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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: Dec 7, 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

Reproducibility across single-cell RNA-seq protocols for spatial ordering analysis.

Morten Seirup^1,2, Li-Fang Chu², Srikumar Sengupta²

¹Molecular and Environmental Toxicology Program, University of Wisconsin Madison, Madison, Wisconsin, United States of America.

|September 28, 2020

Summary

This summary is machine-generated.

Single-cell RNA sequencing protocols vary in read depth and cell capture. This study shows gene expression profiles are reproducible across protocols, but protocol choice impacts analysis of low-expressed genes and isoforms for spatial reconstruction.

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Last Updated: Dec 7, 2025

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
Newer protocols often prioritize high cell numbers over sequencing depth, potentially impacting data utility.
Spatial reconstruction requires robust gene expression data for accurate biological signal recapitulation.

Purpose of the Study:

To evaluate the impact of different scRNA-seq protocols (Smart-seq, MARS-seq, 10X) on spatial reconstruction.
To compare the trade-offs between sequencing depth and cell capture efficiency for various protocols.
To determine the optimal balance of sequencing depth and cell number for efficient scRNA-seq analysis.

Main Methods:

Comparative analysis of gene expression profiles from Smart-seq, MARS-seq, and 10X protocols.
Spatial reconstruction analysis applied to datasets generated by different scRNA-seq protocols.
Subsampling analyses to evaluate protocol performance under varying sequencing depths and cell numbers.

Main Results:

Gene expression profiles after spatial reconstruction were highly reproducible across different scRNA-seq protocols and computational methods.
Smart-seq, with higher read depth, enabled analysis of lowly expressed genes and isoforms.
UMI-based protocols (MARS-seq, 10X) excelled in capturing a larger number of cells.
Subsampling revealed that optimizing the balance between sequencing depth and cell number is critical for resource efficiency.

Conclusions:

Protocol selection for scRNA-seq should be guided by specific biological questions and the features of interest.
Both sequencing depth and cell number are important considerations for robust spatial reconstruction and efficient resource utilization.
Different scRNA-seq protocols offer distinct advantages, impacting the depth and breadth of detectable biological signals.