AN UPDATED DEBARCODING TOOL FOR MASS CYTOMETRY WITH CELL TYPE-SPECIFIC AND CELL SAMPLE-SPECIFIC STRINGENCY...

Area of Science:

• Biotechnology
• Immunology
• Computational Biology

Background:

• Pooled sample analysis via mass cytometry barcoding offers significant advantages, including reduced reagent use and increased throughput.
• Previous debarcoding algorithms had limitations in handling non-uniform barcode staining intensity, potentially introducing bias.
• Variability in barcode staining across pooled samples can affect cell yield and data accuracy.

Purpose of the Study:

• To improve the accuracy and reduce bias in single-cell debarcoding for mass cytometry.
• To develop a flexible debarcoding strategy that accounts for cell-specific and sample-specific variations.
• To provide enhanced data exploration tools for quality control in barcoded mass cytometry datasets.

Main Methods:

• An updated single-cell debarcoding algorithm was developed to output per-cell debarcoding parameters.
• The new algorithm decouples stringency filtering from the sample assignment process.
• Post-assignment filtering is performed using 1- and 2-dimensional gating on debarcoding parameters.

Main Results:

• The updated algorithm allows for visualization and analysis of debarcoding parameters in FCS files.
• This strategy enables the detection of cell type- and sample-specific effects during debarcoding.
• Adjustable stringency filtering effectively removes bias in cell yield introduced by the debarcoding process.

Conclusions:

• The enhanced debarcoding strategy improves the quality and reliability of pooled mass cytometry data.
• This approach offers greater flexibility and accuracy in sample deconvolution, particularly with variable barcode staining.
• The developed data exploration tools serve as a crucial quality check for barcoded mass cytometry experiments.