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Mannens CCA, Hu L, Lönnerberg P, Schipper M, Reagor CC, Li X, He X, Barker RA, Sundström E, Posthuma D, Linnarsson S. Chromatin accessibility during human first-trimester neurodevelopment. Nature 2024:10.1038/s41586-024-07234-1. [PMID: 38693260 DOI: 10.1038/s41586-024-07234-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 02/02/2024] [Indexed: 05/03/2024]
Abstract
The human brain develops through a tightly organized cascade of patterning events, induced by transcription factor expression and changes in chromatin accessibility. Although gene expression across the developing brain has been described at single-cell resolution1, similar atlases of chromatin accessibility have been primarily focused on the forebrain2-4. Here we describe chromatin accessibility and paired gene expression across the entire developing human brain during the first trimester (6-13 weeks after conception). We defined 135 clusters and used multiomic measurements to link candidate cis-regulatory elements to gene expression. The number of accessible regions increased both with age and along neuronal differentiation. Using a convolutional neural network, we identified putative functional transcription factor-binding sites in enhancers characterizing neuronal subtypes. We applied this model to cis-regulatory elements linked to ESRRB to elucidate its activation mechanism in the Purkinje cell lineage. Finally, by linking disease-associated single nucleotide polymorphisms to cis-regulatory elements, we validated putative pathogenic mechanisms in several diseases and identified midbrain-derived GABAergic neurons as being the most vulnerable to major depressive disorder-related mutations. Our findings provide a more detailed view of key gene regulatory mechanisms underlying the emergence of brain cell types during the first trimester and a comprehensive reference for future studies related to human neurodevelopment.
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Affiliation(s)
- Camiel C A Mannens
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Lijuan Hu
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Peter Lönnerberg
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Marijn Schipper
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Caleb C Reagor
- Howard Hughes Medical Institute and Laboratory of Sensory Neuroscience, The Rockefeller University, New York, NY, USA
| | - Xiaofei Li
- Division of Neurodegeneration, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
| | - Xiaoling He
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Roger A Barker
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Erik Sundström
- Division of Neurodegeneration, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sten Linnarsson
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden.
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2
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Mao X, Staiger JF. Multimodal cortical neuronal cell type classification. Pflugers Arch 2024; 476:721-733. [PMID: 38376567 PMCID: PMC11033238 DOI: 10.1007/s00424-024-02923-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 02/01/2024] [Accepted: 02/07/2024] [Indexed: 02/21/2024]
Abstract
Since more than a century, neuroscientists have distinguished excitatory (glutamatergic) neurons with long-distance projections from inhibitory (GABAergic) neurons with local projections and established layer-dependent schemes for the ~ 80% excitatory (principal) cells as well as the ~ 20% inhibitory neurons. Whereas, in the early days, mainly morphological criteria were used to define cell types, later supplemented by electrophysiological and neurochemical properties, nowadays. single-cell transcriptomics is the method of choice for cell type classification. Bringing recent insight together, we conclude that despite all established layer- and area-dependent differences, there is a set of reliably identifiable cortical cell types that were named (among others) intratelencephalic (IT), extratelencephalic (ET), and corticothalamic (CT) for the excitatory cells, which altogether comprise ~ 56 transcriptomic cell types (t-types). By the same means, inhibitory neurons were subdivided into parvalbumin (PV), somatostatin (SST), vasoactive intestinal polypeptide (VIP), and "other (i.e. Lamp5/Sncg)" subpopulations, which altogether comprise ~ 60 t-types. The coming years will show which t-types actually translate into "real" cell types that show a common set of multimodal features, including not only transcriptome but also physiology and morphology as well as connectivity and ultimately function. Only with the better knowledge of clear-cut cell types and experimental access to them, we will be able to reveal their specific functions, a task which turned out to be difficult in a part of the brain being so much specialized for cognition as the cerebral cortex.
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Affiliation(s)
- Xiaoyi Mao
- Institute for Neuroanatomy, University Medical Center Göttingen, Georg-August-University, Kreuzbergring 36, 37075, Göttingen, Germany
| | - Jochen F Staiger
- Institute for Neuroanatomy, University Medical Center Göttingen, Georg-August-University, Kreuzbergring 36, 37075, Göttingen, Germany.
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3
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Kampmann M. Molecular and cellular mechanisms of selective vulnerability in neurodegenerative diseases. Nat Rev Neurosci 2024; 25:351-371. [PMID: 38575768 DOI: 10.1038/s41583-024-00806-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/01/2024] [Indexed: 04/06/2024]
Abstract
The selective vulnerability of specific neuronal subtypes is a hallmark of neurodegenerative diseases. In this Review, I summarize our current understanding of the brain regions and cell types that are selectively vulnerable in different neurodegenerative diseases and describe the proposed underlying cell-autonomous and non-cell-autonomous mechanisms. I highlight how recent methodological innovations - including single-cell transcriptomics, CRISPR-based screens and human cell-based models of disease - are enabling new breakthroughs in our understanding of selective vulnerability. An understanding of the molecular mechanisms that determine selective vulnerability and resilience would shed light on the key processes that drive neurodegeneration and point to potential therapeutic strategies to protect vulnerable cell populations.
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Affiliation(s)
- Martin Kampmann
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA.
- Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA, USA.
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4
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Liu A, Peng B, Pankajam AV, Scheuermann RH, Zhang Y. Discovery of cell type classification marker genes from single cell RNA sequencing data using NS-Forest. bioRxiv 2024:2024.04.22.590194. [PMID: 38712147 PMCID: PMC11071431 DOI: 10.1101/2024.04.22.590194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
The use of single-cell transcriptomic technologies that quantitively describe cell transcriptional phenotypes using single cell/nucleus RNA sequencing (scRNA-seq) is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in eYiciently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an eYective approach to study large-scale scRNA-seq data. NS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and suYicient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package ( https://github.com/JCVenterInstitute/NSForest ), with several enhancements, to select marker gene combinations that exhibit selective expression patterns among closely related cell types and more eYiciently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells. By modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to1, with a metric of 1 given to markers that are only expressed within their target cell types and not in cells of any other cell types. We have applied NS-Forest v4.0 on scRNA-seq datasets from three human organs, including the brain, kidney, and lung. We observe that NS-Forest v4.0 outperforms previous versions on its ability to identify markers with higher On-Target Fraction values for closely related cell types and outperforms other marker gene selection approaches on the classification performance with significantly higher F-beta scores.
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Fei Y, Wu Q, Zhao S, Song K, Han J, Liu C. Diverse and asymmetric patterns of single-neuron projectome in regulating interhemispheric connectivity. Nat Commun 2024; 15:3403. [PMID: 38649683 PMCID: PMC11035633 DOI: 10.1038/s41467-024-47762-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 04/11/2024] [Indexed: 04/25/2024] Open
Abstract
The corpus callosum, historically considered primarily for homotopic connections, supports many heterotopic connections, indicating complex interhemispheric connectivity. Understanding this complexity is crucial yet challenging due to diverse cell-specific wiring patterns. Here, we utilized public AAV bulk tracing and single-neuron tracing data to delineate the anatomical connection patterns of mouse brains and conducted wide-field calcium imaging to assess functional connectivity across various brain states in male mice. The single-neuron data uncovered complex and dense interconnected patterns, particularly for interhemispheric-heterotopic connections. We proposed a metric "heterogeneity" to quantify the complexity of the connection patterns. Computational modeling of these patterns suggested that the heterogeneity of upstream projections impacted downstream homotopic functional connectivity. Furthermore, higher heterogeneity observed in interhemispheric-heterotopic projections would cause lower strength but higher stability in functional connectivity than their intrahemispheric counterparts. These findings were corroborated by our wide-field functional imaging data, underscoring the important role of heterotopic-projection heterogeneity in interhemispheric communication.
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Affiliation(s)
- Yao Fei
- School of Automation, Northwestern Polytechnical University, Xi'an, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Qihang Wu
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shijie Zhao
- School of Automation, Northwestern Polytechnical University, Xi'an, China.
- Research & Development, Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China.
| | - Kun Song
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an, China.
- Research & Development, Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China.
| | - Cirong Liu
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, China.
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6
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Dai R, Zhang M, Chu T, Kopp R, Zhang C, Liu K, Wang Y, Wang X, Chen C, Liu C. Precision and Accuracy of Single-Cell/Nuclei RNA Sequencing Data. bioRxiv 2024:2024.04.12.589216. [PMID: 38659857 PMCID: PMC11042208 DOI: 10.1101/2024.04.12.589216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Single-cell/nuclei RNA sequencing (sc/snRNA-Seq) is widely used for profiling cell-type gene expressions in biomedical research. An important but underappreciated issue is the quality of sc/snRNA-Seq data that would impact the reliability of downstream analyses. Here we evaluated the precision and accuracy in 18 sc/snRNA-Seq datasets. The precision was assessed on data from human brain studies with a total of 3,483,905 cells from 297 individuals, by utilizing technical replicates. The accuracy was evaluated with sample-matched scRNA-Seq and pooled-cell RNA-Seq data of cultured mononuclear phagocytes from four species. The results revealed low precision and accuracy at the single-cell level across all evaluated data. Cell number and RNA quality were highlighted as two key factors determining the expression precision, accuracy, and reproducibility of differential expression analysis in sc/snRNA-Seq. This study underscores the necessity of sequencing enough high-quality cells per cell type per individual, preferably in the hundreds, to mitigate noise in expression quantification.
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Affiliation(s)
- Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Ming Zhang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Tianyao Chu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Richard Kopp
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Chunling Zhang
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Kefu Liu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, VA, USA
| | - Xusheng Wang
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Chao Chen
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Furong Laboratory, Changsha, Hunan, China
- Hunan Key Laboratory of Animal Models for Human Diseases, Central South University, Changsha, China
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
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7
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Kim W, Kim M, Kim B. Unraveling the enigma: housekeeping gene Ugt1a7c as a universal biomarker for microglia. Front Psychiatry 2024; 15:1364201. [PMID: 38666091 PMCID: PMC11043603 DOI: 10.3389/fpsyt.2024.1364201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
Background Microglia, brain resident macrophages, play multiple roles in maintaining homeostasis, including immunity, surveillance, and protecting the central nervous system through their distinct activation processes. Identifying all types of microglia-driven populations is crucial due to the presence of various phenotypes that differ based on developmental stages or activation states. During embryonic development, the E8.5 yolk sac contains erythromyeloid progenitors that go through different growth phases, eventually resulting in the formation of microglia. In addition, microglia are present in neurological diseases as a diverse population. So far, no individual biomarker for microglia has been discovered that can accurately identify and monitor their development and attributes. Summary Here, we highlight the newly defined biomarker of mouse microglia, UGT1A7C, which exhibits superior stability in expression during microglia development and activation compared to other known microglia biomarkers. The UGT1A7C sensing chemical probe labels all microglia in the 3xTG AD mouse model. The expression of Ugt1a7c is stable during development, with only a 4-fold variation, while other microglia biomarkers, such as Csf1r and Cx3cr1, exhibit at least a 10-fold difference. The UGT1A7C expression remains constant throughout its lifespan. In addition, the expression and activity of UGT1A7C are the same in response to different types of inflammatory activators' treatment in vitro. Conclusion We propose employing UGT1A7C as the representative biomarker for microglia, irrespective of their developmental state, age, or activation status. Using UGT1A7C can reduce the requirement for using multiple biomarkers, enhance the precision of microglia analysis, and even be utilized as a standard for gene/protein expression.
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Affiliation(s)
| | | | - Beomsue Kim
- Neural Circuit Research Group, Korea Brain Research Institute, Daegu, Republic of Korea
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Huuki-Myers LA, Montgomery KD, Kwon SH, Cinquemani S, Eagles NJ, Gonzalez-Padilla D, Maden SK, Kleinman JE, Hyde TM, Hicks SC, Maynard KR, Collado-Torres L. Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex. bioRxiv 2024:2024.02.09.579665. [PMID: 38405805 PMCID: PMC10888823 DOI: 10.1101/2024.02.09.579665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Background Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Computational methods for deconvolution have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or immunohistochemistry reference data. A major limitation in developing improved deconvolution algorithms has been the lack of integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue sample. Deconvolution algorithm performance has not yet been evaluated across different RNA extraction methods (cytosolic, nuclear, or whole cell RNA), different library preparation types (mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets. Results A rich multi-assay dataset was generated in postmortem human dorsolateral prefrontal cortex (DLPFC) from 22 tissue blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, bulk RNA-seq (across six library/extraction RNA-seq combinations), and RNAScope/Immunofluorescence (RNAScope/IF) for six broad cell types. The Mean Ratio method, implemented in the DeconvoBuddies R package, was developed for selecting cell type marker genes. Six computational deconvolution algorithms were evaluated in DLPFC and predicted cell type proportions were compared to orthogonal RNAScope/IF measurements. Conclusions Bisque and hspe were the most accurate methods, were robust to differences in RNA library types and extractions. This multi-assay dataset showed that cell size differences, marker genes differentially quantified across RNA libraries, and cell composition variability in reference snRNA-seq impact the accuracy of current deconvolution methods.
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Affiliation(s)
- Louise A. Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Kelsey D. Montgomery
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Sophia Cinquemani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Nicholas J. Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | | | - Sean K. Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kristen R. Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA
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Shen Y, Shao M, Hao ZZ, Huang M, Xu N, Liu S. Multimodal Nature of the Single-cell Primate Brain Atlas: Morphology, Transcriptome, Electrophysiology, and Connectivity. Neurosci Bull 2024; 40:517-532. [PMID: 38194157 PMCID: PMC11003949 DOI: 10.1007/s12264-023-01160-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 09/23/2023] [Indexed: 01/10/2024] Open
Abstract
Primates exhibit complex brain structures that augment cognitive function. The neocortex fulfills high-cognitive functions through billions of connected neurons. These neurons have distinct transcriptomic, morphological, and electrophysiological properties, and their connectivity principles vary. These features endow the primate brain atlas with a multimodal nature. The recent integration of next-generation sequencing with modified patch-clamp techniques is revolutionizing the way to census the primate neocortex, enabling a multimodal neuronal atlas to be established in great detail: (1) single-cell/single-nucleus RNA-seq technology establishes high-throughput transcriptomic references, covering all major transcriptomic cell types; (2) patch-seq links the morphological and electrophysiological features to the transcriptomic reference; (3) multicell patch-clamp delineates the principles of local connectivity. Here, we review the applications of these technologies in the primate neocortex and discuss the current advances and tentative gaps for a comprehensive understanding of the primate neocortex.
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Affiliation(s)
- Yuhui Shen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Mingting Shao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Zhao-Zhe Hao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Mengyao Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Nana Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Sheng Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China.
- Guangdong Province Key Laboratory of Brain Function and Disease, Guangzhou, 510080, China.
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10
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Vásquez CE, Knak Guerra KT, Renner J, Rasia-Filho AA. Morphological heterogeneity of neurons in the human central amygdaloid nucleus. J Neurosci Res 2024; 102:e25319. [PMID: 38629777 DOI: 10.1002/jnr.25319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 02/23/2024] [Accepted: 03/03/2024] [Indexed: 04/19/2024]
Abstract
The central amygdaloid nucleus (CeA) has an ancient phylogenetic development and functions relevant for animal survival. Local cells receive intrinsic amygdaloidal information that codes emotional stimuli of fear, integrate them, and send cortical and subcortical output projections that prompt rapid visceral and social behavior responses. We aimed to describe the morphology of the neurons that compose the human CeA (N = 8 adult men). Cells within CeA coronal borders were identified using the thionine staining and were further analyzed using the "single-section" Golgi method followed by open-source software procedures for two-dimensional and three-dimensional image reconstructions. Our results evidenced varied neuronal cell body features, number and thickness of primary shafts, dendritic branching patterns, and density and shape of dendritic spines. Based on these criteria, we propose the existence of 12 morphologically different spiny neurons in the human CeA and discuss the variability in the dendritic architecture within cellular types, including likely interneurons. Some dendritic shafts were long and straight, displayed few collaterals, and had planar radiation within the coronal neuropil volume. Most of the sampled neurons showed a few to moderate density of small stubby/wide spines. Long spines (thin and mushroom) were observed occasionally. These novel data address the synaptic processing and plasticity in the human CeA. Our morphological description can be combined with further transcriptomic, immunohistochemical, and electrophysiological/connectional approaches. It serves also to investigate how neurons are altered in neurological and psychiatric disorders with hindered emotional perception, in anxiety, following atrophy in schizophrenia, and along different stages of Alzheimer's disease.
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Affiliation(s)
- Carlos E Vásquez
- Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Kétlyn T Knak Guerra
- Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Josué Renner
- Department of Basic Sciences/Physiology and Graduate Program in Biosciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil
| | - Alberto A Rasia-Filho
- Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Department of Basic Sciences/Physiology and Graduate Program in Biosciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil
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11
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Yuan CU, Quah FX, Hemberg M. Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing. Mol Aspects Med 2024; 96:101255. [PMID: 38368637 DOI: 10.1016/j.mam.2024.101255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 02/20/2024]
Abstract
Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.
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Affiliation(s)
- Chengwei Ulrika Yuan
- Department of Biochemistry, University of Cambridge, Cambridge, UK; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Fu Xiang Quah
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Martin Hemberg
- Gene Lay Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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12
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Correa-da-Silva F, Carter J, Wang XY, Sun R, Pathak E, Kuhn JMM, Schriever SC, Maya-Monteiro CM, Jiao H, Kalsbeek MJ, Moraes-Vieira PMM, Gille JJP, Sinnema M, Stumpel CTRM, Curfs LMG, Stenvers DJ, Pfluger PT, Lutter D, Pereira AM, Kalsbeek A, Fliers E, Swaab DF, Wilkinson L, Gao Y, Yi CX. Microglial phagolysosome dysfunction and altered neural communication amplify phenotypic severity in Prader-Willi Syndrome with larger deletion. Acta Neuropathol 2024; 147:64. [PMID: 38556574 PMCID: PMC10982101 DOI: 10.1007/s00401-024-02714-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 02/20/2024] [Accepted: 02/27/2024] [Indexed: 04/02/2024]
Abstract
Prader-Willi Syndrome (PWS) is a rare neurodevelopmental disorder of genetic etiology, characterized by paternal deletion of genes located at chromosome 15 in 70% of cases. Two distinct genetic subtypes of PWS deletions are characterized, where type I (PWS T1) carries four extra haploinsufficient genes compared to type II (PWS T2). PWS T1 individuals display more pronounced physiological and cognitive abnormalities than PWS T2, yet the exact neuropathological mechanisms behind these differences remain unclear. Our study employed postmortem hypothalamic tissues from PWS T1 and T2 individuals, conducting transcriptomic analyses and cell-specific protein profiling in white matter, neurons, and glial cells to unravel the cellular and molecular basis of phenotypic severity in PWS sub-genotypes. In PWS T1, key pathways for cell structure, integrity, and neuronal communication are notably diminished, while glymphatic system activity is heightened compared to PWS T2. The microglial defect in PWS T1 appears to stem from gene haploinsufficiency, as global and myeloid-specific Cyfip1 haploinsufficiency in murine models demonstrated. Our findings emphasize microglial phagolysosome dysfunction and altered neural communication as crucial contributors to the severity of PWS T1's phenotype.
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Affiliation(s)
- Felipe Correa-da-Silva
- Department of Endocrinology and Metabolism, Amsterdam University Medical Centers, Location AMC. University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology and Metabolism, Amsterdam, The Netherlands
- Endocrine Laboratory, Department of Laboratory Medicine, Amsterdam University Medical Centers, Location AMC, Amsterdam, The Netherlands
- Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Jenny Carter
- Neuroscience and Mental Health Innovation Institute, MRC Centre for Neuropsychiatric Genetic and Genomics, School of Medicine, Cardiff University, Cardiff, UK
| | - Xin-Yuan Wang
- Key Laboratory of Cardiovascular and Cerebrovascular Medicine, School of Pharmacy, Nanjing Medical University, Nanjing, China
| | - Rui Sun
- Key Laboratory of Cardiovascular and Cerebrovascular Medicine, School of Pharmacy, Nanjing Medical University, Nanjing, China
| | - Ekta Pathak
- Computational Discovery Unit, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit NeuroBiology of Diabetes, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany
| | - José Manuel Monroy Kuhn
- Computational Discovery Unit, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Sonja C Schriever
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit NeuroBiology of Diabetes, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany
| | - Clarissa M Maya-Monteiro
- Department of Endocrinology and Metabolism, Amsterdam University Medical Centers, Location AMC. University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands
- Laboratory of Immunopharmacology, Oswaldo Cruz Institute, FIOCRUZ, Rio de Janeiro, Brazil
| | - Han Jiao
- Department of Endocrinology and Metabolism, Amsterdam University Medical Centers, Location AMC. University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology and Metabolism, Amsterdam, The Netherlands
- Endocrine Laboratory, Department of Laboratory Medicine, Amsterdam University Medical Centers, Location AMC, Amsterdam, The Netherlands
- Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Martin J Kalsbeek
- Department of Endocrinology and Metabolism, Amsterdam University Medical Centers, Location AMC. University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology and Metabolism, Amsterdam, The Netherlands
| | - Pedro M M Moraes-Vieira
- Laboratory of Immunometabolism, Department of Genetics, Evolution, Microbiology and Immunology, Institute of Biology, University of Campinas, Campinas, São Paulo, Brazil
| | - Johan J P Gille
- Department of Clinical Genetics, Amsterdam University Medical Centers, location VUMC. University of Amsterdam, Amsterdam, The Netherlands
| | - Margje Sinnema
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Constance T R M Stumpel
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Leopold M G Curfs
- Governor Kremers Centre, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Dirk Jan Stenvers
- Department of Endocrinology and Metabolism, Amsterdam University Medical Centers, Location AMC. University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology and Metabolism, Amsterdam, The Netherlands
| | - Paul T Pfluger
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit NeuroBiology of Diabetes, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany
- Division of Neurobiology of Diabetes, TUM School of Medicine, Technical University of Munich, Munich, Germany
| | - Dominik Lutter
- Computational Discovery Unit, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Alberto M Pereira
- Department of Endocrinology and Metabolism, Amsterdam University Medical Centers, Location AMC. University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology and Metabolism, Amsterdam, The Netherlands
| | - Andries Kalsbeek
- Department of Endocrinology and Metabolism, Amsterdam University Medical Centers, Location AMC. University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology and Metabolism, Amsterdam, The Netherlands
- Endocrine Laboratory, Department of Laboratory Medicine, Amsterdam University Medical Centers, Location AMC, Amsterdam, The Netherlands
- Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Eric Fliers
- Department of Endocrinology and Metabolism, Amsterdam University Medical Centers, Location AMC. University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology and Metabolism, Amsterdam, The Netherlands
| | - Dick F Swaab
- Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Lawrence Wilkinson
- Neuroscience and Mental Health Innovation Institute, MRC Centre for Neuropsychiatric Genetic and Genomics, School of Medicine, Cardiff University, Cardiff, UK
| | - Yuanqing Gao
- Key Laboratory of Cardiovascular and Cerebrovascular Medicine, School of Pharmacy, Nanjing Medical University, Nanjing, China
| | - Chun-Xia Yi
- Department of Endocrinology and Metabolism, Amsterdam University Medical Centers, Location AMC. University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands.
- Amsterdam Gastroenterology Endocrinology and Metabolism, Amsterdam, The Netherlands.
- Endocrine Laboratory, Department of Laboratory Medicine, Amsterdam University Medical Centers, Location AMC, Amsterdam, The Netherlands.
- Netherlands Institute for Neuroscience, Amsterdam, The Netherlands.
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13
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Jiang A, Snell RG, Lehnert K. ICARUS v3, a massively scalable web server for single-cell RNA-seq analysis of millions of cells. Bioinformatics 2024; 40:btae167. [PMID: 38539041 PMCID: PMC11007236 DOI: 10.1093/bioinformatics/btae167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 03/18/2024] [Accepted: 03/25/2024] [Indexed: 04/12/2024] Open
Abstract
MOTIVATION In recent years, improvements in throughput of single-cell RNA-seq have resulted in a significant increase in the number of cells profiled. The generation of single-cell RNA-seq datasets comprising >1 million cells is becoming increasingly common, giving rise to demands for more efficient computational workflows. RESULTS We present an update to our single-cell RNA-seq analysis web server application, ICARUS (available at https://launch.icarus-scrnaseq.cloud.edu.au) that allows effective analysis of large-scale single-cell RNA-seq datasets. ICARUS v3 utilizes the geometric cell sketching method to subsample cells from the overall dataset for dimensionality reduction and clustering that can be then projected to the large dataset. We then extend this functionality to select a representative subset of cells for downstream data analysis applications including differential expression analysis, gene co-expression network construction, gene regulatory network construction, trajectory analysis, cell-cell communication inference, and cell cluster associations to GWAS traits. We demonstrate analysis of single-cell RNA-seq datasets using ICARUS v3 of 1.3 million cells completed within the hour. AVAILABILITY AND IMPLEMENTATION ICARUS is available at https://launch.icarus-scrnaseq.cloud.edu.au.
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Affiliation(s)
- Andrew Jiang
- Applied Translational Genetics Group, School of Biological Sciences, The University of Auckland, Auckland 1142, New Zealand
| | - Russell G Snell
- Applied Translational Genetics Group, School of Biological Sciences, The University of Auckland, Auckland 1142, New Zealand
| | - Klaus Lehnert
- Applied Translational Genetics Group, School of Biological Sciences, The University of Auckland, Auckland 1142, New Zealand
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14
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Sagner A. Temporal patterning of the vertebrate developing neural tube. Curr Opin Genet Dev 2024; 86:102179. [PMID: 38490162 DOI: 10.1016/j.gde.2024.102179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/29/2023] [Accepted: 02/20/2024] [Indexed: 03/17/2024]
Abstract
The chronologically ordered generation of distinct cell types is essential for the establishment of neuronal diversity and the formation of neuronal circuits. Recently, single-cell transcriptomic analyses of various areas of the developing vertebrate nervous system have provided evidence for the existence of a shared temporal patterning program that partitions neurons based on the timing of neurogenesis. In this review, I summarize the findings that lead to the proposal of this shared temporal program before focusing on the developing spinal cord to discuss how temporal patterning in general and this program specifically contributes to the ordered formation of neuronal circuits.
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Affiliation(s)
- Andreas Sagner
- Institut für Biochemie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Fahrstraße 17, 91054 Erlangen, Germany.
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15
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Günther DM, Batiuk MY, Petukhov V, De Oliveira R, Wunderle T, Buchholz CJ, Fries P, Khodosevich K. Heterogeneity of layer 4 in visual areas of rhesus macaque cortex. bioRxiv 2024:2024.03.11.584345. [PMID: 38559123 PMCID: PMC10979896 DOI: 10.1101/2024.03.11.584345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Recently, single-cell RNA-sequencing (scRNA-seq) has enabled unprecedented insights to the cellular landscape of the brains of many different species, among them the rhesus macaque as a key animal model. Building on previous, broader surveys of the macaque brain, we closely examined five immediately neighboring areas within the visual cortex of the rhesus macaque: V1, V2, V4, MT and TEO. To facilitate this, we first devised a novel pipeline for brain spatial archive - the BrainSPACE - which enabled robust archiving and sampling from the whole unfixed brain. SnRNA-sequencing of ~100,000 nuclei from visual areas V1 and V4 revealed conservation within the GABAergic neuron subtypes, while seven and one distinct principle neuron subtypes were detected in V1 and V4, respectively, all most likely located in layer 4. Moreover, using small molecule fluorescence in situ hybridization, we identified cell type density gradients across V1, V2, V4, MT, and TEO appearing to reflect the visual hierarchy. These findings demonstrate an association between the clear areal specializations among neighboring areas with the hierarchical levels within the visual cortex of the rhesus macaque.
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Affiliation(s)
- Dorothee M. Günther
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EN Nijmegen, the Netherlands
- Molecular Biotechnology and Gene Therapy, Paul-Ehrlich-Institut, 63225 Langen, Germany
| | - Mykhailo Y. Batiuk
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
- Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Viktor Petukhov
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Romain De Oliveira
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Thomas Wunderle
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany
| | - Christian J. Buchholz
- Molecular Biotechnology and Gene Therapy, Paul-Ehrlich-Institut, 63225 Langen, Germany
| | - Pascal Fries
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EN Nijmegen, the Netherlands
| | - Konstantin Khodosevich
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
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16
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Park YJ, Lu TC, Jackson T, Goodman LD, Ran L, Chen J, Liang CY, Harrison E, Ko C, Hsu AL, Yamamoto S, Qi Y, Bellen HJ, Li H. Whole organism snRNA-seq reveals systemic peripheral changes in Alzheimer's Disease fly models. bioRxiv 2024:2024.03.10.584317. [PMID: 38559164 PMCID: PMC10979927 DOI: 10.1101/2024.03.10.584317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Peripheral tissues become disrupted in Alzheimer's Disease (AD). However, a comprehensive understanding of how the expression of AD-associated toxic proteins, Aβ42 and Tau, in neurons impacts the periphery is lacking. Using Drosophila, a prime model organism for studying aging and neurodegeneration, we generated the Alzheimer's Disease Fly Cell Atlas (AD-FCA): whole-organism single-nucleus transcriptomes of 219 cell types from adult flies neuronally expressing human Aβ42 or Tau. In-depth analyses and functional data reveal impacts on peripheral sensory neurons by Aβ42 and on various non-neuronal peripheral tissues by Tau, including the gut, fat body, and reproductive system. This novel AD atlas provides valuable insights into potential biomarkers and the intricate interplay between the nervous system and peripheral tissues in response to AD-associated proteins.
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Affiliation(s)
- Ye-Jin Park
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA
- Program in Development, Disease Models & Therapeutics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Tzu-Chiao Lu
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Tyler Jackson
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Program in Cancer Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Lindsey D Goodman
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA
| | - Lindsey Ran
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jiaye Chen
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Chung-Yi Liang
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Institute of Biochemistry and Molecular Biology, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Erin Harrison
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Christina Ko
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ao-Lin Hsu
- Institute of Biochemistry and Molecular Biology, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Internal Medicine, Division of Geriatric and Palliative Medicine, University of Michigan, Ann Arbor, MI 28109, USA
| | - Shinya Yamamoto
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA
- Program in Development, Disease Models & Therapeutics, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yanyan Qi
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Hugo J Bellen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA
- Program in Development, Disease Models & Therapeutics, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Hongjie Li
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
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17
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Nardone S, De Luca R, Zito A, Klymko N, Nicoloutsopoulos D, Amsalem O, Brannigan C, Resch JM, Jacobs CL, Pant D, Veregge M, Srinivasan H, Grippo RM, Yang Z, Zeidel ML, Andermann ML, Harris KD, Tsai LT, Arrigoni E, Verstegen AMJ, Saper CB, Lowell BB. A spatially-resolved transcriptional atlas of the murine dorsal pons at single-cell resolution. Nat Commun 2024; 15:1966. [PMID: 38438345 PMCID: PMC10912765 DOI: 10.1038/s41467-024-45907-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 02/07/2024] [Indexed: 03/06/2024] Open
Abstract
The "dorsal pons", or "dorsal pontine tegmentum" (dPnTg), is part of the brainstem. It is a complex, densely packed region whose nuclei are involved in regulating many vital functions. Notable among them are the parabrachial nucleus, the Kölliker Fuse, the Barrington nucleus, the locus coeruleus, and the dorsal, laterodorsal, and ventral tegmental nuclei. In this study, we applied single-nucleus RNA-seq (snRNA-seq) to resolve neuronal subtypes based on their unique transcriptional profiles and then used multiplexed error robust fluorescence in situ hybridization (MERFISH) to map them spatially. We sampled ~1 million cells across the dPnTg and defined the spatial distribution of over 120 neuronal subtypes. Our analysis identified an unpredicted high transcriptional diversity in this region and pinpointed the unique marker genes of many neuronal subtypes. We also demonstrated that many neuronal subtypes are transcriptionally similar between humans and mice, enhancing this study's translational value. Finally, we developed a freely accessible, GPU and CPU-powered dashboard ( http://harvard.heavy.ai:6273/ ) that combines interactive visual analytics and hardware-accelerated SQL into a data science framework to allow the scientific community to query and gain insights into the data.
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Affiliation(s)
- Stefano Nardone
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Roberto De Luca
- Department of Neurology, Division of Sleep Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA
| | - Antonino Zito
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
- Department of Genetics, The Blavatnik Institute, Harvard Medical School, Boston, MA, USA
| | - Nataliya Klymko
- Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02215, USA
| | | | - Oren Amsalem
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Cory Brannigan
- HEAVY.AI, 100 Montgomery St Fl 5, San Francisco, California, 94104, USA
| | - Jon M Resch
- Department of Neuroscience and Pharmacology, University of Iowa, Iowa City, IA, USA
- Fraternal Order of Eagles Diabetes Research Center. University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
| | - Christopher L Jacobs
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Deepti Pant
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Molly Veregge
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Harini Srinivasan
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ryan M Grippo
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Zongfang Yang
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Mark L Zeidel
- Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02215, USA
| | - Mark L Andermann
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Kenneth D Harris
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Linus T Tsai
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Elda Arrigoni
- Department of Neurology, Division of Sleep Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA
| | - Anne M J Verstegen
- Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02215, USA.
| | - Clifford B Saper
- Department of Neurology, Division of Sleep Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA.
| | - Bradford B Lowell
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.
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18
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Khodosevich K, Dragicevic K, Howes O. Drug targeting in psychiatric disorders - how to overcome the loss in translation? Nat Rev Drug Discov 2024; 23:218-231. [PMID: 38114612 DOI: 10.1038/s41573-023-00847-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2023] [Indexed: 12/21/2023]
Abstract
In spite of major efforts and investment in development of psychiatric drugs, many clinical trials have failed in recent decades, and clinicians still prescribe drugs that were discovered many years ago. Although multiple reasons have been discussed for the drug development deadlock, we focus here on one of the major possible biological reasons: differences between the characteristics of drug targets in preclinical models and the corresponding targets in patients. Importantly, based on technological advances in single-cell analysis, we propose here a framework for the use of available and newly emerging knowledge from single-cell and spatial omics studies to evaluate and potentially improve the translational predictivity of preclinical models before commencing preclinical and, in particular, clinical studies. We believe that these recommendations will improve preclinical models and the ability to assess drugs in clinical trials, reducing failure rates in expensive late-stage trials and ultimately benefitting psychiatric drug discovery and development.
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Affiliation(s)
- Konstantin Khodosevich
- Biotech Research and Innovation Centre, Faculty of Health, University of Copenhagen, Copenhagen, Denmark.
| | - Katarina Dragicevic
- Biotech Research and Innovation Centre, Faculty of Health, University of Copenhagen, Copenhagen, Denmark
| | - Oliver Howes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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19
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Ollivier M, Soto JS, Linker KE, Moye SL, Jami-Alahmadi Y, Jones AE, Divakaruni AS, Kawaguchi R, Wohlschlegel JA, Khakh BS. Crym-positive striatal astrocytes gate perseverative behaviour. Nature 2024; 627:358-366. [PMID: 38418885 PMCID: PMC10937394 DOI: 10.1038/s41586-024-07138-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 01/31/2024] [Indexed: 03/02/2024]
Abstract
Astrocytes are heterogeneous glial cells of the central nervous system1-3. However, the physiological relevance of astrocyte diversity for neural circuits and behaviour remains unclear. Here we show that a specific population of astrocytes in the central striatum expresses μ-crystallin (encoded by Crym in mice and CRYM in humans) that is associated with several human diseases, including neuropsychiatric disorders4-7. In adult mice, reducing the levels of μ-crystallin in striatal astrocytes through CRISPR-Cas9-mediated knockout of Crym resulted in perseverative behaviours, increased fast synaptic excitation in medium spiny neurons and dysfunctional excitatory-inhibitory synaptic balance. Increased perseveration stemmed from the loss of astrocyte-gated control of neurotransmitter release from presynaptic terminals of orbitofrontal cortex-striatum projections. We found that perseveration could be remedied using presynaptic inhibitory chemogenetics8, and that this treatment also corrected the synaptic deficits. Together, our findings reveal converging molecular, synaptic, circuit and behavioural mechanisms by which a molecularly defined and allocated population of striatal astrocytes gates perseveration phenotypes that accompany neuropsychiatric disorders9-12. Our data show that Crym-positive striatal astrocytes have key biological functions within the central nervous system, and uncover astrocyte-neuron interaction mechanisms that could be targeted in treatments for perseveration.
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Affiliation(s)
- Matthias Ollivier
- Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Joselyn S Soto
- Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kay E Linker
- Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Stefanie L Moye
- Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Yasaman Jami-Alahmadi
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Anthony E Jones
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ajit S Divakaruni
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Riki Kawaguchi
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - James A Wohlschlegel
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Baljit S Khakh
- Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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20
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Cui H, Wang C, Maan H, Pang K, Luo F, Duan N, Wang B. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat Methods 2024:10.1038/s41592-024-02201-0. [PMID: 38409223 DOI: 10.1038/s41592-024-02201-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 01/30/2024] [Indexed: 02/28/2024]
Abstract
Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between language and cellular biology (in which texts comprise words; similarly, cells are defined by genes), our study probes the applicability of foundation models to advance cellular biology and genetic research. Using burgeoning single-cell sequencing data, we have constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction and gene network inference.
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Affiliation(s)
- Haotian Cui
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontartio, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Chloe Wang
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontartio, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Hassaan Maan
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontartio, Canada
- Vector Institute, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Kuan Pang
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Fengning Luo
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Nan Duan
- Microsoft Research, Redmond, WA, USA
| | - Bo Wang
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontartio, Canada.
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
- Vector Institute, Toronto, Ontario, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
- AI Hub, University Health Network, Toronto, Ontario, Canada.
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21
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Chen AB, Yu X, Thapa KS, Gao H, Reiter JL, Xuei X, Tsai AP, Landreth GE, Lai D, Wang Y, Foroud TM, Tischfield JA, Edenberg HJ, Liu Y. Functional 3'-UTR Variants Identify Regulatory Mechanisms Impacting Alcohol Use Disorder and Related Traits. bioRxiv 2024:2024.01.31.578270. [PMID: 38370821 PMCID: PMC10871301 DOI: 10.1101/2024.01.31.578270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Although genome-wide association studies (GWAS) have identified loci associated with alcohol consumption and alcohol use disorder (AUD), they do not identify which variants are functional. To approach this, we evaluated the impact of variants in 3' untranslated regions (3'-UTRs) of genes in loci associated with substance use and neurological disorders using a massively parallel reporter assay (MPRA) in neuroblastoma and microglia cells. Functionally impactful variants explained a higher proportion of heritability of alcohol traits than non-functional variants. We identified genes whose 3'UTR activities are associated with AUD and alcohol consumption by combining variant effects from MPRA with GWAS results. We examined their effects by evaluating gene expression after CRISPR inhibition of neuronal cells and stratifying brain tissue samples by MPRA-derived 3'-UTR activity. A pathway analysis of differentially expressed genes identified inflammation response pathways. These analyses suggest that variation in response to inflammation contributes to the propensity to increase alcohol consumption.
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Affiliation(s)
- Andy B. Chen
- Department of Medical & Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Xuhong Yu
- Department of Medical & Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Kriti S. Thapa
- Department of Biochemistry & Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Hongyu Gao
- Department of Medical & Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana
- Center for Medical Genomics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Jill L Reiter
- Department of Medical & Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Xiaoling Xuei
- Department of Medical & Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
- Center for Medical Genomics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Andy P. Tsai
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana
| | - Gary E. Landreth
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana
- Department of Anatomy and Cell Biology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Dongbing Lai
- Department of Medical & Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Yue Wang
- Department of Medical & Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Tatiana M. Foroud
- Department of Medical & Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | | | - Howard J. Edenberg
- Department of Medical & Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
- Department of Biochemistry & Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Yunlong Liu
- Department of Medical & Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana
- Center for Medical Genomics, Indiana University School of Medicine, Indianapolis, Indiana
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22
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Beau M, Herzfeld DJ, Naveros F, Hemelt ME, D’Agostino F, Oostland M, Sánchez-López A, Chung YY, Maibach M, Stabb HN, Martínez Lopera MG, Lajko A, Zedler M, Ohmae S, Hall NJ, Clark BA, Cohen D, Lisberger SG, Kostadinov D, Hull C, Häusser M, Medina JF. A deep-learning strategy to identify cell types across species from high-density extracellular recordings. bioRxiv 2024:2024.01.30.577845. [PMID: 38352514 PMCID: PMC10862837 DOI: 10.1101/2024.01.30.577845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but fail to determine each recorded neuron's cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, opening avenues to unveil the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifier's predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our approach provides a general blueprint for cell-type identification from extracellular recordings across the brain.
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Affiliation(s)
- Maxime Beau
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - David J. Herzfeld
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Francisco Naveros
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Department of Computer Engineering, Automation and Robotics, Research Centre for Information and Communication Technologies, University of Granada, Granada, Spain
| | - Marie E. Hemelt
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Federico D’Agostino
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Marlies Oostland
- Wolfson Institute for Biomedical Research, University College London, London, UK
- Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands
| | | | - Young Yoon Chung
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Michael Maibach
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Hannah N. Stabb
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | | | - Agoston Lajko
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Marie Zedler
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Shogo Ohmae
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Nathan J. Hall
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Beverley A. Clark
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Dana Cohen
- The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
| | | | - Dimitar Kostadinov
- Wolfson Institute for Biomedical Research, University College London, London, UK
- Centre for Developmental Neurobiology, King’s College London, London, UK
| | - Court Hull
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Michael Häusser
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Javier F. Medina
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
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23
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Yao S, Harder A, Darki F, Chang YW, Li A, Nikouei K, Volpe G, Lundström JN, Zeng J, Wray N, Lu Y, Sullivan PF, Leffler JH. Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity. medRxiv 2024:2024.01.18.24301478. [PMID: 38410450 PMCID: PMC10896415 DOI: 10.1101/2024.01.18.24301478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Understanding the temporal and spatial brain locations etiological for psychiatric disorders is essential for targeted neurobiological research. Integration of genomic insights from genome-wide association studies with single-cell transcriptomics is a powerful approach although past efforts have necessarily relied on mouse atlases. Leveraging a comprehensive atlas of the adult human brain, we prioritized cell types via the enrichment of SNP-heritabilities for brain diseases, disorders, and traits, progressing from individual cell types to brain regions. Our findings highlight specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals important patterns for schizophrenia with distinct subregions in the hippocampus and amygdala exhibiting the highest significance. Cerebral cortical regions display similar enrichments despite the known prefrontal dysfunction in those with schizophrenia highlighting the importance of subcortical connectivity. Using functional MRI connectivity from cases with schizophrenia and neurotypical controls, we identified brain networks that distinguished cases from controls that also confirmed involvement of the central and lateral amygdala, hippocampal body, and prefrontal cortex. Our findings underscore the value of single-cell transcriptomics in decoding the polygenicity of psychiatric disorders and offer a promising convergence of genomic, transcriptomic, and brain imaging modalities toward common biological targets.
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Affiliation(s)
- Shuyang Yao
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Arvid Harder
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fahimeh Darki
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Yu-Wei Chang
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Ang Li
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Kasra Nikouei
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Johan N Lundström
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Monell Chemical Senses Center, Philadelphia, PA, USA
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Naomi Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Jens Hjerling Leffler
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
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24
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Bauer ME, Pawelec G, Paganelli R. Neuroimmunology and ageing - the state of the art. Immun Ageing 2024; 21:5. [PMID: 38200570 PMCID: PMC10777624 DOI: 10.1186/s12979-024-00411-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 01/02/2024] [Indexed: 01/12/2024]
Affiliation(s)
- Moisés E Bauer
- Laboratory of Immunobiology, School of Health and Life Sciences, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
| | - Graham Pawelec
- Department of Immunology, University of Tübingen, Tübingen, Germany
- Health Sciences North Research Institute, Sudbury, ON, Canada
| | - Roberto Paganelli
- YDA-Institute of Clinical Immunotherapy and Advanced Biological Treatments, Pescara, 65121, Italy.
- Internal Medicine, UniCamillus, International Medical University in Rome, via di Sant'Alessandro 6, Rome, 00131, Italy.
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25
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Wells SB, Rainbow DB, Mark M, Szabo PA, Ergen C, Maceiras AR, Caron DP, Rahmani E, Benuck E, Amiri VVP, Chen D, Wagner A, Howlett SK, Jarvis LB, Ellis KL, Kubota M, Matsumoto R, Mahbubani K, Saeb-Parsy K, Dominguez-Conde C, Richardson L, Xu C, Li S, Mamanova L, Bolt L, Wilk A, Teichmann SA, Farber DL, Sims PA, Jones JL, Yosef N. Multimodal profiling reveals tissue-directed signatures of human immune cells altered with age. bioRxiv 2024:2024.01.03.573877. [PMID: 38260588 PMCID: PMC10802388 DOI: 10.1101/2024.01.03.573877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The immune system comprises multiple cell lineages and heterogeneous subsets found in blood and tissues throughout the body. While human immune responses differ between sites and over age, the underlying sources of variation remain unclear as most studies are limited to peripheral blood. Here, we took a systems approach to comprehensively profile RNA and surface protein expression of over 1.25 million immune cells isolated from blood, lymphoid organs, and mucosal tissues of 24 organ donors aged 20-75 years. We applied a multimodal classifier to annotate the major immune cell lineages (T cells, B cells, innate lymphoid cells, and myeloid cells) and their corresponding subsets across the body, leveraging probabilistic modeling to define bases for immune variations across donors, tissue, and age. We identified dominant tissue-specific effects on immune cell composition and function across lineages for lymphoid sites, intestines, and blood-rich tissues. Age-associated effects were intrinsic to both lineage and site as manifested by macrophages in mucosal sites, B cells in lymphoid organs, and T and NK cells in blood-rich sites. Our results reveal tissue-specific signatures of immune homeostasis throughout the body and across different ages. This information provides a basis for defining the transcriptional underpinnings of immune variation and potential associations with disease-associated immune pathologies across the human lifespan.
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26
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Atamian A, Birtele M, Hosseini N, Nguyen T, Seth A, Del Dosso A, Paul S, Tedeschi N, Taylor R, Coba MP, Samarasinghe R, Lois C, Quadrato G. Human cerebellar organoids with functional Purkinje cells. Cell Stem Cell 2024; 31:39-51.e6. [PMID: 38181749 DOI: 10.1016/j.stem.2023.11.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/30/2023] [Accepted: 11/30/2023] [Indexed: 01/07/2024]
Abstract
Research on human cerebellar development and disease has been hampered by the need for a human cell-based system that recapitulates the human cerebellum's cellular diversity and functional features. Here, we report a human organoid model (human cerebellar organoids [hCerOs]) capable of developing the complex cellular diversity of the fetal cerebellum, including a human-specific rhombic lip progenitor population that have never been generated in vitro prior to this study. 2-month-old hCerOs form distinct cytoarchitectural features, including laminar organized layering, and create functional connections between inhibitory and excitatory neurons that display coordinated network activity. Long-term culture of hCerOs allows healthy survival and maturation of Purkinje cells that display molecular and electrophysiological hallmarks of their in vivo counterparts, addressing a long-standing challenge in the field. This study therefore provides a physiologically relevant, all-human model system to elucidate the cell-type-specific mechanisms governing cerebellar development and disease.
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Affiliation(s)
- Alexander Atamian
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Marcella Birtele
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Negar Hosseini
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Tuan Nguyen
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Anoothi Seth
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Ashley Del Dosso
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Sandeep Paul
- Spatial Genomics, 145 Vista Avenue Suite 111, Pasadena, CA 91107, USA
| | - Neil Tedeschi
- Spatial Genomics, 145 Vista Avenue Suite 111, Pasadena, CA 91107, USA
| | - Ryan Taylor
- Spatial Genomics, 145 Vista Avenue Suite 111, Pasadena, CA 91107, USA
| | - Marcelo P Coba
- Department of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, 1501 San Pablo Street, Los Angeles, CA 90033, USA
| | - Ranmal Samarasinghe
- Department of Clinical Neurophysiology and Neurology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Carlos Lois
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Giorgia Quadrato
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
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27
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Nystuen KL, McNamee SM, Akula M, Holton KM, DeAngelis MM, Haider NB. Alzheimer's Disease: Models and Molecular Mechanisms Informing Disease and Treatments. Bioengineering (Basel) 2024; 11:45. [PMID: 38247923 PMCID: PMC10813760 DOI: 10.3390/bioengineering11010045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/15/2023] [Accepted: 12/22/2023] [Indexed: 01/23/2024] Open
Abstract
Alzheimer's Disease (AD) is a complex neurodegenerative disease resulting in progressive loss of memory, language and motor abilities caused by cortical and hippocampal degeneration. This review captures the landscape of understanding of AD pathology, diagnostics, and current therapies. Two major mechanisms direct AD pathology: (1) accumulation of amyloid β (Aβ) plaque and (2) tau-derived neurofibrillary tangles (NFT). The most common variants in the Aβ pathway in APP, PSEN1, and PSEN2 are largely responsible for early-onset AD (EOAD), while MAPT, APOE, TREM2 and ABCA7 have a modifying effect on late-onset AD (LOAD). More recent studies implicate chaperone proteins and Aβ degrading proteins in AD. Several tests, such as cognitive function, brain imaging, and cerebral spinal fluid (CSF) and blood tests, are used for AD diagnosis. Additionally, several biomarkers seem to have a unique AD specific combination of expression and could potentially be used in improved, less invasive diagnostics. In addition to genetic perturbations, environmental influences, such as altered gut microbiome signatures, affect AD. Effective AD treatments have been challenging to develop. Currently, there are several FDA approved drugs (cholinesterase inhibitors, Aß-targeting antibodies and an NMDA antagonist) that could mitigate AD rate of decline and symptoms of distress.
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Affiliation(s)
- Kaden L. Nystuen
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Shannon M. McNamee
- Schepens Eye Research Institute, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Monica Akula
- Schepens Eye Research Institute, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Kristina M. Holton
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
- Harvard Stem Cell Institute, Cambridge, MA 02138, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Margaret M. DeAngelis
- Department of Ophthalmology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA
| | - Neena B. Haider
- Schepens Eye Research Institute, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
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28
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Sankowski R, Süß P, Benkendorff A, Böttcher C, Fernandez-Zapata C, Chhatbar C, Cahueau J, Monaco G, Gasull AD, Khavaran A, Grauvogel J, Scheiwe C, Shah MJ, Heiland DH, Schnell O, Markfeld-Erol F, Kunze M, Zeiser R, Priller J, Prinz M. Multiomic spatial landscape of innate immune cells at human central nervous system borders. Nat Med 2024; 30:186-198. [PMID: 38123840 PMCID: PMC10803260 DOI: 10.1038/s41591-023-02673-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 10/30/2023] [Indexed: 12/23/2023]
Abstract
The innate immune compartment of the human central nervous system (CNS) is highly diverse and includes several immune-cell populations such as macrophages that are frequent in the brain parenchyma (microglia) and less numerous at the brain interfaces as CNS-associated macrophages (CAMs). Due to their scantiness and particular location, little is known about the presence of temporally and spatially restricted CAM subclasses during development, health and perturbation. Here we combined single-cell RNA sequencing, time-of-flight mass cytometry and single-cell spatial transcriptomics with fate mapping and advanced immunohistochemistry to comprehensively characterize the immune system at human CNS interfaces with over 356,000 analyzed transcriptomes from 102 individuals. We also provide a comprehensive analysis of resident and engrafted myeloid cells in the brains of 15 individuals with peripheral blood stem cell transplantation, revealing compartment-specific engraftment rates across different CNS interfaces. Integrated multiomic and high-resolution spatial transcriptome analysis of anatomically dissected glioblastoma samples shows regionally distinct myeloid cell-type distributions driven by hypoxia. Notably, the glioblastoma-associated hypoxia response was distinct from the physiological hypoxia response in fetal microglia and CAMs. Our results highlight myeloid diversity at the interfaces of the human CNS with the periphery and provide insights into the complexities of the human brain's immune system.
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Affiliation(s)
- Roman Sankowski
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Patrick Süß
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Molecular Neurology, Friedrich Alexander University Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - Alexander Benkendorff
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Chotima Böttcher
- Neuropsychiatry Unit and Laboratory of Molecular Psychiatry, Charité, Universitätsmedizin Berlin and DZNE, Berlin, Germany
| | - Camila Fernandez-Zapata
- Neuropsychiatry Unit and Laboratory of Molecular Psychiatry, Charité, Universitätsmedizin Berlin and DZNE, Berlin, Germany
| | - Chintan Chhatbar
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jonathan Cahueau
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Gianni Monaco
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Institute for Transfusion Medicine and Gene Therapy, Medical Center-University of Freiburg, Freiburg, Germany
| | - Adrià Dalmau Gasull
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ashkan Khavaran
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jürgen Grauvogel
- Department of Neurosurgery, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Christian Scheiwe
- Department of Neurosurgery, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Mukesch Johannes Shah
- Department of Neurosurgery, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Dieter Henrik Heiland
- Department of Neurosurgery, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Oliver Schnell
- Department of Neurosurgery, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Filiz Markfeld-Erol
- Department of Gynecology, Obstetrics, and Perinatology, Faculty of Medicine, University Hospital, Freiburg, Germany
| | - Mirjam Kunze
- Department of Gynecology, Obstetrics, and Perinatology, Faculty of Medicine, University Hospital, Freiburg, Germany
| | - Robert Zeiser
- Department of Internal Medicine I, Faculty of Medicine, Medical Center-University of Freiburg, Freiburg, Germany
- Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany
| | - Josef Priller
- Neuropsychiatry Unit and Laboratory of Molecular Psychiatry, Charité, Universitätsmedizin Berlin and DZNE, Berlin, Germany
- Department of Psychiatry and Psychotherapy, School of Medicine and Health, Technical University of Munich, Munich, Germany
- University of Edinburgh and UK DRI, Edinburgh, UK
| | - Marco Prinz
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany.
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Xu C, Prete M, Webb S, Jardine L, Stewart BJ, Hoo R, He P, Meyer KB, Teichmann SA. Automatic cell-type harmonization and integration across Human Cell Atlas datasets. Cell 2023; 186:5876-5891.e20. [PMID: 38134877 DOI: 10.1016/j.cell.2023.11.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 08/24/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023]
Abstract
Harmonizing cell types across the single-cell community and assembling them into a common framework is central to building a standardized Human Cell Atlas. Here, we present CellHint, a predictive clustering tree-based tool to resolve cell-type differences in annotation resolution and technical biases across datasets. CellHint accurately quantifies cell-cell transcriptomic similarities and places cell types into a relationship graph that hierarchically defines shared and unique cell subtypes. Application to multiple immune datasets recapitulates expert-curated annotations. CellHint also reveals underexplored relationships between healthy and diseased lung cell states in eight diseases. Furthermore, we present a workflow for fast cross-dataset integration guided by harmonized cell types and cell hierarchy, which uncovers underappreciated cell types in adult human hippocampus. Finally, we apply CellHint to 12 tissues from 38 datasets, providing a deeply curated cross-tissue database with ∼3.7 million cells and various machine learning models for automatic cell annotation across human tissues.
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Affiliation(s)
- Chuan Xu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Martin Prete
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Simone Webb
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Biosciences Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Laura Jardine
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Biosciences Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Benjamin J Stewart
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Molecular Immunity Unit, Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, UK; Cambridge University Hospitals NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge CB2 0QQ, UK
| | - Regina Hoo
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Peng He
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
| | - Kerstin B Meyer
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Theory of Condensed Matter Group, Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK.
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30
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Why mega brain project teams need to be talking to each other. Nature 2023; 624:226-226. [PMID: 38092920 DOI: 10.1038/d41586-023-03954-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
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31
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Langlieb J, Sachdev NS, Balderrama KS, Nadaf NM, Raj M, Murray E, Webber JT, Vanderburg C, Gazestani V, Tward D, Mezias C, Li X, Flowers K, Cable DM, Norton T, Mitra P, Chen F, Macosko EZ. The molecular cytoarchitecture of the adult mouse brain. Nature 2023; 624:333-342. [PMID: 38092915 PMCID: PMC10719111 DOI: 10.1038/s41586-023-06818-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 11/01/2023] [Indexed: 12/17/2023]
Abstract
The function of the mammalian brain relies upon the specification and spatial positioning of diversely specialized cell types. Yet, the molecular identities of the cell types and their positions within individual anatomical structures remain incompletely known. To construct a comprehensive atlas of cell types in each brain structure, we paired high-throughput single-nucleus RNA sequencing with Slide-seq1,2-a recently developed spatial transcriptomics method with near-cellular resolution-across the entire mouse brain. Integration of these datasets revealed the cell type composition of each neuroanatomical structure. Cell type diversity was found to be remarkably high in the midbrain, hindbrain and hypothalamus, with most clusters requiring a combination of at least three discrete gene expression markers to uniquely define them. Using these data, we developed a framework for genetically accessing each cell type, comprehensively characterized neuropeptide and neurotransmitter signalling, elucidated region-specific specializations in activity-regulated gene expression and ascertained the heritability enrichment of neurological and psychiatric phenotypes. These data, available as an online resource ( www.BrainCellData.org ), should find diverse applications across neuroscience, including the construction of new genetic tools and the prioritization of specific cell types and circuits in the study of brain diseases.
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Affiliation(s)
| | | | | | - Naeem M Nadaf
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Mukund Raj
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Evan Murray
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | | | | | - Daniel Tward
- Departments of Computational Medicine and Neurology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chris Mezias
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xu Li
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Dylan M Cable
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Partha Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Fei Chen
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Harvard Stem Cell and Regenerative Biology, Cambridge, MA, USA.
| | - Evan Z Macosko
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
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32
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Yao Z, van Velthoven CTJ, Kunst M, Zhang M, McMillen D, Lee C, Jung W, Goldy J, Abdelhak A, Aitken M, Baker K, Baker P, Barkan E, Bertagnolli D, Bhandiwad A, Bielstein C, Bishwakarma P, Campos J, Carey D, Casper T, Chakka AB, Chakrabarty R, Chavan S, Chen M, Clark M, Close J, Crichton K, Daniel S, DiValentin P, Dolbeare T, Ellingwood L, Fiabane E, Fliss T, Gee J, Gerstenberger J, Glandon A, Gloe J, Gould J, Gray J, Guilford N, Guzman J, Hirschstein D, Ho W, Hooper M, Huang M, Hupp M, Jin K, Kroll M, Lathia K, Leon A, Li S, Long B, Madigan Z, Malloy J, Malone J, Maltzer Z, Martin N, McCue R, McGinty R, Mei N, Melchor J, Meyerdierks E, Mollenkopf T, Moonsman S, Nguyen TN, Otto S, Pham T, Rimorin C, Ruiz A, Sanchez R, Sawyer L, Shapovalova N, Shepard N, Slaughterbeck C, Sulc J, Tieu M, Torkelson A, Tung H, Valera Cuevas N, Vance S, Wadhwani K, Ward K, Levi B, Farrell C, Young R, Staats B, Wang MQM, Thompson CL, Mufti S, Pagan CM, Kruse L, Dee N, Sunkin SM, Esposito L, Hawrylycz MJ, Waters J, Ng L, Smith K, Tasic B, Zhuang X, Zeng H. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain. Nature 2023; 624:317-332. [PMID: 38092916 PMCID: PMC10719114 DOI: 10.1038/s41586-023-06812-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 10/31/2023] [Indexed: 12/17/2023]
Abstract
The mammalian brain consists of millions to billions of cells that are organized into many cell types with specific spatial distribution patterns and structural and functional properties1-3. Here we report a comprehensive and high-resolution transcriptomic and spatial cell-type atlas for the whole adult mouse brain. The cell-type atlas was created by combining a single-cell RNA-sequencing (scRNA-seq) dataset of around 7 million cells profiled (approximately 4.0 million cells passing quality control), and a spatial transcriptomic dataset of approximately 4.3 million cells using multiplexed error-robust fluorescence in situ hybridization (MERFISH). The atlas is hierarchically organized into 4 nested levels of classification: 34 classes, 338 subclasses, 1,201 supertypes and 5,322 clusters. We present an online platform, Allen Brain Cell Atlas, to visualize the mouse whole-brain cell-type atlas along with the single-cell RNA-sequencing and MERFISH datasets. We systematically analysed the neuronal and non-neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell-type organization in different brain regions-in particular, a dichotomy between the dorsal and ventral parts of the brain. The dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. Our study also uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types. Finally, we found that transcription factors are major determinants of cell-type classification and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole mouse brain transcriptomic and spatial cell-type atlas establishes a benchmark reference atlas and a foundational resource for integrative investigations of cellular and circuit function, development and evolution of the mammalian brain.
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Affiliation(s)
- Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA.
| | | | | | - Meng Zhang
- Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, Department of Physics, Harvard University, Cambridge, MA, USA
| | | | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Won Jung
- Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, Department of Physics, Harvard University, Cambridge, MA, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Pamela Baker
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Eliza Barkan
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | - Daniel Carey
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Min Chen
- University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jennie Close
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Scott Daniel
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Tim Dolbeare
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - James Gee
- University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Jessica Gloe
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - James Gray
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Windy Ho
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Mike Huang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Madie Hupp
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kelly Jin
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Kanan Lathia
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Arielle Leon
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Su Li
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Brian Long
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Zach Madigan
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Zoe Maltzer
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Naomi Martin
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Rachel McCue
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ryan McGinty
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nicholas Mei
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jose Melchor
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Sven Otto
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Lane Sawyer
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Noah Shepard
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Shane Vance
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Katelyn Ward
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Boaz Levi
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Rob Young
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Brian Staats
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Shoaib Mufti
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Lauren Kruse
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Jack Waters
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Xiaowei Zhuang
- Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, Department of Physics, Harvard University, Cambridge, MA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA.
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Munro V, Kelly V, Messner CB, Kustatscher G. Cellular control of protein levels: A systems biology perspective. Proteomics 2023:e2200220. [PMID: 38012370 DOI: 10.1002/pmic.202200220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/29/2023]
Abstract
How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.
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Affiliation(s)
- Victoria Munro
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK
| | - Van Kelly
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK
| | - Christoph B Messner
- Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK
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Nardone S, De Luca R, Zito A, Klymko N, Nicoloutsopoulos D, Amsalem O, Brannigan C, Resch JM, Jacobs CL, Pant D, Veregge M, Srinivasan H, Grippo RM, Yang Z, Zeidel ML, Andermann ML, Harris KD, Tsai LT, Arrigoni E, Verstegen AMJ, Saper CB, Lowell BB. A spatially-resolved transcriptional atlas of the murine dorsal pons at single-cell resolution. bioRxiv 2023:2023.09.18.558047. [PMID: 38014113 PMCID: PMC10680649 DOI: 10.1101/2023.09.18.558047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
The "dorsal pons", or "dorsal pontine tegmentum" (dPnTg), is part of the brainstem. It is a complex, densely packed region whose nuclei are involved in regulating many vital functions. Notable among them are the parabrachial nucleus, the Kölliker Fuse, the Barrington nucleus, the locus coeruleus, and the dorsal, laterodorsal, and ventral tegmental nuclei. In this study, we applied single-nucleus RNA-seq (snRNA-seq) to resolve neuronal subtypes based on their unique transcriptional profiles and then used multiplexed error robust fluorescence in situ hybridization (MERFISH) to map them spatially. We sampled ~1 million cells across the dPnTg and defined the spatial distribution of over 120 neuronal subtypes. Our analysis identified an unpredicted high transcriptional diversity in this region and pinpointed many neuronal subtypes' unique marker genes. We also demonstrated that many neuronal subtypes are transcriptionally similar between humans and mice, enhancing this study's translational value. Finally, we developed a freely accessible, GPU and CPU-powered dashboard (http://harvard.heavy.ai:6273/) that combines interactive visual analytics and hardware-accelerated SQL into a data science framework to allow the scientific community to query and gain insights into the data.
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Affiliation(s)
- Stefano Nardone
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Roberto De Luca
- Department of Neurology, Division of Sleep Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA
| | - Antonino Zito
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Nataliya Klymko
- Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave., Boston, MA 02215, USA
| | | | - Oren Amsalem
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Cory Brannigan
- HEAVY.AI, 100 Montgomery St Fl 5, San Francisco, California, 94104, USA
| | - Jon M Resch
- Department of Neuroscience and Pharmacology, University of Iowa, Iowa City, IA, USA
- Fraternal Order of Eagles Diabetes Research Center. University of Iowa Carver College of Medicine, Iowa City, IA 52242
| | - Christopher L Jacobs
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Deepti Pant
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Molly Veregge
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Harini Srinivasan
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ryan M Grippo
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Zongfang Yang
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Mark L Zeidel
- Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave., Boston, MA 02215, USA
| | - Mark L Andermann
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Kenneth D Harris
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Linus T Tsai
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Elda Arrigoni
- Department of Neurology, Division of Sleep Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA
| | - Anne M J Verstegen
- Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave., Boston, MA 02215, USA
| | - Clifford B Saper
- Department of Neurology, Division of Sleep Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA
| | - Bradford B Lowell
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
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Herb BR, Glover HJ, Bhaduri A, Colantuoni C, Bale TL, Siletti K, Hodge R, Lein E, Kriegstein AR, Doege CA, Ament SA. Single-cell genomics reveals region-specific developmental trajectories underlying neuronal diversity in the human hypothalamus. Sci Adv 2023; 9:eadf6251. [PMID: 37939194 PMCID: PMC10631741 DOI: 10.1126/sciadv.adf6251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 10/24/2023] [Indexed: 11/10/2023]
Abstract
The development and diversity of neuronal subtypes in the human hypothalamus has been insufficiently characterized. To address this, we integrated transcriptomic data from 241,096 cells (126,840 newly generated) in the prenatal and adult human hypothalamus to reveal a temporal trajectory from proliferative stem cell populations to mature hypothalamic cell types. Iterative clustering of the adult neurons identified 108 robust transcriptionally distinct neuronal subtypes representing 10 hypothalamic nuclei. Pseudotime trajectories provided insights into the genes driving formation of these nuclei. Comparisons to single-cell transcriptomic data from the mouse hypothalamus suggested extensive conservation of neuronal subtypes despite certain differences in species-enriched gene expression. The uniqueness of hypothalamic neuronal lineages was examined developmentally by comparing excitatory lineages present in cortex and inhibitory lineages in ganglionic eminence, revealing both distinct and shared drivers of neuronal maturation across the human forebrain. These results provide a comprehensive transcriptomic view of human hypothalamus development through gestation and adulthood at cellular resolution.
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Affiliation(s)
- Brian R. Herb
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Pharmacology, University of Maryland School of Medicine, Baltimore, MD, USA
- UM-MIND, University of Maryland School of Medicine, Baltimore, MD, USA
- Kahlert Institute for Addiction Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Hannah J. Glover
- Naomi Berrie Diabetes Center, Columbia Stem Cell Initiative, Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA
| | - Aparna Bhaduri
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - Carlo Colantuoni
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tracy L. Bale
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, USA
| | - Kimberly Siletti
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Rebecca Hodge
- Allen Institute for Brain Science, Seattle, WA 98109
| | - Ed Lein
- Allen Institute for Brain Science, Seattle, WA 98109
| | - Arnold R. Kriegstein
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA, USA
| | - Claudia A. Doege
- Naomi Berrie Diabetes Center, Columbia Stem Cell Initiative, Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Seth A. Ament
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- UM-MIND, University of Maryland School of Medicine, Baltimore, MD, USA
- Kahlert Institute for Addiction Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
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36
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Konopka G, Bhaduri A. Functional genomics and systems biology in human neuroscience. Nature 2023; 623:274-282. [PMID: 37938705 DOI: 10.1038/s41586-023-06686-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 09/27/2023] [Indexed: 11/09/2023]
Abstract
Neuroscience research has entered a phase of key discoveries in the realm of neurogenomics owing to strong financial and intellectual support for resource building and tool development. The previous challenge of tissue heterogeneity has been met with the application of techniques that can profile individual cells at scale. Moreover, the ability to perturb genes, gene regulatory elements and neuronal activity in a cell-type-specific manner has been integrated with gene expression studies to uncover the functional underpinnings of the genome at a systems level. Although these insights have necessarily been grounded in model systems, we now have the opportunity to apply these approaches in humans and in human tissue, thanks to advances in human genetics, brain imaging and tissue collection. We acknowledge that there will probably always be limits to the extent to which we can apply the genomic tools developed in model systems to human neuroscience; however, as we describe in this Perspective, the neuroscience field is now primed with an optimal foundation for tackling this ambitious challenge. The application of systems-level network analyses to these datasets will facilitate a deeper appreciation of human neurogenomics that cannot otherwise be achieved from directly observable phenomena.
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Affiliation(s)
- Genevieve Konopka
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA.
- Peter O'Donnell Jr Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA.
| | - Aparna Bhaduri
- Department of Biological Chemistry, University of California, Los Angeles, CA, USA.
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37
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Borm L. Methods section too short? Use online protocols to make complex techniques understandable. Nature 2023:10.1038/d41586-023-03249-2. [PMID: 37845480 DOI: 10.1038/d41586-023-03249-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2023]
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38
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Li YE, Preissl S, Miller M, Johnson ND, Wang Z, Jiao H, Zhu C, Wang Z, Xie Y, Poirion O, Kern C, Pinto-Duarte A, Tian W, Siletti K, Emerson N, Osteen J, Lucero J, Lin L, Yang Q, Zhu Q, Zemke N, Espinoza S, Yanny AM, Nyhus J, Dee N, Casper T, Shapovalova N, Hirschstein D, Hodge RD, Linnarsson S, Bakken T, Levi B, Keene CD, Shang J, Lein E, Wang A, Behrens MM, Ecker JR, Ren B. A comparative atlas of single-cell chromatin accessibility in the human brain. Science 2023; 382:eadf7044. [PMID: 37824643 PMCID: PMC10852054 DOI: 10.1126/science.adf7044] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 09/14/2023] [Indexed: 10/14/2023]
Abstract
Recent advances in single-cell transcriptomics have illuminated the diverse neuronal and glial cell types within the human brain. However, the regulatory programs governing cell identity and function remain unclear. Using a single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq), we explored open chromatin landscapes across 1.1 million cells in 42 brain regions from three adults. Integrating this data unveiled 107 distinct cell types and their specific utilization of 544,735 candidate cis-regulatory DNA elements (cCREs) in the human genome. Nearly a third of the cCREs demonstrated conservation and chromatin accessibility in the mouse brain cells. We reveal strong links between specific brain cell types and neuropsychiatric disorders including schizophrenia, bipolar disorder, Alzheimer's disease (AD), and major depression, and have developed deep learning models to predict the regulatory roles of noncoding risk variants in these disorders.
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Affiliation(s)
- Yang Eric Li
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA 92093, USA
| | - Michael Miller
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA 92093, USA
| | | | - Zihan Wang
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Henry Jiao
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA 92093, USA
| | - Chenxu Zhu
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhaoning Wang
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Yang Xie
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Olivier Poirion
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA 92093, USA
| | - Colin Kern
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA 92093, USA
| | | | - Wei Tian
- The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Kimberly Siletti
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 77 Stockholm, Sweden
| | - Nora Emerson
- The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Julia Osteen
- The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jacinta Lucero
- The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Lin Lin
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA 92093, USA
| | - Qian Yang
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA 92093, USA
| | - Quan Zhu
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA 92093, USA
| | - Nathan Zemke
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA 92093, USA
| | - Sarah Espinoza
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA 92093, USA
| | | | - Julie Nyhus
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tamara Casper
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | | | - Sten Linnarsson
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 77 Stockholm, Sweden
| | - Trygve Bakken
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Boaz Levi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104, USA
| | - Jingbo Shang
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ed Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Allen Wang
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA 92093, USA
| | | | - Joseph R Ecker
- The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Bing Ren
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA 92093, USA
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Tian W, Zhou J, Bartlett A, Zeng Q, Liu H, Castanon RG, Kenworthy M, Altshul J, Valadon C, Aldridge A, Nery JR, Chen H, Xu J, Johnson ND, Lucero J, Osteen JK, Emerson N, Rink J, Lee J, Li Y, Siletti K, Liem M, Claffey N, O’Connor C, Yanny AM, Nyhus J, Dee N, Casper T, Shapovalova N, Hirschstein D, Ding SL, Hodge R, Levi BP, Keene CD, Linnarsson S, Lein E, Ren B, Behrens MM, Ecker JR. Single-cell DNA methylation and 3D genome architecture in the human brain. Science 2023; 382:eadf5357. [PMID: 37824674 PMCID: PMC10572106 DOI: 10.1126/science.adf5357] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 09/05/2023] [Indexed: 10/14/2023]
Abstract
Delineating the gene-regulatory programs underlying complex cell types is fundamental for understanding brain function in health and disease. Here, we comprehensively examined human brain cell epigenomes by probing DNA methylation and chromatin conformation at single-cell resolution in 517 thousand cells (399 thousand neurons and 118 thousand non-neurons) from 46 regions of three adult male brains. We identified 188 cell types and characterized their molecular signatures. Integrative analyses revealed concordant changes in DNA methylation, chromatin accessibility, chromatin organization, and gene expression across cell types, cortical areas, and basal ganglia structures. We further developed single-cell methylation barcodes that reliably predict brain cell types using the methylation status of select genomic sites. This multimodal epigenomic brain cell atlas provides new insights into the complexity of cell-type-specific gene regulation in adult human brains.
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Affiliation(s)
- Wei Tian
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Qiurui Zeng
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92037, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Rosa G. Castanon
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Mia Kenworthy
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jordan Altshul
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Cynthia Valadon
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Andrew Aldridge
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Joseph R. Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Huaming Chen
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jiaying Xu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Nicholas D. Johnson
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jacinta Lucero
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Julia K. Osteen
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Nora Emerson
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jon Rink
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jasper Lee
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Yang Li
- Ludwig Institute for Cancer Research, La Jolla, CA 92037, USA
| | - Kimberly Siletti
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet; 171 77 Stockholm, Sweden
| | - Michelle Liem
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Naomi Claffey
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Caz O’Connor
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | | | - Julie Nyhus
- Allen Institute for Brain Science; Seattle, WA 98109, USA
| | - Nick Dee
- Allen Institute for Brain Science; Seattle, WA 98109, USA
| | - Tamara Casper
- Allen Institute for Brain Science; Seattle, WA 98109, USA
| | | | | | - Song-Lin Ding
- Allen Institute for Brain Science; Seattle, WA 98109, USA
| | - Rebecca Hodge
- Allen Institute for Brain Science; Seattle, WA 98109, USA
| | - Boaz P. Levi
- Allen Institute for Brain Science; Seattle, WA 98109, USA
| | - C. Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Sten Linnarsson
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet; 171 77 Stockholm, Sweden
| | - Ed Lein
- Allen Institute for Brain Science; Seattle, WA 98109, USA
| | - Bing Ren
- Ludwig Institute for Cancer Research, La Jolla, CA 92037, USA
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA 92037, USA
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA 92037, USA
- Institute of Genomic Medicine, University of California, San Diego School of Medicine, La Jolla, CA 92037, USA
- Moores Cancer Center, University of California, San Diego School of Medicine, La Jolla, CA 92037, USA
| | - M. Margarita Behrens
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
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Braun E, Danan-Gotthold M, Borm LE, Lee KW, Vinsland E, Lönnerberg P, Hu L, Li X, He X, Andrusivová Ž, Lundeberg J, Barker RA, Arenas E, Sundström E, Linnarsson S. Comprehensive cell atlas of the first-trimester developing human brain. Science 2023; 382:eadf1226. [PMID: 37824650 DOI: 10.1126/science.adf1226] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 08/09/2023] [Indexed: 10/14/2023]
Abstract
The adult human brain comprises more than a thousand distinct neuronal and glial cell types, a diversity that emerges during early brain development. To reveal the precise sequence of events during early brain development, we used single-cell RNA sequencing and spatial transcriptomics and uncovered cell states and trajectories in human brains at 5 to 14 postconceptional weeks (pcw). We identified 12 major classes that are organized as ~600 distinct cell states, which map to precise spatial anatomical domains at 5 pcw. We described detailed differentiation trajectories of the human forebrain and midbrain and found a large number of region-specific glioblasts that mature into distinct pre-astrocytes and pre-oligodendrocyte precursor cells. Our findings reveal the establishment of cell types during the first trimester of human brain development.
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Affiliation(s)
- Emelie Braun
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 65 Stockholm, Sweden
| | - Miri Danan-Gotthold
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 65 Stockholm, Sweden
| | - Lars E Borm
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 65 Stockholm, Sweden
| | - Ka Wai Lee
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 65 Stockholm, Sweden
| | - Elin Vinsland
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 65 Stockholm, Sweden
| | - Peter Lönnerberg
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 65 Stockholm, Sweden
| | - Lijuan Hu
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 65 Stockholm, Sweden
| | - Xiaofei Li
- Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 171 65 Stockholm, Sweden
| | - Xiaoling He
- John van Geest Centre for Brain Repair, University of Cambridge, Cambridge CB2 0PY, UK
| | - Žaneta Andrusivová
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, 171 65 Solna, Sweden
| | - Joakim Lundeberg
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, 171 65 Solna, Sweden
| | - Roger A Barker
- John van Geest Centre for Brain Repair, University of Cambridge, Cambridge CB2 0PY, UK
| | - Ernest Arenas
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 65 Stockholm, Sweden
| | - Erik Sundström
- Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 171 65 Stockholm, Sweden
| | - Sten Linnarsson
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 65 Stockholm, Sweden
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41
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Krienen FM, Levandowski KM, Zaniewski H, del Rosario RC, Schroeder ME, Goldman M, Wienisch M, Lutservitz A, Beja-Glasser VF, Chen C, Zhang Q, Chan KY, Li KX, Sharma J, McCormack D, Shin TW, Harrahill A, Nyase E, Mudhar G, Mauermann A, Wysoker A, Nemesh J, Kashin S, Vergara J, Chelini G, Dimidschstein J, Berretta S, Deverman BE, Boyden E, McCarroll SA, Feng G. A marmoset brain cell census reveals regional specialization of cellular identities. Sci Adv 2023; 9:eadk3986. [PMID: 37824615 PMCID: PMC10569717 DOI: 10.1126/sciadv.adk3986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 09/26/2023] [Indexed: 10/14/2023]
Abstract
The mammalian brain is composed of many brain structures, each with its own ontogenetic and developmental history. We used single-nucleus RNA sequencing to sample over 2.4 million brain cells across 18 locations in the common marmoset, a New World monkey primed for genetic engineering, and examined gene expression patterns of cell types within and across brain structures. The adult transcriptomic identity of most neuronal types is shaped more by developmental origin than by neurotransmitter signaling repertoire. Quantitative mapping of GABAergic types with single-molecule FISH (smFISH) reveals that interneurons in the striatum and neocortex follow distinct spatial principles, and that lateral prefrontal and other higher-order cortical association areas are distinguished by high proportions of VIP+ neurons. We use cell type-specific enhancers to drive AAV-GFP and reconstruct the morphologies of molecularly resolved interneuron types in neocortex and striatum. Our analyses highlight how lineage, local context, and functional class contribute to the transcriptional identity and biodistribution of primate brain cell types.
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Affiliation(s)
- Fenna M. Krienen
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kirsten M. Levandowski
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Heather Zaniewski
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ricardo C.H. del Rosario
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Margaret E. Schroeder
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Melissa Goldman
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Martin Wienisch
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Alyssa Lutservitz
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Victoria F. Beja-Glasser
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Cindy Chen
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Qiangge Zhang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ken Y. Chan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Katelyn X. Li
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jitendra Sharma
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Dana McCormack
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Tay Won Shin
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Howard Hughes Medical Institute, Cambridge, MA 02139, USA
| | - Andrew Harrahill
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Eric Nyase
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Gagandeep Mudhar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Abigail Mauermann
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Howard Hughes Medical Institute, Cambridge, MA 02139, USA
| | - Alec Wysoker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - James Nemesh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Seva Kashin
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Josselyn Vergara
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Gabriele Chelini
- Center for Mind/Brain Sciences, University of Trento, Piazza della Manifattura n.1, Rovereto (TN) 38068, Italy
| | - Jordane Dimidschstein
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Sabina Berretta
- Basic Neuroscience Division, McLean Hospital, Belmont, MA 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin E. Deverman
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ed Boyden
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Howard Hughes Medical Institute, Cambridge, MA 02139, USA
| | - Steven A. McCarroll
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Guoping Feng
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
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Lee BR, Dalley R, Miller JA, Chartrand T, Close J, Mann R, Mukora A, Ng L, Alfiler L, Baker K, Bertagnolli D, Brouner K, Casper T, Csajbok E, Donadio N, Driessens SLW, Egdorf T, Enstrom R, Galakhova AA, Gary A, Gelfand E, Goldy J, Hadley K, Heistek TS, Hill D, Hou WH, Johansen N, Jorstad N, Kim L, Kocsis AK, Kruse L, Kunst M, León G, Long B, Mallory M, Maxwell M, McGraw M, McMillen D, Melief EJ, Molnar G, Mortrud MT, Newman D, Nyhus J, Opitz-Araya X, Ozsvár A, Pham T, Pom A, Potekhina L, Rajanbabu R, Ruiz A, Sunkin SM, Szöts I, Taskin N, Thyagarajan B, Tieu M, Trinh J, Vargas S, Vumbaco D, Waleboer F, Walling-Bell S, Weed N, Williams G, Wilson J, Yao S, Zhou T, Barzó P, Bakken T, Cobbs C, Dee N, Ellenbogen RG, Esposito L, Ferreira M, Gouwens NW, Grannan B, Gwinn RP, Hauptman JS, Hodge R, Jarsky T, Keene CD, Ko AL, Korshoej AR, Levi BP, Meier K, Ojemann JG, Patel A, Ruzevick J, Silbergeld DL, Smith K, Sørensen JC, Waters J, Zeng H, Berg J, Capogna M, Goriounova NA, Kalmbach B, de Kock CPJ, Mansvelder HD, Sorensen SA, Tamas G, Lein ES, Ting JT. Signature morphoelectric properties of diverse GABAergic interneurons in the human neocortex. Science 2023; 382:eadf6484. [PMID: 37824669 DOI: 10.1126/science.adf6484] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 09/08/2023] [Indexed: 10/14/2023]
Abstract
Human cortex transcriptomic studies have revealed a hierarchical organization of γ-aminobutyric acid-producing (GABAergic) neurons from subclasses to a high diversity of more granular types. Rapid GABAergic neuron viral genetic labeling plus Patch-seq (patch-clamp electrophysiology plus single-cell RNA sequencing) sampling in human brain slices was used to reliably target and analyze GABAergic neuron subclasses and individual transcriptomic types. This characterization elucidated transitions between PVALB and SST subclasses, revealed morphological heterogeneity within an abundant transcriptomic type, identified multiple spatially distinct types of the primate-specialized double bouquet cells (DBCs), and shed light on cellular differences between homologous mouse and human neocortical GABAergic neuron types. These results highlight the importance of multimodal phenotypic characterization for refinement of emerging transcriptomic cell type taxonomies and for understanding conserved and specialized cellular properties of human brain cell types.
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Affiliation(s)
- Brian R Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Rachel Dalley
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Thomas Chartrand
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Jennie Close
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Rusty Mann
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Alice Mukora
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lindsay Ng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lauren Alfiler
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Krissy Brouner
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tamara Casper
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Eva Csajbok
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, 6726 Szeged, Hungary
| | | | - Stan L W Driessens
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Rachel Enstrom
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Anna A Galakhova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | - Amanda Gary
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Emily Gelfand
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kristen Hadley
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tim S Heistek
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | - Dijon Hill
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Wen-Hsien Hou
- Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark
| | | | - Nik Jorstad
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lisa Kim
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Agnes Katalin Kocsis
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, 6726 Szeged, Hungary
| | - Lauren Kruse
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Michael Kunst
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Gabriela León
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Brian Long
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Medea McGraw
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Erica J Melief
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Gabor Molnar
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, 6726 Szeged, Hungary
| | | | - Dakota Newman
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Julie Nyhus
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Attila Ozsvár
- Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark
| | | | - Alice Pom
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Ram Rajanbabu
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Augustin Ruiz
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Susan M Sunkin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ildikó Szöts
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, 6726 Szeged, Hungary
| | - Naz Taskin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jessica Trinh
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Sara Vargas
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - David Vumbaco
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Femke Waleboer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | | | - Natalie Weed
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Grace Williams
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Julia Wilson
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Thomas Zhou
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Pál Barzó
- Department of Neurosurgery, University of Szeged, 6725 Szeged, Hungary
| | - Trygve Bakken
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Charles Cobbs
- Swedish Neuroscience Institute, Seattle, WA 98122, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Richard G Ellenbogen
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Luke Esposito
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Manuel Ferreira
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | | | - Benjamin Grannan
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Ryder P Gwinn
- Swedish Neuroscience Institute, Seattle, WA 98122, USA
| | - Jason S Hauptman
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Rebecca Hodge
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Andrew L Ko
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | | | - Boaz P Levi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kaare Meier
- Department of Neurosurgery, Aarhus University Hospital, 8200 Aarhus, Denmark
- Department of Anesthesiology, Aarhus University Hospital, 8200 Aarhus, Denmark
| | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Anoop Patel
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Jacob Ruzevick
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Daniel L Silbergeld
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Kimberly Smith
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jens Christian Sørensen
- Department of Neurosurgery, Aarhus University Hospital, 8200 Aarhus, Denmark
- Center for Experimental Neuroscience, Aarhus University Hospital, 8200 Aarhus, Denmark
| | - Jack Waters
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jim Berg
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Marco Capogna
- Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark
| | - Natalia A Goriounova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | - Brian Kalmbach
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Christiaan P J de Kock
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | - Huib D Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | | | - Gabor Tamas
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, 6726 Szeged, Hungary
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Jonathan T Ting
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
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Weninger A, Arlotta P. A family portrait of human brain cells. Science 2023; 382:168-169. [PMID: 37824657 DOI: 10.1126/science.adk4857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
A cell census provides information on the source of human brain specialization.
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Affiliation(s)
- Alyssa Weninger
- Department of Psychology and Neuroscience and Department of Nutrition, University of North Carolina Chapel Hill, Chapel Hill, NC, USA
| | - Paola Arlotta
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Conroy G. This is the largest map of the human brain ever made. Nature 2023; 622:679-680. [PMID: 37828214 DOI: 10.1038/d41586-023-03192-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
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