endmayr
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Lisa E Shaw
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Romana Höftberger
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Hendrik J Ankersmit
- Department of Thoracic Surgery, Applied Immunology Laboratory, Medical University of Vienna, Vienna, Austria; Aposcience AG, 1200 Vienna, Austria
| | - Michael Mildner
- Department of Dermatology, Medical University of Vienna, Vienna, Austria.
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31
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Adeuyan O, Gordon ER, Kenchappa D, Bracero Y, Singh A, Espinoza G, Geskin LJ, Saenger YM. An update on methods for detection of prognostic and predictive biomarkers in melanoma. Front Cell Dev Biol 2023; 11:1290696. [PMID: 37900283 PMCID: PMC10611507 DOI: 10.3389/fcell.2023.1290696] [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: 09/07/2023] [Accepted: 10/04/2023] [Indexed: 10/31/2023] Open
Abstract
The approval of immunotherapy for stage II-IV melanoma has underscored the need for improved immune-based predictive and prognostic biomarkers. For resectable stage II-III patients, adjuvant immunotherapy has proven clinical benefit, yet many patients experience significant adverse events and may not require therapy. In the metastatic setting, single agent immunotherapy cures many patients but, in some cases, more intensive combination therapies against specific molecular targets are required. Therefore, the establishment of additional biomarkers to determine a patient's disease outcome (i.e., prognostic) or response to treatment (i.e., predictive) is of utmost importance. Multiple methods ranging from gene expression profiling of bulk tissue, to spatial transcriptomics of single cells and artificial intelligence-based image analysis have been utilized to better characterize the immune microenvironment in melanoma to provide novel predictive and prognostic biomarkers. In this review, we will highlight the different techniques currently under investigation for the detection of prognostic and predictive immune biomarkers in melanoma.
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Affiliation(s)
- Oluwaseyi Adeuyan
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Emily R. Gordon
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Divya Kenchappa
- Albert Einstein College of Medicine, Bronx, NY, United States
| | - Yadriel Bracero
- Albert Einstein College of Medicine, Bronx, NY, United States
| | - Ajay Singh
- Albert Einstein College of Medicine, Bronx, NY, United States
| | | | - Larisa J. Geskin
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, United States
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Lei T, Chen R, Zhang S, Chen Y. Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations. Brief Bioinform 2023; 24:bbad335. [PMID: 37769630 PMCID: PMC10539043 DOI: 10.1093/bib/bbad335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 09/05/2023] [Accepted: 09/06/2023] [Indexed: 10/02/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. We introduce DeepScena, a novel single-cell hierarchical clustering tool that fully incorporates nonlinear dimension reduction, negative binomial-based convolutional autoencoder for data fitting, and a self-supervision model for cell similarity enhancement. In comprehensive evaluation using multiple large-scale scRNA-seq datasets, DeepScena consistently outperformed seven popular clustering tools in terms of accuracy. Notably, DeepScena exhibits high proficiency in identifying rare cell populations within large datasets that contain large numbers of clusters. When applied to scRNA-seq data of multiple myeloma cells, DeepScena successfully identified not only previously labeled large cell types but also subpopulations in CD14 monocytes, T cells and natural killer cells, respectively.
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Affiliation(s)
- Tianyuan Lei
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China
| | - Ruoyu Chen
- Moorestown High School, Moorestown, NJ 08057, USA
| | - Shaoqiang Zhang
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China
| | - Yong Chen
- Department of Biological and Biomedical Sciences, Rowan University, NJ 08028, USA
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Bodea GO, Yemisci M. Editorial: Methods and applications in cellular neuropathology. Front Cell Neurosci 2023; 17:1244414. [PMID: 37496705 PMCID: PMC10367338 DOI: 10.3389/fncel.2023.1244414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 06/23/2023] [Indexed: 07/28/2023] Open
Affiliation(s)
- Gabriela O. Bodea
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - Muge Yemisci
- Institute of Neurological Sciences and Psychiatry, Department of Basic Neurosciences and Psychiatry, Faculty of Medicine, Department of Neurology, Hacettepe University, Sihhiye, Ankara, Türkiye
- Neuroscience and Neurotechnology Center of Excellence (NÖROM), Ankara, Türkiye
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Fernández-Moya SM, Ganesh AJ, Plass M. Neural cell diversity in the light of single-cell transcriptomics. Transcription 2023; 14:158-176. [PMID: 38229529 PMCID: PMC10807474 DOI: 10.1080/21541264.2023.2295044] [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: 06/27/2023] [Accepted: 11/10/2023] [Indexed: 01/18/2024] Open
Abstract
The development of highly parallel and affordable high-throughput single-cell transcriptomics technologies has revolutionized our understanding of brain complexity. These methods have been used to build cellular maps of the brain, its different regions, and catalog the diversity of cells in each of them during development, aging and even in disease. Now we know that cellular diversity is way beyond what was previously thought. Single-cell transcriptomics analyses have revealed that cell types previously considered homogeneous based on imaging techniques differ depending on several factors including sex, age and location within the brain. The expression profiles of these cells have also been exploited to understand which are the regulatory programs behind cellular diversity and decipher the transcriptional pathways driving them. In this review, we summarize how single-cell transcriptomics have changed our view on the cellular diversity in the human brain, and how it could impact the way we study neurodegenerative diseases. Moreover, we describe the new computational approaches that can be used to study cellular differentiation and gain insight into the functions of individual cell populations under different conditions and their alterations in disease.
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Affiliation(s)
- Sandra María Fernández-Moya
- Gene Regulation of Cell Identity, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, L’Hospitalet del Llobregat, Spain
- Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P- CMR[C], Barcelona, L’Hospitalet del Llobregat, Spain
| | - Akshay Jaya Ganesh
- Gene Regulation of Cell Identity, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, L’Hospitalet del Llobregat, Spain
- Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P- CMR[C], Barcelona, L’Hospitalet del Llobregat, Spain
| | - Mireya Plass
- Gene Regulation of Cell Identity, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, L’Hospitalet del Llobregat, Spain
- Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P- CMR[C], Barcelona, L’Hospitalet del Llobregat, Spain
- Center for Networked Biomedical Research on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
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