A paper on DeepSpaceDB, a spatial transcriptomics database, has been published

On Oct 29, 2025, a paper on "DeepSpaceDB," a spatial transcriptomics database developed by Associate Professor VANDENBON Alexis at the Institute for Medical and Biological Engineering, Kyoto University, was published in the Database Issue of the scientific journal "Nucleic Acids Research."

Spatial transcriptomics is a research method that comprehensively investigates which genes are expressed and to what extent in specific locations within a tissue sample. In recent years, with advancements in analytical technology, a large amount of data has been accumulated and made available through public databases. DeepSpaceDB contains most spatial transcriptomics data examined using 10X Genomics Visium, currently the most widely used platform, enabling researchers to perform advanced analyses interactively on the website. Since its release on Sep 4, 2024, it has undergone various improvements and data expansions and is being used by a large number of researchers.

Currently, several databases provide spatial transcriptomics data, but DeepSpaceDB's most distinctive feature is its ability to enable interactive, advanced analysis without requiring specialized bioinformatics skills. Beyond basic functions like searching for samples from specific tissues or states and visualizing gene expression patterns within tissue sections, users can freely select multiple regions within a tissue section using the mouse cursor and compare gene expression between those regions. Comparisons can be made not only between regions within the same section but also between regions in different tissue sections. For example, comparisons can be made between the hippocampal regions of Alzheimer's disease model mice and healthy control mice. All graphs support zooming in and out, and hovering the cursor over a spot displays gene expression levels, pathway activity, and more.

One of the new features added after its release is that users can now analyze their own data using DeepSpaceDB's various functions. Data processing includes normalization, calculation of quality metrics, dimensionality reduction, clustering, and SVG prediction, and users can also compare their own data with sample data within DeepSpaceDB. Uploaded data is stored on the DeepSpaceDB server for a certain period and then automatically deleted, but users can also delete their data from the server at any time. Each uploaded sample is assigned a unique URL that can be shared with collaborators, but the data uploaded by users is not incorporated into DeepSpaceDB's data and is not made public to other users.

This paper describes the various features of DeepSpaceDB in detail, as well as examples of its application in the analysis of breast cancer samples. For more information, please refer to the paper "DeepSpaceDB: a spatial transcriptomics atlas for interactive in-depth analysis of tissues and tissue microenvironments".

＜Number of DeepSpaseDB＞ (As of Oct 29, 2025)

• Human: 1,361 samples
• Mouse: 783 samples

DeepSpaceDB is developed as a part of JST Database Integration Coordination Program (DICP), "Development of a database for spatial genomics data analysis" (Principal Investigator: VANDENBON Alexis, Associate Professor, Institute for Life and Medical Sciences, Kyoto University).