DebarcodeR increases fluorescent cell barcoding capacity and accuracy.

Area of Science:

• Single-cell analysis
• Computational biology
• Flow cytometry

Background:

• Fluorescent cell barcoding (FCB) allows high-throughput sample collection in flow cytometry.
• Current methods lack automated tools to balance dye density, cell complexity, and debarcoding accuracy.
• This limits FCB's integration with high-dimensional cytometry data.

Purpose of the Study:

• Introduce DebarcodeR, a computational framework for automated debarcoding in FCB.
• Improve experimental methods for more robust FCB.
• Enhance the speed, accuracy, and reproducibility of debarcoding.

Main Methods:

• Developed DebarcodeR with adaptive regression and Gaussian mixture modeling for probabilistic cell assignment.
• Implemented assignment tolerance parameters to balance cell recovery and accuracy.
• Introduced external and internal standards for improved experimental control.

Main Results:

• DebarcodeR significantly improved debarcoding speed, accuracy, and reproducibility.
• The method successfully debarcoded cell lines and heterogeneous primary cell mixtures.
• Avoided selective loss of rare cell subsets during analysis.

Conclusions:

• DebarcodeR provides a robust computational solution for FCB debarcoding challenges.
• The integrated experimental improvements enhance FCB reliability.
• DebarcodeR facilitates the application of FCB in complex biological systems.