SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network

Decoding the blueprints of embryo development with single-cell and spatial omics

Embryonic development is a complex and intricately regulated process that encompasses precise control over cell differentiation, morphogenesis, and the underlying gene expression changes. Recent years have witnessed a remarkable acceleration in the development of single-cell and spatial omic technologies, enabling high-throughput profiling of transcriptomic and other multi-omic information at the individual cell level. These innovations offer fresh and multifaceted perspectives for investigating the intricate cellular and molecular mechanisms that govern embryonic development. In this review, we provide an in-depth exploration of the latest technical advancements in single-cell and spatial multi-omic methodologies and compile a systematic catalog of their applications in the field of embryonic development. We deconstruct the research strategies employed by recent studies that leverage single-cell sequencing techniques and underscore the unique advantages of spatial transcriptomics. Furthermore, we delve into both the current applications, data analysis algorithms and the untapped potential of these technologies in advancing our understanding of embryonic development. With the continuous evolution of multi-omic technologies, we anticipate their widespread adoption and profound contributions to unraveling the intricate molecular foundations underpinning embryo development in the foreseeable future.

Advanced Carriers for Precise Delivery and Therapeutic Mechanisms of Traditional Chinese Medicines: Integrating Spatial Multi-Omics and Delivery Visualization

The complex composition of traditional Chinese medicines (TCMs) has posed challenges for in-depth study and global application, despite their abundance of bioactive compounds that make them valuable resources for disease treatment. To overcome these obstacles, it is essential to modernize TCMs by focusing on precise disease treatment. This involves elucidating the structure-activity relationships within their complex compositions, ensuring accurate in vivo delivery, and monitoring the delivery process. This review discusses the research progress of TCMs in precision disease treatment from three perspectives: spatial multi-omics technology for precision therapeutic activity, carrier systems for precise in vivo delivery, and medical imaging technology for visualizing the delivery process. The aim is to establish a novel research paradigm that advances the precision therapy of TCMs.

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.

LETSmix: a spatially informed and learning-based domain adaptation method for cell-type deconvolution in spatial transcriptomics

Spatial transcriptomics (ST) enables the study of gene expression in spatial context, but many ST technologies face challenges due to limited resolution, leading to cell mixtures at each spot. We present LETSmix to deconvolve cell types by integrating spatial correlations through a tailored LETS filter, which leverages layer annotations, expression similarities, image texture features, and spatial coordinates to refine ST data. Additionally, LETSmix employs a mixup-augmented domain adaptation strategy to address discrepancies between ST and reference single-cell RNA sequencing data. Comprehensive evaluations across diverse ST platforms and tissue types demonstrate its high accuracy in estimating cell-type proportions and spatial patterns, surpassing existing methods (URL: https://github.com/ZhanYangen/LETSmix ).

Systematic inference of super-resolution cell spatial profiles from histology images

Inferring cell spatial profiles from histology images is critical for cancer diagnosis and treatment in clinical settings. In this study, we report a weakly-supervised deep-learning method, HistoCell, to directly infer super-resolution cell spatial profiles consisting of cell types, cell states and their spatial network from histology images at the single-nucleus-level. Benchmark analysis demonstrates that HistoCell robustly achieves state-of-the-art performance in terms of cell type/states prediction solely from histology images across multiple cancer tissues. HistoCell can significantly enhance the deconvolution accuracy for the spatial transcriptomics data and enable accurate annotation of subtle cancer tissue architectures. Moreover, HistoCell is applied to de novo discovery of clinically relevant spatial organization indicators, including prognosis and drug response biomarkers, across diverse cancer types. HistoCell also enable image-based screening of cell populations that drives phenotype of interest, and is applied to discover the cell population and corresponding spatial organization indicators associated with gastric malignant transformation risk. Overall, HistoCell emerges as a powerful and versatile tool for cancer studies in histology image-only cohorts.

SpaFun: Discovering Domain-specific Spatial Expression Patterns and New Disease-Relevant Genes using Functional Principal Component Analysis

SpaFun is a novel, non-model-based method developed to address limitations in existing spatially variable gene (SVG) detection techniques, particularly for large-scale spatially resolved transcriptomics (SRT) datasets. These limitations include computational inefficiency, limited statistical power with increasing data size, and the inability to capture spatial heterogeneity and co-expression patterns among genes. Built on functional principal component analysis (fPCA), SpaFun identifies domain-representative genes (DRGs) with significantly better computational efficiency and greater statistical power while accounting for spatial heterogeneity and co-expression patterns among genes. We applied SpaFun to three SRT datasets and demonstrated that SpaFun outperformed state-of-the-art algorithms for identifying representative genes for tumor regions (e.g., DESeq, edgeR, and limma), as well as recently developed novel algorithms designed for spatial omics to identify the representative genes (e.g., SPARK and CSIDE). This highlights SpaFun's ability to accurately identify genes most representative of each spatial domain (e.g., tumor, immune, or stroma regions). By uncovering novel disease-relevant genes overlooked by existing algorithms, SpaFun could provide insights into new molecular mechanisms and propose innovative therapeutic strategies to improve patient outcomes.

stDyer enables spatial domain clustering with dynamic graph embedding

Spatially resolved transcriptomics (SRT) data provide critical insights into gene expression patterns within tissue contexts, necessitating effective methods for identifying spatial domains. We introduce stDyer, an end-to-end deep learning framework for spatial domain clustering in SRT data. stDyer combines Gaussian Mixture Variational AutoEncoder with graph attention networks to learn embeddings and perform clustering. Its dynamic graphs adaptively link units based on Gaussian Mixture assignments, improving clustering and producing smoother domain boundaries. stDyer's mini-batch strategy and multi-GPU support facilitate scalability to large datasets. Benchmarking against state-of-the-art tools, stDyer demonstrates superior performance in spatial domain clustering, multi-slice analysis, and large-scale dataset handling.

Identification and characterization of cell niches in tissue from spatial omics data at single-cell resolution

Deciphering the features, structure, and functions of the cell niche in tissues remains a major challenge. Here, we present scNiche, a computational framework to identify and characterize cell niches from spatial omics data at single-cell resolution. We benchmark scNiche with both simulated and biological datasets, and demonstrate that scNiche can effectively and robustly identify cell niches while outperforming other existing methods. In spatial proteomics data from human triple-negative breast cancer, scNiche reveals the influence of the microenvironment on cellular phenotypes, and further dissects patient-specific niches with distinct cellular compositions or phenotypic characteristics. By analyzing mouse liver spatial transcriptomics data across normal and early-onset liver failure donors, scNiche uncovers disease-specific liver injury niches, and further delineates the niche remodeling from normal liver to liver failure. Overall, scNiche enables decoding the cellular microenvironment in tissues from single-cell spatial omics data.

STMiner: Gene-centric spatial transcriptomics for deciphering tumor tissues

Analyzing spatial transcriptomics data from tumor tissues poses several challenges beyond those of healthy samples, including unclear boundaries between different regions, uneven cell densities, and relatively higher cellular heterogeneity. Collectively, these bias the background against which spatially variable genes are identified, which can result in misidentification of spatial structures and hinder potential insight into complex pathologies. To overcome this problem, STMiner leverages 2D Gaussian mixture models and optimal transport theory to directly characterize the spatial distribution of genes rather than the capture locations of the cells expressing them (spots). By effectively mitigating the impacts of both background bias and data sparsity, STMiner reveals key gene sets and spatial structures overlooked by spot-based analytic tools, facilitating novel biological discoveries. The core concept of directly analyzing overall gene expression patterns also allows for a broader application beyond spatial transcriptomics, positioning STMiner for continuous expansion as spatial omics technologies evolve.

Multi-Omic Analysis of the Differences in Growth and Metabolic Mechanisms Between Chinese Domestic Cattle and Simmental Crossbred Cattle

In livestock production, deeply understanding the molecular mechanisms of growth and metabolic differences in different breeds of cattle is of great significance for optimizing breeding strategies, improving meat quality, and promoting sustainable development. This study aims to comprehensively reveal the molecular-level differences between Chinese domestic cattle and Simmental crossbred cattle through multi-omics analysis, and further provide a theoretical basis for the efficient development of the beef cattle industry. The domestic cattle in China are a unique genetic breed resource. They have characteristics like small size, strong adaptability, and distinctive meat quality. There are significant differences in the growth rate and meat production between these domestic cattle and Simmental hybrid cattle. However, the specific molecular-level differences between them are still unclear. This study conducted a comprehensive comparison between the domestic cattle in China and Simmental crossbred cattle, focusing on microbiology, short-chain fatty acids, blood metabolome, and transcriptome. The results revealed notable differences in the microbial Simpson index between the domestic and Simmental crossbred cattle. The differential strain Akkermansia was found to be highly negatively correlated with the differential short-chain fatty acid isocaproic acid, suggesting that Akkermansia may play a key role in the differences observed in isocaproic acid levels or phenotypes. Furthermore, the transcriptional metabolomics analysis indicated that the differentially expressed genes and metabolites were co-enriched in pathways related to insulin secretion, thyroid hormone synthesis, bile secretion, aldosterone synthesis and secretion, and Cyclic Adenosine Monophosphate (cAMP) signaling pathways. Key genes such as ADCY8 and 1-oleoyl-sn-glycero-3-phosphocholine emerged as crucial regulators of growth and metabolism in beef cattle.

Spatial Patterning Analysis of Cellular Ensembles (SPACE) finds complex spatial organization at the cell and tissue levels

Spatial patterns of cells and other biological elements drive physiologic and pathologic processes within tissues. While many imaging and transcriptomic methods document tissue organization, discerning these patterns is challenging, especially when they involve multiple elements in complex arrangements. To address this challenge, we present Spatial Patterning Analysis of Cellular Ensembles (SPACE), an R package for analysis of high-plex spatial data. SPACE is compatible with any data collection modality that records values (i.e., categorical cell/structure types or quantitative expression levels) at fixed spatial coordinates (i.e., 2d pixels or 3d voxels). SPACE detects not only broad patterns of co-occurrence but also context-dependent associations, quantitative gradients and orientations, and other organizational complexities. Via a robust information theoretic framework, SPACE explores all possible ensembles of tissue elements-single elements, pairs, triplets, and so on-and ranks the most strongly patterned ensembles. For single images, rankings reflect differences from random assortment. For sets of images, rankings reflect differences across sample groups (e.g., genotypes, treatments, timepoints, etc.). Further tools then characterize the nature of each pattern for intuitive interpretation. We validate SPACE and demonstrate its advantages using murine lymph node images for which ground truth has been defined. We then detect new patterns across varied datasets, including tumors and tuberculosis granulomas.

Powerful and accurate case-control analysis of spatial molecular data with deep learning-defined tissue microniches

As spatial molecular data grow in scope and resolution, there is a pressing need to identify key spatial structures associated with disease. Current approaches often rely on hand-crafted features such as local abundances of manually annotated, discrete cell types, which may overlook important signals. Here we introduce variational inference-based microniche analysis (VIMA), a method that combines deep learning with principled statistics to discover associated spatial features with greater flexibility and precision. VIMA uses a variational autoencoder to extract numerical "fingerprints" from small tissue patches that capture their biological content. It uses these fingerprints to define a large number of "microniches" - small, potentially overlapping groups of tissue patches with highly similar biology that span multiple samples. It then uses rigorous statistics to identify microniches whose abundance correlates with case-control status. We show in simulations that VIMA is well calibrated and more powerful and accurate than other approaches. We then apply VIMA to a 140-gene spatial transcriptomics dataset in Alzheimer's dementia, a 54-marker CO-Detection by indEXing (CODEX) dataset in ulcerative colitis (UC), and a 7-marker immunohistochemistry dataset in rheumatoid arthritis (RA), in each case recapitulating known biology and identifying novel spatial features of disease.

Precise gene expression deconvolution in spatial transcriptomics with STged

Spatially resolved transcriptomics (SRT) has transformed tissue biology by linking gene expression profiles with spatial information. However, sequencing-based SRT methods aggregate signals from multiple cell types within capture locations ("spots"), masking cell-type-specific gene expression patterns. Traditional cell-type deconvolution methods estimate cell compositions within spots but fail to resolve cell-type-specific gene expression, limiting their ability to uncover critical biological processes such as cellular interactions and microenvironmental dynamics. Here, we present STged (spatial transcriptomic gene expression deconvolution), a novel computational framework that goes beyond traditional deconvolution by reconstructing cell-type-specific gene expression profiles from mixed spots. STged integrates graph-based spatial correlations and reference-derived gene signatures using a non-negative least-squares regression framework, achieving precise and biologically meaningful deconvolution. Comprehensive simulations show that STged consistently outperforms existing methods in accuracy and robustness. Applications to human pancreatic ductal adenocarcinoma and human squamous cell carcinoma datasets reveal its capacity to identify microenvironment-specific highly variable genes, reconstruct spatial cell-cell communication networks, and resolve tissue architecture at near-single-cell resolution. In mouse kidney tissues, STged uncovers dynamic spatial gene expression patterns and distinct gene programs, advancing our understanding of tissue heterogeneity and cellular dynamics.

SpatialLeiden: spatially aware Leiden clustering

Clustering can identify the natural structure that is inherent to measured data. For single-cell omics, clustering finds cells with similar molecular phenotype after which cell types are annotated. Leiden clustering is one of the algorithms of choice in the single-cell community. In the field of spatial omics, Leiden is often categorized as a "non-spatial" clustering method. However, we show that by integrating spatial information at various steps Leiden clustering is rendered into a computationally highly performant, spatially aware clustering method that compares well with state-of-the art spatial clustering algorithms.

scBSP: A fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data

Emerging spatial omics technologies empower comprehensive exploration of biological systems from multi-omics perspectives in their native tissue location in two and three-dimensional space. However, sparse sequencing capacity and growing spatial resolution in spatial omics present significant computational challenges in identifying biologically meaningful molecules that exhibit variable spatial distributions across different omics. We introduce scBSP, an open-source, versatile, and user-friendly package for identifying spatially variable features in high-resolution spatial omics data. scBSP leverages sparse matrix operation to significantly increase computational efficiency in both computational time and memory usage. In diverse spatial sequencing data and simulations, scBSP consistently and rapidly identifies spatially variable genes and spatially variable peaks across various sequencing techniques and spatial resolutions, handling two- and three-dimensional data with up to millions of cells. It can process high-definition spatial transcriptomics data for 19,950 genes across 181,367 spots within 10 seconds on a typical desktop computer, making it the fastest tool available for handling such high-resolution, sparse spatial omics data while maintaining high accuracy. In a case study of kidney disease using 10x Xenium data, scBSP identified spatially variable genes representative of critical pathological mechanisms associated with histology.

ITGA5+ synovial fibroblasts orchestrate proinflammatory niche formation by remodelling the local immune microenvironment in rheumatoid arthritis

OBJECTIVES

To investigate the phenotypic heterogeneity of tissue-resident synovial fibroblasts and their role in inflammatory response in rheumatoid arthritis (RA).

METHODS

We used single-cell and spatial transcriptomics to profile synovial cells and spatial gene expressions of synovial tissues to identify phenotypic changes in patients with osteoarthritis, RA in sustained remission and active state. Immunohistology, multiplex immunofluorescence and flow cytometry were used to identify synovial fibroblasts subsets. Deconvolution methods further validated our findings in two cohorts (PEAC and R4RA) with treatment response. Cell coculture was used to access the potential cell-cell interactions. Adoptive transfer of synovial cells in collagen-induced arthritis (CIA) mice and bulk RNA sequencing of synovial joints further validate the cellular functions.

RESULTS

We identified a novel tissue-remodelling CD45-CD31-PDPN+ITGA5+ synovial fibroblast population with unique transcriptome of POSTN, COL3A1, CCL5 and TGFB1, and enriched in immunoregulatory pathways. This subset was upregulated in active and lympho-myeloid type of RA, associated with an increased risk of multidrug resistance. Transforming growth factor (TGF)-β1 might participate in the differentiation of this subset. Moreover, ITGA5+ synovial fibroblasts might occur in early stage of inflammation and induce the differentiation of CXCL13hiPD-1hi peripheral helper T cells (TPHs) from naïve CD4+ T cells, by secreting TGF-β1. Intra-articular injection of ITGA5+ synovial fibroblasts exacerbates RA development and upregulates TPHs in CIA mice.

CONCLUSIONS

We demonstrate that ITGA5+ synovial fibroblasts might regulate the RA progression by inducing the differentiation of CXCL13hiPD-1hi TPHs and remodelling the proinflammatory microenvironments. Therapeutic modulation of this subpopulation could therefore be a potential treatment strategy for RA.

Mapping the topography of spatial gene expression with interpretable deep learning

Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of these data complicates analysis of spatial gene expression patterns. We address this issue by deriving a topographic map of a tissue slice-analogous to a map of elevation in a landscape-using a quantity called the isodepth. Contours of constant isodepths enclose domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in expression. We develop GASTON (gradient analysis of spatial transcriptomics organization with neural networks), an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gradients and piecewise linear expression functions that model both continuous gradients and discontinuous variation in gene expression. We show that GASTON accurately identifies spatial domains and marker genes across several tissues, gradients of neuronal differentiation and firing in the brain, and gradients of metabolism and immune activity in the tumor microenvironment.

Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data

In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 34 state-of-the-art methods, classifying SVGs into three categories: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.

Semisupervised Contrastive Learning for Bioactivity Prediction Using Cell Painting Image Data

Morphological profiling has recently demonstrated remarkable potential for identifying the biological activities of small molecules. Alongside the fully supervised and self-supervised machine learning methods recently proposed for bioactivity prediction from Cell Painting image data, we introduce here a semisupervised contrastive (SemiSupCon) learning approach. This approach combines the strengths of using biological annotations in supervised contrastive learning and leveraging large unannotated image data sets with self-supervised contrastive learning. SemiSupCon enhances downstream prediction performance of classifying MeSH pharmacological classifications from PubChem, as well as mode of action and biological target annotations from the Drug Repurposing Hub across two publicly available Cell Painting data sets. Notably, our approach has effectively predicted the biological activities of several unannotated compounds, and these findings were validated through literature searches. This demonstrates that our approach can potentially expedite the exploration of biological activity based on Cell Painting image data with minimal human intervention.

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

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

Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis

Spatially resolved transcriptomics enable comprehensive measurement of gene expression at subcellular resolution while preserving the spatial context of the tissue microenvironment. While deep learning has shown promise in analyzing SCST datasets, most efforts have focused on sequence data and spatial localization, with limited emphasis on leveraging rich histopathological insights from staining images. We introduce GIST, a deep learning-enabled gene expression and histology integration for spatial cellular profiling. GIST employs histopathology foundation models pretrained on millions of histology images to enhance feature extraction and a hybrid graph transformer model to integrate them with transcriptome features. Validated with datasets from human lung, breast, and colorectal cancers, GIST effectively reveals spatial domains and substantially improves the accuracy of segmenting the microenvironment after denoising transcriptomics data. This enhancement enables more accurate gene expression analysis and aids in identifying prognostic marker genes, outperforming state-of-the-art deep learning methods with a total improvement of up to 49.72%. GIST provides a generalizable framework for integrating histology with spatial transcriptome analysis, revealing novel insights into spatial organization and functional dynamics.

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.

Society for Immunotherapy of Cancer: updates and best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) image analysis and data sharing

OBJECTIVES

Multiplex immunohistochemistry and immunofluorescence (mIHC/IF) are emerging technologies that can be used to help define complex immunophenotypes in tissue, quantify immune cell subsets, and assess the spatial arrangement of marker expression. mIHC/IF assays require concerted efforts to optimize and validate the multiplex staining protocols prior to their application on slides. The best practice guidelines for staining and validation of mIHC/IF assays across platforms were previously published by this task force. The current effort represents a complementary manuscript for mIHC/IF analysis focused on the associated image analysis and data management.

METHODS

The Society for Immunotherapy of Cancer convened a task force of pathologists and laboratory leaders from academic centers as well as experts from pharmaceutical and diagnostic companies to develop best practice guidelines for the quantitative image analysis of mIHC/IF output and data management considerations.

RESULTS

Best-practice approaches for image acquisition, color deconvolution and spectral unmixing, tissue and cell segmentation, phenotyping, and algorithm verification are reviewed. Additional quality control (QC) measures such as batch-to-batch correction and QC for assembled images are also discussed. Recommendations for sharing raw outputs, processed results, key analysis programs and source code, and representative photomicrographs from mIHC/IF assays are included. Lastly, multi-institutional harmonization efforts are described.

CONCLUSIONS

mIHC/IF technologies are maturing and are routinely included in research studies and moving towards clinical use. Guidelines for how to perform and standardize image analysis on mIHC/IF-stained slides will likely contribute to more comparable results across laboratories and pave the way for clinical implementation. A checklist encompassing these two-part guidelines for the generation of robust data from quantitative mIHC/IF assays will be provided in a third publication from this task force. While the current effort is mainly focused on best practices for characterizing the tumor microenvironment, these principles are broadly applicable to any mIHC/IF assay and associated image analysis.

Dimensionality reduction for visualizing spatially resolved profiling data using SpaSNE

BACKGROUND

Spatially resolved profiling technologies to quantify transcriptomes, epigenomes, and proteomes have been emerging as groundbreaking methods for comprehensive molecular characterizations. Dimensionality reduction and visualization is an essential step to analyze and interpret spatially resolved profiling data. However, state-of-the-art dimensionality reduction methods for single-cell sequencing data, such as the t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), were not tailored for spatially resolved profiling data.

RESULTS

Here we developed a spatially resolved t-SNE (SpaSNE) method to integrate both spatial and molecular information. We applied it to a variety of public spatially resolved profiling datasets that were generated from 3 experimental platforms and consisted of cells from different diseases, tissues, and cell types. To compare the performances of SpaSNE, t-SNE, and UMAP, we applied them to 4 spatially resolved profiling datasets obtained from 3 distinct experimental platforms (Visium, STARmap, and MERFISH) on both diseased and normal tissues. Comparisons between SpaSNE and these state-of-the-art approaches reveal that SpaSNE achieves more accurate and meaningful visualization that better elucidates the underlying spatial and molecular data structures.

CONCLUSIONS

This work demonstrates the broad application of SpaSNE for reliable and robust interpretation of cell types based on both molecular and spatial information, which can set the foundation for many subsequent analysis steps, such as differential gene expression and trajectory or pseudotime analysis on the spatially resolved profiling data.

SMORE: spatial motifs reveal patterns in cellular architecture of complex tissues

Deciphering the link between tissue architecture and function requires methods to identify and interpret patterns in spatial arrangement of cells. We present SMORE, an approach to detect patterns in sequential arrangements of cells and examine their associated gene expression specializations. Applied to retina, brain, and embryonic tissue maps, SMORE identifies novel spatial motifs, including one that offers a new mechanism of action for type 1b bipolar cells. Structural signatures detected by SMORE also form a basis for classifying tissues. Together, our method provides a new framework for uncovering spatial complexity in tissue organization and offers novel insights into tissue function.

Characterizing cell-type spatial relationships across length scales in spatially resolved omics data

Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we present CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package. To demonstrate the utility of such multi-scale characterization, recapitulate expected cell-type spatial relationships, and evaluate against other cell-type spatial analyses, we apply CRAWDAD to various simulated and real SRO datasets of diverse tissues assayed by diverse SRO technologies. We further demonstrate how such multi-scale characterization enabled by CRAWDAD can be used to compare cell-type spatial relationships across multiple samples. Finally, we apply CRAWDAD to SRO datasets of the human spleen to identify consistent as well as patient and sample-specific cell-type spatial relationships. In general, we anticipate such multi-scale analysis of SRO data enabled by CRAWDAD will provide useful quantitative metrics to facilitate the identification, characterization, and comparison of cell-type spatial relationships across axes of interest.

Cooperative integration of spatially resolved multi-omics data with COSMOS

Recent advancements in biological technologies have enabled the measurement of spatially resolved multi-omics data, yet computational algorithms for this purpose are scarce. Existing tools target either single omics or lack spatial integration. We generate a graph neural network algorithm named COSMOS to address this gap and demonstrated the superior performance of COSMOS in domain segmentation, visualization, and spatiotemporal map for spatially resolved multi-omics data integration tasks.

A Selective Review of Network Analysis Methods for Gene Expression Data

With the development of high-throughput profiling techniques, gene expressions have drawn significant attention due to their important biological implications, widespread data availability, and promising biological findings. The complex interactions and regulations among genes naturally lead to a network structure, which can provide a global view of molecular mechanisms and biological processes. This chapter provides a selective overview of constructing gene expression networks and utilizing them in downstream analysis. It also includes a demonstrating example.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

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.

Computational Approaches in Spatial Transcriptomics for the Study of Mammalian Spermatogenesis

Spermatogenesis is a complex and dynamic cellular differentiation process critical to male fertility. Although the full continuum of gene expression patterns from spermatogonial stem cells (SSCs) to spermatozoa in steady state was characterized using single-cell RNA sequencing technologies, the transcriptional dynamics of spermatogenesis within its native tissue context was largely unexplored. The recent development of spatial transcriptomics (ST) technologies has transformed male fertility research from a single-cell level to a two-dimensional spatial coordinate system and facilitated the study of spermatogenesis in the native environment of both the rodent and human testes. The spatial gene expression information generated by these ST technologies requires new computational approaches to extract novel biological insights. These requirements include, but are not limited to, spatial mapping of testicular cell types, identifying spatially variable genes, and understanding the molecular cross-talk between testicular cell types. Here, we review computational approaches that have been used to dissect mammalian spermatogenesis in the context of ST. We also highlight new computational approaches that can be leveraged to reveal novel insights into male fertility.

SpaInGNN: Enhanced clustering and integration of spatial transcriptomics based on refined graph neural networks

Recent developments in spatial transcriptomics (ST) technology have markedly enhanced the proposed capacity to comprehensively characterize gene expression patterns within tissue microenvironments while crucially preserving spatial context. However, the identification of spatial domains at the single-cell level remains a significant challenge in elucidating biological processes. To address this, SpaInGNN was developed, a sophisticated graph neural network (GNN) framework that accurately delineates spatial domains by integrating spatial location data, histological information, and gene expression profiles into low-dimensional latent embeddings. Additionally, to fully leverage spatial coordinate data, spatial integration using graph neural network (SpaInGNN) refines the graph constructed for spatial locations by incorporating both tissue image distance and Euclidean distance, following a pre-clustering of gene expression profiles. This refined graph is then embedded using a self-supervised GNN, which minimizes self-reconfiguration loss. By applying SpaInGNN to refined graphs across multiple consecutive tissue slices, this study mitigates the impact of batch effects in data analysis. The proposed method demonstrates substantial improvements in the accuracy of spatial domain recognition, providing a more faithful representation of the tissue organization in both mouse olfactory bulb and human lateral prefrontal cortex samples.

Single-cell RNA-sequencing and spatial transcriptomic analysis reveal a distinct population of APOE- cells yielding pathological lymph node metastasis in papillary thyroid cancer

BACKGROUND

Thyroid cancer is one of the most common endocrine tumors worldwide, especially among women and the metastatic mechanism of papillary thyroid carcinoma remains poorly understood.

METHODS

Thyroid cancer tissue samples were obtained for single-cell RNA-sequencing and spatial transcriptomics, aiming to intratumoral and antimetastatic heterogeneity of advanced PTC. The functions of APOE in PTC cell proliferation and invasion were confirmed through in vivo and in vitro assays. Pseudotime analysis and CellChat were performed to explore the the molecular mechanisms of the APOE in PTC progression.

RESULTS

We identified a subpopulation of tumor cells with lower expression levels of APOE, associated with advanced stages of PTC and cervical metastasis. APOE overexpression significantly reduced tumor cell proliferation and invasion, both in vitro and in vivo, by activating the ABCA1-LXR axis. APOE- tumor cells may promote tumor growth by interacting with dendritic cells and CD4+ T cells via CD99- rather than CD6-regulated signaling. We established a machine learning-based scRNA-seq data, 13-gene signature predictive of lymph node metastasis.

CONCLUSIONS

We identified a distinct APOE- tumor cell population associated with cervical metastasis and poor prognosis. Our results and models have potential clinical, prognostic, and therapeutic implications for advanced PTC.

KEY POINTS

A subpopulation of tumor cells with lower expression levels of APOE was strongly associated with more advanced stages and metastasis of PTC. APOE-negative (APOE-) cellsoverall exhibited weaker interactions with immune cells. A machine-learning bioinformatics model based on scRNA-seq data of in-situ thyroid cancer tissue was established to predict lymph node metastasis.

SpaGRA: Graph augmentation facilitates domain identification for spatially resolved transcriptomics

Recent advances in spatially resolved transcriptomics (SRT) have provided new opportunities for characterizing spatial structures of various tissues. Graph-based geometric deep learning has gained widespread adoption for spatial domain identification tasks. Currently, most methods define adjacency relation between cells or spots by their spatial distance in SRT data, which overlooks key biological interactions like gene expression similarities, and leads to inaccuracies in spatial domain identification. To tackle this challenge, we propose a novel method, SpaGRA (https://github.com/sunxue-yy/SpaGRA), for automatic multi-relationship construction based on graph augmentation. SpaGRA uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks (GATs). This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive learning. Additionally, SpaGRA uses these multi-view relationships to construct negative samples, addressing sampling bias posed by random selection. Experimental results show that SpaGRA presents superior domain identification performance on multiple datasets generated from different protocols. Using SpaGRA, we analyze the functional regions in the mouse hypothalamus, identify key genes related to heart development in mouse embryos, and observe cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD data. Overall, SpaGRA can effectively characterize spatial structures across diverse SRT datasets.

Roles of Cellular Neighborhoods in Hepatocellular Carcinoma Pathogenesis

The development of hepatocellular carcinoma (HCC) involves an intricate interplay among various cell types within the liver. Unraveling the orchestration of these cells, particularly in the context of various etiologies, may hold the key to deciphering the underlying mechanisms of this complex disease. The advancement of single-cell and spatial technologies has revolutionized our ability to determine cellular neighborhoods and understand their crucial roles in disease pathogenesis. In this review, we highlight the current research landscape on cellular neighborhoods in chronic liver disease and HCC, as well as the emerging computational approaches applicable to delineate disease-associated cellular neighborhoods, which may offer insights into the molecular mechanisms underlying HCC pathogenesis and pave the way for effective disease interventions.

PCA-based spatial domain identification with state-of-the-art performance

MOTIVATION

The identification of biologically meaningful domains is a central step in the analysis of spatial transcriptomic data.

RESULTS

Following Occam's razor, we show that a simple PCA-based algorithm for unsupervised spatial domain identification rivals the performance of ten competing state-of-the-art methods across six single-cell spatial transcriptomic datasets. Our reductionist approach, NichePCA, provides researchers with intuitive domain interpretation and excels in execution speed, robustness, and scalability.

AVAILABILITY AND IMPLEMENTATION

The code is available at https://github.com/imsb-uke/nichepca.

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 .

Targeting the immune privilege of tumor-initiating cells to enhance cancer immunotherapy

Tumor-initiating cells (TICs) possess the ability to evade anti-tumor immunity, potentially explaining many failures of cancer immunotherapy. Here, we identify CD49f as a prominent marker for discerning TICs in hepatocellular carcinoma (HCC), outperforming other commonly used TIC markers. CD49f-high TICs specifically recruit tumor-promoting neutrophils via the CXCL2-CXCR2 axis and create an immunosuppressive milieu in the tumor microenvironment (TME). Reciprocally, the neutrophils reprogram nearby tumor cells toward a TIC phenotype via secreting CCL4. These cells can evade CD8+ T cell-mediated killing through CCL4/STAT3-induced and CD49f-stabilized CD155 expression. Notably, while aberrant CD155 expression contributes to immune suppression, it also represents a TIC-specific vulnerability. We demonstrate that either CD155 deletion or antibody blockade significantly enhances sensitivity to anti-PD-1 therapy in preclinical HCC models. Our findings reveal a new mechanism of tumor immune evasion and provide a rationale for combining CD155 blockade with anti-PD-1/PD-L1 therapy in HCC.

A comprehensive survey of dimensionality reduction and clustering methods for single-cell and spatial transcriptomics data

In recent years, the application of single-cell transcriptomics and spatial transcriptomics analysis techniques has become increasingly widespread. Whether dealing with single-cell transcriptomic or spatial transcriptomic data, dimensionality reduction and clustering are indispensable. Both single-cell and spatial transcriptomic data are often high-dimensional, making the analysis and visualization of such data challenging. Through dimensionality reduction, it becomes possible to visualize the data in a lower-dimensional space, allowing for the observation of relationships and differences between cell subpopulations. Clustering enables the grouping of similar cells into the same cluster, aiding in the identification of distinct cell subpopulations and revealing cellular diversity, providing guidance for downstream analyses. In this review, we systematically summarized the most widely recognized algorithms employed for the dimensionality reduction and clustering analysis of single-cell transcriptomic and spatial transcriptomic data. This endeavor provides valuable insights and ideas that can contribute to the development of novel tools in this rapidly evolving field.

A comprehensive review of approaches for spatial domain recognition of spatial transcriptomes

In current bioinformatics research, spatial transcriptomics (ST) as a rapidly evolving technology is gradually receiving widespread attention from researchers. Spatial domains are regions where gene expression and histology are consistent in space, and detecting spatial domains can better understand the organization and functional distribution of tissues. Spatial domain recognition is a fundamental step in the process of ST data interpretation, which is also a major challenge in ST analysis. Therefore, developing more accurate, efficient, and general spatial domain recognition methods has become an important and urgent research direction. This article aims to review the current status and progress of spatial domain recognition research, explore the advantages and limitations of existing methods, and provide suggestions and directions for future tool development.

NiCo identifies extrinsic drivers of cell state modulation by niche covariation analysis

Cell states are modulated by intrinsic driving forces such as gene expression noise and extrinsic signals from the tissue microenvironment. The distinction between intrinsic and extrinsic cell state determinants is essential for understanding the regulation of cell fate in tissues during development, homeostasis and disease. The rapidly growing availability of single-cell resolution spatial transcriptomics makes it possible to meet this challenge. However, available computational methods to infer topological tissue domains, spatially variable genes, or ligand-receptor interactions are limited in their capacity to capture cell state changes driven by crosstalk between individual cell types within the same niche. We present NiCo, a computational framework for integrating single-cell resolution spatial transcriptomics with matched single-cell RNA-sequencing reference data to infer the influence of the spatial niche on the cell state. By applying NiCo to mouse embryogenesis, adult small intestine and liver data, we demonstrate the ability to predict novel niche interactions that govern cell state variation underlying tissue development and homeostasis. In particular, NiCo predicts a feedback mechanism between Kupffer cells and neighboring stellate cells dampening stellate cell activation in the normal liver. NiCo provides a powerful tool to elucidate tissue architecture and to identify drivers of cellular states in local niches.

A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics

Spatial transcriptomics technologies enable researchers to accurately quantify and localize messenger ribonucleic acid (mRNA) transcripts at a high resolution while preserving their spatial context. The identification of spatial domains, or the task of spatial clustering, plays a crucial role in investigating data on spatial transcriptomes. One promising approach for classifying spatial domains involves the use of graph neural networks (GNNs) by leveraging gene expressions, spatial locations, and histological images. This study provided a comprehensive overview of the most recent GNN-based methods of spatial clustering methods for the analysis of data on spatial transcriptomics. We extensively evaluated the performance of current methods on prevalent datasets of spatial transcriptomics by considering their accuracy of clustering, robustness, data stabilization, relevant requirements, computational efficiency, and memory use. To this end, we explored 60 clustering scenarios by extending the essential frameworks of spatial clustering for the selection of the GNNs, algorithms of downstream clustering, principal component analysis (PCA)-based reduction, and refined methods of correction. We comparatively analyzed the performance of the methods in terms of spatial clustering to identify their limitations and outline future directions of research in the field. Our survey yielded novel insights, and provided motivation for further investigating spatial transcriptomics.

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.

A contrastive learning approach to integrate spatial transcriptomics and histological images

The rapid growth of spatially resolved transcriptomics technology provides new perspectives on spatial tissue architecture. Deep learning has been widely applied to derive useful representations for spatial transcriptome analysis. However, effectively integrating spatial multi-modal data remains challenging. Here, we present ConGcR, a contrastive learning-based model for integrating gene expression, spatial location, and tissue morphology for data representation and spatial tissue architecture identification. Graph convolution and ResNet were used as encoders for gene expression with spatial location and histological image inputs, respectively. We further enhanced ConGcR with a graph auto-encoder as ConGaR to better model spatially embedded representations. We validated our models using 16 human brains, four chicken hearts, eight breast tumors, and 30 human lung spatial transcriptomics samples. The results showed that our models generated more effective embeddings for obtaining tissue architectures closer to the ground truth than other methods. Overall, our models not only can improve tissue architecture identification's accuracy but also may provide valuable insights and effective data representation for other tasks in spatial transcriptome analyses.

Recent advances in spatially variable gene detection in spatial transcriptomics

With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial patterns across the tissue. SVG detection is an important task since many downstream analyses depend on these selected SVGs. Over the past few years, a plethora of new methods have been proposed for the detection of SVGs, accompanied by numerous innovative concepts and discussions. This article provides a selective review of methods and their practical implementations, offering valuable insights into the current literature in this field.

Spatial domains identification in spatial transcriptomics using modality-aware and subspace-enhanced graph contrastive learning

Spatial transcriptomics (ST) technologies have emerged as an effective tool to identify the spatial architecture of tissues, facilitating a comprehensive understanding of organ function and the tissue microenvironment. Spatial domain identification is the first and most critical step in ST data analysis, which requires thoughtful utilization of tissue microenvironment and morphological priors. Here, we propose a graph contrastive learning framework, GRAS4T, which combines contrastive learning and a subspace analysis model to accurately distinguish different spatial domains by capturing the tissue microenvironment through self-expressiveness of spots within the same domain. To uncover the pertinent features for spatial domain identification, GRAS4T employs a graph augmentation based on histological image priors, preserving structural information crucial for the clustering task. Experimental results on eight ST datasets from five different platforms show that GRAS4T outperforms five state-of-the-art competing methods. Significantly, GRAS4T excels at separating distinct tissue structures and unveiling more detailed spatial domains. GRAS4T combines the advantages of subspace analysis and graph representation learning with extensibility, making it an ideal framework for ST domain identification.

Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics

Embeddings derived from cell graphs hold significant potential for exploring spatial transcriptomics (ST) datasets. Nevertheless, existing methodologies rely on a graph structure defined by spatial proximity, which inadequately represents the diversity inherent in cell-cell interactions (CCIs). This study introduces STAGUE, an innovative framework that concurrently learns a cell graph structure and a low-dimensional embedding from ST data. STAGUE employs graph structure learning to parameterize and refine a cell graph adjacency matrix, enabling the generation of learnable graph views for effective contrastive learning. The derived embeddings and cell graph improve spatial clustering accuracy and facilitate the discovery of novel CCIs. Experimental benchmarks across 86 real and simulated ST datasets show that STAGUE outperforms 15 comparison methods in clustering performance. Additionally, STAGUE delineates the heterogeneity in human breast cancer tissues, revealing the activation of epithelial-to-mesenchymal transition and PI3K/AKT signaling in specific sub-regions. Furthermore, STAGUE identifies CCIs with greater alignment to established biological knowledge than those ascertained by existing graph autoencoder-based methods. STAGUE also reveals the regulatory genes that participate in these CCIs, including those enriched in neuropeptide signaling and receptor tyrosine kinase signaling pathways, thereby providing insights into the underlying biological processes.

AFSC: A self-supervised augmentation-free spatial clustering method based on contrastive learning for identifying spatial domains

Recent research in spatial transcriptomics allows researchers to analyze gene expression without losing spatial information. Spatial information can assist in cell communication, identification of new cell subtypes, which provides important research methods for multiple fields such as microenvironment interactions and pathological processes of diseases. Identifying spatial domains is an important step in spatial transcriptomics analysis, and improving spatial clustering methods can benefit for identifying spatial domains. In addition to eliminating noise in original gene expression, how to use spatial information to assist clustering has also become a new problem. A variety of calculating methods have been applied to spatial clustering, including contrastive learning methods. However, existing spatial clustering methods based on contrastive learning use data augmentation to generate positive and negative pairs, which will inevitably destroy the biological meaning of the data. We propose a new self-supervised spatial clustering method based on contrastive learning, Augmentation-Free Spatial Clustering (AFSC), which integrates spatial information and gene expression to learn latent representations. We construct a contrastive learning module without negative pairs or data augmentation by designing Teacher and Student Encoder. We also design an unsupervised clustering module to make clustering and contrastive learning be trained together. Experiments on multiple spatial transcriptomics datasets at different resolutions demonstrate that our method performs well in self-supervised spatial clustering tasks. Furthermore, the learned representations can be used for various downstream tasks including visualization and trajectory inference.

Computational modeling for deciphering tissue microenvironment heterogeneity from spatially resolved transcriptomics

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

STGAT: Graph attention networks for deconvolving spatial transcriptomics data

BACKGROUND AND OBJECTIVE

Spatially resolved gene expression profiles are crucial for understanding tissue structure and function. However, the lack of single-cell resolution in these profiles demands their integration with single-cell RNA sequencing data for accurate dataset deconvolution. We propose STGAT, an innovative deconvolution method that leverages graph attention networks to enhance spatial transcriptomic (ST) data analysis.

METHODS

STGAT generates pseudo-ST data that more comprehensively represents the cell-type composition within real-ST data by using three different sampling probabilities. A comprehensive combined graph is then constructed to capture the complex relationships both across pseudo- and real-ST data and within each dataset. Moreover, integrating a graph attention network further enables STGAT to dynamically assign the weights to the connections between spots, significantly enhancing the accuracy of cell-type composition predictions.

RESULTS

Extensive comparative experiments on simulated and real-world datasets, demonstrate the superior performance of STGAT for cell-type deconvolution. The method outperforms six established methods and is robust across various biological contexts.

CONCLUSION

STGAT exhibits more precise results in cell-type composition inference that are more consistent with the known knowledge, suggesting its potential utility in improving the resolution and accuracy of spatial transcriptomics data analysis.