Development of an automated identification system for nano-crystal encoded microspheres in flow cytometry

Abstract

Quantum dot encoded microspheres (QDEMs) offer much potential for bead based identification of a variety of biomolecules via flow cytometry (FCM). To date, QDEM subpopulation classification from FCM has required significant instrument modification or multiparameter gating. It is unclear whether or not current data analysis approaches can handle the increased multiplexed capacity offered by these novel encoding schemes. In this thesis the drawbacks of currently available data analysis techniques are demonstrated and novel classification methods proposed to overcome these limitations. A commercially available 20 code QDEM library with fluorescent emissions at 4 distinct wavelengths and 4 different intensity levels was analysed using flow cytometry. Multiparameter gating (MPG) a readily available classification method for subpopulations in FCM was evaluated. A support vector machine (SVM) and two types of artificial neural networks (ANNs), a multilayer perceptron (MLP) and probabilistic radial basis function (PRBF) were also considered. For the supervised models rigorous parameter selection using cross validation (CV) was used to construct the optimum models. Independent test set validation was also carried out. As a further test, external validation of the classifiers was performed using multiplexed QDEMs solutions. The performance of MPG was poor (average misclassification (MC) rate = 9.7%) was a time consuming process requiring fine adjustment of the gates, classifications made on the dataset were poor with multiple classifications on single events and as the multiplex capacity increases the performance is likely to decrease. The SVM had the best performance in independent test validation with 96.33% accuracy on the independent testing (MLP = 96.12%, PRBF = 94.38%). Furthermore the performance of the SVM was superior to both MPG and both ANNs for the external validation set with an average MC rate for MLP = 6.1% and PRBF = 7.5% whereas the SVM MC rate was 2.9%. Assuming that the external test solutions were homogenous the variance between classified results should be minimal hence, the variance of correct classifications (CCs) was used as an additional indicator of classifier performance. The SVM demonstrates the lowest variance for each of the external validation solutions (average σ 2 = 31479) some 50% lower than that of MPG. As a conclusion to the development of the classifier, a user friendly software system has been developed to allow construction and evaluation of multiclass SVMs for use by FCM practitioners in the laboratory. SVMs are a promising classifier for QDEMs that can be rapidly trained and classifications made in real time using standard FCM instrumentation. It is hoped that this work will advance SAT for bioanalytical applications.