Exploring photoacoustic spectroscopy-based machine learning together with metabolomics to assess breast tumor progression in a xenograft model ex vivo.

In the current study, a breast tumor xenograft was established in athymic nude mice by subcutaneous injection of the MCF-7 cell line and assessed the tumor progression by photoacoustic spectroscopy combined with machine learning tools. The advancement of breast tumors in nude mice was validated by tumor volume kinetics and histopathology and corresponding image analysis by TissueQuant software compared to controls. The ex vivo tumors in progressive conditions belonging to time points, day 5th, 10th, 15th & 20th, were excited with 281 nm pulsed laser light and recorded the corresponding photoacoustic spectra in time domain. The spectra were then pre-processed, augmented for a 10-fold increase in the data strength, and subjected to wavelet packet transformation for feature extraction and selection using MATLAB software. In the present study, the top 10 features from all the time point groups under study were selected based on their prediction ranking values using the mRMR algorithm. The chosen features of all the time-point groups were then subjected to multi-class Support Vector Machine (SVM) algorithms for learning and classifying into respective time point groups under study. The analysis demonstrated accuracy values of 95.2%, 99.5%, and 80.3% with SVM- Radial Basis Function (SVM-RBF), SVM-Polynomial & SVM-Linear, respectively. The serum metabolomic levels during tumor progression complemented photoacoustic patterns of tumor progression, depicting breast cancer pathophysiology.

Fluorescence and Photoacoustic Spectroscopy-Based Assessment of Mitochondrial Dysfunction in Oral Cancer Together with Machine Learning: A Pilot Study.

The current study reports an integrated approach of machine learning and tryptophan fluorescence and photoacoustic spectral properties to assess the mitochondrial status under oral pathological conditions. The mitochondria in the study were isolated from oral cancer tissues and adjacent normal counterparts, and the corresponding fluorescence and photoacoustic spectra of tryptophan were recorded at 281 nm pulsed laser excitations. A set of features were selected from the pre-processed spectra and were used to classify the data using support vector machine (SVM) learning in the MATLAB platform. SVM analysis demonstrated clear differentiation between mitochondria isolated from normal and cancer tissues for fluorescence (sensitivity, 86.6%; specificity, 90%) and photoacoustic (sensitivity, 86.6%; specificity, 96.6%) measurements. Further investigation into the influence of change in protein conformation on the nature of tryptophan spectral properties was evaluated by 8-anilino-1-naphthalene sulfonic acid (ANS) fluorescence assay. The impact of protein structural changes on the mitochondrial functions was also estimated by mitochondrial membrane potential (MMP), reactive oxygen species (ROS), and cytochrome c oxidase (COX) assays, suggesting an altered mitochondrial function. The findings indicate that tryptophan fluorescence and photoacoustic spectral properties together with machine learning algorithms may delineate the mitochondrial functional status in vitro, indicating its translational potential.

Interrogation of an autofluorescence-based method for protein fingerprinting.

In the present study, we have designed a laser-induced fluorescence (LIF) based instrumentation and developed a sensitive methodology for the effective separation, visualization, identification and analysis of proteins on a single platform. In this method, intrinsic fluorescence spectra of proteins were detected after separation on 1 or 2 dimensional Sodium Dodecyl Sulfate-Tris(2-carboxyethyl)phosphine (SDS-TCEP) polyacrylamide gel electrophoresis (PAGE) and the data were analyzed. The MATLAB assisted software was designed for the development of PAGE fingerprint for the visualization of protein after 1- and 2-dimensional protein separation. These provided objective parameters of intrinsic fluorescence intensity, emission peak, molecular weight and isoelectric point using a single platform. Further, the current architecture could differentiate the overlapping proteins in the PAGE gels which otherwise were not identifiable by conventional staining, imaging and tagging methods. Categorization of the proteins based on the presence or absence of tyrosine or tryptophan residues and assigning the corresponding emission peaks (309-356 nm) with pseudo colors allowed the detection of proportion of proteins within the given spectrum. The present methodology doesn't use stains or tags, hence amenable to couple with mass spectroscopic measurements. This technique may have relevance in the field of proteomics that is used for innumerable applications.

Monitoring breast tumor progression by photoacoustic measurements: a xenograft mice model study.

The current study reports the photoacoustic spectroscopy-based assessment of breast tumor progression in a nude mice xenograft model. The tumor was induced through subcutaneous injection of MCF-7 cells in female nude mice and was monitored for 20 days until the tumor volume reached 1000 mm3. The tumor tissues were extracted at three different time points (days 10, 15, and 20) after tumor inoculation and subjected to photoacoustic spectral recordings in time domain ex vivo at 281 nm pulsed laser excitations. The spectra were converted into the frequency domain using the fast Fourier transformed tools of MATLAB® algorithms and further utilized to extract seven statistical features (mean, median, area under the curve, variance and standard deviation, skewness and kurtosis) from each time point sample to assess the tumor growth with wavelet principal component analysis based logistic regression analysis performed on the data. The prediction accuracies of the analysis for day 10 versus day 15, day 15 versus day 20, and day 10 versus day 20 were found to be 92.31, 87.5, and 95.2%, respectively. Also, receiver operator characteristics area under the curve analysis for day 10 versus day 15, day 15 versus day 20, and day 10 versus day 20 were found to be 0.95, 0.85, and 0.93, respectively. The ability of photoacoustic measurements in the objective assessment of tumor progression has been clearly demonstrated, indicating its clinical potential.