scSNPdemux: a sensitive demultiplexing pipeline using single nucleotide polymorphisms for improved pooled single-cell...

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) enables high-resolution transcriptomic analysis.
• Sample multiplexing is crucial for increasing throughput and reducing costs in scRNA-seq experiments.
• Accurate sample demultiplexing is essential for reliable downstream analysis.

Purpose of the Study:

• To develop and present scSNPdemux, a novel computational pipeline for accurate sample demultiplexing in scRNA-seq data.
• To leverage natural human genetic variations for sample identification.
• To provide a cost-effective and streamlined solution for multiplexed scRNA-seq experiments.

Main Methods:

• The scSNPdemux pipeline utilizes alignment files from Cell Ranger.
• It incorporates a population single nucleotide polymorphism (SNP) database and sample-specific genotyped SNPs.
• The method analyzes sparse genotyping data in VCF format for sample identification.

Main Results:

• scSNPdemux demonstrated superior demultiplexing performance on both single-cell and single-nuclei RNA sequencing datasets.
• The pipeline showed higher sensitivity and specificity in cell-identity assignment compared to lipid-based methods like CellPlex and Multi-seq.
• Performance was particularly notable for immune cell types, which often have low RNA content.

Conclusions:

• scSNPdemux offers a streamlined approach to single-cell sample demultiplexing.
• The pipeline effectively overcomes common challenges associated with sample multiplexing in scRNA-seq, such as potential impacts on data quality and cost.
• This method provides a robust alternative for researchers conducting multiplexed scRNA-seq studies.