Coincidence Detection in Flow Cytometry Using Pulse-Shape Analysis in Polydisperse Suspensions

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Hematology

Background:

• Flow cytometers are essential for cell characterization in medical diagnostics.
• Accurate cell counting is critical in hematology, necessitating precise coincidence correction methods.
• Current coincidence correction methods have limitations in sensitivity and accuracy.

Purpose of the Study:

• To develop an advanced post-processing procedure for flow cytometer signals to improve coincidence detection.
• To enhance the accuracy and speed of coincidence correction in cell concentration measurements.
• To differentiate coincidences between various particle sizes and cell types.

Main Methods:

• Developed a post-processing method for time-dependent detector signals (pulses).
• Analyzed pulse characteristics including area, maximum intensity, standard deviation, and skewness.
• Utilized standard deviation versus area, and pulse skewness/standard deviation for coincidence detection.

Main Results:

• The standard deviation versus area method significantly improved coincidence detection in monodisperse suspensions compared to traditional methods.
• Successfully identified and distinguished coincidences between different particle sizes (1 and 5 μm) and cell types (thrombocytes, erythrocytes) in polydisperse samples and whole blood.
• The developed procedure identifies coincidences for individual measurements, unlike dilution series methods.

Conclusions:

• The novel approach offers superior sensitivity and accuracy for coincidence detection in flow cytometry.
• This method provides a more precise way to correct for coincidences in cell concentration measurements.
• The technique is applicable to various sample types, including whole blood, enhancing diagnostic capabilities.