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DNA Microarrays

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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: Jun 6, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Mapping cellular interactions from spatially resolved transcriptomics data.

James Zhu¹, Yunguan Wang^1,2,3, Woo Yong Chang¹

¹Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.

|September 3, 2024

Summary

This summary is machine-generated.

Spacia, a new framework, analyzes cell-cell communication using spatial transcriptomics. It overcomes limitations of existing tools, enabling more accurate mapping of gene expression interactions between cells.

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Last Updated: Jun 6, 2026

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Published on: July 6, 2022

Area of Science:

Cell biology
Genomics
Computational biology

Background:

Cell-cell communication (CCC) is fundamental for life.
Spatially resolved transcriptomics (SRT) enables high-throughput mapping of gene expression at single-cell resolution.
Analyzing complex SRT data for CCC presents significant challenges.

Purpose of the Study:

To introduce Spacia, a novel multiple-instance learning framework for detecting CCCs from SRT data.
To leverage the spatial modality of SRT data for improved CCC analysis.
To address limitations of existing CCC inference tools.

Main Methods:

Developed a multiple-instance learning framework named Spacia.
Utilized the spatial information inherent in SRT data.
Evaluated Spacia on data from MERSCOPE/Vizgen, CosMx/NanoString, and Xenium/10x platforms.

Main Results:

Spacia effectively detects cell-cell communications from SRT data.
The framework overcomes limitations such as loss of single-cell resolution and reliance on prior databases.
Spacia addresses the multiple-sender-to-one-receiver paradigm, often missed by other tools.

Conclusions:

Spacia offers a powerful new approach for analyzing CCC from single-cell resolution SRT data.
The framework enhances the accuracy and scope of CCC inference.
Spacia advances quantitative theories and computational analysis of cellular communications.