Accurate and reliable cell counting using holographic microscopy and machine learning algorithms

Reliable and accurate cell counts are crucial for many different purposes such as the maintenance of cell cultures in biological research or in-process controls in industrial bioprocessing.
When considering currently available cell counting options, many users ask which cell counting method is better: manual counting or using automated cell counters. Traditional manual cell counting involves the use of haemocytometers and requires experienced lab personnel to analyze cell samples. In addition to the inherent user-to-user variability, manual counting is also more labour-intensive.
In this study, we evaluated the performance of the fluidlab R-300 cell counter according to the recommendations of the CLSI EP05-A32. To this end, we assessed accuracy, precision, linearity, and repeatability when counting three different cell types (HeLa, PBMCs and yeast) with distinct morphologies and sizes. Our data demonstrates that the fluidlab R-300 cell counter is accurate, precise and ensures linearity of the results over a large range of cell types and concentrations.