Enabling multiple cellular enumeration applications of a bioparticle sensing platform using machine learning

Cellular surface receptors are commonly used as diagnostic and prognostic biomarkers for many infectious diseases. Benchtop clinical instruments, e.g., flow cytometers and fluorescence microscopes are commonly employed for identification and quantification of these biomarkers. These diagnostic techniques suffer from high instrument cost, laborious protocols and limited multiplexing ability. Recently, we have demonstrated the surface receptor detection of blood cells using novel electrically sensitive microparticles. Here, we employ computational pruning and machine learning techniques to improve the detection of cell surface receptors as biomarkers. The data collected from microfluidic impedance flow cytometry consisting of a multifrequency response of metal oxide-coated microparticles conjugated to blood cells (granulocytes) with either CD11b or CD66b surface receptors and video recordings were transformed to numerical values and manually annotated to train machine learning models. Classification accuracies of ∼95% and 97% before and after applying outlier removal techniques were observed when differentiating between cells and cells with 10 nm-Al₂O₃ anti-CD11b conjugated particles. Classification accuracies of ∼94% and ∼96% were observed when differentiating between cells and cells with 20 nm-Al₂O₃ anti-CD66b conjugated particles. Similarly, an improvement in the classification of neural networks was observed for different applications of the impedance spectrometry platform data. This provides preliminary results that impedance cytometer data collected for multiple biomarkers, along with the trained machine-learning models and noise reduction techniques, can efficiently quantify the biomarkers. This study will help in enabling next-generation cytometry technology with a potential for improved disease diagnosis in the future.