Enhancing Medical Imaging Segmentation with GB-SAM: A Novel Approach to Tissue Segmentation Using Granular Box Prompts.

Recent advances in foundation models have revolutionized model development in digital pathology, reducing dependence on extensive manual annotations required by traditional methods. The ability of foundation models to generalize well with few-shot learning addresses critical barriers in adapting models to diverse medical imaging tasks. This work presents the Granular Box Prompt Segment Anything Model (GB-SAM), an improved version of the Segment Anything Model (SAM) fine-tuned using granular box prompts with limited training data. The GB-SAM aims to reduce the dependency on expert pathologist annotators by enhancing the efficiency of the automated annotation process. Granular box prompts are small box regions derived from ground truth masks, conceived to replace the conventional approach of using a single large box covering the entire H&E-stained image patch. This method allows a localized and detailed analysis of gland morphology, enhancing the segmentation accuracy of individual glands and reducing the ambiguity that larger boxes might introduce in morphologically complex regions. We compared the performance of our GB-SAM model against U-Net trained on different sizes of the CRAG dataset. We evaluated the models across histopathological datasets, including CRAG, GlaS, and Camelyon16. GB-SAM consistently outperformed U-Net, with reduced training data, showing less segmentation performance degradation. Specifically, on the CRAG dataset, GB-SAM achieved a Dice coefficient of 0.885 compared to U-Net's 0.857 when trained on 25% of the data. Additionally, GB-SAM demonstrated segmentation stability on the CRAG testing dataset and superior generalization across unseen datasets, including challenging lymph node segmentation in Camelyon16, which achieved a Dice coefficient of 0.740 versus U-Net's 0.491. Furthermore, compared to SAM-Path and Med-SAM, GB-SAM showed competitive performance. GB-SAM achieved a Dice score of 0.900 on the CRAG dataset, while SAM-Path achieved 0.884. On the GlaS dataset, Med-SAM reported a Dice score of 0.956, whereas GB-SAM achieved 0.885 with significantly less training data. These results highlight GB-SAM's advanced segmentation capabilities and reduced dependency on large datasets, indicating its potential for practical deployment in digital pathology, particularly in settings with limited annotated datasets.

A critical assessment of using ChatGPT for extracting structured data from clinical notes.

Existing natural language processing (NLP) methods to convert free-text clinical notes into structured data often require problem-specific annotations and model training. This study aims to evaluate ChatGPT's capacity to extract information from free-text medical notes efficiently and comprehensively. We developed a large language model (LLM)-based workflow, utilizing systems engineering methodology and spiral "prompt engineering" process, leveraging OpenAI's API for batch querying ChatGPT. We evaluated the effectiveness of this method using a dataset of more than 1000 lung cancer pathology reports and a dataset of 191 pediatric osteosarcoma pathology reports, comparing the ChatGPT-3.5 (gpt-3.5-turbo-16k) outputs with expert-curated structured data. ChatGPT-3.5 demonstrated the ability to extract pathological classifications with an overall accuracy of 89%, in lung cancer dataset, outperforming the performance of two traditional NLP methods. The performance is influenced by the design of the instructive prompt. Our case analysis shows that most misclassifications were due to the lack of highly specialized pathology terminology, and erroneous interpretation of TNM staging rules. Reproducibility shows the relatively stable performance of ChatGPT-3.5 over time. In pediatric osteosarcoma dataset, ChatGPT-3.5 accurately classified both grades and margin status with accuracy of 98.6% and 100% respectively. Our study shows the feasibility of using ChatGPT to process large volumes of clinical notes for structured information extraction without requiring extensive task-specific human annotation and model training. The results underscore the potential role of LLMs in transforming unstructured healthcare data into structured formats, thereby supporting research and aiding clinical decision-making.

Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes.

Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.

Interaction between the gut microbiota and colonic enteroendocrine cells regulates host metabolism.

Nutrient handling is an essential function of the gastrointestinal tract. Hormonal responses of small intestinal enteroendocrine cells (EECs) have been extensively studied but much less is known about the role of colonic EECs in metabolic regulation. To address this core question, we investigated a mouse model deficient in colonic EECs. Here we show that colonic EEC deficiency leads to hyperphagia and obesity. Furthermore, colonic EEC deficiency results in altered microbiota composition and metabolism, which we found through antibiotic treatment, germ-free rederivation and transfer to germ-free recipients, to be both necessary and sufficient for the development of obesity. Moreover, studying stool and blood metabolomes, we show that differential glutamate production by intestinal microbiota corresponds to increased appetite and that colonic glutamate administration can directly increase food intake. These observations shed light on an unanticipated host-microbiota axis in the colon, part of a larger gut-brain axis, that regulates host metabolism and body weight.

Deuteration-enhanced neutron contrasts to probe amorphous domain sizes in organic photovoltaic bulk heterojunction films.

An organic photovoltaic bulk heterojunction comprises of a mixture of donor and acceptor materials, forming a semi-crystalline thin film with both crystalline and amorphous domains. Domain sizes critically impact the device performance; however, conventional X-ray scattering techniques cannot detect the contrast between donor and acceptor materials within the amorphous intermixing regions. In this study, we employ neutron scattering and targeted deuteration of acceptor materials to enhance the scattering contrast by nearly one order of magnitude. Remarkably, the PM6:deuterated Y6 system reveals a new length scale, indicating short-range aggregation of Y6 molecules in the amorphous intermixing regions. All-atom molecular dynamics simulations confirm that this short-range aggregation is an inherent morphological advantage of Y6 which effectively assists charge extraction and suppresses charge recombination as shown by capacitance spectroscopy. Our findings uncover the amorphous nanomorphology of organic photovoltaic thin films, providing crucial insights into the morphology-driven device performance.

Susceptible bacteria can survive antibiotic treatment in the mammalian gastrointestinal tract without evolving resistance.

Antibiotic resistance and evasion are incompletely understood and complicated by the fact that murine interval dosing models do not fully recapitulate antibiotic pharmacokinetics in humans. To better understand how gastrointestinal bacteria respond to antibiotics, we colonized germ-free mice with a pan-susceptible genetically barcoded Escherichia coli clinical isolate and administered the antibiotic cefepime via programmable subcutaneous pumps, allowing closer emulation of human parenteral antibiotic dynamics. E. coli was only recovered from intestinal tissue, where cefepime concentrations were still inhibitory. Strikingly, "some" E. coli isolates were not cefepime resistant but acquired mutations in genes involved in polysaccharide capsular synthesis increasing their invasion and survival within human intestinal cells. Deleting wbaP involved in capsular polysaccharide synthesis mimicked this phenotype, allowing increased invasion of colonocytes where cefepime concentrations were reduced. Additionally, "some" mutant strains exhibited a persister phenotype upon further cefepime exposure. This work uncovers a mechanism allowing "select" gastrointestinal bacteria to evade antibiotic treatment.

End Group Engineering for Constructing A-D-A Fused-Ring Photosensitizers with Balanced Phototheranostics Performance.

Phototheranostics continues to flourish in cancer treatment. Due to the competitive relationships between these photophysical processes of fluorescence emission, photothermal conversion, and photodynamic action, it is critical to balance them through subtle photosensitizer designs. Herein, it is provided a useful guideline for constructing A-D-A photosensitizers with superior phototheranostics performance. Various cyanoacetate group-modified end groups containing ester side chains of different length are designed to construct a series of A-D-A photosensitizers (F8CA1 ∼ F8CA4) to study the structure-property relationships. It is surprising to find that the photophysical properties of A-D-A photosensitizers can be precisely regulated by these tiny structural changes. The results reveal that the increase in the steric hindrance of ester side chains has positive impacts on their photothermal conversion capabilities, but adverse impacts on the fluorescence emission and photodynamic activities. Notably, these tiny structural changes lead to their different aggregation behavior. The molecule mechanisms are detailedly explained by theoretical calculations. Finally, F8CA2 nanoparticles with more balanced photophysical properties perform well in fluorescence imaging-guided photothermal and type I&II photodynamic synergistic cancer therapy, even under hypoxic conditions. Therefore, this work provides a novel practicable construction strategy for desired A-D-A photosensitizers.

Whole genome sequencing of 4,787 individuals identifies gene-based rare variants in age-related macular degeneration.

Genome-wide association studies have contributed extensively to the discovery of disease-associated common variants. However, the genetic contribution to complex traits is still largely difficult to interpret. We report a genome-wide association study of 2394 cases and 2393 controls for age-related macular degeneration (AMD) via whole-genome sequencing, with 46.9 million genetic variants. Our study reveals significant single-variant association signals at four loci and independent gene-based signals in CFH, C2, C3, and NRTN. Using data from the Exome Aggregation Consortium (ExAC) for a gene-based test, we demonstrate an enrichment of predicted rare loss-of-function variants in CFH, CFI, and an as-yet unreported gene in AMD, ORMDL2. Our method of using a large variant list without individual-level genotypes as an external reference provides a flexible and convenient approach to leverage the publicly available variant datasets to augment the search for rare variant associations, which can explain additional disease risk in AMD.

Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images.

Recent advancements in tissue imaging techniques have facilitated the visualization and identification of various cell types within physiological and pathological contexts. Despite the emergence of cell-cell interaction studies, there is a lack of methods for evaluating individual spatial interactions. In this study, we introduce Ceograph, a cell spatial organization-based graph convolutional network designed to analyze cell spatial organization (for example,. the cell spatial distribution, morphology, proximity, and interactions) derived from pathology images. Ceograph identifies key cell spatial organization features by accurately predicting their influence on patient clinical outcomes. In patients with oral potentially malignant disorders, our model highlights reduced structural concordance and increased closeness in epithelial substrata as driving features for an elevated risk of malignant transformation. In lung cancer patients, Ceograph detects elongated tumor nuclei and diminished stroma-stroma closeness as biomarkers for insensitivity to EGFR tyrosine kinase inhibitors. With its potential to predict various clinical outcomes, Ceograph offers a deeper understanding of biological processes and supports the development of personalized therapeutic strategies.

Osteosarcoma Explorer: A Data Commons With Clinical, Genomic, Protein, and Tissue Imaging Data for Osteosarcoma Research.

PURPOSE: Osteosarcoma research advancement requires enhanced data integration across different modalities and sources. Current osteosarcoma research, encompassing clinical, genomic, protein, and tissue imaging data, is hindered by the siloed landscape of data generation and storage. MATERIALS AND METHODS: Clinical, molecular profiling, and tissue imaging data for 573 patients with pediatric osteosarcoma were collected from four public and institutional sources. A common data model incorporating standardized terminology was created to facilitate the transformation, integration, and load of source data into a relational database. On the basis of this database, a data commons accompanied by a user-friendly web portal was developed, enabling various data exploration and analytics functions. RESULTS: The Osteosarcoma Explorer (OSE) was released to the public in 2021. Leveraging a comprehensive and harmonized data set on the backend, the OSE offers a wide range of functions, including Cohort Discovery, Patient Dashboard, Image Visualization, and Online Analysis. Since its initial release, the OSE has experienced an increasing utilization by the osteosarcoma research community and provided solid, continuous user support. To our knowledge, the OSE is the largest (N = 573) and most comprehensive research data commons for pediatric osteosarcoma, a rare disease. This project demonstrates an effective framework for data integration and data commons development that can be readily applied to other projects sharing similar goals. CONCLUSION: The OSE offers an online exploration and analysis platform for integrated clinical, molecular profiling, and tissue imaging data of osteosarcoma. Its underlying data model, database, and web framework support continuous expansion onto new data modalities and sources.

OVOL2 sustains postnatal thymic epithelial cell identity.

Distinct pathways and molecules may support embryonic versus postnatal thymic epithelial cell (TEC) development and maintenance. Here, we identify a mechanism by which TEC numbers and function are maintained postnatally. A viable missense allele (C120Y) of Ovol2, expressed ubiquitously or specifically in TECs, results in lymphopenia, in which T cell development is compromised by loss of medullary TECs and dysfunction of cortical TECs. We show that the epithelial identity of TECs is aberrantly subverted towards a mesenchymal state in OVOL2-deficient mice. We demonstrate that OVOL2 inhibits the epigenetic regulatory BRAF-HDAC complex, specifically disrupting RCOR1-LSD1 interaction. This causes inhibition of LSD1-mediated H3K4me2 demethylation, resulting in chromatin accessibility and transcriptional activation of epithelial genes. Thus, OVOL2 controls the epigenetic landscape of TECs to enforce TEC identity. The identification of a non-redundant postnatal mechanism for TEC maintenance offers an entry point to understanding thymic involution, which normally begins in early adulthood.

Genetic determinants underlying the progressive phenotype of ß-lactam/ß-lactamase inhibitor resistance in Escherichia coli.

IMPORTANCE: The increased feasibility of whole-genome sequencing has generated significant interest in using such molecular diagnostic approaches to characterize difficult-to-treat, antimicrobial-resistant (AMR) infections. Nevertheless, there are current limitations in the accurate prediction of AMR phenotypes based on existing AMR gene database approaches, which primarily correlate a phenotype with the presence/absence of a single AMR gene. Our study utilized a large cohort of cephalosporin-susceptible Escherichia coli bacteremia samples to determine how increasing the dosage of narrow-spectrum ß-lactamase-encoding genes in conjunction with other diverse ß-lactam/ß-lactamase inhibitor (BL/BLI) genetic determinants contributes to progressively more severe BL/BLI phenotypes. We were able to characterize the complexity of the genetic mechanisms underlying progressive BL/BLI resistance including the critical role of ß-lactamase encoding gene amplification. For the diverse array of AMR phenotypes with complex mechanisms involving multiple genomic factors, our study provides an example of how composite risk scores may improve understanding of AMR genotype/phenotype correlations.

Unsupervised domain adaptation for nuclei segmentation: Adapting from hematoxylin & eosin stained slides to immunohistochemistry stained slides using a curriculum approach.

BACKGROUND AND OBJECTIVE: Unsupervised domain adaptation (UDA) is a powerful approach in tackling domain discrepancies and reducing the burden of laborious and error-prone pixel-level annotations for instance segmentation. However, the domain adaptation strategies utilized in previous instance segmentation models pool all the labeled/detected instances together to train the instance-level GAN discriminator, which neglects the differences among multiple instance categories. Such pooling prevents UDA instance segmentation models from learning categorical correspondence between source and target domains for accurate instance classification; METHODS: To tackle this challenge, we propose an Instance Segmentation CycleGAN (ISC-GAN) algorithm for UDA multiclass-instance segmentation. We conduct extensive experiments on the multiclass nuclei recognition task to transfer knowledge from hematoxylin and eosin to immunohistochemistry stained pathology images. Specifically, we fuse CycleGAN with Mask R-CNN to learn categorical correspondence with image-level domain adaptation and virtual supervision. Moreover, we utilize Curriculum Learning to separate the learning process into two steps: (1) learning segmentation only on labeled source data, and (2) learning target domain segmentation with paired virtual labels generated by ISC-GAN. The performance was further improved through experiments with other strategies, including Shared Weights, Knowledge Distillation, and Expanded Source Data. RESULTS: Comparing to the baseline model or the three UDA instance detection and segmentation models, ISC-GAN illustrates the state-of-the-art performance, with 39.1% average precision and 48.7% average recall. The source codes of ISC-GAN are available at https://github.com/sdw95927/InstanceSegmentation-CycleGAN. CONCLUSION: ISC-GAN adapted knowledge from hematoxylin and eosin to immunohistochemistry stained pathology images, suggesting the potential for reducing the need for large annotated pathological image datasets in deep learning and computer vision tasks.

ScopeViewer: A Browser-Based Solution for Visualizing Spatial Transcriptomics Data.

Motivation: Spatial transcriptomics (ST) enables a high-resolution interrogation of molecular characteristics within specific spatial contexts and tissue morphology. Despite its potential, visualization of ST data is a challenging task due to the complexities in handling, sharing and visualizing large image datasets together with molecular information. Results: We introduce ScopeViewer, a browser-based software designed to overcome these challenges. ScopeViewer offers the following functionalities: (1) It visualizes large image data and associated annotations at various zoom levels, allowing for intricate exploration of the data; (2) It enables dual interactive viewing of the original images along with their annotations, providing a comprehensive understanding of the context; (3) It displays spatial molecular features with optimized bandwidth, ensuring a smooth user experience; and (4) It bolsters data security by circumventing data transfers. Availability: ScopeViewer is available at: https://datacommons.swmed.edu/scopeviewer.

Deep Learning of Cell Spatial Organizations Identifies Clinically Relevant Insights in Tissue Images.

Recent advancements in tissue imaging techniques have facilitated the visualization and identification of various cell types within physiological and pathological contexts. Despite the emergence of cell-cell interaction studies, there is a lack of methods for evaluating individual spatial interactions. In this study, we introduce Ceograph, a novel cell spatial organization-based graph convolutional network designed to analyze cell spatial organization (i.e. the cell spatial distribution, morphology, proximity, and interactions) derived from pathology images. Ceograph identifies key cell spatial organization features by accurately predicting their influence on patient clinical outcomes. In patients with oral potentially malignant disorders, our model highlights reduced structural concordance and increased closeness in epithelial substrata as driving features for an elevated risk of malignant transformation. In lung cancer patients, Ceograph detects elongated tumor nuclei and diminished stroma-stroma closeness as biomarkers for insensitivity to EGFR tyrosine kinase inhibitors. With its potential to predict various clinical outcomes, Ceograph offers a deeper understanding of biological processes and supports the development of personalized therapeutic strategies.

Ceftolozane/tazobactam heteroresistance in cystic fibrosis-related Pseudomonas aeruginosa infections.

Objectives: Cystic fibrosis (CF) patients are often colonized with Pseudomonas aeruginosa. During treatment, P. aeruginosa can develop subpopulations exhibiting variable in vitro antimicrobial (ABX) susceptibility patterns. Heteroresistance (HR) may underlie reported discrepancies between in vitro susceptibility results and clinical responses to various ABXs. Here, we sought to examine the presence and nature of P. aeruginosa polyclonal HR (PHR) and monoclonal HR (MHR) to ceftolozane/tazobactam in isolates originating from CF pulmonary exacerbations. Methods: This was a single-centre, non-controlled study. Two hundred and forty-six P. aeruginosa isolates from 26 adult CF patients were included. PHR was defined as the presence of different ceftolozane/tazobactam minimum inhibitory concentration (MIC) values among P. aeruginosa isolates originating from a single patient specimen. Population analysis profiles (PAPs) were performed to assess the presence of MHR, defined as ≥4-fold change in the ceftolozane/tazobactam MIC from a single P. aeruginosa colony. Results: Sixteen of 26 patient specimens (62%) contained PHR P. aeruginosa populations. Of these 16 patients, 6 (23%) had specimens in which PHR P. aeruginosa isolates exhibited ceftolozane/tazobactam MICs with categorical differences (i.e. susceptible versus resistant) compared to results reported as part of routine care. One isolate, PSA 1311, demonstrated MHR. Canonical ceftolozane/tazobactam resistance genes were not found in the MHR isolates (MHR PSA 1311 or PHR PSA 6130). Conclusions: Ceftolozane/tazobactam PHR exists among P. aeruginosa isolates in this work, and approximately a quarter of these populations contained isolates with ceftolozane/tazobactam susceptibiilty interpretations different from what was reported clinically, supporting concerns surrounding the utility of traditional susceptibility testing methodology in the setting of CF specimens. Genome sequencing of isolates with acquired MHR to ceftolozane/tazobactam revealed variants of unknown significance. Future work will be centred on determining the significance of these mutations to better understand these data in clinical context.

IsoAnalytics: a single-cell proteomics web server.

Motivation: Single-cell proteomics provide unprecedented resolution to examine biological processes. Customized data analysis and facile data visualization are crucial for scientific discovery. Further, user-friendly data analysis and visualization software that is easily accessible for the general scientific community is essential. Results: We have created a web server, IsoAnalytics, that gives users without computational or bioinformatics background the ability to directly analyze and interactively visualize data obtained from the Isoplexis single cell technology platform. We envision this open-sourced web server will increase research productivity and serve as a free, competitive alternative for single-cell proteomics research. Availability and implementation: IsoAnalytics is free and available at: https://cdc.biohpc.swmed.edu/isoplexis/ and is implemented in Python, with all major browsers supported. Code for IsoAnalytics is free and available at: https://github.com/zhanxw/Isoplexis_Data_Analysis. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization.

Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research. However, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedures and produces massive data that capture tumor histologic details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nucleus segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nucleus segmentation and TME quantification within image patches. However, existing algorithms are computationally intensive and time consuming for WSI analysis. This study presents Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nucleus segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing WSI analysis methods in nucleus detection, classification accuracy, and computation time. We validated the advantages of the system on 3 different tissue types: lung cancer, liver cancer, and breast cancer. For breast cancer, nucleus features by HD-Yolo were more prognostically significant than both the estrogen receptor status by immunohistochemistry and the progesterone receptor status by immunohistochemistry. The WSI analysis pipeline and a real-time nucleus segmentation viewer are available at https://github.com/impromptuRong/hd_wsi.

Immune checkpoint blockade induces gut microbiota translocation that augments extraintestinal antitumor immunity.

Gut microbiota, specifically gut bacteria, are critical for effective immune checkpoint blockade therapy (ICT) for cancer. The mechanisms by which gut microbiota augment extraintestinal anticancer immune responses, however, are largely unknown. Here, we find that ICT induces the translocation of specific endogenous gut bacteria into secondary lymphoid organs and subcutaneous melanoma tumors. Mechanistically, ICT induces lymph node remodeling and dendritic cell (DC) activation, which facilitates the translocation of a selective subset of gut bacteria to extraintestinal tissues to promote optimal antitumor T cell responses in both the tumor-draining lymph nodes (TDLNs) and the primary tumor. Antibiotic treatment results in decreased gut microbiota translocation into mesenteric lymph nodes (MLNs) and TDLNs, diminished DC and effector CD8+ T cell responses, and attenuated responses to ICT. Our findings illuminate a key mechanism by which gut microbiota promote extraintestinal anticancer immunity.

Spatial Transcriptomics Arena (STAr): an Integrated Platform for Spatial Transcriptomics Methodology Research.

The emerging field of spatially resolved transcriptomics (SRT) has revolutionized biomedical research. SRT quantifies expression levels at different spatial locations, providing a new and powerful tool to interrogate novel biological insights. An essential question in the analysis of SRT data is to identify spatially variable (SV) genes; the expression levels of such genes have spatial variation across different tissues. SV genes usually play an important role in underlying biological mechanisms and tissue heterogeneity. Currently, several computational methods have been developed to detect such genes; however, there is a lack of unbiased assessment of these approaches to guide researchers in selecting the appropriate methods for their specific biomedical applications. In addition, it is difficult for researchers to implement different existing methods for either biological study or methodology development. Furthermore, currently available public SRT datasets are scattered across different websites and preprocessed in different ways, posing additional obstacles for quantitative researchers developing computational methods for SRT data analysis. To address these challenges, we designed Spatial Transcriptomics Arena (STAr), an open platform comprising 193 curated datasets from seven technologies, seven statistical methods, and analysis results. This resource allows users to retrieve high-quality datasets, apply or develop spatial gene detection methods, as well as browse and compare spatial gene analysis results. It also enables researchers to comprehensively evaluate SRT methodology research in both simulated and real datasets. Altogether, STAr is an integrated research resource intended to promote reproducible research and accelerate rigorous methodology development, which can eventually lead to an improved understanding of biological processes and diseases. STAr can be accessed at https://lce.biohpc.swmed.edu/star/ .