Supervised machine learning in microfluidic impedance flow cytometry for improved particle size determination. Issue 3 (18th August 2022) - de Bruijn, Douwe S.; ten Eikelder, Henricus R. A.; Papadimitriou, Vasileios A.; Olthuis, Wouter; van den Berg, Albert
Abstract: The assessment of particle and cell size in electrical microfluidic flow cytometers has become common practice. Nevertheless, in flow cytometers with coplanar electrodes accurate determination of particle size is difficult, owing to the inhomogeneous electric field. Pre‐defined signal templates and compensation methods have been introduced to correct for this positional dependence, but are cumbersome when dealing with irregular signal shapes. We introduce a simple and accurate post‐processing method without the use of pre‐defined signal templates and compensation functions using supervised machine learning. We implemented a multiple linear regression model and show an average reduction of the particle diameter variation by 37% with respect to an earlier processing method based on a feature extraction algorithm and compensation function. Furthermore, we demonstrate its application in flow cytometry by determining the size distribution of a population of small (4.6 ± 0.9 μm) and large (5.9 ± 0.8 μm) yeast cells. The improved performance of this coplanar, two electrode chip enables precise cell size determination in easy to fabricate impedance flow cytometers. Abstract : The assessment of particle and cell size in electrical microfluidic flow cytometers has become common practice, nevertheless, determination of the particle size is difficult with easy‐to‐fabricate coplanar electrodes. Here, a simple and accurate post‐processing method is demonstrated without the use of pre‐defined templates and compensation functions using supervised machine learning. The performance of the multiple linear regression model is evaluated with monodisperse beads. Its application in flow cytometry is demonstrated with the size distribution of yeast.