Unveiling dose‐ and time‐dependent osteosarcoma cell responses to the γ‐secretase inhibitor, DAPT, by confocal Raman microscopy

Abstract

Using confocal Raman micro-spectroscopy, this study aims to elucidate the cellular responses of the γ-secretase inhibitor, N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester (DAPT), in osteosarcoma (OS) cells in a dose-and time-dependent manner. The K7M2 murine OS cell line was treated with different DAPT doses (0, 10, 20, and 40 μM) for 24 and 48 hours before investigations. Significant compositional changes (nucleic acids, protein and lipid) after DAPT treatment were addressed, which testified inhibitory effect of DAPT on the growth of OS cells. Moreover, both partial least squares-discriminant analysis (PLS-DA) and principal component analysis-linear discriminant analysis (PCA-LDA) analyses revealed governing composition variations among groups by distinguishing their spectral characteristics. Furthermore, by adopting leave-one-out cross validation method, it is shown that PLS-DA exhibited more classification capacity than PCA-LDA algorithm. Hence, by understanding the DAPT-based cellular variations, the achieved results provided an experimental foundation to establish new DAPT-based anticancer therapeutic strategies, and preclinical Raman analytical methodologies on drug-cell interactions.

A graphical user interface (NWUSA) for Raman spectral processing, analysis and feature recognition

“…2 The PLS-DA and PCA-LDA models were also used to identify osteosarcoma cells treated with different drug concentrations and time periods, providing good analysis and classification results. 23,28,29 These results further confirm the reliability of the developed software system.…”

“…This dataset was randomly selected from our achieved F I G U R E 6 (A) represents the scatter plot of scores for the first two significant LVs; and (B) depicts the PLS-DA loading of LV1. PLS-DA, partial least squares-discriminant analysis Raman spectral datasets of untreated and treated osteosarcoma cells (the cell sample was prepared and investigated following our previous protocols), 23,28,29 which were collected with a confocal micro-Raman spectroscopy system (WITec GmbH, Germany) using a 532-nm excitation laser. In this study, we did not intend to conduct a specific analysis of the dataset, but only use the dataset for software performance testing.…”

“…Moreover, some processing and multivariate analysis algorithms (such as normalization, mean-centered procedure, PCA, LDA, and PLS-DA) in the software system have also been used by our group to identify and understand the subtle biochemical variations underlying cancer progressing, therapeutic schedule, cell screening and cell-drug interactions. 2,23,28,29 For example, the PCA-LDA discriminant model generated sensitivities of 90%, 80%, and 96%, and specificities of 86.2%, 93.8%, and 100% among untreated and microwave ablation-treated lung cancerous tissue, and healthy lung tissue, respectively. 2 The PLS-DA and PCA-LDA models were also used to identify osteosarcoma cells treated with different drug concentrations and time periods, providing good analysis and classification results.…”

“…21 Because the predicted response value obtained by the PLS-DA algorithm is not strictly equal to zero or one, it is necessary to calculate the probability that the sample belongs to a specific class according to the probability density function and Bayes formula, and select the category with the largest probability as the prediction label. 22,23…”

Raman Microspectroscopic Investigation and Classification of Breast Cancer Pathological Characteristics

“…The equipment used for Raman spectroscopy has been described in detail previously [41,42]. Briefly, a single spectrum was collected using a WITec Alpha 500 confocal micro-Raman spectroscopy system (WITec GmbH, Ulm, Germany) using a 633 nm He-Ne laser source (35 mW, Research Electro-Optics, Inc., Boulder, CO, USA).…”