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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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SpaSEG: unsupervised deep learning for multi-task analysis of spatially resolved transcriptomics.

Yong Bai^1,2,3,4, Xiangyu Guo⁵, Keyin Liu^5,6

¹BGI Research, Shenzhen, 518083, China. baiyong@genomics.cn.

|July 30, 2025

Summary

SpaSEG, a new deep learning model, analyzes spatial transcriptomics (SRT) data to reveal cellular differences in tissues. It offers a robust and efficient approach for understanding tissue architecture and disease biology.

Keywords:

Cell–cell interaction Deep learning Multi-section integration Spatial domain identification Spatially resolved transcriptomics Spatially variable gene

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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:

Computational Biology
Genomics
Bioinformatics

Background:

Spatial transcriptomics (SRT) is crucial for understanding cellular heterogeneity within tissue microenvironments.
Existing analytical methods for SRT data often lack robustness and efficiency.
Elucidating gene expression variations in their physiological context is essential for pathological insights.

Purpose of the Study:

To introduce SpaSEG, an unsupervised deep learning model for multiple SRT data analysis tasks.
To demonstrate the robustness and efficiency of SpaSEG across diverse SRT datasets and platforms.
To apply SpaSEG for unraveling intratumoral heterogeneity and immunoregulatory mechanisms in invasive ductal carcinoma.

Main Methods:

Developed SpaSEG, an unsupervised deep learning model based on convolutional neural networks.
Evaluated SpaSEG's performance on various SRT datasets from different platforms.
Applied SpaSEG to analyze invasive ductal carcinoma tissue samples.

Main Results:

SpaSEG exhibited superior robustness and efficiency compared to existing SRT analysis methods.
The model successfully identified intratumoral heterogeneity in invasive ductal carcinoma.
SpaSEG provided novel insights into immunoregulatory mechanisms within the tumor microenvironment.

Conclusions:

SpaSEG is a powerful and versatile tool for analyzing spatial transcriptomics data.
The model has significant potential for advancing the study of tissue architecture and pathological biology.
SpaSEG facilitates a deeper understanding of complex biological systems through spatial gene expression analysis.