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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

DeepSpaceDB: a spatial transcriptomics atlas for interactive in-depth analysis of tissues and tissue

Vladyslav Honcharuk^1,2, Afeefa Zainab¹, Yoshiya Horimoto^3,4

¹Laboratory of Tissue Homeostasis, Institute for Life and Medical Sciences, Kyoto University, Kyoto 606-8507, Japan.

Nucleic Acids Research

|October 29, 2025

Summary

DeepSpaceDB is a new database for spatial transcriptomics data, offering advanced tools for exploring gene expression within tissues. It allows interactive comparison of samples and supports user-uploaded data for deeper biological insights.

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Spatial transcriptomics provides tissue-level gene expression mapping but faces challenges in data accessibility and analysis.
Existing databases lack interactive exploration and cross-sample comparison tools for spatial transcriptomics.
High costs and technical expertise limit the widespread use of spatial transcriptomics.

Purpose of the Study:

Introduce DeepSpaceDB, a next-generation database for spatial transcriptomics data analysis.
Enhance interactive exploration and cross-sample comparison of gene expression data.
Provide advanced analytical tools for understanding tissue organization and disease biology.

Main Methods:

Developed DeepSpaceDB focusing on 10X Genomics Visium samples for high-quality analysis.
Implemented interactive features for exploring gene expression across regions within and between tissue slices.
Integrated quality indicators, database-wide trends, and interactive visualizations (zoomable plots, hover info).
Enabled analysis of user-uploaded spatial transcriptomics samples.

Main Results:

DeepSpaceDB offers enhanced tools for spatial transcriptomics data exploration and comparison.
Users can interactively compare gene expression patterns, for example, in Alzheimer's models versus controls.
The database provides quality metrics and visualizations for deeper biological insights.
Functionality extends to user-uploaded datasets, broadening accessibility.

Conclusions:

DeepSpaceDB is a powerful, interactive resource for spatial transcriptomics research.
It addresses limitations of existing databases by prioritizing functionality and advanced analytics.
Facilitates deeper understanding of tissue organization and disease mechanisms through accessible data exploration.