Distinguishing Different Cancerous Human Cells by Raman Spectroscopy Based on Discriminant Analysis Methods

Optical Diffraction Tomography and Raman Confocal Microscopy for the Investigation of Vacuoles Associated with Cancer Senescent Engulfing Cells

Wild-type p53 cancer therapy-induced senescent cells frequently engulf and degrade neighboring ones inside a massive vacuole in their cytoplasm. After clearance of the internalized cell, the vacuole persists, seemingly empty, for several hours. Despite large vacuoles being associated with cell death, this process is known to confer a survival advantage to cancer engulfing cells, leading to therapy resistance and tumor relapse. Previous attempts to resolve the vacuolar structure and visualize their content using dyes were unsatisfying for lack of known targets and ineffective dye penetration and/or retention. Here, we overcame this problem by applying optical diffraction tomography and Raman spectroscopy to MCF7 doxorubicin-induced engulfing cells. We demonstrated a real ability of cell tomography and Raman to phenotype complex microstructures, such as cell-in-cells and vacuoles, and detect chemical species in extremely low concentrations within live cells in a completely label-free fashion. We show that vacuoles had a density indistinguishable to the medium, but were not empty, instead contained diluted cell-derived macromolecules, and we could discern vacuoles from medium and cells using their Raman fingerprint. Our approach is useful for the noninvasive investigation of senescent engulfing (and other peculiar) cells in unperturbed conditions, crucial for a better understanding of complex biological processes.

Characterizing human mesenchymal stromal cells' immune-modulatory potency using targeted lipidomic profiling of sphingolipids

Cell therapies are expected to increase over the next decade owing to increasing demand for clinical applications. Mesenchymal stromal cells (MSCs) have been explored to treat a number of diseases, with some successes in early clinical trials. Despite early successes, poor MSC characterization results in lessened therapeutic capacity once in vivo. Here, we characterized MSCs derived from bone marrow (BM), adipose tissue and umbilical cord tissue for sphingolipids (SLs), a class of bioactive lipids, using liquid chromatography/tandem mass spectrometry. We found that ceramide levels differed based on the donor's sex in BM-MSCs. We detected fatty acyl chain variants in MSCs from all three sources. Linear discriminant analysis revealed that MSCs separated based on tissue source. Principal component analysis showed that interferon-γ-primed and unstimulated MSCs separated according to their SL signature. Lastly, we detected higher ceramide levels in low indoleamine 2,3-dioxygenase MSCs, indicating that sphingomyelinase or ceramidase enzymatic activity may be involved in their immune potency.

Horse Jumping and Dressage Training Activity Detection Using Accelerometer Data

Equine training activity detection will help to track and enhance the performance and fitness level of riders and their horses. Currently, the equestrian world is eager for a simple solution that goes beyond detecting basic gaits, yet current technologies fall short on the level of user friendliness and detection of main horse training activities. To this end, we collected leg accelerometer data of 14 well-trained horses during jumping and dressage trainings. For the first time, 6 jumping training and 25 advanced horse dressage activities are classified using specifically developed models based on a neural network. A jumping training could be classified with a high accuracy of 100 %, while a dressage training could be classified with an accuracy of 96.29%. Assigning the dressage movements to 11, 6 or 4 superclasses results in higher accuracies of 98.87%, 99.10% and 100%, respectively. Furthermore, during dressage training, the side of movement could be identified with an accuracy of 97.08%. In addition, a velocity estimation model was developed based on the measured velocities of seven horses performing the collected, working, and extended gaits during a dressage training. For the walk, trot, and canter paces, the velocities could be estimated accurately with a low root mean square error of 0.07 m/s, 0.14 m/s, and 0.42 m/s, respectively.

Surface enhanced Raman scattering of extracellular vesicles for cancer diagnostics despite isolation dependent lipoprotein contamination

Given the emerging diagnostic utility of extracellular vesicles (EVs), it is important to account for non-EV contaminants. Lipoprotein present in EV-enriched isolates may inflate particle counts and decrease sensitivity to biomarkers of interest, skewing chemical analyses and perpetuating downstream issues in labeling or functional analysis. Using label free surface enhanced Raman scattering (SERS), we confirm that three common EV isolation methods (differential ultracentrifugation, density gradient ultracentrifugation, and size exclusion chromatography) yield variable lipoprotein content. We demonstrate that a dual-isolation method is necessary to isolate EVs from the major classes of lipoprotein. However, combining SERS analysis with machine learning assisted classification, we show that the disease state is the main driver of distinction between EV samples, and largely unaffected by choice of isolation. Ultimately, this study describes a convenient SERS assay to retain accurate diagnostic information from clinical samples by overcoming differences in lipoprotein contamination according to isolation method.

Molecular monitoring of glioblastoma's immunogenicity using a combination of Raman spectroscopy and chemometrics

Raman spectroscopy (RS) has been used as a powerful diagnostic and non-invasive tool in cancer diagnosis as well as in discrimination of cancer and immune cells. In this study RS in combination with chemometrics was applied to cellular Raman spectral data to distinguish the phenotype of T-cells and monocytes after incubation with media conditioned by glioblastoma stem-cells (GSCs) showing different molecular background. For this purpose, genetic modulations of epithelial-to-mesenchymal transition (EMT) process and expression of immunomodulator CD73 were introduced. Principal component analysis of the Raman spectral data showed that T-cells and monocytes incubated with tumour-conditioned media (TCMs) of GSCs with inhibited EMT activator ZEB1 or CD73 formed distinct clusters compared to controls highlighting their differences. Further discriminatory analysis performed using linear discriminant analysis (LDA) and support vector machine classification (SVM), yielded sensitivities and specificities of over 70 and 67% respectively upon validation against an independent test set. Supporting those results, flow cytometric analysis was performed to test the influence of TCMs on cytokine profile of T-cells and monocytes. We found that ZEB1 and CD73 influence T-cell and monocyte phenotype and promote monocyte differentiation into a population of mixed pro- and anti-tumorigenic macrophages (MΦs) and dendritic cells (DCs) respectively. In conclusion, Raman spectroscopy in combination with chemometrics enabled tracking T-cells and monocytes.

Detection and Classification of Multi-Type Cells by Using Confocal Raman Spectroscopy

Tumor cells circulating in the peripheral blood are the prime cause of cancer metastasis and death, thus the identification and discrimination of these rare cells are crucial in the diagnostic of cancer. As a label-free detection method without invasion, Raman spectroscopy has already been indicated as a promising method for cell identification. This study uses a confocal Raman spectrometer with 532 nm laser excitation to obtain the Raman spectrum of living cells from the kidney, liver, lung, skin, and breast. Multivariate statistical methods are applied to classify the Raman spectra of these cells. The results validate that these cells can be distinguished from each other. Among the models built to predict unknown cell types, the quadratic discriminant analysis model had the highest accuracy. The demonstrated analysis model, based on the Raman spectrum of cells, is propitious and has great potential in the field of biomedical for classifying circulating tumor cells in the future.

Contribution of Raman Spectroscopy to Diagnosis and Grading of Chondrogenic Tumors

In the last decade, Raman Spectroscopy has demonstrated to be a label-free and non-destructive optical spectroscopy able to improve diagnostic accuracy in cancer diagnosis. This is because Raman spectroscopic measurements can reveal a deep molecular understanding of the biochemical changes in cancer tissues in comparison with non-cancer tissues. In this pilot study, we apply Raman spectroscopy imaging to the diagnosis and grading of chondrogenic tumors, including enchondroma and chondrosarcomas of increasing histologic grades. The investigation included the analysis of areas of 50×50 μm² to approximately 200×200 μm², respectively. Multivariate statistical analysis, based on unsupervised (Principal Analysis Components) and supervised (Linear Discriminant Analysis) methods, differentiated between the various tumor samples, between cells and extracellular matrix, and between collagen and non-collagenous components. The results dealt out basic biochemical information on tumor progression giving the possibility to grade with certainty the malignant cartilaginous tumors under investigation. The basic processes revealed by Raman Spectroscopy are the progressive degrading of collagen type-II components, the formation of calcifications and the cell proliferation in tissues ranging from enchondroma to chondrosarcomas. This study highlights that Raman spectroscopy is particularly effective when cartilaginous tumors need to be subjected to histopathological analysis.

A Feasibility Study of a Novel Piezo MEMS Tweezer for Soft Materials Characterization

The opportunity to know the status of a soft tissue (ST) in situ can be very useful for microsurgery or early diagnosis. Since normal and diseased tissues have different mechanical characteristics, many systems have been developed to carry out such measurements locally. Among them, MEMS tweezers are very relevant for their efficiency and relative simplicity compared to the other systems. In this paper a novel piezoelectric MEMS tweezer for soft materials analysis and characterization is presented. A theoretical approach has developed in order to carry out the values of the stiffness, the equivalent Young’s modulus, and the viscous damping coefficients of the analyzed samples. The method has been validated by using both Finite Element Analysis and data from the literature.

Drug-Resistant Staphylococcus aureus Strains Reveal Distinct Biochemical Features with Raman Microspectroscopy

Staphylococcus aureus ( S. aureus) is a leading cause of hospital-acquired infections, such as bacteremia, pneumonia, and endocarditis. Treatment of these infections can be challenging since strains of S. aureus, such as methicillin-resistant S. aureus (MRSA), have evolved resistance to antimicrobials. Current methods to identify infectious agents in hospital environments often rely on time-consuming, multistep culturing techniques to distinguish problematic strains (i.e., antimicrobial resistant variants) of a particular bacterial species. Therefore, a need exists for a rapid, label-free technique to identify drug-resistant bacterial strains to guide proper antibiotic treatment. Here, our findings demonstrate the ability to characterize and identify microbes at the subspecies level using Raman microspectroscopy, which probes the vibrational modes of molecules to provide a biochemical "fingerprint". This technique can distinguish between different isolates of species such as Streptococcus agalactiae and S. aureus. To determine the ability of this analytical approach to detect drug-resistant bacteria, isogenic variants of S. aureus including the comparison of strains lacking or expressing antibiotic resistance determinants were evaluated. Spectral variations observed may be associated with biochemical components such as amino acids, carotenoids, and lipids. Mutants lacking carotenoid production were distinguished from wild-type S. aureus and other strain variants. Furthermore, spectral biomarkers of S. aureus isogenic bacterial strains were identified. These results demonstrate the feasibility of Raman microspectroscopy for distinguishing between various genetically distinct forms of a single bacterial species in situ. This is important for detecting antibiotic-resistant strains of bacteria and indicates the potential for future identification of other multidrug resistant pathogens with this technique.