Surya P. Singh

Oral mucosal epithelial cells express the membrane anchored mucin MUC1

OBJECTIVEnThe presence of a stable salivary pellicle (SP) is essential to provide a wet surface for the oral mucosal epithelia. The oral mucosa is covered by the SP which is suggested to be a mixed film of both salivary and epithelial components. Our aim was to analyse the presence of membrane-anchored mucin MUC1 in the oral mucosal epithelia.nnnDESINGnThe presence of MUC1 was studied by immunohistochemical and immunoelectron microscopical methods in 19 buccal mucosal specimens. The localization and intensity of the epithelial expression were analyzed.nnnRESULTSnStrong staining of MUC1 was found in the epithelial cells of intermediate and superficial layers. Some basal cells were shown faint expression. In the intermediate and superficial layers, the MUC1 expression was seen mainly on the upper cell surface. Furthermore, the expression of MUC1 was noted in the cytoplasm near the nucleus and in the rough granules. By electron microscopy, extracellular domain of membrane-anchored molecules extruded about 15-30nm above the cell surface in the apical cells of the oral epithelium. Immunoelectron microscopic examination shows that MUC1 is mainly localized in the plasma membrane of epithelial cells and also in small vesicles (75-100nm) just below the plasma membrane.nnnCONCLUSIONnThe membrane-anchored MUC1 is expressed in the superficial layer of the oral mucosal epithelium, especially on the upper surface of epithelial cells. MUCI may be the anchoring protein of the salivary pellicle stabilization.

Investigating Effects of Proteasome Inhibitor on Multiple Myeloma Cells Using Confocal Raman Microscopy

Due to its label-free and non-destructive nature, applications of Raman spectroscopic imaging in monitoring therapeutic responses at the cellular level are growing. We have recently developed a high-speed confocal Raman microscopy system to image living biological specimens with high spatial resolution and sensitivity. In the present study, we have applied this system to monitor the effects of Bortezomib, a proteasome inhibitor drug, on multiple myeloma cells. Cluster imaging followed by spectral profiling suggest major differences in the nuclear and cytoplasmic contents of cells due to drug treatment that can be monitored with Raman spectroscopy. Spectra were also acquired from group of cells and feasibility of discrimination among treated and untreated cells using principal component analysis (PCA) was accessed. Findings support the feasibility of Raman technologies as an alternate, novel method for monitoring live cell dynamics with minimal external perturbation.

Objective identification of dental abnormalities with multispectral fluorescence imaging

Sensitive methods that can enable early detection of dental diseases (caries and calculus) are desirable in clinical practice. Optical spectroscopic approaches have emerged as promising alternatives owing to their wealth of molecular information and lack of sample preparation requirements. In the present study, using multispectral fluorescence imaging, we have demonstrated that dental caries and calculus can be objectively identified on extracted tooth. Spectral differences among control, carious and calculus conditions were attributed to the porphyrin pigment content, which is a byproduct of bacterial metabolism. Spectral maps generated using different porphyrin bands offer important clues to the spread of bacterial infection. Statistically significant differences utilizing fluorescence intensity ratios were observed among three groups. In contrast to laser induced fluorescence, these methods can provide information about exact spread of the infection and may aid in long term dental monitoring. Successful adoption of this approach for routine clinical usage can assist dentists in implementing timely remedial measures.

Development of a classification model for non‐alcoholic steatohepatitis (NASH) using confocal Raman micro‐spectroscopy

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disorder in developed countries [1]. A subset of individuals with NAFLD progress to non-alcoholic steatohepatitis (NASH), an advanced form of NAFLD which predisposes individuals to cirrhosis, liver failure and hepatocellular carcinoma. The current gold standard for NASH diagnosis and staging is based on histological evaluation, which is largely semi-quantitative and subjective. To address the need for an automated and objective approach to NASH detection, we combined Raman micro-spectroscopy and machine learning techniques to develop a classification model based on a well-established NASH mouse model, using spectrum pre-processing, biochemical component analysis (BCA) and logistic regression. By employing a selected pool of biochemical components, we identified biochemical changes specific to NASH and show that the classification model is capable of accurately detecting NASH (AUC=0.85-0.87) in mice. The unique biochemical fingerprint generated in this study may serve as a useful criterion to be leveraged for further validation in clinical samples.

Label free assessment of ultra-violet radiation induced damages in skin cells (Conference Presentation)

Changes in the cellular homeostasis in response to a stimuli, disease or therapeutic intervention are multifaceted in nature, and cannot be grasped by routinely employed targeted imaging that focuses on a small set of suspected molecules or genes. Novel approaches relying on global analysis of cellular features, from morphology to the composite biomolecular status (notably chemical composition and molecular conformation), is a pre-requisite for accurate monitoring of cellular processes. In the present study label-free profiling of normal skin fibroblasts (Hs895.Sk) exposed to sub-lethal doses of ultra-violet radiation has been performed using quantitative phase imaging and Raman spectroscopy. Spectral differences in the Raman fingerprint region indicates differences in the protein and nucleic acid composition. These differences were successfully utilized to develop an automated classification model based on principal component analysis. Distinct changes in the cellular morphology were observed and validated through quantitative phase imaging. Significant dose dependent differences in different biophysical parameters such as dry mass and matter density were observed. Combination of these two techniques, one suited for detection of subtle morphological/biophysical alterations while the other appropriate for capturing molecular perturbations, could pave the way to address issues of label-free monitoring of cellular responses in response to an external stimulus. These findings can provide an accurate understanding of different markers associated with radiation damage and would assist in providing a quantitative tool to our future studies on designing alternate diagnostic tools.

Label-free spectrochemical probe for determination of hemoglobin glycation in clinical blood samples

Glycated hemoglobin, HbA1c, is an important biomarker that reveals the average value of blood glucose over the preceding 3 months. While significant recent attention has been focused on the use of optical and direct molecular spectroscopic methods for determination of HbA1c, a facile test that minimizes sample preparation needs and turnaround time still remains elusive. Here, we report a label-free approach for identifying low, mid and high-HbA1c groups in hemolysate and in whole blood samples featuring resonance Raman (RR) spectroscopy and support vector machine (SVM)-based classification of spectral patterns. The diagnostic power of RR measurements stems from its selective enhancement of hemoglobin-specific features, which simultaneously minimizes the blood matrix spectral interference and permits detection in the native solution. In this pilot study, our spectroscopic observations reveal that glycation of hemoglobin results in subtle but reproducible changes even when detected in the whole blood matrix. Leveraging SVM analysis of the principal component scores determined from the RR spectra, we show high degree of accuracy in classifying clinical specimen. We envisage that the promising findings will pave the way for more extensive clinical specimen investigations with the ultimate goal of translating molecular spectroscopy for routine point-of-care testing.

Evaluation of accuracy dependence of Raman spectroscopic models on the ratio of calibration and validation points for non-invasive glucose sensing

AbstractOptical monitoring of blood glucose levels for non-invasive diagnosis is a growing area of research. Recent efforts in this direction have been inclined towards reducing the requirement of calibration framework. Here, we are presenting a systematic investigation on the influence of variation in the ratio of calibration and validation points on the prospective predictive accuracy of spectral models. A fiber-optic probe coupled Raman system has been employed for transcutaneous measurements. Limit of agreement analysis between serum and partial least square regression predicted spectroscopic glucose values has been performed for accurate comparison. Findings are suggestive of strong predictive accuracy of spectroscopic models without requiring substantive calibration measurements.n Graphical abstract

Identification of morphological and biochemical changes in keratin-8/18 knock-down cells using Raman spectroscopy

Accurate understanding of cellular processes and responses to stimuli is of paramount importance in biomedical research and diagnosis. Raman spectroscopy (RS), a label-free and nondestructive spectroscopic method has the potential to serve as a novel theranostics tool. Both fiber-optic and micro-Raman studies have demonstrated efficacy in diagnostics and therapeutic response monitoring. In the present study, we have evaluated the potential of micro-Raman spectroscopic maps in identifying changes induced by loss of K8/18 proteins in a tongue cancer cell line. Furthermore, we also evaluated the efficacy of less expensive and commercially available fiber probes to identify K8/18 wild and knock-down cell pellets, in view of the utility of cell pellet-based studies. The findings suggest that major differences in the cellular morphology and biochemical composition can be objectively identified and can be utilized for classification using both micro-Raman and fiber-probe-based RS. These findings highlight the potential of fiber-optic probe-based RS in noninvasive cellular phenotyping for diagnosis and therapeutic response monitoring, especially in low-resource settings.

A classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy

We combined Raman micro-spectroscopy and machine learning techniques to develop a classification model based on a well-established non-alcoholic steatohepatitis (NASH) mouse model, using spectrum pre-processing, biochemical component analysis (BCA) and logistic regression.