Revealing cell vulnerability in Alzheimer's disease by single-cell transcriptomics

Mining single-cell data for cell type-disease associations

A robust understanding of the cellular mechanisms underlying diseases sets the foundation for the effective design of drugs and other interventions. The wealth of existing single-cell atlases offers the opportunity to uncover high-resolution information on expression patterns across various cell types and time points. To better understand the associations between cell types and diseases, we leveraged previously developed tools to construct a standardized analysis pipeline and systematically explored associations across four single-cell datasets, spanning a range of tissue types, cell types and developmental time periods. We utilized a set of existing tools to identify co-expression modules and temporal patterns per cell type and then investigated these modules for known disease and phenotype enrichments. Our pipeline reveals known and novel putative cell type-disease associations across all investigated datasets. In addition, we found that automatically discovered gene co-expression modules and temporal clusters are enriched for drug targets, suggesting that our analysis could be used to identify novel therapeutic targets.

From Fundamentals to Innovation in Alzheimer's Disease: Molecular Findings and Revolutionary Therapies

Alzheimer's disease (AD) is a global health concern and the leading cause of dementia in the elderly. The prevalence of this neurodegenerative condition is projected to increase concomitantly with increased life expectancy, resulting in a significant economic burden. With very few FDA-approved disease-modifying drugs available for AD, there is an urgent need to develop new compounds capable of impeding the progression of the disease. Given the unclear etiopathogenesis of AD, this review emphasizes the underlying mechanisms of this condition. It explores not only well-studied aspects, such as the accumulation of Aβ plaques and neurofibrillary tangles, but also novel areas, including glymphatic and lymphatic pathways, microbiota and the gut-brain axis, serotoninergic and autophagy alterations, vascular dysfunction, the metal hypothesis, the olfactory pathway, and oral health. Furthermore, the potential molecular targets arising from all these mechanisms have been reviewed, along with novel promising approaches such as nanoparticle-based therapy, neural stem cell transplantation, vaccines, and CRISPR-Cas9-mediated genome editing techniques. Taking into account the overlap of these various mechanisms, individual and combination therapies emerge as the future direction in the AD strategy.

Identification of altered immune cell types and molecular mechanisms in Alzheimer's disease progression by single-cell RNA sequencing

Introduction

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by gradual loss of cognitive function. Understanding the molecular mechanisms is crucial for developing effective therapies.

Methods

Data from single-cell RNA sequencing (scRNA-seq) in the GSE181279 dataset and gene chips in the GSE63060 and GSE63061 datasets were collected and analyzed to identify immune cell types and differentially expressed genes. Cell communication, pseudotime trajectory, enrichment analysis, co- expression network, and short time-series expression miner were analyzed to identify disease-specific molecular and cellular mechanisms.

Results

We identified eight cell types (B cells, monocytes, natural killer cells, gamma-delta T cells, CD8+ T cells, Tem/Temra cytotoxic T cells, Tem/Trm cytotoxic T cells, and mucosal-associated invariant T cells) using scRNA-seq. AD samples were enriched in monocytes, CD8+ T cells, Tem/Temra cytotoxic T cells, and Tem/Trm cytotoxic T cells, whereas samples from healthy controls were enriched in natural killer and mucosal-associated invariant T cells. Five co-expression modules that were identified through weighted gene correlation network analysis were enriched in immune- inflammatory pathways. Candidate genes with higher area under the receiver operating characteristic curve values were screened, and the expression trend of Ubiquitin-Fold Modifier Conjugating Enzyme 1 (UFC1) gradually decreased from healthy controls to mild cognitive impairment and then to AD.

Conclusion

Our study suggests that peripheral immune cells may be a potential therapeutic target for AD. Candidate genes, particularly UFC1, may serve as potential biomarkers for progression of AD.

Association of RDoC dimensions with post-mortem brain transcriptional profiles in Alzheimer's Disease

INTRODUCTION

Neuropsychiatric symptoms are common in people with Alzheimer's disease (AD) across all severity stages. Their heterogeneous presentation and variable temporal association with cognitive decline suggest shared and distinct biological mechanisms. We hypothesized that specific patterns of gene expression associate with distinct NIMH Research Domain Criteria (RDoC) domains in AD.

METHODS

Post-mortem bulk RNAseq on the insula and anterior cingulate cortex from 60 brain donors representing the spectrum of canonical AD neuropathology combined with natural language processing approaches based on the RDoC Clinical Domains.

RESULTS

Distinct sets of >100 genes (p FDR <0.05) were specifically associated with at least one clinical domain (Cognitive, Social, Negative, Positive, Arousal). In addition, dysregulation of immune response pathways was shared across domains and brain regions.

DISCUSSION

Our findings provide evidence for distinct transcriptional profiles associated with RDoC domains suggesting that each dimension is characterized by specific sets of genes providing insight into the underlying mechanisms.

Tryptophan Metabolism in Alzheimer's Disease with the Involvement of Microglia and Astrocyte Crosstalk and Gut-Brain Axis

Alzheimer's disease (AD) is an age-dependent neurodegenerative disease characterized by extracellular Amyloid Aβ peptide (Aβ) deposition and intracellular Tau protein aggregation. Glia, especially microglia and astrocytes are core participants during the progression of AD and these cells are the mediators of Aβ clearance and degradation. The microbiota-gut-brain axis (MGBA) is a complex interactive network between the gut and brain involved in neurodegeneration. MGBA affects the function of glia in the central nervous system (CNS), and microbial metabolites regulate the communication between astrocytes and microglia; however, whether such communication is part of AD pathophysiology remains unknown. One of the potential links in bilateral gut-brain communication is tryptophan (Trp) metabolism. The microbiota-originated Trp and its metabolites enter the CNS to control microglial activation, and the activated microglia subsequently affect astrocyte functions. The present review highlights the role of MGBA in AD pathology, especially the roles of Trp per se and its metabolism as a part of the gut microbiota and brain communications. We (i) discuss the roles of Trp derivatives in microglia-astrocyte crosstalk from a bioinformatics perspective, (ii) describe the role of glia polarization in the microglia-astrocyte crosstalk and AD pathology, and (iii) summarize the potential of Trp metabolism as a therapeutic target. Finally, we review the role of Trp in AD from the perspective of the gut-brain axis and microglia, as well as astrocyte crosstalk, to inspire the discovery of novel AD therapeutics.

Integrating amyloid and tau imaging with proteomics and genomics in Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disease and is characterized by the aggregation of β-amyloid (Aβ) and tau in the brain. Breakthroughs in disease-modifying treatments targeting Aβ bring new hope for the management of AD. But to effectively modify and someday even prevent AD, a better understanding is needed of the biological mechanisms that underlie and link Aβ and tau in AD. Developments of high-throughput omics, including genomics, proteomics, and transcriptomics, together with molecular imaging of Aβ and tau with positron emission tomography (PET), allow us to discover and understand the biological pathways that regulate the aggregation and spread of Aβ and tau in living humans. The field of integrated omics and PET studies of Aβ and tau in AD is growing rapidly. We here provide an update of this field, both in terms of biological insights and in terms of future clinical implications of integrated omics-molecular imaging studies.

Single-Nucleus Landscape of Glial Cells and Neurons in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease with a projected significant increase in incidence. Therefore, this study analyzed single-nucleus AD data to provide a theoretical basis for the clinical development and treatment of AD. We downloaded AD-related monocyte data from the Gene Expression Omnibus database, annotated cells, compared cell abundance between groups, and investigated glial and neuronal cell biological processes and pathways through functional enrichment analysis. Furthermore, we constructed a global regulatory network for AD based on cell communication and ecological analyses. Our findings revealed increased abundance of Capping Protein Regulator And Myosin 1 linker 1 (CARMIL1)+ astrocytes (AST), Immunoglobulin Superfamily Member 21 (IGSF21)+ microglia (MIC), SRY-Box Transcription Factor 6 (SOX6)+ inhibitory neurons (InNeu), and laminin alpha-2 chain (LAMA2)+ oligodendrocytes (OLI) cell subgroups in tissues of patients with AD, while prostaglandin D2 synthase (PTGDS)+ AST, Src Family Tyrosine Kinase (FYN)+ MIC, and Proteolipid Protein 1 (PLP1)+ InNeu subgroups specifically decreased. We found that the cell phenotype of patients with AD shifted from a simpler to a more complex state compared to the control group. Cell communication analysis revealed strong communication between MIC and NEU. Furthermore, AST, MIC, NEU, and OLI were involved in oxidative stress- and inflammation-related pathways, potentially contributing to disease development. This study provides a theoretical basis for further exploring the specific mechanisms underlying AD.

Induced pluripotent stem cell models as a tool to investigate and test fluid biomarkers in Alzheimer's disease and frontotemporal dementia

Neurodegenerative diseases are increasing in prevalence and comprise a large socioeconomic burden on patients and their caretakers. The need for effective therapies and avenues for disease prevention and monitoring is of paramount importance. Fluid biomarkers for neurodegenerative diseases have gained a variety of uses, including informing participant selection for clinical trials, lending confidence to clinical diagnosis and disease staging, determining prognosis, and monitoring therapeutic response. Their role is expected to grow as disease-modifying therapies start to be available to a broader range of patients and as prevention strategies become established. Many of the underlying molecular mechanisms of currently used biomarkers are incompletely understood. Animal models and in vitro systems using cell lines have been extensively employed but face important translatability limitations. Induced pluripotent stem cell (iPSC) technology, where a theoretically unlimited range of cell types can be reprogrammed from peripheral cells sampled from patients or healthy individuals, has gained prominence over the last decade. It is a promising avenue to study physiological and pathological biomarker function and response to experimental therapeutics. Such systems are amenable to high-throughput drug screening or multiomics readouts such as transcriptomics, lipidomics, and proteomics for biomarker discovery, investigation, and validation. The present review describes the current state of biomarkers in the clinical context of neurodegenerative diseases, with a focus on Alzheimer's disease and frontotemporal dementia. We include a discussion of how iPSC models have been used to investigate and test biomarkers such as amyloid-β, phosphorylated tau, neurofilament light chain or complement proteins, and even nominate novel biomarkers. We discuss the limitations of current iPSC methods, mentioning alternatives such as coculture systems and three-dimensional organoids which address some of these concerns. Finally, we propose exciting prospects for stem cell transplantation paradigms using animal models as a preclinical tool to study biomarkers in the in vivo context.

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

Leveraging electronic health records and knowledge networks for Alzheimer's disease prediction and sex-specific biological insights

Identification of Alzheimer's disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.72 (7 years prior) to 0.81 (1 day prior). We further harnessed matched cohort models to identify conditions with predictive power before AD onset. Knowledge networks highlight shared genes between multiple top predictors and AD (for example, APOE, ACTB, IL6 and INS). Genetic colocalization analysis supports AD association with hyperlipidemia at the APOE locus, as well as a stronger female AD association with osteoporosis at a locus near MS4A6A. We therefore show how clinical data can be utilized for early AD prediction and identification of personalized biological hypotheses.

Single-cell RNA sequencing reveals common and unique gene expression profiles in primary CD4+ T cells latently infected with HIV under different conditions

Background

The latent HIV reservoir represents the major barrier to a cure. One curative strategy is targeting diseased cells for elimination based on biomarkers that uniquely define these cells. Single-cell RNA sequencing (scRNA-seq) has enabled the identification of gene expression profiles associated with disease at the single-cell level. Because HIV provirus in many cells during latency is not entirely silent, it became possible to determine gene expression patterns in a subset of cells latently infected with HIV.

Objective

The primary objective of this study was the identification of the gene expression profiles of single latently infected CD4+ T cells using scRNA-seq. Different conditions of latency establishment were considered. The identified profiles were then explored to prioritize the identified genes for future experimental validation.

Methods

To facilitate gene prioritization, three approaches were used. First, we characterized and compared the gene expression profiles of HIV latency established in different environments: in cells that encountered an activation stimulus and then returned to quiescence, and in resting cells that were infected directly via cell-to-cell viral transmission from autologous activated, productively infected cells. Second, we characterized and compared the gene expression profiles of HIV latency established with viruses of different tropisms, using an isogenic pair of CXCR4- and CCR5-tropic viruses. Lastly, we used proviral expression patterns in cells from people with HIV to more accurately define the latently infected cells in vitro.

Results

Our analyses demonstrated that a subset of genes is expressed differentially between latently infected and uninfected cells consistently under most conditions tested, including cells from people with HIV. Our second important observation was the presence of latency signatures, associated with variable conditions when latency was established, including cellular exposure and responsiveness to a T cell receptor stimulus and the tropism of the infecting virus.

Conclusion

Common signatures, specifically genes that encode proteins localized to the cell surface, should be prioritized for further testing at the protein level as biomarkers for the ability to enrich or target latently infected cells. Cell- and tropism-dependent biomarkers may need to be considered in developing targeting strategies to ensure that all the different reservoir subsets are eliminated.

Evaluation of Cell-Specific Alterations in Alzheimer's Disease and Relevance of In Vitro Models

Alzheimer's disease (AD) is a neurodegenerative disorder classically characterized by two neuropathological hallmarks: β-amyloid plaques and tau tangles in the brain. However, the cellular and molecular mechanisms involved in AD are still elusive, which dampens the possibility of finding new and more effective therapeutic interventions. Current in vitro models are limited in modelling the complexity of AD pathogenesis. In this study, we aimed to characterize the AD expression signature upon a meta-analysis of multiple human datasets, including different cell populations from various brain regions, and compare cell-specific alterations in AD patients and in vitro models to highlight the appropriateness and the limitations of the currently available models in recapitulating AD pathology. The meta-analysis showed consistent enrichment of the Rho GTPases signaling pathway among different cell populations and in the models. The accuracy of in vitro models was higher for neurons and lowest for astrocytes. Our study underscores the particularly low fidelity in modelling down-regulated genes across all cell populations. The top enriched pathways arising from meta-analysis of human data differ from the enriched pathways arising from the overlap. We hope that our data will prove useful in indicating a starting point in the development of future, more complex, 3D in vitro models.

Single-cell transcriptomic and genomic changes in the aging human brain

Aging brings dysregulation of various processes across organs and tissues, often stemming from stochastic damage to individual cells over time. Here, we used a combination of single-nucleus RNA-sequencing and single-cell whole-genome sequencing to identify transcriptomic and genomic changes in the prefrontal cortex of the human brain across life span, from infancy to centenarian. We identified infant-specific cell clusters enriched for the expression of neurodevelopmental genes, and a common down-regulation of cell-essential homeostatic genes that function in ribosomes, transport, and metabolism during aging across cell types. Conversely, expression of neuron-specific genes generally remains stable throughout life. We observed a decrease in specific DNA repair genes in aging, including genes implicated in generating brain somatic mutations as indicated by mutation signature analysis. Furthermore, we detected gene-length-specific somatic mutation rates that shape the transcriptomic landscape of the aged human brain. These findings elucidate critical aspects of human brain aging, shedding light on transcriptomic and genomics dynamics.

Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation

Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.

Proteomic analysis across patient iPSC-based models and human post-mortem hippocampal tissue reveals early cellular dysfunction and progression of Alzheimer's disease pathogenesis

The hippocampus is a primary region affected in Alzheimer's disease (AD). Because AD postmortem brain tissue is not available prior to symptomatic stage, we lack understanding of early cellular pathogenic mechanisms. To address this issue, we examined the cellular origin and progression of AD pathogenesis by comparing patient-based model systems including iPSC-derived brain cells transplanted into the mouse brain hippocampus. Proteomic analysis of the graft enabled the identification of pathways and network dysfunction in AD patient brain cells, associated with increased levels of Aβ-42 and β-sheet structures. Interestingly, the host cells surrounding the AD graft also presented alterations in cellular biological pathways. Furthermore, proteomic analysis across human iPSC-based models and human post-mortem hippocampal tissue projected coherent longitudinal cellular changes indicative of early to end stage AD cellular pathogenesis. Our data showcase patient-based models to study the cell autonomous origin and progression of AD pathogenesis.

Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation

Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.

A single-cell transcriptome atlas of glial diversity in the human hippocampus across the postnatal lifespan

The molecular diversity of glia in the human hippocampus and their temporal dynamics over the lifespan remain largely unknown. Here, we performed single-nucleus RNA sequencing to generate a transcriptome atlas of the human hippocampus across the postnatal lifespan. Detailed analyses of astrocytes, oligodendrocyte lineages, and microglia identified subpopulations with distinct molecular signatures and revealed their association with specific physiological functions, age-dependent changes in abundance, and disease relevance. We further characterized spatiotemporal heterogeneity of GFAP-enriched astrocyte subpopulations in the hippocampal formation using immunohistology. Leveraging glial subpopulation classifications as a reference map, we revealed the diversity of glia differentiated from human pluripotent stem cells and identified dysregulated genes and pathological processes in specific glial subpopulations in Alzheimer's disease (AD). Together, our study significantly extends our understanding of human glial diversity, population dynamics across the postnatal lifespan, and dysregulation in AD and provides a reference atlas for stem-cell-based glial differentiation.