Human microglial state dynamics in Alzheimer’s disease progression
Altered microglial states affect neuroinflammation, neurodegeneration, and disease but remain poorly understood. Here, we report 194,000 single-nucleus microglial transcriptomes and epigenomes across 443 human subjects and diverse Alzheimer’s disease (AD) pathological phenotypes. We annotate 12 micr...
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| Published in | Cell Vol. 186; no. 20; pp. 4386 - 4403.e29 |
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| Main Authors | , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
28.09.2023
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Altered microglial states affect neuroinflammation, neurodegeneration, and disease but remain poorly understood. Here, we report 194,000 single-nucleus microglial transcriptomes and epigenomes across 443 human subjects and diverse Alzheimer’s disease (AD) pathological phenotypes. We annotate 12 microglial transcriptional states, including AD-dysregulated homeostatic, inflammatory, and lipid-processing states. We identify 1,542 AD-differentially-expressed genes, including both microglia-state-specific and disease-stage-specific alterations. By integrating epigenomic, transcriptomic, and motif information, we infer upstream regulators of microglial cell states, gene-regulatory networks, enhancer-gene links, and transcription-factor-driven microglial state transitions. We demonstrate that ectopic expression of our predicted homeostatic-state activators induces homeostatic features in human iPSC-derived microglia-like cells, while inhibiting activators of inflammation can block inflammatory progression. Lastly, we pinpoint the expression of AD-risk genes in microglial states and differential expression of AD-risk genes and their regulators during AD progression. Overall, we provide insights underlying microglial states, including state-specific and AD-stage-specific microglial alterations at unprecedented resolution.
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•Single-nucleus transcriptomes and epigenomes of human microglia•Microglia state-specific and disease-stage-specific profile in Alzheimer’s disease•Chromatin accessibility poorly captured microglia transcriptional state diversity•Transcription factor networks regulate microglial states and their transitions
Microglia states showing Alzheimer’s disease (AD)-risk-gene expression and AD-progression-associated expression differences were identified from the microglial transcriptome and epigenomes from the 443 human subjects spanning brain regions and diverse clinical and pathological states. Computational framework and functional studies using iPSC-derived microglia defined the diversity of microglial states across disease, the disease-stage changes of gene expression, and the regulatory network that governs microglial state transitions during the progression of AD. |
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| AbstractList | Altered microglial states affect neuroinflammation, neurodegeneration, and disease but remain poorly understood. Here, we report 194,000 single-nucleus microglial transcriptomes and epigenomes across 443 human subjects and diverse Alzheimer's disease (AD) pathological phenotypes. We annotate 12 microglial transcriptional states, including AD-dysregulated homeostatic, inflammatory, and lipid-processing states. We identify 1,542 AD-differentially-expressed genes, including both microglia-state-specific and disease-stage-specific alterations. By integrating epigenomic, transcriptomic, and motif information, we infer upstream regulators of microglial cell states, gene-regulatory networks, enhancer-gene links, and transcription-factor-driven microglial state transitions. We demonstrate that ectopic expression of our predicted homeostatic-state activators induces homeostatic features in human iPSC-derived microglia-like cells, while inhibiting activators of inflammation can block inflammatory progression. Lastly, we pinpoint the expression of AD-risk genes in microglial states and differential expression of AD-risk genes and their regulators during AD progression. Overall, we provide insights underlying microglial states, including state-specific and AD-stage-specific microglial alterations at unprecedented resolution. Altered microglial states affect neuroinflammation, neurodegeneration, and disease but remain poorly understood. Here, we report 194,000 single-nucleus microglial transcriptomes and epigenomes across 443 human subjects and diverse Alzheimer's disease (AD) pathological phenotypes. We annotate 12 microglial transcriptional states, including AD-dysregulated homeostatic, inflammatory, and lipid-processing states. We identify 1,542 AD-differentially-expressed genes, including both microglia-state-specific and disease-stage-specific alterations. By integrating epigenomic, transcriptomic, and motif information, we infer upstream regulators of microglial cell states, gene-regulatory networks, enhancer-gene links, and transcription-factor-driven microglial state transitions. We demonstrate that ectopic expression of our predicted homeostatic-state activators induces homeostatic features in human iPSC-derived microglia-like cells, while inhibiting activators of inflammation can block inflammatory progression. Lastly, we pinpoint the expression of AD-risk genes in microglial states and differential expression of AD-risk genes and their regulators during AD progression. Overall, we provide insights underlying microglial states, including state-specific and AD-stage-specific microglial alterations at unprecedented resolution.Altered microglial states affect neuroinflammation, neurodegeneration, and disease but remain poorly understood. Here, we report 194,000 single-nucleus microglial transcriptomes and epigenomes across 443 human subjects and diverse Alzheimer's disease (AD) pathological phenotypes. We annotate 12 microglial transcriptional states, including AD-dysregulated homeostatic, inflammatory, and lipid-processing states. We identify 1,542 AD-differentially-expressed genes, including both microglia-state-specific and disease-stage-specific alterations. By integrating epigenomic, transcriptomic, and motif information, we infer upstream regulators of microglial cell states, gene-regulatory networks, enhancer-gene links, and transcription-factor-driven microglial state transitions. We demonstrate that ectopic expression of our predicted homeostatic-state activators induces homeostatic features in human iPSC-derived microglia-like cells, while inhibiting activators of inflammation can block inflammatory progression. Lastly, we pinpoint the expression of AD-risk genes in microglial states and differential expression of AD-risk genes and their regulators during AD progression. Overall, we provide insights underlying microglial states, including state-specific and AD-stage-specific microglial alterations at unprecedented resolution. Altered microglial states affect neuroinflammation, neurodegeneration, and disease but remain poorly understood. Here, we report 194,000 single-nucleus microglial transcriptomes and epigenomes across 443 human subjects and diverse Alzheimer’s disease (AD) pathological phenotypes. We annotate 12 microglial transcriptional states, including AD-dysregulated homeostatic, inflammatory, and lipid-processing states. We identify 1,542 AD-differentially-expressed genes, including both microglia-state-specific and disease-stage-specific alterations. By integrating epigenomic, transcriptomic, and motif information, we infer upstream regulators of microglial cell states, gene-regulatory networks, enhancer-gene links, and transcription-factor-driven microglial state transitions. We demonstrate that ectopic expression of our predicted homeostatic-state activators induces homeostatic features in human iPSC-derived microglia-like cells, while inhibiting activators of inflammation can block inflammatory progression. Lastly, we pinpoint the expression of AD-risk genes in microglial states and differential expression of AD-risk genes and their regulators during AD progression. Overall, we provide insights underlying microglial states, including state-specific and AD-stage-specific microglial alterations at unprecedented resolution. [Display omitted] •Single-nucleus transcriptomes and epigenomes of human microglia•Microglia state-specific and disease-stage-specific profile in Alzheimer’s disease•Chromatin accessibility poorly captured microglia transcriptional state diversity•Transcription factor networks regulate microglial states and their transitions Microglia states showing Alzheimer’s disease (AD)-risk-gene expression and AD-progression-associated expression differences were identified from the microglial transcriptome and epigenomes from the 443 human subjects spanning brain regions and diverse clinical and pathological states. Computational framework and functional studies using iPSC-derived microglia defined the diversity of microglial states across disease, the disease-stage changes of gene expression, and the regulatory network that governs microglial state transitions during the progression of AD. Altered microglial states affect neuroinflammation, neurodegeneration, and disease, but remain poorly understood. Here, we report 194k single-nucleus microglial transcriptomes and epigenomes across 443 human subjects, and diverse Alzheimer’s disease (AD) pathological phenotypes. We annotate 12 microglial transcriptional states, including AD-dysregulated homeostatic, inflammatory, and lipid-processing states. We identify 1,542 AD-differentially-expressed genes, including both microglia-state-specific and disease-stage-specific alterations. By integrating epigenomic, transcriptomic, and motif information, we infer upstream regulators of microglial cell states, gene-regulatory networks, enhancer-gene links, and transcription factor-driven microglial state transitions. We demonstrate that ectopic expression of our predicted homeostatic-state activators induces homeostatic features in human iPSC-derived microglia-like cells, while inhibiting activators of inflammation can block inflammatory progression. Lastly, we pinpoint the expression of AD-risk genes in microglial states and differential expression of AD-risk genes and their regulators during AD progression. Overall, we provide insights underlying microglial states, including state-specific and AD-stage-specific microglial alterations at unprecedented resolution. Microglia states showing AD-risk-gene expression and AD-progression-associated expression differences were identified from the microglial transcriptome and epigenomes from the 443 human subjects spanning brain regions and diverse clinical and pathological states. Computational framework and functional studies using iPSC-derived microglia defined the diversity of microglial states across disease, the disease-stage changes of gene expression, and the regulatory network that governs microglial state transitions during the progression of AD. |
| Author | Mathys, Hansruedi Bennett, David A. James, Benjamin T. Prosper, Shaniah Jiang, Xueqiao Scannail, Aine Ni Park, Yongjin P. Galani, Kyriaki Xiong, Xushen Kellis, Manolis Victor, Matheus B. Leary, Noelle Tsai, Li-Huei Boix, Carles A. Tanigawa, Yosuke Ng, Ayesha P. Sun, Na Viswanathan, Soujanya Luna, Xochitl |
| AuthorAffiliation | 6 Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada 9 These authors contributed equally 3 Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA 10 Lead contact 2 Broad Institute of MIT and Harvard, Cambridge, MA, USA 8 Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA 1 MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA 4 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA 7 Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA 5 Department of Pathology and Laboratory Medicine, Department of Statistics, University of British Columbia, Vancouver, BC, Canada |
| AuthorAffiliation_xml | – name: 9 These authors contributed equally – name: 4 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA – name: 1 MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA – name: 3 Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA – name: 10 Lead contact – name: 5 Department of Pathology and Laboratory Medicine, Department of Statistics, University of British Columbia, Vancouver, BC, Canada – name: 2 Broad Institute of MIT and Harvard, Cambridge, MA, USA – name: 6 Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada – name: 7 Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA – name: 8 Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA |
| Author_xml | – sequence: 1 givenname: Na surname: Sun fullname: Sun, Na organization: MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA – sequence: 2 givenname: Matheus B. surname: Victor fullname: Victor, Matheus B. organization: Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA – sequence: 3 givenname: Yongjin P. surname: Park fullname: Park, Yongjin P. organization: MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA – sequence: 4 givenname: Xushen surname: Xiong fullname: Xiong, Xushen organization: MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA – sequence: 5 givenname: Aine Ni surname: Scannail fullname: Scannail, Aine Ni organization: Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA – sequence: 6 givenname: Noelle surname: Leary fullname: Leary, Noelle organization: Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA – sequence: 7 givenname: Shaniah surname: Prosper fullname: Prosper, Shaniah organization: Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA – sequence: 8 givenname: Soujanya surname: Viswanathan fullname: Viswanathan, Soujanya organization: Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA – sequence: 9 givenname: Xochitl surname: Luna fullname: Luna, Xochitl organization: Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA – sequence: 10 givenname: Carles A. surname: Boix fullname: Boix, Carles A. organization: MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA – sequence: 11 givenname: Benjamin T. surname: James fullname: James, Benjamin T. organization: MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA – sequence: 12 givenname: Yosuke surname: Tanigawa fullname: Tanigawa, Yosuke organization: MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA – sequence: 13 givenname: Kyriaki surname: Galani fullname: Galani, Kyriaki organization: MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA – sequence: 14 givenname: Hansruedi surname: Mathys fullname: Mathys, Hansruedi organization: Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA – sequence: 15 givenname: Xueqiao surname: Jiang fullname: Jiang, Xueqiao organization: Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA – sequence: 16 givenname: Ayesha P. surname: Ng fullname: Ng, Ayesha P. organization: Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA – sequence: 17 givenname: David A. surname: Bennett fullname: Bennett, David A. organization: Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA – sequence: 18 givenname: Li-Huei surname: Tsai fullname: Tsai, Li-Huei email: lhtsai@mit.edu organization: Broad Institute of MIT and Harvard, Cambridge, MA, USA – sequence: 19 givenname: Manolis surname: Kellis fullname: Kellis, Manolis email: manoli@mit.edu organization: MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37774678$$D View this record in MEDLINE/PubMed |
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| IngestDate | Thu Aug 21 18:31:21 EDT 2025 Fri Jul 11 09:33:36 EDT 2025 Fri Jul 11 16:15:31 EDT 2025 Mon Jul 21 06:01:36 EDT 2025 Thu Apr 24 23:03:57 EDT 2025 Tue Jul 01 02:17:13 EDT 2025 Sun Apr 06 06:53:46 EDT 2025 |
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| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 20 |
| Keywords | iPSCs transcription factors inflammation single-cell microglia cell states Alzheimer's disease-stage response |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Author Contributions N.S., M.B.V, L.-H.T. and M.K. conceived and designed the study; M.K. and L.-H.T. supervised the study; N.S. developed the computational framework and conducted data analysis with assistance from Y.P., X.X., C.A.B, B.T.J, and Y.T.; M.B.V., S.V., N.L., X.L., A.N.S., and S.P. performed experiments and analyzed results; K.G., H.M., X.J., and A.P.N. performed snRNA-seq and snATAC-seq profiling; N.S., M.B.V, N.L. and A.N.S. wrote methods; D.A.B. provided post mortem samples and scientific input; and N.S., M.B.V, L.-H.T. and M.K. wrote and revised the manuscript with comments from all authors. |
| OpenAccessLink | https://www.sciencedirect.com/science/article/pii/S0092867423009716 |
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| PublicationDate | 2023-09-28 |
| PublicationDateYYYYMMDD | 2023-09-28 |
| PublicationDate_xml | – month: 09 year: 2023 text: 2023-09-28 day: 28 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
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| PublicationTitle | Cell |
| PublicationTitleAlternate | Cell |
| PublicationYear | 2023 |
| Publisher | Elsevier Inc |
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| Snippet | Altered microglial states affect neuroinflammation, neurodegeneration, and disease but remain poorly understood. Here, we report 194,000 single-nucleus... Altered microglial states affect neuroinflammation, neurodegeneration, and disease, but remain poorly understood. Here, we report 194k single-nucleus... |
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| SubjectTerms | Alzheimer Disease - genetics Alzheimer Disease - pathology Alzheimer's cell states disease progression disease-stage response Epigenome Gene Expression Regulation Humans inflammation Inflammation - pathology iPSCs microglia Microglia - metabolism neurodegenerative diseases neuroglia single-cell transcription (genetics) transcription factors Transcription Factors - metabolism Transcriptome transcriptomics |
| Title | Human microglial state dynamics in Alzheimer’s disease progression |
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