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Yu Z, Coorens THH, Uddin MM, Ardlie KG, Lennon N, Natarajan P. Genetic variation across and within individuals. Nat Rev Genet 2024; 25:548-562. [PMID: 38548833 DOI: 10.1038/s41576-024-00709-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/09/2024] [Indexed: 04/12/2024]
Abstract
Germline variation and somatic mutation are intricately connected and together shape human traits and disease risks. Germline variants are present from conception, but they vary between individuals and accumulate over generations. By contrast, somatic mutations accumulate throughout life in a mosaic manner within an individual due to intrinsic and extrinsic sources of mutations and selection pressures acting on cells. Recent advancements, such as improved detection methods and increased resources for association studies, have drastically expanded our ability to investigate germline and somatic genetic variation and compare underlying mutational processes. A better understanding of the similarities and differences in the types, rates and patterns of germline and somatic variants, as well as their interplay, will help elucidate the mechanisms underlying their distinct yet interlinked roles in human health and biology.
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Affiliation(s)
- Zhi Yu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Md Mesbah Uddin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Niall Lennon
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Pradeep Natarajan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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2
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Mathys H, Boix CA, Akay LA, Xia Z, Davila-Velderrain J, Ng AP, Jiang X, Abdelhady G, Galani K, Mantero J, Band N, James BT, Babu S, Galiana-Melendez F, Louderback K, Prokopenko D, Tanzi RE, Bennett DA, Tsai LH, Kellis M. Single-cell multiregion dissection of Alzheimer's disease. Nature 2024:10.1038/s41586-024-07606-7. [PMID: 39048816 DOI: 10.1038/s41586-024-07606-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 05/24/2024] [Indexed: 07/27/2024]
Abstract
Alzheimer's disease is the leading cause of dementia worldwide, but the cellular pathways that underlie its pathological progression across brain regions remain poorly understood1-3. Here we report a single-cell transcriptomic atlas of six different brain regions in the aged human brain, covering 1.3 million cells from 283 post-mortem human brain samples across 48 individuals with and without Alzheimer's disease. We identify 76 cell types, including region-specific subtypes of astrocytes and excitatory neurons and an inhibitory interneuron population unique to the thalamus and distinct from canonical inhibitory subclasses. We identify vulnerable populations of excitatory and inhibitory neurons that are depleted in specific brain regions in Alzheimer's disease, and provide evidence that the Reelin signalling pathway is involved in modulating the vulnerability of these neurons. We develop a scalable method for discovering gene modules, which we use to identify cell-type-specific and region-specific modules that are altered in Alzheimer's disease and to annotate transcriptomic differences associated with diverse pathological variables. We identify an astrocyte program that is associated with cognitive resilience to Alzheimer's disease pathology, tying choline metabolism and polyamine biosynthesis in astrocytes to preserved cognitive function late in life. Together, our study develops a regional atlas of the ageing human brain and provides insights into cellular vulnerability, response and resilience to Alzheimer's disease pathology.
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Affiliation(s)
- Hansruedi Mathys
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- University of Pittsburgh Brain Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Carles A Boix
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Computational and Systems Biology Program, MIT, Cambridge, MA, USA
| | - Leyla Anne Akay
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Ziting Xia
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Harvard-MIT Health Sciences and Technology Program, MIT, Cambridge, MA, USA
| | | | - Ayesha P Ng
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Xueqiao Jiang
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Ghada Abdelhady
- University of Pittsburgh Brain Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Kyriaki Galani
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Julio Mantero
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Neil Band
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Benjamin T James
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sudhagar Babu
- University of Pittsburgh Brain Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Fabiola Galiana-Melendez
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Kate Louderback
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Dmitry Prokopenko
- Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Rudolph E Tanzi
- Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Li-Huei Tsai
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA.
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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3
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Sullivan PF, Yao S, Hjerling-Leffler J. Schizophrenia genomics: genetic complexity and functional insights. Nat Rev Neurosci 2024:10.1038/s41583-024-00837-7. [PMID: 39030273 DOI: 10.1038/s41583-024-00837-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/04/2024] [Indexed: 07/21/2024]
Abstract
Determining the causes of schizophrenia has been a notoriously intractable problem, resistant to a multitude of investigative approaches over centuries. In recent decades, genomic studies have delivered hundreds of robust findings that implicate nearly 300 common genetic variants (via genome-wide association studies) and more than 20 rare variants (via whole-exome sequencing and copy number variant studies) as risk factors for schizophrenia. In parallel, functional genomic and neurobiological studies have provided exceptionally detailed information about the cellular composition of the brain and its interconnections in neurotypical individuals and, increasingly, in those with schizophrenia. Taken together, these results suggest unexpected complexity in the mechanisms that drive schizophrenia, pointing to the involvement of ensembles of genes (polygenicity) rather than single-gene causation. In this Review, we describe what we now know about the genetics of schizophrenia and consider the neurobiological implications of this information.
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Affiliation(s)
- Patrick F Sullivan
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jens Hjerling-Leffler
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
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4
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Chen R, Nie P, Wang J, Wang GZ. Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1865. [PMID: 38972934 DOI: 10.1002/wrna.1865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 05/05/2024] [Accepted: 05/14/2024] [Indexed: 07/09/2024]
Abstract
The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.
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Affiliation(s)
- Renrui Chen
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Pengxing Nie
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jing Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Guang-Zhong Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
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5
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Strober BJ, Zhang MJ, Amariuta T, Rossen J, Price AL. Fine-mapping causal tissues and genes at disease-associated loci. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.11.01.23297909. [PMID: 37961337 PMCID: PMC10635248 DOI: 10.1101/2023.11.01.23297909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Heritable diseases often manifest in a highly tissue-specific manner, with different disease loci mediated by genes in distinct tissues or cell types. We propose Tissue-Gene Fine-Mapping (TGFM), a fine-mapping method that infers the posterior probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing GWAS summary statistics (and in-sample LD) and leveraging eQTL data from diverse tissues to build cis-predicted expression models; TGFM also assigns PIPs to causal variants that are not mediated by gene expression in assayed genes and tissues. TGFM accounts for both co-regulation across genes and tissues and LD between SNPs (generalizing existing fine-mapping methods), and incorporates genome-wide estimates of each tissue's contribution to disease as tissue-level priors. TGFM was well-calibrated and moderately well-powered in simulations; unlike previous methods, TGFM was able to attain correct calibration by modeling uncertainty in cis-predicted expression models. We applied TGFM to 45 UK Biobank diseases/traits (average N = 316K) using eQTL data from 38 GTEx tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease/trait, of which 11% were gene-tissue pairs. Implicated gene-tissue pairs were concentrated in known disease-critical tissues, and causal genes were strongly enriched in disease-relevant gene sets. Causal gene-tissue pairs identified by TGFM recapitulated known biology (e.g., TPO-thyroid for Hypothyroidism), but also included biologically plausible novel findings (e.g., SLC20A2-artery aorta for Diastolic blood pressure). Further application of TGFM to single-cell eQTL data from 9 cell types in peripheral blood mononuclear cells (PBMC), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs at PIP > 0.5-primarily for autoimmune disease and blood cell traits, including the biologically plausible example of CD52 in classical monocyte cells for Monocyte count. In conclusion, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.
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Affiliation(s)
- Benjamin J. Strober
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Martin Jinye Zhang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Tiffany Amariuta
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Jordan Rossen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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6
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Gao Q, Ai Q. DCRELM: dual correlation reduction network-based extreme learning machine for single-cell RNA-seq data clustering. Sci Rep 2024; 14:13541. [PMID: 38866896 PMCID: PMC11169517 DOI: 10.1038/s41598-024-64217-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 06/06/2024] [Indexed: 06/14/2024] Open
Abstract
Single-cell ribonucleic acid sequencing (scRNA-seq) is a high-throughput genomic technique that is utilized to investigate single-cell transcriptomes. Cluster analysis can effectively reveal the heterogeneity and diversity of cells in scRNA-seq data, but existing clustering algorithms struggle with the inherent high dimensionality, noise, and sparsity of scRNA-seq data. To overcome these limitations, we propose a clustering algorithm: the Dual Correlation Reduction network-based Extreme Learning Machine (DCRELM). First, DCRELM obtains the low-dimensional and dense result features of scRNA-seq data in an extreme learning machine (ELM) random mapping space. Second, the ELM graph distortion module is employed to obtain a dual view of the resulting features, effectively enhancing their robustness. Third, the autoencoder fusion module is employed to learn the attributes and structural information of the resulting features, and merge these two types of information to generate consistent latent representations of these features. Fourth, the dual information reduction network is used to filter the redundant information and noise in the dual consistent latent representations. Last, a triplet self-supervised learning mechanism is utilized to further improve the clustering performance. Extensive experiments show that the DCRELM performs well in terms of clustering performance and robustness. The code is available at https://github.com/gaoqingyun-lucky/awesome-DCRELM .
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Affiliation(s)
- Qingyun Gao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Qing Ai
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
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Gaertner Z, Oram C, Schneeweis A, Schonfeld E, Bolduc C, Chen C, Dombeck D, Parisiadou L, Poulin JF, Awatramani R. Molecular and spatial transcriptomic classification of midbrain dopamine neurons and their alterations in a LRRK2 G2019S model of Parkinson's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597807. [PMID: 38895448 PMCID: PMC11185743 DOI: 10.1101/2024.06.06.597807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Several studies have revealed that midbrain dopamine (DA) neurons, even within a single neuroanatomical area, display heterogeneous properties. In parallel, studies using single cell profiling techniques have begun to cluster DA neurons into subtypes based on their molecular signatures. Recent work has shown that molecularly defined DA subtypes within the substantia nigra (SNc) display distinctive anatomic and functional properties, and differential vulnerability in Parkinson's disease (PD). Based on these provocative results, a granular understanding of these putative subtypes and their alterations in PD models, is imperative. We developed an optimized pipeline for single-nuclear RNA sequencing (snRNA-seq) and generated a high-resolution hierarchically organized map revealing 20 molecularly distinct DA neuron subtypes belonging to three main families. We integrated this data with spatial MERFISH technology to map, with high definition, the location of these subtypes in the mouse midbrain, revealing heterogeneity even within neuroanatomical sub-structures. Finally, we demonstrate that in the preclinical LRRK2G2019S knock-in mouse model of PD, subtype organization and proportions are preserved. Transcriptional alterations occur in many subtypes including those localized to the ventral tier SNc, where differential expression is observed in synaptic pathways, which might account for previously described DA release deficits in this model. Our work provides an advancement of current taxonomic schemes of the mouse midbrain DA neuron subtypes, a high-resolution view of their spatial locations, and their alterations in a prodromal mouse model of PD.
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Affiliation(s)
- Zachary Gaertner
- Northwestern University Feinberg School of Medicine, Dept of Neurology, Chicago, IL 60611
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Cameron Oram
- McGill University (Montreal Neurological Institute), Faculty of Medicine and Health Sciences, Dept of Neurology and Neurosurgery, Montreal (QC), Canada
| | - Amanda Schneeweis
- Northwestern University Feinberg School of Medicine, Dept of Neurology, Chicago, IL 60611
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Elan Schonfeld
- Northwestern University Feinberg School of Medicine, Dept of Neurology, Chicago, IL 60611
| | - Cyril Bolduc
- McGill University (Montreal Neurological Institute), Faculty of Medicine and Health Sciences, Dept of Neurology and Neurosurgery, Montreal (QC), Canada
| | - Chuyu Chen
- Northwestern University Feinberg School of Medicine, Dept of Pharmacology, Chicago, IL 60611
| | - Daniel Dombeck
- Northwestern University, Dept of Neurobiology, Evanston, IL 60201
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Loukia Parisiadou
- Northwestern University Feinberg School of Medicine, Dept of Pharmacology, Chicago, IL 60611
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Jean Francois Poulin
- McGill University (Montreal Neurological Institute), Faculty of Medicine and Health Sciences, Dept of Neurology and Neurosurgery, Montreal (QC), Canada
| | - Rajeshwar Awatramani
- Northwestern University Feinberg School of Medicine, Dept of Neurology, Chicago, IL 60611
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
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8
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Fass SB, Mulvey B, Chase R, Yang W, Selmanovic D, Chaturvedi SM, Tycksen E, Weiss LA, Dougherty JD. Relationship between sex biases in gene expression and sex biases in autism and Alzheimer's disease. Biol Sex Differ 2024; 15:47. [PMID: 38844994 PMCID: PMC11157820 DOI: 10.1186/s13293-024-00622-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 05/23/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Sex differences in the brain may play an important role in sex-differential prevalence of neuropsychiatric conditions. METHODS In order to understand the transcriptional basis of sex differences, we analyzed multiple, large-scale, human postmortem brain RNA-Seq datasets using both within-region and pan-regional frameworks. RESULTS We find evidence of sex-biased transcription in many autosomal genes, some of which provide evidence for pathways and cell population differences between chromosomally male and female individuals. These analyses also highlight regional differences in the extent of sex-differential gene expression. We observe an increase in specific neuronal transcripts in male brains and an increase in immune and glial function-related transcripts in female brains. Integration with single-nucleus data suggests this corresponds to sex differences in cellular states rather than cell abundance. Integration with case-control gene expression studies suggests a female molecular predisposition towards Alzheimer's disease, a female-biased disease. Autism, a male-biased diagnosis, does not exhibit a male predisposition pattern in our analysis. CONCLUSION Overall, these analyses highlight mechanisms by which sex differences may interact with sex-biased conditions in the brain. Furthermore, we provide region-specific analyses of sex differences in brain gene expression to enable additional studies at the interface of gene expression and diagnostic differences.
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Affiliation(s)
- Stuart B Fass
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO, 63110, USA
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO, 63110, USA
| | - Bernard Mulvey
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO, 63110, USA
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO, 63110, USA
- Lieber Institute for Brain Development, 855 North Wolfe St. Ste 300, Baltimore, MD, 21205, USA
| | - Rebecca Chase
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO, 63110, USA
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO, 63110, USA
| | - Wei Yang
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO, 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Din Selmanovic
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO, 63110, USA
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO, 63110, USA
| | - Sneha M Chaturvedi
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO, 63110, USA
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO, 63110, USA
| | - Eric Tycksen
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO, 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Lauren A Weiss
- Institute for Human Genetics, University of California, San Francisco, 513 Parnassus Ave, HSE901, San Francisco, CA, 94143, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, 513 Parnassus Ave, HSE901, San Francisco, CA, 94143, USA
- Weill Institute for Neurosciences, University of California, San Francisco, 513 Parnassus Ave, HSE901, San Francisco, CA, 94143, USA
| | - Joseph D Dougherty
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO, 63110, USA.
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO, 63110, USA.
- Intellectual and Developmental Disabilities Research Center, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO, 63110, USA.
- Department of Genetics, 4566 Scott Ave., Campus Box 8232, St. Louis, MO, 63110-1093, USA.
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9
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Dareng EO, Coetzee SG, Tyrer JP, Peng PC, Rosenow W, Chen S, Davis BD, Dezem FS, Seo JH, Nameki R, Reyes AL, Aben KKH, Anton-Culver H, Antonenkova NN, Aravantinos G, Bandera EV, Beane Freeman LE, Beckmann MW, Beeghly-Fadiel A, Benitez J, Bernardini MQ, Bjorge L, Black A, Bogdanova NV, Bolton KL, Brenton JD, Budzilowska A, Butzow R, Cai H, Campbell I, Cannioto R, Chang-Claude J, Chanock SJ, Chen K, Chenevix-Trench G, Chiew YE, Cook LS, DeFazio A, Dennis J, Doherty JA, Dörk T, du Bois A, Dürst M, Eccles DM, Ene G, Fasching PA, Flanagan JM, Fortner RT, Fostira F, Gentry-Maharaj A, Giles GG, Goodman MT, Gronwald J, Haiman CA, Håkansson N, Heitz F, Hildebrandt MAT, Høgdall E, Høgdall CK, Huang RY, Jensen A, Jones ME, Kang D, Karlan BY, Karnezis AN, Kelemen LE, Kennedy CJ, Khusnutdinova EK, Kiemeney LA, Kjaer SK, Kupryjanczyk J, Labrie M, Lambrechts D, Larson MC, Le ND, Lester J, Li L, Lubiński J, Lush M, Marks JR, Matsuo K, May T, McLaughlin JR, McNeish IA, Menon U, Missmer S, Modugno F, Moffitt M, Monteiro AN, Moysich KB, Narod SA, Nguyen-Dumont T, Odunsi K, Olsson H, Onland-Moret NC, Park SK, Pejovic T, Permuth JB, Piskorz A, Prokofyeva D, Riggan MJ, Risch HA, Rodríguez-Antona C, Rossing MA, Sandler DP, Setiawan VW, Shan K, Song H, Southey MC, Steed H, Sutphen R, Swerdlow AJ, Teo SH, Terry KL, Thompson PJ, Vestrheim Thomsen LC, Titus L, Trabert B, Travis R, Tworoger SS, Valen E, Van Nieuwenhuysen E, Edwards DV, Vierkant RA, Webb PM, Weinberg CR, Weise RM, Wentzensen N, White E, Winham SJ, Wolk A, Woo YL, Wu AH, Yan L, Yannoukakos D, Zeinomar N, Zheng W, Ziogas A, Berchuck A, Goode EL, Huntsman DG, Pearce CL, Ramus SJ, Sellers TA, Freedman ML, Lawrenson K, Schildkraut JM, Hazelett D, Plummer JT, Kar S, Jones MR, Pharoah PDP, Gayther SA. Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions. Am J Hum Genet 2024; 111:1061-1083. [PMID: 38723632 PMCID: PMC11179261 DOI: 10.1016/j.ajhg.2024.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 04/16/2024] [Accepted: 04/16/2024] [Indexed: 06/07/2024] Open
Abstract
To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value <5 × 10-8) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (p value <10-5). Fine mapping identified 4,008 CCVs in these regions, of which 1,452 CCVs were located in ovarian cancer-related chromatin marks with significant enrichment in active enhancers, active promoters, and active regions for CCVs from each EOC histotype. Transcriptome-wide association and colocalization analyses across histotypes using tissue-specific and cross-tissue datasets identified 86 candidate susceptibility genes in known EOC risk regions and 32 genes in 23 additional genomic regions that may represent novel EOC risk loci (false discovery rate <0.05). Finally, by integrating genome-wide HiChIP interactome analysis with transcriptome-wide association study (TWAS), variant effect predictor, transcription factor ChIP-seq, and motifbreakR data, we identified candidate gene-CCV interactions at each locus. This included risk loci where TWAS identified one or more candidate susceptibility genes (e.g., HOXD-AS2, HOXD8, and HOXD3 at 2q31) and other loci where no candidate gene was identified (e.g., MYC and PVT1 at 8q24) by TWAS. In summary, this study describes a functional framework and provides a greater understanding of the biological significance of risk alleles and candidate gene targets at EOC susceptibility loci identified by a genome-wide association study.
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Affiliation(s)
- Eileen O Dareng
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Simon G Coetzee
- Center for Bioinformatics and Functional Genomics and the Cedars Sinai Genomics Core, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jonathan P Tyrer
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Pei-Chen Peng
- Center for Bioinformatics and Functional Genomics and the Cedars Sinai Genomics Core, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Will Rosenow
- 3Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Stephanie Chen
- Center for Bioinformatics and Functional Genomics and the Cedars Sinai Genomics Core, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Applied Genomics, Computation and Translational Core, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Brian D Davis
- Center for Bioinformatics and Functional Genomics and the Cedars Sinai Genomics Core, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Applied Genomics, Computation and Translational Core, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Felipe Segato Dezem
- Center for Bioinformatics and Functional Genomics and the Cedars Sinai Genomics Core, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ji-Heui Seo
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; The Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Robbin Nameki
- Women's Cancer Program, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Alberto L Reyes
- Center for Bioinformatics and Functional Genomics and the Cedars Sinai Genomics Core, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Katja K H Aben
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands; Netherlands Comprehensive Cancer Organisation, Utrecht, the Netherlands
| | - Hoda Anton-Culver
- Department of Medicine, Genetic Epidemiology Research Institute, University of California, Irvine, Irvine, CA, USA
| | - Natalia N Antonenkova
- N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus
| | | | - Elisa V Bandera
- Cancer Prevention and Control Program, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Laura E Beane Freeman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | - Alicia Beeghly-Fadiel
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Javier Benitez
- Human Genetics Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain; Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Marcus Q Bernardini
- Division of Gynecologic Oncology, University Health Network, Princess Margaret Hospital, Toronto, ON, Canada
| | - Line Bjorge
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway; Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Amanda Black
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Natalia V Bogdanova
- N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus; Department of Radiation Oncology, Hannover Medical School, Hannover, Germany; Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
| | - Kelly L Bolton
- Division of Biology and Biomedical Sciences, Washington University, St. Louis, MO, USA
| | - James D Brenton
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Agnieszka Budzilowska
- Department of Pathology and Laboratory Diagnostics, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Ralf Butzow
- Department of Pathology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Hui Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Ian Campbell
- Cancer Genetics Laboratory, Research Division, Peter MacCallum Cancer Center, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Rikki Cannioto
- Cancer Pathology & Prevention, Division of Cancer Prevention and Population Sciences, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Kexin Chen
- Department of Epidemiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Georgia Chenevix-Trench
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Yoke-Eng Chiew
- Centre for Cancer Research, The Westmead Institute for Medical Research, Sydney, NSW, Australia; Department of Gynaecological Oncology, Westmead Hospital, Sydney, NSW, Australia
| | - Linda S Cook
- Epidemiology, School of Public Health, University of Colorado, Aurora, CO, USA; Community Health Sciences, University of Calgary, Calgary, AB, Canada
| | - Anna DeFazio
- Centre for Cancer Research, The Westmead Institute for Medical Research, Sydney, NSW, Australia; Department of Gynaecological Oncology, Westmead Hospital, Sydney, NSW, Australia; The Daffodil Centre, a joint venture with Cancer Council NSW, The University of Sydney, Sydney, NSW, Australia
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jennifer A Doherty
- Huntsman Cancer Institute, Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
| | - Andreas du Bois
- Department of Gynecology and Gynecological Oncology; HSK, Dr. Horst-Schmidt Klinik, Wiesbaden, Wiesbaden, Germany; Department of Gynecology and Gynecologic Oncology, Evangelische Kliniken Essen-Mitte (KEM), Essen, Germany
| | - Matthias Dürst
- Department of Gynaecology, Jena University Hospital - Friedrich Schiller University, Jena, Germany
| | - Diana M Eccles
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Gabrielle Ene
- Division of Gynecologic Oncology, University Health Network, Princess Margaret Hospital, Toronto, ON, Canada
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | - James M Flanagan
- Division of Cancer and Ovarian Cancer Action Research Centre, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Renée T Fortner
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Florentia Fostira
- Molecular Diagnostics Laboratory, INRASTES, National Centre for Scientific Research 'Demokritos', Athens, Greece
| | - Aleksandra Gentry-Maharaj
- MRC Clinical Trials Unit, Institute of Clinical Trials & Methodology, University College London, London, UK
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Marc T Goodman
- Cancer Prevention and Control Program, Cedars-Sinai Cancer, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jacek Gronwald
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Niclas Håkansson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Florian Heitz
- Department of Gynecology and Gynecological Oncology; HSK, Dr. Horst-Schmidt Klinik, Wiesbaden, Wiesbaden, Germany; Department of Gynecology and Gynecologic Oncology, Evangelische Kliniken Essen-Mitte (KEM), Essen, Germany; Center for Pathology, Evangelische Kliniken Essen-Mitte, Essen, Germany
| | | | - Estrid Høgdall
- Department of Pathology, Herlev Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Claus K Høgdall
- Department of Gynaecology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Ruea-Yea Huang
- Center For Immunotherapy, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Allan Jensen
- Department of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Michael E Jones
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Daehee Kang
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea; Cancer Research Institute, Seoul National University, Seoul, Korea
| | - Beth Y Karlan
- David Geffen School of Medicine, Department of Obstetrics and Gynecology, University of California at Los Angeles, Los Angeles, CA, USA
| | - Anthony N Karnezis
- Department of Pathology and Laboratory Medicine, UC Davis Medical Center, Sacramento, CA, USA
| | - Linda E Kelemen
- Hollings Cancer Center, Medical University of South Carolina, Charleston, SC, USA
| | - Catherine J Kennedy
- Centre for Cancer Research, The Westmead Institute for Medical Research, Sydney, NSW, Australia; Department of Gynaecological Oncology, Westmead Hospital, Sydney, NSW, Australia; The University of Sydney, Sydney, NSW, Australia
| | - Elza K Khusnutdinova
- Institute of Biochemistry and Genetics of the Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia; Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, Russia
| | - Lambertus A Kiemeney
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Susanne K Kjaer
- Department of Gynaecology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark; Department of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Jolanta Kupryjanczyk
- Department of Pathology and Laboratory Diagnostics, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Marilyne Labrie
- Department of Immunology and Cell Biology, FMSS - Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Diether Lambrechts
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium; VIB Center for Cancer Biology, VIB, Leuven, Belgium
| | - Melissa C Larson
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, USA
| | - Nhu D Le
- Cancer Control Research, BC Cancer, Vancouver, BC, Canada
| | - Jenny Lester
- David Geffen School of Medicine, Department of Obstetrics and Gynecology, University of California at Los Angeles, Los Angeles, CA, USA
| | - Lian Li
- Department of Epidemiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Jan Lubiński
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jeffrey R Marks
- Department of Surgery, Duke University Hospital, Durham, NC, USA
| | - Keitaro Matsuo
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan; Division of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Taymaa May
- Division of Gynecologic Oncology, University Health Network, Princess Margaret Hospital, Toronto, ON, Canada
| | - John R McLaughlin
- Public Health Ontario, Samuel Lunenfeld Research Institute, Toronto, ON, Canada
| | - Iain A McNeish
- Division of Cancer and Ovarian Cancer Action Research Centre, Department Surgery & Cancer, Imperial College London, London, UK; Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - Usha Menon
- MRC Clinical Trials Unit, Institute of Clinical Trials & Methodology, University College London, London, UK
| | - Stacey Missmer
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Francesmary Modugno
- Women's Cancer Research Center, Magee-Womens Research Institute and Hillman Cancer Center, Pittsburgh, PA, USA; Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Melissa Moffitt
- Department of Gynecologic Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Alvaro N Monteiro
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Kirsten B Moysich
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Steven A Narod
- Women's College Research Institute, University of Toronto, Toronto, ON, Canada
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia; Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia
| | - Kunle Odunsi
- University of Chicago Medicine Comprehensive Cancer Center, Chicago, IL, USA; Department of Obstetrics and Gynecology, University of Chicago, Chicago, IL, USA
| | - Håkan Olsson
- Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - N Charlotte Onland-Moret
- Julius Center for Health Sciences and Primary Care, University Utrecht, UMC Utrecht, Utrecht, the Netherlands
| | - Sue K Park
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea; Cancer Research Institute, Seoul National University, Seoul, Korea; Integrated Major in Innovative Medical Science, Seoul National University College of Medicine, Seoul, South Korea
| | - Tanja Pejovic
- Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, OR, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Jennifer B Permuth
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Anna Piskorz
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Darya Prokofyeva
- Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, Russia
| | - Marjorie J Riggan
- Department of Gynecologic Oncology, Duke University Hospital, Durham, NC, USA
| | - Harvey A Risch
- Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA
| | - Cristina Rodríguez-Antona
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain; Hereditary Endocrine Cancer Group, Spanish National Cancer Research Center (CNIO), Madrid, Spain
| | - Mary Anne Rossing
- Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - V Wendy Setiawan
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kang Shan
- Department of Obstetrics and Gynaecology, Hebei Medical University, Fourth Hospital, Shijiazhuang, China
| | - Honglin Song
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia; Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia
| | - Helen Steed
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Alberta, Edmonton, AB, Canada; Section of Gynecologic Oncology Surgery, Alberta Health Services, North Zone, Edmonton, AB, Canada
| | - Rebecca Sutphen
- Epidemiology Center, College of Medicine, University of South Florida, Tampa, FL, USA
| | - Anthony J Swerdlow
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK; Division of Breast Cancer Research, The Institute of Cancer Research, London, UK
| | - Soo Hwang Teo
- Breast Cancer Research Programme, Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia; Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Kathryn L Terry
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics and Gyneoclogy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Pamela J Thompson
- Samuel Oschin Comprehensive Cancer Institute, Cancer Prevention and Genetics Program, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Liv Cecilie Vestrheim Thomsen
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway; Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Linda Titus
- Muskie School of Public Service, University of Southern Maine, Portland, ME, USA
| | - Britton Trabert
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Ruth Travis
- Cancer Epidemiology Unit, University of Oxford, Oxford, UK
| | - Shelley S Tworoger
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Ellen Valen
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway; Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Els Van Nieuwenhuysen
- Division of Gynecologic Oncology, Department of Gynecology and Obstetrics, Leuven Cancer Institute, Leuven, Belgium
| | - Digna Velez Edwards
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Department of Biomedical Sciences, Women's Health Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Robert A Vierkant
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, USA
| | - Penelope M Webb
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Clarice R Weinberg
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Rayna Matsuno Weise
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Nicolas Wentzensen
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Emily White
- Department of Epidemiology, University of Washington, Seattle, WA, USA; Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Stacey J Winham
- Department of Quantitative Health Sciences, Division of Computational Biology, Mayo Clinic, Rochester, MN, USA
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Yin-Ling Woo
- Department of Obstetrics and Gynaecology, University of Malaya Medical Centre, University of Malaya, Kuala Lumpur, Malaysia
| | - Anna H Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Li Yan
- Department of Molecular Biology, Hebei Medical University, Fourth Hospital, Shijiazhuang, China
| | - Drakoulis Yannoukakos
- Molecular Diagnostics Laboratory, INRASTES, National Centre for Scientific Research 'Demokritos', Athens, Greece
| | - Nur Zeinomar
- Cancer Prevention and Control Program, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Argyrios Ziogas
- Department of Medicine, Genetic Epidemiology Research Institute, University of California, Irvine, Irvine, CA, USA
| | - Andrew Berchuck
- Department of Gynecologic Oncology, Duke University Hospital, Durham, NC, USA
| | - Ellen L Goode
- Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA
| | - David G Huntsman
- Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, BC, Canada; Department of Molecular Oncology, BC Cancer Research Centre, Vancouver, BC, Canada
| | - Celeste L Pearce
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA, USA; Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Susan J Ramus
- School of Women's and Children's Health, Faculty of Medicine and Health, University of NSW Sydney, Sydney, NSW, Australia; Adult Cancer Program, Lowy Cancer Research Centre, University of NSW Sydney, Sydney, NSW, Australia
| | | | - Matthew L Freedman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; The Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kate Lawrenson
- Women's Cancer Program, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Joellen M Schildkraut
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Dennis Hazelett
- Samuel Oschin Comprehensive Cancer Institute, The Center for Bioinformatics and Functional Biology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jasmine T Plummer
- Center for Bioinformatics and Functional Genomics and the Cedars Sinai Genomics Core, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Applied Genomics, Computation and Translational Core, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Siddhartha Kar
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Section of Translational Epidemiology, Division of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Michelle R Jones
- Center for Bioinformatics and Functional Genomics and the Cedars Sinai Genomics Core, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
| | - Simon A Gayther
- Center for Bioinformatics and Functional Genomics and the Cedars Sinai Genomics Core, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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10
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Reiner BC, Chehimi SN, Merkel R, Toikumo S, Berrettini WH, Kranzler HR, Sanchez-Roige S, Kember RL, Schmidt HD, Crist RC. A single-nucleus transcriptomic atlas of medium spiny neurons in the rat nucleus accumbens. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.26.595949. [PMID: 38826289 PMCID: PMC11142250 DOI: 10.1101/2024.05.26.595949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Neural processing of rewarding stimuli involves several distinct regions, including the nucleus accumbens (NAc). The majority of NAc neurons are GABAergic projection neurons known as medium spiny neurons (MSNs). MSNs are broadly defined by dopamine receptor expression, but evidence suggests that a wider array of subtypes exist. To study MSN heterogeneity, we analyzed single-nucleus RNA sequencing data from the largest available rat NAc dataset. Analysis of 48,040 NAc MSN nuclei identified major populations belonging to the striosome and matrix compartments. Integration with mouse and human data indicated consistency across species and disease-relevance scoring using genome-wide association study results revealed potentially differential roles for MSN populations in substance use disorders. Additional high-resolution clustering identified 34 transcriptomically distinct subtypes of MSNs definable by a limited number of marker genes. Together, these data demonstrate the diversity of MSNs in the NAc and provide a basis for more targeted genetic manipulation of specific populations.
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11
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Daskalakis NP, Iatrou A, Chatzinakos C, Jajoo A, Snijders C, Wylie D, DiPietro CP, Tsatsani I, Chen CY, Pernia CD, Soliva-Estruch M, Arasappan D, Bharadwaj RA, Collado-Torres L, Wuchty S, Alvarez VE, Dammer EB, Deep-Soboslay A, Duong DM, Eagles N, Huber BR, Huuki L, Holstein VL, Logue ΜW, Lugenbühl JF, Maihofer AX, Miller MW, Nievergelt CM, Pertea G, Ross D, Sendi MSE, Sun BB, Tao R, Tooke J, Wolf EJ, Zeier Z, Berretta S, Champagne FA, Hyde T, Seyfried NT, Shin JH, Weinberger DR, Nemeroff CB, Kleinman JE, Ressler KJ. Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood. Science 2024; 384:eadh3707. [PMID: 38781393 PMCID: PMC11203158 DOI: 10.1126/science.adh3707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 04/05/2024] [Indexed: 05/25/2024]
Abstract
The molecular pathology of stress-related disorders remains elusive. Our brain multiregion, multiomic study of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) included the central nucleus of the amygdala, hippocampal dentate gyrus, and medial prefrontal cortex (mPFC). Genes and exons within the mPFC carried most disease signals replicated across two independent cohorts. Pathways pointed to immune function, neuronal and synaptic regulation, and stress hormones. Multiomic factor and gene network analyses provided the underlying genomic structure. Single nucleus RNA sequencing in dorsolateral PFC revealed dysregulated (stress-related) signals in neuronal and non-neuronal cell types. Analyses of brain-blood intersections in >50,000 UK Biobank participants were conducted along with fine-mapping of the results of PTSD and MDD genome-wide association studies to distinguish risk from disease processes. Our data suggest shared and distinct molecular pathology in both disorders and propose potential therapeutic targets and biomarkers.
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Affiliation(s)
- Nikolaos P. Daskalakis
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Artemis Iatrou
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Chris Chatzinakos
- McLean Hospital; Belmont, MA, 02478, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, 11203, USA
- VA New York Harbor Healthcare System, Brooklyn, NY, 11209, USA
| | - Aarti Jajoo
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Clara Snijders
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Dennis Wylie
- Center for Biomedical Research Support, The University of Texas at Austin; Austin, TX, 78712, USA
| | - Christopher P. DiPietro
- McLean Hospital; Belmont, MA, 02478, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Ioulia Tsatsani
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
- Department of Psychiatry and Neuropsychology, School for Mental Health, and Neuroscience (MHeNs), Maastricht University, Maastricht, 6229 ER, The Netherlands
| | | | - Cameron D. Pernia
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Marina Soliva-Estruch
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
- Department of Psychiatry and Neuropsychology, School for Mental Health, and Neuroscience (MHeNs), Maastricht University, Maastricht, 6229 ER, The Netherlands
| | - Dhivya Arasappan
- Center for Biomedical Research Support, The University of Texas at Austin; Austin, TX, 78712, USA
| | - Rahul A. Bharadwaj
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Stefan Wuchty
- Departments of Computer Science, University of Miami, Miami, FL, 33146, USA
- Department of Biology, University of Miami, Miami, FL, 33146, USA
| | - Victor E. Alvarez
- Department of Neurology, Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
- VA Bedford Healthcare System, Bedford, MA, 01730, USA
- National Posttraumatic Stress Disorder Brain Bank, VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Eric B Dammer
- Department of Biochemistry, Center for Neurodegenerative Disease, Emory School of Medicine; Atlanta GA, 30329, USA
| | - Amy Deep-Soboslay
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Duc M. Duong
- Department of Biochemistry, Center for Neurodegenerative Disease, Emory School of Medicine; Atlanta GA, 30329, USA
| | - Nick Eagles
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Bertrand R. Huber
- Department of Neurology, Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
- National Posttraumatic Stress Disorder Brain Bank, VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Louise Huuki
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Vincent L Holstein
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Μark W. Logue
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
- Department of Biomedical Genetics, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Justina F. Lugenbühl
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
- Department of Psychiatry and Neuropsychology, School for Mental Health, and Neuroscience (MHeNs), Maastricht University, Maastricht, 6229 ER, The Netherlands
| | - Adam X. Maihofer
- Department of Psychiatry, University of California San Diego; La Jolla, CA, 92093, USA
- Center for Excellence in Stress and Mental Health, Veterans Affairs San Diego Healthcare System; San Diego, CA, 92161, USA
- Research Service, Veterans Affairs San Diego Healthcare System; San Diego, CA, 92161, USA
| | - Mark W. Miller
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Caroline M Nievergelt
- Department of Psychiatry, University of California San Diego; La Jolla, CA, 92093, USA
- Center for Excellence in Stress and Mental Health, Veterans Affairs San Diego Healthcare System; San Diego, CA, 92161, USA
- Research Service, Veterans Affairs San Diego Healthcare System; San Diego, CA, 92161, USA
| | - Geo Pertea
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Deanna Ross
- Department of Psychology, University of Texas at Austin; Austin, TX, 78712, USA
| | - Mohammad S. E Sendi
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | | | - Ran Tao
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - James Tooke
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Erika J. Wolf
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Zane Zeier
- Department of Psychiatry & Behavioral Sciences, Center for Therapeutic Innovation, University of Miami Miller School of Medicine; Miami, FL, 33136, USA
| | | | - Sabina Berretta
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | | | - Thomas Hyde
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
| | - Nicholas T. Seyfried
- Department of Biochemistry, Center for Neurodegenerative Disease, Emory School of Medicine; Atlanta GA, 30329, USA
| | - Joo Heon Shin
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
| | - Charles B. Nemeroff
- Department of Psychology, University of Texas at Austin; Austin, TX, 78712, USA
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin; Austin, TX, 78712, 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 University School of Medicine; Baltimore, MD, 21205, USA
| | - Kerry J. Ressler
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
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12
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Zhang S, Shu H, Zhou J, Rubin-Sigler J, Yang X, Liu Y, Cooper-Knock J, Monte E, Zhu C, Tu S, Li H, Tong M, Ecker JR, Ichida JK, Shen Y, Zeng J, Tsao PS, Snyder MP. Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594252. [PMID: 38798507 PMCID: PMC11118500 DOI: 10.1101/2024.05.14.594252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.
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Affiliation(s)
- Sai Zhang
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
- Departments of Biostatistics & Biomedical Engineering, Genetics Institute, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Hantao Shu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jingtian Zhou
- Arc Institute, Palo Alto, CA, USA
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jasper Rubin-Sigler
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Xiaoyu Yang
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Yuxi Liu
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Emma Monte
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Chenchen Zhu
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sharon Tu
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Han Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Mingming Tong
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Justin K. Ichida
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Yin Shen
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Jianyang Zeng
- School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Philip S. Tsao
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael P. Snyder
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
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13
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Qi T, Song L, Guo Y, Chen C, Yang J. From genetic associations to genes: methods, applications, and challenges. Trends Genet 2024:S0168-9525(24)00095-7. [PMID: 38734482 DOI: 10.1016/j.tig.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/13/2024]
Abstract
Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.
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Affiliation(s)
- Ting Qi
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
| | - Liyang Song
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Yazhou Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Chang Chen
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Jian Yang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
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14
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Chiñas M, Fernandez-Salinas D, Aguiar VRC, Nieto-Caballero VE, Lefton M, Nigrovic PA, Ermann J, Gutierrez-Arcelus M. Functional genomics implicates natural killer cells in the pathogenesis of ankylosing spondylitis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.09.21.23295912. [PMID: 37808698 PMCID: PMC10557806 DOI: 10.1101/2023.09.21.23295912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Objective Multiple lines of evidence indicate that ankylosing spondylitis (AS) is a lymphocyte-driven disease. However, which lymphocyte populations are critical in AS pathogenesis is not known. In this study, we aimed to identify the key cell types mediating the genetic risk in AS using an unbiased functional genomics approach. Methods We integrated genome-wide association study (GWAS) data with epigenomic and transcriptomic datasets of human immune cells. To quantify enrichment of cell type-specific open chromatin or gene expression in AS risk loci, we used three published methods that have successfully identified relevant cell types in other diseases. We performed co-localization analyses between GWAS risk loci and genetic variants associated with gene expression (eQTL) to find putative target genes. Results Natural killer (NK) cell-specific open chromatin regions are significantly enriched in heritability for AS, compared to other immune cell types such as T cells, B cells, and monocytes. This finding was consistent between two AS GWAS. Using RNA-seq data, we validated that genes in AS risk loci are enriched in NK cell-specific gene expression. Using the human Space-Time Gut Cell Atlas, we also found significant upregulation of AS-associated genes predominantly in NK cells. Co-localization analysis revealed four AS risk loci affecting regulation of candidate target genes in NK cells: two known loci, ERAP1 and TNFRSF1A, and two under-studied loci, ENTR1 (aka SDCCAG3) and B3GNT2. Conclusion Our findings suggest that NK cells may play a crucial role in AS development and highlight four putative target genes for functional follow-up in NK cells.
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Affiliation(s)
- Marcos Chiñas
- Division of Immunology, Boston Children’s Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Daniela Fernandez-Salinas
- Division of Immunology, Boston Children’s Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Licenciatura en Ciencias Genomicas, Centro de Ciencias Genomicas, Universidad Nacional Autónoma de México (UNAM), Morelos 62210, Mexico
| | - Vitor R. C. Aguiar
- Division of Immunology, Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Victor E. Nieto-Caballero
- Division of Immunology, Boston Children’s Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Licenciatura en Ciencias Genomicas, Centro de Ciencias Genomicas, Universidad Nacional Autónoma de México (UNAM), Morelos 62210, Mexico
| | - Micah Lefton
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Peter A. Nigrovic
- Division of Immunology, Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Joerg Ermann
- Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Maria Gutierrez-Arcelus
- Division of Immunology, Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
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15
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Lai Q, Dannenfelser R, Roussarie JP, Yao V. Disentangling associations between complex traits and cell types with seismic. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.04.592534. [PMID: 38765980 PMCID: PMC11100625 DOI: 10.1101/2024.05.04.592534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Integrating single-cell RNA sequencing (scRNA-seq) with Genome-Wide Association Studies (GWAS) can help reveal GWAS-associated cell types, furthering our understanding of the cell-type-specific biological processes underlying complex traits and disease. However, current methods have technical limitations that hinder them from making systematic, scalable, interpretable disease-cell-type associations. In order to rapidly and accurately pinpoint associations, we develop a novel framework, seismic, which characterizes cell types using a new specificity score. We compare seismic with alternative methods across over 1,000 cell type characterizations at different granularities and 28 traits, demonstrating that seismic both corroborates findings and identifies trait-relevant cell groups which are not apparent through other methodologies. Furthermore, as part of the seismic framework, the specific genes driving cell type-trait associations can easily be accessed and analyzed, enabling further biological insights. The advantages of seismic are particularly salient in neurodegenerative diseases such as Parkinson's and Alzheimer's, where disease pathology has not only cell-specific manifestations, but also brain region-specific differences. Interestingly, a case study of Alzheimer's disease reveals the importance of considering GWAS endpoints, as studies relying on clinical diagnoses consistently identify microglial associations, while GWAS with a tau biomarker endpoint reveals neuronal associations. In general, seismic is a computationally efficient, powerful, and interpretable approach for identifying associations between complex traits and cell type-specific expression.
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Affiliation(s)
- Qiliang Lai
- Department of Computer Science, Rice University
| | | | | | - Vicky Yao
- Department of Computer Science, Rice University
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16
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Popp JM, Rhodes K, Jangi R, Li M, Barr K, Tayeb K, Battle A, Gilad Y. Cell-type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating cultures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.02.592174. [PMID: 38746382 PMCID: PMC11092595 DOI: 10.1101/2024.05.02.592174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Identifying the molecular effects of human genetic variation across cellular contexts is crucial for understanding the mechanisms underlying disease-associated loci, yet many cell-types and developmental stages remain underexplored. Here we harnessed the potential of heterogeneous differentiating cultures ( HDCs ), an in vitro system in which pluripotent cells asynchronously differentiate into a broad spectrum of cell-types. We generated HDCs for 53 human donors and collected single-cell RNA-sequencing data from over 900,000 cells. We identified expression quantitative trait loci in 29 cell-types and characterized regulatory dynamics across diverse differentiation trajectories. This revealed novel regulatory variants for genes involved in key developmental and disease-related processes while replicating known effects from primary tissues, and dynamic regulatory effects associated with a range of complex traits.
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17
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Zhang K, Kan H, Mao A, Yu F, Geng L, Zhou T, Feng L, Ma X. Integrated Single-Cell Transcriptomic Atlas of Human Kidney Endothelial Cells. J Am Soc Nephrol 2024; 35:578-593. [PMID: 38351505 PMCID: PMC11149048 DOI: 10.1681/asn.0000000000000320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 02/09/2024] [Indexed: 03/23/2024] Open
Abstract
Key Points We created a comprehensive reference atlas of normal human kidney endothelial cells. We confirmed that endothelial cell types in the human kidney were also highly conserved in the mouse kidney. Background Kidney endothelial cells are exposed to different microenvironmental conditions that support specific physiologic processes. However, the heterogeneity of human kidney endothelial cells has not yet been systematically described. Methods We reprocessed and integrated seven human kidney control single-cell/single-nucleus RNA sequencing datasets of >200,000 kidney cells in the same process. Results We identified five major cell types, 29,992 of which were endothelial cells. Endothelial cell reclustering identified seven subgroups that differed in molecular characteristics and physiologic functions. Mapping new data to a normal kidney endothelial cell atlas allows rapid data annotation and analysis. We confirmed that endothelial cell types in the human kidney were also highly conserved in the mouse kidney and identified endothelial marker genes that were conserved in humans and mice, as well as differentially expressed genes between corresponding subpopulations. Furthermore, combined analysis of single-cell transcriptome data with public genome-wide association study data showed a significant enrichment of endothelial cells, especially arterial endothelial cells, in BP heritability. Finally, we identified M1 and M12 from coexpression networks in endothelial cells that may be deeply involved in BP regulation. Conclusions We created a comprehensive reference atlas of normal human kidney endothelial cells that provides the molecular foundation for understanding how the identity and function of kidney endothelial cells are altered in disease, aging, and between species. Finally, we provide a publicly accessible online tool to explore the datasets described in this work (https://vascularmap.jiangnan.edu.cn ).
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Affiliation(s)
- Ka Zhang
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Hao Kan
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Aiqin Mao
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Fan Yu
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Li Geng
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Tingting Zhou
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Lei Feng
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Xin Ma
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
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18
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Pineda SS, Lee H, Ulloa-Navas MJ, Linville RM, Garcia FJ, Galani K, Engelberg-Cook E, Castanedes MC, Fitzwalter BE, Pregent LJ, Gardashli ME, DeTure M, Vera-Garcia DV, Hucke ATS, Oskarsson BE, Murray ME, Dickson DW, Heiman M, Belzil VV, Kellis M. Single-cell dissection of the human motor and prefrontal cortices in ALS and FTLD. Cell 2024; 187:1971-1989.e16. [PMID: 38521060 PMCID: PMC11086986 DOI: 10.1016/j.cell.2024.02.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 11/09/2023] [Accepted: 02/23/2024] [Indexed: 03/25/2024]
Abstract
Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) share many clinical, pathological, and genetic features, but a detailed understanding of their associated transcriptional alterations across vulnerable cortical cell types is lacking. Here, we report a high-resolution, comparative single-cell molecular atlas of the human primary motor and dorsolateral prefrontal cortices and their transcriptional alterations in sporadic and familial ALS and FTLD. By integrating transcriptional and genetic information, we identify known and previously unidentified vulnerable populations in cortical layer 5 and show that ALS- and FTLD-implicated motor and spindle neurons possess a virtually indistinguishable molecular identity. We implicate potential disease mechanisms affecting these cell types as well as non-neuronal drivers of pathogenesis. Finally, we show that neuron loss in cortical layer 5 tracks more closely with transcriptional identity rather than cellular morphology and extends beyond previously reported vulnerable cell types.
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Affiliation(s)
- S Sebastian Pineda
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Hyeseung Lee
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Raleigh M Linville
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Francisco J Garcia
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Kyriakitsa Galani
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | | | | | - Brent E Fitzwalter
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Luc J Pregent
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | - Michael DeTure
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | - Andre T S Hucke
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Dennis W Dickson
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Myriam Heiman
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | | | - Manolis Kellis
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.
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19
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Liu C, Chen Z, Zhang Z, Wang Z, Guo X, Pan Y, Wang Q. Unveiling the Genetic Mechanism of Meat Color in Pigs through GWAS, Multi-Tissue, and Single-Cell Transcriptome Signatures Exploration. Int J Mol Sci 2024; 25:3682. [PMID: 38612491 PMCID: PMC11012088 DOI: 10.3390/ijms25073682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 03/22/2024] [Accepted: 03/23/2024] [Indexed: 04/14/2024] Open
Abstract
Meat color traits directly influence consumer acceptability and purchasing decisions. Nevertheless, there is a paucity of comprehensive investigation into the genetic mechanisms underlying meat color traits in pigs. Utilizing genome-wide association studies (GWAS) on five meat color traits and the detection of selection signatures in pig breeds exhibiting distinct meat color characteristics, we identified a promising candidate SNP, 6_69103754, exhibiting varying allele frequencies among pigs with different meat color characteristics. This SNP has the potential to affect the redness and chroma index values of pork. Moreover, transcriptome-wide association studies (TWAS) analysis revealed the expression of candidate genes associated with meat color traits in specific tissues. Notably, the largest number of candidate genes were observed from transcripts derived from adipose, liver, lung, spleen tissues, and macrophage cell type, indicating their crucial role in meat color development. Several shared genes associated with redness, yellowness, and chroma indices traits were identified, including RINL in adipose tissue, ENSSSCG00000034844 and ITIH1 in liver tissue, TPX2 and MFAP2 in lung tissue, and ZBTB17, FAM131C, KIFC3, NTPCR, and ENGSSSCG00000045605 in spleen tissue. Furthermore, single-cell enrichment analysis revealed a significant association between the immune system and meat color. This finding underscores the significance of the immune system associated with meat color. Overall, our study provides a comprehensive analysis of the genetic mechanisms underlying meat color traits, offering valuable insights for future breeding efforts aimed at improving meat quality.
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Affiliation(s)
- Cheng Liu
- Department of Animal Science, College of Animal Science, Zhejiang University, 866# Yuhangtang Road, Hangzhou 310058, China; (C.L.); (Z.C.); (Z.Z.); (Z.W.); (X.G.); (Y.P.)
| | - Zitao Chen
- Department of Animal Science, College of Animal Science, Zhejiang University, 866# Yuhangtang Road, Hangzhou 310058, China; (C.L.); (Z.C.); (Z.Z.); (Z.W.); (X.G.); (Y.P.)
| | - Zhe Zhang
- Department of Animal Science, College of Animal Science, Zhejiang University, 866# Yuhangtang Road, Hangzhou 310058, China; (C.L.); (Z.C.); (Z.Z.); (Z.W.); (X.G.); (Y.P.)
| | - Zhen Wang
- Department of Animal Science, College of Animal Science, Zhejiang University, 866# Yuhangtang Road, Hangzhou 310058, China; (C.L.); (Z.C.); (Z.Z.); (Z.W.); (X.G.); (Y.P.)
| | - Xiaoling Guo
- Department of Animal Science, College of Animal Science, Zhejiang University, 866# Yuhangtang Road, Hangzhou 310058, China; (C.L.); (Z.C.); (Z.Z.); (Z.W.); (X.G.); (Y.P.)
| | - Yuchun Pan
- Department of Animal Science, College of Animal Science, Zhejiang University, 866# Yuhangtang Road, Hangzhou 310058, China; (C.L.); (Z.C.); (Z.Z.); (Z.W.); (X.G.); (Y.P.)
- Hainan Institute, Zhejiang University, Yongyou Industry Park, Yazhou Bay Sci-Tech City, Sanya 572000, China
| | - Qishan Wang
- Department of Animal Science, College of Animal Science, Zhejiang University, 866# Yuhangtang Road, Hangzhou 310058, China; (C.L.); (Z.C.); (Z.Z.); (Z.W.); (X.G.); (Y.P.)
- Hainan Institute, Zhejiang University, Yongyou Industry Park, Yazhou Bay Sci-Tech City, Sanya 572000, China
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20
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Eshel M, Milon B, Hertzano R, Elkon R. The cells of the sensory epithelium, and not the stria vascularis, are the main cochlear cells related to the genetic pathogenesis of age-related hearing loss. Am J Hum Genet 2024; 111:614-617. [PMID: 38330941 PMCID: PMC10940011 DOI: 10.1016/j.ajhg.2024.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 01/18/2024] [Accepted: 01/19/2024] [Indexed: 02/10/2024] Open
Abstract
Age-related hearing loss (ARHL) is a major health concern among the elderly population. It is hoped that increasing our understanding of its underlying pathophysiological processes will lead to the development of novel therapies. Recent genome-wide association studies (GWASs) discovered a few dozen genetic variants in association with elevated risk for ARHL. Integrated analysis of GWAS results and transcriptomics data is a powerful approach for elucidating specific cell types that are involved in disease pathogenesis. Intriguingly, recent studies that applied such bioinformatics approaches to ARHL resulted in disagreeing findings as for the key cell types that are most strongly linked to the genetic pathogenesis of ARHL. These conflicting studies pointed either to cochlear sensory epithelial or to stria vascularis cells as the cell types most prominently involved in the genetic basis of ARHL. Seeking to resolve this discrepancy, we integrated the analysis of four ARHL GWAS datasets with four independent inner-ear single-cell RNA-sequencing datasets. Our analysis clearly points to the cochlear sensory epithelial cells as the key cells for the genetic predisposition to ARHL. We also explain the limitation of the bioinformatics analysis performed by previous studies that led to missing the enrichment for ARHL GWAS signal in sensory epithelial cells. Collectively, we show that cochlear epithelial cells, not stria vascularis cells, are the main inner-ear cells related to the genetic pathogenesis of ARHL.
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Affiliation(s)
- Mai Eshel
- Department of Human Molecular Genetics and Biochemistry, School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Beatrice Milon
- Neurotology Branch, NIDCD, National Institutes of Health, Bethesda, MD, USA
| | - Ronna Hertzano
- Neurotology Branch, NIDCD, National Institutes of Health, Bethesda, MD, USA.
| | - Ran Elkon
- Department of Human Molecular Genetics and Biochemistry, School of Medicine, Tel Aviv University, Tel Aviv, Israel.
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21
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Valentim WL, Tylee DS, Polimanti R. A perspective on translating genomic discoveries into targets for brain-machine interface and deep brain stimulation devices. WIREs Mech Dis 2024; 16:e1635. [PMID: 38059513 PMCID: PMC11163995 DOI: 10.1002/wsbm.1635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 10/22/2023] [Accepted: 11/17/2023] [Indexed: 12/08/2023]
Abstract
Mental illnesses have a huge impact on individuals, families, and society, so there is a growing need for more efficient treatments. In this context, brain-computer interface (BCI) technology has the potential to revolutionize the options for neuropsychiatric therapies. However, the development of BCI-based therapies faces enormous challenges, such as power dissipation constraints, lack of credible feedback mechanisms, uncertainty of which brain areas and frequencies to target, and even which patients to treat. Some of these setbacks are due to the large gap in our understanding of brain function. In recent years, large-scale genomic analyses uncovered an unprecedented amount of information regarding the biology of the altered brain function observed across the psychopathology spectrum. We believe findings from genetic studies can be useful to refine BCI technology to develop novel treatment options for mental illnesses. Here, we assess the latest advancements in both fields, the possibilities that can be generated from their intersection, and the challenges that these research areas will need to address to ensure that translational efforts can lead to effective and reliable interventions. Specifically, starting from highlighting the overlap between mechanisms uncovered by large-scale genetic studies and the current targets of deep brain stimulation treatments, we describe the steps that could help to translate genomic discoveries into BCI targets. Because these two research areas have not been previously presented together, the present article can provide a novel perspective for scientists with different research backgrounds. This article is categorized under: Neurological Diseases > Genetics/Genomics/Epigenetics Neurological Diseases > Biomedical Engineering.
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Affiliation(s)
- Wander L. Valentim
- Faculty of Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
| | - Daniel S. Tylee
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
- VA CT Healthcare Center, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
- VA CT Healthcare Center, West Haven, CT, USA
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22
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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] [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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23
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Ma Y, Zhou Y, Jiang D, Dai W, Li J, Deng C, Chen C, Zheng G, Zhang Y, Qiu F, Sun H, Xing S, Han H, Qu J, Wu N, Yao Y, Su J. Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19. Cell Prolif 2024; 57:e13558. [PMID: 37807299 PMCID: PMC10905359 DOI: 10.1111/cpr.13558] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/31/2023] [Accepted: 09/18/2023] [Indexed: 10/10/2023] Open
Abstract
Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene-drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID-19) severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID-19 outcomes. Notably, subset of lung mesenchymal stem cells increased proximity with fibroblasts predisposed to repair COVID-19-damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID-19, and this cell subset showed a notable increase in cell-to-cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID-19 in a cell-type-specific manner. Overall, our results showcase that host genetic determinants have cellular-specific contribution to COVID-19 severity, and identification of cell type-specific drug targets may facilitate to develop effective therapeutics for treating severe COVID-19 and its complications.
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Affiliation(s)
- Yunlong Ma
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Yijun Zhou
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Dingping Jiang
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Wei Dai
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China
| | - Jingjing Li
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Chunyu Deng
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Cheng Chen
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Gongwei Zheng
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Yaru Zhang
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Fei Qiu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Haojun Sun
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Shilai Xing
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Haijun Han
- School of Medicine, Hangzhou City University, Hangzhou, China
| | - Jia Qu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Nan Wu
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Key Laboratory of Big Data for Spinal Deformities, Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yinghao Yao
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Jianzhong Su
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
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24
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Shi Y, Huang L, Dong H, Yang M, Ding W, Zhou X, Lu T, Liu Z, Zhou X, Wang M, Zeng B, Sun Y, Zhong S, Wang B, Wang W, Yin C, Wang X, Wu Q. Decoding the spatiotemporal regulation of transcription factors during human spinal cord development. Cell Res 2024; 34:193-213. [PMID: 38177242 PMCID: PMC10907391 DOI: 10.1038/s41422-023-00897-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 11/02/2023] [Indexed: 01/06/2024] Open
Abstract
The spinal cord is a crucial component of the central nervous system that facilitates sensory processing and motor performance. Despite its importance, the spatiotemporal codes underlying human spinal cord development have remained elusive. In this study, we have introduced an image-based single-cell transcription factor (TF) expression decoding spatial transcriptome method (TF-seqFISH) to investigate the spatial expression and regulation of TFs during human spinal cord development. By combining spatial transcriptomic data from TF-seqFISH and single-cell RNA-sequencing data, we uncovered the spatial distribution of neural progenitor cells characterized by combinatorial TFs along the dorsoventral axis, as well as the molecular and spatial features governing neuronal generation, migration, and differentiation along the mediolateral axis. Notably, we observed a sandwich-like organization of excitatory and inhibitory interneurons transiently appearing in the dorsal horns of the developing human spinal cord. In addition, we integrated data from 10× Visium to identify early and late waves of neurogenesis in the dorsal horn, revealing the formation of laminas in the dorsal horns. Our study also illuminated the spatial differences and molecular cues underlying motor neuron (MN) diversification, and the enrichment of Amyotrophic Lateral Sclerosis (ALS) risk genes in MNs and microglia. Interestingly, we detected disease-associated microglia (DAM)-like microglia groups in the developing human spinal cord, which are predicted to be vulnerable to ALS and engaged in the TYROBP causal network and response to unfolded proteins. These findings provide spatiotemporal transcriptomic resources on the developing human spinal cord and potential strategies for spinal cord injury repair and ALS treatment.
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Affiliation(s)
- Yingchao Shi
- Guangdong Institute of Intelligence Science and Technology, Guangdong, China.
| | - Luwei Huang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hao Dong
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Meng Yang
- Changping Laboratory, Beijing, China
| | - Wenyu Ding
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, New Cornerstone Science Laboratory, Beijing Normal University, Beijing, China
| | - Xiang Zhou
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Tian Lu
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | - Xin Zhou
- Changping Laboratory, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, New Cornerstone Science Laboratory, Beijing Normal University, Beijing, China
| | - Mengdi Wang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Bo Zeng
- Changping Laboratory, Beijing, China
| | - Yinuo Sun
- Changping Laboratory, Beijing, China
| | - Suijuan Zhong
- Changping Laboratory, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, New Cornerstone Science Laboratory, Beijing Normal University, Beijing, China
| | - Bosong Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, New Cornerstone Science Laboratory, Beijing Normal University, Beijing, China
| | - Wei Wang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | - Xiaoqun Wang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.
- Changping Laboratory, Beijing, China.
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, New Cornerstone Science Laboratory, Beijing Normal University, Beijing, China.
| | - Qian Wu
- Changping Laboratory, Beijing, China.
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, New Cornerstone Science Laboratory, Beijing Normal University, Beijing, China.
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25
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Maj C, Eberts A, Schumacher J, Dasmeh P. Single-cell analysis reveals the spatial-temporal expression of genes associated with esophageal malformations. Sci Rep 2024; 14:3752. [PMID: 38355689 PMCID: PMC10866870 DOI: 10.1038/s41598-024-53098-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 01/27/2024] [Indexed: 02/16/2024] Open
Abstract
Understanding the molecular mechanisms of congenital diseases is challenging due to their occurrence within specific developmental stages. Esophageal malformations are examples of such conditions, characterized by abnormalities in the development of esophagus during embryogenesis. These developmental malformations encompass a range of anomalies, including esophageal atresia, and tracheoesophageal fistula. Here, we investigated the preferential expression of 29 genes that are implicated in such malformations and their immediate interactome (a total of 67 genes). We conducted our analyses across several single-cell atlases of embryonic development, encompassing approximately 150,000 cells from the mouse foregut, 180,000 cells from human embryos, and 500,000 cells from 24 human organs. Our study, spanning diverse mesodermal and endodermal cell populations and early developmental stages, shows that the genes associated with esophageal malformations show their highest cell-type specific expression in lateral plate mesoderm cells and at the developmental stage of E8.75-E9.0 days. In human embryos, these genes show a significant cell-type specific expression among subpopulations of epithelial cells, fibroblasts and progenitor cells including basal cells. Notably, members of the forkhead-box family of transcription factors, namely FOXF1, FOXC1, and FOXD1, as well as the SRY-box transcription factor, SOX2, demonstrate the most significant preferential expression in both mouse and human embryos. Overall, our findings provide insights into the temporal and cellular contexts contributing to esophageal malformations.
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Affiliation(s)
- Carlo Maj
- Center for Human Genetics, Marburg University and Marburg University Hospital, Marburg, Germany.
| | - Antonia Eberts
- Center for Human Genetics, Marburg University and Marburg University Hospital, Marburg, Germany
| | - Johannes Schumacher
- Center for Human Genetics, Marburg University and Marburg University Hospital, Marburg, Germany.
| | - Pouria Dasmeh
- Center for Human Genetics, Marburg University and Marburg University Hospital, Marburg, Germany.
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, USA.
- Institute for Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.
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26
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Balasubramanian R, Kizhatil K, Li T, Tolman N, Bhandari A, Clark G, Bupp-Chickering V, Kelly RA, Zhou S, Peregrin J, Simón M, Montgomery C, Stamer WD, Qian J, John SWM. Transcriptomic profiling of Schlemm's canal cells reveals a lymphatic-biased identity and three major cell states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.31.555823. [PMID: 37886472 PMCID: PMC10602040 DOI: 10.1101/2023.08.31.555823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Schlemm's canal (SC) is central in intraocular pressure regulation but requires much characterization. It has distinct inner and outer walls, each composed of Schlemm's canal endothelial cells (SECs) with different morphologies and functions. Recent transcriptomic studies of the anterior segment added important knowledge, but were limited in power by SEC numbers or did not focus on SC. To gain a more comprehensive understanding of SC biology, we performed bulk RNA sequencing on C57BL/6J SC, blood vessel, and lymphatic endothelial cells from limbal tissue (~4500 SECs). We also analyzed mouse limbal tissues by single-cell and single-nucleus RNA sequencing (C57BL/6J and 129/Sj strains), successfully sequencing 903 individual SECs. Together, these datasets confirm that SC has molecular characteristics of both blood and lymphatic endothelia with a lymphatic phenotype predominating. SECs are enriched in pathways that regulate cell-cell junction formation pointing to the importance of junctions in determining SC fluid permeability. Importantly, and for the first time, our analyses characterize 3 molecular classes of SECs, molecularly distinguishing inner wall from outer wall SECs and discovering two inner wall cell states that likely result from local environmental differences. Further, and based on ligand and receptor expression patterns, we document key interactions between SECs and cells of the adjacent trabecular meshwork (TM) drainage tissue. Also, we present cell type expression for a collection of human glaucoma genes. These data provide a new molecular foundation that will enable the functional dissection of key homeostatic processes mediated by SECs as well as the development of new glaucoma therapeutics.
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Affiliation(s)
| | | | - Taibo Li
- Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, MD
| | - Nicholas Tolman
- Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA
| | - Aakriti Bhandari
- Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY
- Neuroscience Graduate Program, University of Utah, Salt Lake City, UT
| | | | | | - Ruth A Kelly
- Department of Ophthalmology, Duke University, NC
| | - Sally Zhou
- Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY
- SUNY Downstate Health Sciences University, New York, NY
| | - John Peregrin
- Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY
| | - Marina Simón
- Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY
| | - Christa Montgomery
- Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY
| | | | - Jiang Qian
- Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, MD
| | - Simon W M John
- Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY
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27
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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 : THE PREPRINT SERVER FOR HEALTH SCIENCES 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] [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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28
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Kim SS, Truong B, Jagadeesh K, Dey KK, Shen AZ, Raychaudhuri S, Kellis M, Price AL. Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types. Nat Commun 2024; 15:563. [PMID: 38233398 PMCID: PMC10794712 DOI: 10.1038/s41467-024-44742-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 01/02/2024] [Indexed: 01/19/2024] Open
Abstract
Prioritizing disease-critical cell types by integrating genome-wide association studies (GWAS) with functional data is a fundamental goal. Single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) have characterized cell types at high resolution, and studies integrating GWAS with scRNA-seq have shown promise, but studies integrating GWAS with scATAC-seq have been limited. Here, we identify disease-critical fetal and adult brain cell types by integrating GWAS summary statistics from 28 brain-related diseases/traits (average N = 298 K) with 3.2 million scATAC-seq and scRNA-seq profiles from 83 cell types. We identified disease-critical fetal (respectively adult) brain cell types for 22 (respectively 23) of 28 traits using scATAC-seq, and for 8 (respectively 17) of 28 traits using scRNA-seq. Significant scATAC-seq enrichments included fetal photoreceptor cells for major depressive disorder, fetal ganglion cells for BMI, fetal astrocytes for ADHD, and adult VGLUT2 excitatory neurons for schizophrenia. Our findings improve our understanding of brain-related diseases/traits and inform future analyses.
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Affiliation(s)
- Samuel S Kim
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, UK.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK.
| | - Buu Truong
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, UK.
| | - Karthik Jagadeesh
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK
| | - Kushal K Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amber Z Shen
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Manolis Kellis
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, UK
| | - Alkes L Price
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, UK.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, UK.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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29
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Hekselman I, Vital A, Ziv-Agam M, Kerber L, Yairi I, Yeger-Lotem E. Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data. eLife 2024; 13:e84613. [PMID: 38197427 PMCID: PMC10830129 DOI: 10.7554/elife.84613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/08/2023] [Indexed: 01/11/2024] Open
Abstract
Mendelian diseases tend to manifest clinically in certain tissues, yet their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in Mendelian diseases. Overall, we inferred the likely affected cell types for 328 diseases. We corroborated our findings by literature text-mining, expert validation, and recapitulation in mouse corresponding tissues. Based on these findings, we explored characteristics of disease-affected cell types, showed that diseases manifesting in multiple tissues tend to affect similar cell types, and highlighted cases where gene functions could be used to refine inference. Together, these findings expand the molecular understanding of disease mechanisms and cellular vulnerability.
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Affiliation(s)
- Idan Hekselman
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
| | - Assaf Vital
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
| | - Maya Ziv-Agam
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
| | - Lior Kerber
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
| | - Ido Yairi
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
- The National Institute for Biotechnology in the Negev, Ben-Gurion University of the NegevBe’er ShevaIsrael
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30
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Hamel AR, Yan W, Rouhana JM, Monovarfeshani A, Jiang X, Mehta PA, Advani J, Luo Y, Liang Q, Rajasundaram S, Shrivastava A, Duchinski K, Mantena S, Wang J, van Zyl T, Pasquale LR, Swaroop A, Gharahkhani P, Khawaja AP, MacGregor S, Chen R, Vitart V, Sanes JR, Wiggs JL, Segrè AV. Integrating genetic regulation and single-cell expression with GWAS prioritizes causal genes and cell types for glaucoma. Nat Commun 2024; 15:396. [PMID: 38195602 PMCID: PMC10776627 DOI: 10.1038/s41467-023-44380-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 12/12/2023] [Indexed: 01/11/2024] Open
Abstract
Primary open-angle glaucoma (POAG), characterized by retinal ganglion cell death, is a leading cause of irreversible blindness worldwide. However, its molecular and cellular causes are not well understood. Elevated intraocular pressure (IOP) is a major risk factor, but many patients have normal IOP. Colocalization and Mendelian randomization analysis of >240 POAG and IOP genome-wide association study (GWAS) loci and overlapping expression and splicing quantitative trait loci (e/sQTLs) in 49 GTEx tissues and retina prioritizes causal genes for 60% of loci. These genes are enriched in pathways implicated in extracellular matrix organization, cell adhesion, and vascular development. Analysis of single-nucleus RNA-seq of glaucoma-relevant eye tissues reveals that the POAG and IOP colocalizing genes and genome-wide associations are enriched in specific cell types in the aqueous outflow pathways, retina, optic nerve head, peripapillary sclera, and choroid. This study nominates IOP-dependent and independent regulatory mechanisms, genes, and cell types that may contribute to POAG pathogenesis.
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Affiliation(s)
- Andrew R Hamel
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Wenjun Yan
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - John M Rouhana
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Aboozar Monovarfeshani
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Xinyi Jiang
- MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, UK
| | - Puja A Mehta
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jayshree Advani
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MA, USA
| | - Yuyang Luo
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Qingnan Liang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Skanda Rajasundaram
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Centre for Evidence-Based Medicine, University of Oxford, Oxford, UK
- Faculty of Medicine, Imperial College London, London, UK
| | - Arushi Shrivastava
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Katherine Duchinski
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Bioinformatics and Integrative Genomics (BIG) PhD Program, Harvard Medical School, Boston, MA, USA
| | - Sreekar Mantena
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Harvard/MIT MD-PhD Program, Harvard Medical School, Boston, MA, USA
| | - Jiali Wang
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Tavé van Zyl
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Ophthalmology and Visual Sciences, Yale School of Medicine, New Haven, CT, USA
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Anand Swaroop
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MA, USA
| | - Puya Gharahkhani
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4029, Australia
| | - Anthony P Khawaja
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4029, Australia
| | - Rui Chen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Joshua R Sanes
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Janey L Wiggs
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Ayellet V Segrè
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA.
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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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] [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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Mosquera JV, Auguste G, Wong D, Turner AW, Hodonsky CJ, Alvarez-Yela AC, Song Y, Cheng Q, Lino Cardenas CL, Theofilatos K, Bos M, Kavousi M, Peyser PA, Mayr M, Kovacic JC, Björkegren JLM, Malhotra R, Stukenberg PT, Finn AV, van der Laan SW, Zang C, Sheffield NC, Miller CL. Integrative single-cell meta-analysis reveals disease-relevant vascular cell states and markers in human atherosclerosis. Cell Rep 2023; 42:113380. [PMID: 37950869 DOI: 10.1016/j.celrep.2023.113380] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 09/12/2023] [Accepted: 10/20/2023] [Indexed: 11/13/2023] Open
Abstract
Coronary artery disease (CAD) is characterized by atherosclerotic plaque formation in the arterial wall. CAD progression involves complex interactions and phenotypic plasticity among vascular and immune cell lineages. Single-cell RNA-seq (scRNA-seq) studies have highlighted lineage-specific transcriptomic signatures, but human cell phenotypes remain controversial. Here, we perform an integrated meta-analysis of 22 scRNA-seq libraries to generate a comprehensive map of human atherosclerosis with 118,578 cells. Besides characterizing granular cell-type diversity and communication, we leverage this atlas to provide insights into smooth muscle cell (SMC) modulation. We integrate genome-wide association study data and uncover a critical role for modulated SMC phenotypes in CAD, myocardial infarction, and coronary calcification. Finally, we identify fibromyocyte/fibrochondrogenic SMC markers (LTBP1 and CRTAC1) as proxies of atherosclerosis progression and validate these through omics and spatial imaging analyses. Altogether, we create a unified atlas of human atherosclerosis informing cell state-specific mechanistic and translational studies of cardiovascular diseases.
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Affiliation(s)
- Jose Verdezoto Mosquera
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA; Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Gaëlle Auguste
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Doris Wong
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA; Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Adam W Turner
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Chani J Hodonsky
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | | | - Yipei Song
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Computer Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Qi Cheng
- CVPath Institute, Gaithersburg, MD 20878, USA
| | - Christian L Lino Cardenas
- Cardiovascular Research Center, Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | | | - Maxime Bos
- Department of Epidemiology, Erasmus University Medical Center, 3000 CA Rotterdam, the Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, 3000 CA Rotterdam, the Netherlands
| | - Patricia A Peyser
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48019, USA
| | - Manuel Mayr
- King's British Heart Foundation Centre, King's College London, London WC2R 2LS, UK; National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK
| | - Jason C Kovacic
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia; St. Vincent's Clinical School, University of New South Wales, Sydney, NSW 2052, Australia
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Medicine, Karolinska Institutet, 141 52 Huddinge, Sweden
| | - Rajeev Malhotra
- Cardiovascular Research Center, Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - P Todd Stukenberg
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
| | | | - Sander W van der Laan
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Chongzhi Zang
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA; Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Nathan C Sheffield
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA; Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Clint L Miller
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA; Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA.
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33
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Li P, Wei J, Zhu Y. CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation. Brief Bioinform 2023; 25:bbad417. [PMID: 37995133 PMCID: PMC10790717 DOI: 10.1093/bib/bbad417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/09/2023] [Accepted: 10/29/2023] [Indexed: 11/25/2023] Open
Abstract
Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to consider the critical biological context, such as tissue or cell-type specificity. To address this limitation, we introduced CellGO. CellGO tackles this challenge by leveraging the visible neural network (VNN) and single-cell gene expressions to mimic cell-type-specific signaling propagation along the Gene Ontology tree within a cell. This design enables a novel scoring system to calculate the cell-type-specific gene-pathway paired active scores, based on which, CellGO is able to identify cell-type-specific active pathways associated with single genes. In addition, by aggregating the activities of single genes, CellGO extends its capability to identify cell-type-specific active pathways for a given gene set. To enhance biological interpretation, CellGO offers additional features, including the identification of significantly active cell types and driver genes and community analysis of pathways. To validate its performance, CellGO was assessed using a gene set comprising mixed cell-type markers, confirming its ability to discern active pathways across distinct cell types. Subsequent benchmarking analyses demonstrated CellGO's superiority in effectively identifying cell types and their corresponding cell-type-specific pathways affected by gene knockouts, using either single genes or sets of genes differentially expressed between knockout and control samples. Moreover, CellGO demonstrated its ability to infer cell-type-specific pathogenesis for disease risk genes. Accessible as a Python package, CellGO also provides a user-friendly web interface, making it a versatile and accessible tool for researchers in the field.
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Affiliation(s)
- Peilong Li
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200032, China
| | - Junfeng Wei
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200032, China
| | - Ying Zhu
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200032, China
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34
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Tseng KC, Crump JG. Craniofacial developmental biology in the single-cell era. Development 2023; 150:dev202077. [PMID: 37812056 PMCID: PMC10617621 DOI: 10.1242/dev.202077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
The evolution of a unique craniofacial complex in vertebrates made possible new ways of breathing, eating, communicating and sensing the environment. The head and face develop through interactions of all three germ layers, the endoderm, ectoderm and mesoderm, as well as the so-called fourth germ layer, the cranial neural crest. Over a century of experimental embryology and genetics have revealed an incredible diversity of cell types derived from each germ layer, signaling pathways and genes that coordinate craniofacial development, and how changes to these underlie human disease and vertebrate evolution. Yet for many diseases and congenital anomalies, we have an incomplete picture of the causative genomic changes, in particular how alterations to the non-coding genome might affect craniofacial gene expression. Emerging genomics and single-cell technologies provide an opportunity to obtain a more holistic view of the genes and gene regulatory elements orchestrating craniofacial development across vertebrates. These single-cell studies generate novel hypotheses that can be experimentally validated in vivo. In this Review, we highlight recent advances in single-cell studies of diverse craniofacial structures, as well as potential pitfalls and the need for extensive in vivo validation. We discuss how these studies inform the developmental sources and regulation of head structures, bringing new insights into the etiology of structural birth anomalies that affect the vertebrate head.
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Affiliation(s)
- Kuo-Chang Tseng
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA
| | - J. Gage Crump
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA
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35
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Shi Q, Chen X, Zhang Z. Decoding Human Biology and Disease Using Single-cell Omics Technologies. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:926-949. [PMID: 37739168 PMCID: PMC10928380 DOI: 10.1016/j.gpb.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/22/2023] [Accepted: 06/08/2023] [Indexed: 09/24/2023]
Abstract
Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.
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Affiliation(s)
- Qiang Shi
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Xueyan Chen
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Zemin Zhang
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Changping Laboratory, Beijing 102206, China.
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36
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Ma Y, Deng C, Zhou Y, Zhang Y, Qiu F, Jiang D, Zheng G, Li J, Shuai J, Zhang Y, Yang J, Su J. Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data. CELL GENOMICS 2023; 3:100383. [PMID: 37719150 PMCID: PMC10504677 DOI: 10.1016/j.xgen.2023.100383] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/26/2023] [Accepted: 07/25/2023] [Indexed: 09/19/2023]
Abstract
Advances in single-cell RNA sequencing (scRNA-seq) techniques have accelerated functional interpretation of disease-associated variants discovered from genome-wide association studies (GWASs). However, identification of trait-relevant cell populations is often impeded by inherent technical noise and high sparsity in scRNA-seq data. Here, we developed scPagwas, a computational approach that uncovers trait-relevant cellular context by integrating pathway activation transformation of scRNA-seq data and GWAS summary statistics. scPagwas effectively prioritizes trait-relevant genes, which facilitates identification of trait-relevant cell types/populations with high accuracy in extensive simulated and real datasets. Cellular-level association results identified a novel subpopulation of naive CD8+ T cells related to COVID-19 severity and oligodendrocyte progenitor cell and microglia subsets with critical pathways by which genetic variants influence Alzheimer's disease. Overall, our approach provides new insights for the discovery of trait-relevant cell types and improves the mechanistic understanding of disease variants from a pathway perspective.
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Affiliation(s)
- Yunlong Ma
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Chunyu Deng
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150080, China
| | - Yijun Zhou
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Yaru Zhang
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Fei Qiu
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Dingping Jiang
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Gongwei Zheng
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Jingjing Li
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Jianwei Shuai
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Yan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150080, China
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310012, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Jianzhong Su
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
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37
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Amariuta T, Siewert-Rocks K, Price AL. Modeling tissue co-regulation estimates tissue-specific contributions to disease. Nat Genet 2023; 55:1503-1511. [PMID: 37580597 PMCID: PMC10904330 DOI: 10.1038/s41588-023-01474-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 07/13/2023] [Indexed: 08/16/2023]
Abstract
Integrative analyses of genome-wide association studies and gene expression data have implicated many disease-critical tissues. However, co-regulation of genetic effects on gene expression across tissues impedes distinguishing biologically causal tissues from tagging tissues. In the present study, we introduce tissue co-regulation score regression (TCSC), which disentangles causal tissues from tagging tissues by regressing gene-disease association statistics (from transcriptome-wide association studies) on tissue co-regulation scores, reflecting correlations of predicted gene expression across genes and tissues. We applied TCSC to 78 diseases/traits (average n = 302,000) and gene expression prediction models for 48 GTEx tissues. TCSC identified 21 causal tissue-trait pairs at a 5% false discovery rate (FDR), including well-established findings, biologically plausible new findings (for example, aorta artery and glaucoma) and increased specificity of known tissue-trait associations (for example, subcutaneous adipose, but not visceral adipose, and high-density lipoprotein). TCSC also identified 17 causal tissue-trait covariance pairs at 5% FDR. In conclusion, TCSC is a precise method for distinguishing causal tissues from tagging tissues.
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Affiliation(s)
- Tiffany Amariuta
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA.
- Department of Medicine, University of California San Diego, La Jolla, CA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Katherine Siewert-Rocks
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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38
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Fass SB, Mulvey B, Yang W, Selmanovic D, Chaturvedi S, Tycksen E, Weiss LA, Dougherty JD. Relationship between sex biases in gene expression and sex biases in autism and Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.29.23294773. [PMID: 37693465 PMCID: PMC10491382 DOI: 10.1101/2023.08.29.23294773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Sex differences in the brain may play an important role in sex-differential prevalence of neuropsychiatric conditions. In order to understand the transcriptional basis of sex differences, we analyzed multiple, large-scale, human postmortem brain RNA-seq datasets using both within-region and pan-regional frameworks. We find evidence of sex-biased transcription in many autosomal genes, some of which provide evidence for pathways and cell population differences between chromosomally male and female individuals. These analyses also highlight regional differences in the extent of sex-differential gene expression. We observe an increase in specific neuronal transcripts in male brains and an increase in immune and glial function-related transcripts in female brains. Integration with single-cell data suggests this corresponds to sex differences in cellular states rather than cell abundance. Integration with case-control gene expression studies suggests a female molecular predisposition towards Alzheimer's disease, a female-biased disease. Autism, a male-biased diagnosis, does not exhibit a male predisposition pattern in our analysis. Finally, we provide region specific analyses of sex differences in brain gene expression to enable additional studies at the interface of gene expression and diagnostic differences.
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Affiliation(s)
- Stuart B Fass
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
| | - Bernard Mulvey
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- Lieber Institute for Brain Development, 855 North Wolfe St. Ste 300, Baltimore, MD 21205, USA
| | - Wei Yang
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Din Selmanovic
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
| | - Sneha Chaturvedi
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
| | - Eric Tycksen
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Lauren A Weiss
- Institute for Human Genetics, University of California, San Francisco, 513 Parnassus Ave, HSE901, San Francisco, CA 94143
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, 513 Parnassus Ave, HSE901, San Francisco, CA 94143
- Weill Institute for Neurosciences, University of California, San Francisco, 513 Parnassus Ave, HSE901, San Francisco, CA 94143
| | - Joseph D Dougherty
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- Intellectual and Developmental Disabilities Research Center, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
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39
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Cuomo ASE, Nathan A, Raychaudhuri S, MacArthur DG, Powell JE. Single-cell genomics meets human genetics. Nat Rev Genet 2023; 24:535-549. [PMID: 37085594 PMCID: PMC10784789 DOI: 10.1038/s41576-023-00599-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/29/2023] [Indexed: 04/23/2023]
Abstract
Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.
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Affiliation(s)
- Anna S E Cuomo
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph E Powell
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia.
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40
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Lee EY, Dai Z, Jaiswal A, Wang EHC, Anandasabapathy N, Christiano AM. Functional interrogation of lymphocyte subsets in alopecia areata using single-cell RNA sequencing. Proc Natl Acad Sci U S A 2023; 120:e2305764120. [PMID: 37428932 PMCID: PMC10629527 DOI: 10.1073/pnas.2305764120] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 06/05/2023] [Indexed: 07/12/2023] Open
Abstract
Alopecia areata (AA) is among the most prevalent autoimmune diseases, but the development of innovative therapeutic strategies has lagged due to an incomplete understanding of the immunological underpinnings of disease. Here, we performed single-cell RNA sequencing (scRNAseq) of skin-infiltrating immune cells from the graft-induced C3H/HeJ mouse model of AA, coupled with antibody-based depletion to interrogate the functional role of specific cell types in AA in vivo. Since AA is predominantly T cell-mediated, we focused on dissecting lymphocyte function in AA. Both our scRNAseq and functional studies established CD8+ T cells as the primary disease-driving cell type in AA. Only the depletion of CD8+ T cells, but not CD4+ T cells, NK, B, or γδ T cells, was sufficient to prevent and reverse AA. Selective depletion of regulatory T cells (Treg) showed that Treg are protective against AA in C3H/HeJ mice, suggesting that failure of Treg-mediated immunosuppression is not a major disease mechanism in AA. Focused analyses of CD8+ T cells revealed five subsets, whose heterogeneity is defined by an "effectorness gradient" of interrelated transcriptional states that culminate in increased effector function and tissue residency. scRNAseq of human AA skin showed that CD8+ T cells in human AA follow a similar trajectory, underscoring that shared mechanisms drive disease in both murine and human AA. Our study represents a comprehensive, systematic interrogation of lymphocyte heterogeneity in AA and uncovers a novel framework for AA-associated CD8+ T cells with implications for the design of future therapeutics.
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Affiliation(s)
- Eunice Y. Lee
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY10032
- Medical Scientist Training Program, Columbia University, New York, NY10032
| | - Zhenpeng Dai
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY10032
| | - Abhinav Jaiswal
- Department of Dermatology, Weill Cornell Medicine, New York, NY10021
- Department of Microbiology and Immunology, Weill Cornell Medicine, New York, NY10065
| | - Eddy Hsi Chun Wang
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY10032
| | - Niroshana Anandasabapathy
- Department of Dermatology, Weill Cornell Medicine, New York, NY10021
- Department of Microbiology and Immunology, Weill Cornell Medicine, New York, NY10065
| | - Angela M. Christiano
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY10032
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY10032
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41
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Morabito S, Reese F, Rahimzadeh N, Miyoshi E, Swarup V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. CELL REPORTS METHODS 2023; 3:100498. [PMID: 37426759 PMCID: PMC10326379 DOI: 10.1016/j.crmeth.2023.100498] [Citation(s) in RCA: 63] [Impact Index Per Article: 63.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 02/13/2023] [Accepted: 05/16/2023] [Indexed: 07/11/2023]
Abstract
Biological systems are immensely complex, organized into a multi-scale hierarchy of functional units based on tightly regulated interactions between distinct molecules, cells, organs, and organisms. While experimental methods enable transcriptome-wide measurements across millions of cells, popular bioinformatic tools do not support systems-level analysis. Here we present hdWGCNA, a comprehensive framework for analyzing co-expression networks in high-dimensional transcriptomics data such as single-cell and spatial RNA sequencing (RNA-seq). hdWGCNA provides functions for network inference, gene module identification, gene enrichment analysis, statistical tests, and data visualization. Beyond conventional single-cell RNA-seq, hdWGCNA is capable of performing isoform-level network analysis using long-read single-cell data. We showcase hdWGCNA using data from autism spectrum disorder and Alzheimer's disease brain samples, identifying disease-relevant co-expression network modules. hdWGCNA is directly compatible with Seurat, a widely used R package for single-cell and spatial transcriptomics analysis, and we demonstrate the scalability of hdWGCNA by analyzing a dataset containing nearly 1 million cells.
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Affiliation(s)
- Samuel Morabito
- Mathematical, Computational, and Systems Biology (MCSB) Program, University of California, Irvine, Irvine, CA, USA
- Center for Complex Biological Systems (CCBS), University of California, Irvine, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, Irvine, CA, USA
| | - Fairlie Reese
- Center for Complex Biological Systems (CCBS), University of California, Irvine, Irvine, CA, USA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
| | - Negin Rahimzadeh
- Mathematical, Computational, and Systems Biology (MCSB) Program, University of California, Irvine, Irvine, CA, USA
- Center for Complex Biological Systems (CCBS), University of California, Irvine, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, Irvine, CA, USA
| | - Emily Miyoshi
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, Irvine, CA, USA
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Vivek Swarup
- Center for Complex Biological Systems (CCBS), University of California, Irvine, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, Irvine, CA, USA
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
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42
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Sato G, Shirai Y, Namba S, Edahiro R, Sonehara K, Hata T, Uemura M, Matsuda K, Doki Y, Eguchi H, Okada Y. Pan-cancer and cross-population genome-wide association studies dissect shared genetic backgrounds underlying carcinogenesis. Nat Commun 2023; 14:3671. [PMID: 37340002 DOI: 10.1038/s41467-023-39136-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 05/31/2023] [Indexed: 06/22/2023] Open
Abstract
Integrating genomic data of multiple cancers allows de novo cancer grouping and elucidating the shared genetic basis across cancers. Here, we conduct the pan-cancer and cross-population genome-wide association study (GWAS) meta-analysis and replication studies on 13 cancers including 250,015 East Asians (Biobank Japan) and 377,441 Europeans (UK Biobank). We identify ten cancer risk variants including five pleiotropic associations (e.g., rs2076295 at DSP on 6p24 associated with lung cancer and rs2525548 at TRIM4 on 7q22 nominally associated with six cancers). Quantifying shared heritability among the cancers detects positive genetic correlations between breast and prostate cancer across populations. Common genetic components increase the statistical power, and the large-scale meta-analysis of 277,896 breast/prostate cancer cases and 901,858 controls identifies 91 newly genome-wide significant loci. Enrichment analysis of pathways and cell types reveals shared genetic backgrounds across said cancers. Focusing on genetically correlated cancers can contribute to enhancing our insights into carcinogenesis.
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Affiliation(s)
- Go Sato
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
| | - Tsuyoshi Hata
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Mamoru Uemura
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo, Tokyo, Japan
| | - Yuichiro Doki
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Hidetoshi Eguchi
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan.
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43
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Van de Sande B, Lee JS, Mutasa-Gottgens E, Naughton B, Bacon W, Manning J, Wang Y, Pollard J, Mendez M, Hill J, Kumar N, Cao X, Chen X, Khaladkar M, Wen J, Leach A, Ferran E. Applications of single-cell RNA sequencing in drug discovery and development. Nat Rev Drug Discov 2023; 22:496-520. [PMID: 37117846 PMCID: PMC10141847 DOI: 10.1038/s41573-023-00688-4] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/10/2023] [Indexed: 04/30/2023]
Abstract
Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.
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Affiliation(s)
| | | | | | - Bart Naughton
- Computational Neurobiology, Eisai, Cambridge, MA, USA
| | - Wendi Bacon
- EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
- The Open University, Milton Keynes, UK
| | | | - Yong Wang
- Precision Bioinformatics, Prometheus Biosciences, San Diego, CA, USA
| | | | - Melissa Mendez
- Genomic Sciences, GlaxoSmithKline, Collegeville, PA, USA
| | - Jon Hill
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, USA
| | - Namit Kumar
- Informatics & Predictive Sciences, Bristol Myers Squibb, San Diego, CA, USA
| | - Xiaohong Cao
- Genomic Research Center, AbbVie Inc., Cambridge, MA, USA
| | - Xiao Chen
- Magnet Biomedicine, Cambridge, MA, USA
| | - Mugdha Khaladkar
- Human Genetics and Computational Biology, GlaxoSmithKline, Collegeville, PA, USA
| | - Ji Wen
- Oncology Research and Development Unit, Pfizer, La Jolla, CA, USA
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44
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Innate immune cell genetic risk factors are linked to COVID-19 severity. Nat Genet 2023; 55:742-743. [PMID: 37147488 PMCID: PMC10161972 DOI: 10.1038/s41588-023-01378-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
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45
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Edahiro R, Shirai Y, Takeshima Y, Sakakibara S, Yamaguchi Y, Murakami T, Morita T, Kato Y, Liu YC, Motooka D, Naito Y, Takuwa A, Sugihara F, Tanaka K, Wing JB, Sonehara K, Tomofuji Y, Namkoong H, Tanaka H, Lee H, Fukunaga K, Hirata H, Takeda Y, Okuzaki D, Kumanogoh A, Okada Y. Single-cell analyses and host genetics highlight the role of innate immune cells in COVID-19 severity. Nat Genet 2023; 55:753-767. [PMID: 37095364 PMCID: PMC10181941 DOI: 10.1038/s41588-023-01375-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/15/2023] [Indexed: 04/26/2023]
Abstract
Mechanisms underpinning the dysfunctional immune response in severe acute respiratory syndrome coronavirus 2 infection are elusive. We analyzed single-cell transcriptomes and T and B cell receptors (BCR) of >895,000 peripheral blood mononuclear cells from 73 coronavirus disease 2019 (COVID-19) patients and 75 healthy controls of Japanese ancestry with host genetic data. COVID-19 patients showed a low fraction of nonclassical monocytes (ncMono). We report downregulated cell transitions from classical monocytes to ncMono in COVID-19 with reduced CXCL10 expression in ncMono in severe disease. Cell-cell communication analysis inferred decreased cellular interactions involving ncMono in severe COVID-19. Clonal expansions of BCR were evident in the plasmablasts of patients. Putative disease genes identified by COVID-19 genome-wide association study showed cell type-specific expressions in monocytes and dendritic cells. A COVID-19-associated risk variant at the IFNAR2 locus (rs13050728) had context-specific and monocyte-specific expression quantitative trait loci effects. Our study highlights biological and host genetic involvement of innate immune cells in COVID-19 severity.
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Affiliation(s)
- Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Yusuke Takeshima
- Laboratory of Experimental Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Shuhei Sakakibara
- Laboratory of Immune Regulation, Immunology Frontier Research Center, Osaka University, Suita, Japan
| | - Yuta Yamaguchi
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Teruaki Murakami
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Takayoshi Morita
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Yasuhiro Kato
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Yu-Chen Liu
- Laboratory of Human Immunology (Single Cell Genomics), WPI Immunology Frontier Research Center, Osaka University, Suita, Japan
| | - Daisuke Motooka
- Laboratory of Human Immunology (Single Cell Genomics), WPI Immunology Frontier Research Center, Osaka University, Suita, Japan
- Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
| | - Yoko Naito
- Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Ayako Takuwa
- Laboratory of Human Immunology (Single Cell Genomics), WPI Immunology Frontier Research Center, Osaka University, Suita, Japan
| | - Fuminori Sugihara
- Core Instrumentation Facility, Immunology Frontier Research Center and Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Kentaro Tanaka
- Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - James B Wing
- Laboratory of Human Immunology (Single Cell Immunology), Immunology Frontier Research Center, Osaka University, Suita, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yoshihiko Tomofuji
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Ho Namkoong
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan
| | - Hiromu Tanaka
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Ho Lee
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Haruhiko Hirata
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yoshito Takeda
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Daisuke Okuzaki
- Laboratory of Human Immunology (Single Cell Genomics), WPI Immunology Frontier Research Center, Osaka University, Suita, Japan
- Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
- Japan Agency for Medical Research and Development - Core Research for Evolutional Science and Technology (AMED-CREST), Osaka University, Osaka, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan.
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan.
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan.
- Japan Agency for Medical Research and Development - Core Research for Evolutional Science and Technology (AMED-CREST), Osaka University, Osaka, Japan.
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan.
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
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46
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Chehimi SN, Crist RC, Reiner BC. Unraveling Psychiatric Disorders through Neural Single-Cell Transcriptomics Approaches. Genes (Basel) 2023; 14:genes14030771. [PMID: 36981041 PMCID: PMC10047992 DOI: 10.3390/genes14030771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 03/17/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
The development of single-cell and single-nucleus transcriptome technologies is enabling the unraveling of the molecular and cellular heterogeneity of psychiatric disorders. The complexity of the brain and the relationships between different brain regions can be better understood through the classification of individual cell populations based on their molecular markers and transcriptomic features. Analysis of these unique cell types can explain their involvement in the pathology of psychiatric disorders. Recent studies in both human and animal models have emphasized the importance of transcriptome analysis of neuronal cells in psychiatric disorders but also revealed critical roles for non-neuronal cells, such as oligodendrocytes and microglia. In this review, we update current findings on the brain transcriptome and explore molecular studies addressing transcriptomic alterations identified in human and animal models in depression and stress, neurodegenerative disorders (Parkinson's and Alzheimer's disease), schizophrenia, opioid use disorder, and alcohol and psychostimulant abuse. We also comment on potential future directions in single-cell and single-nucleus studies.
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Affiliation(s)
- Samar N Chehimi
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Richard C Crist
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Benjamin C Reiner
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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47
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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, Cable DM, Norton T, Mitra P, Chen F, Macosko EZ. The cell type composition of the adult mouse brain revealed by single cell and spatial genomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.06.531307. [PMID: 36945580 PMCID: PMC10028805 DOI: 10.1101/2023.03.06.531307] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
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-seq with Slide-seq-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 signaling, 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 (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)
| | | | | | | | - 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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48
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Morris DW. Cell-Specific Gene Expression in the Prenatal Brain and Schizophrenia Risk. Biol Psychiatry 2023; 93:105-106. [PMID: 36517174 DOI: 10.1016/j.biopsych.2022.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 10/24/2022] [Indexed: 12/14/2022]
Affiliation(s)
- Derek W Morris
- Centre for Neuroimaging, Cognition and Genomics, School of Biological and Chemical Sciences, University of Galway, Galway, Ireland.
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49
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Saez A, Herrero-Fernandez B, Gomez-Bris R, Sánchez-Martinez H, Gonzalez-Granado JM. Pathophysiology of Inflammatory Bowel Disease: Innate Immune System. Int J Mol Sci 2023; 24:ijms24021526. [PMID: 36675038 PMCID: PMC9863490 DOI: 10.3390/ijms24021526] [Citation(s) in RCA: 74] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 12/30/2022] [Accepted: 01/10/2023] [Indexed: 01/15/2023] Open
Abstract
Inflammatory bowel disease (IBD), comprising Crohn's disease (CD) and ulcerative colitis (UC), is a heterogeneous state of chronic intestinal inflammation with no exact known cause. Intestinal innate immunity is enacted by neutrophils, monocytes, macrophages, and dendritic cells (DCs), and innate lymphoid cells and NK cells, characterized by their capacity to produce a rapid and nonspecific reaction as a first-line response. Innate immune cells (IIC) defend against pathogens and excessive entry of intestinal microorganisms, while preserving immune tolerance to resident intestinal microbiota. Changes to this equilibrium are linked to intestinal inflammation in the gut and IBD. IICs mediate host defense responses, inflammation, and tissue healing by producing cytokines and chemokines, activating the complement cascade and phagocytosis, or presenting antigens to activate the adaptive immune response. IICs exert important functions that promote or ameliorate the cellular and molecular mechanisms that underlie and sustain IBD. A comprehensive understanding of the mechanisms underlying these clinical manifestations will be important for developing therapies targeting the innate immune system in IBD patients. This review examines the complex roles of and interactions among IICs, and their interactions with other immune and non-immune cells in homeostasis and pathological conditions.
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Affiliation(s)
- Angela Saez
- LamImSys Lab, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), 28041 Madrid, Spain
- Facultad de Ciencias Experimentales, Universidad Francisco de Vitoria (UFV), 28223 Pozuelo de Alarcón, Spain
| | - Beatriz Herrero-Fernandez
- LamImSys Lab, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), 28041 Madrid, Spain
- Departamento de Fisiología, Facultad de Medicina, Universidad Autónoma de Madrid (UAM), 28029 Madrid, Spain
| | - Raquel Gomez-Bris
- LamImSys Lab, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), 28041 Madrid, Spain
- Departamento de Fisiología, Facultad de Medicina, Universidad Autónoma de Madrid (UAM), 28029 Madrid, Spain
| | - Hector Sánchez-Martinez
- LamImSys Lab, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), 28041 Madrid, Spain
| | - Jose M. Gonzalez-Granado
- LamImSys Lab, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), 28041 Madrid, Spain
- Department of Immunology, Ophthalmology and ENT, School of Medicine, Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain
- CIBER de Enfermedades Cardiovasculares (CIBERCV), 28029 Madrid, Spain
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain
- Correspondence: ; Tel.: +34-913908766
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