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Single-cell multiomics reveals disrupted glial gene regulatory programs in Alzheimer's disease via interpretable machine learning
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Abstract
Recent development of single-cell technology across multiple omics platforms has provided new ways to obtain holistic views of cells to study disease pathobiology. Alzheimer's disease (AD) is the most common form of dementia worldwide, yet the detailed understanding of its cellular and molecular mechanisms remains limited. In this study, we analyzed paired single-cell transcriptomic (scRNA-seq) and chromatin accessibility (scATAC-seq) data from the Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) Consortium to investigate the molecular mechanisms of AD at a cell-subpopulation-specific resolution focusing on glial cells. We benchmarked various multi-omics integration methods using diverse metrics and built an analytic workflow that enabled effective batch correction and cross-modality alignment, creating a unified cell state space. Through integrative analysis of 26 human brain samples, we uncovered AD-associated gene expression and pathway changes in glial subpopulations and highlighted important transcriptomic and epigenomic signatures via functional inference and interpretable machine learning paradigms, discovering the profound involvement of the Solute Carrier proteins (SLC) family genes in multiple glial cell types. We also identified glial cell-specific regulatory programs mediated by key transcription factors such as JUN and FOSL2 in astrocytes, the Zinc Finger (ZNF) family genes in microglia, and the SOX family of transcription factors in oligodendrocytes. Our study provides a comprehensive workflow and a high-resolution view of how glial regulatory programs are disrupted in AD. Our findings offer novel insights into disease-related changes in gene regulation and suggest potential targets for further research and therapy.
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Multimodal single-cell assays profile multiple sets of features in the same cells and are widely used for identifying and mapping cell states between chromatin and mRNA and linking regulatory elements to target genes. However, the high dimensionality of input features and shallow sequencing depth compared to unimodal assays pose challenges in data analysis. Here we present scPair, a multimodal single-cell data framework that overcomes these challenges by employing an implicit feature selection approach. scPair uses dual encoder-decoder structures trained on paired data to align cell states across modalities and predict features from one modality to another. We demonstrate that scPair outperforms existing methods in accuracy and execution time, and facilitates downstream tasks such as trajectory inference. We further show scPair can augment smaller multimodal datasets with larger unimodal atlases to increase statistical power to identify groups of transcription factors active during different stages of neural differentiation.
Genetic variants and epigenetic features both contribute to the risk of Alzheimer’s disease (AD). We studied the AD association of CpG-related single nucleotide polymorphisms (CGS), which act as a hub of both the genetic and epigenetic effects, in Caribbean Hispanics (CH) and generalized the findings to Non-Hispanic Whites (NHW). First, we conducted a genome-wide, sliding-window-based association with AD, in 7,155 CH and 1,283 NHW participants. Next, using data from the dorsolateral prefrontal cortex in 179 CH brains, we tested the cis- and trans-effects of AD-associated CGS on brain DNA methylation to mRNA expression. For the genes with significant cis- and trans-effects, we investigated their enriched pathways. We identified six genetic loci in CH with CGS dosage associated with AD at genome-wide significance levels: ADAM20 (Score = 55.19, P = 4.06 × 10–8), the intergenic region between VRTN and SYNDIG1L (Score = − 37.67, P = 2.25 × 10–9), SPG7 (16q24.3) (Score = 40.51, P = 2.23 × 10–8), PVRL2 (Score = 125.86, P = 1.64 × 10–9), TOMM40 (Score = − 18.58, P = 4.61 × 10–8), and APOE (Score = 75.12, P = 7.26 × 10–26). CGSes in PVRL2 and APOE were also significant in NHW. Except for ADAM20, CGSes in the other five loci were associated with CH brain methylation levels (mQTLs) and CGSes in SPG7, PVRL2, and APOE were also mQTLs in NHW. Except for SYNDIG1L (P = 0.08), brain methylation levels in the other five loci affected downstream mRNA expression in CH (P < 0.05), and methylation at VRTN and TOMM40 were also associated with mRNA expression in NHW. Gene expression in these six loci were also regulated by CpG sites in genes that were enriched in the neuron projection and glutamatergic synapse pathways (FDR < 0.05). DNA methylation at all six loci and mRNA expression of SYNDIG1 and TOMM40 were significantly associated with Braak Stage in CH. In summary, we identified six CpG-related genetic loci associated with AD in CH, harboring both genetic and epigenetic risks. However, their downstream effects on mRNA expression maybe ethnic specific and different from NHW.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00401-024-02778-y.
Peroxisome proliferator-activated receptors (PPARs) may play an important role in the pathomechanism/pathogenesis of Alzheimer’s disease (AD) and several other neurological/neuropsychiatric disorders. AD leads to progressive alterations in the redox state, ion homeostasis, lipids, and protein metabolism. Significant alterations in molecular processes and the functioning of several signaling pathways result in the degeneration and death of synapses and neuronal cells, leading to the most severe dementia. Peroxisome proliferator-activated receptor alpha (PPAR-α) is among the processes affected by AD; it regulates the transcription of genes related to the metabolism of cholesterol, fatty acids, other lipids and neurotransmission, mitochondria biogenesis, and function. PPAR-α is involved in the cholesterol transport to mitochondria, the substrate for neurosteroid biosynthesis. PPAR-α-coding enzymes, such as sulfotransferases, which are responsible for neurosteroid sulfation. The relation between PPAR-α and cholesterol/neurosteroids may have a significant impact on the course and progression of neurodegeneration/neuroprotection processes. Unfortunately, despite many years of intensive studies, the pathogenesis of AD is unknown and therapy for AD and other neurodegenerative diseases is symptomatic, presenting a significant goal and challenge today. This review presents recent achievements in therapeutic approaches for AD, which are targeting PPAR-α and its relation to cholesterol and neurosteroids in AD and neuropsychiatric disorders.
Microglial involvement in Alzheimer’s disease (AD) pathology has emerged as a risk-determining pathogenic event. While apolipoprotein E (APOE) is known to modify AD risk, it remains unclear how microglial apoE impacts brain cognition and AD pathology. Here, using conditional mouse models expressing apoE isoforms in microglia and central nervous system-associated macrophages (CAMs), we demonstrate a cell-autonomous effect of apoE3-mediated microglial activation and function, which are negated by apoE4. Expression of apoE3 in microglia/CAMs improves cognitive function, increases microglia surrounding amyloid plaque and reduces amyloid pathology and associated toxicity, whereas apoE4 expression either compromises or has no effects on these outcomes by impairing lipid metabolism. Single-cell transcriptomic profiling reveals increased antigen presentation and interferon pathways upon apoE3 expression. In contrast, apoE4 expression downregulates complement and lysosomal pathways, and promotes stress-related responses. Moreover, in the presence of mouse endogenous apoE, microglial apoE4 exacerbates amyloid pathology. Finally, we observed a reduction in Lgals3-positive responsive microglia surrounding amyloid plaque and an increased accumulation of lipid droplets in APOE4 human brains and induced pluripotent stem cell-derived microglia. Our findings establish critical isoform-dependent effects of microglia/CAM-expressed apoE in brain function and the development of amyloid pathology, providing new insight into how apoE4 vastly increases AD risk.
Jointly profiling the transcriptome, chromatin accessibility and other molecular properties of single cells offers a powerful way to study cellular diversity. Here we present MultiVI, a probabilistic model to analyze such multiomic data and leverage it to enhance single-modality datasets. MultiVI creates a joint representation that allows an analysis of all modalities included in the multiomic input data, even for cells for which one or more modalities are missing. It is available at scvi-tools.org.
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.
Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce ‘bridge integration’, a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a ‘dictionary’, which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit (http://www.satijalab.org/seurat), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities.
As interest in using unsupervised deep learning models to analyze gene expression data has grown, an increasing number of methods have been developed to make these models more interpretable. These methods can be separated into two groups: post hoc analyses of black box models through feature attribution methods and approaches to build inherently interpretable models through biologically-constrained architectures. We argue that these approaches are not mutually exclusive, but can in fact be usefully combined. We propose PAUSE (https://github.com/suinleelab/PAUSE), an unsupervised pathway attribution method that identifies major sources of transcriptomic variation when combined with biologically-constrained neural network models.
Supplementary information
The online version contains supplementary material available at 10.1186/s13059-023-02901-4.
Myelination contributes not only to the rapid nerve conduction but also to axonal insulation and protection. In the central nervous system (CNS), the initial myelination features a multistep process where oligodendrocyte precursor cells undergo proliferation and migration before differentiating into mature oligodendrocytes. Mature oligodendrocytes then extend processes and wrap around axons to form the multilayered myelin sheath. These steps are tightly regulated by various cellular and molecular mechanisms, such as transcription factors (Olig family, Sox family), growth factors (PDGF, BDNF, FGF-2, IGF), chemokines/cytokines (TGF-β, IL-1β, TNFα, IL-6, IFN-γ), hormones (T3), axonal signals (PSA-NCAM, L1-CAM, LINGO-1, neural activity), and intracellular signaling pathways (Wnt/β-catenin, PI3 K/AKT/mTOR, ERK/MAPK). However, the fundamental mechanisms for initial myelination are yet to be fully elucidated. Identifying pivotal mechanisms for myelination onset, development, and repair will become the focus of future studies. This review focuses on the current understanding of how CNS myelination is initiated and also the regulatory mechanisms underlying the process.
TREM2 is exclusively expressed by microglia in the brain and is strongly linked to the risk for Alzheimer’s disease (AD). As microglial responses modulated by TREM2 are central to AD pathogenesis, enhancing TREM2 signaling has been explored as an AD therapeutic strategy. However, the effective therapeutic window targeting TREM2 is unclear. Here, by using microglia-specific inducible mouse models overexpressing human wild-type TREM2 (TREM2-WT) or R47H risk variant (TREM2-R47H), we show that TREM2-WT expression reduces amyloid deposition and neuritic dystrophy only during the early amyloid seeding stage, whereas TREM2-R47H exacerbates amyloid burden during the middle amyloid rapid growth stage. Single-cell RNA sequencing reveals suppressed disease-associated microglia (DAM) signature and reduced DAM population upon TREM2-WT expression in the early stage, whereas upregulated antigen presentation pathway is detected with TREM2-R47H expression in the middle stage. Together, our findings highlight the dynamic effects of TREM2 in modulating AD pathogenesis and emphasize the beneficial effect of enhancing TREM2 function in the early stage of AD development.
Objective:
Bladder cancer (BC) is the most common cancer in urinary system. Recently, the function of family with sequence similarity 107 member A (FAM107A) has been reported in several carcinomas. This study aimed to reveal the potential role of FAM107A in bladder cancer.
Methods:
Bioinformatics analysis was performed to assess the expression level of FAM107A in BC tissues and adjacent tumor-free bladder tissues. The results were confirmed by quantitative real-time polymerase chain reaction (RT-qPCR), western blot and immunohistochemistry staining in BC tissues and adjacent tumor-free bladder tissues as well as BC cell lines. In addition, plasmid was constructed to increase FAM107A protein level in BC cell lines. The effect of FM107A on cell growth, cell migration and invasion were analyzed by CCK8 assay, wound healing assay and transwell-invasion assay.
Results:
The data showed that FAM107A was remarkably down-regulated in bladder cancer tissues and bladder cancer cell lines. Besides, low FAM107A expression was associated with high tumor grade of patients with bladder cancer. Moreover, the restoration of FAM107A remarkably suppressed the cell growth, migration, and invasion of BC cells.
Conclusion:
In summary, FAM107A might serve as a tumor suppressor which inhibits BC cell proliferation, migration, and invasion. This study suggests that FAM107A can be a candidate new diagnostic marker and possible therapeutic target gene of bladder cancer.
Glial cells play crucial roles in brain homeostasis and pathogenesis of central nervous system (CNS) injuries and diseases. However, the roles of these cells and the molecular mechanisms toward regeneration in the CNS have not been fully understood, especially the capacity of them toward demyelinating diseases. Therefore, there are still very limited therapeutic strategies to restore the function of adult CNS in diseases such as multiple sclerosis (MS). Remyelination, a spontaneous regeneration process in the CNS, requires the involvement of multiple cellular and extracellular components. Promoting remyelination by therapeutic interventions is a promising novel approach to restore the CNS function. Herein, we review the role of glial cells in CNS diseases and injuries. Particularly, we discuss the roles of glia and their functional interactions and regulatory mechanisms in remyelination, as well as the current therapeutic strategies for MS.
Astrocytes are abundant glial cells in the central nervous system (CNS) that perform diverse functions in health and disease. Astrocyte dysfunction is found in numerous diseases, including multiple sclerosis, Alzheimer disease, Parkinson disease, Huntington disease and neuropsychiatric disorders. Astrocytes regulate glutamate and ion homeostasis, cholesterol and sphingolipid metabolism and respond to environmental factors, all of which have been implicated in neurological diseases. Astrocytes also exhibit significant heterogeneity, driven by developmental programmes and stimulus-specific cellular responses controlled by CNS location, cell–cell interactions and other mechanisms. In this Review, we highlight general mechanisms of astrocyte regulation and their potential as therapeutic targets, including drugs that alter astrocyte metabolism, and therapies that target transporters and receptors on astrocytes. Emerging ideas, such as engineered probiotics and glia-to-neuron conversion therapies, are also discussed. We further propose a concise nomenclature for astrocyte subsets that we use to highlight the roles of astrocytes and specific subsets in neurological diseases. In this Review, Quintana and colleagues discuss astrocytes, a type of glial cell that could be manipulated to treat neurological conditions. Potential astrocyte targets, and the progess made towards developing astrocyte-directed therapies, are highlighted, along with their potential pitfalls. They also propose a novel nomenclature for astrocyte subsets.
Zinc finger proteins (ZNF) are among the most abundant proteins in eukaryotic genomes. It contains several zinc finger domains that can selectively bind to certain DNA or RNA and associate with proteins, therefore, ZNF can regulate gene expression at the transcriptional and translational levels. In terms of neurological diseases, numerous studies have shown that many ZNF are associated with neurological diseases. The purpose of this review is to summarize the types and roles of ZNF in neuropsychiatric disorders. We will describe the structure and classification of ZNF, then focus on the pathophysiological role of ZNF in neuro-related diseases and summarize the mechanism of action of ZNF in neuro-related diseases.
The recent development of experimental methods for measuring chromatin state at single-cell resolution has created a need for computational tools capable of analyzing these datasets. Here we developed Signac, a comprehensive toolkit for the analysis of single-cell chromatin data. Signac enables an end-to-end analysis of single-cell chromatin data, including peak calling, quantification, quality control, dimension reduction, clustering, integration with single-cell gene expression datasets, DNA motif analysis and interactive visualization. Through its seamless compatibility with the Seurat package, Signac facilitates the analysis of diverse multimodal single-cell chromatin data, including datasets that co-assay DNA accessibility with gene expression, protein abundance and mitochondrial genotype. We demonstrate scaling of the Signac framework to analyze datasets containing over 700,000 cells. The Signac framework enables the end-to-end analysis of single-cell chromatin data and interoperability with the Seurat package for multimodal analysis.
The gene-regulatory landscape of the brain is highly dynamic in health and disease, coordinating a menagerie of biological processes across distinct cell types. Here, we present a multi-omic single-nucleus study of 191,890 nuclei in late-stage Alzheimer’s disease (AD), accessible through our web portal, profiling chromatin accessibility and gene expression in the same biological samples and uncovering vast cellular heterogeneity. We identified cell-type-specific, disease-associated candidate cis-regulatory elements and their candidate target genes, including an oligodendrocyte-associated regulatory module containing links to APOE and CLU. We describe cis-regulatory relationships in specific cell types at a subset of AD risk loci defined by genome-wide association studies, demonstrating the utility of this multi-omic single-nucleus approach. Trajectory analysis of glial populations identified disease-relevant transcription factors, such as SREBF1, and their regulatory targets. Finally, we introduce single-nucleus consensus weighted gene coexpression analysis, a coexpression network analysis strategy robust to sparse single-cell data, and perform a systems-level analysis of the AD transcriptome. An integrative analysis of single-nucleus assay for transposase-accessible chromatin with sequencing and RNA sequencing in normal and Alzheimer’s disease brain tissue identifies cell-type-specific cis-regulatory elements and candidate target genes at disease-associated loci.
The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
Genetic Alzheimer’s disease (AD) risk factors associate with reduced defensive amyloid β plaque-associated microglia (AβAM), but the contribution of modifiable AD risk factors to microglial dysfunction is unknown. In AD mouse models, we observe concomitant activation of the hypoxia-inducible factor 1 (HIF1) pathway and transcription of mitochondrial-related genes in AβAM, and elongation of mitochondria, a cellular response to maintain aerobic respiration under low nutrient and oxygen conditions. Overactivation of HIF1 induces microglial quiescence in cellulo, with lower mitochondrial respiration and proliferation. In vivo, overstabilization of HIF1, either genetically or by exposure to systemic hypoxia, reduces AβAM clustering and proliferation and increases Aβ neuropathology. In the human AD hippocampus, upregulation of HIF1α and HIF1 target genes correlates with reduced Aβ plaque microglial coverage and an increase of Aβ plaque-associated neuropathology. Thus, hypoxia (a modifiable AD risk factor) hijacks microglial mitochondrial metabolism and converges with genetic susceptibility to cause AD microglial dysfunction.
Background
Alzheimer’s disease (AD) is associated with abnormal tau and amyloid-β accumulation in the brain, leading to neurofibrillary tangles, neuropil threads and extracellular amyloid-β plaques. Treatment is limited to symptom management, a disease-modifying therapy is not available. To advance search of therapy approaches, there is a continued need to identify targets for disease intervention both by confirming existing hypotheses and generating new hypotheses.
Methods
We conducted a mRNA-seq study to identify genes associated with AD in post-mortem brain samples from the superior temporal gyrus (STG, n = 76), and inferior frontal gyrus (IFG, n = 65) brain regions. Differentially expressed genes (DEGs) were identified correcting for gender and surrogate variables to capture hidden variation not accounted for by pre-planned covariates. The results from this study were compared with the transcriptome studies from the Accelerated Medicine Partnership – Alzheimer’s Disease (AMP-AD) initiative. Over-representation and gene set enrichment analysis (GSEA) was used to identify disease-associated pathways. Protein-protein interaction (PPI) and weighted gene co-expression network analysis (WGCNA) analyses were carried out and co-expressed gene modules and their hub genes were identified and associated with additional phenotypic traits of interest.
Results
Several hundred mRNAs were differentially expressed between AD cases and cognitively normal controls in the STG, while no and few transcripts met the same criteria (adjusted p less than 0.05 and fold change greater than 1.2) in the IFG. The findings were consistent at the gene set level with two out of three cohorts from AMP-AD. PPI analysis suggested that the DEGs were enriched in protein-protein interactions than expected by random chance. Over-representation and GSEA analysis suggested genes playing roles in neuroinflammation, amyloid-β, autophagy and trafficking being important for the AD disease process. At the gene level, 10 genes from the STG that were consistently differentially expressed in this study and in the MSBB study (one of the three cohorts within the AMP-AD initiative) were enriched in microglial genes (TREM2, C3AR1, ITGAX, OLR1, CD74, and HLA-DRA), but also included genes with a broader cell type expression pattern such as CDK2AP1. Among the DEGs with supporting evidence from an independent study, CDK2AP1 (most abundantly expressed in astrocyte) was the transcript with strongest association with antemortem cognitive measure (last Mini-Mental State Examination score) and neurofibril tangle burden but also associated with amyloid plaque burden, while OLR1 was the transcript with strongest association with amyloid plaque burden. GSEA and over-representation analyses revealed gene sets related to immune processes including neutrophil degranulation, interleukin 10 signaling, and interferon gamma signaling, complement and coagulation cascades, phosphatidylinositol signaling system, phagosome and neurotransmitter receptors and postsynaptic signal transmission were enriched from this study and replicated in an independent study.
Conclusion
This study identified differential gene sets, common with two out of three AMP-AD cohorts (ROSMAP and MSBB) and highlights microglia and astrocyte as the key cell-types with DGEs associated with AD clinical diagnosis, and/or antemortem cognitive measure as well as neuropathological indices. Future meta-analysis and causal inferential analysis will be helpful in pinpointing the most relevant pathways and genes to intervene.
Amyloid-β (Aβ) peptides and hyperphosphorylated tau protein are the most important pathological markers of Alzheimer’s disease (AD). Neuroinflammation and oxidative stress are also involved in the development and pathological mechanism of AD. Hypoxia inducible factor-1α (HIF-1α) is a transcriptional factor responsible for cellular and tissue adaption to low oxygen tension. Emerging evidence has revealed HIF-1α as a potential medicinal target for neurodegenerative diseases. On the one hand, HIF-1α increases AβPP processing and Aβ generation by promoting β/γ-secretases and suppressing α-secretases, inactivates microglia and reduces their activity, contributes to microglia death and neuroinflammation, which promotes AD pathogenesis. On the other hand, HIF-1α could resist the toxic effect of Aβ, inhibits tau hyperphosphorylation and promotes microglial activation. In summary, this review focuses on the potential complex roles and the future perspectives of HIF-1α in AD, in order to provide references for seeking new drug targets and treatment methods for AD.
Over the past decade, research has unveiled the intimate relationship between neuroinflammation and neurodegeneration. Microglia and astrocytes react to brain insult by setting up a multimodal inflammatory state and act as the primary defenders and executioners of neuroinflammatory structural and functional changes. Microglia and astrocytes also play critical roles in the maintenance of normal brain function. This intricate balance of homeostatic and neuroinflammatory functions can influence the onset and the course of neurodegenerative diseases. The emergent role of the microglial-astrocytic axis in neurodegenerative disease presents many druggable targets that may have broad therapeutic benefits across neurodegenerative disease. Here, we provide a brief review of the basal function of both microglia and astrocytes, how they are changed in disease states, the significant differences between mouse and human glia, and use of human induced pluripotent stem cells derived from patients to study cell autonomous changes in human astrocytes and microglia.
Background:
Long intergenic non-coding RNA 00511 (LINC00511) is highly expressed in diverse cancers and has a correlation with poor clinical outcomes for cancer patients. In view of contradictory data among published data, we aim to evaluate the prognostic role of LINC00511 for cancer patients.
Methods:
In the present study, a meta-analysis of related studies has been performed to investigate the prognostic significance of LINC00511 in cancer patients. Relevant studies published before December 22, 2019 were systematically searched online in PubMed, EMBASE, Web of Science, and the Cochrane Library databases. The relationship between LINC00511 expression and cancer patients' survival, including overall survival (OS), disease-free survival (DFS)/relapse-free survival (RFS) and progression-free survival (PFS), was evaluated using pooled hazard ratios (HRs) with their corresponding 95% confidence intervals (CIs). The association between LINC00511 expression and clinicopathological features was assessed using odd ratios (ORs) and their corresponding 95% CIs.
Results:
A total of 14 eligible studies with 1883 patients were enrolled in the present meta-analysis. The results demonstrated that elevated expression of LINC00511 was significantly associated with poor OS (HR = 2.62; 95% CI: 2.00-3.45; p < 0.001), PFS (HR = 1.80; 95% CI: 1.29-2.51; p = 0.001) and DFS/RFS (HR = 2.90; 95% CI: 1.04-8.12; p = 0.04). Additionally, High LINC00511 expression was associated with large tumor size (OR = 3.10; 95% CI: 1.97-4.86; p < 0.00001), lymph node metastasis (OR = 3.11; 95% CI: 2.30-4.21; p < 0.00001), advanced clinical stage (OR = 3.95; 95% CI: 2.68-5.81; p < 0.00001), distant metastasis (OR = 2.39; 95% CI: 1.16-4.93; p = 0.02), and disease recurrence (OR = 4.62; 95% CI: 2.47-8.65; p < 0.00001). Meanwhile, no correlation was found between LINC00511 expression and age, gender, and histological grade. These findings were consolidated by the results of bioinformatics analysis.
Conclusions:
Based on our findings, LINC00511 may serve as a novel prognostic biomarker for cancer patients.
Technological advances have enabled the profiling of multiple molecular layers at single-cell resolution, assaying cells from multiple samples or conditions. Consequently, there is a growing need for computational strategies to analyze data from complex experimental designs that include multiple data modalities and multiple groups of samples. We present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of single-cell multi-modal data. MOFA+ reconstructs a low-dimensional representation of the data using computationally efficient variational inference and supports flexible sparsity constraints, allowing to jointly model variation across multiple sample groups and data modalities.
A new graphical display is proposed for partitioning techniques. Each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation. This silhouette shows which objects lie well within their cluster, and which ones are merely somewhere in between clusters. The entire clustering is displayed by combining the silhouettes into a single plot, allowing an appreciation of the relative quality of the clusters and an overview of the data configuration. The average silhouette width provides an evaluation of clustering validity, and might be used to select an ‘appropriate’ number of clusters.
Background:
Large-scale single-cell transcriptomic datasets generated using different technologies contain batch-specific systematic variations that present a challenge to batch-effect removal and data integration. With continued growth expected in scRNA-seq data, achieving effective batch integration with available computational resources is crucial. Here, we perform an in-depth benchmark study on available batch correction methods to determine the most suitable method for batch-effect removal.
Results:
We compare 14 methods in terms of computational runtime, the ability to handle large datasets, and batch-effect correction efficacy while preserving cell type purity. Five scenarios are designed for the study: identical cell types with different technologies, non-identical cell types, multiple batches, big data, and simulated data. Performance is evaluated using four benchmarking metrics including kBET, LISI, ASW, and ARI. We also investigate the use of batch-corrected data to study differential gene expression.
Conclusion:
Based on our results, Harmony, LIGER, and Seurat 3 are the recommended methods for batch integration. Due to its significantly shorter runtime, Harmony is recommended as the first method to try, with the other methods as viable alternatives.
The emerging diversity of single-cell RNA-seq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies, because biological and technical differences are interspersed. We present Harmony (https://github.com/immunogenomics/harmony), an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms while requiring fewer computational resources. Harmony enables the integration of ~10⁶ cells on a personal computer. We apply Harmony to peripheral blood mononuclear cells from datasets with large experimental differences, five studies of pancreatic islet cells, mouse embryogenesis datasets and the integration of scRNA-seq with spatial transcriptomics data.
Oligodendrocytes are the myelinating cells of the central nervous system (CNS) that are generated from oligodendrocyte progenitor cells (OPC). OPC are distributed throughout the CNS and represent a pool of migratory and proliferative adult progenitor cells that can differentiate into oligodendrocytes. The central function of oligodendrocytes is to generate myelin, which is an extended membrane from the cell that wraps tightly around axons. Due to this energy consuming process and the associated high metabolic turnover oligodendrocytes are vulnerable to cytotoxic and excitotoxic factors. Oligodendrocyte pathology is therefore evident in a range of disorders including multiple sclerosis, schizophrenia and Alzheimer’s disease. Deceased oligodendrocytes can be replenished from the adult OPC pool and lost myelin can be regenerated during remyelination, which can prevent axonal degeneration and can restore function. Cell population studies have recently identified novel immunomodulatory functions of oligodendrocytes, the implications of which, e.g., for diseases with primary oligodendrocyte pathology, are not yet clear. Here, we review the journey of oligodendrocytes from the embryonic stage to their role in homeostasis and their fate in disease. We will also discuss the most common models used to study oligodendrocytes and describe newly discovered functions of oligodendrocytes.
Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using self-organizing maps (SOM) to link scATAC-seq regions with scRNA-seq genes that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of heterogeneous data.
Single-cell RNA sequencing can reveal the transcriptional state of cells, yet provides little insight into the upstream regulatory landscape associated with open or accessible chromatin regions. Joint profiling of accessible chromatin and RNA within the same cells would permit direct matching of transcriptional regulation to its outputs. Here, we describe droplet-based single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq), a method that can link a cell’s transcriptome with its accessible chromatin for sequencing at scale. Specifically, accessible sites are captured by Tn5 transposase in permeabilized nuclei to permit, within many droplets in parallel, DNA barcode tagging together with the mRNA molecules from the same cells. To demonstrate the utility of SNARE-seq, we generated joint profiles of 5,081 and 10,309 cells from neonatal and adult mouse cerebral cortices, respectively. We reconstructed the transcriptome and epigenetic landscapes of major and rare cell types, uncovered lineage-specific accessible sites, especially for low-abundance cells, and connected the dynamics of promoter accessibility with transcription level during neurogenesis.
Proper development and function of the mammalian brain is critically dependent on a steady supply of its chief energy source, glucose. Such supply is mediated by the glucose transporter 1 (Glut1) protein. Paucity of the protein stemming from mutations in the associated SLC2A1 gene deprives the brain of glucose and triggers the infantile‐onset neurodevelopmental disorder, Glut1 deficiency syndrome (Glut1 DS). Considering the monogenic nature of Glut1 DS, the disease is relatively straightforward to model and thus study. Accordingly, Glut1 DS serves as a convenient paradigm to investigate the more general cellular and molecular consequences of brain energy failure. Here, we review how Glut1 DS models have informed the biology of a prototypical brain energy failure syndrome, how these models are facilitating the development of promising new treatments for the human disease, and how important insights might emerge from the study of Glut1 DS to illuminate the myriad conditions involving the Glut1 protein.
Genetic studies of autism spectrum disorder (ASD) have revealed multigene variations that converge on synaptic dysfunction. DOCK4, a gene at 7q31.1 that encodes the Rac1 guanine nucleotide exchange factor Dock4, has been identified as a risk gene for ASD and other neuropsychiatric disorders. However, whether and how Dock4 disruption leads to ASD features through a synaptic mechanism remain unexplored. We generated and characterized a line of Dock4 knockout (KO) mice, which intriguingly displayed a series of ASD-like behaviors, including impaired social novelty preference, abnormal isolation-induced pup vocalizations, elevated anxiety, and perturbed object and spatial learning. Mice with conditional deletion of Dock4 in hippocampal CA1 recapitulated social preference deficit in KO mice. Examination in CA1 pyramidal neurons revealed that excitatory synaptic transmission was drastically attenuated in KO mice, accompanied by decreased spine density and synaptic content of AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid)- and NMDA (N-methyl-D-aspartate)-type glutamate receptors. Moreover, Dock4 deficiency markedly reduced Rac1 activity in the hippocampus, which resulted in downregulation of global protein synthesis and diminished expression of AMPA and NMDA receptor subunits. Notably, Rac1 replenishment in the hippocampal CA1 of Dock4 KO mice restored excitatory synaptic transmission and corrected impaired social deficits in these mice, and pharmacological activation of NMDA receptors also restored social novelty preference in Dock4 KO mice. Together, our findings uncover a previously unrecognized Dock4-Rac1-dependent mechanism involved in regulating hippocampal excitatory synaptic transmission and social behavior.
Ovarian cancer is one of the most common female reproductive system malignancies worldwide. Recently, the aberrant long noncoding RNAs (lncRNAs) expression has been identified in multiple cancers. Emerging evidence has highlighted the critical roles of lncRNAs in carcinogenesis and tumor progression, including ovarian cancer. The objective of this study is to comprehensively analyze lncRNAs expression pattern, and explore their clinical significance and underlying mechanism in human ovarian cancer. In this study, we found hundreds of dysregulated lncRNAs in ovarian cancer by performing genome‐wide analysis using RNA sequencing data from Genotype‐Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) project, and three microarray datasets from Gene Expression Omnibus (GEO). Moreover, our results revealed that up‐ or down‐regulation of some lncRNAs expression in ovarian cancer is accompanied by their genomic loci copy number amplification or deletion. Importantly, some lncRNAs expression levels are significantly associated with ovarian cancer patients’ poor prognosis. Further experimental validation and mechanistic investigation indicate that LINC00511 exerts oncogenic function in ovarian cancer cells through interacting with EZH2 and repressing P21 expression. Taken together, the findings in the current study may provide a useful resource of novel ovarian cancer associated lncRNAs and potential diagnostic biomarker and therapeutic targets for ovarian cancer. In this study, we analyzed lncRNAs profile in ovarian cancer specimen by performing genome‐wide analysis using RNA sequencing date from Genotype‐Tissue Expression (GTEx) and TCGA, and three microarray dataset from GEO. We further experimentally validate the oncogenic function and mechanism of LINC00511 in ovarian cancer cells
Alzheimer disease (AD) is the most common neurodegenerative disease. An imbalance between the production and clearance of Aβ (amyloid beta) is considered to be actively involved in AD pathogenesis. Macroautophagy/autophagy is a major cellular pathway leading to the removal of aggregated proteins, and upregulation of autophagy represents a plausible therapeutic strategy to combat overproduction of neurotoxic Aβ. PPARA/PPARα (peroxisome proliferator activated receptor alpha) is a transcription factor that regulates genes involved in fatty acid metabolism and activates hepatic autophagy. We hypothesized that PPARA regulates autophagy in the nervous system and PPARA-mediated autophagy affects AD. We found that pharmacological activation of PPARA by the PPARA agonists gemfibrozil and Wy14643 induces autophagy in human microglia (HM) cells and U251 human glioma cells stably expressing the human APP (amyloid beta precursor protein) mutant (APP-p.M671L) and this effect is PPARA-dependent. Administration of PPARA agonists decreases amyloid pathology and reverses memory deficits and anxiety symptoms in APP-PSEN1ΔE9 mice. There is a reduced level of soluble Aβ and insoluble Aβ in hippocampus and cortex tissues from APP-PSEN1ΔE9 mice after treatment with either gemfibrozil or Wy14643, which promoted the recruitment of microglia and astrocytes to the vicinity of Aβ plaques and enhanced autophagosome biogenesis. These results indicated that PPARA is an important factor regulating autophagy in the clearance of Aβ and suggested gemfibrozil be assessed as a possible treatment for AD.
Abstract Background Neuronal and glial cell interaction is essential for synaptic homeostasis and may be affected in Alzheimer’s disease (AD). We measured cerebrospinal fluid (CSF) neuronal and glia markers along the AD continuum, to reveal putative protective or harmful stage-dependent patterns of activation. Methods We included healthy controls (n = 36) and Aβ-positive (Aβ+) cases (as defined by pathological CSF amyloid beta 1-42 (Aβ42)) with either subjective cognitive decline (SCD, n = 19), mild cognitive impairment (MCI, n = 39), or AD dementia (n = 27). The following CSF markers were measured: a microglial activation marker—soluble triggering receptor expressed on myeloid cells 2 (sTREM2), a marker of microglial inflammatory reaction—monocyte chemoattractant protein-1 (MCP-1), two astroglial activation markers—chitinase-3-like protein 1 (YKL-40) and clusterin, a neuron-microglia communication marker—fractalkine, and the CSF AD biomarkers (Aβ42, phosphorylated tau (P-tau), total tau (T-tau)). Using ANOVA with planned comparisons, or Kruskal-Wallis tests with Dunn’s pairwise comparisons, CSF levels were compared between clinical groups and between stages of biomarker severity using CSF biomarkers for classification based on amyloid pathology (A), tau pathology (T), and neurodegeneration (N) giving rise to the A/T/N score. Results Compared to healthy controls, sTREM2 was increased in SCD (p
Background
Emerging evidence have illustrated the vital role of long noncoding RNAs (lncRNAs) long intergenic non-protein coding RNA 00511 (LINC00511) on the human cancer progression and tumorigenesis. However, the role of LINC00511 in breast cancer tumourigenesis is still unknown. This research puts emphasis on the function of LINC00511 on the breast cancer tumourigenesis and stemness, and investigates the in-depth mechanism.
Methods
The lncRNA and RNA expression were measured using RT-PCR. Protein levels were measured using western blotting analysis. CCK-8, colony formation assays and transwell assay were performed to evaluate the cell proliferation ability and invasion. Sphere-formation assay was also performed for the stemness. Bioinformatic analysis, chromatin immunoprecipitation (ChIP) and luciferase reporter assays were carried to confirm the molecular binding.
Results
LINC00511 was measured to be highly expressed in the breast cancer specimens and the high-expression was correlated with the poor prognosis. Functionally, the gain and loss-of-functional experiments revealed that LINC00511 promoted the proliferation, sphere-formation ability, stem factors (Oct4, Nanog, SOX2) expression and tumor growth in breast cancer cells. Mechanically, LINC00511 functioned as competing endogenous RNA (ceRNA) for miR-185-3p to positively recover E2F1 protein. Furthermore, transcription factor E2F1 bind with the promoter region of Nanog gene to promote it transcription.
Conclusion
In conclusion, our data concludes that LINC00511/miR-185-3p/E2F1/Nanog axis facilitates the breast cancer stemness and tumorigenesis, providing a vital insight for them.
Electronic supplementary material
The online version of this article (10.1186/s13046-018-0945-6) contains supplementary material, which is available to authorized users.
Dysregulation of neurogenesis in the subgranular zone (SGZ) of the hippocampal dentate gyrus has been related to cognitive deficits and memory loss in neurodegenerative diseases, such as Alzheimer’s disease (AD). Members of the B group of SOX transcription factors play critical roles in regulating neurogenesis in the embryonic and adult nervous system, including maintaining the multipotency, renewal, and cell fate decision of neural stem/progenitor cells. The aim of the present study was to evaluate the expression patterns of selected SOXB proteins in the SGZ, of 8-week-old male and female 5xFAD mice, which represent a transgenic model of AD with a severe and very early development of amyloid pathology. Immunohistochemical analysis showed a significant decrease in the number of cells expressing SOX1, SOX2, and SOX21 transcription factors within the SGZ of 5xFAD mice in comparison to their non-transgenic counterparts which coincidences with reduced number of doublecortin immunoreactive immature neurons found in Tg males. Despite observed changes in expressional pattern of examined SOXB proteins, the proliferative capacity evaluated by the number of Ki-67 immunoreactive cells remained unaffected in transgenic mice of both genders. Based on our results, we suggest that SOXB proteins might be considered as new biomarkers for the detection of early impairments in adult neurogenesis in different animal models or/and new targets in human regenerative medicine.
Background:
Progressive supranuclear palsy (PSP) is a parkinsonian neurodegenerative tauopathy affecting brain regions involved in motor function, including the basal ganglia, diencephalon and brainstem. While PSP is largely considered to be a sporadic disorder, cases with suspected familial inheritance have been identified and the common MAPT H1haplotype is a major genetic risk factor. Due to the relatively low prevalence of PSP, large sample sizes can be difficult to achieve, and this has limited the ability to detect true genetic risk factors at the genome-wide statistical threshold for significance in GWAS data. With this in mind, in this study we genotyped the genetic variants that displayed the strongest degree of association with PSP (P<1E-4) in the previous GWAS in a new cohort of 533 pathologically-confirmed PSP cases and 1172 controls, and performed a combined analysis with the previous GWAS data.
Results:
Our findings validate the known association of loci at MAPT, MOBP, EIF2AK3 and STX6 with risk of PSP, and uncover novel associations with SLCO1A2 (rs11568563) and DUSP10 (rs6687758) variants, both of which were classified as non-significant in the original GWAS.
Conclusions:
Resolving the genetic architecture of PSP will provide mechanistic insights and nominate candidate genes and pathways for future therapeutic intervention strategies.
Significance
Biological samples are often heterogeneous mixtures of different types of cells. Suppose we have two single-cell datasets, each providing information on a different cellular feature and generated on a different sample from this mixture. Then, the clustering of cells in the two samples should be coupled as both clusterings are reflecting the underlying cell types in the same mixture. This “coupled clustering” problem is a new problem not covered by existing clustering methods. In this paper, we develop an approach for its solution based on the coupling of two nonnegative matrix factorizations. The method should be useful for integrative single-cell genomics analysis tasks such as the joint analysis of single-cell RNA-sequencing and single-cell ATAC-sequencing data.
Multi-omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy-chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single-cell multi-omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.
The Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD) is a multifaceted open data resource designed to identify cellular and molecular pathologies that underlie Alzheimer’s disease. Integrating neuropathology, single cell and spatial genomics, and longitudinal clinical metadata, SEA-AD is a unique resource for studying the pathogenesis of Alzheimer’s and related dementias.
The incidence of Alzheimer's disease (AD) is rising globally, yet its treatment and prediction of this condition remain challenging due to the complex pathophysiological mechanisms associated with it. Consequently, the objective of the present study was to analyze and characterize the molecular mechanisms underlying ferroptosis‑related genes (FEGs) in the pathogenesis of AD, as well as to construct a prognostic model. The findings will provide new insights for the future diagnosis and treatment of AD. First, the AD dataset GSE33000 from the Gene Expression Omnibus database and the FEGs from FerrDB were obtained. Next, unsupervised cluster analysis was used to obtain the FEGs that were most relevant to AD. Subsequently, enrichment analyses were performed on the FEGs to explore biological functions. Subsequently, the role of these genes in the immune microenvironment was elucidated through CIBERSORT. Then, the optimal machine learning was selected by comparing the performance of different machine learning models. To validate the prediction efficiency, the models were validated using nomograms, calibration curves, decision curve analysis and external datasets. Furthermore, the expression of FEGs between different groups was verified using reverse transcription quantitative PCR and western blot analysis. In AD, alterations in the expression of FEGs affect the aggregation and infiltration of certain immune cells. This indicated that the occurrence of AD is strongly associated with immune infiltration. Finally, the most appropriate machine learning models were selected, and AD diagnostic models and nomograms were built. The present study provided novel insights that enhance understanding with regard to the molecular mechanism of action of FEGs in AD. Moreover, the present study provided biomarkers that may facilitate the diagnosis of AD.
Communication between the periphery and the brain is key for maintaining energy homeostasis. To do so, peripheral signals from the circulation reach the brain via the circumventricular organs (CVOs), which are characterized by fenestrated vessels lacking the protective blood–brain barrier (BBB). Glial cells, by virtue of their plasticity and their ideal location at the interface of blood vessels and neurons, participate in the integration and transmission of peripheral information to neuronal networks in the brain for the neuroendocrine control of whole-body metabolism. Metabolic diseases, such as obesity and type 2 diabetes, can disrupt the brain-to-periphery communication mediated by glial cells, highlighting the relevance of these cell types in the pathophysiology of such complications. An improved understanding of how glial cells integrate and respond to metabolic and humoral signals has become a priority for the discovery of promising therapeutic strategies to treat metabolic disorders. This Review highlights the role of glial cells in the exchange of metabolic signals between the periphery and the brain that are relevant for the regulation of whole-body energy homeostasis. Nampoothiri et al. discuss the most recent literature that places glial cells as key mediators of energy balance through integration of peripheral signals in discrete brain regions, highlighting the relevance of glia in the pathophysiology of metabolic diseases.
Alzheimer's disease (AD) is one of the foremost neurodegenerative diseases, characterized by beta-amyloid (Aβ) plaques and significant progressive memory loss. In AD, astrocytes are proposed to take up and clear Aβ plaques. However, how Aβ induces pathogenesis and memory impairment in AD remains elusive. We report that normal astrocytes show non-cyclic urea metabolism, whereas Aβ-treated astrocytes show switched-on urea cycle with upregulated enzymes and accumulated entering-metabolite aspartate, starting-substrate ammonia, end-product urea, and side-product putrescine. Gene silencing of astrocytic ornithine decarboxylase-1 (ODC1), facilitating ornithine-to-putrescine conversion, boosts urea cycle and eliminates aberrant putrescine and its toxic byproducts ammonia and H2O2 and its end product GABA to recover from reactive astrogliosis and memory impairment in AD. Our findings implicate that astrocytic urea cycle exerts opposing roles of beneficial Aβ detoxification and detrimental memory impairment in AD. We propose ODC1 inhibition as a promising therapeutic strategy for AD to facilitate removal of toxic molecules and prevent memory loss.
Alzheimer's disease (AD) is a neurodegenerative disorder that usually develops slowly and progressively worsens over time. Although there has been increasing research interest in AD, its pathogenesis is still not well understood. Although most studies primarily focus on neurons, recent research findings suggest that glial cells (especially microglia and astrocytes) are associated with AD pathogenesis and might provide various possible therapeutic targets. Growing evidence suggests that microglia can provide protection against AD pathogenesis, as microglia with weakened functions and impaired responses to Aβ proteins are linked with elevated AD risk. Interestingly, numerous findings also suggest that microglial activation can be detrimental to neurons. Indeed, microglia can induce synapse loss via the engulfment of synapses, possibly through a complement-dependent process. Furthermore, they can worsen tau pathology and release inflammatory factors that cause neuronal damage directly or through the activation of neurotoxic astrocytes. Astrocytes play a significant role in various cerebral activities. Their impairment can mediate neurodegeneration and ultimately the retraction of synapses, resulting in AD-related cognitive deficits. Deposition of Aβ can result in astrocyte reactivity, which can further lead to neurotoxic effects and elevated secretion of inflammatory mediators and cytokines. Moreover, glial-induced inflammation in AD can exert both beneficial and harmful effects. Understanding the activities of astrocytes and microglia in the regulation of AD pathogenesis would facilitate the development of novel therapies. In this article, we address the implications of microglia and astrocytes in AD pathogenesis. We also discuss the mechanisms of therapeutic agents that exhibit anti-inflammatory effects against AD.
Resolving glial contributions to Alzheimer’s disease (AD) is necessary because changes in neuronal function, such as reduced synaptic density, altered electrophysiological properties, and degeneration, are not entirely cell autonomous. To improve understanding of transcriptomic heterogeneity in glia during AD, we used single-nuclei RNA sequencing (snRNA-seq) to characterize astrocytes and oligodendrocytes from apolipoprotein (APOE) Ɛ2/3 human AD and age- and genotype-matched non-symptomatic (NS) brains. We enriched astrocytes before sequencing and characterized pathology from the same location as the sequenced material. We characterized baseline heterogeneity in both astrocytes and oligodendrocytes and identified global and subtype-specific transcriptomic changes between AD and NS astrocytes and oligodendrocytes. We also took advantage of recent human and mouse spatial transcriptomics resources to localize heterogeneous astrocyte subtypes to specific regions in the healthy and inflamed brain. Finally, we integrated our data with published AD snRNA-seq datasets, highlighting the power of combining datasets to resolve previously unidentifiable astrocyte subpopulations.
Cell differentiation and function are regulated across multiple layers of gene regulation, including modulation of gene expression by changes in chromatin accessibility. However, differentiation is an asynchronous process precluding a temporal understanding of regulatory events leading to cell fate commitment. Here we developed simultaneous high-throughput ATAC and RNA expression with sequencing (SHARE-seq), a highly scalable approach for measurement of chromatin accessibility and gene expression in the same single cell, applicable to different tissues. Using 34,774 joint profiles from mouse skin, we develop a computational strategy to identify cis-regulatory interactions and define domains of regulatory chromatin (DORCs) that significantly overlap with super-enhancers. During lineage commitment, chromatin accessibility at DORCs precedes gene expression, suggesting that changes in chromatin accessibility may prime cells for lineage commitment. We computationally infer chromatin potential as a quantitative measure of chromatin lineage-priming and use it to predict cell fate outcomes. SHARE-seq is an extensible platform to study regulatory circuitry across diverse cells in tissues.
A major challenge in the field of neurodegenerative diseases and brain aging is to identify the body's intrinsic mechanism that could sense the central nervous system (CNS) damage early and protect the brain from neurodegeneration. Accumulating evidence suggests that disease-associated microglia (DAM), a recently identified subset of CNS resident macrophages found at sites of neurodegeneration, might play such a protective role. Here, we propose that microglia are endowed with a dedicated sensory mechanism, which includes the Trem2 signaling pathway, to detect damage within the CNS in the form of neurodegeneration-associated molecular patterns (NAMPs). Combining data from transcriptional analysis of DAM at single-cell level and from human genome-wide association studies (GWASs), we discuss potential function of different DAM pathways in the diseased brain and outline how manipulating DAM may create new therapeutic opportunities.