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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 28, 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

Library size confounds biology in spatial transcriptomics data.

Dharmesh D Bhuva^1,2,3, Chin Wee Tan^4,5,6, Agus Salim^4,7

¹South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia. dharmesh.bhuva@adelaide.edu.au.

|April 18, 2024

Summary

This summary is machine-generated.

Normalizing spatial molecular data using single-cell RNA-sequencing methods can distort tissue structure analysis. Researchers should avoid library size correction for accurate spatial domain identification in disease microenvironment studies.

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Area of Science:

Spatial biology
Molecular pathology
Bioinformatics

Background:

Spatial molecular data offers insights into disease microenvironments.
Large datasets present analytical challenges, leading to the adoption of single-cell RNA-sequencing (scRNA-seq) tools.
Normalization methods from scRNA-seq are being applied to spatial data.

Purpose of the Study:

To investigate the impact of scRNA-seq normalization methods on spatial molecular data analysis.
To determine if library size normalization affects spatial domain identification.
To provide recommendations for analyzing spatial molecular data.

Main Methods:

Analysis of spatial molecular datasets.
Application of common scRNA-seq normalization techniques.
Assessment of the effect of normalization on spatial domain identification.

Main Results:

Library size in spatial molecular data is correlated with tissue structure.
Normalization for library size using scRNA-seq methods negatively impacts spatial domain identification.
Direct application of scRNA-seq normalization algorithms requires caution.

Conclusions:

Spatial molecular data should not be normalized for library size before analysis.
Standard scRNA-seq normalization methods can obscure important spatial biological information.
Careful consideration is needed when adapting scRNA-seq tools for spatial data analysis.