Abstract
Raman spectroscopy has been widely used in clinical and molecular biological studies, providing high chemical specificity without the necessity of labels and with little-to-no sample preparation. However, currently performed Raman-based studies of eukaryotic cells are still very laborious and time-consuming, resulting in a low number of sampled cells and questionable statistical validations. Furthermore, the approach requires a trained specialist to perform and analyze the experiments, rendering the method less attractive for most laboratories. In this work, we present a new high-content analysis Raman spectroscopy (HCA-RS) platform that overcomes the current challenges of conventional Raman spectroscopy implementations. HCA-RS allows sampling of a large number of cells under different physiological conditions without any user interaction. The performance of the approach is successfully demonstrated by the development of a Raman-based cell viability assay, i.e., the effect of doxorubicin concentration on monocytic THP-1 cells. A statistical model, principal component analysis combined with support vector machine (PCA-SVM), was found to successfully predict the percentage of viable cells in a mixed population and is in good agreement to results obtained by a standard cell viability assay. This study demonstrates the potential of Raman spectroscopy as a standard high-throughput tool for clinical and biological applications.
Highlights
Raman spectroscopy has attracted a lot of interest as a versatile tool for clinical and biological applications due to its ability to provide molecular fingerprint information of tissue and cells in a label-free manner[1,2,3]
We have extended the previously proposed high-throughput screening Raman spectroscopy (HTS-RS) platform to allow the measurement of cells under different physiological conditions, and used the high-content analysis Raman spectroscopy (HCA-RS) platform to investigate drug-induced changes in cells
The HCA-RS platform requires minimum user input, i.e. the laser power, integration time, number of image frames per well, and number of wells corresponding to different physiological conditions
Summary
Raman spectroscopy has attracted a lot of interest as a versatile tool for clinical and biological applications due to its ability to provide molecular fingerprint information of tissue and cells in a label-free manner[1,2,3]. We have developed a high-throughput screening Raman spectroscopy (HTS-RS) platform that allowed the acquisition of average Raman spectra of cells in an automated fashion, removing user interaction during the data acquisition procedure, resulting in an extreme boost in throughput and rapid sampling of thousands of cells in a short period of time[13]. We have extended the previously proposed HTS-RS platform to allow the measurement of cells under different physiological conditions, and used the high-content analysis Raman spectroscopy (HCA-RS) platform to investigate drug-induced changes in cells. This new extension – HCA-RS - opens a new way to rapidly assess the molecular signature of cells exposed to different physiological conditions. The newly proposed HCA-RS platform creates a paradigm change for the application of Raman spectroscopy for the molecular analysis and characterization of eukaryotic cells
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