Spatially Resolved Transcriptomes-Next Generation Tools for Tissue Exploration

Assessing spatial sequencing and imaging approaches to capture the molecular and pathological heterogeneity of archived cancer tissues

Spatial transcriptomics (ST) offers enormous potential to decipher the biological and pathological heterogeneity in precious archival cancer tissues. Traditionally, these tissues have rarely been used and only examined at a low throughput, most commonly by histopathological staining. ST adds thousands of times as many molecular features to histopathological images, but critical technical issues and limitations require more assessment of how ST performs on fixed archival tissues. In this work, we addressed this in a cancer-heterogeneity pipeline, starting with an exploration of the whole transcriptome by two sequencing-based ST protocols capable of measuring coding and non-coding RNAs. We optimised the two protocols to work with challenging formalin-fixed paraffin-embedded (FFPE) tissues, derived from skin. We then assessed alternative imaging methods, including multiplex RNAScope single-molecule imaging and multiplex protein imaging (CODEX). We evaluated the methods' performance for tissues stored from 4 to 14 years ago, covering a range of RNA qualities, allowing us to assess variation. In addition to technical performance metrics, we determined the ability of these methods to quantify tumour heterogeneity. We integrated gene expression profiles with pathological information, charting a new molecular landscape on the pathologically defined tissue regions. Together, this work provides important and comprehensive experimental technical perspectives to consider the applications of ST in deciphering the cancer heterogeneity in archived tissues. © 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach

MOTIVATION

Spatial transcriptomics is a state-of-art technique that allows researchers to study gene expression patterns in tissues over the spatial domain. As a result of technical limitations, the majority of spatial transcriptomics techniques provide bulk data for each sequencing spot. Consequently, in order to obtain high-resolution spatial transcriptomics data, performing deconvolution becomes essential. Most existing deconvolution methods rely on reference data (e.g., single-cell data), which may not be available in real applications. Current reference-free methods encounter limitations due to their dependence on distribution assumptions, reliance on marker genes, or the absence of leveraging histology and spatial information. Consequently, there is a critical need for the development of highly flexible, robust, and user-friendly reference-free deconvolution methods capable of unifying or leveraging case-specific information in the analysis of spatial transcriptomics data.

RESULTS

We propose a novel reference-free method based on regularized non-negative matrix factorization (NMF), named Flexible Analysis of Spatial Transcriptomics (FAST), that can effectively incorporate gene expression data, spatial, and histology information into a unified deconvolution framework. Compared to existing methods, FAST imposes fewer distribution assumptions, utilizes the spatial structure information of tissues, and encourages interpretable factorization results. These features enable greater flexibility and accuracy, making FAST an effective tool for deciphering the complex cell-type composition of tissues and advancing our understanding of various biological processes and diseases. Extensive simulation studies have shown that FAST outperforms other existing reference-free methods. In real data applications, FAST is able to uncover the underlying tissue structures and identify the corresponding marker genes.

Novel photocrosslinking chemical probes utilized for high-resolution spatial transcriptomics

The architecture of cells and the tissue they form within multicellular organisms are highly complex and dynamic. Cells optimize their function within tissue microenvironments by expressing specific subsets of RNAs. Advances in cell tagging methods enable spatial understanding of RNA expression when merged with transcriptomics. However, these techniques are currently limited by the spatial resolution of the tagging, the number of RNAs that can be sequenced, and multiplexing to isolate spatially-distinct cells within the same tissue landscape. To address these limitations, we developed CrossSeq, which employs photocrosslinking fluorescent probes and confocal microscopy activation to demarcate user-defined regions of interest on fixed cells for multiplexed spatial transcriptomic analysis. We investigate phenyl azide and diazirine crosslinking scaffolds and define their photoactivity profiles. We then deploy the aryl azide scaffold with three fluorophores for multiplexing on glyoxal fixed cells and analyze the defined populations using flow cytometry. Finally, we apply CrossSeq to investigate an in vitro MDA-MB-231-LM2 metastatic cancer migration model to evaluate changes in gene expression at the migratory cell front versus the exterior population. We anticipate this new technology will be a valuable tool addition as it will enable easier access to spatial transcriptomic analysis for the scientific community using conventional microscopy and analysis techniques.

Techniques and analytic workflow for spatial transcriptomics and its application to allergy and inflammation

Spatial profiling, through single-cell gene-level expression data paired with cell localization, offers unprecedented biologic insights within the intact spatial context of cells in healthy and diseased tissue, adding a novel dimension to data interpretation. This review summarizes recent developments in this field, its application to allergy and inflammation, and recent single-cell resolution platforms designed for spatial transcriptomics with a focus on data processing and analyses for efficient biologic interpretation of data. By preserving spatial context, these technologies provide critical insights into tissue architecture and cellular interactions that are unattainable with traditional transcriptomics methods, such as revealing localized inflammatory cell network in atopic dermatitis and T-cell interactions in the lung in chronic obstructive pulmonary disease. Spatial profiling offers opportunities for discovering novel biomarkers, defining compartmentalization of immune responses within tissues and individual diseases, and accelerating novel discoveries toward a greater understanding of fundamental disease mechanisms and, eventually, toward the development of future targeted therapies.

Environmental community transcriptomics: strategies and struggles

Transcriptomics is the study of RNA transcripts, the portion of the genome that is transcribed, in a specific cell, tissue, or organism. Transcriptomics provides insight into gene expression patterns, regulation, and the underlying mechanisms of cellular processes. Community transcriptomics takes this a step further by studying the RNA transcripts from environmental assemblies of organisms, with the intention of better understanding the interactions between members of the community. Community transcriptomics requires successful extraction of RNA from a diverse set of organisms and subsequent analysis via mapping those reads to a reference genome or de novo assembly of the reads. Both, extraction protocols and the analysis steps can pose hurdles for community transcriptomics. This review covers advances in transcriptomic techniques and assesses the viability of applying them to community transcriptomics.

Applications of Nanotechnology for Spatial Omics: Biological Structures and Functions at Nanoscale Resolution

Spatial omics methods are extensions of traditional histological methods that can illuminate important biomedical mechanisms of physiology and disease by examining the distribution of biomolecules, including nucleic acids, proteins, lipids, and metabolites, at microscale resolution within tissues or individual cells. Since, for some applications, the desired resolution for spatial omics approaches the nanometer scale, classical tools have inherent limitations when applied to spatial omics analyses, and they can measure only a limited number of targets. Nanotechnology applications have been instrumental in overcoming these bottlenecks. When nanometer-level resolution is needed for spatial omics, super resolution microscopy or detection imaging techniques, such as mass spectrometer imaging, are required to generate precise spatial images of target expression. DNA nanostructures are widely used in spatial omics for purposes such as nucleic acid detection, signal amplification, and DNA barcoding for target molecule labeling, underscoring advances in spatial omics. Other properties of nanotechnologies include advanced spatial omics methods, such as microfluidic chips and DNA barcodes. In this review, we describe how nanotechnologies have been applied to the development of spatial transcriptomics, proteomics, metabolomics, epigenomics, and multiomics approaches. We focus on how nanotechnology supports improved resolution and throughput of spatial omics, surpassing traditional techniques. We also summarize future challenges and opportunities for the application of nanotechnology to spatial omics methods.

Clonal phylogenies inferred from bulk, single cell, and spatial transcriptomic analysis of epithelial cancers

Epithelial cancers are typically heterogeneous with primary prostate cancer being a typical example of histological and genomic variation. Prior studies of primary prostate cancer tumour genetics revealed extensive inter and intra-patient genomic tumour heterogeneity. Recent advances in machine learning have enabled the inference of ground-truth genomic single-nucleotide and copy number variant status from transcript data. While these inferred SNV and CNV states can be used to resolve clonal phylogenies, however, it is still unknown how faithfully transcript-based tumour phylogenies reconstruct ground truth DNA-based tumour phylogenies. We sought to study the accuracy of inferred-transcript to recapitulate DNA-based tumour phylogenies. We first performed in-silico comparisons of inferred and directly resolved SNV and CNV status, from single cancer cells, from three different cell lines. We found that inferred SNV phylogenies accurately recapitulate DNA phylogenies (entanglement = 0.097). We observed similar results in iCNV and CNV based phylogenies (entanglement = 0.11). Analysis of published prostate cancer DNA phylogenies and inferred CNV, SNV and transcript based phylogenies demonstrated phylogenetic concordance. Finally, a comparison of pseudo-bulked spatial transcriptomic data to adjacent sections with WGS data also demonstrated recapitulation of ground truth (entanglement = 0.35). These results suggest that transcript-based inferred phylogenies recapitulate conventional genomic phylogenies. Further work will need to be done to increase accuracy, genomic, and spatial resolution.

SpatialData: an open and universal data framework for spatial omics

Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study.

Accurate Spatial Heterogeneity Dissection and Gene Regulation Interpretation for Spatial Transcriptomics using Dual Graph Contrastive Learning

Recent advances in spatial transcriptomics have enabled simultaneous preservation of high-throughput gene expression profiles and the spatial context, enabling high-resolution exploration of distinct regional characterization in tissue. To effectively understand the underlying biological mechanisms within tissue microenvironments, there is a requisite for methods that can accurately capture external spatial heterogeneity and interpret internal gene regulation from spatial transcriptomics data. However, current methods for region identification often lack the simultaneous characterizing of spatial structure and gene regulation, thereby limiting the ability of spatial dissection and gene interpretation. Here, stDCL is developed, a dual graph contrastive learning method to identify spatial domains and interpret gene regulation in spatial transcriptomics data. stDCL adaptively incorporates gene expression data and spatial information via a graph embedding autoencoder, thereby preserving critical information within the latent embedding representations. In addition, dual graph contrastive learning is proposed to train the model, ensuring that the latent embedding representation closely resembles the actual spatial distribution and exhibits cluster similarity. Benchmarking stDCL against other state-of-the-art clustering methods using complex cortex datasets demonstrates its superior accuracy and effectiveness in identifying spatial domains. Our analysis of the imputation matrices generated by stDCL reveals its capability to reconstruct spatial hierarchical structures and refine differential expression assessment. Furthermore, it is demonstrated that the versatility of stDCL in interpretability of gene regulation, spatial heterogeneity at high resolution, and embryonic developmental patterns. In addition, it is also showed that stDCL can successfully annotate disease-associated astrocyte subtypes in Alzheimer's disease and unravel multiple relevant pathways and regulatory mechanisms.

ImSpiRE: image feature-aided spatial resolution enhancement method

The resolution of most spatially resolved transcriptomic technologies usually cannot attain the single-cell level, limiting their applications in biological discoveries. Here, we introduce ImSpiRE, an image feature-aided spatial resolution enhancement method for in situ capturing spatial transcriptome. Taking the information stored in histological images, ImSpiRE solves an optimal transport problem to redistribute the expression profiles of spots to construct new transcriptional profiles with enhanced resolution, together with extending the gene expression profiles into unmeasured regions. Applications to multiple datasets confirm that ImSpiRE can enhance spatial resolution to the subspot level while contributing to the discovery of tissue domains, signaling communication patterns, and spatiotemporal characterization.

Application of Spatial Transcriptomics in Digestive System Tumors

In the field of digestive system tumor research, spatial transcriptomics technologies are used to delve into the spatial structure and the spatial heterogeneity of tumors and to analyze the tumor microenvironment (TME) and the inter-cellular interactions within it by revealing gene expression in tumors. These technologies are also instrumental in the diagnosis, prognosis, and treatment of digestive system tumors. This review provides a concise introduction to spatial transcriptomics and summarizes recent advances, application prospects, and technical challenges of these technologies in digestive system tumor research. This review also discusses the importance of combining spatial transcriptomics with single-cell RNA sequencing (scRNA-seq), artificial intelligence, and machine learning in digestive system cancer research.

Tumor infiltration of inactive CD8 + T cells was associated with poor prognosis in Gastric Cancer

BACKGROUND

Gastric cancer (GC) shows limited response to immune checkpoint inhibitors due to its complex tumor immune microenvironment (TIME). This study explores the functions of various immune cells in the complex TIME in GC.

METHODS

We assessed CD8 + T-cell infiltration of GC tissues by immunohistochemistry, and performed single-cell RNA sequencing (scRNA-seq) of tumor and normal tissues from 34 patients with GC.

RESULTS

We categorized 157 GC patients into LOW, MID, and HIGH groups based on their CD8 + T-cell infiltration. Overall survival was notably lower for the HIGH and LOW groups compared with the MID group. Our scRNA-seq data analysis showed that CD8 + T-cell activity markers in the HIGH group were expressed at lower levels than in normal tissue, but the T-cell-attracting chemokine CCL5 was expressed at a higher level. Notably, CD8 + T-cells in the HIGH group displayed lower PD1 expression and higher CTLA4 expression. TCR repertoire analysis using only Epstein-Barr virus-negative cases showed that CD8 + T-cell receptor clonality was lower in the HIGH group than in the MID group. Furthermore, in the HIGH group, the antigen-presenting capacity of type 1 conventional dendritic cells was lower, the immunosuppressive capacity of myeloid-derived suppressor cells was higher, and the expression of CTLA4 in regulatory T-cells was higher.

CONCLUSION

The present data suggest that the infiltration of inactive CD8 + T-cells with low clonality is induced by chemotaxis in the HIGH group, possibly leading to a poor prognosis for patients with GC.

SpatialCVGAE: Consensus Clustering Improves Spatial Domain Identification of Spatial Transcriptomics Using VGAE

The advent of spatially resolved transcriptomics (SRT) has provided critical insights into the spatial context of tissue microenvironments. Spatial clustering is a fundamental aspect of analyzing spatial transcriptomics data. However, spatial clustering methods often suffer from instability caused by the sparsity and high noise in the SRT data. To address this challenge, we propose SpatialCVGAE, a consensus clustering framework designed for SRT data analysis. SpatialCVGAE adopts the expression of high-variable genes from different dimensions along with multiple spatial graphs as inputs to variational graph autoencoders (VGAEs), learning multiple latent representations for clustering. These clustering results are then integrated using a consensus clustering approach, which enhances the model's stability and robustness by combining multiple clustering outcomes. Experiments demonstrate that SpatialCVGAE effectively mitigates the instability typically associated with non-ensemble deep learning methods, significantly improving both the stability and accuracy of the results. Compared to previous non-ensemble methods in representation learning and post-processing, our method fully leverages the diversity of multiple representations to accurately identify spatial domains, showing superior robustness and adaptability. All code and public datasets used in this paper are available at https://github.com/wenwenmin/SpatialCVGAE .

Harnessing Single-Cell and Spatial Transcriptomics for Crop Improvement

Single-cell and spatial transcriptomics technologies have significantly advanced our understanding of the molecular mechanisms underlying crop biology. This review presents an update on the application of these technologies in crop improvement. The heterogeneity of different cell populations within a tissue plays a crucial role in the coordinated response of an organism to its environment. Single-cell transcriptomics enables the dissection of this heterogeneity, offering insights into the cell-specific transcriptomic responses of plants to various environmental stimuli. Spatial transcriptomics technologies complement single-cell approaches by preserving the spatial context of gene expression profiles, allowing for the in situ localization of transcripts. Together, single-cell and spatial transcriptomics facilitate the discovery of novel genes and gene regulatory networks that can be targeted for genetic manipulation and breeding strategies aimed at enhancing crop yield, quality, and resilience. This review highlights significant findings from recent studies, discusses the expanding roles of these technologies, and explores future opportunities for their application in crop improvement.

Tackling the Complexity of Spatial Transcriptomics Data Interpretation with Large Language Models

Spatial transcriptomics offers unprecedented insights into the complex cellular landscapes of tissues, particularly in cancer research where understanding the tumor microenvironment is crucial. However, interpreting the vast and intricate data generated by this technology remains a significant challenge. This study explores the potential of Large Language Models (LLMs) to assist in the analysis and interpretation of spatial transcriptomic data from a murine melanoma tumor model. We first evaluated the performance of multiple LLM models in describing and quantifying spatial gene expression patterns. Our benchmarking revealed that spatial transcriptomics data interpretation proved challenging for most models, with only a few demonstrating sufficient capability for this complex task. Using Claude 3.5 Sonnet, which showed the highest accuracy in spot quantification and pattern recognition, we developed a systematic workflow for analyzing the tumor immune landscape. The model first assisted in identifying and prioritizing panels of M1 and M2 macrophage-associated markers through knowledge-driven scoring. It then demonstrated remarkable ability to integrate spatial expression data with extensive immunological knowledge, providing sophisticated interpretation of local immune organization. When analyzing individual tumor regions, the model identified coordinated immunosuppressive mechanisms including metabolic barriers and disrupted pro-inflammatory signaling cascades, findings that both aligned with and extended current understanding of tumor immunology. This study highlights the potential of LLMs as powerful assistive tools in spatial transcriptomics analysis, capable of combining advanced pattern recognition with extensive knowledge integration to enhance data interpretation. While significant development work remains to make such workflows scalable, our proof of concept demonstrates that LLMs can help accelerate the translation of spatial transcriptomics data into biological insights.

Deep clustering representation of spatially resolved transcriptomics data using multi-view variational graph auto-encoders with consensus clustering

The rapid development of spatial transcriptomics (ST) technology has provided unprecedented opportunities to understand tissue relationships and functions within specific spatial contexts. Accurate identification of spatial domains is crucial for downstream spatial transcriptomics analysis. However, effectively combining gene expression data, histological images and spatial coordinate data to identify spatial domains remains a challenge. To this end, we propose STMVGAE, a novel spatial transcriptomics analysis tool that combines a multi-view variational graph autoencoder with a consensus clustering framework. STMVGAE begins by extracting histological images features using a pre-trained convolutional neural network (CNN) and integrates these features with gene expression data to generate augmented gene expression profiles. Subsequently, multiple graphs (views) are constructed using various similarity measures, capturing different aspects of the spatial and transcriptional relationships. These views, combined with the augmented gene expression data, are then processed through variational graph auto-encoders (VGAEs) to learn multiple low-dimensional latent embeddings. Finally, the model employs a consensus clustering method to integrate the clustering results derived from these embeddings, significantly improving clustering accuracy and stability. We applied STMVGAE to five real datasets and compared it with five state-of-the-art methods, showing that STMVGAE consistently achieves competitive results. We assessed its capabilities in spatial domain identification and evaluated its performance across various downstream tasks, including UMAP visualization, PAGA trajectory inference, spatially variable gene (SVG) identification, denoising, batch integration, and other analyses. All code and public datasets used in this paper is available at https://github.com/wenwenmin/STMVGAE and https://zenodo.org/records/13119867.

Fhl1, a new spatially specific protein, regulates vein graft neointimal hyperplasia

BACKGROUND

Vein grafts are commonly employed in revascularisation surgery for multivessel coronary artery disease, yet neointimal hyperplasia (NIH) remains a critical impediment to the long-term patency of these grafts. Despite this, effective methods to precisely identify and target interventions for the neointima are still inadequate.

METHODS

In this study, Sprague-Dawley (SD) rats were used to establish an external jugular vein transplantation model, and the NIH pathophysiological process was tracked across 11 time points (0-35 days) using various histological stains. Spatial transcriptomics was performed on normal veins and 19-day grafts to explore gene expression in neointimal regions. Immunohistochemical analysis identified neointima-specific markers, while NIH progression was assessed in SD rats with four and a half LIM domains protein 1 (Fhl1) knockout and in human saphenous veins (HSV) with adenovirus-mediated Fhl1 overexpression.

RESULTS

Typical neointimal formation commenced by day 11 postgrafting and peaked at day 19. Neointimal cells originated from newly generated α-SMA(+) repair cells located outside the grafted vein, displaying a hybrid fibroblast-smooth muscle cell phenotype. Spatial transcriptomics identified stable and sustained Fhl1 expression within the neointima throughout the entire NIH phase. Systemic knockout of Fhl1 in SD rats via the phosphoinositide 3-kinase pathway exacerbated graft inflammation, heightened cell proliferation, and accelerated NIH. Conversely, FHL1 overexpression in cultured HSV suppressed NIH.

CONCLUSION

These findings indicate that, following grafting into the arterial system, the newly formed repair cells external to the grafted vein play a pivotal role in NIH, with neointimal cells exhibiting stable and continuous Fhl1 expression. Fhl1 serves as a protective factor against NIH both in vivo and in HSV, likely due to its anti-inflammatory and anti-proliferative effects.

KEY POINTS

This study firstly used spatial transcriptomics technique to analyse the neointima and generated a specific neointimal transcriptomic atlas. Fhl1 exhibits specific and stable expression in the spatial region of the neointima. It has thus far the highest enrichment of expression in the neointima in NIH phases, suggesting that it is a prominent molecular biomarker of neointima. We generated rats with a Fhl1 deletion and found that insufficient Fhl1 expression caused an increase in the severity of vascular inflammation and proliferation during neointimal hyperplasia. Adenovirus-mediated FHL1 overexpression in human saphenous vein have beneficial effects in preventing neointimal hyperplasia. These highlight its potential as a therapeutic target for mitigating vein graft failure associated with cardiovascular procedures. Spatial transcriptomics profiles and morphological observations demonstrated that a newly generated cell population outside the grafted vein with hybrid phenotype between SMCs and fibroblasts contributes to neointimal formation.

Deciphering spatial domains from spatially resolved transcriptomics through spatially regularized deep graph networks

BACKGROUND

Recent advancements in spatially resolved transcriptomics (SRT) have opened up unprecedented opportunities to explore gene expression patterns within spatial contexts. Deciphering spatial domains is a critical task in spatial transcriptomic data analysis, aiding in the elucidation of tissue structural heterogeneity and biological functions. However, existing spatial domain detection methods ignore the consistency of expression patterns and spatial arrangements between spots, as well as the severe gene dropout phenomenon present in SRT data, resulting in suboptimal performance in identifying tissue spatial heterogeneity.

RESULTS

In this paper, we introduce a novel framework, spatially regularized deep graph networks (SR-DGN), which integrates gene expression profiles with spatial information to learn spatially-consistent and informative spot representations. Specifically, SR-DGN employs graph attention networks (GAT) to adaptively aggregate gene expression information from neighboring spots, considering local expression patterns between spots. In addition, the spatial regularization constraint ensures the consistency of neighborhood relationships between physical and embedded spaces in an end-to-end manner. SR-DGN also employs cross-entropy (CE) loss to model gene expression states, effectively mitigating the impact of noisy gene dropouts.

CONCLUSIONS

Experimental results demonstrate that SR-DGN outperforms state-of-the-art methods in spatial domain identification across SRT data from different sequencing platforms. Moreover, SR-DGN is capable of recovering known microanatomical structures, yielding clearer low-dimensional visualizations and more accurate spatial trajectory inferences.

Spatial dissection of tumour microenvironments in gastric cancers reveals the immunosuppressive crosstalk between CCL2+ fibroblasts and STAT3-activated macrophages

BACKGROUND

A spatially resolved, niche-level analysis of tumour microenvironments (TME) can provide insights into cellular interactions and their functional impacts in gastric cancers (GC).

OBJECTIVE

Our goal was to translate the spatial organisation of GC ecosystems into a functional landscape of cellular interactions involving malignant, stromal and immune cells.

DESIGN

We performed spatial transcriptomics on nine primary GC samples using the Visium platform to delineate the transcriptional landscape and dynamics of malignant, stromal and immune cells within the GC tissue architecture, highlighting cellular crosstalks and their functional consequences in the TME.

RESULTS

GC spatial transcriptomes with substantial cellular heterogeneity were delineated into six regional compartments. Specifically, the fibroblast-enriched TME upregulates epithelial-to-mesenchymal transformation and immunosuppressive response in malignant and TME cells, respectively. Cell type-specific transcriptional dynamics revealed that malignant and endothelial cells promote the cellular proliferations of TME cells, whereas the fibroblasts and immune cells are associated with procancer and anticancer immunity, respectively. Ligand-receptor analysis revealed that CCL2-expressing fibroblasts promote the tumour progression via JAK-STAT3 signalling and inflammatory response in tumour-infiltrated macrophages. CCL2+ fibroblasts and STAT3-activated macrophages are co-localised and their co-abundance was associated with unfavourable prognosis. We experimentally validated that CCL2+ fibroblasts recruit myeloid cells and stimulate STAT3 activation in recruited macrophages. The development of immunosuppressive TME by CCL2+ fibroblasts were also validated in syngeneic mouse models.

CONCLUSION

GC spatial transcriptomes revealed functional cellular crosstalk involving multiple cell types among which the interaction between CCL2+ fibroblasts and STAT3-activated macrophages plays roles in establishing immune-suppressive GC TME with potential clinical relevance.

stHGC: a self-supervised graph representation learning for spatial domain recognition with hybrid graph and spatial regularization

Advancements in spatial transcriptomics (ST) technology have enabled the analysis of gene expression while preserving cellular spatial information, greatly enhancing our understanding of cellular interactions within tissues. Accurate identification of spatial domains is crucial for comprehending tissue organization. However, the effective integration of spatial location and gene expression still faces significant challenges. To address this challenge, we propose a novel self-supervised graph representation learning framework named stHGC for identifying spatial domains. Firstly, a hybrid neighbor graph is constructed by integrating different similarity metrics to represent spatial proximity and high-dimensional gene expression features. Secondly, a self-supervised graph representation learning framework is introduced to learn the representation of spots in ST data. Within this framework, the graph attention mechanism is utilized to characterize relationships between adjacent spots, and the self-supervised method ensures distinct representations for non-neighboring spots. Lastly, a spatial regularization constraint is employed to enable the model to retain the structural information of spatial neighbors. Experimental results demonstrate that stHGC outperforms state-of-the-art methods in identifying spatial domains across ST datasets with different resolutions. Furthermore, stHGC has been proven to be beneficial for downstream tasks such as denoising and trajectory inference, showcasing its scalability in handling ST data.

STCGAN: a novel cycle-consistent generative adversarial network for spatial transcriptomics cellular deconvolution

MOTIVATION

Spatial transcriptomics (ST) technologies have revolutionized our ability to map gene expression patterns within native tissue context, providing unprecedented insights into tissue architecture and cellular heterogeneity. However, accurately deconvolving cell-type compositions from ST spots remains challenging due to the sparse and averaged nature of ST data, which is essential for accurately depicting tissue architecture. While numerous computational methods have been developed for cell-type deconvolution and spatial distribution reconstruction, most fail to capture tissue complexity at the single-cell level, thereby limiting their applicability in practical scenarios.

RESULTS

To this end, we propose a novel cycle-consistent generative adversarial network named STCGAN for cellular deconvolution in spatial transcriptomic. STCGAN first employs a cycle-consistent generative adversarial network (CGAN) to pre-train on ST data, ensuring that both the mapping from ST data to latent space and its reverse mapping are consistent, capturing complex spatial gene expression patterns and learning robust latent representations. Based on the learned representation, STCGAN then optimizes a trainable cell-to-spot mapping matrix to integrate scRNA-seq data with ST data, accurately estimating cellular composition within each capture spot and effectively reconstructing the spatial distribution of cells across the tissue. To further enhance deconvolution accuracy, we incorporate spatial-aware regularization that ensures accurate cellular distribution reconstruction within the spatial context. Benchmarking against seven state-of-the-art methods on five simulated and real datasets from various tissues, STCGAN consistently delivers superior cell-type deconvolution performance.

AVAILABILITY

The code of STCGAN can be downloaded from https://github.com/cs-wangbo/STCGAN and all the mentioned datasets are available on Zenodo at https://zenodo.org/doi/10.5281/zenodo.10799113.

STMGraph: spatial-context-aware of transcriptomes via a dual-remasked dynamic graph attention model

Spatial transcriptomics (ST) technologies enable dissecting the tissue architecture in spatial context. To perceive the global contextual information of gene expression patterns in tissue, the spatial dependence of cells must be fully considered by integrating both local and non-local features by means of spatial-context-aware. However, the current ST integration algorithm ignores for ST dropouts, which impedes the spatial-aware of ST features, resulting in challenges in the accuracy and robustness of microenvironmental heterogeneity detecting, spatial domain clustering, and batch-effects correction. Here, we developed an STMGraph, a universal dual-view dynamic deep learning framework that combines dual-remask (MASK-REMASK) with dynamic graph attention model (DGAT) to exploit ST data outperforming pre-existing tools. The dual-remask mechanism masks the embeddings before encoding and decoding, establishing dual-decoding-view to share features mutually. DGAT leverages self-supervision to update graph linkage relationships from two distinct perspectives, thereby generating a comprehensive representation for each node. Systematic benchmarking against 10 state-of-the-art tools revealed that the STMGraph has the optimal performance with high accuracy and robustness on spatial domain clustering for the datasets of diverse ST platforms from multi- to sub-cellular resolutions. Furthermore, STMGraph aggregates ST information cross regions by dual-remask to realize the batch-effects correction implicitly, allowing for spatial domain clustering of ST multi-slices. STMGraph is platform independent and superior in spatial-context-aware to achieve microenvironmental heterogeneity detection, spatial domain clustering, batch-effects correction, and new biological discovery, and is therefore a desirable novel tool for diverse ST studies.

The advance of single cell transcriptome to study kidney immune cells in diabetic kidney disease

Diabetic kidney disease (DKD) is a prevalent microvascular complication of diabetes mellitus and a primary cause of end-stage renal disease (ESRD). Increasing studies suggest that immune cells are involved in regulating renal inflammation, which contributes to the progression of DKD. Compared with conventional methods, single-cell sequencing technology is more developed technique that has advantages in resolving cellular heterogeneity, parallel multi-omics studies, and discovering new cell types. ScRNA-seq helps researchers to analyze specifically gene expressions, signaling pathways, intercellular communication as well as their regulations in various immune cells of kidney biopsy and urine samples. It is still challenging to investigate the function of each cell type in the pathophysiology of kidney due to its complex and heterogeneous structure and function. Here, we discuss the application of single-cell transcriptomics in the field of DKD and highlight several recent studies that explore the important role of immune cells including macrophage, T cells, B cells etc. in DKD through scRNA-seq analyses. Through combing the researches of scRNA-seq on immune cells in DKD, this review provides novel perspectives on the pathogenesis and immune therapeutic strategy for DKD.

Sainsc: A Computational Tool for Segmentation-Free Analysis of In Situ Capture Data

Spatially resolved transcriptomics (SRT) has become the method of choice for characterising the complexity of biomedical tissue samples. Until recently, scientists were restricted to SRT methods that can profile a limited set of target genes at high spatial resolution or transcriptome-wide but at a low spatial resolution. Through recent developments, there are now methods that offer both subcellular spatial resolution and full transcriptome coverage. However, utilising these new methods' high spatial resolution and gene resolution remains elusive due to several factors, including low detection efficiency and high computational costs. Here, we present Sainsc (Segmentation-free analysis of in situ capture data), which combines a cell-segmentation-free approach with efficient data processing of transcriptome-wide nanometre-resolution spatial data. Sainsc can generate cell-type maps with accurate cell-type assignment at the nanometre scale, together with corresponding maps of the assignment scores that facilitate interpretation of the local confidence of cell-type assignment. We demonstrate its utility and accuracy for different tissues and technologies. Compared to other methods, Sainsc requires lower computational resources and has scalable performance, enabling interactive data exploration. Sainsc is compatible with common data analysis frameworks and is available as open-source software in multiple programming languages.

SpottedPy quantifies relationships between spatial transcriptomic hotspots and uncovers environmental cues of epithelial-mesenchymal plasticity in breast cancer

Spatial transcriptomics is revolutionizing the exploration of intratissue heterogeneity in cancer, yet capturing cellular niches and their spatial relationships remains challenging. We introduce SpottedPy, a Python package designed to identify tumor hotspots and map spatial interactions within the cancer ecosystem. Using SpottedPy, we examine epithelial-mesenchymal plasticity in breast cancer and highlight stable niches associated with angiogenic and hypoxic regions, shielded by CAFs and macrophages. Hybrid and mesenchymal hotspot distribution follows transformation gradients reflecting progressive immunosuppression. Our method offers flexibility to explore spatial relationships at different scales, from immediate neighbors to broader tissue modules, providing new insights into tumor microenvironment dynamics.

IAMSAM: image-based analysis of molecular signatures using the Segment Anything Model

Spatial transcriptomics is a cutting-edge technique that combines gene expression with spatial information, allowing researchers to study molecular patterns within tissue architecture. Here, we present IAMSAM, a user-friendly web-based tool for analyzing spatial transcriptomics data focusing on morphological features. IAMSAM accurately segments tissue images using the Segment Anything Model, allowing for the semi-automatic selection of regions of interest based on morphological signatures. Furthermore, IAMSAM provides downstream analysis, such as identifying differentially expressed genes, enrichment analysis, and cell type prediction within the selected regions. With its simple interface, IAMSAM empowers researchers to explore and interpret heterogeneous tissues in a streamlined manner.

Application of spatial-omics to the classification of kidney biopsy samples in transplantation

Improvement of long-term outcomes through targeted treatment is a primary concern in kidney transplant medicine. Currently, the validation of a rejection diagnosis and subsequent treatment depends on the histological assessment of allograft biopsy samples, according to the Banff classification system. However, the lack of (early) disease-specific tissue markers hinders accurate diagnosis and thus timely intervention. This challenge mainly results from an incomplete understanding of the pathophysiological processes underlying late allograft failure. Integration of large-scale multimodal approaches for investigating allograft biopsy samples might offer new insights into this pathophysiology, which are necessary for the identification of novel therapeutic targets and the development of tailored immunotherapeutic interventions. Several omics technologies - including transcriptomic, proteomic, lipidomic and metabolomic tools (and multimodal data analysis strategies) - can be applied to allograft biopsy investigation. However, despite their successful application in research settings and their potential clinical value, several barriers limit the broad implementation of many of these tools into clinical practice. Among spatial-omics technologies, mass spectrometry imaging, which is under-represented in the transplant field, has the potential to enable multi-omics investigations that might expand the insights gained with current clinical analysis technologies.

Beyond genomic studies of congenital heart defects through systematic modelling and phenotyping

Congenital heart defects (CHDs), the most common congenital anomalies, are considered to have a significant genetic component. However, despite considerable efforts to identify pathogenic genes in patients with CHDs, few gene variants have been proven as causal. The complexity of the genetic architecture underlying human CHDs likely contributes to this poor genetic discovery rate. However, several other factors are likely to contribute. For example, the level of patient phenotyping required for clinical care may be insufficient for research studies focused on mechanistic discovery. Although several hundred mouse gene knockouts have been described with CHDs, these are generally not phenotyped and described in the same way as CHDs in patients, and thus are not readily comparable. Moreover, most patients with CHDs carry variants of uncertain significance of crucial cardiac genes, further complicating comparisons between humans and mouse mutants. In spite of major advances in cardiac developmental biology over the past 25 years, these advances have not been well communicated to geneticists and cardiologists. As a consequence, the latest data from developmental biology are not always used in the design and interpretation of studies aimed at discovering the genetic causes of CHDs. In this Special Article, while considering other in vitro and in vivo models, we create a coherent framework for accurately modelling and phenotyping human CHDs in mice, thereby enhancing the translation of genetic and genomic studies into the causes of CHDs in patients.

Single-cell and spatial transcriptomics: Discovery of human placental development and disease

The human placenta is a vital organ, encompassing many distinct cell types, that maintains the growth and development of the fetus and is essential for substance exchange, defense, synthesis, and immunity. Abnormalities in placental cells can lead to various pregnancy complications, but the mechanisms remain largely unclear. Single-cell and spatial transcriptomics technologies have been developed in recent years to demonstrate placental cell heterogeneity and spatial molecular localization. Here, we review and summarize the current literature, demonstrating these technologies and showing the heterogeneity of various placenta cells and cell-cell communication of normal human placenta, as well as placenta-related diseases, such as preeclampsia, gestational diabetes mellitus, advanced maternal age, recurrent pregnancy loss, and placenta accreta spectrum disorders. Meanwhile, current weaknesses and future direction were discussed.

DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax

Although spatial transcriptomics data provide valuable insights into gene expression profiles and the spatial structure of tissues, most studies rely solely on gene expression information, underutilizing the spatial data. To fully leverage the potential of spatial transcriptomics and graph neural networks, the DGSI (Deep Graph Structure Infomax) model is proposed. This innovative graph data processing model uses graph convolutional neural networks and employs an unsupervised learning approach. It maximizes the mutual information between graph-level and node-level representations, emphasizing flexible sampling and aggregation of nodes and their neighbors. This effectively captures and incorporates local information from nodes into the overall graph structure. Additionally, this paper developed the DGSIST framework, an unsupervised cell clustering method that integrates the DGSI model, SVD dimensionality reduction algorithm, and k-means++ clustering algorithm. This aims to identify cell types accurately. DGSIST fully uses spatial transcriptomics data and outperforms existing methods in accuracy. Demonstrations of DGSIST's capability across various tissue types and technological platforms have shown its effectiveness in accurately identifying spatial domains in multiple tissue sections. Compared to other spatial clustering methods, DGSIST excels in cell clustering and effectively eliminates batch effects without needing batch correction. DGSIST excels in spatial clustering analysis, spatial variation identification, and differential gene expression detection and directly applies to graph analysis tasks, such as node classification, link prediction, or graph clustering. Anticipation lies in the contribution of the DGSIST framework to a deeper understanding of the spatial organizational structures of diseases such as cancer.

Spatially resolved transcriptomic signatures of hippocampal subregions and Arc-expressing ensembles in active place avoidance memory

The rodent hippocampus is a spatially organized neuronal network that supports the formation of spatial and episodic memories. We conducted bulk RNA sequencing and spatial transcriptomics experiments to measure gene expression changes in the dorsal hippocampus following the recall of active place avoidance (APA) memory. Through bulk RNA sequencing, we examined the gene expression changes following memory recall across the functionally distinct subregions of the dorsal hippocampus. We found that recall induced differentially expressed genes (DEGs) in the CA1 and CA3 hippocampal subregions were enriched with genes involved in synaptic transmission and synaptic plasticity, while DEGs in the dentate gyrus (DG) were enriched with genes involved in energy balance and ribosomal function. Through spatial transcriptomics, we examined gene expression changes following memory recall across an array of spots encompassing putative memory-associated neuronal ensembles marked by the expression of the IEGs Arc, Egr1, and c-Jun. Within samples from both trained and untrained mice, the subpopulations of spatial transcriptomic spots marked by these IEGs were transcriptomically and spatially distinct from one another. DEGs detected between Arc + and Arc- spots exclusively in the trained mouse were enriched in several memory-related gene ontology terms, including "regulation of synaptic plasticity" and "memory." Our results suggest that APA memory recall is supported by regionalized transcriptomic profiles separating the CA1 and CA3 from the DG, transcriptionally and spatially distinct IEG expressing spatial transcriptomic spots, and biological processes related to synaptic plasticity as a defining the difference between Arc + and Arc- spatial transcriptomic spots.

Mimicking and analyzing the tumor microenvironment

The tumor microenvironment (TME) is increasingly appreciated to play a decisive role in cancer development and response to therapy in all solid tumors. Hypoxia, acidosis, high interstitial pressure, nutrient-poor conditions, and high cellular heterogeneity of the TME arise from interactions between cancer cells and their environment. These properties, in turn, play key roles in the aggressiveness and therapy resistance of the disease, through complex reciprocal interactions between the cancer cell genotype and phenotype, and the physicochemical and cellular environment. Understanding this complexity requires the combination of sophisticated cancer models and high-resolution analysis tools. Models must allow both control and analysis of cellular and acellular TME properties, and analyses must be able to capture the complexity at high depth and spatial resolution. Here, we review the advantages and limitations of key models and methods in order to guide further TME research and outline future challenges.

Graph attention automatic encoder based on contrastive learning for domain recognition of spatial transcriptomics

Spatial transcriptomics is an emerging technology that enables the profiling of gene expression in tissues while preserving spatial location information. This innovative approach is anticipated to provide a comprehensive understanding of the spatial distribution of different cells within tissues and facilitate in-depth analysis of tissue structure. To accurately recognize spatial domains from spatial transcriptomics, we have introduced a generalized deep learning method called GAAEST (Graph Attention-based Autoencoder for Spatial Transcriptomics). Our proposed approach effectively integrates both spatial location information and gene expression data from spatial transcriptomics. Specifically, it leverages spatial location details to construct a neighborhood graph and employs a graph attention network-based encoder to embed gene expression information into a spatially informed space. At the same time, to further optimize the learned potential embedding, self-supervised contrastive learning is introduced to capture spatial information at three levels: local, global and contextual feature of spots. Finally, the decoder reconstructs gene expressions, which are then clustered to identify spatial domains with similar expression patterns and spatial proximity. Based on our experiments conducted on multiple datasets, GAAEST consistently outperforms existing state-of-the-art methods. The proposed GAAEST demonstrates excellent capabilities in spatial domain recognition, positioning it as an ideal tool for advancing spatial transcriptomics research.

Implementing distinct spatial proteogenomic technologies: opportunities, challenges, and key considerations

Our ability to understand the cellular complexity of tissues has been revolutionized in recent years with significant advances in proteogenomic technologies including those enabling spatial analyses. This has led to numerous consortium efforts, such as the human cell atlas initiative which aims to profile all cells in the human body in healthy and diseased contexts. The availability of such information will subsequently lead to the identification of novel biomarkers of disease and of course therapeutic avenues. However, before such an atlas of any given healthy or diseased tissue can be generated, several factors should be considered including which specific techniques are optimal for the biological question at hand. In this review, we aim to highlight some of the considerations we believe to be important in the experimental design and analysis process, with the goal of helping to navigate the rapidly changing landscape of technologies available.

SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data

Spatial barcoding-based transcriptomic (ST) data require deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER tackles platform effects between ST and scRNA-seq data, ensuring a linear relationship between them while addressing sparsity and spatial correlations in cell types across capture spots. SDePER estimates cell-type proportions, enabling enhanced resolution tissue mapping by imputing cell-type compositions and gene expressions at unmeasured locations. Applications to simulated data and four real datasets showed SDePER's superior accuracy and robustness over existing methods.

Discovery of prognostic lncRNAs in colorectal cancer using spatial transcriptomics

Colorectal cancer (CRC) exhibits significant genetic and epigenetic diversity, evolving into sub-clonal populations with varied metastatic potentials and treatment responses. Predicting metastatic disease in CRC patients remains challenging, underscoring the need for reliable biomarkers. While most research on therapeutic targets and biomarkers has focused on proteins, non-coding RNAs such as long non-coding RNAs (lncRNAs) comprise most of the transcriptome and demonstrate superior tissue- and cancer-specific expression. We utilised spatial transcriptomics to investigate lncRNAs in CRC tumours, offering more precise cell-type-specific expression data compared to bulk RNA sequencing. Our analysis identified 301 lncRNAs linked to malignant CRC regions, which we validated with public data. Further validation using RNA-FISH revealed three lncRNAs (LINC01978, PLAC4, and LINC01303) that are detectable in stage II tumours but not in normal epithelium and are upregulated in metastatic tissues. These lncRNAs hold potential as biomarkers for early risk assessment of metastatic disease.

STAREG: Statistical replicability analysis of high throughput experiments with applications to spatial transcriptomic studies

Replicable signals from different yet conceptually related studies provide stronger scientific evidence and more powerful inference. We introduce STAREG, a statistical method for replicability analysis of high throughput experiments, and apply it to analyze spatial transcriptomic studies. STAREG uses summary statistics from multiple studies of high throughput experiments and models the the joint distribution of p-values accounting for the heterogeneity of different studies. It effectively controls the false discovery rate (FDR) and has higher power by information borrowing. Moreover, it provides different rankings of important genes. With the EM algorithm in combination with pool-adjacent-violator-algorithm (PAVA), STAREG is scalable to datasets with millions of genes without any tuning parameters. Analyzing two pairs of spatially resolved transcriptomic datasets, we are able to make biological discoveries that otherwise cannot be obtained by using existing methods.

Next-generation spatial transcriptomics: unleashing the power to gear up translational oncology

The growing advances in spatial transcriptomics (ST) stand as the new frontier bringing unprecedented influences in the realm of translational oncology. This has triggered systemic experimental design, analytical scope, and depth alongside with thorough bioinformatics approaches being constantly developed in the last few years. However, harnessing the power of spatial biology and streamlining an array of ST tools to achieve designated research goals are fundamental and require real-world experiences. We present a systemic review by updating the technical scope of ST across different principal basis in a timeline manner hinting on the generally adopted ST techniques used within the community. We also review the current progress of bioinformatic tools and propose in a pipelined workflow with a toolbox available for ST data exploration. With particular interests in tumor microenvironment where ST is being broadly utilized, we summarize the up-to-date progress made via ST-based technologies by narrating studies categorized into either mechanistic elucidation or biomarker profiling (translational oncology) across multiple cancer types and their ways of deploying the research through ST. This updated review offers as a guidance with forward-looking viewpoints endorsed by many high-resolution ST tools being utilized to disentangle biological questions that may lead to clinical significance in the future.

BayeSMART: Bayesian clustering of multi-sample spatially resolved transcriptomics data

The field of spatially resolved transcriptomics (SRT) has greatly advanced our understanding of cellular microenvironments by integrating spatial information with molecular data collected from multiple tissue sections or individuals. However, methods for multi-sample spatial clustering are lacking, and existing methods primarily rely on molecular information alone. This paper introduces BayeSMART, a Bayesian statistical method designed to identify spatial domains across multiple samples. BayeSMART leverages artificial intelligence (AI)-reconstructed single-cell level information from the paired histology images of multi-sample SRT datasets while simultaneously considering the spatial context of gene expression. The AI integration enables BayeSMART to effectively interpret the spatial domains. We conducted case studies using four datasets from various tissue types and SRT platforms, and compared BayeSMART with alternative multi-sample spatial clustering approaches and a number of state-of-the-art methods for single-sample SRT analysis, demonstrating that it surpasses existing methods in terms of clustering accuracy, interpretability, and computational efficiency. BayeSMART offers new insights into the spatial organization of cells in multi-sample SRT data.

SpaGIC: graph-informed clustering in spatial transcriptomics via self-supervised contrastive learning

Spatial transcriptomics technologies enable the generation of gene expression profiles while preserving spatial context, providing the potential for in-depth understanding of spatial-specific tissue heterogeneity. Leveraging gene and spatial data effectively is fundamental to accurately identifying spatial domains in spatial transcriptomics analysis. However, many existing methods have not yet fully exploited the local neighborhood details within spatial information. To address this issue, we introduce SpaGIC, a novel graph-based deep learning framework integrating graph convolutional networks and self-supervised contrastive learning techniques. SpaGIC learns meaningful latent embeddings of spots by maximizing both edge-wise and local neighborhood-wise mutual information of graph structures, as well as minimizing the embedding distance between spatially adjacent spots. We evaluated SpaGIC on seven spatial transcriptomics datasets across various technology platforms. The experimental results demonstrated that SpaGIC consistently outperformed existing state-of-the-art methods in several tasks, such as spatial domain identification, data denoising, visualization, and trajectory inference. Additionally, SpaGIC is capable of performing joint analyses of multiple slices, further underscoring its versatility and effectiveness in spatial transcriptomics research.

Scoping Review: Methods and Applications of Spatial Transcriptomics in Tumor Research

Spatial transcriptomics (ST) examines gene expression within its spatial context on tissue, linking morphology and function. Advances in ST resolution and throughput have led to an increase in scientific interest, notably in cancer research. This scoping study reviews the challenges and practical applications of ST, summarizing current methods, trends, and data analysis techniques for ST in neoplasm research. We analyzed 41 articles published by the end of 2023 alongside public data repositories. The findings indicate cancer biology is an important focus of ST research, with a rising number of studies each year. Visium (10x Genomics, Pleasanton, CA, USA) is the leading ST platform, and SCTransform from Seurat R library is the preferred method for data normalization and integration. Many studies incorporate additional data types like single-cell sequencing and immunohistochemistry. Common ST applications include discovering the composition and function of tumor tissues in the context of their heterogeneity, characterizing the tumor microenvironment, or identifying interactions between cells, including spatial patterns of expression and co-occurrence. However, nearly half of the studies lacked comprehensive data processing protocols, hindering their reproducibility. By recommending greater transparency in sharing analysis methods and adapting single-cell analysis techniques with caution, this review aims to improve the reproducibility and reliability of future studies in cancer research.

Systematic comparison of sequencing-based spatial transcriptomic methods

Recent developments of sequencing-based spatial transcriptomics (sST) have catalyzed important advancements by facilitating transcriptome-scale spatial gene expression measurement. Despite this progress, efforts to comprehensively benchmark different platforms are currently lacking. The extant variability across technologies and datasets poses challenges in formulating standardized evaluation metrics. In this study, we established a collection of reference tissues and regions characterized by well-defined histological architectures, and used them to generate data to compare 11 sST methods. We highlighted molecular diffusion as a variable parameter across different methods and tissues, significantly affecting the effective resolutions. Furthermore, we observed that spatial transcriptomic data demonstrate unique attributes beyond merely adding a spatial axis to single-cell data, including an enhanced ability to capture patterned rare cell states along with specific markers, albeit being influenced by multiple factors including sequencing depth and resolution. Our study assists biologists in sST platform selection, and helps foster a consensus on evaluation standards and establish a framework for future benchmarking efforts that can be used as a gold standard for the development and benchmarking of computational tools for spatial transcriptomic analysis.

Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology

Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.

Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs

MOTIVATION

Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images provides a scalable alternative approach to decoding tissue complexity.

RESULTS

Here, we present a graph neural network based framework to predict the spatial expression of highly expressed genes from tissue histological images. Extensive experiments on two separate breast cancer data cohorts demonstrate that our method improves the prediction performance compared to the state-of-the-art, and that our model can be used to better delineate spatial domains of biological interest.

AVAILABILITY AND IMPLEMENTATION

https://github.com/song0309/asGNN/.

STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics

In in situ capturing-based spatial transcriptomics, spots of the same size and printed at fixed locations cannot precisely capture the randomly-located single cells, therefore inherently failing to profile transcriptome at the single-cell level. To this end, we present STIE, an Expectation Maximization algorithm that aligns the spatial transcriptome to its matched histology image-based nuclear morphology and recovers missing cells from ~70% gap area, thereby achieving the real single-cell level and whole-slide scale deconvolution, convolution, and clustering for both low- and high-resolution spots. STIE characterizes cell-type-specific gene expression and demonstrates outperforming concordance with true cell-type-specific transcriptomic signatures than the other spot- and subspot-level methods. Furthermore, STIE reveals the single-cell level insights, for instance, lower actual spot resolution than its reported spot size, unbiased evaluation of cell type colocalization, superior power of high-resolution spot in distinguishing nuanced cell types, and spatial cell-cell interactions at the single-cell level other than spot level.

Spatiotemporal omics for biology and medicine

The completion of the Human Genome Project has provided a foundational blueprint for understanding human life. Nonetheless, understanding the intricate mechanisms through which our genetic blueprint is involved in disease or orchestrates development across temporal and spatial dimensions remains a profound scientific challenge. Recent breakthroughs in cellular omics technologies have paved new pathways for understanding the regulation of genomic elements and the relationship between gene expression, cellular functions, and cell fate determination. The advent of spatial omics technologies, encompassing both imaging and sequencing-based methodologies, has enabled a comprehensive understanding of biological processes from a cellular ecosystem perspective. This review offers an updated overview of how spatial omics has advanced our understanding of the translation of genetic information into cellular heterogeneity and tissue structural organization and their dynamic changes over time. It emphasizes the discovery of various biological phenomena, related to organ functionality, embryogenesis, species evolution, and the pathogenesis of diseases.

How Can Spatial Transcriptomic Profiling Advance Our Understanding of Skin Diseases?

Spatial transcriptomic (ST) profiling is the mapping of gene expression within cell populations with preservation of positional context and represents an exciting new approach to develop our understanding of local and regional influences upon skin biology in health and disease. With the ability to probe from a few hundred transcripts to the entire transcriptome, multiple ST approaches are now widely available. In this paper, we review the ST field and discuss its application to dermatology. Its potential to advance our understanding of skin biology in health and disease is highlighted through the illustrative examples of 3 research areas: cutaneous aging, tumorigenesis, and psoriasis.

Spatial technologies to evaluate the HIV-1 reservoir and its microenvironment in the lymph node

The presence of the HIV-1 reservoir, a group of immune cells that contain intact, integrated, and replication-competent proviruses, is a major challenge to cure HIV-1. HIV-1 reservoir cells are largely unaffected by the cytopathic effects of viruses, antiviral immune responses, or antiretroviral therapy (ART). The HIV-1 reservoir is seeded early during HIV-1 infection and augmented during active viral replication. CD4+ T cells are the primary target for HIV-1 infection, and recent studies suggest that memory T follicular helper cells within the lymph node, more precisely in the B cell follicle, harbor integrated provirus, which contribute to viral rebound upon ART discontinuation. The B cell follicle, more specifically the germinal center, possesses a unique environment because of its distinct property of being partly immune privileged, potentially allowing HIV-1-infected cells within the lymph nodes to be protected from CD8+ T cells. This modified immune response in the germinal center of the follicle is potentially explained by the exclusion of CD8+ T cells and the presence of T regulatory cells at the junction of the follicle and extrafollicular region. The proviral makeup of HIV-1-infected cells is similar in lymph nodes and blood, suggesting trafficking between these compartments. Little is known about the cell-to-cell interactions, microenvironment of HIV-1-infected cells in the follicle, and trafficking between the lymph node follicle and other body compartments. Applying a spatiotemporal approach that integrates genomics, transcriptomics, and proteomics to investigate the HIV-1 reservoir and its neighboring cells in the lymph node has promising potential for informing HIV-1 cure efforts.

Benchmarking clustering, alignment, and integration methods for spatial transcriptomics

BACKGROUND

Spatial transcriptomics (ST) is advancing our understanding of complex tissues and organisms. However, building a robust clustering algorithm to define spatially coherent regions in a single tissue slice and aligning or integrating multiple tissue slices originating from diverse sources for essential downstream analyses remains challenging. Numerous clustering, alignment, and integration methods have been specifically designed for ST data by leveraging its spatial information. The absence of comprehensive benchmark studies complicates the selection of methods and future method development.

RESULTS

In this study, we systematically benchmark a variety of state-of-the-art algorithms with a wide range of real and simulated datasets of varying sizes, technologies, species, and complexity. We analyze the strengths and weaknesses of each method using diverse quantitative and qualitative metrics and analyses, including eight metrics for spatial clustering accuracy and contiguity, uniform manifold approximation and projection visualization, layer-wise and spot-to-spot alignment accuracy, and 3D reconstruction, which are designed to assess method performance as well as data quality. The code used for evaluation is available on our GitHub. Additionally, we provide online notebook tutorials and documentation to facilitate the reproduction of all benchmarking results and to support the study of new methods and new datasets.

CONCLUSIONS

Our analyses lead to comprehensive recommendations that cover multiple aspects, helping users to select optimal tools for their specific needs and guide future method development.