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Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining the spatial context of the tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to use their spatial information carefully. To this end, we develop a graph attention auto-encoder framework STAGATE to accurately identify spatial domains by learning low-dimensional latent embeddings via integrating spatial information and gene expression profiles. To better characterize the spatial similarity at the boundary of spatial domains, STAGATE adopts an attention mechanism to adaptively learn the similarity of neighboring spots, and an optional cell type-aware module through integrating the pre-clustering of gene expressions. We validate STAGATE on diverse spatial transcriptomics datasets generated by different platforms with different spatial resolutions. STAGATE could substantially improve the identification accuracy of spatial domains, and denoise the data while preserving spatial expression patterns. Importantly, STAGATE could be extended to multiple consecutive sections to reduce batch effects between sections and extracting three-dimensional (3D) expression domains from the reconstructed 3D tissue effectively. Breakthrough technologies for spatially resolved transcriptomics have enabled genome-wide profiling of gene expressions in captured locations. Here the authors integrate gene expressions and spatial locations to identify spatial domains using an adaptive graph attention auto-encoder.

One major challenge in analyzing spatial transcriptomic datasets is to simultaneously incorporate the cell transcriptome similarity and their spatial locations. Here, we introduce SpaceFlow, which generates spatially-consistent low-dimensional embeddings by incorporating both expression similarity and spatial information using spatially regularized deep graph networks. Based on the embedding, we introduce a pseudo-Spatiotemporal Map that integrates the pseudotime concept with spatial locations of the cells to unravel spatiotemporal patterns of cells. By comparing with multiple existing methods on several spatial transcriptomic datasets at both spot and single-cell resolutions, SpaceFlow is shown to produce a robust domain segmentation and identify biologically meaningful spatiotemporal patterns. Applications of SpaceFlow reveal evolving lineage in heart developmental data and tumor-immune interactions in human breast cancer data. Our study provides a flexible deep learning framework to incorporate spatiotemporal information in analyzing spatial transcriptomic data. A critical task in spatial transcriptomics analysis is to understand inherently spatial relationships among cells. Here, the authors present a deep learning framework to integrate spatial and transcriptional information, spatially extending pseudotime and revealing spatiotemporal organization of cells.

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic. Single-cell and spatial transcriptomics are transforming our understanding of cell plasticity and tissue diversity. This Review discusses technical and computational advancements and challenges in characterizing cell states and tissues during embryogenesis, tumorigenesis and immune responses, and the application of these tools to the clinic.

Different sets of neurons encode the spatial position and orientation of an organism. However, social animals need to know the position of other individuals for social interactions, observational learning, and group navigation. Surprisingly, very little is known about how the position of other animals is represented in the brain. Danjo et al. and Omer et al. now report the discovery of a subgroup of neurons in hippocampal area CA1 that encodes the presence of conspecifics in rat and bat brains, respectively. Science , this issue p. 213 , p. 218 A subpopulation of bat hippocampal CA1 neurons represents the spatial position of another bat. Social animals have to know the spatial positions of conspecifics. However, it is unknown how the position of others is represented in the brain. We designed a spatial observational-learning task, in which an observer bat mimicked a demonstrator bat while we recorded hippocampal dorsal-CA1 neurons from the observer bat. A neuronal subpopulation represented the position of the other bat, in allocentric coordinates. About half of these “social place-cells” represented also the observer’s own position—that is, were place cells. The representation of the demonstrator bat did not reflect self-movement or trajectory planning by the observer. Some neurons represented also the position of inanimate moving objects; however, their representation differed from the representation of the demonstrator bat. This suggests a role for hippocampal CA1 neurons in social-spatial cognition.

Among the methods valuable for assessing spatial learning and memory impairments in rodents, the Barnes maze (BM) task deserves special attention. It is based on the assumption that the animal placed into the aversive environment should learn and remember the location of an escape box located below the surface of the platform. Different phases of the task allow to measure spatial learning, memory retrieval, and cognitive flexibility. Herein, we summarize current knowledge about the BM procedure, its variations and critical parameters measured in the task. We highlight confounding factors which should be taken into account when conducting BM task, discussing briefly its advantages and disadvantages. We then propose an extended version of the BM protocol which allows to measure different aspects of spatial learning and memory in rodents. We believe that this review will help to standardize the BM methodology across the laboratories and eventually make the results comparable.

Spatial transcriptomics (ST) technologies detect mRNA expression in single cells/spots while preserving their two-dimensional (2D) spatial coordinates, allowing researchers to study the spatial distribution of the transcriptome in tissues; however, joint analysis of multiple ST slices and aligning them to construct a three-dimensional (3D) stack of the tissue still remain a challenge. Here, we introduce spatial architecture characterization by deep learning (SPACEL) for ST data analysis. SPACEL comprises three modules: Spoint embeds a multiple-layer perceptron with a probabilistic model to deconvolute cell type composition for each spot in a single ST slice; Splane employs a graph convolutional network approach and an adversarial learning algorithm to identify spatial domains that are transcriptomically and spatially coherent across multiple ST slices; and Scube automatically transforms the spatial coordinate systems of consecutive slices and stacks them together to construct a 3D architecture of the tissue. Comparisons against 19 state-of-the-art methods using both simulated and real ST datasets from various tissues and ST technologies demonstrate that SPACEL outperforms the others for cell type deconvolution, for spatial domain identification, and for 3D alignment, thus showcasing SPACEL as a valuable integrated toolkit for ST data processing and analysis. Spatial transcriptomics (ST) technologies detect transcript distribution in space. Here, authors present a deep learning based method SPACEL for cell type deconvolution, spatial domain identification and 3D alignment, showcasing it as a valuable toolkit for ST data analysis

Activation of microglia is a prominent pathological feature in tauopathies, including Alzheimer’s disease. How microglia activation contributes to tau toxicity remains largely unknown. Here we show that nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling, activated by tau, drives microglial-mediated tau propagation and toxicity. Constitutive activation of microglial NF-κB exacerbated, while inactivation diminished, tau seeding and spreading in young PS19 mice. Inhibition of NF-κB activation enhanced the retention while reduced the release of internalized pathogenic tau fibrils from primary microglia and rescued microglial autophagy deficits. Inhibition of microglial NF-κB in aged PS19 mice rescued tau-mediated learning and memory deficits, restored overall transcriptomic changes while increasing neuronal tau inclusions. Single cell RNA-seq revealed that tau-associated disease states in microglia were diminished by NF-κB inactivation and further transformed by constitutive NF-κB activation. Our study establishes a role for microglial NF-κB signaling in mediating tau spreading and toxicity in tauopathy. Wang et al show that microglial NF-κB activation is essential for tau spreading and tau-mediated spatial learning and memory deficits in tauopathy mice. Inactivation of NF-κB reversed tau associated microglial states and rescued autophagy deficits.

Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and frameworks in the Methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper sheds some light the topic for the community and will inspire further research efforts in the change detection task.

There has long been discussion regarding the positive effects of physical exercise on brain activity. However, physical exercise has only recently begun to receive the attention of the scientific community, with major interest in its effects on the cognitive functions, spatial learning and memory, as a non-drug method of maintaining brain health and treating neurodegenerative and/or psychiatric conditions. In humans, several studies have shown the beneficial effects of aerobic and resistance exercises in adult and geriatric populations. More recently, studies employing animal models have attempted to elucidate the mechanisms underlying neuroplasticity related to physical exercise-induced spatial learning and memory improvement, even under neurodegenerative conditions. In an attempt to clarify these issues, the present review aims to discuss the role of physical exercise in the improvement of spatial learning and memory and the cellular and molecular mechanisms involved in neuroplasticity.

Sex differences are reported in hippocampal plasticity, cognition, and in a number of disorders that target the integrity of the hippocampus. For example, meta-analyses reveal that males outperform females on hippocampus-dependent tasks in rodents and in humans, furthermore women are more likely to experience greater cognitive decline in Alzheimer’s disease and depression, both diseases characterized by hippocampal dysfunction. The hippocampus is a highly plastic structure, important for processing higher order information and is sensitive to the environmental factors such as stress. The structure retains the ability to produce new neurons and this process plays an important role in pattern separation, proactive interference, and cognitive flexibility. Intriguingly, there are prominent sex differences in the level of neurogenesis and the activation of new neurons in response to hippocampus-dependent cognitive tasks in rodents. However, sex differences in spatial performance can be nuanced as animal studies have demonstrated that there are task, and strategy choice dependent sex differences in performance, as well as sex differences in the subregions of the hippocampus influenced by learning. This review discusses sex differences in pattern separation, pattern completion, spatial learning, and links between adult neurogenesis and these cognitive functions of the hippocampus. We emphasize the importance of including both sexes when studying genomic, cellular, and structural mechanisms of the hippocampal function.