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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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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

Spatial analysis toolkits for RNA in situ sequencing.

Jiayu Chen¹, Rongqin Ke¹

¹School of Medicine, Huaqiao University, Xiamen, Fujian, China.

Wiley Interdisciplinary Reviews. RNA

|April 12, 2024

Summary

This summary is machine-generated.

This review covers bioinformatic tools for in situ sequencing (ISS) data analysis, a spatial transcriptomics (ST) method. It details workflows for analyzing ISS data to understand gene expression in tissue context.

Keywords:

data analysis gene expression profiling in situ sequencing spatial transcriptomics

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Last Updated: Jun 28, 2025

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Published on: July 6, 2022

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Identification of Circular RNAs using RNA Sequencing

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Published on: November 14, 2019

Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

Spatial transcriptomics (ST) enables gene expression profiling within native tissue environments, crucial for studying gene regulatory networks.
In situ sequencing (ISS) is a high-resolution ST technology detecting hundreds to thousands of genes at the subcellular level.
Growing interest in ISS necessitates scalable, integrated data analysis workflows for diverse research applications.

Purpose of the Study:

To present state-of-the-art bioinformatic toolkits for analyzing in situ sequencing (ISS) data.
To review the application and compatibility of various tools with ISS data for the research community.
To discuss future perspectives and challenges in integrating ISS analysis tools into user-friendly pipelines.

Main Methods:

Comprehensive review of existing bioinformatic toolkits for ISS data analysis.
Detailed examination of upstream and downstream analysis workflows, including image analysis, cell segmentation, clustering, functional enrichment, and spatial analysis.
Evaluation of tool compatibility and application specifically for ISS data.

Main Results:

Identification and categorization of current bioinformatic tools for ISS data analysis.
Detailed review of tool functionalities covering image processing to advanced spatial analyses like cell-cell interactions and trajectory inference.
Assessment of tool applicability and integration challenges for ISS data.

Conclusions:

Effective bioinformatic tools are essential for unlocking the full potential of ISS-based spatial transcriptomics.
Standardized and integrated analysis pipelines are needed to facilitate broader adoption of ISS technology across research fields.
Addressing current challenges will pave the way for more accessible and powerful ISS data analysis.