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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...

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Updated: Jul 12, 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

SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions,

Tiantian Guo^1,2, Zhiyuan Yuan³, Yan Pan⁴

¹School of Software Engineering, Beijing Jiaotong University, Beijing, 100044, China.

|October 20, 2023

Summary

SPIRAL integrates spatially resolved transcriptomics (SRT) data from multiple batches. This method improves batch effect removal and spatial alignment for comprehensive spatial atlases.

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Spatially resolved transcriptomics (SRT) provides gene expression data with spatial information.
Integrating SRT data from different batches is crucial for building comprehensive spatial atlases.
Existing methods face challenges in accurate data integration and coordinate alignment across batches.

Purpose of the Study:

To develop a novel computational framework, SPIRAL, for integrating multi-batch SRT data.
To improve batch effect removal and enable accurate alignment of spatial coordinates.
To facilitate the construction of a unified spatial transcriptome atlas.

Main Methods:

SPIRAL employs a two-module approach: SPIRAL-integration and SPIRAL-alignment.
SPIRAL-integration utilizes graph domain adaptation for data integration.
SPIRAL-alignment uses cluster-aware optimal transport for coordinate alignment.

Main Results:

SPIRAL-integration effectively removes batch effects and identifies joint spatial domains.
SPIRAL-integration outperforms existing methods in integrating SRT data.
SPIRAL-alignment achieves more accurate coordinate alignments compared to current methods.
SPIRAL demonstrates robust performance on both synthetic and real SRT datasets.

Conclusions:

SPIRAL provides an effective solution for integrating multi-batch SRT data.
The proposed method enables the creation of more comprehensive and accurate spatial transcriptome atlases.
SPIRAL advances the field of spatial transcriptomics analysis.