SpatialDB: a database for spatially resolved transcriptomes

Abstract

Spatially resolved transcriptomic techniques allow the characterization of spatial organization of cells in tissues, which revolutionize the studies of tissue function and disease pathology. New strategies for detecting spatial gene expression patterns are emerging, and spatially resolved transcriptomic data are accumulating rapidly. However, it is not convenient for biologists to exploit these data due to the diversity of strategies and complexity in data analysis. Here, we present SpatialDB, the first manually curated database for spatially resolved transcriptomic techniques and datasets. The current version of SpatialDB contains 24 datasets (305 sub-datasets) from 5 species generated by 8 spatially resolved transcriptomic techniques. SpatialDB provides a user-friendly web interface for visualization and comparison of spatially resolved transcriptomic data. To further explore these data, SpatialDB also provides spatially variable genes and their functional enrichment annotation. SpatialDB offers a repository for research community to investigate the spatial cellular structure of tissues, and may bring new insights into understanding the cellular microenvironment in disease. SpatialDB is freely available at https://www.spatialomics.org/SpatialDB.

A Picture Worth a Thousand Molecules—Integrative Technologies for Mapping Subcellular Molecular Organization and Plasticity in Developing Circuits

“…Also, proteomic (Sharma et al, 2015;Koopmans et al, 2019;Perez-Riverol et al, 2019) and transcriptomic (Ecker et al, 2017;Keil et al, 2018;Solanelles-Farré and Telley, 2021) databases inform hypotheses for gain/loss-of-function studies and probe design/selection for spatial analyses. In a complementary way, spatial transcriptomic databases are resources for validating and mapping spatial gene expression patterns in circuits (Fan et al, 2020). As technical advances increase experimental throughput, a central analytical challenge is the computational integration of multiomic and spatial information within user-friendly environments to extract biological results from big data (Ritchie et al, 2015;Conesa and Beck, 2019;Leonavicius et al, 2019;Leonelli, 2019;Brademan et al, 2020).…”

Pinpointing Cell Identity in Time and Space

“…This enables the platform to handle data sets from multiple techniques already described here, and extract and compare information across different experiments. More recently, SpatialDB (Fan et al, 2020) has been set up as a manually curated and explorable repository of spatially resolved transcriptomic datasets from multiple techniques. As these analytical platforms develop, we expect that the integration of each of these tools into a single framework, in a modular manner, will be beneficial to researchers seeking to understand cell identity and differences in biology.…”

Inferring functionally relevant molecular tissue substructures by agglomerative clustering of digitized spatial transcriptomes

“…Hyppocampus and Cortex high-resolution Slide-seq maps 20 were obtained from the SpatialDB database 22 . Pancreatic ductal adenocarcinoma data generated by Moncada et al 33 ; were obtained from GEO database (GSE111672…”