Methodologies for Sample Multiplexing and Computational Deconvolution in Single-Cell Sequencing

Area of Science:

• Genomics and Molecular Biology
• Bioinformatics

Background:

• Single-cell sequencing offers high cellular resolution but faces cost and batch effect limitations in multi-sample studies.
• Sample multiplexing addresses these limitations by uniquely tagging cells for pooled sequencing.

Purpose of the Study:

• To provide a comprehensive review of single-cell multiplexing techniques.
• To highlight the interplay between experimental design and computational accuracy in sample deconvolution.
• To summarize diverse applications of single-cell multiplexing.

Main Methods:

• Survey of major experimental strategies for sample multiplexing.
• Review of critical computational algorithms for accurate sample deconvolution.
• Analysis of the link between experimental design and computational outcomes.

Main Results:

• Sample multiplexing significantly boosts throughput and data reliability by minimizing technical variability.
• Accurate sample deconvolution is crucial for interpreting multiplexed single-cell data.
• Successful applications span large clinical cohorts, multi-omics, developmental biology, and drug screening.

Conclusions:

• Single-cell multiplexing is a key technology for advancing biological research.
• This review empowers researchers to choose optimal methods for discovery.
• Methods accelerate understanding of disease mechanisms, therapeutic responses, and development.