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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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Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

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stTrace: detecting spatial-temporal domains from spatial transcriptome to trace developmental path.

Zhangdi Song^1,2, Changyu Zheng¹, Jiaxing Chen¹

¹Guangdong Provincial/Zhuhai Key Laboratory IRADS, Beijing Normal-Hong Kong Baptist University, 2000 Jintong Road, Tangjiawan, Zhuhai 519087, Guangdong Province, China.

Briefings in Bioinformatics

|November 16, 2025

Summary

This summary is machine-generated.

We developed stTrace, a new algorithm using spatial transcriptomics to map cell development in tissues. It reveals spatial-temporal domains and developmental paths, improving understanding of organismal growth and disease progression.

Keywords:

development spatial transcriptome spatial-temporal domain trajectory

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Area of Science:

Developmental Biology
Computational Biology
Genomics

Background:

Studying organismal development is crucial for understanding complex biological systems.
Current methods using single-cell gene expression data overlook spatial cell interactions.
Existing spatial transcriptomics algorithms primarily identify regions, not developmental connections.

Purpose of the Study:

To introduce stTrace, an algorithm for detecting spatial-temporal domains to trace developmental paths using spatial transcriptomics.
To integrate cell development, gene expression, and spatial location for comprehensive developmental analysis.
To overcome limitations of current methods in capturing tissue development.

Main Methods:

Developed stTrace algorithm integrating spatial transcriptomics data.
Incorporated cell development degree, gene expression, and spatial location.
Identified 'spatial-temporal domains' representing cells with similar functions and developmental stages.
Analyzed hierarchical relationships among domains to infer developmental connections.

Main Results:

stTrace achieved higher resolution and developmental consistency compared to traditional algorithms on mouse embryo and human breast cancer datasets.
Identified distinct spatial-temporal domains in mouse brain and eye tissues with significant gene expression differences.
Reconstructed a developmental tree for human cancer data, inferring cancer cell spread directions.

Conclusions:

stTrace effectively maps developmental trajectories in tissues by identifying spatial-temporal domains.
The algorithm provides novel insights into organismal development and disease progression, such as cancer cell migration.
This approach enhances the analysis of spatial transcriptomics data for understanding complex biological processes.