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Recurrence and metastasis are the major issues for papillary thyroid cancer (PTC). Current morphological and molecular classification systems are not satisfied for PTC diagnosis due to lacking variant‐specific morphological criteria and high signal‐to‐noise in mutation‐based diagnosis, respectively. Importantly, intratumor heterogeneity is largely lost in current molecular classification system, which can be resolved by single cell RNA sequencing (scRNA‐seq). However, scRNA‐seq loses spatial information and morphological features. Herein, scRNA‐seq is integrated and spatially‐resolved transcriptomics (SRT) to elaborate the mechanisms underlying the spatial heterogeneity, malignancy and metastasis of PTCs by associating transcriptome and local morphology. This results demonstrated that PTC cells evolved with multiple routes, driven by the enhanced aerobic metabolism and the suppressed mRNA translation and protein synthesis and the involvement of cell–cell interaction. Two curated malignant and metastatic footprints can discriminate PTC cells from normal thyrocytes. Ferroptosis resistance contributed to PTC evolution. This results will advance the knowledge of intratumor spatial heterogeneity and evolution of PTCs at spatial and single‐cell levels, and propose better diagnostic strategy. Integrated analysis of scRNA‐seq and SRT, by associating transcriptome and local morphology, reveals that enhanced aerobic metabolism, suppressed mRNA translation and protein synthesis, and cell–cell interactions collectively drive the evolution of PTC cells. This finding further elucidates the mechanisms of spatial heterogeneity, malignancy, and metastasis of PTC.

Revealing fine-scale cellular heterogeneity among spatial context and the functional and structural foundations of tissue architecture is fundamental within biological research and pharmacology. Unlike traditional approaches involving single molecules or bulk omics, cutting-edge, spatially resolved transcriptomics techniques offer near-single-cell or even subcellular resolution within tissues. Massive information across higher dimensions along with position-coordinating labels can better map the whole 3D transcriptional landscape of tissues. In this review, we focus on developments and strategies in spatially resolved transcriptomics, compare the cell and gene throughput and spatial resolution in detail for existing methods, and highlight the enormous potential in biomedical research. To accurately reflect organ architecture, spatially resolved transcriptomics aims to provide spatial and expression information at the single cellular level for higher-order reconstruction.In silico methods combine single-cell RNA sequencing (scRNA-seq), in situ hybridization, and prior knowledge to reconstruct spatial transcriptomes of tissues but cannot match coordinates and tend to simplify.Laser capture microdissection (LCM)-based approaches allow full gene single-cell profiling plus position information, but assay only a few cells.RNA imaging provides the expression landscape for millions of cells in situ but detects only targeted transcripts.In situ sequencing provides spatial whole genome-wide expression at the micron level by combining barcoding with NGS but fails to describe individual cells.

Background Spatially-resolved transcriptomics has now enabled the quantification of high-throughput and transcriptome-wide gene expression in intact tissue while also retaining the spatial coordinates. Incorporating the precise spatial mapping of gene activity advances our understanding of intact tissue-specific biological processes. In order to interpret these novel spatial data types, interactive visualization tools are necessary. Results We describe spatialLIBD , an R/Bioconductor package to interactively explore spatially-resolved transcriptomics data generated with the 10x Genomics Visium platform. The package contains functions to interactively access, visualize, and inspect the observed spatial gene expression data and data-driven clusters identified with supervised or unsupervised analyses, either on the user’s computer or through a web application. Conclusions spatialLIBD is available at https://bioconductor.org/packages/spatialLIBD . It is fully compatible with SpatialExperiment and the Bioconductor ecosystem. Its functionality facilitates analyzing and interactively exploring spatially-resolved data from the Visium platform.

Current clustering analysis of spatial transcriptomics data primarily relies on molecular information and fails to fully exploit the morphological features present in histology images, leading to compromised accuracy and interpretability. To overcome these limitations, we have developed a multi-stage statistical method called iIMPACT. It identifies and defines histology-based spatial domains based on AI-reconstructed histology images and spatial context of gene expression measurements, and detects domain-specific differentially expressed genes. Through multiple case studies, we demonstrate iIMPACT outperforms existing methods in accuracy and interpretability and provides insights into the cellular spatial organization and landscape of functional genes within spatial transcriptomics data.

Bioinformatics is a rapidly evolving field charged with cataloging, disseminating, and analyzing biological data. Bioinformatics started with genomics, but while genomics focuses more narrowly on the genes comprising a genome, bioinformatics now encompasses a much broader range of omics technologies. Overcoming barriers of scale and effort that plagued earlier sequencing methods, bioinformatics adopted an ambitious strategy involving high-throughput and highly automated assays. However, as the list of omics technologies continues to grow, the field of bioinformatics has changed in two fundamental ways. Despite enormous success in expanding our understanding of the biological world, the failure of bulk methods to account for biologically important variability among cells of the same or different type has led to a major shift toward single-cell and spatially resolved omics methods, which attempt to disentangle the conflicting signals contained in heterogeneous samples by examining individual cells or cell clusters. The second major shift has been the attempt to integrate two or more different classes of omics data in a single multimodal analysis to identify patterns that bridge biological layers. For example, unraveling the cause of disease may reveal a metabolite deficiency caused by the failure of an enzyme to be phosphorylated because a gene is not expressed due to aberrant methylation as a result of a rare germline variant. Conclusions: There is a fine line between superficial understanding and analysis paralysis, but like a detective novel, multi-omics increasingly provides the clues we need, if only we are able to see them.

Abstract Microorganisms have been detected in various tumors, and research on the tumor microbiome has received increasing attention. However, the investigation of the cancer microbiome at the spatial resolution level remains a challenging issue. The emergence of spatially resolved transcriptomics technology has enabled to map transcripts at the single-cell resolution in various cancer types. Here, we constructed a comprehensive spatial meta-transcriptome resource by manually curating 203 fresh frozen slices from 20 cancer types encompassing 334,253 spots and 1,908,646 cells. A spatial meta-transcriptome database (SMTdb; http://bio-bigdata.hrbmu.edu.cn/SMTdb/) was constructed to provide detailed insights into the abundance, distribution, and enriched tumor microenvironment (TME) regions of 1,218 microbiota in spatial tissue slices. SMTdb enables to explore the vast interactive data of spatial distribution and expression of microbiota, provides host gene modules associated with certain microbiota, and contains data on the co-occurrence between the microbiota and immune cells within the TME. The atlas resource serves as a comprehensive and structured platform to investigate the interactions between microbial ecosystems and hosts in cancer. Graphical Abstract Graphical Abstract

Spatially resolved transcriptomics (SRT) facilitates the study of cell–cell interactions within native tissue environments. To support method development and benchmarking, we introduce sCCIgen, a real-data-based simulator that generates high-fidelity synthetic SRT data with known interaction features. sCCIgen preserves transcriptomic and spatial characteristics and provides key interaction features, including cell colocalization, spatial dependence of gene expression, and gene–gene interactions between neighboring cells. It supports input from SRT data, single-cell expression data alone, and unpaired expression and spatial data. sCCIgen is interactive, user-friendly, reproducible, and well-documented for studying cellular interactions and spatial biology.

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.

Spatially resolved transcriptomics (SRT) for characterizing spatial cellular heterogeneities in tissue environments requires systematic analytical approaches to elucidate gene expression variations within their physiological context. Here, we introduce SpaSEG, an unsupervised deep learning model utilizing convolutional neural networks for multiple SRT analysis tasks. Extensive evaluations across diverse SRT datasets generated by various platforms demonstrate SpaSEG’s superior robustness and efficiency compared to existing methods. In the application analysis of invasive ductal carcinoma, SpaSEG successfully unravels intratumoral heterogeneity and delivers insights into immunoregulatory mechanisms. These results highlight SpaSEG’s substantial potential for exploring tissue architectures and pathological biology.

Background Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have led to groundbreaking advancements in life sciences. To develop bioinformatics tools for scRNA-seq and SRT data and perform unbiased benchmarks, data simulation has been widely adopted by providing explicit ground truth and generating customized datasets. However, the performance of simulation methods under multiple scenarios has not been comprehensively assessed, making it challenging to choose suitable methods without practical guidelines. Results We systematically evaluated 49 simulation methods developed for scRNA-seq and/or SRT data in terms of accuracy, functionality, scalability, and usability using 152 reference datasets derived from 24 platforms. SRTsim, scDesign3, ZINB-WaVE, and scDesign2 have the best accuracy performance across various platforms. Unexpectedly, some methods tailored to scRNA-seq data have potential compatibility for simulating SRT data. Lun, SPARSim, and scDesign3-tree outperform other methods under corresponding simulation scenarios. Phenopath, Lun, Simple, and MFA yield high scalability scores but they cannot generate realistic simulated data. Users should consider the trade-offs between method accuracy and scalability (or functionality) when making decisions. Additionally, execution errors are mainly caused by failed parameter estimations and appearance of missing or infinite values in calculations. We provide practical guidelines for method selection, a standard pipeline Simpipe ( https://github.com/duohongrui/simpipe ; https://doi.org/10.5281/zenodo.11178409 ), and an online tool Simsite ( https://www.ciblab.net/software/simshiny/ ) for data simulation. Conclusions No method performs best on all criteria, thus a good-yet-not-the-best method is recommended if it solves problems effectively and reasonably. Our comprehensive work provides crucial insights for developers on modeling gene expression data and fosters the simulation process for users.