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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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Related Experiment Video

Updated: May 24, 2025

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

STExplore: An Integrated Online Platform for Comprehensive Analysis and Visualization of Spatial Transcriptomics

Yongtian Wang^1,2,3,4, Jintian Luo⁵, Shaoqing Jiao⁵

¹School of Computer Science, Northwestern Polytechnical University, No.1 Dongxiang Road, Xi'an, Shaanxi, 710129, China.

|March 6, 2025

Summary

This summary is machine-generated.

STExplore is a new online platform that analyzes spatial transcriptomics data from various technologies. It offers a complete workflow for gene expression, cell organization, and communication analysis in tissues.

Keywords:

data analysis interactive visualization single‐cell RNA sequencing spatial transcriptomics web server

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatial transcriptomics integrates spatial data with gene expression for tissue analysis.
Current platforms struggle with diverse techniques, multi-omics integration, and comprehensive workflows.
Addressing these limitations is crucial for advancing spatial biology research.

Purpose of the Study:

To develop STExplore, an advanced online platform for spatial transcriptomics analysis.
To provide a unified and user-friendly solution for diverse spatial transcriptomics techniques.
To enable comprehensive analysis from preprocessing to cell-cell communication studies.

Main Methods:

STExplore supports both sequencing-based and image-based spatial transcriptomics methods.
The platform offers a complete analysis workflow: preprocessing, scRNA-seq integration, cluster/gene-level analysis, and cell-cell communication.
Interactive visualizations and dynamic parameter adjustments are integrated throughout the workflow.

Main Results:

STExplore successfully processed spatial transcriptomics data for neurogenesis, Alzheimer's disease, and brain tissue architecture.
The platform demonstrated enhanced gene expression analysis and revealed cellular spatial organizations.
Intercellular communication patterns within tissues were effectively uncovered.

Conclusions:

STExplore provides a comprehensive, versatile, and user-friendly platform for spatial transcriptomics research.
The platform facilitates deeper insights into tissue organization and cellular interactions.
STExplore addresses the growing need for integrated analysis in the field of spatial biology.