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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: Jun 1, 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 practical guide for choosing an optimal spatial transcriptomics technology from seven major commercially available

Hyun Ju Lim¹, Ye Wang², Anton Buzdin^3,4

¹UCLA Technology Center for Genomics & Bioinformatics, Department of Pathology & Laboratory Medicine, 650 Charles E Young Dr. South, Los Angeles, CA, 90095, USA.

|January 20, 2025

Summary

Spatial transcriptomics offers insights into tissue gene expression. This guide compares seven platforms to help researchers choose the best spatial technology for their specific needs.

Keywords:

10X Visium 10X visium HD CosMx SMI GeoMx DSP Merscope RNA sequencing Spatial transcriptomics Stereoseq Xenium

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Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

Spatial transcriptomics technologies map gene expression within intact tissues.
These methods provide insights into cellular organization and function in situ.
Numerous commercial platforms exist, each with unique technical specifications.

Purpose of the Study:

To compare and analyze seven major commercially available spatial transcriptomics platforms.
To guide researchers in selecting the most appropriate platform for their research objectives.

Main Methods:

Comparative analysis of seven leading spatial transcriptomics platforms.
Evaluation based on key parameters: cellular resolution, detection sensitivity, gene coverage, and throughput.

Main Results:

Detailed comparison of the technical specifications and performance of each platform.
Identification of strengths and limitations of each technology for different research applications.

Conclusions:

Provides a framework for researchers to make informed decisions when choosing spatial transcriptomics technology.
Aims to reduce confusion and streamline the selection process for optimal experimental design.