Multivariate Analysis of Mixed Lipid Aggregate Phase Transitions Monitored Using Raman Spectroscopy

Brillouin Spectroscopy of Binary Phospholipid-Cholesterol Bilayers

Multicomponent lipid bilayers are used as models for searching the origin of spatial heterogeneities in biomembranes called lipid rafts, implying the coexistence of domains of different phases and compositions within the lipid bilayer. The spatial organization of multicomponent lipid bilayers on a scale of a hundred nanometers remains unknown. Brillouin spectroscopy providing information about the acoustic phonons with the wavelength of several hundred nanometers has an unexplored potential for this problem. Here, we applied Brillouin spectroscopy for three binary bilayers composed of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), 1,2-palmitoyl-sn-glycero-3-phosphocholine (DPPC), and cholesterol. The Brillouin experiment for the oriented planar multibilayers was realized for two scattering geometries involving phonons for the lateral and normal directions of the propagation. The DPPC-DOPC mixtures known for the coexistence of the solid-ordered and liquid-disordered phases had bimodal Brillouin peaks, revealing the phase domains with sizes more than a hundred nanometers. Analysis of the Brillouin data for the binary mixtures concluded that the lateral phonons are preferable for testing the lateral homogeneity of the bilayers, while the phonons spreading across the bilayers are sensitive to the layered packing at the mesoscopic scale.

Biochemical changes in the cytoplasm of bovine oocytes during the in vitro maturation process: a Raman microscopy study

The sequence and chronology of the main biochemical changes occurring in the cytoplasm of bovine oocytes during the in vitro maturation process were tracked by Raman microscopy applied to cells previously subjected to enzymatic digestion of the zona pellucida. Specific spectral markers for proteins, lipids and carbohydrates were used to evaluate the developmental status of the ooplasm at four different times. Spectral changes revealed that lipid accumulation was dominant during the first six hours of culture while protein content reached the average levels characteristic of mature oocytes within the last four hours of the maturation process. A time-dependent decrease in carbohydrates was also observed. Finally, the carbohydrate-to-protein (P₁₀₃₇/P₁₀₀₂) ratio proved to be sensitive enough to determine the cytoplasmic maturation state of bovine oocytes and promises to be useful in future research aimed at optimizing culture conditions through the promotion of protein accumulation in the ooplasm.

Continuous gradient temperature Raman spectroscopy of 1-stearoyl- 2-docosahexaenoyl, 1-stearoyl- 2-arachidonoyl, and 1,2-stearoyl phosphocholines

Mixed chain phospholipids containing a saturated fatty acid at sn1 and a polyunsaturated fatty acid in sn2 are common in the specialized biological membranes prevalent in neural, retinal and organ tissues. Particularly important are mixed lipids containing palmitic or stearic acid and arachidonic or docosahexaenoic acid. Gradient temperature Raman spectroscopy (GTRS) applies the temperature gradients utilized in differential scanning calorimetry to Raman spectroscopy, providing a straightforward technique to identify molecular rearrangements and phase transitions. Herein we utilize GTRS for 1-18:0, 2-20:4n-6 PC; 1-18:0 2-22:6n-3 PC; and 1-18:0, 2-18:0 PC from -80 to 50 °C temperatures. 20 Mb three-dimensional data arrays with 0.2 °C increments and first/second derivatives allowed detailed vibrational mode assignment and analysis. Samples were analyzed neat and with molecular hydration. Previously reported phase transitions for hydrated 18:0-20:4PC and 18:0-22:6PC and numerous spectral differences resulting from hydration and the double bond structure were clearly observed. Molecular models showed that the addition of minimal water molecules results in significant structural differences compared to the neat molecules; 18:0-22:6PC is strikingly compact with water when viewed from the hydrophilic end. This precise Raman data cannot be observed in typically utilized fully hydrated vesicle samples, however the improved GTRS will allow for more precise analysis in fully hydrated vesicles because the underlying modes in the unavoidably broadened spectra can be identified.

Analysis and Classification of Hepatitis Infections Using Raman Spectroscopy and Multiscale Convolutional Neural Networks

Hepatitis infections represent a major health concern worldwide. Numerous computer-aided approaches have been devised for the early detection of hepatitis. In this study, we propose a method for the analysis and classification of cases of hepatitis-B virus ( HBV), hepatitis-C virus (HCV), and healthy subjects using Raman spectroscopy and a multiscale convolutional neural network (MSCNN). In particular, serum samples of HBV-infected patients (435 cases), HCV-infected patients (374 cases), and healthy persons (499 cases) are analyzed via Raman spectroscopy. The differences between Raman peaks in the measured serum spectra indicate specific biomolecular differences among the three classes. The dimensionality of the spectral data is reduced through principal component analysis. Subsequently, features are extracted, and then feature normalization is applied. Next, the extracted features are used to train different classifiers, namely MSCNN, a single-scale convolutional neural network, and other traditional classifiers. Among these classifiers, the MSCNN model achieved the best outcomes with a precision of 98.89%, sensitivity of 97.44%, specificity of 94.54%, and accuracy of 94.92%. Overall, the results demonstrate that Raman spectral analysis and MSCNN can be effectively utilized for rapid screening of hepatitis B and C cases.

Rise of Raman spectroscopy in neurosurgery: a review

SIGNIFICANCE

Although the clinical potential for Raman spectroscopy (RS) has been anticipated for decades, it has only recently been used in neurosurgery. Still, few devices have succeeded in making their way into the operating room. With recent technological advancements, however, vibrational sensing is poised to be a revolutionary tool for neurosurgeons.

AIM

We give a summary of neurosurgical workflows and key translational milestones of RS in clinical use and provide the optics and data science background required to implement such devices.

APPROACH

We performed an extensive review of the literature, with a specific emphasis on research that aims to build Raman systems suited for a neurosurgical setting.

RESULTS

The main translatable interest in Raman sensing rests in its capacity to yield label-free molecular information from tissue intraoperatively. Systems that have proven usable in the clinical setting are ergonomic, have a short integration time, and can acquire high-quality signal even in suboptimal conditions. Moreover, because of the complex microenvironment of brain tissue, data analysis is now recognized as a critical step in achieving high performance Raman-based sensing.

CONCLUSIONS

The next generation of Raman-based devices are making their way into operating rooms and their clinical translation requires close collaboration between physicians, engineers, and data scientists.

Deep learning-based component identification for the Raman spectra of mixtures

Raman spectroscopy is widely used as a fingerprint technique for molecular identification. However, Raman spectra contain molecular information from multiple components and interferences from noise and instrumentation. Thus, component identification using Raman spectra is still challenging, especially for mixtures. In this study, a novel approach entitled deep learning-based component identification (DeepCID) was proposed to solve this problem. Convolution neural network (CNN) models were established to predict the presence of components in mixtures. Comparative studies showed that DeepCID could learn spectral features and identify components in both simulated and real Raman spectral datasets of mixtures with higher accuracy and significantly lower false positive rates. In addition, DeepCID showed better sensitivity when compared with the logistic regression (LR) with L1-regularization, k-nearest neighbor (kNN), random forest (RF) and back propagation artificial neural network (BP-ANN) models for ternary mixture spectral datasets. In conclusion, DeepCID is a promising method for solving the component identification problem in the Raman spectra of mixtures.