1Stomic data processingSource: STOmics DB (ID: STT0000048 )

Developing a series algorithm to generate more accurate single cell stomics data.

2SAW: An efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomicsSource: STOmics DB (ID: STT0000038 )

The basic analysis steps of spatial transcriptomics involves obtaining gene expression information from both space and cells. This process requires a set of tools to be completed, and existing tools face performance issues when dealing with large data sets. These issues include computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the process. To address these issues, a high-performance and accurate spatial transcriptomics data analysis workflow called Stereo-Seq Analysis Workflow (SAW) has been developed for the Stereo-Seq technology developed by BGI. This workflow includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation and clustering, and generate results files in a universal format for subsequent personalized analysis. The excutation time for the entire analysis process is ~148 minutes on 1G reads 1*1 cm chip test data, 1.8 times faster than unoptimized workflow.

3Spatiotemporal transcriptomic landscape of rice embryonic cells during seed germinationSource: STOmics DB (ID: STT0000049 )

Characterizing cell features of germinating seeds is essential for understanding the complex biological functions of different embryonic cells in regulating seed vigor and seedling establishment. In this study, we performed spatially enhanced resolution omics (Stereo-seq) and single-cell RNA sequencing (scRNA-seq) to capture spatially resolved single-cell transcriptomes of germinating rice embryos. An automated cell-segmentation model was developed based on deep-learning to accommodate the analysis requirements. The spatial transcriptome revealed known and novel cell types of rice embryo, including two new scutellum cell types. The temporal transcriptome revealed the expression dynamics of genes in different types of embryonic cells during the time course of seed germination and the key genes, particularly related to nutrient metabolism and biosynthesis and signaling of phytohormonesand were reprogrammed in a cell-type-specific manner. Our study provides a panoramic spatiotemporal transcriptome of rice embryo and a novel methodology for exploring the roles of different embryonic cells in seed germination.

4Integrated transcriptomic atlas facilitates the understanding of organ development in soybeanSource: STOmics DB (ID: STT0000020 )

Revealing the temporal and spatial expression of each gene is critical for the understanding of plant organ development. To this end, there is increasing demand for the construction of integrated transcriptomic atlases using multiple types of transcriptome data. In this study, we sequenced 315 soybean tissues over the course of the full developmental cycle by bulk RNA-seq and sequenced 5 representative organs (roots, nodules, shoot apical meristems, leaves and stems) by snRNA-seq and Stereo-seq. Integrating these transcriptome data, we constructed a comprehensive soybean transcriptomic atlas. Taking the investigations of genes related to blade, vascular bundles, and nodule development as examples, we demonstrate that the integrated transcriptomic atlas has powerful potential for exploring key genes in organ formation. Together, the panoramic soybean transcriptomic atlas provides a valuable resource and will greatly promote functional studies in the future.

5A new cell typing method: Spatial-IDSource: STOmics DB (ID: STT0000022 )

A novel method (Spatial-ID) was represented for cell annotation. A series of comparison experiments demonstrate the superiority of this method in cell type annotation compared with other state-of-the-art methods. Besdes, the application of Spatial-ID on a Stereo-seq SRT dataset with 3D spatial dimension shows its advancement on the large field tissues (~1cm2) with subcellular spatial resolution. Moreover, by mapping the identified cell types with identified spatial gene patterns, the significant GO terms of the spatial gene patterns further reveal the functions and underlying biological processes of the identified cell types.

6MouseBrain_FFPESource: STOmics DB (ID: STT0000037 )

An FFPE version Stereo-seq sample data for technical test usage.

7Spatiotemporal transcriptome studies of human embryonic developmentSource: STOmics DB (ID: STT0000025 )

This project uses spatiotemporal multi-omics technology to construct spatiotemporal mapping of human embryonic organs within 3 to 8 weeks after fertilization, excavate the mechanisms of embryo development process at different stages, explore the development process of embryos and other major basic scientific research issues.

8StereoCell: a highly accurate single-cell gene expression processing software for high-resolution spatial transcriptomicsSource: STOmics DB (ID: STT0000027 )

Owing to recent advances in resolution and field-of-view, spatially resolved sequencing has emerged as a cutting-edge technology that provides a technical foundation for the interpretation of large tissues at the single-cell level. To generate accurate single-cell spatial gene expression profiles from high-resolution spatial omics data and associated images, a powerful tool is required. Here we present StereoCell, an image-facilitated one-stop software for high-resolution and large field-of-view spatial transcriptomic data . StereoCell provides a comprehensive and systematic platform for the generation of high-confidence single-cell spatial gene expression profiles, which includes image stitching, image registration, tissue segmentation, nuclei segmentation and molecule labeling. StereoCell has been applied to obtain reliable single-cell spatial gene expression profiles for mouse embryo and continuous mouse brain slices in previous published works. StereoCell is user-friendly and doesn’t require a specific level of expertise. For mouse brain dataset (131,990,020 genes and 117 tiles), StereoCell can be carried out in 4.8h on a server with 40-core CPU, 128GB of RAM and 24GB of GPU.

9Single-cell and spatiotemporal transcriptomic reveals the effects of microorganisms on immunity and metabolism in mouse liverSource: STOmics DB (ID: STT0000034 )

The gut-liver axis is a complex bidirectional communication pathway between the intestine and the liver in which microorganisms and their metabolites from the intestine flow through the portal vein to the liver and influence liver function. In a sterile environment, the phenotype or function of the liver is altered, yet there are few studies on the specific cellular and molecular effects of microorganisms on the liver. Towards this aim, we constructed single-cell and spatial transcriptomic (ST) profiles of germ-free (GF) and specific pathogen-free (SPF) mice livers. The single cell RNA sequencing (scRNA-seq) found that the proportion of the vast majority of immune cells in GF mice was significantly reduced, especially natural killer T (NKT) cells, IgA plasma cells (IgAs), and Kupffer cells (KCs). The spatial enhanced resolution omics-sequencing (Stereo-seq) confirmed that microorganisms mediated the accumulation of Kupffer cells in the periportal zone. We also unexpectedly found that IgA plasma cells were more numerous and concentrated in the periportal vein in sections from SPF mice, while they were fewer and scattered in GF mice. ST technology also enables precise zonation of liver lobules into eight layers and three patterns based on gene expression level in each layer, allowing us to further investigate the effects of microbes on gene zonation patterns and functions. Furthermore, the untargeted metabolism experiments in the liver discovered that propionic acid levels were significantly lower in GF mice and may be related to the control of genes involved in bile acid and fatty acid metabolism. In conclusion, the combined study of scRNA-seq, Stereo-seq, and untargeted metabolomics revealed immune system defects as well as altered bile acid and lipid metabolic processes at the single-cell and spatial levels in the livers of GF mice. This study has great value for understanding host-microbiota interactions.

10The Single-cell Stereo-seq reveals region-specific cell subtypes and transcriptome profiling in Arabidopsis leavesSource: STOmics DB (ID: STT0000023 )

Understanding the complex functions of plant leaves requires a thorough characterization of discrete cell features. Although single-cell gene expression profiling technologies have been developed, their application in characterizing cell subtypes has not been achieved yet. Here, we present scStereo-seq (single-cell SpaTial Enhanced REsolution Omics-sequencing) that enabled us to firstly show the bona fide single-cell spatial transcriptome profiles of Arabidopsis leaves. Subtle but significant transcriptomic differences between upper and lower epidermal cells have been successfully distinguished. Furthermore, we discovered cell-type-specific gene expression gradients from the main vein to the leaf edge, which for the first time led to the finding of distinct spatial developmental trajectories of vascular cells and guard cells. Our study showcases the importance of physical locations of individual cells for exerting complex biologic functions in plants and demonstrates that scStereo-seq is a powerful tool to integrate single cell location and transcriptome information for plant biology study.