High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis

Image guided construction of a common coordinate framework for spatial transcriptome data

Spatial transcriptomics is a powerful technology for high-resolution mapping of gene expression in tissue samples, enabling a molecular level understanding of tissue architecture. The acquisition entails dissecting and profiling micron-thick tissue slices, with multiple slices often needed for a comprehensive study. However, the lack of a common coordinate framework (CCF) among slices, due to slicing and displacement variations, can hinder data analysis, making data comparison and integration challenging, and potentially compromising analysis accuracy. Here we present a deep learning algorithm STaCker that unifies the coordinates of transcriptomic slices via an image registration process. STaCker derives a composite image representation by integrating tissue image and gene expression that are transformed to be resilient to noise and batch effects. STaCker overcomes the training data scarcity by training exclusively on diverse synthetic data. Its performance on various benchmarking datasets shows a significant increase in spatial concordance in aligned slices, surpassing existing methods. STaCker also successfully harmonizes multiple real spatial transcriptome datasets acquired from various platforms. These results indicate that STaCker is a valuable computational tool for constructing a CCF with spatial transcriptome data.

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.

Cell simulation as cell segmentation

Single-cell spatial transcriptomics promises a highly detailed view of a cell's transcriptional state and microenvironment, yet inaccurate cell segmentation can render these data murky by misattributing large numbers of transcripts to nearby cells or conjuring nonexistent cells. We adopt methods from ab initio cell simulation, in a method called Proseg (probabilistic segmentation), to rapidly infer morphologically plausible cell boundaries. Benchmarking applied to datasets generated by three commercial platforms shows superior performance and computational efficiency of Proseg when compared to existing methods. We show that improved accuracy in cell segmentation aids greatly in detection of difficult-to-segment tumor-infiltrating immune cells such as neutrophils and T cells. Last, through improvements in our ability to delineate subsets of tumor-infiltrating T cells, we show that CXCL13-expressing CD8+ T cells tend to be more closely associated with tumor cells than their CXCL13-negative counterparts in data generated from samples from patients with renal cell carcinoma.

A single-cell spatial chart of the airway wall reveals proinflammatory cellular ecosystems and their interactions in health and asthma

Determining spatial location of cells within tissues gives vital insight into the interactions between resident and inflammatory cells and is a critical factor for uncoupling the mechanisms driving disease. Here, we apply single-cell spatial transcriptomics to reveal the airway wall landscape in health and during asthma. We identified proinflammatory cellular ecosystems that exist within discrete spatial niches in healthy and asthma samples. These cellular hubs are characterized by a high level of chemokine and alarmin expression, along with unique combinations of stromal cells. Mechanistically, we demonstrated that receptors, such as ACKR1, retain immune mediators locally, while amphiregulin-expressing mast cells are prominent within these proinflammatory hubs. Despite anti-inflammatory treatments, the asthma airway mucosa exhibited a distinct remodeling program within these cellular ecosystems, marked by increased proximity between key cell types. This study provides an unprecedented view of the topography of the airway wall, revealing distinct, specific ecosystems within spatial niches to target for therapeutic intervention.

Scalable image-based visualization and alignment of spatial transcriptomics datasets

We present the "spatial transcriptomics imaging framework" (STIM), an imaging-based computational framework focused on visualizing and aligning high-throughput spatial sequencing datasets. STIM is built on the powerful, scalable ImgLib2 and BigDataViewer (BDV) image data frameworks and thus enables novel development or transfer of existing computer vision techniques to the sequencing domain characterized by datasets with irregular measurement-spacing and arbitrary spatial resolution, such as spatial transcriptomics data generated by multiplexed targeted hybridization or spatial sequencing technologies. We illustrate STIM's capabilities by representing, interactively visualizing, 3D rendering, automatically registering, and segmenting publicly available spatial sequencing data from 13 serial sections of mouse brain tissue and from 19 sections of a human metastatic lymph node. We demonstrate that the simplest alignment mode of STIM achieves human-level accuracy.

UTX (KDM6A) promotes differentiation noncatalytically in somatic self-renewing epithelia

The X-linked histone demethylase, UTX (KDM6A), is a master regulator of gene enhancers, though its role in self-renewing epithelia like the skin is not well understood. Here, we find that UTX is a key regulator of skin differentiation via the regulation of retinoic acid (RA) signaling, an essential metabolic pathway in both skin homeostasis, as well as in the treatment of an array of skin conditions ranging from cancer and acne to aging. Through deletion of Utx in the skin, we demonstrate direct regulation of both retinoid metabolic genes such as Crabp2, as well as key genes involved in epidermal stem cell fate and differentiation (i.e., Cdh1, Grhl3, Ctnnb1). Spatial analyses show that UTX loss dysregulates epidermal, sebaceous, and hair follicle differentiation programs. Strikingly, this only occurs in homozygous females, demonstrating that UTX's Y-linked paralog, UTY (Kdm6c), can compensate in males. Further, we observe genome-wide losses of H3K27 acetylation (H3K27ac) with minimal changes in H3K27 trimethylation (H3K27me3), revealing that UTX functions primarily noncatalytically to promote skin homeostasis. Together, the elucidation of these links between epigenetics, metabolic signaling, and epithelial differentiation offers new insights into how epigenetic modulation may allow for fine-tuning of key signaling pathways to treat disease.

The Role of Omics Techniques in Diabetic Wound Healing: Recent Insights into the Application of Single-Cell RNA Sequencing, Bulk RNA Sequencing, Spatial Transcriptomics, and Proteomics

Diabetic foot ulcers (DFUs) are a devastating complication of diabetes mellitus (DM) that affect millions of people worldwide every year. They have a long-term impact on patients' quality of life and pose a significant challenge for both patients and clinicians, alongside negative economic implications on affected individuals. The current therapeutic approaches are costly and, in many cases, ineffective, highlighting the urgent need to develop novel, affordable, more efficient, and personalized treatments. Recent advances in high-throughput omics technologies, including proteomics, bulk RNA sequencing (bulk RNA-seq), single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics in both preclinical animal and human clinical studies, have enhanced our understanding of the molecular function and mechanisms of DFUs, thereby offering potential for targeted therapies. Additionally, these technologies provide valuable insights behind the mechanism of action of novel wound dressings and treatments. In this review, we outline the latest application of omics technologies in DFU preclinical animal and human clinical research on diabetic wound healing, and spotlight recent findings.A graphical abstract is available with this article.

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.

Cell-type deconvolution methods for spatial transcriptomics

Spatial transcriptomics is a powerful method for studying the spatial organization of cells, which is a critical feature in the development, function and evolution of multicellular life. However, sequencing-based spatial transcriptomics has not yet achieved cellular-level resolution, so advanced deconvolution methods are needed to infer cell-type contributions at each location in the data. Recent progress has led to diverse tools for cell-type deconvolution that are helping to describe tissue architectures in health and disease. In this Review, we describe the varied types of cell-type deconvolution methods for spatial transcriptomics, contrast their capabilities and summarize them in a web-based, interactive table to enable more efficient method selection.

Combining spatial transcriptomics with tissue morphology

Spatial transcriptomics has transformed our understanding of tissue architecture by preserving the spatial context of gene expression patterns. Simultaneously, advances in imaging AI have enabled extraction of morphological features describing the tissue. This review introduces a framework for categorizing methods that combine spatial transcriptomics with tissue morphology, focusing on either translating or integrating morphological features into spatial transcriptomics. Translation involves using morphology to predict gene expression, creating super-resolution maps or inferring genetic information from H&E-stained samples. Integration enriches spatial transcriptomics by identifying morphological features that complement gene expression. We also explore learning strategies and future directions for this emerging field.

Transcriptome analysis of archived tumors by Visium, GeoMx DSP, and Chromium reveals patient heterogeneity

Recent advancements in probe-based, full-transcriptome technologies for FFPE tissues, such as Visium CytAssist, Chromium Flex, and GeoMx DSP, enable analysis of archival samples, facilitating the generation of data from extensive cohorts. However, these methods can be labor-intensive and costly, requiring informed selection based on research objectives. We compare these methods on FFPE tumor samples in Breast, NSCLC and DLBCL showing 1) good-quality, highly reproducible data from all methods; 2) GeoMx data containing cell mixtures despite marker-based preselection; 3) Visium and Chromium outperform GeoMx in discovering tumor heterogeneity and potential drug targets. We recommend the use of Visium and Chromium for high-throughput and discovery projects, while the manually more challenging GeoMx platform with targeted regions remains valuable for specialized questions.

ONTraC characterizes spatially continuous variations of tissue microenvironment through niche trajectory analysis

Recent technological advances enable mapping of tissue spatial organization at single-cell resolution, but methods for analyzing spatially continuous microenvironments are still lacking. We introduce ONTraC, a graph neural network-based framework for constructing spatial trajectories at niche-level. Through benchmarking analyses using multiple simulated and real datasets, we show that ONTraC outperforms existing methods. ONTraC captures both normal anatomical structures and disease-associated tissue microenvironment changes. In addition, it identifies tissue microenvironment-dependent shifts in gene expression, regulatory network, and cell-cell interaction patterns. Taken together, ONTraC provides a useful framework for characterizing the structural and functional organization of tissue microenvironments.

ROICellTrack: a deep learning framework for integrating cellular imaging modalities in subcellular spatial transcriptomic profiling of tumor tissues

MOTIVATION

Spatial transcriptomic (ST) technologies, such as GeoMx Digital Spatial Profiler, are increasingly utilized to investigate the role of diverse tumor microenvironment components, particularly in relation to cancer progression, treatment response, and therapeutic resistance. However, in many ST studies, the spatial information obtained from immunofluorescence imaging is primarily used for identifying regions of interest (ROIs) rather than as an integral part of downstream transcriptomic data analysis and interpretation.

RESULTS

We developed ROICellTrack, a deep learning-based framework that better integrates cellular imaging with spatial transcriptomic profiling. By analyzing 56 ROIs from urothelial carcinoma of the bladder and upper tract urothelial carcinoma, ROICellTrack identified distinct cancer-immune cell mixtures, characterized by specific transcriptomic and morphological signatures and receptor-ligand interactions linked to tumor content and immune infiltrations. Our findings demonstrate the value of integrating imaging with transcriptomics to analyze spatial omics data, improving our understanding of tumor heterogeneity and its relevance to personalized and targeted therapies.

AVAILABILITY AND IMPLEMENTATION

ROICellTrack is publicly available at https://github.com/wanglab1/ROICellTrack.

Induced B cell receptor diversity predicts PD-1 blockade immunotherapy response

Immune checkpoint inhibitors such as anti-Programmed Death-1 antibodies (aPD-1) can be effective in treating advanced cancers. However, many patients do not respond, and the mechanisms underlying these differences remain incompletely understood. In this study, we profile a cohort of patients with locally advanced or metastatic basal cell carcinoma undergoing aPD-1 therapy using single-cell RNA sequencing, high-definition spatial transcriptomics in tumors and draining lymph nodes, and spatial immunoreceptor profiling, with long-term clinical follow-up. We find that successful responses to PD-1 inhibition are characterized by an induction of B cell receptor (BCR) clonal diversity after treatment initiation. These induced BCR clones spatially colocalize with T cell clones, facilitate their activation, and traffic alongside them between tumor and draining lymph nodes to enhance tumor clearance. Furthermore, we validated aPD-1-induced BCR diversity as a predictor of clinical response in a larger cohort of glioblastoma, melanoma, and head and neck squamous cell carcinoma patients, suggesting that this is a generalizable predictor of treatment response across many types of cancers. We find that pretreatment tumors harbor a characteristic gene expression signature that portends a higher probability of inducing BCR clonal diversity after aPD-1 therapy, and we develop a machine learning model that predicts PD-1-induced BCR clonal diversity from baseline tumor RNA sequencing. These findings underscore a dynamic role of B cell diversity during immunotherapy, highlighting its importance as a prognostic marker and a potential target for intervention in non-responders.

Identification of Target Genes Using Innovative Screening Systems

Advances in cancer biology have been achieved by the identification of oncogenes and tumor suppressor genes through the remarkable progression of next-generation sequencing. New techniques, such as single-cell analysis, help uncover cancer progression and heterogeneity. Reverse genetic screenings, including methods like random mutagenesis via retroviruses, transposons, RNA interference, and CRISPR, are useful for exploring gene functions and their roles in cancer. Especially in random mutagenesis, CRISPR screening and its modifications have recently emerged as powerful tools due to their comprehensiveness and simplicity in inducing genetic mutations. Initially, CRISPR screening focused on analyzing biological phenotypes in a cell population. It has since evolved to incorporate advanced techniques, such as combining single-cell and spatial analyses. These developments enable the investigation of cell-cell and spatial interactions, which more closely mimic In Vivo microenvironments, offering deeper insights into complex biological processes. These approaches allow for the identification of essential genes involved in cancer survival, drug resistance, and tumorigenesis. Together, these technologies are advancing cancer research and therapeutic development.

snPATHO-seq: A Detailed Protocol for Single Nucleus RNA Sequencing From FFPE

Formalin-fixed paraffin-embedded (FFPE) samples remain an underutilized resource in single-cell omics due to RNA degradation from formalin fixation. Here, we present snPATHO-seq, a robust and adaptable approach that enables the generation of high-quality single-nucleus (sn) transcriptomic data from FFPE tissues, utilizing advancements in single-cell genomic techniques. The snPATHO-seq workflow integrates optimized nuclei isolation with the 10× Genomics Flex assay, targeting short RNA fragments to mitigate FFPE-related RNA degradation. Benchmarking against standard 10× 3' and Flex assays for fresh/frozen tissues confirmed robust detection of transcriptomic signatures and cell types. snPATHO-seq demonstrated high performance across diverse FFPE samples, including diseased tissues like breast cancer. It seamlessly integrates with FFPE spatial transcriptomics (e.g., FFPE Visium) for multi-modal spatial and single-nucleus profiling. Compared to workflows like 10× Genomics' snFFPE, snPATHO-seq delivers superior data quality by reducing tissue debris and preserving RNA integrity via nuclei isolation. This cost-effective workflow enables high-resolution transcriptomics of archival FFPE samples, advancing single-cell omics in translational and clinical research. Key features • Optimized nuclei isolation from FFPE tissues enables high-quality single-nucleus transcriptomics by minimizing debris and maximizing intact nuclear yield. • Compatible with 10× Genomics Flex, leveraging short RNA probes to overcome FFPE RNA fragmentation challenges. • Outperforms existing FFPE workflows in cell type detection sensitivity across archival, degraded, or aged samples. • Low-cost, accessible protocol using off-the-shelf reagents, suitable for broad translational and archival tissue applications.

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.

Spatially Resolved Metabolomics Reveals Metabolic Heterogeneity Among Pulmonary Fibrosis

Pulmonary fibrosis (PF) is a chronic and progressive lung disease with fatal consequences. The study of PF is challenging due to the complex mechanism involved, the need to understand the heterogeneity and spatial organization within lung tissues. In this study, we investigate the metabolic heterogeneity between two forms of lung fibrosis: idiopathic pulmonary fibrosis (IPF) and silicosis, using advanced spatially-resolved metabolomics techniques. Employing high-resolution mass spectrometry imaging, we spatially mapped and identified over 260 metabolites in lung tissue sections from mouse models of IPF and silicosis. Histological analysis confirmed fibrosis in both models, with distinct pathological features: alveolar destruction and collagen deposition in IPF, and nodule formation in silicosis. Metabolomic analysis revealed significant differences between IPF and silicosis in key metabolic pathways, including phospholipid metabolism, purine/pyrimidine metabolism, and the TCA cycle. Notably, phosphocholine was elevated in silicosis but reduced in IPF, while carnitine levels decreased in both conditions. Additionally, glycolytic activity was increased in both models, but TCA cycle intermediates showed opposing trends. These findings highlight the spatial metabolic heterogeneity of PF and suggest potential metabolic targets for therapeutic intervention. Further investigation into the regulatory mechanisms behind these metabolic shifts may open new avenues for fibrosis treatment.

UCS: A Unified Approach to Cell Segmentation for Subcellular Spatial Transcriptomics

Subcellular Spatial Transcriptomics (SST) represents an innovative technology enabling researchers to investigate gene expression at the subcellular level within tissues. To comprehend the spatial architecture of a given tissue, cell segmentation plays a crucial role in attributing the measured transcripts to individual cells. However, existing cell segmentation methods for SST datasets still face challenges in accurately distinguishing cell boundaries due to the varying characteristics of SST technologies. In this study, a unified approach is proposed to cell segmentation (UCS) specifically designed for SST data obtained from diverse platforms, including 10X Xenium, NanoString CosMx, MERSCOPE, and Stereo-seq. UCS leverages deep learning techniques to achieve high accuracy in cell segmentation by integrating nuclei segmentation from nuclei staining and transcript data. Compared to current methods, UCS not only provides more precise transcript assignment to individual cells but also offers computational advantages for large-scale SST data analysis. The analysis output of UCS further supports versatile downstream analyses, such as subcellular gene classification and missing cell detection. By employing UCS, researchers gain the ability to characterize gene expression patterns at both the cellular and subcellular levels, leading to a deeper understanding of tissue architecture and function.

Integrating Spatially-Resolved Transcriptomics Data Across Tissues and Individuals: Challenges and Opportunities

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. The lowering cost of SRT data generation presents an unprecedented opportunity to create large-scale spatial atlases and enable population-level investigation, integrating SRT data across multiple tissues, individuals, species, or phenotypes. Here, unique challenges are described in the SRT data integration, where the analytic impact of varying spatial and biological resolutions is characterized and explored. A succinct review of spatially-aware integration methods and computational strategies is provided. Exciting opportunities to advance computational algorithms amenable to atlas-scale datasets along with standardized preprocessing methods, leading to improved sensitivity and reproducibility in the future are further highlighted.

SpaNorm: spatially-aware normalization for spatial transcriptomics data

Normalization of spatial transcriptomics data is challenging due to spatial association between region-specific library size and biology. We develop SpaNorm, the first spatially-aware normalization method that concurrently models library size effects and the underlying biology, segregates these effects, and thereby removes library size effects without removing biological information. Using 27 tissue samples from 6 datasets spanning 4 technological platforms, SpaNorm outperforms commonly used single-cell normalization approaches while retaining spatial domain information and detecting spatially variable genes. SpaNorm is versatile and works equally well for multicellular and subcellular spatial transcriptomics data with relatively robust performance under different segmentation methods.

Unveiling fine-scale spatial structures and amplifying gene expression signals in ultra-large ST slices with HERGAST

We propose HERGAST, a system for spatial structure identification and signal amplification in ultra-large-scale and ultra-high-resolution spatial transcriptomics data. To handle ultra-large spatial transcriptomics (ST) data, we consider the divide and conquer strategy and devise a Divide-Iterate-Conquer framework especially for spatial transcriptomics data analysis, which can also be adopted by other computational methods for extending to ultra-large-scale ST data analysis. To tackle the potential over-smoothing problem arising from data splitting, we construct a heterogeneous graph network to incorporate both local and global spatial relationships. In simulations, HERGAST consistently outperforms other methods across all settings with more than a 10% increase in average adjusted rand index (ARI). In real-world datasets, HERGAST's high-precision spatial clustering identifies SPP1+ macrophages intermingled within colorectal tumors, while the enhanced gene expression signals reveal unique spatial expression patterns of key genes in breast cancer.

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.

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.

Dissection of tumoral niches using spatial transcriptomics and deep learning

This study introduces TG-ME, an innovative computational framework that integrates transformer with graph variational autoencoder (GraphVAE) models for dissection of tumoral niches using spatial transcriptomics data and morphological images. TG-ME effectively identifies and characterizes niches in bench datasets and a high resolution NSCLC dataset. The pipeline consists in different stages that include normalization, spatial information integration, morphological feature extraction, gene expression quantification, single cell expression characterization, and tumor niche characterization. For this, TG-ME leverages advanced deep learning techniques that achieve robust clustering and profiling of niches across cancer stages. TG-ME can potentially provide insights into the spatial organization of tumor microenvironments (TME), highlighting specific niche compositions and their molecular changes along cancer progression. TG-ME is a promising tool for guiding personalized treatment strategies by uncovering microenvironmental signatures associated with disease prognosis and therapeutic outcomes.

Determinants of Response to Sequential Pembrolizumab with Trastuzumab plus Platinum/5-FU in HER2-Positive Gastric Cancer: A Phase II Chemoimmunotherapy Trial

PURPOSE

Adding pembrolizumab to first-line fluoropyrimidine (5-FU)/platinum chemotherapy plus trastuzumab improves outcomes in advanced HER2+ gastroesophageal adenocarcinomas, but the benefit is largely confined to dual HER2+ and PD-L1+ patients. To assess the contributions of components, we conducted a phase II trial evaluating 5-FU/platinum/trastuzumab and added pembrolizumab in cycle 2 in patients with metastatic HER2+ disease.

PATIENTS AND METHODS

Treatment-naïve patients with advanced HER2+ gastroesophageal cancer underwent a baseline biopsy and received a single dose of 5-FU/platinum with trastuzumab followed by repeat biopsy. Pembrolizumab was added, and a third biopsy was performed after six cycles. The primary endpoint was the objective response rate. Secondary endpoints included progression-free and overall survival. Exploratory biomarker analysis and dynamic changes in HER2 and PD-L1 were prespecified.

RESULTS

Sixteen patients were enrolled. The objective response rate was 69%, and the median progression-free survival was 11.9 months. Serial whole-exome, single-cell RNA, T-cell receptor sequencing, and spatial transcriptomics from pretreatment and on-treatment samples revealed early trastuzumab-induced NK cell infiltration in HER2+ tumor beds and an increase in Fc receptor gamma III expression in macrophages, suggesting that trastuzumab directs Fc receptor-mediated antibody-dependent cytotoxicity. This favorable remodeling was enhanced by the addition of pembrolizumab, primarily in PD-L1+ samples. We observed TGF-β signaling in HER2-negative tumor regions, which was associated with nonresponder status.

CONCLUSIONS

These data highlight the biology of intratumoral heterogeneity and the impact of tumor and immune cell features on clinical outcomes and may partly explain the lesser magnitude of pembrolizumab benefit in HER2+ and PD-L1-negative subgroups.

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.

Spatial genomics: Mapping the landscape of fibrosis

Organ fibrosis causes major morbidity and mortality worldwide. Treatments for fibrosis are limited, with organ transplantation being the only cure. Here, we review how various state-of-the-art spatial genomics approaches are being deployed to interrogate fibrosis across multiple organs, providing exciting insights into fibrotic disease pathogenesis. These include the detailed topographical annotation of pathogenic cell populations and states, detection of transcriptomic perturbations in morphologically normal tissue, characterization of fibrotic and homeostatic niches and their cellular constituents, and in situ interrogation of ligand-receptor interactions within these microenvironments. Together, these powerful readouts enable detailed analysis of fibrosis evolution across time and space.

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

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

Evidence of off-target probe binding in the 10x Genomics Xenium v1 Human Breast Gene Expression Panel compromises accuracy of spatial transcriptomic profiling

The accuracy of spatial gene expression profiles generated by probe-based in situ spatially-resolved transcriptomic technologies depends on the specificity with which probes bind to their intended target gene. Off-target binding, defined as a probe binding to something other than the target gene, can distort a gene's true expression profile, making probe specificity essential for reliable transcriptomics. Here, we investigate off-target binding in the 10x Genomics Xenium v1 Human Breast Gene Expression Panel. We developed a software tool, Off-target Probe Tracker (OPT), to identify putative off-target binding via alignment of probe sequences and found at least 21 out of the 280 genes in the panel impacted by off-target binding to protein-coding genes. To substantiate our predictions, we leveraged a previously published Xenium breast cancer dataset generated using this gene panel and compared results to orthogonal spatial and single-cell transcriptomic profiles from Visium CytAssist and 3' single-cell RNA-seq derived from the same tumor block. Our findings indicate that for some genes, the expression patterns detected by Xenium demonstrably reflect the aggregate expression of the target and predicted off-target genes based on Visium and single-cell RNA-seq rather than the target gene alone. Overall, this work enhances the biological interpretability of spatial transcriptomics data and improves reproducibility in spatial transcriptomics research.

EcDNA-borne PVT1 fusion stabilizes oncogenic mRNAs

Extrachromosomal DNA (ecDNA) amplifications are prevalent drivers of human cancers. We show that ecDNAs exhibit elevated structural variants leading to gene fusions that produce oncogene fusion transcripts. The long noncoding RNA (lncRNA) gene PVT1 is the most recurrent structural variant across cancer genomes, with PVT1-MYC fusions arising most frequently on ecDNA. PVT1 exon 1 is the predominant 5' partner fused to MYC or other oncogenes on the 3' end. Mechanistic studies demonstrate that PVT1 exon 1 confers enhanced RNA stability for fusion transcripts, which requires PVT1 exon 1 interaction with SRSF1 protein. Genetic rescue of MYC-addicted cancer models and isoform-specific single-cell RNA sequencing of tumors reveal that PVT1-MYC better supports MYC dependency and better activates MYC target genes in vivo . Thus, the mutagenic landscape of ecDNA contributes to genome instability and generates chimeric fusions of lncRNA and mRNA genes, selecting PVT1 5' region as a stabilizer of oncogene mRNAs.

MYC ecDNA promotes intratumour heterogeneity and plasticity in PDAC

Intratumour heterogeneity and phenotypic plasticity drive tumour progression and therapy resistance1,2. Oncogene dosage variation contributes to cell-state transitions and phenotypic heterogeneity3, thereby providing a substrate for somatic evolution. Nonetheless, the genetic mechanisms underlying phenotypic heterogeneity are still poorly understood. Here we show that extrachromosomal DNA (ecDNA) is a major source of high-level focal amplification in key oncogenes and a major contributor of MYC heterogeneity in pancreatic ductal adenocarcinoma (PDAC). We demonstrate that ecDNAs drive varying levels of MYC dosage, depending on their regulatory landscape, enabling cancer cells to rapidly and reversibly adapt to microenvironmental changes. In the absence of selective pressure, a high ecDNA copy number imposes a substantial fitness cost on PDAC cells. We also show that MYC dosage affects cell morphology and dependence of cancer cells on stromal niche factors. Our work provides a detailed analysis of ecDNAs in PDAC and describes a new genetic mechanism driving MYC heterogeneity in PDAC.

Quantitative characterization of cell niches in spatially resolved omics data

Spatial omics enable the characterization of colocalized cell communities that coordinate specific functions within tissues. These communities, or niches, are shaped by interactions between neighboring cells, yet existing computational methods rarely leverage such interactions for their identification and characterization. To address this gap, here we introduce NicheCompass, a graph deep-learning method that models cellular communication to learn interpretable cell embeddings that encode signaling events, enabling the identification of niches and their underlying processes. Unlike existing methods, NicheCompass quantitatively characterizes niches based on communication pathways and consistently outperforms alternatives. We show its versatility by mapping tissue architecture during mouse embryonic development and delineating tumor niches in human cancers, including a spatial reference mapping application. Finally, we extend its capabilities to spatial multi-omics, demonstrate cross-technology integration with datasets from different sequencing platforms and construct a whole mouse brain spatial atlas comprising 8.4 million cells, highlighting NicheCompass' scalability. Overall, NicheCompass provides a scalable framework for identifying and analyzing niches through signaling events.

Single-cell omics technologies - Fundamentals on how to create single-cell looking glasses for reproductive health

Over the last decade, in line with the goals of precision medicine to offer individualized patient care, various single-cell technologies measuring gene and proteomic expression in various tissues have rapidly advanced to study health and disease at the single cell level. Precisely understanding cell composition, position within tissues, signaling pathways, and communication can reveal insights into disease mechanisms and systemic changes during development, pregnancy, and gynecologic disorders across the lifespan. Single-cell technologies dissect the complex cellular compositions of reproductive tract tissues, providing insights into mechanisms behind reproductive tract dysfunction which impact wellness and quality of life. These technologies aim to understand basic tissue and organ functions and, clinically, to develop novel diagnostics, early disease biomarkers, and cell-targeted therapies for currently suboptimally-treated disorders. Increasingly, they are applied to pregnancy and pregnancy disorders, gynecologic malignancies, and uterine and ovarian physiology and aging, which are discussed in more detail in manuscripts in this special issue of AJOG. Here, we review recent applications of single-cell technologies to the study of gynecologic disorders and systemic biological adaptations during fetal development, pregnancy, and across a woman's lifespan. We discuss sequencing- and proteomic-based single-cell methods, as well as spatial transcriptomics and high-dimensional proteomic imaging, describing each technology's mechanism, workflow, quality control, and highlighting specific benefits, drawbacks, and utility in the context of reproductive medicine. We consider analytical methods for the high-dimensional single-cell data generated, highlighting statistical constraints and recent computational techniques for downstream clinical translation. Overall, current and evolving single-cell "looking glasses", or perspectives, have the potential to transform fundamental understanding of women's health and reproductive disorders and alter the trajectory of clinical practice and patient outcomes in the future.

Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows

The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species, comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open-source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as an independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets.

The promising role of nanopore sequencing in cancer diagnostics and treatment

Cancer arises from genetic alterations that impact both the genome and transcriptome. The utilization of nanopore sequencing offers a powerful means of detecting these alterations due to its unique capacity for long single-molecule sequencing. In the context of DNA analysis, nanopore sequencing excels in identifying structural variations (SVs), copy number variations (CNVs), gene fusions within SVs, and mutations in specific genes, including those involving DNA modifications and DNA adducts. In the field of RNA research, nanopore sequencing proves invaluable in discerning differentially expressed transcripts, uncovering novel elements linked to transcriptional regulation, and identifying alternative splicing events and RNA modifications at the single-molecule level. Furthermore, nanopore sequencing extends its reach to detecting microorganisms, encompassing bacteria and viruses, that are intricately associated with tumorigenesis and the development of cancer. Consequently, the application prospects of nanopore sequencing in tumor diagnosis and personalized treatment are expansive, encompassing tasks such as tumor identification and classification, the tailoring of treatment strategies, and the screening of prospective patients. In essence, this technology stands poised to unearth novel mechanisms underlying tumorigenesis while providing dependable support for the diagnosis and treatment of cancer.

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.

HISSTA: a human in situ single-cell transcriptome atlas

MOTIVATION

Spatial transcriptomics holds great promise for revolutionizing biology and medicine by providing gene expression profiles with spatial information. Until recently, spatial resolution has been limited, but advances in high-throughput in situ imaging technologies now offer new opportunities by covering thousands of genes at a single-cell or even subcellular resolution, necessitating databases dedicated to comprehensive coverage and analysis with user-friendly intefaces.

RESULTS

We introduce the HISSTA database, which facilitates the archival and analysis of in situ transcriptome data at single-cell resolution from various human tissues. We have collected and annotated spatial transcriptome data generated by MERFISH, CosMx SMI, and Xenium techniques, encompassing 112 samples and 28 million cells across 16 tissue types from 63 studies. To decipher spatial contexts, we have implemented advanced tools for cell type annotation, spatial colocalization, spatial cellular communication, and niche analyses. Notably, all datasets and annotations are interactively accessible through Vitessce, allowing users to focus on regions of interest and examine gene expression in detail. HISSTA is a unique database designed to manage the rapidly growing dataset of in situ transcriptomes at single-cell resolution. Given its comprehensive data content and advanced analysis tools with interactive visualizations, HISSTA is poised to significantly impact cancer diagnosis, precision medicine, and digital pathology.

AVAILABILITY AND IMPLEMENTATION

HISSTA is freely accessible at https://kbds.re.kr/hissta/. The source code is available at https://doi.org/10.5281/zenodo.14904523.

Single-cell light-sheet fluorescence 3D images of tumour-stroma spheroid multicultures

Spheroids are widely used in oncology for testing drugs, but models composed of a single cell line do not fully capture the complexity of the in vivo tumours targeted by chemotherapy. Developing 3D in vitro models that better mimic tumour architecture is a crucial step for the scientific community. To enable more reliable drug testing, we generated multiculture spheroids and analysed cell morphology and distribution over time. This dataset is the first publicly available single-cell light-sheet fluorescence microscopy image collection of 3D multiculture tumour models comprising of three different cell lines analysed at different time points. Specifically, we created models composed of one cancer cell line (melanoma, breast cancer, or osteosarcoma) alongside two stromal cell lines (fibroblasts and endothelial cells). Then, we acquired single-cell resolution light-sheet fluorescence 3D images of the spheroids to analyse spheroid morphology after 24, 48, and 96 hours. The image collection, whole spheroid annotations, and extracted features are publicly available for further research and can support the development of automated analysis models.

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.

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.

Segger: Fast and accurate cell segmentation of imaging-based spatial transcriptomics data

The accurate assignment of transcripts to their cells of origin remains the Achilles heel of imaging-based spatial transcriptomics, despite being critical for nearly all downstream analyses. Current cell segmentation methods are prone to over- and under-segmentation, misassign transcripts to cells, require manual intervention, and suffer from low sensitivity and scalability. We introduce segger, a versatile graph neural network based on a heterogeneous graph representation of individual transcripts and cells, that frames cell segmentation as a transcript-to-cell link prediction task and can leverage single-cell RNA-seq information to improve transcript assignments. On multiple Xenium dataset benchmarks, segger exhibits superior sensitivity and specificity, while requiring orders of magnitude less compute time than existing methods. The user-friendly open-source software implementation has extensive documentation (https://elihei2.github.io/segger_dev/), requires little manual intervention, integrates seamlessly into existing workflows, and enables atlas-scale applications.

Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments

The tumor microenvironment (TME) plays a crucial role in orchestrating tumor cell behavior and cancer progression. Recent advances in spatial profiling technologies have uncovered novel spatial signatures, including univariate distribution patterns, bivariate spatial relationships, and higher-order structures. These signatures have the potential to revolutionize tumor mechanism and treatment. In this review, we summarize the current state of spatial signature research, highlighting computational methods to uncover spatially relevant biological significance. We discuss the impact of these advances on fundamental cancer biology and translational research, address current challenges and future research directions.

Thor: a platform for cell-level investigation of spatial transcriptomics and histology

Spatial transcriptomics integrates transcriptomics data with histological tissue images, offering deeper insights into cellular organization and molecular functions. However, existing computational platforms mainly focus on genomic analysis, leaving a gap in the seamless integration of genomic and image analysis. To address this, we introduce Thor, a comprehensive computational platform for multi-modal analysis of spatial transcriptomics and histological images. Thor utilizes an anti-shrinking Markov diffusion method to infer single-cell spatial transcriptomes from spot-level data, effectively integrating cell morphology with spatial transcriptomics. The platform features 10 modules designed for cell-level genomic and image analysis. Additionally, we present Mjolnir, a web-based tool for interactive tissue analysis using vivid gigapixel images that display information on histology, gene expression, pathway enrichment, and immune response. Thor's accuracy was validated through simulations and ISH, MERFISH, Xenium, and Stereo-seq datasets. To demonstrate its versatility, we applied Thor for joint genomic-histology analysis across various datasets. In in-house heart failure patient samples, Thor identified a regenerative signature in heart failure, with protein presence confirmed in blood vessels through immunofluorescence staining. Thor also revealed the layered structure of the mouse olfactory bulb, performed unbiased screening of breast cancer hallmarks, elucidated the heterogeneity of immune responses, and annotated fibrotic regions in multiple heart failure zones using a semi-supervised approach. Furthermore, Thor imputed high-resolution spatial transcriptomics data in an in-house bladder cancer sample sequenced using Visium HD, uncovering stronger spatial patterns that align more closely with histology. Bridging the gap between genomic and image analysis in spatial biology, Thor offers a powerful tool for comprehensive cellular and molecular analysis.

ENACT: End-to-End Analysis of Visium High Definition (HD) Data

MOTIVATION

Spatial transcriptomics (ST) enables the study of gene expression within its spatial context in histopathology samples. To date, a limiting factor has been the resolution of sequencing based ST products. The introduction of the Visium High Definition (HD) technology opens the door to cell resolution ST studies. However, challenges remain in the ability to accurately map transcripts to cells and in assigning cell types based on the transcript data.

RESULTS

We developed ENACT, a self-contained pipeline that integrates advanced cell segmentation with Visium HD transcriptomics data to infer cell types across whole tissue sections. Our pipeline incorporates novel bin-to-cell assignment methods, enhancing the accuracy of single-cell transcript estimates. Validated on diverse synthetic and real datasets, our approach is both scalable to samples with hundreds of thousands of cells and effective, offering a robust solution for spatially resolved transcriptomics analysis.

AVAILABILITY AND IMPLEMENTATION

ENACT source code is available at https://github.com/Sanofi-Public/enact-pipeline. Experimental data are available at https://zenodo.org/records/14748859.

From morphology to single-cell molecules: high-resolution 3D histology in biomedicine

High-resolution three-dimensional (3D) tissue analysis has emerged as a transformative innovation in the life sciences, providing detailed insights into the spatial organization and molecular composition of biological tissues. This review begins by tracing the historical milestones that have shaped the development of high-resolution 3D histology, highlighting key breakthroughs that have facilitated the advancement of current technologies. We then systematically categorize the various families of high-resolution 3D histology techniques, discussing their core principles, capabilities, and inherent limitations. These 3D histology techniques include microscopy imaging, tomographic approaches, single-cell and spatial omics, computational methods and 3D tissue reconstruction (e.g. 3D cultures and spheroids). Additionally, we explore a wide range of applications for single-cell 3D histology, demonstrating how single-cell and spatial technologies are being utilized in the fields such as oncology, cardiology, neuroscience, immunology, developmental biology and regenerative medicine. Despite the remarkable progress made in recent years, the field still faces significant challenges, including high barriers to entry, issues with data robustness, ambiguous best practices for experimental design, and a lack of standardization across methodologies. This review offers a thorough analysis of these challenges and presents recommendations to surmount them, with the overarching goal of nurturing ongoing innovation and broader integration of cellular 3D tissue analysis in both biology research and clinical practice.

Profiling immune cell tissue niches in the spatial -omics era

Immune responses require complex, spatially coordinated interactions between immune cells and their tissue environment. For decades, we have imaged tissue sections to visualize a limited number of immune-related macromolecules in situ, functioning as surrogates for cell types or processes of interest. However, this inevitably provides a limited snapshot of the tissue's immune landscape. Recent developments in high-throughput spatial -omics technologies, particularly spatial transcriptomics, and its application to human samples has facilitated a more comprehensive understanding of tissue immunity by mapping fine-grained immune cell states to their precise tissue location while providing contextual information about their immediate cellular and tissue environment. These data provide opportunities to investigate mechanisms underlying the spatial distribution of immune cells and its functional implications, including the identification of immune niches, although the criteria used to define this term have been inconsistent. Here, we review recent technological and analytic advances in multiparameter spatial profiling, focusing on how these methods have generated new insights in translational immunology. We propose a 3-step framework for the definition and characterization of immune niches, which is powerfully facilitated by new spatial profiling methodologies. Finally, we summarize current approaches to analyze adaptive immune repertoires and lymphocyte clonal expansion in a spatially resolved manner.

Towards deciphering the bone marrow microenvironment with spatial multi-omics

The tissue microenvironment refers to a localised tissue area where a complex combination of cells, structural components, and signalling molecules work together to support specific biological activities. A prime example is the bone marrow microenvironment, particularly the hematopoietic stem cell (HSC) niche, which is of immense interest due to its critical role in supporting lifelong blood cell production and the growth of malignant cells. In this review, we summarise the current understanding of HSC niche biology, highlighting insights gained from advanced imaging and genomic techniques. We also discuss the potential of emerging technologies such as spatial multi-omics to unravel bone marrow architecture in unprecedented detail.

Tissue-resident memory CD8 T cell diversity is spatiotemporally imprinted

Tissue-resident memory CD8 T (TRM) cells provide protection from infection at barrier sites. In the small intestine, TRM cells are found in at least two distinct subpopulations: one with higher expression of effector molecules and another with greater memory potential1. However, the origins of this diversity remain unknown. Here we proposed that distinct tissue niches drive the phenotypic heterogeneity of TRM cells. To test this, we leveraged spatial transcriptomics of human samples, a mouse model of acute systemic viral infection and a newly established strategy for pooled optically encoded gene perturbations to profile the locations, interactions and transcriptomes of pathogen-specific TRM cell differentiation at single-transcript resolution. We developed computational approaches to capture cellular locations along three anatomical axes of the small intestine and to visualize the spatiotemporal distribution of cell types and gene expression. Our study reveals that the regionalized signalling of the intestinal architecture supports two distinct TRM cell states: differentiated TRM cells and progenitor-like TRM cells, located in the upper villus and lower villus, respectively. This diversity is mediated by distinct ligand-receptor activities, cytokine gradients and specialized cellular contacts. Blocking TGFβ or CXCL9 and CXCL10 sensing by antigen-specific CD8 T cells revealed a model consistent with anatomically delineated, early fate specification. Ultimately, our framework for the study of tissue immune networks reveals that T cell location and functional state are fundamentally intertwined.

Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE

Recent advances in spatial transcriptomics (ST) technologies have transformed our ability to profile gene expression while retaining the crucial spatial context within tissues. However, existing ST platforms suffer from high costs, long turnaround times, low resolution, limited gene coverage, and small tissue capture areas, which hinder their broad applications. Here we present iSCALE, a method that predicts super-resolution gene expression and automatically annotates cellular-level tissue architecture for large-sized tissues that exceed the capture areas of standard ST platforms. The accuracy of iSCALE were validated by comprehensive evaluations, involving benchmarking experiments, immunohistochemistry staining, and manual annotation by pathologists. When applied to multiple sclerosis human brain samples, iSCALE uncovered lesion associated cellular characteristics that were undetectable by conventional ST experiments. Our results demonstrate iSCALE's utility in analyzing large-sized tissues with automatic and unbiased tissue annotation, inferring cell type composition, and pinpointing regions of interest for features not discernible through human visual assessment.