Conservation and divergence of cortical cell organization in human and mouse revealed by MERFISH

Microglia transcriptional states and their functional significance: Context drives diversity

In the brain, microglia are continuously exposed to a dynamic microenvironment throughout life, requiring them to adapt accordingly to specific developmental or disease-related demands. The advent of single-cell sequencing technologies has revealed the diversity of microglial transcriptional states. In this review, we explore the various contexts that drive transcriptional diversity in microglia and assess the extent to which non-homeostatic conditions induce context-specific signatures. We discuss our current understanding and knowledge gaps regarding the relationship between transcriptional states and microglial function, review the influence of complex microenvironments and prior experiences on microglial state induction, and highlight strategies to bridge the gap between mouse and human studies to advance microglia-targeting therapeutics.

Decoding human brain evolution: Insights from genomics

The human brain has undergone remarkable structural and functional specializations compared to that of nonhuman primates (NHPs), underlying the advanced cognitive abilities unique to humans. However, the cellular and genetic basis driving these specializations remains largely unknown. Comparing humans to our closest living relatives, chimpanzee and other great apes, is essential for identifying truly human-specific features. Recent comparative studies with closely related NHPs at the single-cell resolution using multimodal genomic profiling, assisted with high-throughput functional screening have provided unprecedented insights into human-specific brain features and their genetic underpinnings. In this review, we synthesize the current knowledge of human brain evolution at cellular and molecular levels, emphasizing how genetic changes have shaped these adaptations. We also discuss the emerging opportunities presented by new technologies and comprehensive atlases for advancing our understanding of human brain evolution.

Boosting multiplexing capabilities for error-robust spatial transcriptomic methods using a set exchange approach

In the last decades, image-based transcriptomic and proteomic experiments have moved from single-target probes to multiplexed experiments, allowing researchers to study hundreds or even thousands of mRNA and protein targets simultaneously. This large increase in scope necessitates methods in either increased specificity or in error correction, such as the Hamming codes used in the imaging-based spatial transcriptomic method MERFISH. For some experimental conditions, Hamming codes are efficient in encoding the highest possible number of genes for spatial analysis. However, for most experimental parameters, the optimal generation of error-robust codebooks is an unsolved mathematical problem. Here, we present a method to generate highly optimized extended Hamming codebooks compatible with established error-correctable methodologies such as MERFISH. Our method uses an iterative set-exchange approach and generally reaches over 90% of the theoretical maximum limit of gene set complexity. We also provide ready-to-use codebooks and discuss the advantages and disadvantages of changing probe density.

DSCT: a novel deep-learning framework for rapid and accurate spatial transcriptomic cell typing

Unraveling complex cell-type-composition and gene-expression patterns at the cellular spatial resolution is crucial for understanding intricate cell functions in the brain. In this study, we developed Deep Neural Network-based Spatial Cell Typing (DSCT)-an innovative framework for spatial cell typing within spatial transcriptomic data sets. This approach utilizes a synergistic integration of an enhanced gene-selection strategy and a lightweight deep neural network for data training, offering a more rapid and accurate solution for the analysis of spatial transcriptomic data. Based on comprehensive analysis, DSCT achieved exceptional accuracy in cell-type identification across various brain regions, species and spatial transcriptomic platforms. It also performed well in mapping finer cell types, thereby showcasing its versatility and adaptability across diverse data sets. Strikingly, DSCT exhibited high efficiency and remarkable processing speed, with fewer computational resource demands. As such, this novel approach opens new avenues for exploring the spatial organization of cell types and gene-expression patterns, advancing our understanding of biological functions and pathologies within the nervous system.

Development of GABAergic Interneurons in the Human Cerebral Cortex

GABAergic interneurons are critical regulators of information processing in the cerebral cortex. They constitute a heterogeneous group of neurons with unique spatial and temporal capabilities to control information flow and influence neural network dynamics through inhibitory and disinhibitory mechanisms. Interneuron diversity is largely conserved between rodents and primates, which indicates that the addition of new types of GABAergic neurons is not the most critical innovation of the primate cortex. In contrast, interneurons are much more abundant and seem more widely interconnected in the cerebral cortex of primates than in rodents, suggesting selective evolutionary pressure in the mechanisms regulating the generation, survival and maturation of cortical interneurons. Recent studies are beginning to shed light on the cellular and molecular mechanisms controlling the development of cortical interneurons in humans, from their generation in the embryonic telencephalon to their early integration in cortical networks. These studies identified many features in the development of human cortical interneurons that are shared with other mammals, along with distinctive features that seem characteristic of the primate brain, such as a previously unrecognised protracted period of neurogenesis and migration that extends the earliest stages of interneuron development into the first months of postnatal life in humans.

Glial changes and gene expression in Alzheimer's disease from snRNA-Seq and spatial transcriptomics

BackgroundAlzheimer's disease (AD) is characterized by cortical atrophy, glutamatergic neuron loss, and cognitive decline. However, large-scale quantitative assessments of cellular changes during AD pathology remain scarce.ObjectiveThis study aims to integrate single-nuclei sequencing data from the Seattle Alzheimer's Disease Cortical Atlas (SEA-AD) with spatial transcriptomics to quantify cellular changes in the prefrontal cortex and temporal gyrus, regions vulnerable to AD neuropathological changes (ADNC).MethodsWe mapped differentially expressed genes (DEGs) and analyzed their interactions with pathological factors such as APOE expression and Lewy bodies. Cellular proportions were assessed, focusing on neurons, glial cells, and immune cells.ResultsRORB-expressing L4-like neurons, though vulnerable to ADNC, exhibited stable cell numbers throughout disease progression. In contrast, astrocytes displayed increased reactivity, with upregulated cytokine signaling and oxidative stress responses, suggesting a role in neuroinflammation. A reduction in synaptic maintenance pathways indicated a decline in astrocytic support functions. Microglia showed heightened immune surveillance and phagocytic activity, indicating their role in maintaining cortical homeostasis.ConclusionsThe study underscores the critical roles of glial cells, particularly astrocytes and microglia, in AD progression. These findings contribute to a better understanding of cellular dynamics and may inform therapeutic strategies targeting glial cell function in AD.

Cross-Species Multi-Omics Analysis Reveals Myeloid-Driven Endothelial Oxidative Stress in Ischemic Stroke

BACKGROUND

Ischemic stroke is a leading cause of mortality and disability worldwide, yet the interplay between peripheral and central immune responses is still only partially understood. Emerging evidence suggests that myeloid cells, when activated in the periphery, infiltrate the ischemic brain and contribute to the disruption of the blood-brain barrier (BBB) through both inflammatory and metabolic mechanisms.

METHODS

In this study, we integrated bulk RNA-sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq), spatial transcriptomics, and flow cytometry data from human and mouse models of ischemic stroke. Mouse stroke models were induced by transient middle cerebral artery occlusion (tMCAO), and brain tissues were later collected at specified time points for analysis. We examined time-dependent transcriptional changes in the peripheral blood, delineated cell-type-specific responses by single-cell profiling, and validated myeloid infiltration into the ischemic brain. We also investigated endothelial metabolic reprogramming and oxidative stress by combining scMetabolism analyses (a computational R package for inferring metabolic pathway activity at the single-cell level) with in vitro oxygen-glucose deprivation/reperfusion (OGD/R) experiments.

RESULTS

Cross-species bulk RNA-seq revealed a modest early immune shift at 3 h post-stroke, escalating significantly by 24 h, with robust myeloid-centric gene signatures conserved in humans and mice. Single-cell analyses confirmed a pronounced expansion of neutrophils, monocytes, and megakaryocytes in peripheral blood, coupled with a decrease in T and B lymphocytes. Spatial transcriptomics and flow cytometry demonstrated substantial infiltration of CD11b+ myeloid cells into the infarct core, which showed extensive interaction with endothelial cells. Endothelial scRNA-seq data showed reductions in the oxidative phosphorylation, glutathione, and nicotinate metabolic pathways, together with elevated pentose phosphate pathway activity, suggestive of oxidative stress and compromised antioxidant capacity. Functional scoring further indicated diminished endothelial inflammation/repair potential, while in vitro OGD/R experiments revealed morphological disruption, CD31 downregulation, and increased 4-hydroxynonenal (4-HNE), underscoring the importance of endothelial oxidative damage in BBB breakdown.

CONCLUSIONS

These multi-omics findings highlight the existence of a coordinated peripheral-central immune axis in ischemic stroke, wherein myeloid cell recruitment and endothelial metabolic vulnerability jointly exacerbate inflammation and oxidative stress. The targeting of endothelial oxidative injury and myeloid-endothelial crosstalk may represent a promising strategy to mitigate secondary brain injury in ischemic stroke.

Transfer learning of multicellular organization via single-cell and spatial transcriptomics

Biological tissues exhibit complex gene expression and multicellular patterns that are valuable to dissect. Single-cell RNA sequencing (scRNA-seq) provides full coverages of genes, but lacks spatial information, whereas spatial transcriptomics (ST) measures spatial locations of individual or group of cells, with more restrictions on gene information. Here we show a transfer learning method named iSORT to decipher spatial organization of cells by integrating scRNA-seq and ST data. iSORT trains a neural network that maps gene expressions to spatial locations. iSORT can find spatial patterns at single-cell scale, identify spatial-organizing genes (SOGs) that drive the patterning, and infer pseudo-growth trajectories using a concept of SpaRNA velocity. Benchmarking on a range of biological systems, such as human cortex, mouse embryo, mouse brain, Drosophila embryo, and human developmental heart, demonstrates iSORT's accuracy and practicality in reconstructing multicellular organization. We further conducted scRNA-seq and ST sequencing from normal and atherosclerotic arteries, and the functional enrichment analysis shows that SOGs found by iSORT are strongly associated with vascular structural anomalies.

Single-cell spatial transcriptome atlas and whole-brain connectivity of the macaque claustrum

Claustrum orchestrates brain functions via its connections with numerous brain regions, but its molecular and cellular organization remains unresolved. Single-nucleus RNA sequencing of 227,750 macaque claustral cells identified 48 transcriptome-defined cell types, with most glutamatergic neurons similar to deep-layer insular neurons. Comparison of macaque, marmoset, and mouse transcriptomes revealed macaque-specific cell types. Retrograde tracer injections at 67 cortical and 7 subcortical regions defined four distinct distribution zones of retrogradely labeled claustral neurons. Joint analysis of whole-brain connectivity and single-cell spatial transcriptome showed that these four zones containing distinct compositions of glutamatergic (but not GABAergic) cell types preferentially connected to specific brain regions with a strong ipsilateral bias. Several macaque-specific glutamatergic cell types in ventral vs. dorsal claustral zones selectively co-projected to two functionally related areas-entorhinal cortex and hippocampus vs. motor cortex and putamen, respectively. These data provide the basis for elucidating the neuronal organization underlying diverse claustral functions.

SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data

Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to reliably capture spatial regulations. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free deep learning framework that disentangles cell intrinsic and spatial-induced latent variables in spatial omics data with rigorous theoretical support. By this disentanglement, SIMVI enables estimation of spatial effects at a single-cell resolution, and empowers various downstream analyses. We demonstrate the superior performance of SIMVI across datasets from diverse platforms and tissues. SIMVI illuminates the cyclical spatial dynamics of germinal center B cells in human tonsil. Applying SIMVI to multiome melanoma data reveals potential tumor epigenetic reprogramming states. On our newly-collected cohort-level CosMx melanoma data, SIMVI uncovers space-and-outcome-dependent macrophage states and cellular communication machinery in tumor microenvironments.

Spatial transcriptomics in the adult Drosophila brain and body

Recently, we have achieved a significant milestone with the creation of the Fly Cell Atlas. This single-nuclei atlas encompasses the entire fly, covering the entire head and body, in addition to all major organs. This atlas catalogs many hundreds of cell types, of which we annotated 250. Thus, a large number of clusters remain to be fully characterized, in particular in the brain. Furthermore, by applying single-nuclei sequencing, all information about the spatial location of the cells in the body and of about possible subcellular localization of the mRNAs within these cells is lost. Spatial transcriptomics promises to tackle these issues. In a proof-of-concept study, we have here applied spatial transcriptomics using a selected gene panel to pinpoint the locations of 150 mRNA species in the adult fly. This enabled us to map unknown clusters identified in the Fly Cell Atlas to their spatial locations in the fly brain. Additionally, spatial transcriptomics discovered interesting principles of mRNA localization and transcriptional diversity within the large and crowded muscle cells that may spark future mechanistic investigations. Furthermore, we present a set of computational tools that will allow for easier integration of spatial transcriptomics and single-cell datasets.

Advances in Spatial Omics Technologies

Rapidly developing spatial omics technologies provide us with new approaches to deeply understanding the diversity and functions of cell types within organisms. Unlike traditional approaches, spatial omics technologies enable researchers to dissect the complex relationships between tissue structure and function at the cellular or even subcellular level. The application of spatial omics technologies provides new perspectives on key biological processes such as nervous system development, organ development, and tumor microenvironment. This review focuses on the advancements and strategies of spatial omics technologies, summarizes their applications in biomedical research, and highlights the power of spatial omics technologies in advancing the understanding of life sciences related to development and disease.

Opportunities and challenges for the use of human samples in translational cardiovascular research: a scientific statement of the ESC Working Group on Cellular Biology of the Heart, the ESC Working Group on Cardiovascular Surgery, the ESC Council on Basic Cardiovascular Science, the ESC Scientists of Tomorrow, the European Association of Percutaneous Cardiovascular Interventions of the ESC, and the Heart Failure Association of the ESC

Animal models offer invaluable insights into disease mechanisms but cannot entirely mimic the variability and heterogeneity of human populations, nor the increasing prevalence of multi-morbidity. Consequently, employing human samples-such as whole blood or fractions, valvular and vascular tissues, myocardium, pericardium, or human-derived cells-is essential for enhancing the translational relevance of cardiovascular research. For instance, myocardial tissue slices, which preserve crucial structural and functional characteristics of the human heart, can be used in vitro to examine drug responses. Human blood serves as a rich source of biomarkers, including extracellular vesicles, various types of RNA (miRNA, lncRNA, and circRNAs), circulating inflammatory cells, and endothelial colony-forming cells, facilitating detailed studies of cardiovascular diseases. Primary cardiomyocytes and vascular cells isolated from human tissues are invaluable for mechanistic investigations in vitro. In cases where these are unavailable, human induced pluripotent stem cells serve as effective substitutes, albeit with specific limitations. However, the use of human samples presents challenges such as ethical approvals, tissue procurement and storage, variability in patient genetics and treatment regimens, and the selection of appropriate control samples. Biobanks are central to the efficient use of these scarce and valuable resources. This scientific statement discusses opportunities to implement the use of human samples for cardiovascular research within specific clinical contexts, offers a practical framework for acquiring and utilizing different human materials, and presents examples of human sample applications for specific cardiovascular diseases, providing a valuable resource for clinicians, translational and basic scientists engaged in cardiovascular research.

Efficient coding in biophysically realistic excitatory-inhibitory spiking networks

The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.

Uncovering dynamic transcriptional regulation of methanogenesis via single-cell imaging of archaeal gene expression

Archaeal methanogenesis is a dynamic process regulated by various cellular and environmental signals. However, understanding this regulation is technically challenging due to the difficulty of measuring gene expression dynamics in individual archaeal cells. Here, we develop a multi-round hybridization chain reaction (HCR)-assisted single-molecule fluorescence in situ hybridization (FISH) method to quantify the transcriptional dynamics of 12 genes involved in methanogenesis in individual cells of Methanococcoides orientis. Under optimal growth condition, most of these genes appear to be expressed in a temporal order matching metabolic reaction order. Interestingly, an important environmental factor, Fe(III), stimulates cellular methane production without upregulating methanogenic gene expression, likely through a Fenton-reaction-triggered mechanism. Through single-cell clustering and kinetic analyses, we associate these gene expression patterns to a dynamic mixture of distinct cellular states, potentially regulated by a set of shared factors. Our work provides a quantitative framework for uncovering the mechanisms of metabolic regulation in archaea.

Imaging the Intestinal Transcriptome With Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH)

Multiplexed error-robust fluorescence in situ hybridization (MERFISH) is a massively multiplexed single RNA-molecule imaging technique capable of spatially resolved single-cell transcriptomic profiling of thousands of genes in millions of cells within intact tissue slices. Initially introduced for brain tissues, MERFISH has since been extended to other tissues, where rapid RNA degradation during the preparation process can pose challenges. This protocol outlines the application of MERFISH in one such challenging tissue, the mammalian gastrointestinal tract. We describe two complementary protocols leveraging either fresh frozen or fixed frozen approaches and describe methods for combining RNA imaging with immunofluorescence. While these protocols were designed and validated in gut tissues, we anticipate that they will be useful resources for the application to other challenging tissue types. © 2025 Wiley Periodicals LLC. Basic Protocol 1: Fixed-frozen sample preparation Basic Protocol 2: Fresh-frozen sample preparation Basic Protocol 3: Encoding probe construction Basic Protocol 4: MERFISH imaging Basic Protocol 5: Image decoding Support Protocol 1: Coverslip silanization Support Protocol 2: Poly-d-lysine (PDL) coating of the coverslips Support Protocol 3: Hybridization buffer preparation Support Protocol 4: Trolox quinone stock preparation Support Protocol 5: TCEP stock preparation Alternate Protocol 1: MERFISH-compatible immunofluorescent boundary stains in fresh frozen tissue Alternate Protocol 2: Immunofluorescent boundary stains with methacrylate-NHS-anchored antibodies for PFA-fixed samples Alternate Protocol 3: Guanidine-HCl tissue clearing.

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.

Cell type-specific 3D-genome organization and transcription regulation in the brain

3D organization of the genome plays a critical role in regulating gene expression. How 3D-genome organization differs among different cell types and relates to cell type-dependent transcriptional regulation remains unclear. Here, we used genome-scale DNA and RNA imaging to investigate 3D-genome organization in transcriptionally distinct cell types in the mouse cerebral cortex. We uncovered a wide spectrum of differences in the nuclear architecture and 3D-genome organization among different cell types, ranging from the size of the cell nucleus to higher-order chromosome structures and radial positioning of chromatin loci within the nucleus. These cell type-dependent variations in nuclear architecture and chromatin organization exhibit strong correlations with both the total transcriptional activity of the cell and transcriptional regulation of cell type-specific marker genes. Moreover, we found that the methylated DNA binding protein MeCP2 promotes active-inactive chromatin segregation and regulates transcription in a nuclear radial position-dependent manner that is highly correlated with its function in modulating active-inactive chromatin compartmentalization.

A platform for multimodal in vivo pooled genetic screens reveals regulators of liver function

Organ function requires coordinated activities of thousands of genes in distinct, spatially organized cell types. Understanding the basis of emergent tissue function requires approaches to dissect the genetic control of diverse cellular and tissue phenotypes in vivo. Here, we develop paired imaging and sequencing methods to construct large-scale, multi-modal genotype-phenotypes maps in tissue with pooled genetic perturbations. Using imaging, we identify genetic perturbations in individual cells while simultaneously measuring their gene expression and subcellular morphology. Using single-cell sequencing, we measure transcriptomic responses to the same genetic perturbations. We apply this approach to study hundreds of genetic perturbations in the mouse liver. Our study reveals regulators of hepatocyte zonation and liver unfolded protein response, as well as distinct pathways that cause hepatocyte steatosis. Our approach enables new ways of interrogating the genetic basis of complex cellular and organismal physiology and provides crucial training data for emerging machine-learning models of cellular function.

A Massively Parallel CRISPR-Based Screening Platform for Modifiers of Neuronal Activity

Understanding the complex interplay between gene expression and neuronal activity is crucial for unraveling the molecular mechanisms underlying cognitive function and neurological disorders. Here, we developed pooled screens for neuronal activity, using CRISPR interference (CRISPRi) and the fluorescent calcium integrator CaMPARI2. Using this screening method, we evaluated 1343 genes for their effect on excitability in human iPSC-derived neurons, revealing potential links to neurodegenerative and neurodevelopmental disorders. These genes include known regulators of neuronal excitability, such as TARPs and ion channels, as well as genes associated with autism spectrum disorder and Alzheimer's disease not previously described to affect neuronal excitability. This CRISPRi-based screening platform offers a versatile tool to uncover molecular mechanisms controlling neuronal activity in health and disease.

CelloType: a unified model for segmentation and classification of tissue images

Cell segmentation and classification are critical tasks in spatial omics data analysis. Here we introduce CelloType, an end-to-end model designed for cell segmentation and classification for image-based spatial omics data. Unlike the traditional two-stage approach of segmentation followed by classification, CelloType adopts a multitask learning strategy that integrates these tasks, simultaneously enhancing the performance of both. CelloType leverages transformer-based deep learning techniques for improved accuracy in object detection, segmentation and classification. It outperforms existing segmentation methods on a variety of multiplexed fluorescence and spatial transcriptomic images. In terms of cell type classification, CelloType surpasses a model composed of state-of-the-art methods for individual tasks and a high-performance instance segmentation model. Using multiplexed tissue images, we further demonstrate the utility of CelloType for multiscale segmentation and classification of both cellular and noncellular elements in a tissue. The enhanced accuracy and multitask learning ability of CelloType facilitate automated annotation of rapidly growing spatial omics data.

Spatial transcriptomic clocks reveal cell proximity effects in brain ageing

Old age is associated with a decline in cognitive function and an increase in neurodegenerative disease risk1. Brain ageing is complex and is accompanied by many cellular changes2. Furthermore, the influence that aged cells have on neighbouring cells and how this contributes to tissue decline is unknown. More generally, the tools to systematically address this question in ageing tissues have not yet been developed. Here we generate a spatially resolved single-cell transcriptomics brain atlas of 4.2 million cells from 20 distinct ages across the adult lifespan and across two rejuvenating interventions-exercise and partial reprogramming. We build spatial ageing clocks, machine learning models trained on this spatial transcriptomics atlas, to identify spatial and cell-type-specific transcriptomic fingerprints of ageing, rejuvenation and disease, including for rare cell types. Using spatial ageing clocks and deep learning, we find that T cells, which increasingly infiltrate the brain with age, have a marked pro-ageing proximity effect on neighbouring cells. Surprisingly, neural stem cells have a strong pro-rejuvenating proximity effect on neighbouring cells. We also identify potential mediators of the pro-ageing effect of T cells and the pro-rejuvenating effect of neural stem cells on their neighbours. These results suggest that rare cell types can have a potent influence on their neighbours and could be targeted to counter tissue ageing. Spatial ageing clocks represent a useful tool for studying cell-cell interactions in spatial contexts and should allow scalable assessment of the efficacy of interventions for ageing and disease.

Mapping the cellular etiology of schizophrenia and complex brain phenotypes

Psychiatric disorders are multifactorial and effective treatments are lacking. Probable contributing factors to the challenges in therapeutic development include the complexity of the human brain and the high polygenicity of psychiatric disorders. Combining well-powered genome-wide and brain-wide genetics and transcriptomics analyses can deepen our understanding of the etiology of psychiatric disorders. Here, we leverage two landmark resources to infer the cell types involved in the etiology of schizophrenia, other psychiatric disorders and informative comparison of brain phenotypes. We found both cortical and subcortical neuronal associations for schizophrenia, bipolar disorder and depression. These cell types included somatostatin interneurons, excitatory neurons from the retrosplenial cortex and eccentric medium spiny-like neurons from the amygdala. In contrast we found T cell and B cell associations with multiple sclerosis and microglial associations with Alzheimer's disease. We provide a framework for a cell-type-based classification system that can lead to drug repurposing or development opportunities and personalized treatments. This work formalizes a data-driven, cellular and molecular model of complex brain disorders.

Human brain tissue cultures: a unique ex vivo model to unravel the pathogenesis of neurotropic arboviruses

Arboviruses are transmitted by arthropods, and their spread from endemic to nonendemic regions has been accelerated by deforestation, climate change, and global mobility. Arbovirus infection in human results in symptoms ranging from mild to life-threatening, with the impairment of central nervous system functions being reported in severe cases. Despite its clinical relevance, the mechanisms by which arboviruses led to neural dysfunction are still poorly understood. The lack of a widespread human central nervous system model to study the virus-host interaction challenges the advance of our knowledge on these mechanisms. In this context, human brain-derived ex vivo models have the advantage of preserving cellular diversity, cell connections, and tissue cytoarchitecture found in human brain, raising them as a powerful strategy to elucidate the cellular-molecular alterations underlying brain diseases. Here, we review recent advances in the field of neurotropic arboviruses obtained using ex vivo human brain tissue as the experimental model.

SpaIM: Single-cell Spatial Transcriptomics Imputation via Style Transfer

Spatial transcriptomics (ST) technologies have revolutionized our understanding of cellular ecosystems. However, these technologies face challenges such as sparse gene signals and limited gene detection capacities, which hinder their ability to fully capture comprehensive spatial gene expression profiles. To address these limitations, we propose leveraging single-cell RNA sequencing (scRNA-seq), which provides comprehensive gene expression data but lacks spatial context, to enrich ST profiles. Herein, we introduce SpaIM, an innovative style transfer learning model that utilizes scRNA-seq information to predict unmeasured gene expressions in ST data, thereby improving gene coverage and expressions. SpaIM segregates scRNA-seq and ST data into data-agnostic contents and data-specific styles, with the contents capture the commonalities between the two data types, while the styles highlight their unique differences. By integrating the strengths of scRNA-seq and ST, SpaIM overcomes data sparsity and limited gene coverage issues, making significant advancements over 12 existing methods. This improvement is demonstrated across 53 diverse ST datasets, spanning sequencing- and imaging-based spatial technologies in various tissue types. Additionally, SpaIM enhances downstream analyses, including the detection of ligand-receptor interactions, spatial domain characterization, and identification of differentially expressed genes. Released as open-source software, SpaIM increases accessibility for spatial transcriptomics analysis. In summary, SpaIM represents a pioneering approach to enrich spatial transcriptomics using scRNA-seq data, enabling precise gene expression imputation and advancing the field of spatial transcriptomics research.

STAIG: Spatial transcriptomics analysis via image-aided graph contrastive learning for domain exploration and alignment-free integration

Spatial transcriptomics is an essential application for investigating cellular structures and interactions and requires multimodal information to precisely study spatial domains. Here, we propose STAIG, a deep-learning model that integrates gene expression, spatial coordinates, and histological images using graph-contrastive learning coupled with high-performance feature extraction. STAIG can integrate tissue slices without prealignment and remove batch effects. Moreover, it is designed to accept data acquired from various platforms, with or without histological images. By performing extensive benchmarks, we demonstrate the capability of STAIG to recognize spatial regions with high precision and uncover new insights into tumor microenvironments, highlighting its promising potential in deciphering spatial biological intricates.

The Impact of Klotho in Cancer: From Development and Progression to Therapeutic Potential

Klotho, initially identified as an anti-aging gene, has been shown to play significant roles in cancer biology. Alongside α-Klotho, the β-Klotho and γ-Klotho isoforms have also been studied; these studies showed that Klotho functions as a potential tumor suppressor in many different cancers by inhibiting cancer cell proliferation, inducing apoptosis and modulating critical signaling pathways such as the Wnt/β-catenin and PI3K/Akt pathways. In cancers such as breast cancer, colorectal cancer, hepatocellular carcinoma, ovarian cancer, and renal cell carcinoma, reduced Klotho expression often correlates with a poor prognosis. In addition, Klotho's role in enhancing chemotherapy sensitivity and its epigenetic regulation further underscores its potential as a target for cancer treatments. This review details Klotho's multifaceted contributions to cancer suppression and its potential as a therapeutic target, enhancing the understanding of its significance in cancer treatment and prognoses.

The left amygdala is genetically sexually-dimorphic: multi-omics analysis of structural MRI volumes

Brain anatomy plays a key role in complex behaviors and mental disorders that are sexually divergent. While our understanding of the sex differences in the brain anatomy remains relatively limited, particularly of the underlying genetic and molecular mechanisms that contribute to these differences. We performed the largest study of sex differences in brain volumes (N = 33,208) by examining sex differences both in the raw brain volumes and after controlling the whole brain volumes. Genetic correlation analysis revealed sex differences only in the left amygdala. We compared transcriptome differences between males and females using data from GTEx and characterized cell-type compositions using GTEx bulk amygdala RNA-seq data and LIBD amygdala single-cell reference profiles. We also constructed polygenic risk scores (PRS) to investigate sex-specific genetic correlations between left amygdala volume and mental disorders (N = 25,576~105,318) of Psychiatric Genomics Consortium and other traits of UKB (N = 347,996). Although there were pronounced sex differences in brain volumes, there was no difference in the heritability between sexes. There was a significant sex-specific genetic correlation between male and female left amygdala. We identified sex-differentiated genetic effects of PRSs for schizophrenia on left amygdala volume, as well as significant sex-differentiated genetic correlations between PRSs of left amygdala and six traits in UKB. We also found several sex-differentially expressed genes in the amygdala. These findings not only advanced the current knowledge of genetic basis of sex differences in brain anatomy, but also presented an important clue for future research on the mechanism of sex differences in mental disorders and targeted treatments.

Alterations in Prefrontal Cortical Somatostatin Neurons in Schizophrenia: Evidence for Weaker Inhibition of Pyramidal Neuron Dendrites

BACKGROUND

Certain cognitive processes require inhibition provided by the somatostatin (SST) class of GABA (gamma-aminobutyric acid) neurons in the dorsolateral prefrontal cortex (DLPFC). This inhibition onto pyramidal neuron dendrites depends on both SST and GABA signaling. Although SST messenger RNA (mRNA) levels are lower in the DLPFC in schizophrenia, it is not known whether SST neurons exhibit alterations in the capacity to synthesize GABA, principally via the 67-kilodalton isoform of glutamic acid decarboxylase (GAD67).

METHODS

GAD67 and SST mRNA levels were quantified in individual SST neurons using fluorescence in situ hybridization in DLPFC layers 2/superficial 3, where SST neurons are enriched, in individuals with schizophrenia (n = 46) and unaffected comparison (n = 46) individuals. Findings were compared with GAD67 and SST mRNA levels quantified by polymerase chain reaction and to final educational attainment, a proxy measure for cognitive functioning.

RESULTS

GAD67 (F1,84 = 13.1, p = .0005, Cohen's d = -0.78) and SST (F1,84 = 10.1, p = .002, Cohen's d = -0.64) mRNA levels in SST neurons were lower in schizophrenia, with no group differences in the relative density of SST neurons (F1,84 = 0.21, p = .65). A presynaptic index of dendritic inhibition, derived by summing the alterations in GAD67 and SST mRNAs, was lower in 80.4% of individuals with schizophrenia and was associated with final educational attainment (adjusted odds ratio = 1.44, p = .022).

CONCLUSIONS

Deficits in both GAD67 and SST mRNAs within SST neurons indicate that these neurons have a markedly reduced ability to inhibit postsynaptic pyramidal neuron dendrites in schizophrenia. These alterations likely contribute to cognitive dysfunction in schizophrenia.

Spatial-Omics Methods and Applications

Traditional tissue profiling approaches have evolved from bulk studies to single-cell analysis over the last decade; however, the spatial context in tissues and microenvironments has always been lost. Over the last 5 years, spatial technologies have emerged that enabled researchers to investigate tissues in situ for proteins and transcripts without losing anatomy and histology. The field of spatial-omics enables highly multiplexed analysis of biomolecules like RNAs and proteins in their native spatial context-and has matured from initial proof-of-concept studies to a thriving field with widespread applications from basic research to translational and clinical studies. While there has been wide adoption of spatial technologies, there remain challenges with the standardization of methodologies, sample compatibility, throughput, resolution, and ease of use. In this chapter, we discuss the current state of the field and highlight technological advances and limitations.

A comparative view of human and mouse telencephalon inhibitory neuron development

Human GABAergic inhibitory neurons (INs) in the telencephalon play crucial roles in modulating neural circuits, generating cortical oscillations, and maintaining the balance between excitation and inhibition. The major IN subtypes are based on their gene expression profiles, morphological diversity and circuit-specific functions. Although previous foundational work has established that INs originate in the ganglionic eminence regions in mice, recent studies have questioned origins in humans and non-human primates. We review the origins of INs in mice and compare with recent findings from primary human prenatal brain tissue culture experiments and lineage analysis from somatic variants in neurotypical human cadavers and human brain organoids. Together, these studies suggest potential primate- or human-specific processes that may have been overlooked in mouse models and could have implications for brain disorders.

Three-dimensional single-cell transcriptome imaging of thick tissues

Multiplexed error-robust fluorescence in situ hybridization (MERFISH) allows genome-scale imaging of RNAs in individual cells in intact tissues. To date, MERFISH has been applied to image thin-tissue samples of ~10 µm thickness. Here, we present a thick-tissue three-dimensional (3D) MERFISH imaging method, which uses confocal microscopy for optical sectioning, deep learning for increasing imaging speed and quality, as well as sample preparation and imaging protocol optimized for thick samples. We demonstrated 3D MERFISH on mouse brain tissue sections of up to 200 µm thickness with high detection efficiency and accuracy. We anticipate that 3D thick-tissue MERFISH imaging will broaden the scope of questions that can be addressed by spatial genomics.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Transcriptomic landscape of mammalian ventral pallidum at single-cell resolution

The ventral pallidum (VP) is critical for motivated behaviors. While contemporary work has begun to elucidate the functional diversity of VP neurons, the molecular heterogeneity underlying this functional diversity remains incompletely understood. We used single-nucleus RNA sequencing and in situ hybridization to define the transcriptional taxonomy of VP cell types in mice, macaques, and baboons. We found transcriptional conservation between all three species, within the broader neurochemical cell types. Unique dopaminoceptive and cholinergic subclusters were identified and conserved across both primate species but had no homolog in mice. This harmonized consensus VP cellular atlas will pave the way for understanding the structure and function of the VP and identified key neuropeptides, neurotransmitters, and neurotransmitter receptors that could be targeted within specific VP cell types for functional investigations.

Decoding the mosaic of inflammatory bowel disease: Illuminating insights with single-cell RNA technology

Inflammatory bowel diseases (IBD), comprising ulcerative colitis (UC) and Crohn's disease (CD), are complex chronic inflammatory intestinal conditions with a multifaceted pathology, influenced by immune dysregulation and genetic susceptibility. The challenges in understanding IBD mechanisms and implementing precision medicine include deciphering the contributions of individual immune and non-immune cell populations, pinpointing specific dysregulated genes and pathways, developing predictive models for treatment response, and advancing molecular technologies. Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to address these challenges, offering comprehensive transcriptome profiles of various cell types at the individual cell level in IBD patients, overcoming limitations of bulk RNA sequencing. Additionally, single-cell proteomics analysis, T-cell receptor repertoire analysis, and epigenetic profiling provide a comprehensive view of IBD pathogenesis and personalized therapy. This review summarizes significant advancements in single-cell sequencing technologies for enhancing our understanding of IBD, covering pathogenesis, diagnosis, treatment, and prognosis. Furthermore, we discuss the challenges that persist in the context of IBD research, including the need for longitudinal studies, integration of multiple single-cell and spatial transcriptomics technologies, and the potential of microbial single-cell RNA-seq to shed light on the role of the gut microbiome in IBD.

Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

Over the past decade, single-cell genomics technologies have allowed scalable profiling of cell-type-specific features, which has substantially increased our ability to study cellular diversity and transcriptional programs in heterogeneous tissues. Yet our understanding of mechanisms of gene regulation or the rules that govern interactions between cell types is still limited. The advent of new computational pipelines and technologies, such as single-cell epigenomics and spatially resolved transcriptomics, has created opportunities to explore two new axes of biological variation: cell-intrinsic regulation of cell states and expression programs and interactions between cells. Here, we summarize the most promising and robust technologies in these areas, discuss their strengths and limitations and discuss key computational approaches for analysis of these complex datasets. We highlight how data sharing and integration, documentation, visualization and benchmarking of results contribute to transparency, reproducibility, collaboration and democratization in neuroscience, and discuss needs and opportunities for future technology development and analysis.

Computational modeling for deciphering tissue microenvironment heterogeneity from spatially resolved transcriptomics

Spatial transcriptomics techniques, while measuring gene expression, retain spatial location information, aiding in situ studies of organismal tissue architecture and the progression of pathological processes. These techniques generate vast amounts of omics data, necessitating the development of computational methods to reveal the underlying tissue microenvironment heterogeneity. The main directions in spatial transcriptomics data analysis are spatial domain detection and spatial deconvolution, which can identify spatial functional regions and parse the distribution of cell types in spatial transcriptomics data by integrating single-cell transcriptomics data. In these two research directions, many computational methods have been successively proposed. This article will categorize them into three types: machine learning-based methods, probabilistic models-based methods, and deep learning-based methods. It will list and discuss the representative algorithms of each type along with their advantages and disadvantages and describe the datasets and evaluation metrics used to assess these computational methods, facilitating researchers in selecting suitable computational methods according to their research needs. Finally, combining the latest technological developments and the advantages and disadvantages of current algorithms, this article will look forward to the future directions of computational method development.

Integrated multimodal cell atlas of Alzheimer's disease

Alzheimer's disease (AD) is the leading cause of dementia in older adults. Although AD progression is characterized by stereotyped accumulation of proteinopathies, the affected cellular populations remain understudied. Here we use multiomics, spatial genomics and reference atlases from the BRAIN Initiative to study middle temporal gyrus cell types in 84 donors with varying AD pathologies. This cohort includes 33 male donors and 51 female donors, with an average age at time of death of 88 years. We used quantitative neuropathology to place donors along a disease pseudoprogression score. Pseudoprogression analysis revealed two disease phases: an early phase with a slow increase in pathology, presence of inflammatory microglia, reactive astrocytes, loss of somatostatin+ inhibitory neurons, and a remyelination response by oligodendrocyte precursor cells; and a later phase with exponential increase in pathology, loss of excitatory neurons and Pvalb+ and Vip+ inhibitory neuron subtypes. These findings were replicated in other major AD studies.

Probe set selection for targeted spatial transcriptomics

Targeted spatial transcriptomic methods capture the topology of cell types and states in tissues at single-cell and subcellular resolution by measuring the expression of a predefined set of genes. The selection of an optimal set of probed genes is crucial for capturing the spatial signals present in a tissue. This requires selecting the most informative, yet minimal, set of genes to profile (gene set selection) for which it is possible to build probes (probe design). However, current selections often rely on marker genes, precluding them from detecting continuous spatial signals or new states. We present Spapros, an end-to-end probe set selection pipeline that optimizes both gene set specificity for cell type identification and within-cell type expression variation to resolve spatially distinct populations while considering prior knowledge as well as probe design and expression constraints. We evaluated Spapros and show that it outperforms other selection approaches in both cell type recovery and recovering expression variation beyond cell types. Furthermore, we used Spapros to design a single-cell resolution in situ hybridization on tissues (SCRINSHOT) experiment of adult lung tissue to demonstrate how probes selected with Spapros identify cell types of interest and detect spatial variation even within cell types.

Transcriptional patterns of brain structural abnormalities in CSVD-related cognitive impairment

Background

Brain structural abnormalities have been associated with cognitive impairment in individuals with small cerebral vascular disease (CSVD). However, the molecular and cellular factors making the different brain structural regions more vulnerable to CSVD-related cognitive impairment remain largely unknown.

Materials and methods

Voxel-based morphology (VBM) was performed on the structural magnetic resonance imaging data of 46 CSVD-related cognitive impairment and 73 healthy controls to analyze and compare the gray matter volume (GMV) between the 2 groups. Transcriptome-neuroimaging spatial correlation analysis was carried out in combination with the Allen Human Brain Atlas to explore gene expression profiles associated with changes in cortical morphology in CSVD-related cognitive impairment.

Results

VBM analysis demonstrated extensive decreased GMV in CSVD-related cognitive impairment in the bilateral temporal lobe and thalamus, especially the hippocampus, thalamus, parahippocampus, and fusiform, and the left temporal lobe showed a more severe atrophy than the right temporal lobe. These brain structural alterations were closely related to memory and executive function deficits in CSVD-related cognitive impairment. Furthermore, a total of 1,580 genes were revealed to be significantly associated with regional change in GMV. The negatively and positively GMV-linked gene expression profiles were mainly enriched in RNA polymerase II, catalytic activity acting on a nucleic acid, aminoacyltransferase activity, axonogenesis, Golgi membrane, and cell junction organization.

Conclusion

Our findings suggest that brain morphological abnormalities in CSVD-related cognitive impairment are linked to molecular changes involving complex polygenic mechanisms, highlighting the interplay between genetic influences and structural alterations relevant to CSVD-related cognitive impairment.

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.

The 3D Genome in Brain Development: An Exploration of Molecular Mechanisms and Experimental Methods

The human brain contains multiple cell types that are spatially organized into functionally distinct regions. The proper development of the brain requires complex gene regulation mechanisms in both neurons and the non-neuronal cell types that support neuronal function. Studies across the last decade have discovered that the 3D nuclear organization of the genome is instrumental in the regulation of gene expression in the diverse cell types of the brain. In this review, we describe the fundamental biochemical mechanisms that regulate the 3D genome, and comprehensively describe in vitro and ex vivo studies on mouse and human brain development that have characterized the roles of the 3D genome in gene regulation. We highlight the significance of the 3D genome in linking distal enhancers to their target promoters, which provides insights on the etiology of psychiatric and neurological disorders, as the genetic variants associated with these disorders are primarily located in noncoding regulatory regions. We also describe the molecular mechanisms that regulate chromatin folding and gene expression in neurons. Furthermore, we describe studies with an evolutionary perspective, which have investigated features that are conserved from mice to human, as well as human gained 3D chromatin features. Although most of the insights on disease and molecular mechanisms have been obtained from bulk 3C based experiments, we also highlight other approaches that have been developed recently, such as single cell 3C approaches, as well as non-3C based approaches. In our future perspectives, we highlight the gaps in our current knowledge and emphasize the need for 3D genome engineering and live cell imaging approaches to elucidate mechanisms and temporal dynamics of chromatin interactions, respectively.

Astrocyte regional specialization is shaped by postnatal development

Astrocytes are an abundant class of glial cells with critical roles in neural circuit assembly and function. Though many studies have uncovered significant molecular distinctions between astrocytes from different brain regions, how this regionalization unfolds over development is not fully understood. We used single-nucleus RNA sequencing to characterize the molecular diversity of brain cells across six developmental stages and four brain regions in the mouse and marmoset brain. Our analysis of over 170,000 single astrocyte nuclei revealed striking regional heterogeneity among astrocytes, particularly between telencephalic and diencephalic regions, at all developmental time points surveyed in both species. At the stages sampled, most of the region patterning was private to astrocytes and not shared with neurons or other glial types. Though astrocytes were already regionally patterned in late embryonic stages, this region-specific astrocyte gene expression signature changed dramatically over postnatal development, and its composition suggests that regional astrocytes further specialize postnatally to support their local neuronal circuits. Comparing across species, we found divergence in the expression of astrocytic region- and age-differentially expressed genes and the timing of astrocyte maturation relative to birth between mouse and marmoset, as well as hundreds of species differentially expressed genes. Finally, we used expansion microscopy to show that astrocyte morphology is largely conserved across gray matter regions of prefrontal cortex, striatum, and thalamus in the mouse, despite substantial molecular divergence.

Unraveling mechanisms of human brain evolution

Evolutionary changes in human brain structure and function have enabled our specialized cognitive abilities. How these changes have come about genetically and functionally has remained an open question. However, new methods are providing a wealth of information about the genetic, epigenetic, and transcriptomic differences that set the human brain apart. Combined with in vitro models that allow access to developing brain tissue and the cells of our closest living relatives, the puzzle pieces are now coming together to yield a much more complete picture of what is actually unique about the human brain. The challenge now will be linking these observations and making the jump from correlation to causation. However, elegant genetic manipulations are now possible and, when combined with model systems such as organoids, will uncover a mechanistic understanding of how evolutionary changes at the genetic level have led to key differences in development and function that enable human cognition.

SIMVI reveals intrinsic and spatial-induced states in spatial omics data

Spatial omics technologies enable the analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to capture spatial regulations for further biological discoveries. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free framework that disentangles cell intrinsic and spatial-induced latent variables for modeling gene expression in spatial omics data. We derive theoretical support for SIMVI in disentangling intrinsic and spatial-induced variations. By this disentanglement, SIMVI enables estimation of spatial effects (SE) at a single-cell resolution, and opens up various opportunities for novel downstream analyses. To demonstrate the potential of SIMVI, we applied SIMVI to spatial omics data from diverse platforms and tissues (MERFISH human cortex, Slide-seqv2 mouse hippocampus, Slide-tags human tonsil, spatial multiome human melanoma, cohort-level CosMx melanoma). In all tested datasets, SIMVI effectively disentangles variations and infers accurate spatial effects compared with alternative methods. Moreover, on these datasets, SIMVI uniquely uncovers complex spatial regulations and dynamics of biological significance. In the human tonsil data, SIMVI illuminates the cyclical spatial dynamics of germinal center B cells during maturation. Applying SIMVI to both RNA and ATAC modalities of the multiome melanoma data reveals potential tumor epigenetic reprogramming states. Application of SIMVI on our newly-collected cohort-level CosMx melanoma dataset uncovers space-and-outcome-dependent macrophage states and the underlying cellular communication machinery in the tumor microenvironments.

Evolutionary neurogenomics at single-cell resolution

The human brain is composed of increasingly recognized heterogeneous cell types. Applying single-cell genomics to brain tissue can elucidate relative cell type proportions as well as differential gene expression and regulation among humans and other species. Here, we review recent studies that utilized high-throughput genomics approaches to compare brains among species at single-cell resolution. These studies identified genomic elements that are similar among species as well as evolutionary novelties on the human lineage. We focus on those human-relevant innovations and discuss the biological implications of these modifications. Finally, we discuss areas of comparative single-cell genomics that remain unexplored either due to needed technological advances or due to biological availability at the brain region or species level.

Transcriptomic landscape of mammalian ventral pallidum at single-cell resolution

The ventral pallidum (VP) is critical for motivated behaviors. While contemporary work has begun to elucidate the functional diversity of VP neurons, the molecular heterogeneity underlying this functional diversity remains incompletely understood. We used snRNA-seq and in situ hybridization to define the transcriptional taxonomy of VP cell types in mice, macaques, and baboons. We found transcriptional conservation between all three species, within the broader neurochemical cell types. Unique dopaminoceptive and cholinergic subclusters were identified and conserved across both primate species but had no homolog in mice. This harmonized consensus VP cellular atlas will pave the way for understanding the structure and function of the VP and identified key neuropeptides, neurotransmitters, and neuro receptors that could be targeted within specific VP cell types for functional investigations.

Teaser

Genetic identity of ventral pallidum cell types is conserved across rodents and primates at the transcriptional level.

Experimental Cell Models for Investigating Neurodegenerative Diseases

Experimental models play a pivotal role in biomedical research, facilitating the understanding of disease mechanisms and the development of novel therapeutics. This is particularly true for neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, and motor neuron disease, which present complex challenges for research and therapy development. In this work, we review the recent literature about experimental models and motor neuron disease. We identified three main categories of models that are highly studied by scientists. In fact, experimental models for investigating these diseases encompass a variety of approaches, including modeling the patient's cell culture, patient-derived induced pluripotent stem cells, and organoids. Each model offers unique advantages and limitations, providing researchers with a range of tools to address complex biological questions. Here, we discuss the characteristics, applications, and recent advancements in terms of each model system, highlighting their contributions to advancing biomedical knowledge and translational research.

Tracing the evolution of single-cell cancer 3D genomes: an atlas for cancer gene discovery

Although three-dimensional (3D) genome structures are altered in cancer cells, little is known about how these changes evolve and diversify during cancer progression. Leveraging genome-wide chromatin tracing to visualize 3D genome folding directly in tissues, we generated 3D genome cancer atlases of murine lung and pancreatic adenocarcinoma. Our data reveal stereotypical, non-monotonic, and stage-specific alterations in 3D genome folding heterogeneity, compaction, and compartmentalization as cancers progress from normal to preinvasive and ultimately to invasive tumors, discovering a potential structural bottleneck in early tumor progression. Remarkably, 3D genome architectures distinguish histologic cancer states in single cells, despite considerable cell-to-cell heterogeneity. Gene-level analyses of evolutionary changes in 3D genome compartmentalization not only showed compartment-associated genes are more homogeneously regulated, but also elucidated prognostic and dependency genes in lung adenocarcinoma and a previously unappreciated role for polycomb-group protein Rnf2 in 3D genome regulation. Our results demonstrate the utility of mapping the single-cell cancer 3D genome in tissues and illuminate its potential to identify new diagnostic, prognostic, and therapeutic biomarkers in cancer.

Graph Fourier transform for spatial omics representation and analyses of complex organs

Spatial omics technologies decipher functional components of complex organs at cellular and subcellular resolutions. We introduce Spatial Graph Fourier Transform (SpaGFT) and apply graph signal processing to a wide range of spatial omics profiling platforms to generate their interpretable representations. This representation supports spatially variable gene identification and improves gene expression imputation, outperforming existing tools in analyzing human and mouse spatial transcriptomics data. SpaGFT can identify immunological regions for B cell maturation in human lymph nodes Visium data and characterize variations in secondary follicles using in-house human tonsil CODEX data. Furthermore, it can be integrated seamlessly into other machine learning frameworks, enhancing accuracy in spatial domain identification, cell type annotation, and subcellular feature inference by up to 40%. Notably, SpaGFT detects rare subcellular organelles, such as Cajal bodies and Set1/COMPASS complexes, in high-resolution spatial proteomics data. This approach provides an explainable graph representation method for exploring tissue biology and function.