Raman spectroscopy using a multiclass extension of Fisher-based Feature Selection Support Vector Machines (FFS-SVM) for characterizing in-vitro apoptotic cell death induced by paclitaxel

Raman microspectroscopy combined with advanced data mining methods are used to demonstrate proof-of-concept for the development of a non-invasive, real-time in vitro assay platform for the classification and characterization of anti-cancer agents. Breast cancer cells were investigated over a 48 h time course of treatment with Paclitaxel. Raman spectroscopic analysis is used with a multiclass One-versus-One Support Vector Machines classification algorithm to classify cell death over a 48 h period. The Fisher-based Feature Selection method provides discriminative features descriptive of the apoptotic process during time-course. Spectral datasets collected at each of the time-points during a separate 48 h 3-point time course study are used as the testing datasets. The features, or spectral peaks, output directly as wavenumbers are correlated to corresponding biochemical species for each time point yielding an analysis of the biochemical compositional changes. Conventional assay methods are employed to validate and confirm results of the Raman spectroscopic analysis. © 2014 Springer International Publishing.