Integrated multimodal cell atlas of Alzheimer's disease

The single-cell opioid responses in the context of HIV (SCORCH) consortium

Substance use disorders (SUD) and drug addiction are major threats to public health, impacting not only the millions of individuals struggling with SUD, but also surrounding families and communities. One of the seminal challenges in treating and studying addiction in human populations is the high prevalence of co-morbid conditions, including an increased risk of contracting a human immunodeficiency virus (HIV) infection. Of the ~15 million people who inject drugs globally, 17% are persons with HIV. Conversely, HIV is a risk factor for SUD because chronic pain syndromes, often encountered in persons with HIV, can lead to an increased use of opioid pain medications that in turn can increase the risk for opioid addiction. We hypothesize that SUD and HIV exert shared effects on brain cell types, including adaptations related to neuroplasticity, neurodegeneration, and neuroinflammation. Basic research is needed to refine our understanding of these affected cell types and adaptations. Studying the effects of SUD in the context of HIV at the single-cell level represents a compelling strategy to understand the reciprocal interactions among both conditions, made feasible by the availability of large, extensively-phenotyped human brain tissue collections that have been amassed by the Neuro-HIV research community. In addition, sophisticated animal models that have been developed for both conditions provide a means to precisely evaluate specific exposures and stages of disease. We propose that single-cell genomics is a uniquely powerful technology to characterize the effects of SUD and HIV in the brain, integrating data from human cohorts and animal models. We have formed the Single-Cell Opioid Responses in the Context of HIV (SCORCH) consortium to carry out this strategy.

All the single cells: Single-cell transcriptomics/epigenomics experimental design and analysis considerations for glial biologists

Single-cell transcriptomics, epigenomics, and other 'omics applied at single-cell resolution can significantly advance hypotheses and understanding of glial biology. Omics technologies are revealing a large and growing number of new glial cell subtypes, defined by their gene expression profile. These subtypes have significant implications for understanding glial cell function, cell-cell communications, and glia-specific changes between homeostasis and conditions such as neurological disease. For many, the training in how to analyze, interpret, and understand these large datasets has been through reading and understanding literature from other fields like biostatistics. Here, we provide a primer for glial biologists on experimental design and analysis of single-cell RNA-seq datasets. Our goal is to further the understanding of why decisions are made about datasets and to enhance biologists' ability to interpret and critique their work and the work of others. We review the steps involved in single-cell analysis with a focus on decision points and particular notes for glia. The goal of this primer is to ensure that single-cell 'omics experiments continue to advance glial biology in a rigorous and replicable way.

Generation of Mammalian Astrocyte Functional Heterogeneity

Mammalian astrocytes have regional roles within the brain parenchyma. Indeed, the notion that astrocytes are molecularly heterogeneous could help explain how the central nervous system (CNS) retains embryonic positional information through development into specialized regions into adulthood. A growing body of evidence supports the concept of morphological and molecular differences between astrocytes in different brain regions, which might relate to their derivation from regionally patterned radial glia and/or local neuron inductive cues. Here, we review evidence for regionally encoded functions of astrocytes to provide an integrated concept on lineage origins and heterogeneity to understand regional brain organization, as well as emerging technologies to identify and further investigate novel roles for astrocytes.

scEMB: Learning context representation of genes based on large-scale single-cell transcriptomics

Background

The rapid advancement of single-cell transcriptomic technologies has led to the curation of millions of cellular profiles, providing unprecedented insights into cellular heterogeneity across various tissues and developmental stages. This growing wealth of data presents an opportunity to uncover complex gene-gene relationships, yet also poses significant computational challenges.

Results

We present scEMB, a transformer-based deep learning model developed to capture context-aware gene embeddings from large-scale single-cell transcriptomics data. Trained on over 30 million single-cell transcriptomes, scEMB utilizes an innovative binning strategy that integrates data across multiple platforms, effectively preserving both gene expression hierarchies and cell-type specificity. In downstream tasks such as batch integration, clustering, and cell type annotation, scEMB demonstrates superior performance compared to existing models like scGPT and Geneformer. Notably, scEMB excels in silico correlation analysis, accurately predicting gene perturbation effects in CRISPR-edited datasets and microglia state transition, identifying a few known Alzheimer's disease (AD) risks genes in top gene list. Additionally, scEMB offers robust fine-tuning capabilities for domain-specific applications, making it a versatile tool for tackling diverse biological problems such as therapeutic target discovery and disease modeling.

Conclusions

scEMB represents a powerful tool for extracting biologically meaningful insights from complex gene expression data. Its ability to model in silico perturbation effects and conduct correlation analyses in the embedding space highlights its potential to accelerate discoveries in precision medicine and therapeutic development.

A suite of enhancer AAVs and transgenic mouse lines for genetic access to cortical cell types

The mammalian cortex is comprised of cells classified into types according to shared properties. Defining the contribution of each cell type to the processes guided by the cortex is essential for understanding its function in health and disease. We used transcriptomic and epigenomic cortical cell type taxonomies from mouse and human to define marker genes and putative enhancers and created a large toolkit of transgenic lines and enhancer AAVs for selective targeting of cortical cell populations. We report evaluation of fifteen new transgenic driver lines, two new reporter lines, and >800 different enhancer AAVs covering most subclasses of cortical cells. The tools reported here as well as the scaled process of tool creation and modification enable diverse experimental strategies towards understanding mammalian cortex and brain function.

gsQTL: Associating genetic risk variants with gene sets by exploiting their shared variability

To investigate the functional significance of genetic risk loci identified through genome-wide association studies (GWASs), genetic loci are linked to genes based on their capacity to account for variation in gene expression, resulting in expression quantitative trait loci (eQTL). Following this, gene set analyses are commonly used to gain insights into functionality. However, the efficacy of this approach is hampered by small effect sizes and the burden of multiple testing. We propose an alternative approach: instead of examining the cumulative associations of individual genes within a gene set, we consider the collective variation of the entire gene set. We introduce the concept of gene set QTL (gsQTL), and show it to be more adept at identifying links between genetic risk variants and specific gene sets. Notably, gsQTL experiences less susceptibility to inflation or deflation of significant enrichments compared with conventional methods. Furthermore, we demonstrate the broader applicability of shared variability within gene sets. This is evident in scenarios such as the coordinated regulation of genes by a transcription factor or coordinated differential expression.

Single-Cell Cortical Transcriptomics Reveals Common and Distinct Changes in Cell-Cell Communication in Alzheimer's and Parkinson's Disease

Alzheimer's disease (AD) and Parkinson's disease (PD) cause significant neuronal loss and severely impair daily living. Despite different clinical manifestations, these disorders share common pathological molecular hallmarks, including mitochondrial dysfunction and synaptic degeneration. A detailed comparison of molecular changes at single-cell resolution in the cortex, as one of the main brain regions affected in both disorders, may reveal common susceptibility factors and disease mechanisms. We performed single-cell transcriptomic analyses of post-mortem cortical tissue from AD and PD subjects and controls to identify common and distinct disease-associated changes in individual genes, cellular pathways, molecular networks, and cell-cell communication events, and to investigate common mechanisms. The results revealed significant disease-specific, shared, and opposing gene expression changes, including cell type-specific signatures for both diseases. Hypoxia signaling and lipid metabolism emerged as significantly modulated cellular processes in both AD and PD, with contrasting expression alterations between the two diseases. Furthermore, both pathway and cell-cell communication analyses highlighted shared significant alterations involving the JAK-STAT signaling pathway, which has been implicated in the inflammatory response in several neurodegenerative disorders. Overall, the analyses revealed common and distinct alterations in gene signatures, pathway activities, and gene regulatory subnetworks in AD and PD. The results provide insights into coordinated changes in pathway activity and cell-cell communication that may guide future diagnostics and therapeutics.

Single-cell multi-omics analysis reveals cooperative transcription factors for gene regulation in oligodendrocytes

Oligodendrocytes are the myelinating cells within the central nervous system. Many oligodendrocyte genes have been associated with brain disorders. However, how transcription factors (TFs) cooperate for gene regulation in oligodendrocytes remains largely uncharacterized. To address this, we integrated scRNA-seq and scATAC-seq data to identify the cooperative TFs that co-regulate the target gene (TG) expression in oligodendrocytes. First, we identified co- binding TF pairs whose binding sites overlapped in oligodendrocyte-specific regulatory regions. Second, we trained a deep learning model to predict the expression level of each TG using the expression levels of co-binding TFs. Third, using the trained models, we computed the TF importance and TF-TF interaction scores for predicting TG expression by the Shapley interaction scores. We found that the co-binding TF pairs involving known important TF pairs for oligodendrocyte differentiation, such as SOX10-TCF12, SOX10-MYRF, and SOX10-OLIG2, exhibited significantly higher Shapley scores than others (t-test, p-value < 1e-4). Furthermore, we identified 153 oligodendrocyte-associated eQTLs that reside in oligodendrocyte-specific enhancers or promoters where their eGenes (TGs) are regulated by cooperative TFs, suggesting potential novel regulatory roles from genetic variants. We also experimentally validated some identified TF pairs such as SOX10-OLIG2 and SOX10-NKX2.2 by co-enrichment analysis, using ChIP-seq data from rat peripheral nerve.

In-vivo neuronal dysfunction by Aβ and tau overlaps with brain-wide inflammatory mechanisms in Alzheimer's disease

The molecular mechanisms underlying neuronal dysfunction in Alzheimer's disease (AD) remain uncharacterized. Here, we identify genes, molecular pathways and cellular components associated with whole-brain dysregulation caused by amyloid-beta (Aβ) and tau deposits in the living human brain. We obtained in-vivo resting-state functional MRI (rs-fMRI), Aβ- and tau-PET for 47 cognitively unimpaired and 16 AD participants from the Translational Biomarkers in Aging and Dementia cohort. Adverse neuronal activity impacts by Aβ and tau were quantified with personalized dynamical models by fitting pathology-mediated computational signals to the participant's real rs-fMRIs. Then, we detected robust brain-wide associations between the spatial profiles of Aβ-tau impacts and gene expression in the neurotypical transcriptome (Allen Human Brain Atlas). Within the obtained distinctive signature of in-vivo neuronal dysfunction, several genes have prominent roles in microglial activation and in interactions with Aβ and tau. Moreover, cellular vulnerability estimations revealed strong association of microglial expression patterns with Aβ and tau's synergistic impact on neuronal activity (q < 0.001). These results further support the central role of the immune system and neuroinflammatory pathways in AD pathogenesis. Neuronal dysregulation by AD pathologies also associated with neurotypical synaptic and developmental processes. In addition, we identified drug candidates from the vast LINCS library to halt or reduce the observed Aβ-tau effects on neuronal activity. Top-ranked pharmacological interventions target inflammatory, cancer and cardiovascular pathways, including specific medications undergoing clinical evaluation in AD. Our findings, based on the examination of molecular-pathological-functional interactions in humans, may accelerate the process of bringing effective therapies into clinical practice.

Molecular and cellular mechanisms of selective vulnerability in neurodegenerative diseases

The selective vulnerability of specific neuronal subtypes is a hallmark of neurodegenerative diseases. In this Review, I summarize our current understanding of the brain regions and cell types that are selectively vulnerable in different neurodegenerative diseases and describe the proposed underlying cell-autonomous and non-cell-autonomous mechanisms. I highlight how recent methodological innovations - including single-cell transcriptomics, CRISPR-based screens and human cell-based models of disease - are enabling new breakthroughs in our understanding of selective vulnerability. An understanding of the molecular mechanisms that determine selective vulnerability and resilience would shed light on the key processes that drive neurodegeneration and point to potential therapeutic strategies to protect vulnerable cell populations.

Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets

Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.

Cellular underpinnings of the selective vulnerability to tauopathic insults in Alzheimer's disease

Neurodegenerative diseases such as Alzheimer's disease (AD) exhibit pathological changes in the brain that proceed in a stereotyped and regionally specific fashion, but the cellular and molecular underpinnings of regional vulnerability are currently poorly understood. Recent work has identified certain subpopulations of neurons in a few focal regions of interest, such as the entorhinal cortex, that are selectively vulnerable to tau pathology in AD. However, the cellular underpinnings of regional susceptibility to tau pathology are currently unknown, primarily because whole-brain maps of a comprehensive collection of cell types have been inaccessible. Here, we deployed a recent cell-type mapping pipeline, Matrix Inversion and Subset Selection (MISS), to determine the brain-wide distributions of pan-hippocampal and neocortical neuronal and non-neuronal cells in the mouse using recently available single-cell RNA sequencing (scRNAseq) data. We then performed a robust set of analyses to identify general principles of cell-type-based selective vulnerability using these cell-type distributions, utilizing 5 transgenic mouse studies that quantified regional tau in 12 distinct PS19 mouse models. Using our approach, which constitutes the broadest exploration of whole-brain selective vulnerability to date, we were able to discover cell types and cell-type classes that conferred vulnerability and resilience to tau pathology. Hippocampal glutamatergic neurons as a whole were strongly positively associated with regional tau deposition, suggesting vulnerability, while cortical glutamatergic and GABAergic neurons were negatively associated. Among glia, we identified oligodendrocytes as the single-most strongly negatively associated cell type, whereas microglia were consistently positively correlated. Strikingly, we found that there was no association between the gene expression relationships between cell types and their vulnerability or resilience to tau pathology. When we looked at the explanatory power of cell types versus GWAS-identified AD risk genes, cell type distributions were consistently more predictive of end-timepoint tau pathology than regional gene expression. To understand the functional enrichment patterns of the genes that were markers of the identified vulnerable or resilient cell types, we performed gene ontology analysis. We found that the genes that are directly correlated to tau pathology are functionally distinct from those that constitutively embody the vulnerable cells. In short, we have demonstrated that regional cell-type composition is a compelling explanation for the selective vulnerability observed in tauopathic diseases at a whole-brain level and is distinct from that conferred by risk genes. These findings may have implications in identifying cell-type-based therapeutic targets.

Morphoelectric and transcriptomic divergence of the layer 1 interneuron repertoire in human versus mouse neocortex

Neocortical layer 1 (L1) is a site of convergence between pyramidal-neuron dendrites and feedback axons where local inhibitory signaling can profoundly shape cortical processing. Evolutionary expansion of human neocortex is marked by distinctive pyramidal neurons with extensive L1 branching, but whether L1 interneurons are similarly diverse is underexplored. Using Patch-seq recordings from human neurosurgical tissue, we identified four transcriptomic subclasses with mouse L1 homologs, along with distinct subtypes and types unmatched in mouse L1. Subclass and subtype comparisons showed stronger transcriptomic differences in human L1 and were correlated with strong morphoelectric variability along dimensions distinct from mouse L1 variability. Accompanied by greater layer thickness and other cytoarchitecture changes, these findings suggest that L1 has diverged in evolution, reflecting the demands of regulating the expanded human neocortical circuit.

Identification of key lipid metabolism-related genes in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) represents profound degenerative conditions of the brain that cause significant deterioration in memory and cognitive function. Despite extensive research on the significant contribution of lipid metabolism to AD progression, the precise mechanisms remain incompletely understood. Hence, this study aimed to identify key differentially expressed lipid metabolism-related genes (DELMRGs) in AD progression.

METHODS

Comprehensive analyses were performed to determine key DELMRGs in AD compared to controls in GSE122063 dataset from Gene Expression Omnibus. Additionally, the ssGSEA algorithm was utilized for estimating immune cell levels. Subsequently, correlations between key DELMRGs and each immune cell were calculated specifically in AD samples. The key DELMRGs expression levels were validated via two external datasets. Furthermore, gene set enrichment analysis (GSEA) was utilized for deriving associated pathways of key DELMRGs. Additionally, miRNA-TF regulatory networks of the key DELMRGs were constructed using the miRDB, NetworkAnalyst 3.0, and Cytoscape software. Finally, based on key DELMRGs, AD samples were further segmented into two subclusters via consensus clustering, and immune cell patterns and pathway differences between the two subclusters were examined.

RESULTS

Seventy up-regulated and 100 down-regulated DELMRGs were identified. Subsequently, three key DELMRGs (DLD, PLPP2, and PLAAT4) were determined utilizing three algorithms [(i) LASSO, (ii) SVM-RFE, and (iii) random forest]. Specifically, PLPP2 and PLAAT4 were up-regulated, while DLD exhibited downregulation in AD cerebral cortex tissue. This was validated in two separate external datasets (GSE132903 and GSE33000). The AD group exhibited significantly altered immune cell composition compared to controls. In addition, GSEA identified various pathways commonly associated with three key DELMRGs. Moreover, the regulatory network of miRNA-TF for key DELMRGs was established. Finally, significant differences in immune cell levels and several pathways were identified between the two subclusters.

CONCLUSION

This study identified DLD, PLPP2, and PLAAT4 as key DELMRGs in AD progression, providing novel insights for AD prevention/treatment.