Automated analysis of flow cytometric data for measuring neutrophil CD64 expression using a multi-instrument...

Area of Science:

• Flow cytometry
• Immunology
• Infectious disease diagnostics

Background:

• Leuko64™ assay measures neutrophil CD64 expression as an indicator of infection, sepsis, or systemic inflammation.
• Current Leuko64 analysis uses QuantiCALC software, a semi-automated method with limited interanalyst variability (<5%).
• This study introduces and evaluates a fully automated approach using GemStone™ software and probability state modeling (PSM).

Purpose of the Study:

• To evaluate a fully automated method for measuring neutrophil CD64 expression using GemStone and PSM.
• To compare the performance of the automated method against the existing semi-automated QuantiCALC method.
• To assess the impact of automation on interanalyst variability and precision in CD64 index quantification.

Main Methods:

• Processed 457 human blood samples using the Leuko64 assay across four different flow cytometer models.
• Developed a PSM to automatically identify calibration beads and leukocyte subpopulations.
• PSM automatically calculated CD64 index values, with data analysis and quality control performed by GemStone software without operator intervention.

Main Results:

• The automated PSM with GemStone showed excellent correlation with expert manual analysis (r² = 0.99675) for neutrophil CD64 index values.
• Interanalyst imprecision was reduced from 1.06% with QuantiCALC to 0.00% with GemStone PSM.
• Operator-to-operator agreement in GemStone was a perfect correlation (r² = 1.000).

Conclusions:

• Automated quantification of CD64 index values using PSM strongly correlates with expert analysis.
• PSM successfully processed flow cytometric data from multiple instruments and kit lots.
• The probability-based method offers enhanced objectivity, speed, and precision for in vitro diagnostic flow cytometry assays.