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Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy: An analytical technique to understand therapeutic responses at the molecular level.

ABSTRACT: Rapid monitoring of the response to treatment in cancer patients is essential to predict the outcome of the therapeutic regimen early in the course of the treatment. The conventional methods are laborious, time-consuming, subjective and lack the ability to study different biomolecules and their interactions, simultaneously. Since; mechanisms of cancer and its response to therapy is dependent on molecular interactions and not on single biomolecules, an assay capable of studying molecular interactions as a whole, is preferred. Fourier Transform Infrared (FTIR) spectroscopy has become a popular technique in the field of cancer therapy with an ability to elucidate molecular interactions. The aim of this study, was to explore the utility of the FTIR technique along with multivariate analysis to understand whether the method has the resolution to identify the differences in the mechanism of therapeutic response. Towards achieving the aim, we utilized the mouse xenograft model of retinoblastoma and nanoparticle mediated targeted therapy. The results indicate that the mechanism underlying the response differed between the treated and untreated group which can be elucidated by unique spectral signatures generated by each group. The study establishes the efficiency of non-invasive, label-free and rapid FTIR method in assessing the interactions of nanoparticles with cellular macromolecules towards monitoring the response to cancer therapeutics.

SUBMITTER: Kalmodia S

PROVIDER: S-EPMC4645174 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy: An analytical technique to understand therapeutic responses at the molecular level.

Kalmodia Sushma S Parameswaran Sowmya S Yang Wenrong W Barrow Colin J CJ Krishnakumar Subramanian S

Scientific reports 20151116

Rapid monitoring of the response to treatment in cancer patients is essential to predict the outcome of the therapeutic regimen early in the course of the treatment. The conventional methods are laborious, time-consuming, subjective and lack the ability to study different biomolecules and their interactions, simultaneously. Since; mechanisms of cancer and its response to therapy is dependent on molecular interactions and not on single biomolecules, an assay capable of studying molecular interact ...[more]

PMID: 26568521

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Project description:BackgroundJapanese knotweed (Reynoutria japonica var. japonica), a problematic invasive species, has a wide geographical distribution. We have previously shown the potential for attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and chemometrics to segregate regional differentiation between Japanese knotweed plants. However, the contribution of environment to spectral differences remains unclear. Herein, the response of Japanese knotweed to varied environmental habitats has been studied. Eight unique growth environments were created by manipulation of the red: far-red light ratio (R: FR), water availability, nitrogen, and micronutrients. Their impacts on plant growth, photosynthetic parameters, and ATR-FTIR spectral profiles, were explored using chemometric techniques, including principal component analysis (PCA), linear discriminant analysis, support vector machines (SVM) and partial least squares regression. Key wavenumbers responsible for spectral differences were identified with PCA loadings, and molecular biomarkers were assigned. Partial least squared regression (PLSR) of spectral absorbance and root water potential (RWP) data was used to create a predictive model for RWP.ResultsSpectra from plants grown in different environments were differentiated using ATR-FTIR spectroscopy coupled with SVM. Biomarkers highlighted through PCA loadings corresponded to several molecules, most commonly cell wall carbohydrates, suggesting that these wavenumbers could be consistent indicators of plant stress across species. R: FR most affected the ATR-FTIR spectra of intact dried leaf material. PLSR prediction of root water potential achieved an R2 of 0.8, supporting the potential use of ATR-FTIR spectrometers as sensors for prediction of plant physiological parameters.ConclusionsJapanese knotweed exhibits environmentally induced phenotypes, indicated by measurable differences in their ATR-FTIR spectra. This high environmental plasticity reflected by key biomolecular changes may contribute to its success as an invasive species. Light quality (R: FR) appears critical in defining the growth and spectral response to environment. Cross-species conservation of biomarkers suggest that they could function as indicators of plant-environment interactions including abiotic stress responses and plant health.

Project description:BACKGROUND:The topical HIV prevention (microbicides) field is in acute need of a method to rapidly and objectively measure adherence to product use in clinical trials. Infrared (IR) spectroscopy has been used in many pharmaceutical and forensic applications but has yet to be applied to adherence monitoring. In this study, we report on efforts to test the feasibility of using IR spectroscopy as a means to measure residual active or placebo vaginal product, semen exposure and vaginal insertion from a single swab. METHODS:A portable IR spectrometer equipped with diamond attenuated total reflectance (ATR) was used to capture spectra of unused vs. vaginally-used swabs, vaginal swabs containing semen, and vaginal swabs to which either tenofovir-containing or matching placebo products (vaginal gel or insert) were added. Spectral data obtained from swabs placed directly on the spectrometer were divided into calibration and testing sets for developing and validating discriminant models set up to provide yes/no predictions of: vaginal vs. non-vaginal use, presence vs. no presence of each test product, and presence vs. no presence of semen. Further validation of models was performed using vaginal swabs collected from a clinical study evaluating vaginally administered placebo insert formulations. RESULTS:For each discriminant model developed to predict vaginal vs. non-vaginal use, presence vs. no presence of each test product, and presence vs. no presence of semen, classified validation samples not included in the model development were correctly identified into their respective classes with minimal prediction error. Clinically obtained vaginal swabs collected 15-60 minutes after placebo insert use were also correctly identified, further validating the models. CONCLUSION:Our findings demonstrate the proof of concept that IR spectroscopy can be a method for rapid detection and characterization of microbicide products and biological fluids present in vaginal swabs. This novel method has potential to support real-time, on-site adherence monitoring in clinical or field settings.

Project description:In the aging process, the presence of interleukin (IL)-17-producing CD4+CD28-NKG2D+T cells (called pathogenic CD4+ T cells) is strongly associated with inflammation and the development of various diseases. Thus, their presence needs to be monitored. The emergence of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy empowered with machine learning is a breakthrough in the field of medical diagnostics. This study aimed to discriminate between the elderly with a low percentage (LP; ≤3%) and a high percentage (HP; ≥6%) of pathogenic CD4+CD28-NKG2D+IL17+ T cells by utilizing ATR-FTIR coupled with machine learning algorithms. ATR spectra of serum, exosome, and HDL from both groups were explored in this study. Only exosome spectra in the 1700-1500 cm-1 region exhibited possible discrimination for the LP and HP groups based on principal component analysis (PCA). Furthermore, partial least square-discriminant analysis (PLS-DA) could differentiate both groups using the 1700-1500 cm-1 region of exosome ATR spectra with 64% accuracy, 69% sensitivity, and 61% specificity. To obtain better classification performance, several spectral models were then established using advanced machine learning algorithms, including J48 decision tree, support vector machine (SVM), random forest (RF), and neural network (NN). Herein, NN was considered to be the best model with an accuracy of 100%, sensitivity of 100%, and specificity of 100% using serum spectra in the region of 1800-900 cm-1. Exosome spectra in the 1700-1500 and combined 3000-2800 and 1800-900 cm-1 regions using the NN algorithm gave the same accuracy performance of 95% with a variation in sensitivity and specificity. HDL spectra with the NN algorithm also showed excellent test performance in the 1800-900 cm-1 region with 97% accuracy, 100% sensitivity, and 95% specificity. This study demonstrates that ATR-FTIR coupled with machine learning algorithms can be used to study immunosenescence. Furthermore, this approach can possibly be applied to monitor the presence of pathogenic CD4+ T cells in the elderly. Due to the limited number of samples used in this study, it is necessary to conduct a large-scale study to obtain more robust classification models and to assess the true clinical diagnostic performance.

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Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy: An analytical technique to understand therapeutic responses at the molecular level.

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Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy: An analytical technique to understand therapeutic responses at the molecular level.

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