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Nystuen KL, McNamee SM, Akula M, Holton KM, DeAngelis MM, Haider NB. Alzheimer's Disease: Models and Molecular Mechanisms Informing Disease and Treatments. Bioengineering (Basel) 2024; 11:45. [PMID: 38247923 PMCID: PMC10813760 DOI: 10.3390/bioengineering11010045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/15/2023] [Accepted: 12/22/2023] [Indexed: 01/23/2024] Open
Abstract
Alzheimer's Disease (AD) is a complex neurodegenerative disease resulting in progressive loss of memory, language and motor abilities caused by cortical and hippocampal degeneration. This review captures the landscape of understanding of AD pathology, diagnostics, and current therapies. Two major mechanisms direct AD pathology: (1) accumulation of amyloid β (Aβ) plaque and (2) tau-derived neurofibrillary tangles (NFT). The most common variants in the Aβ pathway in APP, PSEN1, and PSEN2 are largely responsible for early-onset AD (EOAD), while MAPT, APOE, TREM2 and ABCA7 have a modifying effect on late-onset AD (LOAD). More recent studies implicate chaperone proteins and Aβ degrading proteins in AD. Several tests, such as cognitive function, brain imaging, and cerebral spinal fluid (CSF) and blood tests, are used for AD diagnosis. Additionally, several biomarkers seem to have a unique AD specific combination of expression and could potentially be used in improved, less invasive diagnostics. In addition to genetic perturbations, environmental influences, such as altered gut microbiome signatures, affect AD. Effective AD treatments have been challenging to develop. Currently, there are several FDA approved drugs (cholinesterase inhibitors, Aß-targeting antibodies and an NMDA antagonist) that could mitigate AD rate of decline and symptoms of distress.
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Affiliation(s)
- Kaden L. Nystuen
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Shannon M. McNamee
- Schepens Eye Research Institute, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Monica Akula
- Schepens Eye Research Institute, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Kristina M. Holton
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
- Harvard Stem Cell Institute, Cambridge, MA 02138, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Margaret M. DeAngelis
- Department of Ophthalmology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA
| | - Neena B. Haider
- Schepens Eye Research Institute, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
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202
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Alldred MJ, Pidikiti H, Ibrahim KW, Lee SH, Heguy A, Hoffman GE, Mufson EJ, Stutzmann GE, Ginsberg SD. Hippocampal CA1 Pyramidal Neurons Display Sublayer and Circuitry Dependent Degenerative Expression Profiles in Aged Female Down Syndrome Mice. J Alzheimers Dis 2024; 100:S341-S362. [PMID: 39031371 PMCID: PMC11497160 DOI: 10.3233/jad-240622] [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: 07/22/2024]
Abstract
Background Individuals with Down syndrome (DS) have intellectual disability and develop Alzheimer's disease (AD) pathology during midlife, particularly in the hippocampal component of the medial temporal lobe memory circuit. However, molecular and cellular mechanisms underlying selective vulnerability of hippocampal CA1 neurons remains a major knowledge gap during DS/AD onset. This is compounded by evidence showing spatial (e.g., deep versus superficial) localization of pyramidal neurons (PNs) has profound effects on activity and innervation within the CA1 region. Objective We investigated whether there is a spatial profiling difference in CA1 PNs in an aged female DS/AD mouse model. We posit dysfunction may be dependent on spatial localization and innervation patterns within discrete CA1 subfields. Methods Laser capture microdissection was performed on trisomic CA1 PNs in an established mouse model of DS/AD compared to disomic controls, isolating the entire CA1 pyramidal neuron layer and sublayer microisolations of deep and superficial PNs from the distal CA1 (CA1a) region. Results RNA sequencing and bioinformatic inquiry revealed dysregulation of CA1 PNs based on spatial location and innervation patterns. The entire CA1 region displayed the most differentially expressed genes (DEGs) in trisomic mice reflecting innate DS vulnerability, while trisomic CA1a deep PNs exhibited fewer but more physiologically relevant DEGs, as evidenced by bioinformatic inquiry. Conclusions CA1a deep neurons displayed numerous DEGs linked to cognitive functions whereas CA1a superficial neurons, with approximately equal numbers of DEGs, were not linked to pathways of dysregulation, suggesting the spatial location of vulnerable CA1 PNs plays an important role in circuit dissolution.
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Affiliation(s)
- Melissa J. Alldred
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY, USA
- Department of Psychiatry, School of Medicine, New York, NY, USA
| | - Harshitha Pidikiti
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY, USA
| | | | - Sang Han Lee
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY, USA
- Department of Psychiatry, School of Medicine, New York, NY, USA
| | - Adriana Heguy
- Genome Technology Center, School of Medicine, New York, NY, USA
| | - Gabriel E. Hoffman
- Department of Genetics and Genomic Sciences and Psychiatry and the Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Elliott J. Mufson
- Department of Translational Neuroscience and Neurology and Barrow Neurological Institute, Phoenix, AZ, USA
| | - Grace E. Stutzmann
- Center for Neurodegenerative Disease and Therapeutics, Rosalind Franklin University/The Chicago Medical School, North Chicago, IL, USA
| | - Stephen D. Ginsberg
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY, USA
- Department of Psychiatry, School of Medicine, New York, NY, USA
- Neuroscience & Physiology, School of Medicine, New York, NY, USA
- NYU Neuroscience Institute, New York University School of Medicine, New York, NY, USA
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203
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Huang Z. Evidence that Alzheimer's Disease Is a Disease of Competitive Synaptic Plasticity Gone Awry. J Alzheimers Dis 2024; 99:447-470. [PMID: 38669548 PMCID: PMC11119021 DOI: 10.3233/jad-240042] [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: 04/28/2024]
Abstract
Mounting evidence indicates that a physiological function of amyloid-β (Aβ) is to mediate neural activity-dependent homeostatic and competitive synaptic plasticity in the brain. I have previously summarized the lines of evidence supporting this hypothesis and highlighted the similarities between Aβ and anti-microbial peptides in mediating cell/synapse competition. In cell competition, anti-microbial peptides deploy a multitude of mechanisms to ensure both self-protection and competitor elimination. Here I review recent studies showing that similar mechanisms are at play in Aβ-mediated synapse competition and perturbations in these mechanisms underpin Alzheimer's disease (AD). Specifically, I discuss evidence that Aβ and ApoE, two crucial players in AD, co-operate in the regulation of synapse competition. Glial ApoE promotes self-protection by increasing the production of trophic monomeric Aβ and inhibiting its assembly into toxic oligomers. Conversely, Aβ oligomers, once assembled, promote the elimination of competitor synapses via direct toxic activity and amplification of "eat-me" signals promoting the elimination of weak synapses. I further summarize evidence that neuronal ApoE may be part of a gene regulatory network that normally promotes competitive plasticity, explaining the selective vulnerability of ApoE expressing neurons in AD brains. Lastly, I discuss evidence that sleep may be key to Aβ-orchestrated plasticity, in which sleep is not only induced by Aβ but is also required for Aβ-mediated plasticity, underlining the link between sleep and AD. Together, these results strongly argue that AD is a disease of competitive synaptic plasticity gone awry, a novel perspective that may promote AD research.
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Affiliation(s)
- Zhen Huang
- Departments of Neuroscience and Neurology, University of Wisconsin-Madison, Madison, WI, USA
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204
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Sun MK, Alkon DL. Treating Alzheimer's Disease: Focusing on Neurodegenerative Consequences. J Alzheimers Dis 2024; 101:S263-S274. [PMID: 39422958 DOI: 10.3233/jad-240479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
Neurodegenerative disorders involve progressive dysfunction and loss of synapses and neurons and brain atrophy, slowly declining memories and cognitive skills, throughout a long process. Alzheimer's disease (AD), the leading neurodegenerative disorder, suffers from a lack of effective therapeutic drugs. Decades of efforts targeting its pathologic hallmarks, amyloid plaques and neurofibrillary tangles, in clinical trials have produced therapeutics with marginal benefits that lack meaningful clinical improvements in cognition. Delivering meaningful clinical therapeutics to treat or prevent neurodegenerative disorders thus remains a great challenge to scientists and clinicians. Emerging evidence, however, suggests that dysfunction of various synaptogenic signaling pathways participates in the neurodegenerative progression, resulting in deterioration of operation/structure of the synaptic networks involved in cognition. These derailed endogenous signaling pathways and disease processes are potential pharmacological targets for the therapies. Therapeutics with meaningful clinical benefit in cognition may depend on the effectiveness of arresting and reversing the neurodegenerative process through these targets. In essence, promoting neuro-regeneration may represent the only option to recover degenerated synapses and neurons. These potential directions in clinical trials for AD therapeutics with meaningful clinical benefit in cognitive function are summarized and discussed.
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205
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Lam M, Lee D, Kosater I, Khairallah A, Taga M, Zhang Y, Fujita M, Nag S, Bennett DA, De Jager P, Menon V. Human disease-specific cell signatures in non-lesional tissue in Multiple Sclerosis detected by single-cell and spatial transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.20.572491. [PMID: 38187779 PMCID: PMC10769298 DOI: 10.1101/2023.12.20.572491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Recent investigations of cell type changes in Multiple Sclerosis (MS) using single-cell profiling methods have focused on active lesional and peri-lesional brain tissue, and have implicated a number of peripheral and central nervous system cell types. However, an important question is the extent to which so-called "normal-appearing" non-lesional tissue in individuals with MS accumulate changes over the lifespan. Here, we compared post-mortem non-lesional brain tissue from donors with a pathological or clinical diagnosis of MS from the Religious Orders Study or Rush Memory and Aging Project (ROSMAP) cohorts to age- and sex-matched brains from persons without MS (controls). We profiled three brain regions using single-nucleus RNA-seq: dorsolateral prefrontal cortex (DLPFC), normal appearing white matter (NAWM) and the pulvinar in thalamus (PULV), from 15 control individuals, 8 individuals with MS, and 5 individuals with other detrimental pathologies accompanied by demyelination, resulting in a total of 78 samples. We identified region- and cell type-specific differences in non-lesional samples from individuals diagnosed with MS and/or exhibiting secondary demyelination with other neurological conditions, as compared to control donors. These differences included lower proportions of oligodendrocytes with expression of myelination related genes MOBP, MBP, PLP1, as well as higher proportions of CRYAB+ oligodendrocytes in all three brain regions. Among microglial signatures, we identified subgroups that were higher in both demyelination (TMEM163+/ERC2+), as well as those that were specifically higher in MS donors (HIF1A+/SPP1+) and specifically in donors with secondary demyelination (SOCS6+/MYO1E+), in both white and grey matter. To validate our findings, we generated Visium spatial transcriptomics data on matched tissue from 13 donors, and recapitulated our observations of gene expression differences in oligodendrocytes and microglia. Finally, we show that some of the differences observed between control and MS donors in NAWM mirror those previously reported between control WM and active lesions in MS donors. Overall, our investigation sheds additional light on cell type- and disease-specific differences present even in non-lesional white and grey matter tissue, highlighting widespread cellular signatures that may be associated with downstream pathological changes.
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Affiliation(s)
- Matti Lam
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center
| | - Dylan Lee
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center
| | - Ivy Kosater
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center
| | - Anthony Khairallah
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center
| | - Mariko Taga
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center
| | - Ya Zhang
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center
| | - Masashi Fujita
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center
| | - Sukriti Nag
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL
| | - Philip De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center
| | - Vilas Menon
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center
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206
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Montine KS, Berson E, Phongpreecha T, Huang Z, Aghaeepour N, Zou JY, MacCoss MJ, Montine TJ. Understanding the molecular basis of resilience to Alzheimer's disease. Front Neurosci 2023; 17:1311157. [PMID: 38192507 PMCID: PMC10773681 DOI: 10.3389/fnins.2023.1311157] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024] Open
Abstract
The cellular and molecular distinction between brain aging and neurodegenerative disease begins to blur in the oldest old. Approximately 15-25% of observations in humans do not fit predicted clinical manifestations, likely the result of suppressed damage despite usually adequate stressors and of resilience, the suppression of neurological dysfunction despite usually adequate degeneration. Factors during life may predict the clinico-pathologic state of resilience: cardiovascular health and mental health, more so than educational attainment, are predictive of a continuous measure of resilience to Alzheimer's disease (AD) and AD-related dementias (ADRDs). In resilience to AD alone (RAD), core features include synaptic and axonal processes, especially in the hippocampus. Future focus on larger and more diverse cohorts and additional regions offer emerging opportunities to understand this counterforce to neurodegeneration. The focus of this review is the molecular basis of resilience to AD.
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Affiliation(s)
| | - Eloïse Berson
- Department of Pathology, Stanford University, Stanford, CA, United States
- Department of Anesthesiology, Stanford University, Stanford, CA, United States
| | - Thanaphong Phongpreecha
- Department of Pathology, Stanford University, Stanford, CA, United States
- Department of Anesthesiology, Stanford University, Stanford, CA, United States
| | - Zhi Huang
- Department of Pathology, Stanford University, Stanford, CA, United States
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Nima Aghaeepour
- Department of Anesthesiology, Stanford University, Stanford, CA, United States
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - James Y. Zou
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
- Department of Computer Science, Stanford University, Stanford, CA, United States
| | - Michael J. MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA, United States
| | - Thomas J. Montine
- Department of Pathology, Stanford University, Stanford, CA, United States
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207
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Levites Y, Dammer EB, Ran Y, Tsering W, Duong D, Abreha M, Gadhavi J, Lolo K, Trejo-Lopez J, Phillips JL, Iturbe A, Erqiuzi A, Moore BD, Ryu D, Natu A, Dillon KD, Torrellas J, Moran C, Ladd TB, Afroz KF, Islam T, Jagirdar J, Funk CC, Robinson M, Borchelt DR, Ertekin-Taner N, Kelly JW, Heppner FL, Johnson EC, McFarland K, Levey AL, Prokop S, Seyfried NT, Golde TE. Aβ Amyloid Scaffolds the Accumulation of Matrisome and Additional Proteins in Alzheimer's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.568318. [PMID: 38076912 PMCID: PMC10705437 DOI: 10.1101/2023.11.29.568318] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/14/2024]
Abstract
We report a highly significant correlation in brain proteome changes between Alzheimers disease (AD) and CRND8 APP695NL/F transgenic mice. However, integrating protein changes observed in the CRND8 mice with co-expression networks derived from human AD, reveals both conserved and divergent module changes. For the most highly conserved module (M42, matrisome) we find many proteins accumulate in plaques, cerebrovascular amyloid (CAA), dystrophic processes, or a combination thereof. Overexpression of two M42 proteins, midkine (Mdk) and pleiotrophin (PTN), in CRND8 mice brains leads to increased accumulation of A β ; in plaques and in CAA; further, recombinant MDK and PTN enhance A β ; aggregation into amyloid. Multiple M42 proteins, annotated as heparan sulfate binding proteins, bind to fibrillar A β 42 and a non-human amyloid fibril in vitro. Supporting this binding data, MDK and PTN co-accumulate with transthyretin (TTR) amyloid in the heart and islet amyloid polypeptide (IAPP) amyloid in the pancreas. Our findings establish several critical insights. Proteomic changes in modules observed in human AD brains define an A β ; amyloid responsome that is well conserved from mouse model to human. Further, distinct amyloid structures may serve as scaffolds, facilitating the co-accumulation of proteins with signaling functions. We hypothesize that this co-accumulation may contribute to downstream pathological sequalae. Overall, this contextualized understanding of proteomic changes and their interplay with amyloid deposition provides valuable insights into the complexity of AD pathogenesis and potential biomarkers and therapeutic targets.
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208
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The Neuroscientist Comments. Neuroscientist 2023; 29:663. [PMID: 37915220 DOI: 10.1177/10738584231207804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
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209
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Konopka G, Bhaduri A. Functional genomics and systems biology in human neuroscience. Nature 2023; 623:274-282. [PMID: 37938705 PMCID: PMC11465930 DOI: 10.1038/s41586-023-06686-1] [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: 02/23/2023] [Accepted: 09/27/2023] [Indexed: 11/09/2023]
Abstract
Neuroscience research has entered a phase of key discoveries in the realm of neurogenomics owing to strong financial and intellectual support for resource building and tool development. The previous challenge of tissue heterogeneity has been met with the application of techniques that can profile individual cells at scale. Moreover, the ability to perturb genes, gene regulatory elements and neuronal activity in a cell-type-specific manner has been integrated with gene expression studies to uncover the functional underpinnings of the genome at a systems level. Although these insights have necessarily been grounded in model systems, we now have the opportunity to apply these approaches in humans and in human tissue, thanks to advances in human genetics, brain imaging and tissue collection. We acknowledge that there will probably always be limits to the extent to which we can apply the genomic tools developed in model systems to human neuroscience; however, as we describe in this Perspective, the neuroscience field is now primed with an optimal foundation for tackling this ambitious challenge. The application of systems-level network analyses to these datasets will facilitate a deeper appreciation of human neurogenomics that cannot otherwise be achieved from directly observable phenomena.
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Affiliation(s)
- Genevieve Konopka
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA.
- Peter O'Donnell Jr Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA.
| | - Aparna Bhaduri
- Department of Biological Chemistry, University of California, Los Angeles, CA, USA.
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210
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Reardon S. The brain cells linked to protection against dementia. Nature 2023; 622:229-230. [PMID: 37770661 DOI: 10.1038/d41586-023-03012-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
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211
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Sun N, Victor MB, Park YP, Xiong X, Scannail AN, Leary N, Prosper S, Viswanathan S, Luna X, Boix CA, James BT, Tanigawa Y, Galani K, Mathys H, Jiang X, Ng AP, Bennett DA, Tsai LH, Kellis M. Human microglial state dynamics in Alzheimer's disease progression. Cell 2023; 186:4386-4403.e29. [PMID: 37774678 PMCID: PMC10644954 DOI: 10.1016/j.cell.2023.08.037] [Citation(s) in RCA: 155] [Impact Index Per Article: 77.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 03/21/2023] [Accepted: 08/29/2023] [Indexed: 10/01/2023]
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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Affiliation(s)
- Na Sun
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matheus B Victor
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yongjin P Park
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Pathology and Laboratory Medicine, Department of Statistics, University of British Columbia, Vancouver, BC, Canada; Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Xushen Xiong
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aine Ni Scannail
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Noelle Leary
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shaniah Prosper
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Soujanya Viswanathan
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Xochitl Luna
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Carles A Boix
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin T James
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yosuke Tanigawa
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kyriaki Galani
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hansruedi Mathys
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Xueqiao Jiang
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ayesha P Ng
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Li-Huei Tsai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Manolis Kellis
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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212
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Xiong X, James BT, Boix CA, Park YP, Galani K, Victor MB, Sun N, Hou L, Ho LL, Mantero J, Scannail AN, Dileep V, Dong W, Mathys H, Bennett DA, Tsai LH, Kellis M. Epigenomic dissection of Alzheimer's disease pinpoints causal variants and reveals epigenome erosion. Cell 2023; 186:4422-4437.e21. [PMID: 37774680 PMCID: PMC10782612 DOI: 10.1016/j.cell.2023.08.040] [Citation(s) in RCA: 76] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 04/04/2023] [Accepted: 08/29/2023] [Indexed: 10/01/2023]
Abstract
Recent work has identified dozens of non-coding loci for Alzheimer's disease (AD) risk, but their mechanisms and AD transcriptional regulatory circuitry are poorly understood. Here, we profile epigenomic and transcriptomic landscapes of 850,000 nuclei from prefrontal cortexes of 92 individuals with and without AD to build a map of the brain regulome, including epigenomic profiles, transcriptional regulators, co-accessibility modules, and peak-to-gene links in a cell-type-specific manner. We develop methods for multimodal integration and detecting regulatory modules using peak-to-gene linking. We show AD risk loci are enriched in microglial enhancers and for specific TFs including SPI1, ELF2, and RUNX1. We detect 9,628 cell-type-specific ATAC-QTL loci, which we integrate alongside peak-to-gene links to prioritize AD variant regulatory circuits. We report differential accessibility of regulatory modules in late AD in glia and in early AD in neurons. Strikingly, late-stage AD brains show global epigenome dysregulation indicative of epigenome erosion and cell identity loss.
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Affiliation(s)
- Xushen Xiong
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; Liangzhu Laboratory, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
| | - Benjamin T James
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Carles A Boix
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Yongjin P Park
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA; Department of Pathology and Laboratory Medicine, Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Kyriaki Galani
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Matheus B Victor
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Na Sun
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Lei Hou
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Li-Lun Ho
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Julio Mantero
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Aine Ni Scannail
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Vishnu Dileep
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Weixiu Dong
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Hansruedi Mathys
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Li-Huei Tsai
- The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.
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213
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Luo W, Qu W, Gan L. The AD odyssey 2023: Tales of single cell. Cell 2023; 186:4257-4259. [PMID: 37774675 DOI: 10.1016/j.cell.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/01/2023] [Accepted: 09/01/2023] [Indexed: 10/01/2023]
Abstract
Deciphering cellular changes in Alzheimer's disease (AD) using large cohorts with defined clinical stages is essential for understanding the diverse trajectories of AD progression. In this issue of Cell, five studies harnessed the power of single-nuclei RNA sequencing (snRNA-seq) and single-nuclei ATAC sequencing (snATAC-seq) at unprecedented scale and revealed exciting insights into cell-type-specific mechanisms underlying the progression of AD pathogenesis.
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Affiliation(s)
- Wenjie Luo
- Helen and Robert Appel Alzheimer's Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Wenhui Qu
- Helen and Robert Appel Alzheimer's Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Li Gan
- Helen and Robert Appel Alzheimer's Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
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