Fluorescence Flow Cytometry: The next step in urine particle analysis

Fluorescence staining is the first step of the measurement process. Here, specific fluorochromes are used to label the core (DNA) and surface (membrane) structures of the particles in a sample. The fluorochromes are chosen based on their ability to emit fluorescent light when excited by a specific wavelength of light. This enables the characteristics of each particle to be identified and measured, allowing for a wider range of clinical parameters to be determined.

Hydrodynamic focusing is the second step. In this step, a sheath reagent flow is used to separate and align the particles in the sample. The sheath reagent flow works by surrounding the sample with a fluid stream that directs the particles in a single file through the laser beam. This ensures that each particle is measured individually and accurately, leading to more precise results.

Particle characterisation by laser scanning is the third step. In this step, each particle passes through the 488nm-laser beam, which excites the individual particles and can separate the cells using four different signals. The signals collected by the detectors provide information on each particle's size, shape, depolarisation and fluorescence intensity.

The detected light signal patterns are translated into individual 'fingerprints', which enable each particle to be identified and allocated to the relevant parameter. This leads to the quantification of each parameter, providing detailed information about the particles in the sample.

Finally, the signals collected from one measurement are allocated to the corresponding parameters and summarised in scattergrams. Each scattergram represents the signals of one sample for one or more parameters, providing a comprehensive analysis of the particles and their characteristics. With fluorescent flow cytometry, large numbers of cells can be analysed in a short amount of time, providing detailed information about cell populations and their characteristics. This information can then be used to support clinical decision making.