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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 14, 2025

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Published on: March 23, 2022

Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data.

Yile Fu¹, Mohamad Youness¹, Alessia Virzì¹

¹Laboratory of Experimental Cardiology, Department of Cardiovascular Sciences, KU Leuven, Herestraat 49, 3000 Leuven, Belgium.

Briefings in Bioinformatics

|July 24, 2025

Summary

Benchmarking sample demultiplexing software for single-nucleus RNA sequencing (snRNA-Seq) reveals Vireo as the most accurate tool. This study provides crucial guidance for selecting demultiplexing methods to improve cost-efficiency in complex tissue analysis.

Keywords:

donor demultiplexing genetic variation snRNA-Seq

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Last Updated: Sep 14, 2025

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

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Published on: March 23, 2022

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Published on: September 16, 2019

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Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Published on: February 23, 2024

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Single-nucleus RNA sequencing (snRNA-Seq) offers deep insights into cellular heterogeneity and gene expression in complex tissues.
High costs associated with snRNA-Seq limit the number of samples that can be analyzed.
Sample pooling and subsequent demultiplexing using genetic variants present a cost-effective solution for increasing sample throughput.

Purpose of the Study:

To comprehensively benchmark leading software tools for genetic variant-based sample demultiplexing in snRNA-Seq.
To compare variant calling from SNP array and bulk RNA-Seq data for demultiplexing.
To evaluate the impact of doublet percentages and variant calling tools on demultiplexing performance.

Main Methods:

Benchmarking of Vireo, Souporcell, Freemuxlet, and scSplit software for sample demultiplexing using genetic variants.
Comparison of genetic variant data derived from SNP array (gDNA) and sample-matched bulk RNA-Seq.
Evaluation using simulated multiplexed datasets (2, 4, 6 samples; 0-30% doublets) and validation with sex-linked genes.

Main Results:

All tested tools, except scSplit, achieved high recall and precision (80-85% accuracy), with Vireo demonstrating the best performance.
Demultiplexing accuracy was influenced by the choice of variant calling tool and decreased with increasing doublet percentages.
Successful deployment of demultiplexing on real-world 10x RNA-Seq data from human heart and cross-species samples was demonstrated.

Conclusions:

Vireo is recommended as the top-performing software for genetic variant-based sample demultiplexing in snRNA-Seq.
The choice of variant calling method impacts demultiplexing tool performance, highlighting the need for careful consideration.
Demultiplexing strategies enhance cost-efficiency, improve doublet detection, and facilitate analysis of pooled snRNA-Seq data.