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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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Updated: Jan 17, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Mining Spatial Transcriptomics Datasets using DeepSpaceDB.

Nupura Prabhune¹, Yilin Du², Afeefa Zainab³

¹Department of Integrative Bioanalytics, Institute of Development, Aging and Cancer, Tohoku University; Department of Life Science and Medical Bioscience, Graduate School of Advanced Science and Engineering, Waseda University.

Journal of Visualized Experiments : Jove

|September 22, 2025

Summary

This summary is machine-generated.

DeepSpaceDB offers a user-friendly platform for exploring spatial transcriptomics data. This resource aids researchers in analyzing gene expression patterns and tissue organization, accelerating biological and disease research.

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Last Updated: Jan 17, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

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Published on: September 5, 2025

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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

Area of Science:

Bioinformatics
Genomics
Molecular Biology

Background:

Spatial transcriptomics captures gene expression with positional data, crucial for understanding tissue heterogeneity.
Increasing availability of spatial transcriptomics datasets necessitates advanced analytical tools.
Technical and financial barriers limit widespread adoption of spatial transcriptomics.

Purpose of the Study:

To introduce DeepSpaceDB, a comprehensive database for spatial transcriptomics data exploration.
To provide detailed workflows and examples for navigating and utilizing the database.
To facilitate deeper insights into tissue organization and disease biology through accessible spatial data analysis.

Main Methods:

Development of DeepSpaceDB, a dynamic database for spatial transcriptomics data.
Implementation of interactive features for data exploration and visualization.
Demonstration of analysis workflows using mouse brain and liver cancer datasets.

Main Results:

DeepSpaceDB enables exploration of quality indicators, spatially variable genes, and pathways.
Analysis revealed gene expression variations between mouse brain regions (hippocampus vs. hypothalamus).
Identification and annotation of immune-related differentially expressed genes in colorectal metastasis models.

Conclusions:

DeepSpaceDB serves as a valuable resource for spatial transcriptomics research.
The database enhances the ability to investigate tissue organization and disease mechanisms.
DeepSpaceDB lowers barriers to entry for spatial transcriptomics data analysis.