CapMux: a Snakemake pipeline for early demultiplexing of split-pool scRNA-seq data into sample-resolved outputs

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Split-pool combinatorial barcoding enhances high-throughput single-cell RNA sequencing (scRNA-seq).
• Current methods often pool reads from multiple samples, hindering individual sample analysis.
• Sample identity is typically encoded early, complicating downstream data separation.

Purpose of the Study:

• To introduce CapMux, a Snakemake-based pipeline for processing split-pool scRNA-seq data.
• To enable the reconstruction of sample identity from pooled sequencing reads.
• To facilitate per-sample analysis and selective data extraction from combinatorial barcoding experiments.

Main Methods:

• Developed a Snakemake pipeline (CapMux) for split-pool scRNA-seq data processing.
• Integrated sub-library index information with experimental plate layouts to reconstruct sample identity.
• Configured the pipeline for CapSeq and adaptable to related combinatorial barcoding designs.

Main Results:

• CapMux successfully resolved pooled scRNA-seq data into individual sample outputs in a cell line mixing experiment.
• Enabled independent quality control, mapping statistics, and count matrices for each resolved sample.
• Demonstrated that the secondary demultiplexing step introduces only a modest computational overhead.

Conclusions:

• CapMux provides a practical and adaptable solution for recovering sample-level resolution in split-pool scRNA-seq data.
• The pipeline facilitates more robust and detailed analysis of individual samples within pooled experiments.
• CapMux enhances the utility of high-throughput scRNA-seq methods utilizing combinatorial barcoding.