Three-dimensional intact-tissue sequencing of single-cell transcriptional states

Single-cell and spatial omics: exploring hypothalamic heterogeneity

Elucidating the complex dynamic cellular organization in the hypothalamus is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.

CellLENS enables cross-domain information fusion for enhanced cell population delineation in single-cell spatial omics data

Delineating cell populations is crucial for understanding immune function in health and disease. Spatial omics technologies offer insights by capturing three complementary domains: single-cell molecular biomarker expression, cellular spatial relationships and tissue architecture. However, current computational methods often fail to fully integrate these multidimensional data, particularly for immune cell populations and intrinsic functional states. We introduce Cell Local Environment and Neighborhood Scan (CellLENS), a self-supervised computational method that learns cellular representations by fusing information across three spatial omics domains (expression, neighborhood and image). CellLENS markedly enhances de novo discovery of biologically relevant immune cell populations at fine granularity by integrating individual cells' molecular profiles with their neighborhood context and tissue localization. By applying CellLENS to diverse spatial proteomic and transcriptomic datasets across multiple tissue types and disease settings, we uncover unique immune cell populations functionally stratified according to their spatial contexts. Our work demonstrates the power of multi-domain data integration in spatial omics to reveal insights into immune cell heterogeneity and tissue-specific functions.

A postsynaptic GPR158-PLCXD2 complex controls spine apparatus abundance and dendritic spine maturation

The spine apparatus (SA), an endoplasmic reticulum (ER)-related organelle present in a subset of dendritic spines, plays a key role in postsynaptic development and is implicated in various neurological disorders. The molecular mechanisms that dictate SA localization at selected synapses remain elusive. Here, we identify a postsynaptic signaling complex comprising the G protein-coupled receptor (GPCR)- GPR158 and a constitutively active phospholipase C (PLC) family member, PLC X-domain containing 2 (PLCXD2), that controls SA abundance. Sparse genetic manipulations of mouse cortical neurons in vivo demonstrate that, in the absence of GPR158, unrestrained PLCXD2 activity impedes postsynaptic SA incorporation and hampers structural and functional dendritic spine maturation. Extracellular heparan sulfate proteoglycan (HSPG) binding modulates the GPR158-PLCXD2 interaction, providing spatiotemporal control over GPR158 signaling. Together, our findings uncover a direct GPCR-like receptor-to-PLC signaling pathway that bypasses canonical PLC regulation via G proteins. This GPR158-PLCXD2 module regulates SA abundance, essential for proper postsynaptic structure and function.

Advances in spatial transcriptomics and its application in the musculoskeletal system

While bulk RNA sequencing and single-cell RNA sequencing have shed light on cellular heterogeneity and potential molecular mechanisms in the musculoskeletal system in both physiological and various pathological states, the spatial localization of cells and molecules and intercellular interactions within the tissue context require further elucidation. Spatial transcriptomics has revolutionized biological research by simultaneously capturing gene expression profiles and in situ spatial information of tissues, gradually finding applications in musculoskeletal research. This review provides a summary of recent advances in spatial transcriptomics and its application to the musculoskeletal system. The classification and characteristics of data acquisition techniques in spatial transcriptomics are briefly outlined, with an emphasis on widely-adopted representative technologies and the latest technological breakthroughs, accompanied by a concise workflow for incorporating spatial transcriptomics into musculoskeletal system research. The role of spatial transcriptomics in revealing physiological mechanisms of the musculoskeletal system, particularly during developmental processes, is thoroughly summarized. Furthermore, recent discoveries and achievements of this emerging omics tool in addressing inflammatory, traumatic, degenerative, and tumorous diseases of the musculoskeletal system are compiled. Finally, challenges and potential future directions for spatial transcriptomics, both as a field and in its applications in the musculoskeletal system, are discussed.

Light-microscopy-based connectomic reconstruction of mammalian brain tissue

The information-processing capability of the brain's cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution1,2 with dense cellular labelling. Light microscopy is uniquely positioned to visualize specific molecules, but dense, synapse-level circuit reconstruction by light microscopy has been out of reach, owing to limitations in resolution, contrast and volumetric imaging capability. Here we describe light-microscopy-based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning-based segmentation and analysis of connectivity, thereby directly incorporating molecular information into synapse-level reconstructions of brain tissue. LICONN will allow synapse-level phenotyping of brain tissue in biological experiments in a readily adoptable manner.

m2ST: dual multi-scale graph clustering for spatially resolved transcriptomics

MOTIVATION

Spatial clustering is a key analytical technique for exploring spatial transcriptomics data. Recent graph neural network-based methods have shown promise in spatial clustering but face notable challenges. One significant issue is that analyzing the functions and complex mechanisms of organisms from a single scale is difficult and most methods focus exclusively on the single-scale representation of transcriptomic data, potentially limiting the discriminative power of extracted features for spatial domain clustering. Furthermore, classical clustering algorithms are often applied directly to latent representation, making it a worthwhile endeavor to explore a tailored clustering method to further improve the accuracy of spatial domain annotation.

RESULTS

To address these limitations, we propose m2ST, a novel dual multi-scale graph clustering method. m2ST first uses a multi-scale masked graph autoencoder to extract representations across different scales from spatial transcriptomic data. To effectively compress and distill meaningful knowledge embedded in the data, m2ST introduces a random masking mechanism for node features and uses a scaled cosine error as the loss function. Additionally, we introduce a tailored multi-scale clustering framework that integrates scale-common and scale-specific information exploration into the clustering process, achieving more robust annotation performance. Shannon entropy is finally utilized to dynamically adjust the importance of different scales. Extensive experiments on multiple spatial transcriptomic datasets demonstrate the superior performance of m2ST compared to existing methods.

AVAILABILITY AND IMPLEMENTATION

https://github.com/BBKing49/m2ST.

Emerging Techniques in Spatial Multiomics: Fundamental Principles and Applications to Dermatology

Molecular pathology, such as high-throughput genomic and proteomic profiling, identifies precise disease targets from biopsies but require tissue dissociation, losing valuable histologic and spatial context. Emerging spatial multi-omic technologies now enable multiplexed visualization of genomic, proteomic, and epigenomic targets within a single tissue slice, eliminating the need for labeling multiple adjacent slices. Although early work focused on RNA (spatial transcriptomics), spatial technologies can now concurrently capture DNA, genome accessibility, histone modifications, and proteins with spatially-resolved single-cell resolution. This review outlines the principles, advantages, limitations, and potential for spatial technologies to advance dermatologic research. By jointly profiling multiple molecular channels, spatial multiomics enables novel studies of copy number variations, clonal heterogeneity, and enhancer dysregulation, replete with spatial context, illuminating the skin's complex heterogeneity.

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.

Sequencing technologies to measure translation in single cells

Translation is one of the most energy-intensive processes in a cell and, accordingly, is tightly regulated. Genome-wide methods to measure translation and the translatome and to study the complex regulation of protein synthesis have enabled unprecedented characterization of this crucial step of gene expression. However, technological limitations have hampered our understanding of translation control in multicellular tissues, rare cell types and dynamic cellular processes. Recent optimizations, adaptations and new techniques have enabled these measurements to be made at single-cell resolution. In this Progress, we discuss single-cell sequencing technologies to measure translation, including ribosome profiling, ribosome affinity purification and spatial translatome methods.

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.

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.

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.

Perturb-tracing enables high-content screening of multi-scale 3D genome regulators

Three-dimensional (3D) genome organization becomes altered during development, aging and disease, but the factors regulating chromatin topology are incompletely understood and currently no technology can efficiently screen for new regulators of multi-scale chromatin organization. Here, we developed an image-based high-content screening platform (Perturb-tracing) that combines pooled CRISPR screens, a cellular barcode readout method (BARC-FISH) and chromatin tracing. We performed a loss-of-function screen in human cells, and visualized alterations to their 3D chromatin folding conformations, alongside perturbation-paired barcode readout in the same single cells. We discovered tens of new regulators of chromatin folding at different length scales, ranging from chromatin domains and compartments to chromosome territory. A subset of the regulators exhibited 3D genome effects associated with loop extrusion and A-B compartmentalization mechanisms, while others were largely unrelated to these known 3D genome mechanisms. Finally, we identified new regulators of nuclear architectures and found a functional link between chromatin compaction and nuclear shape. Altogether, our method enables scalable, high-content identification of chromatin and nuclear topology regulators that will stimulate new insights into the 3D genome.

Spatially Aware Domain Adaptation Enables Cell Type Deconvolution from Multi-Modal Spatially Resolved Transcriptomics

Spatially Resolved Transcriptomics (SRT) offers unprecedented opportunities to elucidate the cellular arrangements within tissues. Nevertheless, the absence of deconvolution methods that simultaneously model multi-modal features has impeded progress in understanding cellular heterogeneity in spatial contexts. To address this issue, SpaDA is developed, a novel spatially aware domain adaptation method that integrates multi-modal data (i.e., transcriptomics, histological images, and spatial locations) from SRT to accurately estimate the spatial distribution of cell types. SpaDA utilizes a self-expressive variational autoencoder, coupled with deep spatial distribution alignment, to learn and align spatial and graph representations from spatial multi-modal SRT data and single-cell RNA sequencing (scRNA-seq) data. This strategy facilitates the transfer of cell type annotation information across these two similarity graphs, thereby enhancing the prediction accuracy of cell type composition. The results demonstrate that SpaDA surpasses existing methods in cell type deconvolution and the identification of cell types and spatial domains across diverse platforms. Moreover, SpaDA excels in identifying spatially colocalized cell types and key marker genes in regions of low-quality measurements, exemplified by high-resolution mouse cerebellum SRT data. In conclusion, SpaDA offers a powerful and flexible framework for the analysis of multi-modal SRT datasets, advancing the understanding of complex biological systems.

Exploring the Latent Information in Spatial Transcriptomics Data via Multi-View Graph Convolutional Network Based on Implicit Contrastive Learning

Latest developments in spatial transcriptomics enable thoroughly profiling of gene expression while preserving tissue microenvironment. Connecting gene expression with spatial arrangement is key for precise spatial domain identification, enhancing the comprehension of tissue microenvironments and biological processes. However, accurately analyzing spatial domains with similar gene expression and histological features is still challenging. This study introduces STMIGCL, a novel framework that leverages a multi-view graph convolutional network and implicit contrastive learning. First, it creates neighbor graphs from gene expression and spatial coordinates, and then combines these with gene expression through multi-view learning to learn low-dimensional representations. To further refine the obtained low-dimensional representations, a graph contrastive learning method with contrastive enhancement in the latent space is employed, aiming to better capture critical information in the data and improve the accuracy and discriminative power of the embeddings. Finally, an attention mechanism is used to adaptively integrate different views, capturing the importance of spots in various views to obtain the final spot representation. Experimental data confirms that STMIGCL significantly enhances spatial domain recognition precision and outperforms all baseline methods in tasks such as trajectory inference and Spatially Variable Genes (SVGs) recognition.

Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis

Understanding the dynamic nature of biological systems is fundamental to deciphering cellular behavior, developmental processes, and disease progression. Single-cell RNA sequencing (scRNA-seq) has provided static snapshots of gene expression, offering valuable insights into cellular states at a single time point. Recent advancements in temporally resolved scRNA-seq, spatial transcriptomics (ST), and time-series spatial transcriptomics (temporal-ST) have further revolutionized our ability to study the spatiotemporal dynamics of individual cells. These technologies, when combined with computational frameworks such as Markov chains, stochastic differential equations (SDEs), and generative models like optimal transport and Schrödinger bridges, enable the reconstruction of dynamic cellular trajectories and cell fate decisions. This review discusses how these dynamical system approaches offer new opportunities to model and infer cellular dynamics from a systematic perspective.

Spatial Omics in Clinical Research: A Comprehensive Review of Technologies and Guidelines for Applications

Spatial omics integrates molecular profiling with spatial tissue context, enabling high-resolution analysis of gene expression, protein interactions, and epigenetic modifications. This approach provides critical insights into disease mechanisms and therapeutic responses, with applications in cancer, neurology, and immunology. Spatial omics technologies, including spatial transcriptomics, proteomics, and epigenomics, facilitate the study of cellular heterogeneity, tissue organization, and cell-cell interactions within their native environments. Despite challenges in data complexity and integration, advancements in multi-omics pipelines and computational tools are enhancing data accuracy and biological interpretation. This review provides a comprehensive overview of key spatial omics technologies, their analytical methods, validation strategies, and clinical applications. By integrating spatially resolved molecular data with traditional omics, spatial omics is transforming precision medicine, biomarker discovery, and personalized therapy. Future research should focus on improving standardization, reproducibility, and multimodal data integration to fully realize the potential of spatial omics in clinical and translational research.

Temporal and spatial omics technologies for 4D profiling

Cells have distinct molecular repertoires on their surfaces and unique intracellular biomolecular profiles that play pivotal roles in orchestrating a myriad of biological responses in the context of growth, development and disease. A persistent challenge in the deep exploration of these cues has been in our inability to effectively and precisely capture the temporal and spatial characteristics of living cells. In this Perspective, we delve into techniques for temporal and two- and three-dimensional spatial omics analyses and underscore how their harmonious fusion promises to unlock insights into the dynamics and diversity of individual cells within biological systems such as tissues and organoids. We then explore four-dimensional profiling, a nascent but promising frontier that adds a temporal (fourth-dimension) component to three-dimensional omics; highlight the advancements, challenges and gaps in the field; and discuss potential strategies for further technological development.

Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics

Understanding complex biological systems requires tracing cellular dynamic changes across conditions, time, and space. However, integrating multi-sample data in a unified way to explore cellular heterogeneity remains challenging. Here, we present Stereopy, a flexible framework for modeling and dissecting comparative and spatiotemporal patterns in multi-sample spatial transcriptomics with interactive data visualization. To optimize this framework, we devise a universal container, a scope controller, and an integrative transformer tailored for multi-sample multimodal data storage, management, and processing. Stereopy showcases three representative applications: investigating specific cell communities and genes responsible for pathological changes, detecting spatiotemporal gene patterns by considering spatial and temporal features, and inferring three-dimensional niche-based cell-gene interaction network that bridges intercellular communications and intracellular regulations. Stereopy serves as both a comprehensive bioinformatics toolbox and an extensible framework that empowers researchers with enhanced data interpretation abilities and new perspectives for mining multi-sample spatial transcriptomics data.

SGTB: A graph representation learning model combining transformer and BERT for optimizing gene expression analysis in spatial transcriptomics data

In recent years, spatial transcriptomics (ST) has emerged as an innovative technology that enables the simultaneous acquisition of gene expression information and its spatial distribution at the single-cell or regional level, providing deeper insights into cellular interactions and tissue organization, this technology provides a more holistic view of tissue organization and intercellular dynamics. However, existing methods still face certain limitations in data representation capabilities, making it challenging to fully capture complex spatial dependencies and global features. To address this, this paper proposes an innovative spatial multi-scale graph convolutional network (SGTB) based on large language models, integrating graph convolutional networks (GCN), Transformer, and BERT language models to optimize the representation of spatial transcriptomics data. The Graph Convolutional Network (GCN) employs a multi-layer architecture to extract features from gene expression matrices. Through iterative aggregation of neighborhood information, it captures spatial dependencies among cells and gene co-expression patterns, thereby constructing hierarchical cell embeddings. Subsequently, the model integrates an attention mechanism to assign weights to critical features and leverages Transformer layers to model global relationships, refining the ability of learned representations to reflect variations in spatial patterns. Finally, the model incorporates the BERT language model, mapping cell embeddings into textual inputs to exploit its deep semantic representation capabilities for high-dimensional feature extraction. These features are then fused with the embeddings generated by the Transformer, further optimizing feature learning for spatial transcriptomics data. This approach holds significant application value in improving the accuracy of tasks such as cell type classification and gene regulatory network construction, providing a novel computational framework for deep mining of spatial multi-scale biological data.

Deep-tissue transcriptomics and subcellular imaging at high spatial resolution

Limited color channels in fluorescence microscopy have long constrained spatial analysis in biological specimens. We introduce cycle hybridization chain reaction (cycleHCR), a method that integrates multicycle DNA barcoding with HCR to overcome this limitation. cycleHCR enables highly multiplexed imaging of RNA and proteins using a unified barcode system. Whole-embryo transcriptomics imaging achieved precise three-dimensional gene expression and cell fate mapping across a specimen depth of ~310 μm. When combined with expansion microscopy, cycleHCR revealed an intricate network of 10 subcellular structures in mouse embryonic fibroblasts. In mouse hippocampal slices, multiplex RNA and protein imaging uncovered complex gene expression gradients and cell-type-specific nuclear structural variations. cycleHCR provides a quantitative framework for elucidating spatial regulation in deep tissue contexts for research and has potential diagnostic applications.

CellSP: Module discovery and visualization for subcellular spatial transcriptomics data

Spatial transcriptomics has enabled the study of mRNA distributions within cells, a key aspect of cellular function. However, there is a dearth of tools that can identify and interpret functionally relevant spatial patterns of subcellular transcript distribution. To address this, we present CellSP, a computational framework for identifying, visualizing, and characterizing consistent subcellular spatial patterns of mRNA. CellSP introduces the concept of "gene-cell modules", which are gene sets with coordinated subcellular transcript distributions in many cells. It provides intuitive visualizations of the captured patterns and offers functional insights into each discovered module. We demonstrate that CellSP reliably identifies functionally significant modules across diverse tissues and technologies. We use the tool to discover subcellular spatial phenomena related to myelination, axonogenesis and synapse formation in the mouse brain. We find immune response-related modules that change between kidney cancer and healthy samples, and myelination-related modules specific to mouse models of Alzheimer's Disease.

Imaging 3D cell cultures with optical microscopy

Three-dimensional (3D) cell cultures have gained popularity in recent years due to their ability to represent complex tissues or organs more faithfully than conventional two-dimensional (2D) cell culture. This article reviews the application of both 2D and 3D microscopy approaches for monitoring and studying 3D cell cultures. We first summarize the most popular optical microscopy methods that have been used with 3D cell cultures. We then discuss the general advantages and disadvantages of various microscopy techniques for several broad categories of investigation involving 3D cell cultures. Finally, we provide perspectives on key areas of technical need in which there are clear opportunities for innovation. Our goal is to guide microscope engineers and biomedical end users toward optimal imaging methods for specific investigational scenarios and to identify use cases in which additional innovations in high-resolution imaging could be helpful.

Simultaneous CRISPR screening and spatial transcriptomics reveal intracellular, intercellular, and functional transcriptional circuits

Pooled optical screens have enabled the study of cellular interactions, morphology, or dynamics at massive scale, but they have not yet leveraged the power of highly plexed single-cell resolved transcriptomic readouts to inform molecular pathways. Here, we present a combination of imaging spatial transcriptomics with parallel optical detection of in situ amplified guide RNAs (Perturb-FISH). Perturb-FISH recovers intracellular effects that are consistent with single-cell RNA-sequencing-based readouts of perturbation effects (Perturb-seq) in a screen of lipopolysaccharide response in cultured monocytes, and it uncovers intercellular and density-dependent regulation of the innate immune response. Similarly, in three-dimensional xenograft models, Perturb-FISH identifies tumor-immune interactions altered by genetic knockout. When paired with a functional readout in a separate screen of autism spectrum disorder risk genes in human-induced pluripotent stem cell (hIPSC) astrocytes, Perturb-FISH shows common calcium activity phenotypes and their associated genetic interactions and dysregulated molecular pathways. Perturb-FISH is thus a general method for studying the genetic and molecular associations of spatial and functional biology at single-cell resolution.

SpaGRN: Investigating spatially informed regulatory paths for spatially resolved transcriptomics data

Cells spatially organize into distinct cell types or functional domains through localized gene regulatory networks. However, current spatially resolved transcriptomics analyses fail to integrate spatial constraints and proximal cell influences, limiting the mechanistic understanding of tissue organization. Here, we introduce SpaGRN, a statistical framework that reconstructs cell-type- or functional-domain-specific, dynamic, and spatial regulons by coupling intracellular spatial regulatory causality with extracellular signaling path information. Benchmarking across synthetic and real datasets demonstrates SpaGRN's superior precision over state-of-the-art tools in identifying context-dependent regulons. Applied to diverse spatially resolved transcriptomics platforms (Stereo-seq, STARmap, MERFISH, CosMx, Slide-seq, and 10x Visium), complex cancerous samples, and 3D datasets of developing Drosophila embryos and larvae, SpaGRN not only provides a versatile toolkit for decoding receptor-mediated spatial regulons but also reveals spatiotemporal regulatory mechanisms underlying organogenesis and inflammation.

Advancing super-resolution microscopy with metal complexes: functional imaging agents for nanoscale visualization

Super-resolution microscopy (SRM) has transformed biological imaging by overcoming the diffraction limit, offering nanoscale visualization of cellular structures and processes. However, the widespread use of organic fluorescent probes is often hindered by limitations such as photobleaching, short photostability, and inadequate performance in deep-tissue imaging. Metal complexes, with their superior photophysical properties, including exceptional photostability, tuneable luminescence, and extended excited-state lifetimes, address these challenges, enabling precise subcellular targeting and long-term imaging. Beyond imaging, their theranostic potential unlocks real-time diagnostics and treatments for diseases such as cancer and bacterial infections. This review explores recent advancements in applying metal complexes for SRM, focusing on their utility in visualizing intricate subcellular structures, capturing temporal dynamics in live cells and elucidating in vivo spatial organization. We emphasize how rational design strategies optimize biocompatibility, organelle specificity, and deep-tissue penetration, expanding their applicability in multiplexed imaging. Furthermore, we discuss the design of various metal nanoparticles (NPs) for SRM, along with emerging hybrid nanoscale probes that integrate metal complexes with gold (Au) scaffolds, offering promising avenues for overcoming current limitations. By highlighting both established successes and potential frontiers, this review provides a roadmap for leveraging metal complexes as versatile tools in advancing SRM applications.

Spatial omics for accelerating plant research and crop improvement

Plant cells communicate information to regulate developmental processes and respond to environmental stresses. This communication spans various 'omics' layers within a cell and operates through intricate regulatory networks. The emergence of spatial omics presents a promising approach to thoroughly analyze cells, allowing the combined analysis of diverse modalities either in parallel or on the same tissue section. Here, we provide an overview of recent advancements in spatial omics and delineate scientific discoveries in plant research enabled by these technologies. We delve into experimental and computational challenges and outline strategies to navigate these challenges for advancing breeding efforts. With ongoing insightful discoveries and improved accessibility, spatial omics stands on the brink of playing a crucial role in designing future crops.

Benchmarking computational methods for detecting spatial domains and domain-specific spatially variable genes from spatial transcriptomics data

Advances in spatially resolved transcriptomics (SRT) have led to the emergence of numerous computational methods for identifying spatial domains and spatially variable genes (SVGs); however, a comprehensive assessment of existing methods is lacking. We comprehensively benchmarked 19 methods for detecting spatial domains and domain-specific SVGs from SRT data, using 30 real-world datasets covering six SRT technologies and 27 synthetic datasets. We first evaluated the performance of these methods on spatial domain identification in terms of accuracy, stability, generalizability, and scalability. Results reveal that there is no single method that works best for all datasets, and the optimal method depends on the data, especially the SRT platform. Further, we proposed a quantitative strategy to evaluate domain-specific SVG recognition results and assessed the impact of spatial domains on SVG detection. We found that SVG detection based on spatial domains identified by different GNN methods have high accuracy but low concordance. Generally, the more accurate the recognized spatial domains, the higher the number and accuracy of domain-specific SVGs detected. Moreover, integrating spatial clustering results from different methods can lead to more robust and better clustering and SVG results. Practical guidelines were provided for choosing appropriate methods for spatial domain and domain-specific SVG identification.

Spatial transcriptomics AI agent charts hPSC-pancreas maturation in vivo

Spatial transcriptomics has revolutionized our understanding of tissue organization by simultaneously capturing gene expression and spatial localization within intact tissues. However, analyzing these increasingly complex datasets requires specialized expertise across computational biology, statistics, and biological context. To address this challenge, we introduce the Spatial Transcriptomics AI Agent (STAgent), an autonomous multimodal agentic AI that integrates multimodal large language models (LLMs) with specialized computational tools to transform weeks-long analysis tasks into minutes of automated processing. Unlike conventional machine learning approaches that are limited to narrow, predefined tasks, STAgent leverages the emergent capabilities of multimodal LLMs - such as flexible reasoning, contextual understanding, and cross-modal integration - which allow it to adapt to novel data, execute multi-step analyses, and generate biologically meaningful insights with minimal human input. STAgent enables autonomous deep research through integrated capabilities, including dynamic code generation for complex analytical workflows, visual reasoning for interpreting spatial patterns, real-time retrieval of relevant peer-reviewd scientific literature, and synthesis of comprehensive, actionable reports. We applied STAgent to investigate the in vivo maturation of human stem cell-derived pancreatic cells (SC-pancreas) transplanted into immunodeficient mice. We generated single-cell spatial transcriptomics data spanning multiple developmental timepoints. STAgent autonomously (1) identified the maturation of initially scattered endocrine cells into well-defined islet-like structures, with predominantly peripheral α-cells surrounding β-cell cores supported by an expanding mesenchymal network; (2) revealed strengthening endocrine-endocrine cell interactions over time and, through context-aware gene set analysis, uncovered spatially resolved biological processes driving maturation; (3) unlike traditional analytical approaches, STAgent offers mechanistic explanations of spatial patterns, contextualizing findings with relevant literatures and developing cohesive insights into human pancreatic development. This agentic approach establishes a new paradigm in spatial transcriptomics analysis by substantially lowering the expertise barrier and reducing analysis time, accelerating biological and biomedical discovery.

Scalable spatial transcriptomics through computational array reconstruction

Spatial transcriptomics enables gene expression mapping within tissues but is often limited by imaging constraints. We present an imaging-free approach that reconstructs spatial barcode locations using molecular diffusion and dimensionality reduction. Validated against ground truth imaging, our method achieves high fidelity and scales to centimeter-sized tissues. This approach enhances spatial transcriptomics' accessibility and throughput, enabling large-scale studies without specialized imaging equipment.

Recurrent Composite Markers of Cell Types and States

Biological function is mediated by the hierarchical organization of cell types and states within tissue ecosystems. Identifying interpretable composite marker sets that both define and distinguish hierarchical cell identities is essential for decoding biological complexity, yet remains a major challenge. Here, we present RECOMBINE, an algorithm that identifies recurrent composite marker sets to define hierarchical cell identities. Validation using both simulated and biological datasets demonstrates that RECOMBINE achieves higher accuracy in identifying discriminative markers compared to existing approaches, including differential gene expression analysis. When applied to single-cell data and validated with spatial transcriptomics data from the mouse visual cortex, RECOMBINE identified key cell type markers and generated a robust gene panel for targeted spatial profiling. It also uncovered markers of CD8+; T cell states, including GZMK+;HAVCR2-; effector memory cells associated with anti-PD-1 therapy response, and revealed a rare intestinal subpopulation with composite markers in mice. Finally, using data from the Tabula Sapiens project, RECOMBINE identified composite marker sets across a broad range of human tissues. Together, these results highlight RECOMBINE as a robust, data-driven framework for optimized marker selection, enabling the discovery and validation of hierarchical cell identities across diverse tissue contexts.

mcDETECT: Decoding 3D Spatial Synaptic Transcriptomes with Subcellular-Resolution Spatial Transcriptomics

Spatial transcriptomics (ST) has shown great potential for unraveling the molecular mechanisms of neurodegenerative diseases. However, most existing analyses of ST data focus on bulk or single-cell resolution, overlooking subcellular compartments such as synapses, which are fundamental structures of the brain's neural network. Here we present mcDETECT, a novel framework that integrates machine learning algorithms and in situ ST (iST) with targeted gene panels to study synapses. mcDETECT identifies individual synapses based on the aggregation of synaptic mRNAs in three-dimensional (3D) space, allowing for the construction of single-synapse spatial transcriptome profiles. By benchmarking the synapse density measured by volume electron microscopy and genetic labeling, we demonstrate that mcDETECT can faithfully and accurately recover the spatial location of single synapses using iST data from multiple platforms, including Xenium, Xenium 5K, MERSCOPE, and CosMx. Based on the subsequent transcriptome profiling, we further stratify total synapses into various subtypes and explore their pathogenic dysregulation associated with Alzheimer's disease (AD) progression, which provides potential targets for synapse-specific therapies in AD progression.

Assessing the virological response to direct-acting antiviral therapies in the HBV cure programme

Hepatitis B virus (HBV) is a global health problem with over 250 million people affected worldwide. Nucleos(t)ide analogues remain the standard of care and suppress production of progeny virions; however, they have limited effect on the viral transcriptome and long-term treatment is associated with off-target toxicities. Promising results are emerging from clinical trials and several drug classes have been evaluated, including capsid assembly modulators and RNA interfering agents. Whilst peripheral biomarkers are used to monitor responses and define treatment endpoints, they fail to reflect the full reservoir of infected hepatocytes. Given these limitations, consideration should be given to the merits of sampling liver tissue, especially in the context of clinical trials. In this review article, we will discuss methods for profiling HBV in liver tissue and their value to the HBV cure programme.

SpaMask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics

Understanding the spatial locations of cell within tissues is crucial for unraveling the organization of cellular diversity. Recent advancements in spatial resolved transcriptomics (SRT) have enabled the analysis of gene expression while preserving the spatial context within tissues. Spatial domain characterization is a critical first step in SRT data analysis, providing the foundation for subsequent analyses and insights into biological implications. Graph neural networks (GNNs) have emerged as a common tool for addressing this challenge due to the structural nature of SRT data. However, current graph-based deep learning approaches often overlook the instability caused by the high sparsity of SRT data. Masking mechanisms, as an effective self-supervised learning strategy, can enhance the robustness of these models. To this end, we propose SpaMask, dual masking graph autoencoder with contrastive learning for SRT analysis. Unlike previous GNNs, SpaMask masks a portion of spot nodes and spot-to-spot edges to enhance its performance and robustness. SpaMask combines Masked Graph Autoencoders (MGAE) and Masked Graph Contrastive Learning (MGCL) modules, with MGAE using node masking to leverage spatial neighbors for improved clustering accuracy, while MGCL applies edge masking to create a contrastive loss framework that tightens embeddings of adjacent nodes based on spatial proximity and feature similarity. We conducted a comprehensive evaluation of SpaMask on eight datasets from five different platforms. Compared to existing methods, SpaMask achieves superior clustering accuracy and effective batch correction.

STopover captures spatial colocalization and interaction in the tumor microenvironment using topological analysis in spatial transcriptomics data

Unraveling the spatial configuration of the tumor microenvironment (TME) is crucial for elucidating tumor-immune interactions based on immuno-oncology. We present STopover, a novel approach utilizing spatially resolved transcriptomics (SRT) data and topological analysis to investigate the TME. By gradually lowering the feature threshold, connected components (CCs) are extracted based on spatial distance and persistence, with Jaccard indices quantifying their spatial overlap, and transcriptomic profiles are permutated to assess statistical significance. Applied to lung and breast cancer SRT, STopover revealed immune and stromal cell infiltration patterns, predicted key cell-cell communication, and identified relevant regions, shedding light on cancer pathophysiology (URL: https://github.com/bsungwoo/STopover ).

Quantitative characterization of tissue states using multiomics and ecological spatial analysis

The spatial organization of cells in tissues underlies biological function, and recent advances in spatial profiling technologies have enhanced our ability to analyze such arrangements to study biological processes and disease progression. We propose MESA (multiomics and ecological spatial analysis), a framework drawing inspiration from ecological concepts to delineate functional and spatial shifts across tissue states. MESA introduces metrics to systematically quantify spatial diversity and identify hot spots, linking spatial patterns to phenotypic outcomes, including disease progression. Furthermore, MESA integrates spatial and single-cell multiomics data to facilitate an in-depth, molecular understanding of cellular neighborhoods and their spatial interactions within tissue microenvironments. Applying MESA to diverse datasets demonstrates additional insights it brings over prior methods, including newly identified spatial structures and key cell populations linked to disease states. Available as a Python package, MESA offers a versatile framework for quantitative decoding of tissue architectures in spatial omics across health and disease.

X-scPAE: An explainable deep learning model for embryonic lineage allocation prediction based on single-cell transcriptomics revealing key genes in embryonic cell development

In single-cell transcriptomics research, accurately predicting cell lineage allocation and identifying differences between lineages are crucial for understanding cell differentiation processes and reducing early pregnancy miscarriages in humans. This paper introduces an explainable PCA-based deep learning attention autoencoder model, X-scPAE (eXplained Single Cell PCA - Attention Auto Encoder), which is built on the Counterfactual Gradient Attribution (CGA) algorithm. The model is designed to predict lineage allocation in human and mouse single-cell transcriptomic data, while identifying and interpreting gene expression differences across lineages to extract key genes. It first reduces dimensionality using Principal Component Analysis (PCA) and ranks the importance of principal components. An autoencoder is then employed for feature extraction, integrating an attention mechanism to capture interactions between features. Finally, the Counterfactual Gradient Attribution algorithm calculates the importance of each feature. The model achieved an accuracy of 0.945 on the test set and 0.977 on the validation set, with other metrics such as F1-score, Precision, and Recall all reaching 0.94. It significantly outperformed both baseline algorithms (XGBoost, SVM, RF, and LR) and advanced approaches like F-Score-SVM, CV2-LR, scChrBin, and TripletCell. Notably, the explainability analysis uncovered key lineage predictor genes for both humans and mice and identified crucial genes distinguishing between developmental stages and lineages. A logistic regression model built using the extracted key genes still achieved an AUROC of 0.92, surpassing the performance of other feature extraction methods, including F-Score, CV2, PCA, random feature selection, and the interpretability method Shapley. Lastly, ablation studies demonstrated the effectiveness of each model component.

Sequencing-free whole genome spatial transcriptomics at molecular resolution in intact tissue

Recent breakthroughs in spatial transcriptomics technologies have enhanced our understanding of diverse cellular identities, compositions, interactions, spatial organizations, and functions. Yet existing spatial transcriptomics tools are still limited in either transcriptomic coverage or spatial resolution. Leading spatial-capture or spatial-tagging transcriptomics techniques that rely on in-vitro sequencing offer whole-transcriptome coverage, in principle, but at the cost of lower spatial resolution compared to image-based techniques. In contrast, high-performance image-based spatial transcriptomics techniques, which rely on in situ hybridization or in situ sequencing, achieve single-molecule spatial resolution and retain sub-cellular morphologies, but are limited by probe libraries that target only a subset of the transcriptome, typically covering several hundred to a few thousand transcript species. Together, these limitations hinder unbiased, hypothesis-free transcriptomic analyses at high spatial resolution. Here we develop a new image-based spatial transcriptomics technology termed Reverse-padlock Amplicon Encoding FISH (RAEFISH) with whole-genome level coverage while retaining single-molecule spatial resolution in intact tissues. We demonstrate image-based spatial transcriptomics targeting 23,000 human transcript species or 22,000 mouse transcript species, including nearly the entire protein-coding transcriptome and several thousand long-noncoding RNAs, in single cells in cultures and in tissue sections. Our analyses reveal differential subcellular localizations of diverse transcripts, cell-type-specific and cell-type-invariant tissue zonation dependent transcriptome, and gene expression programs underlying preferential cell-cell interactions. Finally, we further develop our technology for direct spatial readout of gRNAs in an image-based high-content CRISPR screen. Overall, these developments provide the research community with a broadly applicable technology that enables high-coverage, high-resolution spatial profiling of both long and short, native and engineered RNA species in many biomedical contexts.

Benchmarking algorithms for spatially variable gene identification in spatial transcriptomics

MOTIVATION

The rapid development of spatial transcriptomics has underscored the importance of identifying spatially variable genes. As a fundamental task in spatial transcriptomic data analysis, spatially variable gene identification has been extensively studied. However, the lack of comprehensive benchmark makes it difficult to validate the effectiveness of various algorithms scattered across a large number of studies with real-world datasets.

RESULTS

In response, this article proposes a benchmark framework to evaluate algorithms for identifying spatially variable genes through the analysis of 30 synthesized and 74 real-world datasets, aiming to identify the best algorithms and their corresponding application scenarios. This framework can assist medical and life scientists in selecting suitable algorithms for their research, while also aid bioinformatics scientists in developing more powerful and efficient computational methods in spatial transcriptomic research.

AVAILABILITY AND IMPLEMENTATION

The source code of this benchmarking framework is available at both Github (https://github.com/XiDsLab/svg-benchmark) and Zenodo (https://doi.org/10.5281/zenodo.15031083). In addition, all real and synthetic datasets considered in this study are also publicly available at Zenodo (https://doi.org/10.5281/zenodo.7227771).

Benchmarking spatial transcriptomics technologies with the multi-sample SpatialBenchVisium dataset

BACKGROUND

Spatial transcriptomics allows gene expression to be measured within complex tissue contexts. Among the array of spatial capture technologies available is 10x Genomics' Visium platform, a popular method which enables transcriptome-wide profiling of tissue sections. Visium offers a range of sample handling and library construction methods which introduces a need for benchmarking to compare data quality and assess how well the technology can recover expected tissue features and biological signatures.

RESULTS

Here we present SpatialBenchVisium, a unique reference dataset generated from spleen tissue of mice responding to malaria infection spanning several tissue preparation protocols (both fresh frozen and FFPE, with either manual or CytAssist tissue placement). We note better quality control metrics in reference samples prepared using probe-based capture methods, particularly those processed with CytAssist, validating the improvement in data quality produced with the platform. Our analysis of replicate samples extends to explore spatially variable gene detection, the outcomes of clustering and cell deconvolution using matched single-cell RNA-sequencing data and publicly available reference data to identify cell types and tissue regions expected in the spleen. Multi-sample differential expression analysis recovered known gene signatures related to biological sex or gene knockout.

Spatial transcriptomic imaging of an intact organism using volumetric DNA microscopy

Lymphatic, nervous and tumor tissues exhibit complex physiology arising from three-dimensional interactions within genetically unique microenvironments. Here we develop a technology capable of volumetrically imaging transcriptomes, genotypes and morphologies in a single measurement, without relying on prior knowledge of spatial organization or genetic sequences. Our method extends DNA microscopy into three dimensions at scales involving 107 molecules by forming a distributed intermolecular network of proximal unique DNA barcodes tagging complementary DNA molecules inside the specimen. After sequencing the DNA-encoded network, an image of molecular positions is inferred using geodesic spectral embeddings, a dimensionality reduction approach that we show to be especially suitable for this data-inverse problem. Applying whole-transcriptome volumetric DNA microscopy to intact zebrafish embryos, we demonstrate that three-dimensional image inference recapitulates zebrafish morphology and known gene expression patterns, capturing the spatial organization of gene sequences. Our extension of spatial genetic measurements to three dimensions, independent of prior templates, opens the door to detailed joint resolution of genomics and morphology in biological tissues.

Bridging the gap between histopathology and genomics: Spotlighting spatial omics

Spatial biology has emerged as a transformative field, offering insights into cellular interactions and organization within tissues. The field has evolved rapidly since the coining of the term "spatial omics." Now, the ability to spatially resolve proteins, RNA, chromatin, and lipids is becoming widespread, and the technologies are continually refined. Reagents to support the analysis of veterinary species are available and more are emerging. These new tools will allow pathologists and scientists to unravel the intricate interplay between tissue architecture and diverse cellular phenotypes. By integrating histological observations with spatially resolved genomic data, spatial biology holds immense potential for advancing diagnostic and therapeutic strategies in veterinary medicine. These tools will undoubtedly equip veterinary pathologists to better decipher complex disease processes and identify novel therapeutic targets.

Mapping molecular landscapes in triple-negative breast cancer: insights from spatial transcriptomics

The tumor microenvironment (TME) of triple-negative breast cancer (TNBC) is a highly heterogeneous and very aggressive form of the disease that has few suitable treatment options; however, spatial transcriptomics (ST) is a powerful tool for elucidation of the TME in TNBC. Because of its spatial context preservation, ST has a unique capability to map tumor-stroma interactions, immune infiltration, and therapy resistance mechanisms (which are key to understanding TNBC progression), compared with conventional transcriptomics. This review shows the use of ST in TNBC, its utilization in spatial biomarker identification, intratumoral heterogeneity definition, molecular subtyping refinement, and prediction of immunotherapy responses. Recent insight from ST-driven insights has explained the key spatial patterns on immune evasion, chemotherapy resistance, racial disparities of TNBC, and aspects for patient stratification and therapeutic decision. With the integration of ST with the subjects of proteomics and imaging mass cytometry, this approach has been enlarged and is now applied in precision medicine and biomarker discovery. Recently, advancements in AI-based spatial analysis for tumor classification, identification of biomarkers, and creation of therapy prediction models have occurred. However, continued developments in ST technologies, computational tools, and partnerships amongst multiple centers to facilitate the integration of ST into clinical routine practice are needed to unlock novel therapeutic targets.

Single-cell spatial transcriptomic atlas of the whole mouse brain

A comprehensive atlas of genes, cell types, and their spatial distribution across a whole mammalian brain is fundamental for understanding the function of the brain. Here, using single-nucleus RNA sequencing (snRNA-seq) and Stereo-seq techniques, we generated a mouse brain atlas with spatial information for 308 cell clusters at single-cell resolution, involving over 4 million cells, as well as for 29,655 genes. We have identified cell clusters exhibiting preference for cortical subregions and explored their associations with brain-related diseases. Additionally, we pinpointed 155 genes with distinct regional expression patterns within the brainstem and unveiled 513 long non-coding RNAs showing region-enriched expression in the adult brain. Parcellation of brain regions based on spatial transcriptomic information revealed fine structure for several brain areas. Furthermore, we have uncovered 411 transcription factor regulons showing distinct spatiotemporal dynamics during neurodevelopment. Thus, we have constructed a single-cell-resolution spatial transcriptomic atlas of the mouse brain with genome-wide coverage.

Brainstem neuropeptidergic neurons link a neurohumoral axis to satiation

Hunger is evolutionarily hardwired to ensure that an animal has sufficient energy to survive and reproduce. Just as important as knowing when to start eating is knowing when to stop eating. Here, using spatially resolved single-cell phenotyping, we characterize a population of neuropeptidergic neurons in the brainstem's dorsal raphe nucleus (DRN) and describe how they regulate satiation. These neurons track food from sensory presentation through ingestion, integrate these signals with slower-acting humoral cues, and express cholecystokinin (CCK). These CCK neurons bidirectionally regulate meal size, driving a sustained meal termination signal with a built-in delay. They are also well positioned to sense and respond to ingestion: they express a host of metabolic signaling factors and are integrated into an extended network known to regulate feeding. Together, this work demonstrates how DRN CCK neurons regulate satiation and identifies a likely conserved cellular mechanism that transforms diverse neurohumoral signals into a key behavioral output.

SPARROW reveals microenvironment-zone-specific cell states in healthy and diseased tissues

Spatially resolved transcriptomics technologies have advanced our understanding of cellular characteristics within tissue contexts. However, current analytical tools often treat cell-type inference and cellular neighborhood identification as separate and hard clustering processes, limiting comparability across scales and samples. SPARROW addresses these challenges by jointly learning latent embeddings and soft clusterings of cell types and cellular organization. It outperformed state-of-the-art methods in cell-type inference and microenvironment zone delineation and uncovered zone-specific cell states in human and mouse tissues that competing methods missed. By integrating spatially resolved transcriptomics and single-cell RNA sequencing (scRNA-seq) data in a shared latent space, SPARROW achieves single-cell spatial resolution and whole-transcriptome coverage, enabling the discovery of both established and unknown microenvironment zone-specific ligand-receptor interactions in the human tonsil. Overall, SPARROW is a computational framework that provides a comprehensive characterization of tissue features across scales, samples, and conditions.

A tissue-scale strategy for sensing threats in barrier organs

Barrier organs rely on a limited set of pattern recognition receptors (PRRs) to detect diverse immunogenic challenges. How organs assess threats and adjust immune responses to balance host protection with collateral tissue damage remains unclear. Here, by analyzing influenza infection in the lung using single-molecule imaging and spatial transcriptomics, we discovered a tiered threat-sensing strategy at the tissue scale, where the probability of detecting and responding to infection is lowest in the outermost epithelia and highest in the inner stroma. This strategy emerges from spatially graded PRR expression that results in cell-type-specific probabilities of threat-sensing across the tissue, a design broadly adopted by barrier organs. Selectively increasing PRR expression in lung epithelia in vivo exacerbated tissue damage upon inflammatory challenge. These results reveal a spatially tiered strategy to tolerate threats restricted within the epithelia, and yet enable progressively potent immune responses as threats invade deeper into the tissue.

VISTA Uncovers Missing Gene Expression and Spatial-induced Information for Spatial Transcriptomic Data Analysis

Characterizing cell activities within a spatially resolved context is essential to enhance our understanding of spatially-induced cellular states and features. While single-cell RNA-seq (scRNA-seq) offers comprehensive profiling of cells within a tissue, it fails to capture spatial context. Conversely, subcellular spatial transcriptomics (SST) technologies provide high-resolution spatial profiles of gene expression, yet their utility is constrained by the limited number of genes they can simultaneously profile. To address this limitation, we introduce VISTA, a novel approach designed to predict the expression levels of unobserved genes specifically tailored for SST data. VISTA jointly models scRNA-seq data and SST data based on variational inference and geometric deep learning, and incorporates uncertainty quantification. Using four SST datasets, we demonstrate VISTA's superior performance in imputation and in analyzing large-scale SST datasets with satisfactory time efficiency and memory consumption. The imputation of VISTA enables a multitude of downstream applications, including the detection of new spatially variable genes, the discovery of novel ligand-receptor interactions, the inference of spatial RNA velocity, the generation for spatial transcriptomics with in-silico perturbation, and an improved decomposition of spatial and intrinsic variations.

MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning

Spatially resolved transcriptomics (SRT) simultaneously measure spatial location, histology images, and transcriptional profiles of cells or regions in undissociated tissues. Integrative analysis of multi-modal SRT data holds immense potential for understanding biological mechanisms. Here, we present a flexible multi-modal contrastive learning for the integration of SRT data (MuCST), which joins denoising, heterogeneity elimination, and compatible feature learning. MuCST accurately identifies spatial domains and is applicable to diverse datasets platforms. Overall, MuCST provides an alternative for integrative analysis of multi-modal SRT data ( https://github.com/xkmaxidian/MuCST ).

Transcending the hegemony of the molecular machine through an organic renewal of biology and biomedicine

The dominant approach to the study of living systems in the 20th century into today has been that of a reductionist approach focused on genetics and biochemistry. The hunt for genes and the elucidation of their biochemical outputs has organized funding in research, educational curricula, academic promotion, and the distribution of prestige through awards. Such reductionism has gone hand in hand with an ontology of the machine. We will discuss how viewing life as if it emanated from a set of molecular machines is the main bottleneck in addressing key questions in biology. We will discuss how moving beyond it is not contingent on new technologies but rather a refreshed perspective of life that can be termed "organic". Furthermore, we suggest that the study of how form arises, morphogenesis, is the key to an organic renewal of biology and biomedicine. Although morphogenesis is currently seen as a subsidiary branch of developmental biology as well as the consequence of molecular patterning processes at the subcellular scale, we will argue that morphology and its self-organizing capacity at the supracellular scale is the fundamental nexus in embryonic development as well as disease. We see the inability to appreciate form through an organic supracellular perspective as the principal bottleneck for making inroads into health issues such as cancer and the chronic disease epidemic.