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Related Concept Videos

Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

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Related Experiment Video

Updated: Jun 18, 2026

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

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

Published on: July 6, 2022

Empowering multifaceted analysis of spatial transcriptomics data with RGAST.

Yuqiao Gong1, Xin Yuan1,2,3,4, Zhangsheng Yu1,2,3,5

  • 1Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, Shanghai, 200240, China.

Briefings in Bioinformatics
|June 16, 2026
PubMed
Summary
This summary is machine-generated.

RGAST, a new framework for spatial transcriptomics (ST) analysis, integrates spatial proximity and gene expression data. This advanced tool enhances spatial domain identification, gene expression accuracy, and 3D tissue reconstruction for diverse ST research.

Keywords:
RGASTcell–cell communicationheterogeneous graph networkrelational graph attentionspatial transcriptomics

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Last Updated: Jun 18, 2026

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

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

Published on: July 6, 2022

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Spatial transcriptomics (ST) provides gene expression data within tissue context, crucial for understanding cellular interactions and tissue architecture.
  • Current ST analysis workflows often require separate algorithms for distinct tasks, limiting efficiency and integration.
  • Existing methods may struggle to capture both local and global spatial structures or long-range interactions.

Purpose of the Study:

  • To introduce RGAST (Relational Graph Attention network for ST analysis), a unified framework for diverse ST data analysis tasks.
  • To develop a method that jointly models spatial proximity and gene expression similarity for comprehensive ST data interpretation.
  • To improve the accuracy and efficiency of spatial domain identification, gene expression analysis, and 3D reconstruction in ST data.

Main Methods:

  • Development of RGAST, a framework extending the HERGAST model using a relational graph attention auto-encoder.
  • Joint modeling of spatial proximity and gene expression similarity to capture local and global data structures.
  • Comprehensive benchmarking across multiple ST datasets and platforms, including the dorsolateral prefrontal cortex and mouse hypothalamus.

Main Results:

  • RGAST achieved superior performance in spatial domain identification, outperforming existing models by approximately 10% in adjusted rand index on the dorsolateral prefrontal cortex dataset.
  • The framework accurately reconstructed known neuroglial interaction patterns and long-range signaling pathways in the mouse hypothalamus.
  • RGAST demonstrated improved accuracy in identifying spatially variable genes, precise inference of developmental trajectories in the human cortex, and robust 3D tissue reconstruction.

Conclusions:

  • RGAST offers a coherent and powerful solution for advancing spatial transcriptomics data analysis across various research scenarios.
  • The unified framework streamlines diverse downstream tasks, enhancing the interpretation of complex spatial gene expression data.
  • RGAST's ability to integrate spatial and expression information provides deeper insights into tissue architecture and cellular communication.