1Spatiotemporal Stereo-seq reveals strain-specific and persistent neuroinflammation and neurodegeneration in long COVIDSource: STOmics DB (ID: STT0000161 )

Stereo-seq data of hamster brains after SARS-CoV-2 infection.

2stRNA Identifies Zonated Ferroptosis of Medullary Ray S3 Proximal Tubule Cells as the Hallmark of Viral NephropathiesSource: STOmics DB (ID: STT0000164 )

This study utilized 10x Visium HD spatial transcriptomics to profile sagittal sections of kidneys from control, H1N1-infected, and SARS-CoV-2-infected mouse models, investigating the association between renal zonation and viral pathogenesis. Spatial mapping of transcriptomic data defined eleven major cell clusters and accurately annotated them to distinct anatomical regions of the kidney. We further identified three spatially resolved subtypes of proximal tubule cells (PTCs): an S1 cluster (marked by Slc5a2, Slc5a12) in the cortical labyrinth, an S2 cluster (marked by Slc22a6, Slc13a3) in the cortical labyrinth-medullary ray transition region, and an S3 cluster (marked by Slc5a10, Atp11a) in the medullary ray. Analysis revealed a gradient of injury, with severity increasing from S1 to S3, as evidenced by the zonal upregulation of injury biomarkers Havcr1 (KIM-1) and Nqo. To define the cell death pathways underlying this zonated injury, we applied the AddModuleScore analysis to assess key mechanisms. While multiple forms of cell death were detected, ferroptosis demonstrated the highest activity score in infected mice. Crucially, ferroptosis activity was exclusively and significantly enriched within the corticomedullary junction (CMJ), the region where the vulnerable S3 PTCs are localized. In contrast, apoptosis, necroptosis, and pyroptosis lacked this distinct spatial preference. In conclusion, our spatial transcriptomics analysis pinpoints the selective induction of ferroptosis in the medullary ray S3 PTCs as the dominant and spatially restricted cell death signature driving viral-associated acute kidney injury, providing a novel mechanistic insight into the zonated pathology of viral nephropathies.

3Single-cell spatial multi-omics research on gastrointestinal tumorsSource: STOmics DB (ID: STT0000222 )

Colorectal and gastric cancers are common, high-incidence malignancies of the digestive tract, ranking among the top five cancers worldwide in both incidence and mortality. Based on SURF, a foundation model developed for matched spatial transcriptomics at pixel-level resolution, this study aims to develop artificial intelligence algorithms for colorectal cancer-related downstream applications.By leveraging spatial omics to enhance H&E-based intelligent diagnosis and fully integrating the advantages of big data and AI, the project seeks to create a clinically competitive pathology diagnostic product. This multi-omics pathology data resource links histology, spatial transcriptomics, and cell-level annotations to support reproducible biomedical research.

4Maternal microbiota shapes brain and neuroendocrine development in pigletsSource: STOmics DB (ID: STT0000218 )

This study investigates how maternal microbiota exposure is associated with brain and neuroendocrine development in piglets. Using germ-free and maternal microbiota-transplanted piglet models, we generated an integrated multi-organ dataset combining single-nucleus RNA sequencing, spatial transcriptomics, intestinal bulk RNA sequencing, and metabolomics. The dataset covers five brain regions, the hypothalamic-pituitary-adrenal axis, and multiple intestinal segments, providing a resource for investigating microbiota-associated cellular, transcriptional, spatial, and metabolic changes during early postnatal development.

5Spatiotemporal transcriptomic maps of mouse intracerebral hemorrhage at single-cell resolutionSource: STOmics DB (ID: STT0000047 )

Intracerebral hemorrhage (ICH) is a prevalent disease with high mortality. Despite advances in clinical care, the prognosis of ICH remains poor due to an incomplete understanding of the complex pathological processes. To address this challenge, we generated single-cell resolution spatiotemporal transcriptomic maps of mouse brain after ICH. This data set is the most extended resource available that provides detailed information about temporal expression of genes together with a high resolution cellular profile and preserved cellular organization. We identified 100 distinct cellular subclasses, 17 of which were found to play significant roles in the pathophysiology of ICH. We also report similarities and differences between two experimental ICH models and human postmortem ICH brain tissue. This study advances the understanding of the local and global responses of brain cells to ICH, and provides a valuable resource that can facilitate future research and aid the development of novel therapies for this devastating condition.

6Using multi-omics techniques to construct spatial expression atlas of muscle-invasive bladder cancerSource: STOmics DB (ID: STT0000103 )

Bladder cancer is the most common cancer in the urinary system in China, with approximately 82,300 new cases diagnosed each year. It is also the tenth most commonly diagnosed cancer worldwide, with 573,278 new cases and 212,536 deaths annually. At the time of initial diagnosis, about 70% of patients are classified as having non-muscle invasive bladder cancer (NMIBC, Tis, Ta, T1), while the remaining 30% have muscle-invasive bladder cancer (MIBC, T2-4). The main treatment for MIBC is radical cystectomy, which is a painful surgical procedure, and there is a lack of effective treatment options for advanced-stage patients. Furthermore, NMIBC rarely progresses to MIBC (10-15%), indicating that NMIBC and MIBC are not different stages of the same disease, but rather have significant differences in terms of genetic background, treatment approaches, and patient prognosis. In the past, research on MIBC has been primarily limited to Next-Generation Sequencing (NGS) techniques, using mixed samples and lacking high-resolution spatial information. As a result, only a preliminary genetic profile of MIBC has been outlined, and the core driving factors for MIBC invasion into the muscle layer have not been fully elucidated. Therefore, the development of high spatiotemporal-resolution MIBC maps is of paramount importance for treatment decision-making, drug development, molecular-guided surgery, and real-time monitoring, as it holds transformative implications for clinical management of MIBC. This study aims to construct a spatial omics map of MIBC using high-resolution spatial transcriptomics and single-cell nuclear multi-omics techniques. It aims to decipher the specific driving genes and related cellular molecular mechanisms underlying the muscle invasion of bladder cancer, providing more potential targets for the treatment of MIBC.

7Spatiotemporal Decoding of Atherosclerosis at Single-Cell ResolutionSource: STOmics DB (ID: STT0000118 )

Atherosclerosis is a spatially and temporally organized disease in which vascular cell identities are reshaped within the constrained architecture of the arterial wall. Yet how spatial context regulates vascular cell state transitions contributing to atherosclerosis progression remains unresolved. Here, we generate a single-cell-resolution spatial transcriptomic atlas of atherosclerosis in mice, anchored in extensive validation from human lesions, comprising ~3 million cells across multiple disease stages and vascular regions. We develop a spatially informed, cross-species framework that reconstructs smooth muscle cell (SMC) migration and transdifferentiation along the intima-adventitia axis. This reveals that loss of PI16 drives SMC-to-fibroblast fate switching, linking defective extracellular matrix remodeling to fibrous-cap destabilization. We further identify hub transcription factors in endothelial cells and SMCs that control early atherogenic remodeling. Together, our study establishes a unified spatial framework for vascular cell plasticity in atherosclerosis with direct translational relevance. The data of scRNA could be found in https://db.cngb.org/data_resources/project/CNP0009396/

8multi-omics study of germ-free miceSource: STOmics DB (ID: STT0000178 )

This study aims to elucidate disease-associated mechanisms through integrated analyses of single-cell, single-nucleus, and spatial transcriptomic datasets derived from multiple organs across distinct mouse models.

9A unified spatial transcriptome profiling of ten mouse organsSource: STOmics DB (ID: STT0000184 )

Spatial transcriptomics has enabled numerous deep learning models in this area, and training them requires large amounts of high-quality data, especially expression matrices paired with histological images. Here, we present a unified spatial transcriptomic dataset generated using the Stereo-seq platform, covering 10 mouse organs-including brain, kidney, lung, thymus, large intestine, skin, spleen, ovary, testis, and uterus-encompassing 23 tissue sections generated from 21 chips, each with matched ssDNA or H&E staining images. The dataset comprises single-cell-resolution or square bin-50 expression matrices for each sample, accompanied by corresponding cell type annotations. Annotation robustness was further supported by concordance across different sections of the same tissue and corroboration with canonical marker gene expression patterns. Finally, we compared the characteristics of the cell-bin and bin-50 expression matrices and demonstrated the advantages of cell-bin resolution for cell annotation. This dataset provides a standardized resource for spatial transcriptomics method development, benchmarking, and multimodal analysis.

10Spatiotemporal transcriptome atlas of human embryos after gastrulationSource: STOmics DB (ID: STT0000025 )

Leveraging Stereo-seq technology, we generated spatial transcriptomic profiles across 77 sagittal sections of 13 whole human embryos ranging from Carnegie stage 12 to 23, integrated with single-nucleus RNA-seq to elucidate gene expression patterns within defined cellular contexts, revealing the cellular heterogeneity that drives organ-specific differentiation.