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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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Related Experiment Video

Updated: Sep 18, 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

A comprehensive review of spatial transcriptomics data alignment and integration.

Muiz Khan¹, Suzan Arslanturk¹, Sorin Draghici^1,2

¹Department of Computer Science, Wayne State University, Detroit, 48202 Michigan, United States.

Nucleic Acids Research

|June 26, 2025

Summary

This summary is machine-generated.

Automated alignment and integration of multiple spatial transcriptomics slices are crucial for analyzing whole tissues. This review categorizes 24 tools, highlighting challenges and proposing a general pipeline for robust multi-slice data analysis.

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Last Updated: Sep 18, 2025

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Published on: July 6, 2022

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatial transcriptomics (ST) technologies quantify molecular expression with spatial context.
Analyzing whole tissues requires aligning and integrating multiple ST slices, a complex task due to tissue heterogeneity.
Manual alignment is time-consuming and requires expertise, necessitating automated solutions.

Purpose of the Study:

To comprehensively review methodologies for spatial transcriptomics data alignment and integration.
To explain the challenges and scope of multi-slice ST data alignment.
To propose a general pipeline for ST data alignment and integration.

Main Methods:

Reviewed 24 tools for multi-slice ST alignment and integration, excluding single-slice or multi-omics tools.
Categorized approaches by methodology: statistical mapping, image processing/registration, and graph-based.
Evaluated tools based on strengths, limitations, task scope, and potential for biological insights.

Main Results:

Identified key challenges in aligning heterogeneous tissue slices.
Assessed the performance and applicability of existing ST alignment and integration tools.
Found that despite advances, robust alignment across diverse slices remains a significant challenge.

Conclusions:

Automated and robust alignment and integration of multiple ST slices are essential for advancing biological insights.
The proposed general pipeline provides a framework for understanding and developing ST data integration methodologies.
Further research is needed to overcome persistent challenges in cross-slice alignment for heterogeneous tissues.