Impact of Panel Size, Fluorochrome Selection, and Unmixing Algorithms on Ultra-High Parameter Flow Cytometry Analysis

Area of Science:

• Immunology
• Biotechnology
• Data Science

Background:

• Full spectral flow cytometry enables ultra-high-dimensional immunophenotyping with up to 50 fluorochromes.
• This advancement presents significant analytical challenges in unmixing accuracy, population spread, and panel design.

Purpose of the Study:

• To evaluate the impact of various unmixing algorithms on biological interpretation using OMIP datasets.
• To identify limitations in current tools and propose strategies for accurate data interpretation in high-parameter cytometry.

Main Methods:

• Comparative analysis of different unmixing algorithms on OMIP datasets.
• Utilized metrics such as Median Mismatch Index (MMI), Spillover Spread Matrix (SSM), and robust Standard Deviation (rSD).

Main Results:

• Algorithmic discrepancies can lead to loss of resolution, population misidentification, and incorrect biological interpretation.
• Current tools, including SSM, may not be suitable for predicting spillover spread in ultra-large panels.
• Highlighted limitations of current tools and proposed strategies for optimized single stain use and unmixing accuracy prediction.

Conclusions:

• Unmixing algorithm choice critically affects biological interpretation in high-parameter flow cytometry.
• Optimized strategies for single stain use and accurate unmixing are essential for reliable data interpretation.
• Further development is needed for tools to accurately predict spillover spread in ultra-large spectral flow cytometry panels.