MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices.

Area of Science:

• Molecular Biology
• Genomics
• Biotechnology

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
• Sample multiplexing reduces costs and aids in identifying artifacts like cell doublets.
• Existing universal and scalable sample barcoding strategies for scRNA-seq are lacking.

Purpose of the Study:

• To develop a universal and scalable sample barcoding strategy for single-cell and single-nucleus RNA sequencing.
• To introduce MULTI-seq, a novel method for multiplexing samples using lipid-tagged indices.
• To enhance data quality and preserve biological information in scRNA-seq experiments.

Main Methods:

• Developed MULTI-seq, a method utilizing lipid-tagged indices for sample barcoding.
• Applied MULTI-seq reagents to various cell types and species with accessible plasma membranes.
• Minimized sample processing to preserve cell viability and endogenous gene expression.

Main Results:

• MULTI-seq successfully barcodes diverse cell types and nuclei across species.
• Barcode abundances accurately classify cells into sample groups, improving data quality.
• Enhanced doublet identification and recovery of low-RNA content cells were achieved.
• Demonstrated utility in tracking T-cell activation dynamics and large-scale perturbation experiments.

Conclusions:

• MULTI-seq provides a universal, scalable, and cost-effective solution for sample multiplexing in scRNA-seq.
• The method enhances scRNA-seq data quality by improving doublet detection and cell recovery.
• MULTI-seq is versatile, applicable to various biological systems including primary cells and complex disease models.