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Diez D, Morte B, Bernal J. Single-Cell Transcriptome Profiling of Thyroid Hormone Effectors in the Human Fetal Neocortex: Expression of SLCO1C1, DIO2, and THRB in Specific Cell Types. Thyroid 2021; 31:1577-1588. [PMID: 34114484 DOI: 10.1089/thy.2021.0057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Background: Thyroid hormones are crucial for brain development, acting through the thyroid hormone nuclear receptors (TR)α1 and β to control gene expression. Triiodothyronine (T3), the receptor-ligand, is transported into the brain from the blood by the monocarboxylate transporter 8 (MCT8). Another source of brain T3 is from the local deiodination of thyroxine (T4) by type 2 deiodinase (DIO2). While these mechanisms are very similar in mice and humans, important species-specific differences confound our understanding of disease using mouse models. To fill this knowledge gap on thyroid hormone action in the human fetal brain, we analyzed the expression of transporters, DIO2, and TRs, which we call thyroid hormone effectors, at single-cell resolution. Methods: We analyzed publicly available single-cell transcriptome data sets of isolated cerebral cortex neural cells from three different studies, with expression data from 393 to almost 40,000 cells. We generated Uniform Manifold Approximation and Projection scatterplots and cell clusters to identify differentially expressed genes between clusters, and correlated their gene signatures with the expression of thyroid effectors. Results: The radial glia, mainly the outer radial glia, and astrocytes coexpress SLCO1C1 and DIO2, indicating close cooperation between the T4 transporter OATP1C1 and DIO2 in local T3 formation. Strikingly, THRB was mainly present in two classes of interneurons: a majority expressing CALB2/calretinin, from the caudal ganglionic eminence, and in somatostatin-expressing interneurons from the medial ganglionic eminence. By contrast, many cell types express SLC16A2 and THRA. Conclusions:SLCO1C1 and DIO2 coexpression in the outer radial glia, the universal stem cell of the cerebral cortex, highlights the likely importance of brain-generated T3 in neurogenesis. The unique expression of THRB in discrete subsets of interneurons is a novel finding whose pathophysiological meaning deserves further investigation.
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Affiliation(s)
- Diego Diez
- Immunology Frontier Research Center, Osaka University, Suita, Japan
| | - Beatriz Morte
- Center for Biomedical Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
- Instituto de Investigaciones Biomedicas Alberto Sols, Consejo Superior de Investigaciones Científicas (CSIC) and Universidad Autónoma de Madrid, Madrid, Spain
| | - Juan Bernal
- Instituto de Investigaciones Biomedicas Alberto Sols, Consejo Superior de Investigaciones Científicas (CSIC) and Universidad Autónoma de Madrid, Madrid, Spain
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202
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Dong J, Zhou P, Wu Y, Chen Y, Xie H, Gao Y, Lu J, Yang J, Zhang X, Wen L, Li T, Tang F. Integrating single-cell datasets with ambiguous batch information by incorporating molecular network features. Brief Bioinform 2021; 23:6373559. [PMID: 34553223 DOI: 10.1093/bib/bbab366] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 08/17/2021] [Accepted: 08/18/2021] [Indexed: 01/30/2023] Open
Abstract
With the rapid development of single-cell sequencing techniques, several large-scale cell atlas projects have been launched across the world. However, it is still challenging to integrate single-cell RNA-seq (scRNA-seq) datasets with diverse tissue sources, developmental stages and/or few overlaps, due to the ambiguity in determining the batch information, which is particularly important for current batch-effect correction methods. Here, we present SCORE, a simple network-based integration methodology, which incorporates curated molecular network features to infer cellular states and generate a unified workflow for integrating scRNA-seq datasets. Validating on real single-cell datasets, we showed that regardless of batch information, SCORE outperforms existing methods in accuracy, robustness, scalability and data integration.
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Affiliation(s)
- Ji Dong
- Guangzhou Laboratory, Guangzhou, China
| | - Peijie Zhou
- Department of Mathematics, University of California at Irvine, Irvine, CA, USA
| | - Yichong Wu
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Yidong Chen
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China
| | - Haoling Xie
- Beijing Advanced Innovation Center for Genomics, College of Life Sciences, Peking University, Beijing, China
| | - Yuan Gao
- Beijing Advanced Innovation Center for Genomics, College of Life Sciences, Peking University, Beijing, China
| | - Jiansen Lu
- Beijing Advanced Innovation Center for Genomics, College of Life Sciences, Peking University, Beijing, China
| | - Jingwei Yang
- Beijing Advanced Innovation Center for Genomics, College of Life Sciences, Peking University, Beijing, China
| | - Xiannian Zhang
- School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Lu Wen
- Beijing Advanced Innovation Center for Genomics, College of Life Sciences, Peking University, Beijing, China
| | - Tiejun Li
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Fuchou Tang
- Beijing Advanced Innovation Center for Genomics, College of Life Sciences, Peking University, Beijing, China
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203
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Han S, Okawa S, Wilkinson GA, Ghazale H, Adnani L, Dixit R, Tavares L, Faisal I, Brooks MJ, Cortay V, Zinyk D, Sivitilli A, Li S, Malik F, Ilnytskyy Y, Angarica VE, Gao J, Chinchalongporn V, Oproescu AM, Vasan L, Touahri Y, David LA, Raharjo E, Kim JW, Wu W, Rahmani W, Chan JAW, Kovalchuk I, Attisano L, Kurrasch D, Dehay C, Swaroop A, Castro DS, Biernaskie J, Del Sol A, Schuurmans C. Proneural genes define ground-state rules to regulate neurogenic patterning and cortical folding. Neuron 2021; 109:2847-2863.e11. [PMID: 34407390 DOI: 10.1016/j.neuron.2021.07.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 05/19/2021] [Accepted: 07/08/2021] [Indexed: 02/06/2023]
Abstract
Asymmetric neuronal expansion is thought to drive evolutionary transitions between lissencephalic and gyrencephalic cerebral cortices. We report that Neurog2 and Ascl1 proneural genes together sustain neurogenic continuity and lissencephaly in rodent cortices. Using transgenic reporter mice and human cerebral organoids, we found that Neurog2 and Ascl1 expression defines a continuum of four lineage-biased neural progenitor cell (NPC) pools. Double+ NPCs, at the hierarchical apex, are least lineage restricted due to Neurog2-Ascl1 cross-repression and display unique features of multipotency (more open chromatin, complex gene regulatory network, G2 pausing). Strikingly, selectively eliminating double+ NPCs by crossing Neurog2-Ascl1 split-Cre mice with diphtheria toxin-dependent "deleter" strains locally disrupts Notch signaling, perturbs neurogenic symmetry, and triggers cortical folding. In support of our discovery that double+ NPCs are Notch-ligand-expressing "niche" cells that control neurogenic periodicity and cortical folding, NEUROG2, ASCL1, and HES1 transcript distribution is modular (adjacent high/low zones) in gyrencephalic macaque cortices, prefiguring future folds.
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Affiliation(s)
- Sisu Han
- Sunnybrook Research Institute, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada; Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Satoshi Okawa
- Computational Biology Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4362 Esch-sur-Alzette, Luxembourg; Integrated BioBank of Luxembourg, 3555, 3531 Dudelange, Luxembourg
| | - Grey Atteridge Wilkinson
- Department of Biochemistry and Molecular Biology, ACHRI, HBI, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Hussein Ghazale
- Sunnybrook Research Institute, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada; Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Lata Adnani
- Sunnybrook Research Institute, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada; Department of Biochemistry and Molecular Biology, ACHRI, HBI, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Rajiv Dixit
- Sunnybrook Research Institute, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada; Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Ligia Tavares
- i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal
| | - Imrul Faisal
- Sunnybrook Research Institute, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada; Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Matthew J Brooks
- Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD 20892-1204, USA
| | - Veronique Cortay
- Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Dawn Zinyk
- Sunnybrook Research Institute, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada
| | - Adam Sivitilli
- Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada; Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Saiqun Li
- Department of Biochemistry and Molecular Biology, ACHRI, HBI, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Faizan Malik
- Department of Medical Genetics, ACHRI, HBI, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Yaroslav Ilnytskyy
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
| | - Vladimir Espinosa Angarica
- Computational Biology Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4362 Esch-sur-Alzette, Luxembourg
| | - Jinghua Gao
- Sunnybrook Research Institute, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada; Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Vorapin Chinchalongporn
- Sunnybrook Research Institute, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada; Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Ana-Maria Oproescu
- Sunnybrook Research Institute, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Lakshmy Vasan
- Sunnybrook Research Institute, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Yacine Touahri
- Sunnybrook Research Institute, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada; Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Luke Ajay David
- Sunnybrook Research Institute, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Eko Raharjo
- Department of Comparative Biology and Experimental Medicine, HBI, ACHRI, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Jung-Woong Kim
- Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD 20892-1204, USA
| | - Wei Wu
- Department of Pathology and Laboratory Medicine, Charbonneau Cancer Institute, HBI, University of Calgary, Calgary, AB T2N 4Z6, Canada
| | - Waleed Rahmani
- Department of Comparative Biology and Experimental Medicine, HBI, ACHRI, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Jennifer Ai-Wen Chan
- Department of Pathology and Laboratory Medicine, Charbonneau Cancer Institute, HBI, University of Calgary, Calgary, AB T2N 4Z6, Canada
| | - Igor Kovalchuk
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
| | - Liliana Attisano
- Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada; Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Deborah Kurrasch
- Department of Medical Genetics, ACHRI, HBI, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Colette Dehay
- Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Anand Swaroop
- Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD 20892-1204, USA
| | - Diogo S Castro
- i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal
| | - Jeff Biernaskie
- Department of Comparative Biology and Experimental Medicine, HBI, ACHRI, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Antonio Del Sol
- Computational Biology Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4362 Esch-sur-Alzette, Luxembourg; CIC bioGUNE, Bizkaia Technology Park, 48160 Derio, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao 48013, Spain
| | - Carol Schuurmans
- Sunnybrook Research Institute, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada; Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada; Department of Biochemistry and Molecular Biology, ACHRI, HBI, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada.
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204
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Liu H, Sun Y, Zhang Q, Jin W, Gordon RE, Zhang Y, Wang J, Sun C, Wang ZJ, Qi X, Zhang J, Huang B, Gui Q, Yuan H, Chen L, Ma X, Fang C, Liu YQ, Yu X, Feng S. Pro-inflammatory and proliferative microglia drive progression of glioblastoma. Cell Rep 2021; 36:109718. [PMID: 34525361 DOI: 10.1016/j.celrep.2021.109718] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 04/01/2021] [Accepted: 08/25/2021] [Indexed: 12/13/2022] Open
Abstract
Scant understanding of the glioblastoma microenvironment and molecular bases hampers development of efficient treatment strategies. Analyses of gene signatures of human gliomas demonstrate that the SETD2 mutation is correlated with poor prognosis of IDH1/2 wild-type (IDH-WT) adult glioblastoma patients. To better understand the crosstalk between SETD2 mutant (SETD2-mut) glioblastoma cells and the tumor microenvironment, we leverage single-cell transcriptomics to comprehensively map cellular populations in glioblastoma. In this study, we identify a specific subtype of high-grade glioma-associated microglia (HGG-AM). Further analysis shows that transforming growth factor (TGF)-β1 derived from SETD2-mut/IDH-WT tumor cells activates HGG-AM, exhibiting pro-inflammation and proliferation signatures. Particularly, HGG-AM secretes interleukin (IL)-1β via the apolipoprotein E (ApoE)-mediated NLRP1 inflammasome, thereby promoting tumor progression. HGG-AM present extensive proliferation and infiltration to supplement the activated microglia pool. Notably, TGF-β1/TβRI depletion dramatically reduces HGG-AM density and suppresses tumor growth. Altogether, our studies identify a specific microglia subpopulation and establish the cellular basis of interactions between HGG-AM and glioblastoma cells.
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Affiliation(s)
- Hailong Liu
- Department of Neurosurgery, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100853, P.R. China; Department of Radiotherapy, Beijing Tiantan Hospital, Capital Medical University, Beijing 10070, P.R. China
| | - Youliang Sun
- School of Basic Medical Science, Capital Medical University, Beijing 100069, P.R. China
| | - Qian Zhang
- Medical Laboratory Center, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, P.R. China; Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518000, P.R. China
| | - Wei Jin
- Department of Pathology, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100853, P.R. China
| | | | - Yanyang Zhang
- Department of Neurosurgery, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100853, P.R. China
| | - Jian Wang
- Department of Neurosurgery, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100853, P.R. China
| | - Caihong Sun
- Department of Neurosurgery, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100853, P.R. China
| | - Zeyuan John Wang
- School of Pharmaceutical Sciences, Temple University, Philadelphia, PA 19140, USA
| | - Xueling Qi
- Department of Neuro-Pathology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, P. R. China
| | - Junping Zhang
- Department of Neuro-Oncology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, P.R. China
| | - Boyuan Huang
- Department of Neurosurgery, Beijing Electric Power Hospital, Beijing 100073, P.R. China
| | - Qiuping Gui
- Department of Pathology, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100853, P.R. China
| | - Hongyu Yuan
- State Key Laboratory of Molecular Oncology, Chinese Academy of Medical Science Cancer Hospital/National Cancer Center, Beijing 100021, P.R. China
| | - Ling Chen
- Department of Neurosurgery, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100853, P.R. China
| | - Xiaodong Ma
- Department of Neurosurgery, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100853, P.R. China
| | - Chuan Fang
- Department of Neurosurgery, The Affiliated Hospital of Hebei University, Baoding 122311, P.R. China
| | - Yong-Qiang Liu
- Key Laboratory of Chinese Medicinal Resource from Lingnan, Ministry of Education, Guangzhou University of Chinese Medicine, Guangzhou 510006, P.R. China.
| | - Xinguang Yu
- Department of Neurosurgery, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100853, P.R. China.
| | - Shiyu Feng
- Department of Neurosurgery, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100853, P.R. China.
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205
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Huang X, Lin X, Liu F, Wu G, Yang Z, Meng A. The rise of developmental biology in China. Dev Growth Differ 2021; 64:106-115. [PMID: 34510425 DOI: 10.1111/dgd.12751] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 08/16/2021] [Accepted: 08/21/2021] [Indexed: 11/30/2022]
Abstract
Developmental biology research in China started from experimental embryology, in particular from studies on aquatic and reptile animals. The recent growth of the developmental biology community in China parallels the increased governmental funding support and the recruitment of overseas talents. This flourishing field in China embraces the activities of developmental biology-related societies, national meetings, key research initiatives and talented scientists. The first Development paper from China, published in 2000, marked the beginning of a new era. More recently, the second decade in the 21st century witnessed the blossoming of developmental biology research in China. Significant research spotlights, technical advances, and up-and-coming areas will be discussed in this overview.
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Affiliation(s)
- Xun Huang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Xinhua Lin
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Feng Liu
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Gen Wu
- High Technology Research and Development Center, Beijing, China
| | - Zhongzhou Yang
- State Key Laboratory of Pharmaceutical Biotechnology and MOE Key Laboratory of Model Animal for Disease Study, Model Animal Research Center, Medical School of Nanjing University, Nanjing, China
| | - Anming Meng
- State Key Laboratory of Membrane Biology, Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, China
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206
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Huang WK, Wong SZH, Pather SR, Nguyen PTT, Zhang F, Zhang DY, Zhang Z, Lu L, Fang W, Chen L, Fernandes A, Su Y, Song H, Ming GL. Generation of hypothalamic arcuate organoids from human induced pluripotent stem cells. Cell Stem Cell 2021; 28:1657-1670.e10. [PMID: 33961804 PMCID: PMC8419002 DOI: 10.1016/j.stem.2021.04.006] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 02/21/2021] [Accepted: 04/07/2021] [Indexed: 12/12/2022]
Abstract
Human brain organoids represent remarkable platforms for recapitulating features of human brain development and diseases. Existing organoid models do not resolve fine brain subregions, such as different nuclei in the hypothalamus. We report the generation of arcuate organoids (ARCOs) from human induced pluripotent stem cells (iPSCs) to model the development of the human hypothalamic arcuate nucleus. Single-cell RNA sequencing of ARCOs revealed significant molecular heterogeneity underlying different arcuate cell types, and machine learning-aided analysis based on the neonatal human hypothalamus single-nucleus transcriptome further showed a human arcuate nucleus molecular signature. We also explored ARCOs generated from Prader-Willi syndrome (PWS) patient iPSCs. These organoids exhibit aberrant differentiation and transcriptomic dysregulation similar to postnatal hypothalamus of PWS patients, indicative of cellular differentiation deficits and exacerbated inflammatory responses. Thus, patient iPSC-derived ARCOs represent a promising experimental model for investigating nucleus-specific features and disease-relevant mechanisms during early human arcuate development.
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Affiliation(s)
- Wei-Kai Huang
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Graduate Program in Pathobiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Samuel Zheng Hao Wong
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Graduate Program in Cellular and Molecular Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Sarshan R Pather
- Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Phuong T T Nguyen
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Feng Zhang
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel Y Zhang
- Biochemistry and Molecular Biophysics Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zhijian Zhang
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lu Lu
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Wanqi Fang
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Luyun Chen
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Analiese Fernandes
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yijing Su
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hongjun Song
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Institute for Regenerative Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; The Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Guo-Li Ming
- Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Institute for Regenerative Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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207
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Playfoot CJ, Duc J, Sheppard S, Dind S, Coudray A, Planet E, Trono D. Transposable elements and their KZFP controllers are drivers of transcriptional innovation in the developing human brain. Genome Res 2021; 31:1531-1545. [PMID: 34400477 PMCID: PMC8415367 DOI: 10.1101/gr.275133.120] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 07/15/2021] [Indexed: 11/25/2022]
Abstract
Transposable elements (TEs) account for more than 50% of the human genome and many have been co-opted throughout evolution to provide regulatory functions for gene expression networks. Several lines of evidence suggest that these networks are fine-tuned by the largest family of TE controllers, the KRAB-containing zinc finger proteins (KZFPs). One tissue permissive for TE transcriptional activation (termed "transposcription") is the adult human brain, however comprehensive studies on the extent of this process and its potential contribution to human brain development are lacking. To elucidate the spatiotemporal transposcriptome of the developing human brain, we have analyzed two independent RNA-seq data sets encompassing 16 brain regions from eight weeks postconception into adulthood. We reveal a distinct KZFP:TE transcriptional profile defining the late prenatal to early postnatal transition, and the spatiotemporal and cell type-specific activation of TE-derived alternative promoters driving the expression of neurogenesis-associated genes. Long-read sequencing confirmed these TE-driven isoforms as significant contributors to neurogenic transcripts. We also show experimentally that a co-opted antisense L2 element drives temporal protein relocalization away from the endoplasmic reticulum, suggestive of novel TE dependent protein function in primate evolution. This work highlights the widespread dynamic nature of the spatiotemporal KZFP:TE transcriptome and its importance throughout TE mediated genome innovation and neurotypical human brain development. To facilitate interactive exploration of these spatiotemporal gene and TE expression dynamics, we provide the "Brain TExplorer" web application freely accessible for the community.
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Affiliation(s)
- Christopher J Playfoot
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Julien Duc
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Shaoline Sheppard
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Sagane Dind
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Alexandre Coudray
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Evarist Planet
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Didier Trono
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
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208
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Larsson I, Dalmo E, Elgendy R, Niklasson M, Doroszko M, Segerman A, Jörnsten R, Westermark B, Nelander S. Modeling glioblastoma heterogeneity as a dynamic network of cell states. Mol Syst Biol 2021; 17:e10105. [PMID: 34528760 PMCID: PMC8444284 DOI: 10.15252/msb.202010105] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 08/16/2021] [Accepted: 08/17/2021] [Indexed: 12/13/2022] Open
Abstract
Tumor cell heterogeneity is a crucial characteristic of malignant brain tumors and underpins phenomena such as therapy resistance and tumor recurrence. Advances in single-cell analysis have enabled the delineation of distinct cellular states of brain tumor cells, but the time-dependent changes in such states remain poorly understood. Here, we construct quantitative models of the time-dependent transcriptional variation of patient-derived glioblastoma (GBM) cells. We build the models by sampling and profiling barcoded GBM cells and their progeny over the course of 3 weeks and by fitting a mathematical model to estimate changes in GBM cell states and their growth rates. Our model suggests a hierarchical yet plastic organization of GBM, where the rates and patterns of cell state switching are partly patient-specific. Therapeutic interventions produce complex dynamic effects, including inhibition of specific states and altered differentiation. Our method provides a general strategy to uncover time-dependent changes in cancer cells and offers a way to evaluate and predict how therapy affects cell state composition.
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Affiliation(s)
- Ida Larsson
- Department of Immunology, Genetics and PathologyUppsala UniversityUppsalaSweden
| | - Erika Dalmo
- Department of Immunology, Genetics and PathologyUppsala UniversityUppsalaSweden
| | - Ramy Elgendy
- Department of Immunology, Genetics and PathologyUppsala UniversityUppsalaSweden
| | - Mia Niklasson
- Department of Immunology, Genetics and PathologyUppsala UniversityUppsalaSweden
| | - Milena Doroszko
- Department of Immunology, Genetics and PathologyUppsala UniversityUppsalaSweden
| | - Anna Segerman
- Department of Immunology, Genetics and PathologyUppsala UniversityUppsalaSweden
- Department of Medical SciencesCancer Pharmacology and Computational MedicineUppsala University HospitalUppsalaSweden
| | - Rebecka Jörnsten
- Mathematical SciencesChalmers University of TechnologyGothenburgSweden
| | - Bengt Westermark
- Department of Immunology, Genetics and PathologyUppsala UniversityUppsalaSweden
| | - Sven Nelander
- Department of Immunology, Genetics and PathologyUppsala UniversityUppsalaSweden
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209
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Cheng J, Liao J, Shao X, Lu X, Fan X. Multiplexing Methods for Simultaneous Large-Scale Transcriptomic Profiling of Samples at Single-Cell Resolution. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2101229. [PMID: 34240574 PMCID: PMC8425911 DOI: 10.1002/advs.202101229] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/28/2021] [Indexed: 05/19/2023]
Abstract
Barcoding technology has greatly improved the throughput of cells and genes detected in single-cell RNA sequencing (scRNA-seq) studies. Recently, increasing studies have paid more attention to the use of this technology to increase the throughput of samples, as it has greatly reduced the processing time, technical batch effects, and library preparation costs, and lowered the per-sample cost. In this review, the various DNA-based barcoding methods for sample multiplexing are focused on, specifically, on the four major barcoding strategies. A detailed comparison of the barcoding methods is also presented, focusing on aspects such as sample/cell throughput and gene detection, and guidelines for choosing the most appropriate barcoding technique according to the personalized requirements are developed. Finally, the critical applications of sample multiplexing and technical challenges in combinatorial labeling, barcoding in vivo, and multimodal tagging at the spatially resolved resolution, as well as, the future prospects of multiplexed scRNA-seq, for example, prioritizing and predicting the severity of coronavirus disease 2019 (COVID-19) in patients of different gender and age are highlighted.
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Affiliation(s)
- Junyun Cheng
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhou310058China
| | - Jie Liao
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhou310058China
| | - Xin Shao
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhou310058China
| | - Xiaoyan Lu
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhou310058China
| | - Xiaohui Fan
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhou310058China
- Innovation Center in Zhejiang UniversityState Key Laboratory of Component‐Based Chinese MedicineHangzhou310058China
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210
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Wightman DP, Jansen IE, Savage JE, Shadrin AA, Bahrami S, Holland D, Rongve A, Børte S, Winsvold BS, Drange OK, Martinsen AE, Skogholt AH, Willer C, Bråthen G, Bosnes I, Nielsen JB, Fritsche LG, Thomas LF, Pedersen LM, Gabrielsen ME, Johnsen MB, Meisingset TW, Zhou W, Proitsi P, Hodges A, Dobson R, Velayudhan L, Heilbron K, Auton A, Sealock JM, Davis LK, Pedersen NL, Reynolds CA, Karlsson IK, Magnusson S, Stefansson H, Thordardottir S, Jonsson PV, Snaedal J, Zettergren A, Skoog I, Kern S, Waern M, Zetterberg H, Blennow K, Stordal E, Hveem K, Zwart JA, Athanasiu L, Selnes P, Saltvedt I, Sando SB, Ulstein I, Djurovic S, Fladby T, Aarsland D, Selbæk G, Ripke S, Stefansson K, Andreassen OA, Posthuma D. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease. Nat Genet 2021; 53:1276-1282. [PMID: 34493870 PMCID: PMC10243600 DOI: 10.1038/s41588-021-00921-z] [Citation(s) in RCA: 452] [Impact Index Per Article: 150.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 07/16/2021] [Indexed: 12/12/2022]
Abstract
Late-onset Alzheimer's disease is a prevalent age-related polygenic disease that accounts for 50-70% of dementia cases. Currently, only a fraction of the genetic variants underlying Alzheimer's disease have been identified. Here we show that increased sample sizes allowed identification of seven previously unidentified genetic loci contributing to Alzheimer's disease. This study highlights microglia, immune cells and protein catabolism as relevant to late-onset Alzheimer's disease, while identifying and prioritizing previously unidentified genes of potential interest. We anticipate that these results can be included in larger meta-analyses of Alzheimer's disease to identify further genetic variants that contribute to Alzheimer's pathology.
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Affiliation(s)
- Douglas P Wightman
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands
| | - Iris E Jansen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands
| | - Jeanne E Savage
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands
| | - Alexey A Shadrin
- NORMENT Centre, University of Oslo, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- NORMENT Centre, University of Oslo, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dominic Holland
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Arvid Rongve
- Department of Research and Innovation, Helse Fonna, Haugesund Hospital, Haugesund, Norway
- The University of Bergen, Institute of Clinical Medicine (K1), Bergen, Norway
| | - Sigrid Børte
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Research and Communication Unit for Musculoskeletal Health (FORMI), Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bendik S Winsvold
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Ole Kristian Drange
- Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Division of Mental Health Care, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Amy E Martinsen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Anne Heidi Skogholt
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Cristen Willer
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Geir Bråthen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway
- Department of Geriatrics, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Ingunn Bosnes
- Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Psychiatry, Hospital Namsos, Nord-Trøndelag Health Trust, Namsos, Norway
| | - Jonas Bille Nielsen
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Lars G Fritsche
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Laurent F Thomas
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Linda M Pedersen
- Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Maiken E Gabrielsen
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Marianne Bakke Johnsen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Research and Communication Unit for Musculoskeletal Health (FORMI), Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tore Wergeland Meisingset
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway
| | - Wei Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Petroula Proitsi
- Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK
| | - Angela Hodges
- Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, UK
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
- Health Data Research UK London, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, UK
| | - Latha Velayudhan
- Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK
| | | | | | - Julia M Sealock
- Division of Genetic Medicine, Department of Medicine Vanderbilt University Medical Center Nashville, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lea K Davis
- Division of Genetic Medicine, Department of Medicine Vanderbilt University Medical Center Nashville, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Chandra A Reynolds
- Department of Psychology, University of California-Riverside, Riverside, CA, USA
| | - Ida K Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Institute of Gerontology and Aging Research Network - Jönköping (ARN-J), School of Health and Welfare, Jönköping University, Jönköping, Sweden
| | | | | | | | - Palmi V Jonsson
- Department of Geriatric Medicine, Landspitali University Hospital, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Jon Snaedal
- Department of Geriatric Medicine, Landspitali University Hospital, Reykjavik, Iceland
| | - Anna Zettergren
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
| | - Ingmar Skoog
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - Silke Kern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - Margda Waern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychosis Clinic, Gothenburg, Sweden
| | - Henrik Zetterberg
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Eystein Stordal
- Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Psychiatry, Hospital Namsos, Nord-Trøndelag Health Trust, Namsos, Norway
| | - Kristian Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - John-Anker Zwart
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Lavinia Athanasiu
- NORMENT Centre, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Per Selnes
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | - Ingvild Saltvedt
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Geriatrics, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Sigrid B Sando
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway
| | - Ingun Ulstein
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Tormod Fladby
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | - Dag Aarsland
- Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK
- Centre of Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Geir Selbæk
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
| | - Stephan Ripke
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin, Berlin, Germany
| | | | - Ole A Andreassen
- NORMENT Centre, University of Oslo, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands.
- Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam University Medical Center, Amsterdam, the Netherlands.
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211
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Chau KK, Zhang P, Urresti J, Amar M, Pramod AB, Chen J, Thomas A, Corominas R, Lin GN, Iakoucheva LM. Full-length isoform transcriptome of the developing human brain provides further insights into autism. Cell Rep 2021; 36:109631. [PMID: 34469739 PMCID: PMC8437376 DOI: 10.1016/j.celrep.2021.109631] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 04/23/2021] [Accepted: 08/09/2021] [Indexed: 12/13/2022] Open
Abstract
Alternative splicing plays an important role in brain development, but its global contribution to human neurodevelopmental diseases (NDDs) requires further investigation. Here we examine the relationships between splicing isoform expression in the brain and de novo loss-of-function mutations from individuals with NDDs. We analyze the full-length isoform transcriptome of the developing human brain and observe differentially expressed isoforms and isoform co-expression modules undetectable by gene-level analyses. These isoforms are enriched in loss-of-function mutations and microexons, are co-expressed with a unique set of partners, and have higher prenatal expression. We experimentally test the effect of splice-site mutations and demonstrate exon skipping in five NDD risk genes, including SCN2A, DYRK1A, and BTRC. Our results suggest that the splice site mutation in BTRC reduces translational efficiency, likely affecting Wnt signaling through impaired degradation of β-catenin. We propose that functional effects of mutations should be investigated at the isoform- rather than gene-level resolution.
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Affiliation(s)
- Kevin K Chau
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Department of Biology, University of California, San Diego, La Jolla, CA, USA
| | - Pan Zhang
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Jorge Urresti
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Megha Amar
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Akula Bala Pramod
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Jiaye Chen
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Amy Thomas
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Roser Corominas
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Guan Ning Lin
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Lilia M Iakoucheva
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA.
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212
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Wu H, Wang X, Chu M, Xiang R, Zhou K. FRMC: a fast and robust method for the imputation of scRNA-seq data. RNA Biol 2021; 18:172-181. [PMID: 34459719 DOI: 10.1080/15476286.2021.1960688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
The high-resolution feature of single-cell transcriptome sequencing technology allows researchers to observe cellular gene expression profiles at the single-cell level, offering numerous possibilities for subsequent biomedical investigation. However, the unavoidable technical impact of high missing values in the gene-cell expression matrices generated by insufficient RNA input severely hampers the accuracy of downstream analysis. To address this problem, it is essential to develop a more rapid and stable imputation method with greater accuracy, which should not only be able to recover the missing data, but also effectively facilitate the following biological mechanism analysis. The existing imputation methods all have their drawbacks and limitations, some require pre-assumed data distribution, some cannot distinguish between technical and biological zeros, and some have poor computational performance. In this paper, we presented a novel imputation software FRMC for single-cell RNA-Seq data, which innovates a fast and accurate singular value thresholding approximation method. The experiments demonstrated that FRMC can not only precisely distinguish 'true zeros' from dropout events and correctly impute missing values attributed to technical noises, but also effectively enhance intracellular and intergenic connections and achieve accurate clustering of cells in biological applications. In summary, FRMC can be a powerful tool for analysing single-cell data because it ensures biological significance, accuracy, and rapidity simultaneously. FRMC is implemented in Python and is freely accessible to non-commercial users on GitHub: https://github.com/HUST-DataMan/FRMC.
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Affiliation(s)
- Honglong Wu
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science & Technology, Wuhan, Hubei, China.,BGI PathoGenesis Pharmaceutical Technology, BGI-Shenzhen, Shenzhen 518083, China
| | - Xuebin Wang
- BGI PathoGenesis Pharmaceutical Technology, BGI-Shenzhen, Shenzhen 518083, China
| | - Mengtian Chu
- BGI PathoGenesis Pharmaceutical Technology, BGI-Shenzhen, Shenzhen 518083, China
| | - Ruizhi Xiang
- BGI PathoGenesis Pharmaceutical Technology, BGI-Shenzhen, Shenzhen 518083, China
| | - Ke Zhou
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science & Technology, Wuhan, Hubei, China
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213
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Rybak-Wolf A, Plass M. RNA Dynamics in Alzheimer's Disease. Molecules 2021; 26:5113. [PMID: 34500547 PMCID: PMC8433936 DOI: 10.3390/molecules26175113] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 08/09/2021] [Accepted: 08/17/2021] [Indexed: 02/06/2023] Open
Abstract
Alzheimer's disease (AD) is the most common age-related neurodegenerative disorder that heavily burdens healthcare systems worldwide. There is a significant requirement to understand the still unknown molecular mechanisms underlying AD. Current evidence shows that two of the major features of AD are transcriptome dysregulation and altered function of RNA binding proteins (RBPs), both of which lead to changes in the expression of different RNA species, including microRNAs (miRNAs), circular RNAs (circRNAs), long non-coding RNAs (lncRNAs), and messenger RNAs (mRNAs). In this review, we will conduct a comprehensive overview of how RNA dynamics are altered in AD and how this leads to the differential expression of both short and long RNA species. We will describe how RBP expression and function are altered in AD and how this impacts the expression of different RNA species. Furthermore, we will also show how changes in the abundance of specific RNA species are linked to the pathology of AD.
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Affiliation(s)
- Agnieszka Rybak-Wolf
- Max Delbrück Center for Molecular Medicine (MDC), Berlin Institute for Medical Systems Biology (BIMSB), 10115 Berlin, Germany
| | - Mireya Plass
- Gene Regulation of Cell Identity, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908 Barcelona, Spain
- Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P-CMR[C], L'Hospitalet del Llobregat, 08908 Barcelona, Spain
- Center for Networked Biomedical Research on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029 Madrid, Spain
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214
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Abstract
In the mammalian neocortex, projection neuron types are sequentially generated by the same pool of neural progenitors. How neuron type specification is related to developmental timing remains unclear. To determine whether temporal gene expression in neural progenitors correlates with neuron type specification, we performed single-cell RNA sequencing (scRNA-Seq) analysis of the developing mouse neocortex. We uncovered neuroepithelial cell enriched genes such as Hmga2 and Ccnd1 when compared to radial glial cells (RGCs). RGCs display dynamic gene expression over time; for instance, early RGCs express higher levels of Hes5, and late RGCs show higher expression of Pou3f2 Interestingly, intermediate progenitor cell marker gene Eomes coexpresses temporally with known neuronal identity genes at different developmental stages, though mostly in postmitotic cells. Our results delineate neural progenitor cell diversity in the developing mouse neocortex and support that neuronal identity genes are transcriptionally evident in Eomes-positive cells.
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215
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Lei T, Liao X, Chen X, Zhao T, Xu Y, Xia M, Zhang J, Xia Y, Sun X, Wei Y, Men W, Wang Y, Hu M, Zhao G, Du B, Peng S, Chen M, Wu Q, Tan S, Gao JH, Qin S, Tao S, Dong Q, He Y. Progressive Stabilization of Brain Network Dynamics during Childhood and Adolescence. Cereb Cortex 2021; 32:1024-1039. [PMID: 34378030 DOI: 10.1093/cercor/bhab263] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 07/14/2021] [Accepted: 07/15/2021] [Indexed: 11/14/2022] Open
Abstract
Functional brain networks require dynamic reconfiguration to support flexible cognitive function. However, the developmental principles shaping brain network dynamics remain poorly understood. Here, we report the longitudinal development of large-scale brain network dynamics during childhood and adolescence, and its connection with gene expression profiles. Using a multilayer network model, we show the temporally varying modular architecture of child brain networks, with higher network switching primarily in the association cortex and lower switching in the primary regions. This topographical profile exhibits progressive maturation, which manifests as reduced modular dynamics, particularly in the transmodal (e.g., default-mode and frontoparietal) and sensorimotor regions. These developmental refinements mediate age-related enhancements of global network segregation and are linked with the expression profiles of genes associated with the enrichment of ion transport and nucleobase-containing compound transport. These results highlight a progressive stabilization of brain dynamics, which expand our understanding of the neural mechanisms that underlie cognitive development.
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Affiliation(s)
- Tianyuan Lei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Xiaodan Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yuehua Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Jiaying Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yunman Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xiaochen Sun
- Department of Linguistics, Beijing Language and Culture University, Beijing 100083, China
| | - Yongbin Wei
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Mingming Hu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Bin Du
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Siya Peng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Menglu Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qian Wu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.,Chinese Institute for Brain Research, Beijing 102206, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.,Chinese Institute for Brain Research, Beijing 102206, China
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216
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Cho AN, Jin Y, An Y, Kim J, Choi YS, Lee JS, Kim J, Choi WY, Koo DJ, Yu W, Chang GE, Kim DY, Jo SH, Kim J, Kim SY, Kim YG, Kim JY, Choi N, Cheong E, Kim YJ, Je HS, Kang HC, Cho SW. Microfluidic device with brain extracellular matrix promotes structural and functional maturation of human brain organoids. Nat Commun 2021; 12:4730. [PMID: 34354063 PMCID: PMC8342542 DOI: 10.1038/s41467-021-24775-5] [Citation(s) in RCA: 160] [Impact Index Per Article: 53.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 07/06/2021] [Indexed: 11/10/2022] Open
Abstract
Brain organoids derived from human pluripotent stem cells provide a highly valuable in vitro model to recapitulate human brain development and neurological diseases. However, the current systems for brain organoid culture require further improvement for the reliable production of high-quality organoids. Here, we demonstrate two engineering elements to improve human brain organoid culture, (1) a human brain extracellular matrix to provide brain-specific cues and (2) a microfluidic device with periodic flow to improve the survival and reduce the variability of organoids. A three-dimensional culture modified with brain extracellular matrix significantly enhanced neurogenesis in developing brain organoids from human induced pluripotent stem cells. Cortical layer development, volumetric augmentation, and electrophysiological function of human brain organoids were further improved in a reproducible manner by dynamic culture in microfluidic chamber devices. Our engineering concept of reconstituting brain-mimetic microenvironments facilitates the development of a reliable culture platform for brain organoids, enabling effective modeling and drug development for human brain diseases.
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Affiliation(s)
- Ann-Na Cho
- Department of Biotechnology, Yonsei University, Seoul, Republic of Korea
| | - Yoonhee Jin
- Department of Biotechnology, Yonsei University, Seoul, Republic of Korea
| | - Yeonjoo An
- Department of Biotechnology, Yonsei University, Seoul, Republic of Korea
| | - Jin Kim
- Department of Biotechnology, Yonsei University, Seoul, Republic of Korea
| | - Yi Sun Choi
- Department of Biotechnology, Yonsei University, Seoul, Republic of Korea
| | - Jung Seung Lee
- Department of Biotechnology, Yonsei University, Seoul, Republic of Korea
| | - Junghoon Kim
- Department of Biotechnology, Yonsei University, Seoul, Republic of Korea
| | - Won-Young Choi
- Department of Biochemistry, Yonsei University, Seoul, Republic of Korea
| | - Dong-Jun Koo
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul, Republic of Korea
| | - Weonjin Yu
- Signature Program in Neuroscience and Behavioral Disorders, Duke-NUS Medical School, Singapore, Singapore
| | - Gyeong-Eon Chang
- Department of Biotechnology, Yonsei University, Seoul, Republic of Korea
| | - Dong-Yoon Kim
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul, Republic of Korea
| | - Sung-Hyun Jo
- Department of Chemical Engineering, Soongsil University, Seoul, Republic of Korea
| | - Jihun Kim
- Division of Pediatric Neurology, Department of Pediatrics, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sung-Yon Kim
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul, Republic of Korea
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Yun-Gon Kim
- Department of Chemical Engineering, Soongsil University, Seoul, Republic of Korea
| | - Ju Young Kim
- Department of Advanced Materials Engineering, Kangwon National University, Samcheok, Republic of Korea
| | - Nakwon Choi
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
| | - Eunji Cheong
- Department of Biotechnology, Yonsei University, Seoul, Republic of Korea
| | - Young-Joon Kim
- Department of Biochemistry, Yonsei University, Seoul, Republic of Korea
| | - Hyunsoo Shawn Je
- Signature Program in Neuroscience and Behavioral Disorders, Duke-NUS Medical School, Singapore, Singapore
| | - Hoon-Chul Kang
- Division of Pediatric Neurology, Department of Pediatrics, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seung-Woo Cho
- Department of Biotechnology, Yonsei University, Seoul, Republic of Korea.
- Center for Nanomedicine, Institute for Basic science (IBS), Seoul, Republic of Korea.
- Graduate Program of Nano Biomedical Engineering (NanoBME), Advanced Science Institute, Yonsei University, Seoul, Republic of Korea.
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217
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Current tools to interrogate microglial biology. Neuron 2021; 109:2805-2819. [PMID: 34390649 DOI: 10.1016/j.neuron.2021.07.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 06/23/2021] [Accepted: 07/06/2021] [Indexed: 12/26/2022]
Abstract
Microglial cells perform a plethora of functions in the central nervous system (CNS), involving them in brain development, maintenance of homeostasis in adulthood, and CNS diseases. Significant technical advancements have prompted the development of novel systems adapted to analyze microglia with increasing specificity and intricacy. The advent of single-cell technologies combined with targeted mouse models has been decisive in deciphering microglia heterogeneity and dissecting microglial functions. However sophisticated these tools have become, clear limitations remain. Understanding their pitfalls and advantages ensures their correct application. Therefore, we provide a guide to the cutting-edge methods currently available to dissect microglial biology.
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218
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Zhu J, Chen F, Luo L, Wu W, Dai J, Zhong J, Lin X, Chai C, Ding P, Liang L, Wang S, Ding X, Chen Y, Wang H, Qiu J, Wang F, Sun C, Zeng Y, Fang J, Jiang X, Liu P, Tang G, Qiu X, Zhang X, Ruan Y, Jiang S, Li J, Zhu S, Xu X, Li F, Liu Z, Cao G, Chen D. Single-cell atlas of domestic pig cerebral cortex and hypothalamus. Sci Bull (Beijing) 2021; 66:1448-1461. [PMID: 36654371 DOI: 10.1016/j.scib.2021.04.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 11/07/2020] [Accepted: 03/12/2021] [Indexed: 01/20/2023]
Abstract
The brain of the domestic pig (Sus scrofa domesticus) has drawn considerable attention due to its high similarities to that of humans. However, the cellular compositions of the pig brain (PB) remain elusive. Here we investigated the single-nucleus transcriptomic profiles of five regions of the PB (frontal lobe, parietal lobe, temporal lobe, occipital lobe, and hypothalamus) and identified 21 cell subpopulations. The cross-species comparison of mouse and pig hypothalamus revealed the shared and specific gene expression patterns at the single-cell resolution. Furthermore, we identified cell types and molecular pathways closely associated with neurological disorders, bridging the gap between gene mutations and pathogenesis. We reported, to our knowledge, the first single-cell atlas of domestic pig cerebral cortex and hypothalamus combined with a comprehensive analysis across species, providing extensive resources for future research regarding neural science, evolutionary developmental biology, and regenerative medicine.
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Affiliation(s)
- Jiacheng Zhu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Fang Chen
- BGI-Shenzhen, Shenzhen 518083, China; MGI, BGI-Shenzhen, Shenzhen 518083, China
| | - Lihua Luo
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Weiying Wu
- BGI-Shenzhen, Shenzhen 518083, China; Department of Neurobiology, NHC and CAMS Key Laboratory of Medical Neurobiology, School of Brain Science and Brian Medicine, and the MOE Frontier Science Center for Brain Research and Brain-Machine Integration, Zhejiang University School of Medicine, Hangzhou 310031, China
| | - Jinxia Dai
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China; College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China
| | - Jixing Zhong
- School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Xiumei Lin
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Chaochao Chai
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Peiwen Ding
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Langchao Liang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Shiyou Wang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Xiangning Ding
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Yin Chen
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Haoyu Wang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Jiaying Qiu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI-Shenzhen, Shenzhen 518083, China
| | | | - Chengcheng Sun
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI-Shenzhen, Shenzhen 518083, China; School of Basic Medicine, Qingdao University, Qingdao 266071, China
| | - Yuying Zeng
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI-Shenzhen, Shenzhen 518083, China; College of Life Science, South China Agricultural University, Guangzhou 510642, China
| | - Jian Fang
- BGI-Shenzhen, Shenzhen 518083, China
| | - Xiaosen Jiang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Ping Liu
- BGI-Shenzhen, Shenzhen 518083, China; MGI, BGI-Shenzhen, Shenzhen 518083, China
| | - Gen Tang
- Shenzhen Children's Hospital, Shenzhen 518083, China
| | - Xin Qiu
- Shenzhen Children's Hospital, Shenzhen 518083, China
| | | | - Yetian Ruan
- Research Center for Translational Medicine, East Hospital, Tongji University School of Medicine, Shanghai 200120, China
| | | | | | - Shida Zhu
- BGI-Shenzhen, Shenzhen 518083, China
| | - Xun Xu
- BGI-Shenzhen, Shenzhen 518083, China
| | - Fang Li
- Research Center for Translational Medicine, East Hospital, Tongji University School of Medicine, Shanghai 200120, China
| | - Zhongmin Liu
- Research Center for Translational Medicine, East Hospital, Tongji University School of Medicine, Shanghai 200120, China
| | - Gang Cao
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China; College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China; College of Biomedicine and Health, Huazhong Agricultural University, Wuhan 430070, China.
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219
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Xie Z, Wang M, Liu Z, Shang C, Zhang C, Sun L, Gu H, Ran G, Pei Q, Ma Q, Huang M, Zhang J, Lin R, Zhou Y, Zhang J, Zhao M, Luo M, Wu Q, Cao P, Wang X. Transcriptomic encoding of sensorimotor transformation in the midbrain. eLife 2021; 10:e69825. [PMID: 34318750 PMCID: PMC8341986 DOI: 10.7554/elife.69825] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/25/2021] [Indexed: 12/31/2022] Open
Abstract
Sensorimotor transformation, a process that converts sensory stimuli into motor actions, is critical for the brain to initiate behaviors. Although the circuitry involved in sensorimotor transformation has been well delineated, the molecular logic behind this process remains poorly understood. Here, we performed high-throughput and circuit-specific single-cell transcriptomic analyses of neurons in the superior colliculus (SC), a midbrain structure implicated in early sensorimotor transformation. We found that SC neurons in distinct laminae expressed discrete marker genes. Of particular interest, Cbln2 and Pitx2 were key markers that define glutamatergic projection neurons in the optic nerve (Op) and intermediate gray (InG) layers, respectively. The Cbln2+ neurons responded to visual stimuli mimicking cruising predators, while the Pitx2+ neurons encoded prey-derived vibrissal tactile cues. By forming distinct input and output connections with other brain areas, these neuronal subtypes independently mediated behaviors of predator avoidance and prey capture. Our results reveal that, in the midbrain, sensorimotor transformation for different behaviors may be performed by separate circuit modules that are molecularly defined by distinct transcriptomic codes.
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Affiliation(s)
- Zhiyong Xie
- National Institute of Biological SciencesBeijingChina
| | - Mengdi Wang
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology (Shanghai), Institute of Biophysics, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Zeyuan Liu
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology (Shanghai), Institute of Biophysics, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Congping Shang
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory)GuangzhouChina
| | - Changjiang Zhang
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology (Shanghai), Institute of Biophysics, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Le Sun
- Beijing Institute for Brain Disorders, Capital Medical UniversityBeijingChina
| | - Huating Gu
- National Institute of Biological SciencesBeijingChina
| | - Gengxin Ran
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology (Shanghai), Institute of Biophysics, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Qing Pei
- National Institute of Biological SciencesBeijingChina
| | - Qiang Ma
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology (Shanghai), Institute of Biophysics, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Meizhu Huang
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory)GuangzhouChina
| | - Junjing Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijingChina
| | - Rui Lin
- National Institute of Biological SciencesBeijingChina
| | - Youtong Zhou
- National Institute of Biological SciencesBeijingChina
| | - Jiyao Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijingChina
| | - Miao Zhao
- National Institute of Biological SciencesBeijingChina
| | - Minmin Luo
- National Institute of Biological SciencesBeijingChina
- Chinese Institute for Brain ResearchBeijingChina
| | - Qian Wu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijingChina
| | - Peng Cao
- National Institute of Biological SciencesBeijingChina
- Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua UniversityBeijingChina
| | - Xiaoqun Wang
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology (Shanghai), Institute of Biophysics, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory)GuangzhouChina
- Beijing Institute for Brain Disorders, Capital Medical UniversityBeijingChina
- Chinese Institute for Brain ResearchBeijingChina
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University & Capital Medical UniversityBeijingChina
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220
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La Manno G, Siletti K, Furlan A, Gyllborg D, Vinsland E, Mossi Albiach A, Mattsson Langseth C, Khven I, Lederer AR, Dratva LM, Johnsson A, Nilsson M, Lönnerberg P, Linnarsson S. Molecular architecture of the developing mouse brain. Nature 2021; 596:92-96. [PMID: 34321664 DOI: 10.1038/s41586-021-03775-x] [Citation(s) in RCA: 210] [Impact Index Per Article: 70.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 06/28/2021] [Indexed: 01/13/2023]
Abstract
The mammalian brain develops through a complex interplay of spatial cues generated by diffusible morphogens, cell-cell interactions and intrinsic genetic programs that result in probably more than a thousand distinct cell types. A complete understanding of this process requires a systematic characterization of cell states over the entire spatiotemporal range of brain development. The ability of single-cell RNA sequencing and spatial transcriptomics to reveal the molecular heterogeneity of complex tissues has therefore been particularly powerful in the nervous system. Previous studies have explored development in specific brain regions1-8, the whole adult brain9 and even entire embryos10. Here we report a comprehensive single-cell transcriptomic atlas of the embryonic mouse brain between gastrulation and birth. We identified almost eight hundred cellular states that describe a developmental program for the functional elements of the brain and its enclosing membranes, including the early neuroepithelium, region-specific secondary organizers, and both neurogenic and gliogenic progenitors. We also used in situ mRNA sequencing to map the spatial expression patterns of key developmental genes. Integrating the in situ data with our single-cell clusters revealed the precise spatial organization of neural progenitors during the patterning of the nervous system.
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Affiliation(s)
- Gioele La Manno
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden. .,Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - Kimberly Siletti
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
| | - Alessandro Furlan
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden.,Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Daniel Gyllborg
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - Elin Vinsland
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
| | - Alejandro Mossi Albiach
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
| | | | - Irina Khven
- Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Alex R Lederer
- Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Lisa M Dratva
- Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Anna Johnsson
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - Peter Lönnerberg
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
| | - Sten Linnarsson
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden.
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221
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Liu Y, Feng W, Dai Y, Bao M, Yuan Z, He M, Qin Z, Liao S, He J, Huang Q, Yu Z, Zeng Y, Guo B, Huang R, Yang R, Jiang Y, Liao J, Xiao Z, Zhan X, Lin C, Xu J, Ye Y, Ma J, Wei Q, Mo Z. Single-Cell Transcriptomics Reveals the Complexity of the Tumor Microenvironment of Treatment-Naive Osteosarcoma. Front Oncol 2021; 11:709210. [PMID: 34367994 PMCID: PMC8335545 DOI: 10.3389/fonc.2021.709210] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 07/02/2021] [Indexed: 12/03/2022] Open
Abstract
Osteosarcoma (OS), which occurs most commonly in adolescents, is associated with a high degree of malignancy and poor prognosis. In order to develop an accurate treatment for OS, a deeper understanding of its complex tumor microenvironment (TME) is required. In the present study, tissues were isolated from six patients with OS, and then subjected to single-cell RNA sequencing (scRNA-seq) using a 10× Genomics platform. Multiplex immunofluorescence staining was subsequently used to validate the subsets identified by scRNA-seq. ScRNA-seq of six patients with OS was performed prior to neoadjuvant chemotherapy, and data were obtained on 29,278 cells. A total of nine major cell types were identified, and the single-cell transcriptional map of OS was subsequently revealed. Identified osteoblastic OS cells were divided into five subsets, and the subsets of those osteoblastic OS cells with significant prognostic correlation were determined using a deconvolution algorithm. Thereby, different transcription patterns in the cellular subtypes of osteoblastic OS cells were reported, and key transcription factors associated with survival prognosis were identified. Furthermore, the regulation of osteolysis by osteoblastic OS cells via receptor activator of nuclear factor kappa-B ligand was revealed. Furthermore, the role of osteoblastic OS cells in regulating angiogenesis through vascular endothelial growth factor-A was revealed. C3_TXNIP+ macrophages and C5_IFIT1+ macrophages were found to regulate regulatory T cells and participate in CD8+ T cell exhaustion, illustrating the possibility of immunotherapy that could target CD8+ T cells and macrophages. Our findings here show that the role of C1_osteoblastic OS cells in OS is to promote osteolysis and angiogenesis, and this is associated with survival prognosis. In addition, T cell depletion is an important feature of OS. More importantly, the present study provided a valuable resource for the in-depth study of the heterogeneity of the OS TME.
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Affiliation(s)
- Yun Liu
- Department of Spinal Bone Disease, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Wenyu Feng
- Department of Trauma Orthopedic and Hand Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yan Dai
- Center for Genomic and Personalized Medicine, School of Preclinical Medicine, Guangxi Medical University, Nanning, China.,Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Key Laboratory of Colleges and Universities, Nanning, China.,Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Mengying Bao
- Center for Genomic and Personalized Medicine, School of Preclinical Medicine, Guangxi Medical University, Nanning, China.,Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Key Laboratory of Colleges and Universities, Nanning, China.,Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Zhenchao Yuan
- Department of Bone and Soft Tissue Surgery, The Affiliated Tumor Hospital, Guangxi Medical University, Nanning, China
| | - Mingwei He
- Department of Trauma Orthopedic and Hand Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zhaojie Qin
- Department of Spinal Bone Disease, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Shijie Liao
- Department of Trauma Orthopedic and Hand Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Juliang He
- Department of Trauma Orthopedic and Hand Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Qian Huang
- Department of Trauma Orthopedic and Hand Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zhenyuan Yu
- Center for Genomic and Personalized Medicine, School of Preclinical Medicine, Guangxi Medical University, Nanning, China.,Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Key Laboratory of Colleges and Universities, Nanning, China.,Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Yanyu Zeng
- Center for Genomic and Personalized Medicine, School of Preclinical Medicine, Guangxi Medical University, Nanning, China.,Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Key Laboratory of Colleges and Universities, Nanning, China.,Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Binqian Guo
- Center for Genomic and Personalized Medicine, School of Preclinical Medicine, Guangxi Medical University, Nanning, China.,Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Key Laboratory of Colleges and Universities, Nanning, China.,Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Rong Huang
- Center for Genomic and Personalized Medicine, School of Preclinical Medicine, Guangxi Medical University, Nanning, China.,Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Key Laboratory of Colleges and Universities, Nanning, China.,Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Rirong Yang
- Center for Genomic and Personalized Medicine, School of Preclinical Medicine, Guangxi Medical University, Nanning, China.,Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Key Laboratory of Colleges and Universities, Nanning, China.,Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Yonghua Jiang
- Center for Genomic and Personalized Medicine, School of Preclinical Medicine, Guangxi Medical University, Nanning, China.,Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Key Laboratory of Colleges and Universities, Nanning, China.,Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Jinling Liao
- Center for Genomic and Personalized Medicine, School of Preclinical Medicine, Guangxi Medical University, Nanning, China.,Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Key Laboratory of Colleges and Universities, Nanning, China.,Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Zengming Xiao
- Department of Spinal Bone Disease, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xinli Zhan
- Department of Spinal Bone Disease, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chengsen Lin
- Department of Trauma Orthopedic and Hand Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jiake Xu
- School of Biomedical Sciences, The University of Western Australia, Perth, WA, Australia
| | - Yu Ye
- Center for Genomic and Personalized Medicine, School of Preclinical Medicine, Guangxi Medical University, Nanning, China.,Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Key Laboratory of Colleges and Universities, Nanning, China.,Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Jie Ma
- Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Qingjun Wei
- Department of Spinal Bone Disease, First Affiliated Hospital of Guangxi Medical University, Nanning, China.,Guangxi Key Laboratory of Regenerative Medicine, Research Centre for Regenerative Medicine, Guangxi Medical University, Nanning, China
| | - Zengnan Mo
- Center for Genomic and Personalized Medicine, School of Preclinical Medicine, Guangxi Medical University, Nanning, China.,Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Key Laboratory of Colleges and Universities, Nanning, China.,Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
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222
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Hu G, Li J, Wang GZ. Significant Evolutionary Constraints on Neuron Cells Revealed by Single-Cell Transcriptomics. Genome Biol Evol 2021; 12:300-308. [PMID: 32176293 PMCID: PMC7186789 DOI: 10.1093/gbe/evaa054] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2020] [Indexed: 12/30/2022] Open
Abstract
Recent advances in single-cell RNA-sequencing technology have enabled us to characterize a variety of different cell types in each brain region. However, the evolutionary differences among these cell types remain unclear. Here, we analyzed single-cell RNA-seq data of >280,000 cells and developmental transcriptomes of bulk brain tissues. At the single-cell level, we found that the evolutionary constraints on the cell types of different organs significantly overlap with each other and the transcriptome of neuron cells is one of the most restricted evolutionarily. In addition, mature neurons are under more constraints than neuron stem cells as well as nascent neurons and the order of the constraints of various cell types of the brain is largely conserved in different subregions. We also found that although functionally similar brain regions have comparable evolutionary constraints, the early fetal brain is the least constrained and this pattern is conserved in the mouse, macaque, and humans. These results demonstrate the importance of maintaining the plasticity of early brain development during evolution. The delineation of evolutionary differences between brain cell types has great potential for an improved understanding of the pathogenesis of neurological diseases and drug development efforts aimed at the manipulation of molecular activities at the single-cell level.
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Affiliation(s)
- Ganlu Hu
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jie Li
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Guang-Zhong Wang
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
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223
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Hyman LB, Christopher CR, Romero PA. Single-cell nucleic acid profiling in droplets (SNAPD) enables high-throughput analysis of heterogeneous cell populations. Nucleic Acids Res 2021; 49:e103. [PMID: 34233007 PMCID: PMC8501953 DOI: 10.1093/nar/gkab577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 06/17/2021] [Accepted: 06/21/2021] [Indexed: 12/03/2022] Open
Abstract
Experimental methods that capture the individual properties of single cells are revealing the key role of cell-to-cell variability in countless biological processes. These single-cell methods are becoming increasingly important across the life sciences in fields such as immunology, regenerative medicine and cancer biology. In addition to high-dimensional transcriptomic techniques such as single-cell RNA sequencing, there is a need for fast, simple and high-throughput assays to enumerate cell samples based on RNA biomarkers. In this work, we present single-cell nucleic acid profiling in droplets (SNAPD) to analyze sets of transcriptional markers in tens of thousands of single mammalian cells. Individual cells are encapsulated in aqueous droplets on a microfluidic chip and the RNA markers in each cell are amplified. Molecular logic circuits then integrate these amplicons to categorize cells based on the transcriptional markers and produce a detectable fluorescence output. SNAPD is capable of analyzing over 100,000 cells per hour and can be used to quantify distinct cell types within heterogeneous populations, detect rare cells at frequencies down to 0.1% and enrich specific cell types using microfluidic sorting. SNAPD provides a simple, rapid, low cost and scalable approach to study complex phenotypes in heterogeneous cell populations.
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Affiliation(s)
- Leland B Hyman
- Graduate Program in Cell and Molecular Biology, University of Wisconsin-Madison, Madison, WI 53706, USA.,Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Clare R Christopher
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Philip A Romero
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA.,Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.,The University of Wisconsin Carbone Cancer Center, Madison, WI 53706, USA
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224
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Saito K, Shigetomi E, Koizumi S. [Alexander disease: diversity of cell population and interactions between neuron and glia]. Nihon Yakurigaku Zasshi 2021; 156:239-243. [PMID: 34193704 DOI: 10.1254/fpj.21028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Alexander disease (AxD) is a rare neurodegenerative disorder caused by the mutations in glial fibrillary acidic protein (GFAP) gene. Rosenthal fiber formations in astrocytes are the pathological hallmarks of AxD. Astrocyte dysfunction in the AxD brain is considered to be involved in its pathogenesis. We have previously reported that in AxD model mice aberrant Ca2+ signals in astrocytes were associated with the upregulation of reactive phenotype. Reactive astrocytes are conditions that lead to morphological, functional, and molecular changes by responding to various pathological insults (trauma, inflammation, ischemia), and environmental stimuli. Recent technological advances in single-cell gene expression analysis have revealed that astrocytes have heterogeneity by indicating that they form sub population with different characteristics depending on the brain region, the growth development, aging stage, and the pathological condition. AxD astrocytes are also thought to constitute a heterogeneous population with diverse properties and functions. Moreover, it is presumed that AxD pathogenesis occur due to interactions with neurons and other glial cells, as well as the microenvironment in tissues. Research strategies based on these perspectives will help us understand AxD pathology better and may lead to the elucidation of disease modifiers and clinical diversity.
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Affiliation(s)
- Kozo Saito
- Department of Neuropharmcology, Interdisciplinary Graduate School of Medicine
| | - Eiji Shigetomi
- Department of Neuropharmcology, Interdisciplinary Graduate School of Medicine
| | - Schuichi Koizumi
- Department of Neuropharmcology, Interdisciplinary Graduate School of Medicine
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225
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Li Y, Zeng W, Li T, Guo Y, Zheng G, He X, Bai L, Ding G, Jin L, Liu X. Integrative Single-Cell Transcriptomic Analysis of Human Fetal Thymocyte Development. Front Genet 2021; 12:679616. [PMID: 34276782 PMCID: PMC8284395 DOI: 10.3389/fgene.2021.679616] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 06/03/2021] [Indexed: 11/23/2022] Open
Abstract
Intrathymic differentiation of T lymphocytes begins as early as intrauterine stage, yet the T cell lineage decisions of human fetal thymocytes at different gestational ages are not currently understood. Here, we performed integrative single-cell analyses of thymocytes across gestational ages. We identified conserved candidates underlying the selection of T cell receptor (TCR) lineages in different human fetal stages. The trajectory of early thymocyte commitment during fetal growth was also characterized. Comparisons with mouse data revealed conserved and species-specific transcriptional dynamics of thymocyte proliferation, apoptosis and selection. Genome-wide association study (GWAS) data associated with multiple autoimmune disorders were analyzed to characterize susceptibility genes that are highly expressed at specific stages during fetal thymocyte development. In summary, our integrative map describes previously underappreciated aspects of human thymocyte development, and provides a comprehensive reference for understanding T cell lymphopoiesis in a self-tolerant and functional adaptive immune system.
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Affiliation(s)
- Yuchen Li
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Key Laboratory of Embryo Original Disease, Shanghai, China.,Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences, Shanghai, China
| | - Weihong Zeng
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Key Laboratory of Embryo Original Disease, Shanghai, China.,Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences, Shanghai, China
| | - Tong Li
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Key Laboratory of Embryo Original Disease, Shanghai, China.,Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences, Shanghai, China
| | - Yanyan Guo
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Key Laboratory of Embryo Original Disease, Shanghai, China.,Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences, Shanghai, China
| | - Guangyong Zheng
- Bio-Med Big Data Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xiaoying He
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Key Laboratory of Embryo Original Disease, Shanghai, China
| | - Lilian Bai
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Key Laboratory of Embryo Original Disease, Shanghai, China.,Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences, Shanghai, China
| | - Guolian Ding
- Shanghai Key Laboratory of Embryo Original Disease, Shanghai, China.,Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences, Shanghai, China.,Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China
| | - Li Jin
- Shanghai Key Laboratory of Embryo Original Disease, Shanghai, China.,Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China
| | - Xinmei Liu
- Shanghai Key Laboratory of Embryo Original Disease, Shanghai, China.,Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences, Shanghai, China.,Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China
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226
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Yang AC, Kern F, Losada PM, Agam MR, Maat CA, Schmartz GP, Fehlmann T, Stein JA, Schaum N, Lee DP, Calcuttawala K, Vest RT, Berdnik D, Lu N, Hahn O, Gate D, McNerney MW, Channappa D, Cobos I, Ludwig N, Schulz-Schaeffer WJ, Keller A, Wyss-Coray T. Dysregulation of brain and choroid plexus cell types in severe COVID-19. Nature 2021; 595:565-571. [PMID: 34153974 PMCID: PMC8400927 DOI: 10.1038/s41586-021-03710-0] [Citation(s) in RCA: 381] [Impact Index Per Article: 127.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 06/07/2021] [Indexed: 01/08/2023]
Abstract
Although SARS-CoV-2 primarily targets the respiratory system, patients with and survivors of COVID-19 can suffer neurological symptoms1-3. However, an unbiased understanding of the cellular and molecular processes that are affected in the brains of patients with COVID-19 is missing. Here we profile 65,309 single-nucleus transcriptomes from 30 frontal cortex and choroid plexus samples across 14 control individuals (including 1 patient with terminal influenza) and 8 patients with COVID-19. Although our systematic analysis yields no molecular traces of SARS-CoV-2 in the brain, we observe broad cellular perturbations indicating that barrier cells of the choroid plexus sense and relay peripheral inflammation into the brain and show that peripheral T cells infiltrate the parenchyma. We discover microglia and astrocyte subpopulations associated with COVID-19 that share features with pathological cell states that have previously been reported in human neurodegenerative disease4-6. Synaptic signalling of upper-layer excitatory neurons-which are evolutionarily expanded in humans7 and linked to cognitive function8-is preferentially affected in COVID-19. Across cell types, perturbations associated with COVID-19 overlap with those found in chronic brain disorders and reside in genetic variants associated with cognition, schizophrenia and depression. Our findings and public dataset provide a molecular framework to understand current observations of COVID-19-related neurological disease, and any such disease that may emerge at a later date.
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Affiliation(s)
- Andrew C Yang
- Department of Bioengineering, Stanford University School of Medicine, Stanford, CA, USA
- ChEM-H, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Fabian Kern
- Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany
| | - Patricia M Losada
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Maayan R Agam
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Christina A Maat
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Georges P Schmartz
- Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany
| | - Tobias Fehlmann
- Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany
| | - Julian A Stein
- Institute for Neuropathology, Saarland University Hospital and Medical Faculty of Saarland University, Homburg, Germany
| | - Nicholas Schaum
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Davis P Lee
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Kruti Calcuttawala
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Ryan T Vest
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Daniela Berdnik
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Nannan Lu
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Oliver Hahn
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - David Gate
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - M Windy McNerney
- Department of Psychiatry, Stanford University School of Medicine, Stanford, CA, USA
| | - Divya Channappa
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Inma Cobos
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Nicole Ludwig
- Department of Human Genetics, Saarland University, Homburg, Germany
| | - Walter J Schulz-Schaeffer
- Institute for Neuropathology, Saarland University Hospital and Medical Faculty of Saarland University, Homburg, Germany
| | - Andreas Keller
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany.
| | - Tony Wyss-Coray
- ChEM-H, Stanford University, Stanford, CA, USA.
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
- Paul F. Glenn Center for the Biology of Aging, Stanford University School of Medicine, Stanford, CA, USA.
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227
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Cardona-Alberich A, Tourbez M, Pearce SF, Sibley CR. Elucidating the cellular dynamics of the brain with single-cell RNA sequencing. RNA Biol 2021; 18:1063-1084. [PMID: 33499699 PMCID: PMC8216183 DOI: 10.1080/15476286.2020.1870362] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 12/17/2020] [Accepted: 12/24/2020] [Indexed: 12/18/2022] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) has emerged in recent years as a breakthrough technology to understand RNA metabolism at cellular resolution. In addition to allowing new cell types and states to be identified, scRNA-seq can permit cell-type specific differential gene expression changes, pre-mRNA processing events, gene regulatory networks and single-cell developmental trajectories to be uncovered. More recently, a new wave of multi-omic adaptations and complementary spatial transcriptomics workflows have been developed that facilitate the collection of even more holistic information from individual cells. These developments have unprecedented potential to provide penetrating new insights into the basic neural cell dynamics and molecular mechanisms relevant to the nervous system in both health and disease. In this review we discuss this maturation of single-cell RNA-sequencing over the past decade, and review the different adaptations of the technology that can now be applied both at different scales and for different purposes. We conclude by highlighting how these methods have already led to many exciting discoveries across neuroscience that have furthered our cellular understanding of the neurological disease.
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Affiliation(s)
- Aida Cardona-Alberich
- Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Sciences, Edinburgh University, Edinburgh, UK
| | - Manon Tourbez
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
| | - Sarah F. Pearce
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
| | - Christopher R. Sibley
- Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Sciences, Edinburgh University, Edinburgh, UK
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
- Euan MacDonald Centre for MND Research, University of Edinburgh, Edinburgh, UK
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228
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Ballarin L, Karahan A, Salvetti A, Rossi L, Manni L, Rinkevich B, Rosner A, Voskoboynik A, Rosental B, Canesi L, Anselmi C, Pinsino A, Tohumcu BE, Jemec Kokalj A, Dolar A, Novak S, Sugni M, Corsi I, Drobne D. Stem Cells and Innate Immunity in Aquatic Invertebrates: Bridging Two Seemingly Disparate Disciplines for New Discoveries in Biology. Front Immunol 2021; 12:688106. [PMID: 34276677 PMCID: PMC8278520 DOI: 10.3389/fimmu.2021.688106] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 05/31/2021] [Indexed: 12/12/2022] Open
Abstract
The scopes related to the interplay between stem cells and the immune system are broad and range from the basic understanding of organism's physiology and ecology to translational studies, further contributing to (eco)toxicology, biotechnology, and medicine as well as regulatory and ethical aspects. Stem cells originate immune cells through hematopoiesis, and the interplay between the two cell types is required in processes like regeneration. In addition, stem and immune cell anomalies directly affect the organism's functions, its ability to cope with environmental changes and, indirectly, its role in ecosystem services. However, stem cells and immune cells continue to be considered parts of two branches of biological research with few interconnections between them. This review aims to bridge these two seemingly disparate disciplines towards much more integrative and transformative approaches with examples deriving mainly from aquatic invertebrates. We discuss the current understanding of cross-disciplinary collaborative and emerging issues, raising novel hypotheses and comments. We also discuss the problems and perspectives of the two disciplines and how to integrate their conceptual frameworks to address basic equations in biology in a new, innovative way.
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Affiliation(s)
| | - Arzu Karahan
- Middle East Technical University, Institute of Marine Sciences, Erdemli, Mersin, Turkey
| | - Alessandra Salvetti
- Department of Clinical and Experimental Medicine, Unit of Experimental Biology and Genetics, University of Pisa, Pisa, Italy
| | - Leonardo Rossi
- Department of Clinical and Experimental Medicine, Unit of Experimental Biology and Genetics, University of Pisa, Pisa, Italy
| | - Lucia Manni
- Department of Biology, University of Padua, Padua, Italy
| | - Baruch Rinkevich
- Department of Biology, Israel Oceanographic and Limnological Research, National Institute of Oceanography, Haifa, Israel
| | - Amalia Rosner
- Department of Biology, Israel Oceanographic and Limnological Research, National Institute of Oceanography, Haifa, Israel
| | - Ayelet Voskoboynik
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, United States
- Department of Biology, Stanford University, Hopkins Marine Station, Pacific Grove, CA, United States
- Department of Biology, Chan Zuckerberg Biohub, San Francisco, CA, United States
| | - Benyamin Rosental
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Center for Regenerative Medicine and Stem Cells, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Laura Canesi
- Department of Earth Environment and Life Sciences (DISTAV), University of Genoa, Genoa, Italy
| | - Chiara Anselmi
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, United States
- Department of Biology, Stanford University, Hopkins Marine Station, Pacific Grove, CA, United States
| | - Annalisa Pinsino
- Institute for Biomedical Research and Innovation, National Research Council, Palermo, Italy
| | - Begüm Ece Tohumcu
- Middle East Technical University, Institute of Marine Sciences, Erdemli, Mersin, Turkey
| | - Anita Jemec Kokalj
- Department of Biology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Andraž Dolar
- Department of Biology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Sara Novak
- Department of Biology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Michela Sugni
- Department of Environmental Science and Policy, University of Milan, Milan, Italy
| | - Ilaria Corsi
- Department of Physical, Earth and Environmental Sciences, University of Siena, Siena, Italy
| | - Damjana Drobne
- Department of Biology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
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229
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Xu Y, Kong J, Hu P. Computational Drug Repurposing for Alzheimer's Disease Using Risk Genes From GWAS and Single-Cell RNA Sequencing Studies. Front Pharmacol 2021; 12:617537. [PMID: 34276354 PMCID: PMC8277916 DOI: 10.3389/fphar.2021.617537] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 06/15/2021] [Indexed: 01/14/2023] Open
Abstract
Background: Traditional therapeutics targeting Alzheimer's disease (AD)-related subpathologies have so far proved ineffective. Drug repurposing, a more effective strategy that aims to find new indications for existing drugs against other diseases, offers benefits in AD drug development. In this study, we aim to identify potential anti-AD agents through enrichment analysis of drug-induced transcriptional profiles of pathways based on AD-associated risk genes identified from genome-wide association analyses (GWAS) and single-cell transcriptomic studies. Methods: We systematically constructed four gene lists (972 risk genes) from GWAS and single-cell transcriptomic studies and performed functional and genes overlap analyses in Enrichr tool. We then used a comprehensive drug repurposing tool Gene2Drug by combining drug-induced transcriptional responses with the associated pathways to compute candidate drugs from each gene list. Prioritized potential candidates (eight drugs) were further assessed with literature review. Results: The genomic-based gene lists contain late-onset AD associated genes (BIN1, ABCA7, APOE, CLU, and PICALM) and clinical AD drug targets (TREM2, CD33, CHRNA2, PRSS8, ACE, TKT, APP, and GABRA1). Our analysis identified eight AD candidate drugs (ellipticine, alsterpaullone, tomelukast, ginkgolide A, chrysin, ouabain, sulindac sulfide and lorglumide), four of which (alsterpaullone, ginkgolide A, chrysin and ouabain) have shown repurposing potential for AD validated by their preclinical evidence and moderate toxicity profiles from literature. These support the value of pathway-based prioritization based on the disease risk genes from GWAS and scRNA-seq data analysis. Conclusion: Our analysis strategy identified some potential drug candidates for AD. Although the drugs still need further experimental validation, the approach may be applied to repurpose drugs for other neurological disorders using their genomic information identified from large-scale genomic studies.
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Affiliation(s)
- Yun Xu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada
| | - Jiming Kong
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada
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230
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Pan X, Zhao J, Zhou Z, Chen J, Yang Z, Wu Y, Bai M, Jiao Y, Yang Y, Hu X, Cheng T, Lu Q, Wang B, Li CL, Lu YJ, Diao L, Zhong YQ, Pan J, Zhu J, Xiao HS, Qiu ZL, Li J, Wang Z, Hui J, Bao L, Zhang X. 5'-UTR SNP of FGF13 causes translational defect and intellectual disability. eLife 2021; 10:63021. [PMID: 34184986 PMCID: PMC8241442 DOI: 10.7554/elife.63021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 06/17/2021] [Indexed: 12/15/2022] Open
Abstract
The congenital intellectual disability (ID)-causing gene mutations remain largely unclear, although many genetic variations might relate to ID. We screened gene mutations in Chinese Han children suffering from severe ID and found a single-nucleotide polymorphism (SNP) in the 5′-untranslated region (5′-UTR) of fibroblast growth factor 13 (FGF13) mRNA (NM_001139500.1:c.-32c>G) shared by three male children. In both HEK293 cells and patient-derived induced pluripotent stem cells, this SNP reduced the translation of FGF13, which stabilizes microtubules in developing neurons. Mice carrying the homologous point mutation in 5′-UTR of Fgf13 showed delayed neuronal migration during cortical development, and weakened learning and memory. Furthermore, this SNP reduced the interaction between FGF13 5′-UTR and polypyrimidine-tract-binding protein 2 (PTBP2), which was required for FGF13 translation in cortical neurons. Thus, this 5′-UTR SNP of FGF13 interferes with the translational process of FGF13 and causes deficits in brain development and cognitive functions.
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Affiliation(s)
- Xingyu Pan
- Institute of Neuroscience and State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,Shanghai Brain-Intelligence Project Center, Shanghai, China
| | - Jingrong Zhao
- Institute of Neuroscience and State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Zhiying Zhou
- Shanghai Clinical Center, Chinese Academy of Sciences/Xu-Hui Central Hospital, Shanghai, China
| | - Jijun Chen
- Shanghai Brain-Intelligence Project Center, Shanghai, China
| | - Zhenxing Yang
- Shanghai Clinical Center, Chinese Academy of Sciences/Xu-Hui Central Hospital, Shanghai, China
| | - Yuxuan Wu
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Meizhu Bai
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Yang Jiao
- School of Life Science and Technology, Shanghai Tech University, Shanghai, China
| | - Yun Yang
- CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai, China
| | - Xuye Hu
- Shanghai Brain-Intelligence Project Center, Shanghai, China.,Shanghai Clinical Center, Chinese Academy of Sciences/Xu-Hui Central Hospital, Shanghai, China
| | - Tianling Cheng
- Institute of Neuroscience and State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Qianyun Lu
- CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai, China
| | - Bin Wang
- Shanghai Brain-Intelligence Project Center, Shanghai, China.,State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Chang-Lin Li
- Shanghai Brain-Intelligence Project Center, Shanghai, China.,Shanghai Clinical Center, Chinese Academy of Sciences/Xu-Hui Central Hospital, Shanghai, China
| | - Ying-Jin Lu
- Shanghai Brain-Intelligence Project Center, Shanghai, China.,Shanghai Clinical Center, Chinese Academy of Sciences/Xu-Hui Central Hospital, Shanghai, China
| | - Lei Diao
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Yan-Qing Zhong
- Institute of Neuroscience and State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jing Pan
- Shanghai Brain-Intelligence Project Center, Shanghai, China
| | - Jianmin Zhu
- Shanghai Clinical Center, Chinese Academy of Sciences/Xu-Hui Central Hospital, Shanghai, China
| | - Hua-Sheng Xiao
- Shanghai Clinical Center, Chinese Academy of Sciences/Xu-Hui Central Hospital, Shanghai, China
| | - Zi-Long Qiu
- Institute of Neuroscience and State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jinsong Li
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Zefeng Wang
- CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai, China
| | - Jingyi Hui
- State Key Laboratory of Molecular Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Lan Bao
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, Shanghai Tech University, Shanghai, China
| | - Xu Zhang
- Institute of Neuroscience and State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,Shanghai Brain-Intelligence Project Center, Shanghai, China.,Shanghai Clinical Center, Chinese Academy of Sciences/Xu-Hui Central Hospital, Shanghai, China.,School of Life Science and Technology, Shanghai Tech University, Shanghai, China.,Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China
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231
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Schaffer LV, Ideker T. Mapping the multiscale structure of biological systems. Cell Syst 2021; 12:622-635. [PMID: 34139169 PMCID: PMC8245186 DOI: 10.1016/j.cels.2021.05.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 05/04/2021] [Accepted: 05/14/2021] [Indexed: 01/14/2023]
Abstract
Biological systems are by nature multiscale, consisting of subsystems that factor into progressively smaller units in a deeply hierarchical structure. At any level of the hierarchy, an ever-increasing diversity of technologies can be applied to characterize the corresponding biological units and their relations, resulting in large networks of physical or functional proximities-e.g., proximities of amino acids within a protein, of proteins within a complex, or of cell types within a tissue. Here, we review general concepts and progress in using network proximity measures as a basis for creation of multiscale hierarchical maps of biological systems. We discuss the functionalization of these maps to create predictive models, including those useful in translation of genotype to phenotype, along with strategies for model visualization and challenges faced by multiscale modeling in the near future. Collectively, these approaches enable a unified hierarchical approach to biological data, with application from the molecular to the macroscopic.
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Affiliation(s)
- Leah V Schaffer
- Division of Genetics, Department of Medicine, University of California San Diego, San Diego, La Jolla, CA 92093, USA
| | - Trey Ideker
- Division of Genetics, Department of Medicine, University of California San Diego, San Diego, La Jolla, CA 92093, USA.
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232
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Hara T, Chanoch-Myers R, Mathewson ND, Myskiw C, Atta L, Bussema L, Eichhorn SW, Greenwald AC, Kinker GS, Rodman C, Gonzalez Castro LN, Wakimoto H, Rozenblatt-Rosen O, Zhuang X, Fan J, Hunter T, Verma IM, Wucherpfennig KW, Regev A, Suvà ML, Tirosh I. Interactions between cancer cells and immune cells drive transitions to mesenchymal-like states in glioblastoma. Cancer Cell 2021; 39:779-792.e11. [PMID: 34087162 PMCID: PMC8366750 DOI: 10.1016/j.ccell.2021.05.002] [Citation(s) in RCA: 262] [Impact Index Per Article: 87.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 02/19/2021] [Accepted: 05/05/2021] [Indexed: 02/07/2023]
Abstract
The mesenchymal subtype of glioblastoma is thought to be determined by both cancer cell-intrinsic alterations and extrinsic cellular interactions, but remains poorly understood. Here, we dissect glioblastoma-to-microenvironment interactions by single-cell RNA sequencing analysis of human tumors and model systems, combined with functional experiments. We demonstrate that macrophages induce a transition of glioblastoma cells into mesenchymal-like (MES-like) states. This effect is mediated, both in vitro and in vivo, by macrophage-derived oncostatin M (OSM) that interacts with its receptors (OSMR or LIFR) in complex with GP130 on glioblastoma cells and activates STAT3. We show that MES-like glioblastoma states are also associated with increased expression of a mesenchymal program in macrophages and with increased cytotoxicity of T cells, highlighting extensive alterations of the immune microenvironment with potential therapeutic implications.
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Affiliation(s)
- Toshiro Hara
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Rony Chanoch-Myers
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Nathan D Mathewson
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Cancer Immunology and Virology, Department of Microbiology and Immunobiology, Department of Neurology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA
| | - Chad Myskiw
- Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Lyla Atta
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Lillian Bussema
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Stephen W Eichhorn
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA; Department of Chemistry and Chemical Biology, Department of Physics, Harvard University, Cambridge, MA 02138, USA
| | - Alissa C Greenwald
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 761001, Israel
| | - Gabriela S Kinker
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 761001, Israel; Department of Physiology, Institute of Bioscience, University of Sao Paulo, Sao Paulo, Brazil
| | - Christopher Rodman
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - L Nicolas Gonzalez Castro
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Neurology and Center for Neuro-Oncology, Brigham and Women's - Dana-Farber Cancer Center and Harvard Medical School, Boston, MA 02115, USA
| | - Hiroaki Wakimoto
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Xiaowei Zhuang
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA; Department of Chemistry and Chemical Biology, Department of Physics, Harvard University, Cambridge, MA 02138, USA
| | - Jean Fan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Tony Hunter
- Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Inder M Verma
- Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Kai W Wucherpfennig
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Cancer Immunology and Virology, Department of Microbiology and Immunobiology, Department of Neurology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA 02139, USA
| | - Mario L Suvà
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
| | - Itay Tirosh
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 761001, Israel.
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233
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Zhang W, Wang W, Zhao M, Turck CW, Zhu Y, Wang GZ. Rapid Body-Wide Transcriptomic Turnover During Rhesus Macaque Perinatal Development. Front Physiol 2021; 12:690540. [PMID: 34177627 PMCID: PMC8223001 DOI: 10.3389/fphys.2021.690540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Accepted: 05/17/2021] [Indexed: 11/13/2022] Open
Abstract
An hourglass cup-shape pattern of regulation at the molecular level was detected during the development of the primate brain. Specifically, a peak of temporally differentially expressed genes around the time of birth has been observed in the human brain. However, to what extend this peak of regulation exists among species has not been investigated in great detail. Here, by integrating multiple large-scale transcriptome data from rhesus macaques, we confirmed that a similar differential expression peak exists during the development of the macaque brain. We also found that a similar peak exists during the development of other organs, such as liver, testis, kidney and heart. Furthermore, we found that distinct pathways are regulated in the peak period of those organs. Our results highlight the importance of co-evolution of diverse organs during critical periods of perinatal development in primates.
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Affiliation(s)
- Wenqian Zhang
- School of Life Sciences, Guizhou Normal University, Guiyang, China
| | - Wei Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Manman Zhao
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Christoph W Turck
- Max Planck Institute of Psychiatry, Proteomics and Biomarkers, Munich, Germany
| | - Ying Zhu
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Guang-Zhong Wang
- School of Life Sciences, Guizhou Normal University, Guiyang, China.,CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
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234
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Williams JB, Cao Q, Yan Z. Transcriptomic analysis of human brains with Alzheimer's disease reveals the altered expression of synaptic genes linked to cognitive deficits. Brain Commun 2021; 3:fcab123. [PMID: 34423299 PMCID: PMC8374979 DOI: 10.1093/braincomms/fcab123] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 03/30/2021] [Accepted: 04/22/2021] [Indexed: 11/14/2022] Open
Abstract
Alzheimer's disease is a progressive neurodegenerative disorder associated with memory loss and impaired executive function. The molecular underpinnings causing cognitive deficits in Alzheimer's disease are loosely understood. Here, we performed cross-study large-scale transcriptomic analyses of postmortem prefrontal cortex derived from Alzheimer's disease patients to reveal the role of aberrant gene expression in this disease. We identified that one of the most prominent changes in prefrontal cortex of Alzheimer's disease humans was the downregulation of genes in excitatory and inhibitory neurons that are associated with synaptic functions, particularly the SNARE-binding complex, which is essential for vesicle docking and neurotransmitter release. Comparing genomic data of Alzheimer's disease with proteomic data of cognitive trajectory, we found that many of the lost synaptic genes in Alzheimer's disease encode hub proteins whose increased abundance is required for cognitive stability. This study has revealed potential molecular targets for therapeutic intervention of cognitive decline associated with Alzheimer's disease.
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Affiliation(s)
- Jamal B Williams
- Department of Physiology and Biophysics, State University of New York at Buffalo, Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY 14203, USA
| | - Qing Cao
- Department of Physiology and Biophysics, State University of New York at Buffalo, Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY 14203, USA
| | - Zhen Yan
- Department of Physiology and Biophysics, State University of New York at Buffalo, Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY 14203, USA
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235
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Ou MY, Xiao Q, Ju XC, Zeng PM, Huang J, Sheng AL, Luo ZG. The CTNNBIP1-CLSTN1 fusion transcript regulates human neocortical development. Cell Rep 2021; 35:109290. [PMID: 34192541 DOI: 10.1016/j.celrep.2021.109290] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 02/17/2021] [Accepted: 06/02/2021] [Indexed: 12/21/2022] Open
Abstract
Fusion transcripts or RNAs have been found in both disordered and healthy human tissues and cells; however, their physiological functions in the brain development remain unknown. In the analysis of deposited RNA-sequence libraries covering early to middle embryonic stages, we identify 1,055 fusion transcripts present in the developing neocortex. Interestingly, 98 fusion transcripts exhibit distinct expression patterns in various neural progenitors (NPs) or neurons. We focus on CTNNBIP1-CLSTN1 (CTCL), which is enriched in outer radial glial cells that contribute to cortex expansion during human evolution. Intriguingly, downregulation of CTCL in cultured human cerebral organoids causes marked reduction in NPs and precocious neuronal differentiation, leading to impairment of organoid growth. Furthermore, the expression of CTCL fine-tunes Wnt/β-catenin signaling that controls cortex patterning. Together, this work provides evidence indicating important roles of fusion transcript in human brain development and evolution.
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Affiliation(s)
- Min-Yi Ou
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qi Xiao
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiang-Chun Ju
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Peng-Ming Zeng
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jing Huang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Ai-Li Sheng
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhen-Ge Luo
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
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236
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Bocchi VD, Conforti P, Vezzoli E, Besusso D, Cappadona C, Lischetti T, Galimberti M, Ranzani V, Bonnal RJP, De Simone M, Rossetti G, He X, Kamimoto K, Espuny-Camacho I, Faedo A, Gervasoni F, Vuono R, Morris SA, Chen J, Felsenfeld D, Pavesi G, Barker RA, Pagani M, Cattaneo E. The coding and long noncoding single-cell atlas of the developing human fetal striatum. Science 2021; 372:372/6542/eabf5759. [PMID: 33958447 DOI: 10.1126/science.abf5759] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 03/29/2021] [Indexed: 12/12/2022]
Abstract
Deciphering how the human striatum develops is necessary for understanding the diseases that affect this region. To decode the transcriptional modules that regulate this structure during development, we compiled a catalog of 1116 long intergenic noncoding RNAs (lincRNAs) identified de novo and then profiled 96,789 single cells from the early human fetal striatum. We found that D1 and D2 medium spiny neurons (D1- and D2-MSNs) arise from a common progenitor and that lineage commitment is established during the postmitotic transition, across a pre-MSN phase that exhibits a continuous spectrum of fate determinants. We then uncovered cell type-specific gene regulatory networks that we validated through in silico perturbation. Finally, we identified human-specific lincRNAs that contribute to the phylogenetic divergence of this structure in humans. This work delineates the cellular hierarchies governing MSN lineage commitment.
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Affiliation(s)
- Vittoria Dickinson Bocchi
- Dipartimento di Bioscienze, Università degli Studi di Milano, Milan, Italy.,INGM, Istituto Nazionale Genetica Molecolare, Milan, Italy
| | - Paola Conforti
- Dipartimento di Bioscienze, Università degli Studi di Milano, Milan, Italy.,INGM, Istituto Nazionale Genetica Molecolare, Milan, Italy
| | - Elena Vezzoli
- Dipartimento di Bioscienze, Università degli Studi di Milano, Milan, Italy.,INGM, Istituto Nazionale Genetica Molecolare, Milan, Italy
| | - Dario Besusso
- Dipartimento di Bioscienze, Università degli Studi di Milano, Milan, Italy.,INGM, Istituto Nazionale Genetica Molecolare, Milan, Italy
| | - Claudio Cappadona
- Dipartimento di Bioscienze, Università degli Studi di Milano, Milan, Italy.,INGM, Istituto Nazionale Genetica Molecolare, Milan, Italy
| | - Tiziana Lischetti
- Dipartimento di Bioscienze, Università degli Studi di Milano, Milan, Italy.,INGM, Istituto Nazionale Genetica Molecolare, Milan, Italy
| | - Maura Galimberti
- Dipartimento di Bioscienze, Università degli Studi di Milano, Milan, Italy.,INGM, Istituto Nazionale Genetica Molecolare, Milan, Italy
| | | | | | | | | | - Xiaoling He
- WT-MRC Cambridge Stem Cell Institute and Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK
| | - Kenji Kamimoto
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA.,Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Ira Espuny-Camacho
- Dipartimento di Bioscienze, Università degli Studi di Milano, Milan, Italy.,INGM, Istituto Nazionale Genetica Molecolare, Milan, Italy
| | - Andrea Faedo
- Dipartimento di Bioscienze, Università degli Studi di Milano, Milan, Italy.,INGM, Istituto Nazionale Genetica Molecolare, Milan, Italy
| | - Federica Gervasoni
- INGM, Istituto Nazionale Genetica Molecolare, Milan, Italy.,Dipartimento di Biotecnologie Mediche e Medicina Traslazionale, Università degli Studi di Milano, Milan, Italy
| | - Romina Vuono
- WT-MRC Cambridge Stem Cell Institute and Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK
| | - Samantha A Morris
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA.,Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jian Chen
- CHDI Management/CHDI Foundation, New York, NY, USA
| | | | - Giulio Pavesi
- Dipartimento di Bioscienze, Università degli Studi di Milano, Milan, Italy
| | - Roger A Barker
- WT-MRC Cambridge Stem Cell Institute and Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK
| | - Massimiliano Pagani
- INGM, Istituto Nazionale Genetica Molecolare, Milan, Italy. .,Dipartimento di Biotecnologie Mediche e Medicina Traslazionale, Università degli Studi di Milano, Milan, Italy
| | - Elena Cattaneo
- Dipartimento di Bioscienze, Università degli Studi di Milano, Milan, Italy. .,INGM, Istituto Nazionale Genetica Molecolare, Milan, Italy
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237
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Yuan P, Ding L, Chen H, Wang Y, Li C, Zhao S, Yang X, Ma Y, Zhu J, Qi X, Zhang Y, Xia X, Zheng JC. Neural Stem Cell-Derived Exosomes Regulate Neural Stem Cell Differentiation Through miR-9-Hes1 Axis. Front Cell Dev Biol 2021; 9:601600. [PMID: 34055767 PMCID: PMC8155619 DOI: 10.3389/fcell.2021.601600] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 04/16/2021] [Indexed: 12/27/2022] Open
Abstract
Exosomes, a key element of the central nervous system microenvironment, mediate intercellular communication via horizontally transferring bioactive molecules. Emerging evidence has implicated exosomes in the regulation of neurogenesis. Recently, we compared the neurogenic potential of exosomes released from primary mouse embryonic neural stem cells (NSCs) and astrocyte-reprogrammed NSCs, and observed diverse neurogenic potential of those two exosome populations in vitro. However, the roles of NSC-derived exosomes on NSC differentiation and the underlying mechanisms remain largely unknown. In this study, we firstly demonstrated that NSC-derived exosomes facilitate the differentiation of NSCs and the maturation of both neuronal and glial cells in defined conditions. We then identified miR-9, a pro-neural miRNA, as the most abundantly expressed miRNA in NSC-derived exosomes. The silencing of miR-9 in exosomes abrogates the positive effects of NSC-derived exosomes on the differentiation of NSCs. We further identified Hes1 as miR-9 downstream target, as the transfection of Hes1 siRNA restored the differentiation promoting potential of NSC-derived exosomes after knocking down exosomal miR-9. Thus, our data indicate that NSC-derived exosomes facilitate the differentiation of NSCs via transferring miR-9, which sheds light on the development of cell-free therapeutic strategies for treating neurodegeneration.
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Affiliation(s)
- Ping Yuan
- Center for Translational Neurodegeneration and Regenerative Therapy, Tenth People's Hospital of Tongji University, Shanghai, China.,Department of Cardio-Pulmonary Circulation, School of Medicine, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China
| | - Lu Ding
- Center for Translational Neurodegeneration and Regenerative Therapy, Tenth People's Hospital of Tongji University, Shanghai, China
| | - Huili Chen
- Center for Translational Neurodegeneration and Regenerative Therapy, Tenth People's Hospital of Tongji University, Shanghai, China
| | - Yi Wang
- Center for Translational Neurodegeneration and Regenerative Therapy, Tenth People's Hospital of Tongji University, Shanghai, China
| | - Chunhong Li
- Center for Translational Neurodegeneration and Regenerative Therapy, Tenth People's Hospital of Tongji University, Shanghai, China
| | - Shu Zhao
- Center for Translational Neurodegeneration and Regenerative Therapy, Tenth People's Hospital of Tongji University, Shanghai, China
| | - Xiaoyu Yang
- Center for Translational Neurodegeneration and Regenerative Therapy, Tenth People's Hospital of Tongji University, Shanghai, China
| | - Yizhao Ma
- Center for Translational Neurodegeneration and Regenerative Therapy, Tenth People's Hospital of Tongji University, Shanghai, China
| | - Jie Zhu
- Center for Translational Neurodegeneration and Regenerative Therapy, Tenth People's Hospital of Tongji University, Shanghai, China
| | - Xinrui Qi
- Center for Translational Neurodegeneration and Regenerative Therapy, Tenth People's Hospital of Tongji University, Shanghai, China
| | - Yanyan Zhang
- Center for Translational Neurodegeneration and Regenerative Therapy, Tenth People's Hospital of Tongji University, Shanghai, China
| | - Xiaohuan Xia
- Center for Translational Neurodegeneration and Regenerative Therapy, Tenth People's Hospital of Tongji University, Shanghai, China.,Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital affiliated to Tongji University School of Medicine, Shanghai, China
| | - Jialin C Zheng
- Center for Translational Neurodegeneration and Regenerative Therapy, Tenth People's Hospital of Tongji University, Shanghai, China.,Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital affiliated to Tongji University School of Medicine, Shanghai, China.,Collaborative Innovation Center for Brain Science, Tongji University, Shanghai, China
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238
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Sevoflurane impairs m6A-mediated mRNA translation and leads to fine motor and cognitive deficits. Cell Biol Toxicol 2021; 38:347-369. [PMID: 33928466 DOI: 10.1007/s10565-021-09601-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 03/10/2021] [Indexed: 12/14/2022]
Abstract
Clinical surgical practices have found that children who undergo multiple anesthesia may have an increased risk of deficiencies in cognition and fine motor control. Here, we report that YT521-B homology domain family 1 (YTHDF1), a critical reader protein for N6-methyladenosine-modified mRNA, was significantly downregulated in the prefrontal cortex of young mice after multiple sevoflurane anesthesia exposures. Importantly, sevoflurane led to a decrease in protein synthesis in mouse cortical neurons that was fully rescued by YTHDF1, suggesting that anesthesia may affect early brain development by affecting m6A-dependent mRNA translation. Transcriptome-wide experiments showed that numerous mRNA targets related to synaptic functions in the prefrontal mouse cortex were associated with m6A methylation and YTHDF1. In particular, we found that synaptophysin, a critical presynaptic protein, was specifically modified by m6A methylation and associated with YTHDF1, and m6A methylation of synaptophysin decreased with multiple sevoflurane exposures. Importantly, we showed that fine motor control skills and cognitive functions were impaired in mice with multiple anesthesia exposures, and these effects were fully reversed by reintroducing YTHDF1 through a blood-brain barrier (BBB)-crossing viral delivery system. Finally, we found that the fine motor skills in children who underwent prolonged anesthesia were compromised 6 months after surgery. Our findings indicated that impairment in the translational regulation of mRNA via N6-methyladenosine methylation is a potential mechanism underlying the effects of anesthesia on neural development in the young brain. 1. N6-methyladenosine (m6A) modifications were involved in anesthesia-induced neurotoxicity. 2. Sevoflurane impairs m6A-mediated mRNA translation and leads to fine motor deficits in young mice. 3. YTHDF1, a m6A reader protein, rescued sevoflurane-induced protein synthesis inhibition and fine motor deficits in young mice.
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239
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Michki NS, Li Y, Sanjasaz K, Zhao Y, Shen FY, Walker LA, Cao W, Lee CY, Cai D. The molecular landscape of neural differentiation in the developing Drosophila brain revealed by targeted scRNA-seq and multi-informatic analysis. Cell Rep 2021; 35:109039. [PMID: 33909998 PMCID: PMC8139287 DOI: 10.1016/j.celrep.2021.109039] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 01/19/2021] [Accepted: 04/06/2021] [Indexed: 01/16/2023] Open
Abstract
The Drosophila type II neuroblast lineages present an attractive model to investigate the neurogenesis and differentiation process as they adapt to a process similar to that in the human outer subventricular zone. We perform targeted single-cell mRNA sequencing in third instar larval brains to study this process of the type II NB lineage. Combining prior knowledge, in silico analyses, and in situ validation, our multi-informatic investigation describes the molecular landscape from a single developmental snapshot. 17 markers are identified to differentiate distinct maturation stages. 30 markers are identified to specify the stem cell origin and/or cell division numbers of INPs, and at least 12 neuronal subtypes are identified. To foster future discoveries, we provide annotated tables of pairwise gene-gene correlation in single cells and MiCV, a web tool for interactively analyzing scRNA-seq datasets. Taken together, these resources advance our understanding of the neural differentiation process at the molecular level.
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Affiliation(s)
- Nigel S Michki
- Biophysics LS&A, University of Michigan, Ann Arbor, MI, USA
| | - Ye Li
- Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Kayvon Sanjasaz
- Molecular, Cellular, and Developmental Biology LS&A, University of Michigan, Ann Arbor, MI, USA
| | - Yimeng Zhao
- Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Fred Y Shen
- Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Logan A Walker
- Biophysics LS&A, University of Michigan, Ann Arbor, MI, USA
| | - Wenjia Cao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Cheng-Yu Lee
- Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA; Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA; Division of Genetic Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Dawen Cai
- Biophysics LS&A, University of Michigan, Ann Arbor, MI, USA; Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA; Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, MI, USA.
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240
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Aichmüller CF, Iskar M, Jones DTW, Korshunov A, Radlwimmer B, Kool M, Ernst A, Pfister SM, Lichter P, Zapatka M. Pilocytic astrocytoma demethylation and transcriptional landscapes link bZIP transcription factors to immune response. Neuro Oncol 2021; 22:1327-1338. [PMID: 32052037 DOI: 10.1093/neuonc/noaa035] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Pilocytic astrocytoma (PA) is the most common pediatric brain tumor. While genome and transcriptome landscapes are well studied, data of the complete methylome, tumor cell composition, and immune infiltration are scarce. METHODS We generated whole genome bisulfite sequence (WGBS) data of 9 PAs and 16 control samples and integrated available 154 PA and 57 control methylation array data. RNA sequence data of 49 PAs and 11 control samples as well as gene expression arrays of 248 PAs and 28 controls were used to assess transcriptional activity. RESULTS DNA-methylation patterns of partially methylated domains suggested high stability of the methylomes during tumorigenesis. Comparing tumor and control tissues of infra- and supratentorial location using methylation arrays revealed a site specific pattern. Analysis of WGBS data revealed 9381 significantly differentially methylated regions (DMRs) in PA versus control tissue. Enhancers and transcription factor (TF) motifs of five distinct TF families were found to be enriched in DMRs. Methylation together with gene expression data-based in silico tissue deconvolution analysis indicated a striking variation in the immune cell infiltration in PA. A TF network analysis showed a regulatory relation between basic leucine zipper (bZIP) transcription factors and genes involved in immune-related processes. CONCLUSION We provide evidence for a link of focal methylation differences and differential gene expression to immune infiltration.
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Affiliation(s)
| | - Murat Iskar
- Division of Molecular Genetics, German Cancer Research Center, Heidelberg, Germany
| | - David T W Jones
- Hopp Children's Cancer Center Heidelberg, Heidelberg, Germany.,Pediatric Glioma Research Group, German Cancer Research Center, Heidelberg, Germany.,German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Andrey Korshunov
- Department of Neuropathology, Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.,Clinical Cooperation Unit Neuropathology, German Consortium for Translational Cancer Research, German Cancer Research Center, Heidelberg, Germany
| | - Bernhard Radlwimmer
- Division of Molecular Genetics, German Cancer Research Center, Heidelberg, Germany
| | - Marcel Kool
- Hopp Children's Cancer Center Heidelberg, Heidelberg, Germany.,Pediatric Glioma Research Group, German Cancer Research Center, Heidelberg, Germany
| | - Aurelie Ernst
- Group Genome Instability in Tumors, German Cancer Research Center, Heidelberg, Germany
| | - Stefan M Pfister
- Hopp Children's Cancer Center Heidelberg, Heidelberg, Germany.,Pediatric Glioma Research Group, German Cancer Research Center, Heidelberg, Germany.,Division of Pediatric Neurooncology, German Cancer Consortium and German Cancer Research Center, Heidelberg, Germany.,Department of Pediatric Oncology, Hematology, and Immunology, Heidelberg University Hospital, Heidelberg, Germany.,German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Peter Lichter
- Division of Molecular Genetics, German Cancer Research Center, Heidelberg, Germany.,German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Marc Zapatka
- Division of Molecular Genetics, German Cancer Research Center, Heidelberg, Germany
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241
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Zhang YH, Xu M, Shi X, Sun XL, Mu W, Wu H, Wang J, Li S, Su P, Gong L, He M, Yao M, Wu QF. Cascade diversification directs generation of neuronal diversity in the hypothalamus. Cell Stem Cell 2021; 28:1483-1499.e8. [PMID: 33887179 DOI: 10.1016/j.stem.2021.03.020] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 03/02/2021] [Accepted: 03/24/2021] [Indexed: 11/28/2022]
Abstract
The hypothalamus contains an astounding heterogeneity of neurons that regulate endocrine, autonomic, and behavioral functions. However, its molecular developmental trajectory and origin of neuronal diversity remain unclear. Here, we profile the transcriptome of 43,261 cells derived from Rax+ hypothalamic neuroepithelium to map the developmental landscape of the mouse hypothalamus and trajectory of radial glial cells (RGCs), intermediate progenitor cells (IPCs), nascent neurons, and peptidergic neurons. We show that RGCs adopt a conserved strategy for multipotential differentiation but generate Ascl1+ and Neurog2+ IPCs. Ascl1+ IPCs differ from their telencephalic counterpart by displaying fate bifurcation, and postmitotic nascent neurons resolve into multiple peptidergic neuronal subtypes. Clonal analysis further demonstrates that single RGCs can produce multiple neuronal subtypes. Our study reveals that multiple cell types along the lineage hierarchy contribute to fate diversification of hypothalamic neurons in a stepwise fashion, suggesting a cascade diversification model that deconstructs the origin of neuronal diversity.
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Affiliation(s)
- Yu-Hong Zhang
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100101, China
| | - Mingrui Xu
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100101, China
| | - Xiang Shi
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100101, China
| | - Xue-Lian Sun
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100101, China
| | - Wenhui Mu
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Haoda Wu
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100101, China
| | - Jingjing Wang
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Si Li
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100101, China
| | - Pengfei Su
- Institutes of Biomedical Sciences, Shanxi University, Taiyuan 030006, China
| | - Ling Gong
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai 200032, China
| | - Miao He
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai 200032, China
| | - Mingze Yao
- Institutes of Biomedical Sciences, Shanxi University, Taiyuan 030006, China
| | - Qing-Feng Wu
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100101, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China; Chinese Institute for Brain Research, Beijing 102206, China.
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242
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Lanshakov DA, Sukhareva EV, Bulygina VV, Bannova AV, Shaburova EV, Kalinina TS. Single neonatal dexamethasone administration has long-lasting outcome on depressive-like behaviour, Bdnf, Nt-3, p75ngfr and sorting receptors (SorCS1-3) stress reactive expression. Sci Rep 2021; 11:8092. [PMID: 33854153 PMCID: PMC8046778 DOI: 10.1038/s41598-021-87652-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 03/31/2021] [Indexed: 12/22/2022] Open
Abstract
Elevated glucocorticoid level in the early postnatal period is associated with glucocorticoid therapy prescribed at preterm delivery most often has severe long-lasting neurodevelopmental and behavioural effects. Detailed molecular mechanisms of such programming action of antenatal glucocorticoids on behaviour are still poorly understood. To address this question we studied neurotrophins: Bdnf, Nt-3, Ngf and their receptors: p75ngfr, Sorcs3 expression changes after subcutaneous dexamethasone (DEX) 0.2 mg/kg injection to P2 rat pups. Neurotrophins expression level was studied in the hippocampus (HPC). Disturbances in these brain regions have been implicated in the emergence of multiple psychopathologies. p75ngfr and Sorcs3 expression was studied in the brainstem—region where monoamine neurons are located. Immunohistochemically P75NTR protein level changes after DEX were investigated in the brainstem Locus Coereleus norepinephrine neurons (NE). In the first hours after DEX administration elevation of neurotrophins expression in HPC and decline of receptor’s expression in the NE brainstem neurons were observed. Another critical time point during maturation is adolescence. Impact of elevated glucocorticoid level in the neonatal period and unpredictable stress (CMUS) at the end of adolescence on depressive-like behaviour was studied. Single neonatal DEX injection leads to decrease in depressive-like behaviour, observed in FST, independently from chronic stress. Neonatal DEX administration decreased Ntf3 and SorCS1 expression in the brainstem. Also Bdnf mRNA level in the brainstem of these animals didn’t decrease after FST. CMUS at the end of adolescence changed p75ngfr and SorCS3 expression in the brainstem in the animals that received single neonatal DEX administration.
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Affiliation(s)
- D A Lanshakov
- Laboratory of Postgenomics Neurobiology, Institute of Cytology and Genetics, Russian Academy of Science, Novosibirsk, Russian Federation, 630090.
| | - E V Sukhareva
- Functional Neurogenomics Laboratory, Institute of Cytology and Genetics, Russian Academy of Science, Novosibirsk, Russian Federation, 630090.,Department of Natural Sciences, Novosibirsk State University, Novosibirsk, Russian Federation, 630090
| | - V V Bulygina
- Functional Neurogenomics Laboratory, Institute of Cytology and Genetics, Russian Academy of Science, Novosibirsk, Russian Federation, 630090
| | - A V Bannova
- Functional Neurogenomics Laboratory, Institute of Cytology and Genetics, Russian Academy of Science, Novosibirsk, Russian Federation, 630090
| | - E V Shaburova
- Functional Neurogenomics Laboratory, Institute of Cytology and Genetics, Russian Academy of Science, Novosibirsk, Russian Federation, 630090.,Department of Natural Sciences, Novosibirsk State University, Novosibirsk, Russian Federation, 630090
| | - T S Kalinina
- Functional Neurogenomics Laboratory, Institute of Cytology and Genetics, Russian Academy of Science, Novosibirsk, Russian Federation, 630090.,Department of Natural Sciences, Novosibirsk State University, Novosibirsk, Russian Federation, 630090
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243
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Morson S, Yang Y, Price DJ, Pratt T. Expression of Genes in the 16p11.2 Locus during Development of the Human Fetal Cerebral Cortex. Cereb Cortex 2021; 31:4038-4052. [PMID: 33825894 PMCID: PMC8328201 DOI: 10.1093/cercor/bhab067] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 02/25/2021] [Accepted: 02/26/2021] [Indexed: 12/27/2022] Open
Abstract
The 593 kbp 16p11.2 copy number variation (CNV) affects the gene dosage of 29 protein coding genes, with heterozygous 16p11.2 microduplication or microdeletion implicated in about 1% of autism spectrum disorder (ASD) cases. The 16p11.2 CNV is frequently associated with macrocephaly or microcephaly indicating early defects of neurogenesis may contribute to subsequent ASD symptoms, but it is unknown which 16p11.2 transcripts are expressed in progenitors and whose levels are likely, therefore, to influence neurogenesis. Analysis of human fetal gene expression data revealed that KIF22, ALDOA, HIRIP3, PAGR1, and MAZ transcripts are expressed in neural progenitors with ALDOA and KIF22 significantly enriched compared to post-mitotic cells. To investigate the possible roles of ALDOA and KIF22 proteins in human cerebral cortex development we used immunohistochemical staining to describe their expression in late first and early second trimester human cerebral cortex. KIF22 protein is restricted to proliferating cells with its levels increasing during the cell cycle and peaking at mitosis. ALDOA protein is expressed in all cell types and does not vary with cell-cycle phase. Our expression analysis suggests the hypothesis that altered neurogenesis in the cerebral cortex contributes to ASD in 16p11.2 CNV patients.
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Affiliation(s)
- Sarah Morson
- Simons Initiative for the Developing Brain, Hugh Robson Building, Edinburgh Medical School Biomedical Sciences, The University of Edinburgh, Edinburgh EH8 9XD, UK.,Centre for Discovery Brain Sciences, Hugh Robson Building, Edinburgh Medical School Biomedical Sciences, The University of Edinburgh, Edinburgh EH8 9XD, UK
| | - Yifei Yang
- Simons Initiative for the Developing Brain, Hugh Robson Building, Edinburgh Medical School Biomedical Sciences, The University of Edinburgh, Edinburgh EH8 9XD, UK.,Centre for Discovery Brain Sciences, Hugh Robson Building, Edinburgh Medical School Biomedical Sciences, The University of Edinburgh, Edinburgh EH8 9XD, UK
| | - David J Price
- Simons Initiative for the Developing Brain, Hugh Robson Building, Edinburgh Medical School Biomedical Sciences, The University of Edinburgh, Edinburgh EH8 9XD, UK.,Centre for Discovery Brain Sciences, Hugh Robson Building, Edinburgh Medical School Biomedical Sciences, The University of Edinburgh, Edinburgh EH8 9XD, UK
| | - Thomas Pratt
- Simons Initiative for the Developing Brain, Hugh Robson Building, Edinburgh Medical School Biomedical Sciences, The University of Edinburgh, Edinburgh EH8 9XD, UK.,Centre for Discovery Brain Sciences, Hugh Robson Building, Edinburgh Medical School Biomedical Sciences, The University of Edinburgh, Edinburgh EH8 9XD, UK
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244
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Chan WK, Fetit R, Griffiths R, Marshall H, Mason JO, Price DJ. Using organoids to study human brain development and evolution. Dev Neurobiol 2021; 81:608-622. [PMID: 33773072 DOI: 10.1002/dneu.22819] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 03/19/2021] [Accepted: 03/21/2021] [Indexed: 12/22/2022]
Abstract
Recent advances in methods for making cerebral organoids have opened a window of opportunity to directly study human brain development and disease, countering limitations inherent in non-human-based approaches. Whether freely patterned, guided into a region-specific fate or fused into assembloids, organoids have successfully recapitulated key features of in vivo neurodevelopment, allowing its examination from early to late stages. Although organoids have enormous potential, their effective use relies on understanding the extent of their limitations in accurately reproducing specific processes and components in the developing human brain. Here we review the potential of cerebral organoids to model and study human brain development and evolution and discuss the progress and current challenges in their use for reproducing specific human neurodevelopmental processes.
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Affiliation(s)
- Wai-Kit Chan
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
| | - Rana Fetit
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
| | - Rosie Griffiths
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
| | - Helen Marshall
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
| | - John O Mason
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
| | - David J Price
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
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245
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Ulmke PA, Sakib MS, Ditte P, Sokpor G, Kerimoglu C, Pham L, Xie Y, Mao X, Rosenbusch J, Teichmann U, Nguyen HP, Fischer A, Eichele G, Staiger JF, Tuoc T. Molecular Profiling Reveals Involvement of ESCO2 in Intermediate Progenitor Cell Maintenance in the Developing Mouse Cortex. Stem Cell Reports 2021; 16:968-984. [PMID: 33798452 PMCID: PMC8072132 DOI: 10.1016/j.stemcr.2021.03.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 03/05/2021] [Accepted: 03/08/2021] [Indexed: 12/12/2022] Open
Abstract
Intermediate progenitor cells (IPCs) are neocortical neuronal precursors. Although IPCs play crucial roles in corticogenesis, their molecular features remain largely unknown. In this study, we aimed to characterize the molecular profile of IPCs. We isolated TBR2-positive (+) IPCs and TBR2-negative (-) cell populations in the developing mouse cortex. Comparative genome-wide gene expression analysis of TBR2+ IPCs versus TBR2- cells revealed differences in key factors involved in chromatid segregation, cell-cycle regulation, transcriptional regulation, and cell signaling. Notably, mutation of many IPC genes in human has led to intellectual disability and caused a wide range of cortical malformations, including microcephaly and agenesis of corpus callosum. Loss-of-function experiments in cortex-specific mutants of Esco2, one of the novel IPC genes, demonstrate its critical role in IPC maintenance, and substantiate the identification of a central genetic determinant of IPC biogenesis. Our data provide novel molecular characteristics of IPCs in the developing mouse cortex.
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Affiliation(s)
- Pauline Antonie Ulmke
- Institute for Neuroanatomy, University Medical Center, Georg-August-University, Goettingen, Germany
| | - M Sadman Sakib
- German Center for Neurodegenerative Diseases, Goettingen, Germany
| | - Peter Ditte
- Max-Planck-Institute for Biophysical Chemistry, Goettingen, Germany
| | - Godwin Sokpor
- Institute for Neuroanatomy, University Medical Center, Georg-August-University, Goettingen, Germany; Department of Human Genetics, Ruhr University of Bochum, Bochum, Germany
| | - Cemil Kerimoglu
- German Center for Neurodegenerative Diseases, Goettingen, Germany
| | - Linh Pham
- Institute for Neuroanatomy, University Medical Center, Georg-August-University, Goettingen, Germany; Department of Human Genetics, Ruhr University of Bochum, Bochum, Germany
| | - Yuanbin Xie
- Institute for Neuroanatomy, University Medical Center, Georg-August-University, Goettingen, Germany
| | - Xiaoyi Mao
- Institute for Neuroanatomy, University Medical Center, Georg-August-University, Goettingen, Germany
| | - Joachim Rosenbusch
- Institute for Neuroanatomy, University Medical Center, Georg-August-University, Goettingen, Germany
| | - Ulrike Teichmann
- Max-Planck-Institute for Biophysical Chemistry, Goettingen, Germany
| | - Huu Phuc Nguyen
- Department of Human Genetics, Ruhr University of Bochum, Bochum, Germany
| | - Andre Fischer
- German Center for Neurodegenerative Diseases, Goettingen, Germany; Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), Goettingen, Germany
| | - Gregor Eichele
- Max-Planck-Institute for Biophysical Chemistry, Goettingen, Germany
| | - Jochen F Staiger
- Institute for Neuroanatomy, University Medical Center, Georg-August-University, Goettingen, Germany
| | - Tran Tuoc
- Institute for Neuroanatomy, University Medical Center, Georg-August-University, Goettingen, Germany; Department of Human Genetics, Ruhr University of Bochum, Bochum, Germany.
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246
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Afridi R, Seol S, Kang HJ, Suk K. Brain-immune interactions in neuropsychiatric disorders: Lessons from transcriptome studies for molecular targeting. Biochem Pharmacol 2021; 188:114532. [PMID: 33773976 DOI: 10.1016/j.bcp.2021.114532] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/18/2021] [Accepted: 03/18/2021] [Indexed: 12/12/2022]
Abstract
Understanding the pathophysiological mechanisms of neuropsychiatric disorders has been a challenging quest for neurobiologists. Recent years have witnessed enormous technological advances in the field of neuroimmunology, blurring boundaries between the central nervous system and the periphery. Consequently, the discipline has expanded to cover interactions between the nervous and immune systems in health and diseases. The complex interplay between the peripheral and central immune pathways in neuropsychiatric disorders has recently been documented in various studies, but the genetic determinants remain elusive. Recent transcriptome studies have identified dysregulated genes involved in peripheral immune cell activation, blood-brain barrier integrity, glial cell activation, and synaptic plasticity in major depressive disorder, bipolar disorder, autism spectrum disorder, and schizophrenia. Herein, the key transcriptomic techniques applied in investigating differentially expressed genes and pathways responsible for altered brain-immune interactions in neuropsychiatric disorders are discussed. The application of transcriptomics that can aid in identifying molecular targets in various neuropsychiatric disorders is highlighted.
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Affiliation(s)
- Ruqayya Afridi
- Department of Pharmacology, Brain Science & Engineering Institute, BK21 Plus KNU Biomedical Convergence Program, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Sihwan Seol
- Department of Life Science, Chung-Ang University, Seoul, Republic of Korea
| | - Hyo Jung Kang
- Department of Life Science, Chung-Ang University, Seoul, Republic of Korea.
| | - Kyoungho Suk
- Department of Pharmacology, Brain Science & Engineering Institute, BK21 Plus KNU Biomedical Convergence Program, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
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247
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Gusic M, Prokisch H. Genetic basis of mitochondrial diseases. FEBS Lett 2021; 595:1132-1158. [PMID: 33655490 DOI: 10.1002/1873-3468.14068] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 02/17/2021] [Accepted: 02/18/2021] [Indexed: 12/13/2022]
Abstract
Mitochondrial disorders are monogenic disorders characterized by a defect in oxidative phosphorylation and caused by pathogenic variants in one of over 340 different genes. The implementation of whole-exome sequencing has led to a revolution in their diagnosis, duplicated the number of associated disease genes, and significantly increased the diagnosed fraction. However, the genetic etiology of a substantial fraction of patients exhibiting mitochondrial disorders remains unknown, highlighting limitations in variant detection and interpretation, which calls for improved computational and DNA sequencing methods, as well as the addition of OMICS tools. More intriguingly, this also suggests that some pathogenic variants lie outside of the protein-coding genes and that the mechanisms beyond the Mendelian inheritance and the mtDNA are of relevance. This review covers the current status of the genetic basis of mitochondrial diseases, discusses current challenges and perspectives, and explores the contribution of factors beyond the protein-coding regions and monogenic inheritance in the expansion of the genetic spectrum of disease.
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Affiliation(s)
- Mirjana Gusic
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany.,Institute of Human Genetics, Technical University of Munich, Germany.,DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Germany
| | - Holger Prokisch
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany.,Institute of Human Genetics, Technical University of Munich, Germany
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248
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Liu SJ, Magill ST, Vasudevan HN, Hilz S, Villanueva-Meyer JE, Lastella S, Daggubati V, Spatz J, Choudhury A, Orr BA, Demaree B, Seo K, Ferris SP, Abate AR, Oberheim Bush NA, Bollen AW, McDermott MW, Costello JF, Raleigh DR. Multiplatform Molecular Profiling Reveals Epigenomic Intratumor Heterogeneity in Ependymoma. Cell Rep 2021; 30:1300-1309.e5. [PMID: 32023450 PMCID: PMC7313374 DOI: 10.1016/j.celrep.2020.01.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 06/19/2019] [Accepted: 01/06/2020] [Indexed: 12/24/2022] Open
Abstract
Ependymomas exist within distinct genetic subgroups, but the molecular diversity within individual ependymomas is unknown. We perform multiplatform molecular profiling of 6 spatially distinct samples from an ependymoma with C11orf95-RELA fusion. DNA methylation and RNA sequencing distinguish clusters of samples according to neuronal development gene expression programs that could also be delineated by differences in magnetic resonance blood perfusion. Exome sequencing and phylogenetic analysis reveal epigenomic intratumor heterogeneity and suggest that chromosomal structural alterations may precede accumulation of single-nucleotide variants during ependymoma tumorigenesis. In sum, these findings shed light on the oncogenesis and intratumor heterogeneity of ependymoma. Tumor heterogeneity poses a barrier to cancer treatment. Liu etal. investigate radiographically distinct regions of an ependymoma tumor using transcriptomic, genetic, and epigenomic profiling and discover axes of gene expression programs that recapitulate normal brain development in addition to phylogenies that shed light on the tumorigenesis of ependymoma.
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Affiliation(s)
- S John Liu
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Stephen T Magill
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Harish N Vasudevan
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Stephanie Hilz
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Javier E Villanueva-Meyer
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Sydney Lastella
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Vikas Daggubati
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Jordan Spatz
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Abrar Choudhury
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Brent A Orr
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Benjamin Demaree
- Department of Bioengineering and Therapeutic Sciences, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Kyounghee Seo
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Sean P Ferris
- Department of Pathology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Adam R Abate
- Department of Bioengineering and Therapeutic Sciences, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Nancy Ann Oberheim Bush
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Andrew W Bollen
- Department of Pathology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Michael W McDermott
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Joseph F Costello
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - David R Raleigh
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.
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249
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Masuda T, Sankowski R, Staszewski O, Prinz M. Microglia Heterogeneity in the Single-Cell Era. Cell Rep 2021; 30:1271-1281. [PMID: 32023447 DOI: 10.1016/j.celrep.2020.01.010] [Citation(s) in RCA: 394] [Impact Index Per Article: 131.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 10/19/2019] [Accepted: 01/02/2020] [Indexed: 12/30/2022] Open
Abstract
Microglia are resident immune cells in the central nervous system (CNS) that are capable of carrying out prominent and various functions during development and adulthood under both homeostatic and disease conditions. Although microglia are traditionally thought to be heterogeneous populations, which potentially allows them to achieve a wide range of responses to environmental changes for the maintenance of CNS homeostasis, a lack of unbiased and high-throughput methods to assess microglia heterogeneity has prevented the study of spatially and temporally distributed microglia subsets. The recent emergence of novel single-cell techniques, such as cytometry by time-of-flight mass spectrometry (CyTOF) and single-cell RNA sequencing, enabled scientists to overcome such limitations and reveal the surprising context-dependent heterogeneity of microglia. In this review, we summarize the current knowledge about the spatial, temporal, and functional diversity of microglia during development, homeostasis, and disease in mice and humans.
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Affiliation(s)
- Takahiro Masuda
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Roman Sankowski
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Berta-Ottenstein-Programme for Clinician Scientists, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ori Staszewski
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Berta-Ottenstein-Programme for Clinician Scientists, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marco Prinz
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany; Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany.
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250
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Resolving organoid brain region identities by mapping single-cell genomic data to reference atlases. Cell Stem Cell 2021; 28:1148-1159.e8. [PMID: 33711282 DOI: 10.1016/j.stem.2021.02.015] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 10/23/2020] [Accepted: 02/08/2021] [Indexed: 12/13/2022]
Abstract
Self-organizing tissues resembling brain structures generated from human stem cells offer exciting possibilities to study human brain development, disease, and evolution. These 3D models are complex and can contain cells at various stages of differentiation from different brain regions. Single-cell genomic methods provide powerful approaches to explore cell composition, differentiation trajectories, and genetic perturbations in brain organoid systems. However, it remains a major challenge to understand the heterogeneity observed within and between individual organoids. Here, we develop a set of computational tools (VoxHunt) to assess brain organoid patterning, developmental state, and cell identity through comparisons to spatial and single-cell transcriptome reference datasets. We use VoxHunt to characterize and visualize cell compositions using single-cell and bulk genomic data from multiple organoid protocols modeling different brain structures. VoxHunt will be useful to assess organoid engineering protocols and to annotate cell fates that emerge in organoids during genetic and environmental perturbation experiments.
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