The use of Raman spectroscopy to differentiate between different prostatic adenocarcinoma cell lines

P Crow, B Barrass, C Kendall, M Hart-Prieto, M Wright, R Persad, N Stone, P Crow, B Barrass, C Kendall, M Hart-Prieto, M Wright, R Persad, N Stone

Abstract

Raman spectroscopy (RS) is an optical technique that provides an objective method of pathological diagnosis based on the molecular composition of tissue. Studies have shown that the technique can accurately identify and grade prostatic adenocarcinoma (CaP) in vitro. This study aimed to determine whether RS was able to differentiate between CaP cell lines of varying degrees of biological aggressiveness. Raman spectra were measured from two well-differentiated, androgen-sensitive cell lines (LNCaP and PCa 2b) and two poorly differentiated, androgen-insensitive cell lines (DU145 and PC 3). Principal component analysis was used to study the molecular differences that exist between cell lines and, in conjunction with linear discriminant analysis, was applied to 200 spectra to construct a diagnostic algorithm capable of differentiating between the different cell lines. The algorithm was able to identify the cell line of each individual cell with an overall sensitivity of 98% and a specificity of 99%. The results further demonstrate the ability of RS to differentiate between CaP samples of varying biological aggressiveness. RS shows promise for application in the diagnosis and grading of CaP in clinical practise as well as providing molecular information on CaP samples in a research setting.

Figures

Figure 1
Figure 1
A Raman spectrum from a prostate cell with wavenumbers labelled for major peaks.
Figure 2
Figure 2
Renishaw 1000 Raman system.
Figure 3
Figure 3
Mean Raman spectra measured from each of the cell lines.
Figure 4
Figure 4
Loads of the first three principal components. These describe the greatest variance in the spectra.
Figure 5
Figure 5
A three-dimensional scatter plot of the scores of PCs 1, 2 and 3. This demonstrates the natural clustering of the four cell lines spectra, without any modification of the algorithm to maximise group separation.
Figure 6
Figure 6
A three-dimensional scatter plot of the scores of LDFS 1, 2 and 3 demonstrating the clustering of the four cell lines achieved by the PCA/LDA algorithm.
Figure 7
Figure 7
A bar chart demonstrating the prediction power of the diagnostic algorithm.

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