The traditional method for counting cells is electrical impedance, also known as the Coulter Principle. Whole blood is passed between two electrodes through an aperture so narrow that only one cell can pass through at a time. The impedance changes as a cell passes through. The change in impedance is proportional to cell volume, resulting in a cell count and measure of volume.
The principle of impedance counting, also known as the Coulter principle after its inventor Wallace Coulter, is the passage of cells suspended in a known dilution through a small orifice. The electrolyte-containing diluent serves as a conductor of a constant electrical current between two electrodes. Cells are poor electrical conductors and as they pass through the orifice, they impede the passage of current, which is detected as an increase in electrical resistance. Each cell will cause a resistance pulse, thus allowing cell counting. Furthermore, the magnitude of the resistance peak is directly related to cell volume.
Impedance counting is schematically illustrated in Figure 1. A vacuum draws the cells suspended in conductive diluent from left to right through the orifice. Passage of each cell is registered as a peak in electrical resistance between the two electrodes.
Impedance platelet counting is typically performed in the presence of red blood cells (RBC), which are counted simultaneously. Platelets are differentiated from RBCs based on histogram analysis of the accumulated resistance events, meaning their size. Thresholds are used to find optimal separation between the two cell populations.
Over years of development, technical nuances have been introduced into impedance counting for improving accuracy. One of the known limitations of impedance counting is the potential for a phenomenon called “recirculation” that can cause falsely increased cell counts. This phenomenon (Figure 2) occurs when cells that have traversed the orifice become caught in eddy currents behind the orifice. These cells recirculate in and around the detection zone and can be recounted, which obviously results in spuriously higher counts.
Various approaches to resolve this artifact have been applied. Some instrument manufacturers use lateral flow of reagents to sweep already counted cells away from the detection zone. Other manufacturers use a plate close to the orifice, which ensures that any cell recirculation takes place away from the detection zone. These devices are called after their inventor, von Behren’s plates (Figure 3).
Another alternative is the use of hydrodynamic focusing. This technique employs a sheath of fast moving fluid that guides and confines the cell suspension, ensuring that during analysis the cells are continuously propelled forwards through and beyond the orifice and therefore away from the impedance detection zone (Figure 4).
One further advantage of using hydrodynamic focusing is that it focuses the cells on the very center of the orifice Instruments that do not use hydrodynamic focusing are prone to what is known as the “edge effect”. This phenomenon implies that cells flowing through a simple bulk flow transducer may traverse the orifice at its center, but may also pass at the periphery of the orifice. The consequence then is an irregular impedance profile, resulting in false estimates of cell size (Figure 5). Although electronic and algorithmic editing of the pulses can correct this phenomenon to some extent, hydrodynamic focusing is the preferred means of resolving edge effects.
Impedance analysis has some benefits. The method has historically been widely accepted and from an economic perspective, impedance detectors can be cheaply manufactured. The disadvantage of impedance analysis is that the discrimination between platelet and non-platelet events is purely based on size. Although normal sized platelets and normal sized RBCs show little overlap, this may not be true in cases of pathology. Specific examples of poor impedance separation are shown in Figures 6B and 6C. In some impedance analyzers attempts have been made to optimize separation between platelets and RBCs, for example by using dynamic thresholding to find valleys between the two cell populations. Alternatively, software algorithms have been developed that improve the accuracy of platelet counts in comparison with the use of fixed thresholds.
Figure 6. Histograms of CELL-DYN Sapphire platelet impedance measurements. 6A: (left) normal platelet count and normal MCV; no overlap with RBC. 6B: (center) normal platelet count and low MCV; clear overlap between platelets and microcytic RBC. 6C: (right) very low platelet count; the separation between platelets and non-platelets is difficult to define.