Raman micro spectroscopy study of the interaction of vincristine with A549 cells supported by expression analysis of bcl-2 protein

Cellular discrimination using in vitro Raman micro spectroscopy: the role of the nucleolus

“…16,17 Thus, the potential applications extend beyond disease diagnostics to the label free in vitro screening of cytological processes, such as drug or nanoparticle uptake and mechanisms of interaction, and toxicology. 16,[18][19][20] There has been a wide range of studies to date demonstrating the potential of Raman micro spectroscopy to map live and fixed cells with subcellular resolution, [21][22][23][24][25] profile the distribution of anticancer agents [26][27][28][29][30] and nanoparticles in cells 16,31,32 and monitor subcellular processes 33 and toxicological responses. [34][35][36][37] Fundamental to the development of applications of Raman micro spectroscopy for disease diagnostics as well as analysis of cytological processes is an understanding of the variability of the spectral signatures across the subcellular environment, their potential for differentiation of cell phenotype or diseased state, and their sensitivity to external perturbation, such as viral infection, radiation damage, or chemical stress due to, for example, toxic or chemotherapeutic agents.…”

Spectral pre and post processing for infrared and Raman spectroscopy of biological tissues and cells

“…To extract information regarding the biochemical changes underpinning the spectral changes, a more sophisticated data processing toolbox is required. Supervised methods such as (Linear and Nonlinear) Partial Least Squares Regression (PLSR) can be employed to identify spectral variables which are specifically correlated with an external agent [5,6] or indeed an observed physiological effect such as viability or proliferative capacity [7]. In such studies, correlation with accepted or "gold standard" assays can be used to guide and validate the interpretation of the vibrational spectroscopic results.…”

“…In PLSR, PLS Jack-knifing has been demonstrated to allow the reduction of the number of variables [74] avoiding over fitting and improving performance. PLS Jack-knifing produces results that are readily interpretable in terms of highlighting the systematic variation of important spectral features within the regression model, and allows visualization of PLSR coefficients and their uncertainty, and their use in analysing spectroscopic responses associated with chemotherapeutic agents has been demonstrated [6,7]. Whereas PCA and PLSR, for example, may be considered linear analytical methods, in that they attempt to describe the variability of the dataset according to single parameters, multiparameter or nonlinear statistical approaches can prove more powerful in applications to biospectroscopical datasets which have a high degree of variability.…”

“…Furthermore, feature selection techniques such as PLSR coefficients, Jack-Knifing (JK) and genetic algorithms, amongst others [92], can be employed to identify the most statistically relevant spectral changes, such that the biological mechanisms underlying the spectral changes can explored and understood. A good practical example of this method in action in Raman spectral data is outlined in two studies by Nawaz et al, [6,7] in which, the aim was to investigate the capability of Raman as a technique to study drug interactions in cells and the physiological response. Looking specifically at cisplatin as an example chemotherapeutic drug, these studies were able to extract information relating to drug action in the cells via regression of the Raman dataset against cytotoxicological data and dose, extracting out features from the Raman spectra which correspond to changes to protein conformation and structural alterations of DNA [6].…”

“…IR absorption and Raman spectroscopy are very distinct physical processes [2], and therefore the spectral distortions can be of very different physical origin. Although many biomedical applications of the techniques have been targeted towards disease diagnostics, for which high specificity and sensitivity classification algorithms are desirable, more recent applications have been targeted at, for example, disease aetiology [3,4], radiation dosimetry [5], drug screening [6][7][8][9][10] and nanotoxicology [11][12][13], for which a range of other data mining and analysis protocols have been explored [14]. It is important, however, that these protocols are well validated, in order to progress the field with confidence.…”