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Zu S, Li YE, Wang K, Armand EJ, Mamde S, Amaral ML, Wang Y, Chu A, Xie Y, Miller M, Xu J, Wang Z, Zhang K, Jia B, Hou X, Lin L, Yang Q, Lee S, Li B, Kuan S, Liu H, Zhou J, Pinto-Duarte A, Lucero J, Osteen J, Nunn M, Smith KA, Tasic B, Yao Z, Zeng H, Wang Z, Shang J, Behrens MM, Ecker JR, Wang A, Preissl S, Ren B. Single-cell analysis of chromatin accessibility in the adult mouse brain. Nature 2023; 624:378-389. [PMID: 38092917 PMCID: PMC10719105 DOI: 10.1038/s41586-023-06824-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 11/01/2023] [Indexed: 12/17/2023]
Abstract
Recent advances in single-cell technologies have led to the discovery of thousands of brain cell types; however, our understanding of the gene regulatory programs in these cell types is far from complete1-4. Here we report a comprehensive atlas of candidate cis-regulatory DNA elements (cCREs) in the adult mouse brain, generated by analysing chromatin accessibility in 2.3 million individual brain cells from 117 anatomical dissections. The atlas includes approximately 1 million cCREs and their chromatin accessibility across 1,482 distinct brain cell populations, adding over 446,000 cCREs to the most recent such annotation in the mouse genome. The mouse brain cCREs are moderately conserved in the human brain. The mouse-specific cCREs-specifically, those identified from a subset of cortical excitatory neurons-are strongly enriched for transposable elements, suggesting a potential role for transposable elements in the emergence of new regulatory programs and neuronal diversity. Finally, we infer the gene regulatory networks in over 260 subclasses of mouse brain cells and develop deep-learning models to predict the activities of gene regulatory elements in different brain cell types from the DNA sequence alone. Our results provide a resource for the analysis of cell-type-specific gene regulation programs in both mouse and human brains.
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Affiliation(s)
- Songpeng Zu
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Yang Eric Li
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
- Department of Neurosurgery and Genetics, Washington University School of Medicine, St Louis, MO, USA
| | - Kangli Wang
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Ethan J Armand
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Sainath Mamde
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Maria Luisa Amaral
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Yuelai Wang
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Andre Chu
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Yang Xie
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Michael Miller
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Jie Xu
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Zhaoning Wang
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Kai Zhang
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Bojing Jia
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Xiaomeng Hou
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Lin Lin
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Qian Yang
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Seoyeon Lee
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Bin Li
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Samantha Kuan
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Jacinta Lucero
- The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Julia Osteen
- The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Michael Nunn
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | | | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Zihan Wang
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Jingbo Shang
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | | | - Joseph R Ecker
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Allen Wang
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Bing Ren
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA.
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA.
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Prater KE, Green KJ, Mamde S, Sun W, Cochoit A, Smith CL, Chiou KL, Heath L, Rose SE, Wiley J, Keene CD, Kwon RY, Snyder-Mackler N, Blue EE, Logsdon B, Young JE, Shojaie A, Garden GA, Jayadev S. Human microglia show unique transcriptional changes in Alzheimer's disease. Nat Aging 2023; 3:894-907. [PMID: 37248328 PMCID: PMC10353942 DOI: 10.1038/s43587-023-00424-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 04/25/2023] [Indexed: 05/31/2023]
Abstract
Microglia, the innate immune cells of the brain, influence Alzheimer's disease (AD) progression and are potential therapeutic targets. However, microglia exhibit diverse functions, the regulation of which is not fully understood, complicating therapeutics development. To better define the transcriptomic phenotypes and gene regulatory networks associated with AD, we enriched for microglia nuclei from 12 AD and 10 control human dorsolateral prefrontal cortices (7 males and 15 females, all aged >60 years) before single-nucleus RNA sequencing. Here we describe both established and previously unrecognized microglial molecular phenotypes, the inferred gene networks driving observed transcriptomic change, and apply trajectory analysis to reveal the putative relationships between microglial phenotypes. We identify microglial phenotypes more prevalent in AD cases compared with controls. Further, we describe the heterogeneity in microglia subclusters expressing homeostatic markers. Our study demonstrates that deep profiling of microglia in human AD brain can provide insight into microglial transcriptional changes associated with AD.
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Affiliation(s)
| | - Kevin J Green
- Department of Neurology, University of Washington, Seattle, WA, USA
| | - Sainath Mamde
- Department of Neurology, University of Washington, Seattle, WA, USA
| | - Wei Sun
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Carole L Smith
- Department of Neurology, University of Washington, Seattle, WA, USA
| | - Kenneth L Chiou
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
- School of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
| | | | - Shannon E Rose
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | | | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Ronald Y Kwon
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
- Department of Orthopaedics and Sports Medicine, University of Washington, Seattle, WA, USA
| | - Noah Snyder-Mackler
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
- School of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, USA
| | - Elizabeth E Blue
- Division of Medical Genetics, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Benjamin Logsdon
- Sage Bionetworks, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | - Jessica E Young
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Ali Shojaie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Gwenn A Garden
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Suman Jayadev
- Department of Neurology, University of Washington, Seattle, WA, USA.
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA.
- Division of Medical Genetics, University of Washington, Seattle, WA, USA.
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4
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Oruganty K, Campit SE, Mamde S, Lyssiotis CA, Chandrasekaran S. Common biochemical properties of metabolic genes recurrently dysregulated in tumors. Cancer Metab 2020; 8:5. [PMID: 32411371 PMCID: PMC7206696 DOI: 10.1186/s40170-020-0211-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 02/03/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Tumor initiation and progression are associated with numerous metabolic alterations. However, the biochemical drivers and constraints that contribute to metabolic gene dysregulation are unclear. METHODS Here, we present MetOncoFit, a computational model that integrates 142 metabolic features that can impact tumor fitness, including enzyme catalytic activity, pathway association, network topology, and reaction flux. MetOncoFit uses genome-scale metabolic modeling and machine-learning to quantify the relative importance of various metabolic features in predicting cancer metabolic gene expression, copy number variation, and survival data. RESULTS Using MetOncoFit, we performed a meta-analysis of 9 cancer types and over 4500 samples from TCGA, Prognoscan, and COSMIC tumor databases. MetOncoFit accurately predicted enzyme differential expression and its impact on patient survival using the 142 attributes of metabolic enzymes. Our analysis revealed that enzymes with high catalytic activity were frequently upregulated in many tumors and associated with poor survival. Topological analysis also identified specific metabolites that were hot spots of dysregulation. CONCLUSIONS MetOncoFit integrates a broad range of datasets to understand how biochemical and topological features influence metabolic gene dysregulation across various cancer types. MetOncoFit was able to achieve significantly higher accuracy in predicting differential expression, copy number variation, and patient survival than traditional modeling approaches. Overall, MetOncoFit illuminates how enzyme activity and metabolic network architecture influences tumorigenesis.
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Affiliation(s)
- Krishnadev Oruganty
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48105 USA
- Present Address: Genpact, New York, NY 10036 USA
| | - Scott Edward Campit
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48105 USA
| | - Sainath Mamde
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48105 USA
| | - Costas A. Lyssiotis
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48105 USA
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48105 USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48105 USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109 USA
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