A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex

Spatial transcriptomics reveals tumor microenvironment heterogeneity in EBV positive diffuse large B cell lymphoma

Accumulating research suggests that Epstein-Barr Virus-positive Diffuse Large B-cell Lymphoma (EBV+DLBCL) is associated with immune dysfunction and tumor microenvironment (TME) heterogeneity. While the prognostic role of the TME in EBV-DLBCL is established, its impact on EBV+DLBCL survival remains unclear. Here, we integrated 10X Visium spatial transcriptomics (ST) with single-cell RNA sequencing (scRNA-seq) to map TME heterogeneity in EBV+DLBCL. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses identified PD-1/PD-L1 signaling as a hallmark of EBV+DLBCL's immunosuppressive TME. Functional validation using the PD-1/PD-L1 inhibitor BMS202 revealed dose-dependent suppression of proliferation and enhanced apoptosis in EBV+Farage cells, with TLR4 emerging as a downstream effector showing EBV-status-dependent regulation. These findings not only link TME-driven PD-1/PD-L1 activation to EBV+DLBCL's poor prognosis but also provide preclinical evidence for PD-1/PD-L1 blockade as a therapeutic strategy.

Spatial Transcriptomics: Biotechnologies, Computational Tools, and Neuroscience Applications

Spatial transcriptomics (ST) represents a revolutionary approach in molecular biology, providing unprecedented insights into the spatial organization of gene expression within tissues. This review aims to elucidate advancements in ST technologies, their computational tools, and their pivotal applications in neuroscience. It is begun with a historical overview, tracing the evolution from early image-based techniques to contemporary sequence-based methods. Subsequently, the computational methods essential for ST data analysis, including preprocessing, cell type annotation, spatial clustering, detection of spatially variable genes, cell-cell interaction analysis, and 3D multi-slices integration are discussed. The central focus of this review is the application of ST in neuroscience, where it has significantly contributed to understanding the brain's complexity. Through ST, researchers advance brain atlas projects, gain insights into brain development, and explore neuroimmune dysfunctions, particularly in brain tumors. Additionally, ST enhances understanding of neuronal vulnerability in neurodegenerative diseases like Alzheimer's and neuropsychiatric disorders such as schizophrenia. In conclusion, while ST has already profoundly impacted neuroscience, challenges remain issues such as enhancing sequencing technologies and developing robust computational tools. This review underscores the transformative potential of ST in neuroscience, paving the way for new therapeutic insights and advancements in brain research.

Chimeric brain models: Unlocking insights into human neural development, aging, diseases, and cell therapies

Human-rodent chimeric brain models serve as a unique platform for investigating the pathophysiology of human cells within a living brain environment. These models are established by transplanting human tissue- or human pluripotent stem cell (hPSC)-derived macroglial, microglial, or neuronal lineage cells, as well as cerebral organoids, into the brains of host animals. This approach has opened new avenues for exploring human brain development, disease mechanisms, and regenerative processes. Here, we highlight recent advancements in using chimeric models to study human neural development, aging, and disease. Additionally, we explore the potential applications of these models for studying human glial cell-replacement therapies, studying in vivo human glial-to-neuron reprogramming, and harnessing single-cell omics and advanced functional assays to uncover detailed insights into human neurobiology. Finally, we discuss strategies to enhance the precision and translational relevance of these models, expanding their impact in stem cell and neuroscience research.

Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex

Cellular deconvolution of bulk RNA-sequencing data using single cell/nuclei RNA-seq reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as the human brain. Here, we generate a multi-assay dataset in postmortem human dorsolateral prefrontal cortex from 22 tissue blocks, including bulk RNA-seq, reference snRNA-seq, and orthogonal measurement of cell type proportions with RNAScope/ImmunoFluorescence. We use this dataset to evaluate six deconvolution algorithms. Bisque and hspe were the most accurate methods. The dataset, as well as the Mean Ratio gene marker finding method, is made available in the DeconvoBuddies R/Bioconductor package.

A large-scale integrated transcriptomic atlas for soybean organ development

Soybean is one of the most important crops globally, and its production must be significantly increased to meet increasing demand. Elucidating the genetic regulatory networks underlying soybean organ development is essential for breeding elite and resilient varieties to ensure increased soybean production under climate change. An integrated transcriptomic atlas that leverages multiple types of transcriptomics data can facilitate the characterization of temporal-spatial expression patterns of most organ development-related genes and thereby help us to understand organ developmental processes. Here, we constructed a comprehensive, integrated transcriptomic atlas for soybeans, integrating bulk RNA sequencing (RNA-seq) datasets from 314 samples across the soybean life cycle, along with single-nucleus RNA-seq and spatially enhanced resolution omics sequencing datasets from five organs: root, nodule, shoot apex, leaf, and stem. Investigating genes related to organ specificity, blade development, and nodule formation, we demonstrate that the atlas has robust power for exploring key genes involved in organ formation. In addition, we developed a user-friendly panoramic database for the transcriptomic atlas, enabling easy access and queries, which will serve as a valuable resource to significantly advance future soybean functional studies.

Organizational Principles of the Primate Cerebral Cortex at the Single-Cell Level

The primate cerebral cortex, the major organ for cognition, consists of an immense number of neurons. However, the organizational principles governing these neurons remain unclear. By accessing the single-cell spatial transcriptome of over 25 million neuron cells across the entire macaque cortex, it is discovered that the distribution of neurons within cortical layers is highly non-random. Strikingly, over three-quarters of these neurons are located in distinct neuronal clusters. Within these clusters, different cell types tend to collaborate rather than function independently. Typically, excitatory neuron clusters mainly consist of excitatory-excitatory combinations, while inhibitory clusters primarily contain excitatory-inhibitory combinations. Both cluster types have roughly equal numbers of neurons in each layer. Importantly, most excitatory and inhibitory neuron clusters form spatial partnerships, indicating a balanced local neuronal network and correlating with specific functional regions. These organizational principles are conserved across mouse cortical regions. These findings suggest that different brain regions of the cortex may exhibit similar mechanisms at the neuronal population level.

Integrative Genomics Approach Identifies Glial Transcriptomic Dysregulation and Risk in the Cortex of Individuals With Alcohol Use Disorder

BACKGROUND

Alcohol use disorder (AUD) is a prevalent neuropsychiatric disorder that is a major global health concern, affecting millions of people worldwide. Previous studies of AUD used underpowered single-cell analysis or bulk homogenates of postmortem brain tissue, which obscure gene expression changes in specific cell types. Therefore, we sought to conduct the largest-to-date single-nucleus RNA sequencing (snRNA-seq) postmortem brain study in AUD to elucidate transcriptomic pathology with cell type-specific resolution.

METHODS

Here, we performed snRNA-seq and high-dimensional network analysis of 73 postmortem samples from individuals with AUD (n = 36, nnuclei = 248,873) and neurotypical control individuals (n = 37, nnuclei = 210,573) in the dorsolateral prefrontal cortex from both male and female donors. Additionally, we performed analysis for cell type-specific enrichment of aggregate genetic risk for AUD as well as integration of the AUD proteome for secondary validation.

RESULTS

We identified 32 distinct cell clusters and found widespread cell type-specific transcriptomic changes across the cortex in AUD, particularly affecting glial populations. We found the greatest dysregulation in novel microglial and astrocytic subtypes that accounted for the majority of differential gene expression and coexpression modules linked to AUD. Differential gene expression was secondarily validated by integration of a publicly available AUD proteome. Finally, analysis for aggregate genetic risk for AUD identified subtypes of glia as potential key players not only affected by but also causally linked to the progression of AUD.

CONCLUSIONS

These results highlight the importance of cell type-specific molecular changes in AUD and offer opportunities to identify novel targets for treatment on the single-nucleus level.

Topic modeling analysis of the Allen Human Brain Atlas

The human brain is a complex interconnected structure controlling all elementary and high-level cognitive tasks. It is composed of many regions that exhibit specific distributions of cell types and distinct patterns of functional connections. This complexity is rooted in differential transcription. The constituent cell types of different brain regions express distinctive combinations of genes as they develop and mature, ultimately shaping their functional state in adulthood. How precisely the genetic information of anatomical structures is connected to their underlying biological functions remains an open question in modern neuroscience. A major challenge is the identification of "universal patterns", which do not depend on the particular individual, but are instead basic structural properties shared by all brains. Despite the vast amount of gene expression data available at both the bulk and single-cell levels, this task remains challenging, mainly due to the lack of suitable data mining tools. In this paper, we propose an approach to address this issue based on a hierarchical version of Stochastic Block Modeling. Thanks to its specific choice of priors, the method is particularly effective in identifying these universal features. We use as a laboratory to test our algorithm a dataset obtained from six independent human brains from the Allen Human Brain Atlas. We show that the proposed method is indeed able to identify universal patterns much better than more traditional algorithms such as Latent Dirichlet Allocation or Weighted Correlation Network Analysis. The probabilistic association between genes and samples that we find well represents the known anatomical and functional brain organization. Moreover, leveraging the peculiar "fuzzy" structure of the gene sets obtained with our method, we identify examples of transcriptional and post-transcriptional pathways associated with specific brain regions, highlighting the potential of our approach.

Spatiotemporal analysis of gene expression in the human dentate gyrus reveals age-associated changes in cellular maturation and neuroinflammation

The dentate gyrus of the hippocampus is important for many cognitive functions, including learning, memory, and mood. Here, we present transcriptome-wide spatial gene expression maps of the human dentate gyrus and investigate age-associated changes across the lifespan. Genes associated with neurogenesis and the extracellular matrix are enriched in infants and decline throughout development and maturation. Following infancy, inhibitory neuron markers increase, and cellular proliferation markers decrease. We also identify spatio-molecular signatures that support existing evidence for protracted maturation of granule cells during adulthood and age-associated increases in neuroinflammation-related gene expression. Our findings support the notion that the hippocampal neurogenic niche undergoes major changes following infancy and identify molecular regulators of brain aging in glial- and neuropil-enriched tissue.

Stereo-seq of the prefrontal cortex in aging and Alzheimer's disease

Aging increases the risk for Alzheimer's disease (AD), driving pathological changes like amyloid-β (Aβ) buildup, inflammation, and oxidative stress, especially in the prefrontal cortex (PFC). We present the first subcellular-resolution spatial transcriptome atlas of the human prefrontal cortex (PFC), generated with Stereo-seq from six male AD cases at varying neuropathological stages and six age-matched male controls. Our analyses revealed distinct transcriptional alterations across PFC layers, highlighted disruptions in laminar structure, and exposed AD-related shifts in layer-to-layer and cell-cell interactions. Notably, we identified genes highly upregulated in stressed neurons and nearby glial cells, where AD diminished stress-response interactions that promote Aβ clearance. Further, cell-type-specific co-expression analysis highlighted three neuronal modules linked to neuroprotection, protein dephosphorylation, and Aβ regulation, with all modules downregulated as AD progresses. We identified ZNF460 as a transcription factor regulating these modules, offering a potential therapeutic target. In summary, this spatial transcriptome atlas provides valuable insight into AD's molecular mechanisms.

Integrative genomics approach identifies glial transcriptomic dysregulation and risk in the cortex of individuals with Alcohol Use Disorder

Alcohol use disorder (AUD) is a prevalent neuropsychiatric disorder that is a major global health concern, affecting millions of people worldwide. Past molecular studies of AUD used underpowered single cell analysis or bulk homogenates of postmortem brain tissue, which obscures gene expression changes in specific cell types. Here we performed single nuclei RNA-sequencing analysis of 73 post-mortem samples from individuals with AUD (N=36, N nuclei = 248,873) and neurotypical controls (N=37, N nuclei = 210,573) in both sexes across two institutional sites. We identified 32 clusters and found widespread cell type-specific transcriptomic changes across the cortex in AUD, particularly affecting glia. We found the greatest dysregulation in novel microglial and astrocytic subtypes that accounted for the majority of differential gene expression and co-expression modules linked to AUD. Analysis for cell type-specific enrichment of aggregate genetic risk for AUD identified subtypes of microglia and astrocytes as potential key players not only affected by but causally linked to the progression of AUD. These results highlight the importance of cell-type specific molecular changes in AUD and offer opportunities to identify novel targets for treatment.

The gut microbiota-oligodendrocyte axis: A promising pathway for modulating oligodendrocyte homeostasis and demyelination-associated disorders

The bidirectional regulation between the gut microbiota and brain, known as gut-brain axis, has received significant attention. The myelin sheath, produced by oligodendrocytes or Schwann cells, is essential for efficient nervous signal transmission and the maintenance of brain function. Growing evidence shows that both oligodendrogenesis and myelination are modulated by gut microbiota and its metabolites, and when dysbiosis occurs, changes in the microbiota composition and/or associated metabolites may impact developmental myelination and the occurrence of neurodevelopmental disabilities. Although the link between the microbiota and demyelinating disease such as multiple sclerosis has been extensively studied, our knowledge about the role of the microbiota in other myelin-related disorders, such as neurodegenerative diseases, is limited. Mechanistically, the microbiota-oligodendrocyte axis is primarily mediated by factors such as inflammation, the vagus nerve, endocrine hormones, and microbiota metabolites as evidenced by metagenomics, metabolomics, vagotomy, and morphological and molecular approaches. Treatments targeting this axis include probiotics, prebiotics, microbial metabolites, herbal bioactive compounds, and specific dietary management. In addition to the commonly used approaches, viral vector-mediated tracing and gene manipulation, integrated multiomics and multicenter clinical trials will greatly promote the mechanistic and interventional studies and ultimately, the development of new preventive and therapeutic strategies against gut-oligodendrocyte axis-mediated brain impairments. Interestingly, recent findings showed that microbiota dysbiosis can be induced by hippocampal myelin damage and is reversible by myelin-targeted drugs, which provides new insights into understanding how hippocampus-based functional impairment (such as in neurodegenerative Alzheimer's disease) regulates the peripheral homeostasis of microbiota and associated systemic disorders.

White matter damage and degeneration in traumatic brain injury

Traumatic brain injury (TBI) is a complex condition that can resolve over time but all too often leads to persistent symptoms, and the risk of poor patient outcomes increases with aging. TBI damages neurons and long axons within white matter tracts that are critical for communication between brain regions; this causes slowed information processing and neuronal circuit dysfunction. This review focuses on white matter injury after TBI and the multifactorial processes that underlie white matter damage, potential for recovery, and progression of degeneration. A multiscale perspective across clinical and preclinical advances is presented to encourage interdisciplinary insights from whole-brain neuroimaging of white matter tracts down to cellular and molecular responses of axons, myelin, and glial cells within white matter tissue.

Integrating gene expression and imaging data across Visium capture areas with visiumStitched

Background

Visium is a widely-used spatially-resolved transcriptomics assay available from 10x Genomics. Standard Visium capture areas (6.5mm by 6.5mm) limit the survey of larger tissue structures, but combining overlapping images and associated gene expression data allow for more complex study designs. Current software can handle nested or partial image overlaps, but is designed for merging up to two capture areas, and cannot account for some technical scenarios related to capture area alignment.

Results

We generated Visium data from a postmortem human tissue sample such that two capture areas were partially overlapping and a third one was adjacent. We developed the R/Bioconductor package visiumStitched, which facilitates stitching the images together with Fiji (ImageJ), and constructing SpatialExperiment R objects with the stitched images and gene expression data. visiumStitched constructs an artificial hexagonal array grid which allows seamless downstream analyses such as spatially-aware clustering without discarding data from overlapping spots. Data stitched with visiumStitched can then be interactively visualized with spatialLIBD.

Conclusions

visiumStitched provides a simple, but flexible framework to handle various multi-capture area study design scenarios. Specifically, it resolves a data processing step without disrupting analysis workflows and without discarding data from overlapping spots. visiumStiched relies on affine transformations by Fiji, which have limitations and are less accurate when aligning against an atlas or other situations. visiumStiched provides an easy-to-use solution which expands possibilities for designing multi-capture area study designs.

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.

Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood

The molecular pathology of stress-related disorders remains elusive. Our brain multiregion, multiomic study of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) included the central nucleus of the amygdala, hippocampal dentate gyrus, and medial prefrontal cortex (mPFC). Genes and exons within the mPFC carried most disease signals replicated across two independent cohorts. Pathways pointed to immune function, neuronal and synaptic regulation, and stress hormones. Multiomic factor and gene network analyses provided the underlying genomic structure. Single nucleus RNA sequencing in dorsolateral PFC revealed dysregulated (stress-related) signals in neuronal and non-neuronal cell types. Analyses of brain-blood intersections in >50,000 UK Biobank participants were conducted along with fine-mapping of the results of PTSD and MDD genome-wide association studies to distinguish risk from disease processes. Our data suggest shared and distinct molecular pathology in both disorders and propose potential therapeutic targets and biomarkers.