Raman spectra of amino acids and their aqueous solutions

Blueshift of the CN stretching vibration of acetonitrile in solution: computational and experimental study

Acetonitrile, a polar molecule that cannot form hydrogen bonds on its own, interacts with solvent molecules mainly through the lone pair of its nitrogen atom and the π electrons of its CN triple bond [Correction added on 17 July 2024, after first online publication: Acetole has been changed to Acetonitrile in the preceeding sentence.]. Interestingly, acetonitrile exhibits an unexpected strengthening of the triple bond's force constant in an aqueous environment, leading to an upshift (blueshift) in the corresponding stretching vibration: this effect contrasts with the usual consequence of hydrogen bonding on the vibrational frequencies of the acceptor groups, that is, frequency redshift. This investigation elucidates this phenomenon using Raman spectroscopy to examine the behavior of acetonitrile in organic solvent, water, and silver ion aqueous solutions, where an even more pronounced upshift is observed. Raman spectroscopy is particularly well suited for analyzing aqueous solutions due to the minimal scattering effect of water molecules across most of the vibrational spectrum. Computational approaches, both static and dynamical, based on Density Functional Theory and hybrid functionals, are employed here to interpret these findings, and accurately reproduce the vibrational frequencies of acetonitrile in different environments. Our calculations also allow an explanation for this unique behavior in terms of electric charge displacements. On the other hand, the study of the interaction of acetonitrile with water molecules and metal ions is relevant for the use of this molecule as a solvent in both chemical and pharmaceutical applications.

Focused ion beam-fabricated nanorod substrate for label-free surface-enhanced Raman spectroscopy and enabling dual virus detection

The COVID-19 pandemic presents global challenges, notably with co-infections in respiratory tract involving SARS-CoV-2 variants and influenza strains. Detecting multiple viruses simultaneously is crucial for accurate diagnosis, effective tracking infectious sources, and containment of the epidemic. This study uses a label-free surface-enhanced Raman spectroscopy (SERS) method using Au NPs/pZrO2 (250) and FIB-made Au NRs (100) to detect dual viruses, including SARS-CoV-2 Delta variant (D) and influenza A (A) or B (B) virus. Results demonstrate distinct peaks facilitating virus differentiation, especially between D and A or B, with clear disparities between substrates; specific peaks at 950 and 1337 cm-1 are pivotal for discerning viruses using Au NPs/pZrO2 (250), while those at 1050, 1394, and 1450 cm-1 and 1033, 1165, 1337, and 1378 cm-1 are key for validation using Au NRs (100). Differences in substrate surface morphology and spatial disposition of accommodating viruses significantly influence hotspot formation and Raman signal amplification efficiency, thereby affecting the ability to distinguish various viruses. Furthermore, both substrates offer insights, even in the presence of oxymetazoline hydrochloride (an interfering substance), with practical implications in viral diagnosis. The customized design and reproducibility underscore efficient Raman signal amplification, even in challenging environments, highlighting potential for widespread virus detection.

Biomolecules in Pleistocene fossils from tropical cave indicate fossil biofilm

Finding biomolecules in fossils is a challenging task due to their degradation over time from physical, chemical, and biological factors. The primary hypothesis for explaining the presence of biomolecules in fossilized bones tissues suggests their survival in the fossilization process. In contrast, some of these biomolecules could either derive from bacteria biofilm, thus without a direct relationship with the fossil record or could be an artifact from measurement procedures. Raman spectroscopy studies across various fossil ages and environments have detected multiple bands ranging from 1200 to 1800 cm-1 associative of organic compounds. However, the significance of these bands remains elusive. Our research aims to address this issue through a deep Raman spectroscopy investigation on Pleistocene teeth from Tayassu and Smilodon populator. These fossils were obtained from a well-preserved stratigraphic succession in Toca de Cima do Pilão cave, near the National Park of Serra da Capivara in semiarid Brazil. We propose two hypotheses to explain the presence of organic compounds related to 1200 to 1800 cm-1 Raman spectral range in fossil tissues: (i) these bands are biological signatures of preserved fossil biomolecules, or (ii) they are exogenous biological signatures associated with the bacterial biofilm formation during post-depositional processes. Our results align with the latter hypothesis, followed by biofilm degradation. However, the specific mechanisms involved in the natural biofilm degradation in fossil records remain unexplored in this study. In our case, the formation of biofilm on fossil bones is attributed to the oligotrophic conditions of the cave sediment matrix. We present a comprehensive model to elucidate the existence of biofilm on fossilized tissues, emphasizing the pivotal role of post-depositional processes, especially water action, in the cave environment. As the fossils were discovered in a cave setting, post-depositional processes significantly contribute to the formation of the biofilm matrix. Although our study provides insights into biofilm formation, further research is needed to delve into the specific mechanisms driving natural biofilm degradation in fossils.

Multifunctional magneto-plasmonic lipogel based on peptide hydrogel for application in combined cancer therapy

Supramolecular hydrogels, particularly low-molecular-weight peptide hydrogels, are promising drug delivery systems due to their ability to change the solubility, targeting, metabolism and toxicity of drugs. Magneto-plasmonic liposomes, in addition to being remotely controllable with the application of an external magnetic field, also increase the efficiency of encapsulated drug release through thermal stimulation, for example, with magnetic and optical hyperthermia. Thus, the combination of those two materials-giving magneto-plasmonic lipogels-brings together several functionalities, among which are hyperthermia and spatiotemporally controlled drug delivery. In this work, a novel dehydrodipeptide hydrogelator was synthesised, and the respective hydrogel was functionalized with magneto-plasmonic liposomes. After individually characterising the components with regard to their rheological, spectroscopic and magnetic properties, the magneto-plasmonic lipogel was equally characterised and evaluated concerning its ability to deliver drugs in a controlled fashion. To this end, the response of the 5(6)-carboxyfluorescein-loaded magneto-plasmonic lipogel to near-infrared light was assessed. The results showed that the system is a proper carrier of hydrophilic drugs and allows to envisage photo-responsive drug delivery. These facts, together with the magnetic guidance and hyperthermia capabilities of the developed composite gel, may pave the way to a new era in the treatment of cancer and other diseases.

Rapid discrimination between wild and cultivated Ophiocordyceps sinensis through comparative analysis of label-free SERS technique and mass spectrometry

Ophiocordyceps sinensis is a genus of ascomycete fungi that has been widely used as a valuable tonic or medicine. However, due to over-exploitation and the destruction of natural ecosystems, the shortage of wild O. sinensis resources has led to an increase in artificially cultivated O. sinensis. To rapidly and accurately identify the molecular differences between cultivated and wild O. sinensis, this study employs surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms to distinguish the two O. sinensis categories. Specifically, we collected SERS spectra for wild and cultivated O. sinensis and validated the metabolic profiles of SERS spectra using Ultra-Performance Liquid Chromatography coupled with Orbitrap High-Resolution Mass Spectrometry (UPLC-Orbitrap-HRMS). Subsequently, we constructed machine learning classifiers to mine potential information from the spectral data, and the spectral feature importance map is determined through an optimized algorithm. The results indicate that the representative characteristic peaks in the SERS spectra are consistent with the metabolites identified through metabolomics analysis, confirming the feasibility of the SERS method. The optimized support vector machine (SVM) model achieved the most accurate and efficient capacity in discriminating between wild and cultivated O. sinensis (accuracy = 98.95%, 5-fold cross-validation = 98.38%, time = 0.89s). The spectral feature importance map revealed subtle compositional differences between wild and cultivated O. sinensis. Taken together, these results are expected to enable the application of SERS in the quality control of O. sinensis raw materials, providing a foundation for the efficient and rapid identification of their quality and origin.

Probing Biological Nitrogen Fixation in Legumes Using Raman Spectroscopy

Biological nitrogen fixation (BNF) by symbiotic bacteria plays a vital role in sustainable agriculture. However, current quantification methods are often expensive and impractical. This study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans. Raman spectra were obtained from soybean plants grown with and without rhizobia bacteria to identify spectral signatures associated with BNF. δN15 isotope ratio mass spectrometry (IRMS) was used to determine actual BNF percentages. Partial least squares regression (PLSR) was employed to develop a model for BNF quantification based on Raman spectra. The model explained 80% of the variation in BNF activity. To enhance the model's specificity for BNF detection regardless of nitrogen availability, a subsequent elastic net (Enet) regularisation strategy was implemented. This approach provided insights into key wavenumbers and biochemicals associated with BNF in soybeans.

Ultrabroadband two-beam coherent anti-Stokes Raman scattering and spontaneous Raman spectroscopy of organic fluids: A comparative study

Spontaneous Raman spectroscopy is a well-established diagnostic tool, allowing for the identification of all Raman active species with a single measurement. Yet, it may suffer from low-signal intensity and fluorescent background. In contrast, coherent anti-Stokes Raman scattering (CARS) offers laser-like signals, but the traditional approach lacks the multiplex capability of spontaneous Raman spectroscopy. We present an ultrabroadband CARS setup which aims at exciting the full spectrum (300-3700 cm-1) of biological molecules. A dual-output optical parametric amplifier provides a ~7 fs pump/Stokes and a ~700 fs probe pulse. CARS spectra of DMSO, ethanol, and methanol show great agreement with spontaneous Raman spectroscopy and superiority in fluorescent environments. The spectral resolution proves sufficient to differentiate between the complex spectra of L-proline and hydroxyproline. Moreover, decay constants in the sub picosecond range are determined for individual Raman transitions, providing an additional approach for sample characterization.

A Novel Non-Destructive Rapid Tool for Estimating Amino Acid Composition and Secondary Structures of Proteins in Solution

Amino-acid protein composition plays an important role in biology, medicine, and nutrition. Here, a groundbreaking protein analysis technique that quickly estimates amino acid composition and secondary structure across various protein sizes, while maintaining their natural states is introduced and validated. This method combines multivariate statistics and the thermostable Raman interaction profiling (TRIP) technique, eliminating the need for complex preparations. In order to validate the approach, the Raman spectra are constructed of seven proteins of varying sizes by utilizing their amino acid frequencies and the Raman spectra of individual amino acids. These constructed spectra exhibit a close resemblance to the actual measured Raman spectra. Specific vibrational modes tied to free amino and carboxyl termini of the amino acids disappear as signals linked to secondary structures emerged under TRIP conditions. Furthermore, the technique is used inversely to successfully estimate amino acid compositions and secondary structures of unknown proteins across a range of sizes, achieving impressive accuracy ranging between 1.47% and 5.77% of root mean square errors (RMSE). These results extend the uses for TRIP beyond interaction profiling, to probe amino acid composition and structure.

Fabrication of immobilized lipases from Candida rugosa on hierarchical mesoporous silica for enzymatic enrichment of ω-3 polyunsaturated fatty acids by selective hydrolysis

In this study, lipase from Candida rugosa was immobilized on hydrophobic hierarchical porous hollow silica microsphere (HPHSM-C3) via adsorption. The prepared biocatalyst HPHSM-C3@CRL exhibited higher activity, thermal and pH stability. HPHSM-C3@CRL remained 70.2% of initial activity after 30 days of storage at 24 °C and 50.4% of initial activity after 10 cycles. Moreover, HPHSM-C3@CRL was utilized in enzymatic enrichment of omega-3 polyunsaturated fatty acids (ω-3 PUFAs) in glycerides, achieving ω-3 PUFAs content of 53.42% with the hydrolysis rate of 48.78% under optimal condition. The Km and Vmax value of HPHSM-C3@CRL was 42.2% lower and 63.5% higher than those of CRL, respectively. The 3D structure analysis of CRL, substrates and pore structure of HPHSM-C3 suggested that the hierarchical pore improved activity and selectivity of immobilized lipase. This result demonstrated that HPHSM-C3@CRL may be an effective biocatalyst for the enzymatic enrichment of ω-3 PUFAs in food industries.

Raman Spectra of Amino Acids and Peptides from Machine Learning Polarizabilities

Raman spectroscopy is an important tool in the study of vibrational properties and composition of molecules, peptides, and even proteins. Raman spectra can be simulated based on the change of the electronic polarizability with vibrations, which can nowadays be efficiently obtained via machine learning models trained on first-principles data. However, the transferability of the models trained on small molecules to larger structures is unclear, and direct training on large structures is prohibitively expensive. In this work, we first train two machine learning models to predict the polarizabilities of all 20 amino acids. Both models are carefully benchmarked and compared to density functional theory (DFT) calculations, with the neural network method being found to offer better transferability. By combination of machine learning models with classical force field molecular dynamics, Raman spectra of all amino acids are also obtained and investigated, showing good agreement with experiments. The models are further extended to small peptides. We find that adding structures containing peptide bonds to the training set greatly improves predictions, even for peptides not included in training sets.

In situ brain tumor detection using a Raman spectroscopy system-results of a multicenter study

Safe and effective brain tumor surgery aims to remove tumor tissue, not non-tumoral brain. This is a challenge since tumor cells are often not visually distinguishable from peritumoral brain during surgery. To address this, we conducted a multicenter study testing whether the Sentry System could distinguish the three most common types of brain tumors from brain tissue in a label-free manner. The Sentry System is a new real time, in situ brain tumor detection device that merges Raman spectroscopy with machine learning tissue classifiers. Nine hundred and seventy-six in situ spectroscopy measurements and colocalized tissue specimens were acquired from 67 patients undergoing surgery for glioblastoma, brain metastases, or meningioma to assess tumor classification. The device achieved diagnostic accuracies of 91% for glioblastoma, 97% for brain metastases, and 96% for meningiomas. These data show that the Sentry System discriminated tumor containing tissue from non-tumoral brain in real time and prior to resection.

A metabolic map and artificial intelligence-aided identification of nasopharyngeal carcinoma via a single-cell Raman platform

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a complex cancer influenced by various factors. This study explores the use of single-cell Raman spectroscopy as a potential diagnostic tool for investigating biomolecular changes associated with NPC carcinogenesis.

METHODS

Seven NPC cell lines, one immortalised nasopharyngeal epithelial cell line, six nasopharyngeal mucosa tissues and seven NPC tissue samples were analysed by performing confocal Raman spectroscopic measurements and imaging. The single-cell Raman spectral dataset was used to quantify relevant biomolecules and build machine learning classification models. Metabolomic profiles were investigated using ultra-performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS).

RESULTS

By generating a metabolic map of seven NPC cell lines, we identified an interplay of altered metabolic processes involving nucleic acids, amino acids, lipids and sugars. The results from spatially resolved Raman maps and UPLC-MS/MS metabolomics were consistent, revealing an increase of unsaturated fatty acids in cancer cells, particularly in highly metastatic 5-8F and poorly differentiated CNE2 cells. The classification model achieved a nearly perfect classification when identifying NPC and non-NPC cells with an ROC-AUC of 0.99 and a value of 0.97 when identifying 13 tissue samples.

CONCLUSION

This study unveils a complex interplay of metabolic network and highlights the potential roles of unsaturated fatty acids in NPC progression and metastasis. This renders further research to provide deeper insights into NPC pathogenesis, identify new metabolic targets and improve the efficacy of targeted therapies in NPC. Artificial intelligence-aided analysis of single-cell Raman spectra has achieved high accuracies in the classification of both cancer cells and patient tissues, paving the way for a simple, less invasive and accurate diagnostic test.

Exploring the cellular antioxidant mechanism against cytotoxic silver nanoparticles: a Raman spectroscopic analysis

Silver nanoparticles (AgNPs) hold great promise for several different applications, from colorimetric sensors to antimicrobial agents. Despite their widespread incorporation in consumer products, limited understanding of the detrimental effects and cellular antioxidant responses associated with AgNPs at sublethal concentrations persists, raising concerns for human and ecological well-being. To address this gap, we synthesized AgNPs of varying sizes and evaluated their cytotoxicity against human dermal fibroblasts (HDF). Our study revealed that toxicity of AgNPs is a time- and size-dependent process, even at low exposure levels. AgNPs exhibited low short-term cytotoxicity but high long-term impact, particularly for the smallest NPs tested. Raman microspectroscopy was employed for in-time investigations of intracellular molecular variations during the first 24 h of exposure to AgNPs of 35 nm. Subtle protein and lipid degradations were detected, but no discernible damage to the DNA was observed. Signals associated with antioxidant proteins, such as superoxide dismutase (SOD), catalase (CAT) and metallothioneins (MTs), increased over time, reflecting the heightened production of these defense agents. Fluorescence microscopy further confirmed the efficacy of overexpressed antioxidant proteins in mitigating ROS formation during short-term exposure to AgNPs. This work provides valuable insights into the molecular changes and remedial strategies within the cellular environment, utilizing Raman microspectroscopy as an advanced analytical technique. These findings offer a novel perspective on the cytotoxicity mechanism of AgNPs, contributing to the development of safer materials and advice on regulatory guidelines for their biomedical applications.

Low-abundance proteins-based label-free SERS approach for high precision detection of liver cancer with different stages

BACKGROUND

Surface-enhanced Raman scattering (SERS) technology have unique advantages of rapid, simple, and highly sensitive in the detection of serum, it can be used for the detection of liver cancer. However, some protein biomarkers in body fluids are often present at ultra-low concentrations and severely interfered with by the high-abundance proteins (HAPs), which will affect the detection of specificity and accuracy in cancer screening based on the SERS immunoassay. Clearly, there is a need for an unlabeled SERS method based on low abundance proteins, which is rapid, noninvasive, and capable of high precision detection and screening of liver cancer.

RESULTS

Serum samples were collected from 60 patients with liver cancer (27 patients with stage T1 and T2 liver cancer, 33 patients with stage T3 and T4 liver cancer) and 40 healthy volunteers. Herein, immunoglobulin and albumin were separated by immune sorption and Cohn ethanol fractionation. Then, the low abundance protein (LAPs) was enriched, and high-quality SERS spectral signals were detected and obtained. Finally, combined with the principal component analysis-linear discriminant analysis (PCA-LDA) algorithm, the SERS spectrum of early liver cancer (T1-T2) and advanced liver cancer (T3-T4) could be well distinguished from normal people, and the accuracy rate was 98.5% and 100%, respectively. Moreover, SERS technology based on serum LAPs extraction combined with the partial least square-support vector machine (PLS-SVM) successfully realized the classification and prediction of normal volunteers and liver cancer patients with different tumor (T) stages, and the diagnostic accuracy of PLS-SVM reached 87.5% in the unknown testing set.

SIGNIFICANCE

The experimental results show that the serum LAPs SERS detection combined with multivariate statistical algorithms can be used for effectively distinguishing liver cancer patients from healthy volunteers, and even achieved the screening of early liver cancer with high accuracy (T1 and T2 stage). These results showed that serum LAPs SERS detection combined with a multivariate statistical diagnostic algorithm has certain application potential in early cancer screening.

Metabolic profiling of murine radiation-induced lung injury with Raman spectroscopy and comparative machine learning

Radiation-induced lung injury (RILI) is a dose-limiting toxicity for cancer patients receiving thoracic radiotherapy. As such, it is important to characterize metabolic associations with the early and late stages of RILI, namely pneumonitis and pulmonary fibrosis. Recently, Raman spectroscopy has shown utility for the differentiation of pneumonitic and fibrotic tissue states in a mouse model; however, the specific metabolite-disease associations remain relatively unexplored from a Raman perspective. This work harnesses Raman spectroscopy and supervised machine learning to investigate metabolic associations with radiation pneumonitis and pulmonary fibrosis in a mouse model. To this end, Raman spectra were collected from lung tissues of irradiated/non-irradiated C3H/HeJ and C57BL/6J mice and labelled as normal, pneumonitis, or fibrosis, based on histological assessment. Spectra were decomposed into metabolic scores via group and basis restricted non-negative matrix factorization, classified with random forest (GBR-NMF-RF), and metabolites predictive of RILI were identified. To provide comparative context, spectra were decomposed and classified via principal component analysis with random forest (PCA-RF), and full spectra were classified with a convolutional neural network (CNN), as well as logistic regression (LR). Through leave-one-mouse-out cross-validation, we observed that GBR-NMF-RF was comparable to other methods by measure of accuracy and log-loss (p > 0.10 by Mann-Whitney U test), and no methodology was dominant across all classification tasks by measure of area under the receiver operating characteristic curve. Moreover, GBR-NMF-RF results were directly interpretable and identified collagen and specific collagen precursors as top fibrosis predictors, while metabolites with immune and inflammatory functions, such as serine and histidine, were top pneumonitis predictors. Further support for GBR-NMF-RF and the identified metabolite associations with RILI was found as CNN interpretation heatmaps revealed spectral regions consistent with these metabolites.

AI-Driven Spectral Decomposition: Predicting the Most Probable Protein Compositions from Surface Enhanced Raman Spectroscopy Spectra of Amino Acids

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for elucidating the molecular makeup of materials. It possesses the unique characteristics of single-molecule sensitivity and extremely high specificity. However, the true potential of SERS, particularly in capturing the biochemical content of particles, remains underexplored. In this study, we harnessed transformer neural networks to interpret SERS spectra, aiming to discern the amino acid profiles within proteins. By training the network on the SERS profiles of 20 amino acids of human proteins, we explore the feasibility of predicting the predominant proteins within the µL-scale detection volume of SERS. Our results highlight a consistent alignment between the model's predictions and the protein's known amino acid compositions, deepening our understanding of the inherent information contained within SERS spectra. For instance, the model achieved low root mean square error (RMSE) scores and minimal deviation in the prediction of amino acid compositions for proteins such as Bovine Serum Albumin (BSA), ACE2 protein, and CD63 antigen. This novel methodology offers a robust avenue not only for protein analytics but also sets a precedent for the broader realm of spectral analyses across diverse material categories. It represents a solid step forward to establishing SERS-based proteomics.

Simultaneous prediction of 16 quality attributes during protein A chromatography using machine learning based Raman spectroscopy models

Several key technologies for advancing biopharmaceutical manufacturing depend on the successful implementation of process analytical technologies that can monitor multiple product quality attributes in a continuous in-line setting. Raman spectroscopy is an emerging technology in the biopharma industry that promises to fit this strategic need, yet its application is not widespread due to limited success for predicting a meaningful number of quality attributes. In this study, we addressed this very problem by demonstrating new capabilities for preprocessing Raman spectra using a series of Butterworth filters. The resulting increase in the number of spectral features is paired with a machine learning algorithm and laboratory automation hardware to drive the automated collection and training of a calibration model that allows for the prediction of 16 different product quality attributes in an in-line mode. The demonstrated ability to generate these Raman-based models for in-process product quality monitoring is the breakthrough to increase process understanding by delivering product quality data in a continuous manner. The implementation of this multiattribute in-line technology will create new workflows within process development, characterization, validation, and control.

Highly sensitive detection of tryptophan based on Schiff base reaction and surface-enhanced Raman spectroscopy

In this study, a simple, rapid and sensitive method combining surface-enhanced Raman spectroscopy and Schiff base reaction was developed for the detection of tryptophan. This method does not require product separation to obtain a significant Raman signal of the derivatized product, and the derivatization reaction can be controlled by experimental parameters such as reaction temperature, time, concentration of derivatization reagent and concentration of sodium nitrite. The characteristic peak of the derivative of tryptophan (1620 cm-1) was selected for quantitative analysis, and the intensity of the characteristic Raman spectrum peak showed a linear relationship with the concentration of tryptophan (10-8-10-4 mol/L) in the range of with a correlation coefficient R2 of 0.9922. This assay combines surface-enhanced Raman spectroscopy and Schiff base reaction, which is characterized by high sensitivity and easy operation, and has good application prospects in the detection of tryptophan in food.

Tip-enhanced Raman spectroscopy reveals the structural rearrangements of tau protein aggregates at the growth phase

Tau protein aggregates inside neurons in the course of Alzheimer's disease (AD). Because of the enormous number of people suffering from AD, this disease has become one of the world's major health and social problems. The presence of tau lesions clearly correlates with cognitive impairments in AD patients, thus, tau is the target of potential treatments for AD, next to amyloid-β. The exact mechanism of tau aggregation has not been understood in detail so far; especially little is known about the structural rearrangements of tau aggregates at the growth phase. The research into tau conformation at each step of the aggregation pathway will contribute to the design of effective therapeutic approaches. To follow the secondary structure of individual tau aggregates at the growth phase, we applied tip-enhanced Raman spectroscopy (TERS). The nanospectroscopic approach enabled us to follow the structure of individual aggregates occurring in the subsequent phases of tau aggregation. We applied multivariate data analysis to extract the spectral differences for tau aggregates at different aggregation phases. Moreover, atomic force microscopy (AFM) allowed the tracking of the morphological alterations for species occurring with the progression of tau aggregation.

Convolutional Neural Networks Guided Raman Spectroscopy as a Process Analytical Technology (PAT) Tool for Monitoring and Simultaneous Prediction of Monoclonal Antibody Charge Variants

BACKGROUND

Charge related heterogeneities of monoclonal antibody (mAb) based therapeutic products are increasingly being considered as a critical quality attribute (CQA). They are typically estimated using analytical cation exchange chromatography (CEX), which is time consuming and not suitable for real time control. Raman spectroscopy coupled with artificial intelligence (AI) tools offers an opportunity for real time monitoring and control of charge variants.

OBJECTIVE

We present a process analytical technology (PAT) tool for on-line and real-time charge variant determination during process scale CEX based on Raman spectroscopy employing machine learning techniques.

METHOD

Raman spectra are collected from a reference library of samples with distribution of acidic, main, and basic species from 0-100% in a mAb concentration range of 0-20 g/L generated from process-scale CEX. The performance of different machine learning techniques for spectral processing is compared for predicting different charge variant species.

RESULT

A convolutional neural network (CNN) based model was successfully calibrated for quantification of acidic species, main species, basic species, and total protein concentration with R2 values of 0.94, 0.99, 0.96 and 0.99, respectively, and the Root Mean Squared Error (RMSE) of 0.1846, 0.1627, and 0.1029 g/L, respectively, and 0.2483 g/L for the total protein concentration.

CONCLUSION

We demonstrate that Raman spectroscopy combined with AI-ML frameworks can deliver rapid and accurate determination of product related impurities. This approach can be used for real time CEX pooling decisions in mAb production processes, thus enabling consistent charge variant profiles to be achieved.

A novel method for detection of pancreatic Ductal Adenocarcinoma using explainable machine learning

BACKGROUND AND OBJECTIVE

Pancreatic Ductal Adenocarcinoma (PDAC) is a form of pancreatic cancer that is one of the primary causes of cancer-related deaths globally, with less than 10 % of the five years survival rate. The prognosis of pancreatic cancer has remained poor in the last four decades, mainly due to the lack of early diagnostic mechanisms. This study proposes a novel method for detecting PDAC using explainable and supervised machine learning from Raman spectroscopic signals.

METHODS

An insightful feature set consisting of statistical, peak, and extended empirical mode decomposition features is selected using the support vector machine recursive feature elimination method integrated with a correlation bias reduction. Explicable features successfully identified mutations in Kirsten rat sarcoma viral oncogene homolog (KRAS) and tumor suppressor protein53 (TP53) in the fingerprint region for the first time in the literature. PDAC and normal pancreas are classified using K-nearest neighbor, linear discriminant analysis, and support vector machine classifiers.

RESULTS

This study achieved a classification accuracy of 98.5% using a nonlinear support vector machine. Our proposed method reduced test time by 28.5 % and saved 85.6 % memory utilization, which reduces complexity significantly and is more accurate than the state-of-the-art method. The generalization of the proposed method is assessed by fifteen-fold cross-validation, and its performance is evaluated using accuracy, specificity, sensitivity, and receiver operating characteristic curves.

CONCLUSIONS

In this study, we proposed a method to detect and define the fingerprint region for PDAC using explainable machine learning. This simple, accurate, and efficient method for PDAC detection in mice could be generalized to examine human pancreatic cancer and provide a basis for precise chemotherapy for early cancer treatment.

Quasi-spherical silver nanoparticles for human prolactin detection by surface-enhanced Raman spectroscopy

Prolactin is a polypeptide hormone made of 199 amino acids; 50% of the amino acid chain forms helices, and the rest forms loops. This hormone is typically related to initiating and maintaining lactation, although it is also elevated in various pathological conditions. Serum prolactin levels of 2 to 18 ng ml-1 in men, up to 30 ng ml-1 in women, and 10 to 210 ng ml-1 in pregnant women are considered normal. Immunoassay techniques used for detection are susceptible to error in different clinical conditions. Surface-enhanced Raman spectroscopy (SERS) is a technique that allows for obtaining the protein spectrum in a simple, fast, and reproducible manner. Nonetheless, proper characterization of human prolactin's Raman/SERS spectrum at different concentrations has so far not been deeply discussed. This study aims to characterize the Raman spectrum of human prolactin at physiological concentrations using silver nanoparticles (AgNPs) as the SERS substrate. The Raman spectrum of prolactin at 20 ng ul-1 was acquired. Quasi-spherical AgNPs were obtained using chemical synthesis. For SERS characterization, decreasing dilutions of the protein were made by adding deionized water and then a 1 : 1 volume of the AgNPs colloid. For each mixture, the Raman spectrum was determined. The spectrum of prolactin by SERS was obtained with a concentration of up to 0.1 ng ml-1. It showed characteristic bands corresponding to the side chains of aromatic amino acids in the protein's primary structure and the alpha helices of the secondary structure of prolactin. In conclusion, using quasi-spherical silver nanoparticles as the SERS substrate, the Raman spectrum of human prolactin at physiological concentration was determined.

Metabolic stressful environment drives epigenetic modifications in oviduct epithelial cells

The oviduct provides a suitable microenvironment from the gametes' final maturation until initial embryo development. Dynamic functional changes are observed in the oviduct cells, mainly controlled by steroid hormones and well-orchestrated during the estrous cycle. However, based on the roles played by the oviduct, additional layers of complexity might be present in its regulatory process. There is a cellular process that includes metabolic adaptation that can guide molecular modifications. This process is known as metaboloepigenetics. Therefore, we aimed to better understand how this crosstalk occurs in oviductal epithelial cells (OEC). Due to limited in situ access to the oviduct, we used the primary in vitro cell culture as a culture model and glucose as a metabolic disturbed factor. For that, cells derived from the oviductal epithelial layer were collected from cows at either follicular or luteal stages (n = 4 animals per group). They were cultured on a monolayer culture system under normoglycemic (2.7 mM glucose) or hyperglycemic conditions (27 mM glucose). On day five of culture, attached cells were submitted to analysis of mitochondrial metabolism (mitochondrial membrane potential - MMP) and epigenetics markers (5- methylcytosine - 5 mC and histone 3 lysine 9 acetylation - H3K9ac). Moreover, the culture media were submitted to the metabolites analysis profile by Raman spectrometry. Data were analyzed considering the effect of glucose level (normoglycemic vs. hyperglycemic), stages when OEC were harvested (follicular vs. luteal), and their interaction (glucose level * cycle stage) by two-way ANOVA. As a result, the high glucose level decreased the H3K9ac and MMP levels but did not affect the 5 mC. Regardless of the metabolic profile of the culture media, the glucose level was the only factor that changed the Raman shifts abundance. Although this present study evaluated oviductal epithelial cells after being submitted to an in vitro monolayer culture system, which is known to lead to cell dedifferentiation, yet, these results provide evidence of a relationship between epigenetic reprogramming and energy metabolism under these cell culture conditions. In conclusion, the levels of metabolites in culture media may be crucial for cellular function and differentiation, meaning that it should be considered in studies culturing oviductal cells.

Surface-enhanced Raman scattering (SERS) and tip-enhanced Raman scattering (TERS) in label-free characterization of erythrocyte membranes and extracellular vesicles at the nano-scale and molecular level

The manuscript presents the potential of surface-enhanced Raman spectroscopy (SERS) and tip-enhanced Raman spectroscopy (TERS) for label-free characterization of extracellular microvesicles (EVs) and their isolated membranes derived from red blood cells (RBCs) at the nanoscale and at the single-molecule level, providing detection of a few individual amino acids, protein and lipid membrane compartments. The study shows future directions for research, such as investigating the use of the mentioned techniques for the detection and diagnosis of diseases. We demonstrate that SERS and TERS are powerful techniques for identifying the biochemical composition of EVs and their membranes, allowing the detection of small molecules, lipids, and proteins. Furthermore, extracellular vesicles released from red blood cells (REVs) can be broadly classified into exosomes, microvesicles, and apoptotic bodies, based on their size and biogenesis pathways. Our study specifically focuses on microvesicles that range from 100 to 1000 nanometres in diameter, as presented in AFM images. Using SERS and TERS spectra obtained for REVs and their membranes, we were able to characterize the chemical and structural properties of microvesicle membranes with high sensitivity and specificity. This information may help better distinguish and categorize different types of EVs, leading to a better understanding of their functions and potential biomedical applications.

Raman spectra simulation of antiplatelet drug-platelet interaction using DFT

The paper reflects the results of molecular docking and mathematical DFT simulation for antiplatelet drugs and the target platelet receptor/ferment interaction in the limited area. The results of Raman spectra simulation are implemented and obtained from the interaction of the clopidogrel metabolite of the P2Y12 receptor. The interaction of aspirin with the COX-1 enzyme was also investigated. As a result, theoretical Raman spectra of the drug-receptor area were obtained. The theoretical data were compared with the experimental SERS results. The characteristic bands corresponding to metabolite/ferment and antiplatelet drug vibrations were clarified. The prospects of obtaining results for pathologies based on platelet conformations during cardiovascular diseases have been demonstrated.

Self-Healable Adhesive Hydrogel with a Preserved Underwater Adhesive Ability Based on Histidine-Zinc Coordination and a Bioengineered Hybrid Mussel Protein

Mussels are marine organisms that are capable of constructing an underwater adhesion between their bodies and rigid structures. It is well known that mussels achieve underwater adhesion through the presence of mussel adhesive proteins (MAPs) that contain high levels of 3,4-dihydroxyphenylalanine (DOPA). Although the extraordinary underwater adhesive properties of mussels are attributed to DOPA, its capacity to play a dual role in surface adhesion and internal cohesion is inherently limited. However, mussels employ a combination of chemical moieties, not just DOPA, along with anatomical components, such as plaque and byssus, in underwater adhesion. This also involves junction proteins that connect the plaque and byssus. In this study, a novel hybrid MAP was bioengineered via the fusion of the plaque protein (foot protein type 1) and the histidine-rich domain of the junction protein (foot protein type 4). To achieve direct adhesion underwater, the adhesive should maintain surface adhesion without disintegrating. Notably, the histidine-Zn-coordinated hybrid MAP hydrogel maintained a high surface adhesion ability even after cross-linking because of the preservation of its unoxidized and non-cross-linked DOPA moieties. The formulated adhesive hydrogel system based on the bioengineered hybrid MAP exhibited self-healing properties, owing to the reversible metal coordination bonds. The developed adhesive hydrogel exhibits outstanding levels of bulk adhesion in underwater environments, highlighting its potential as an effective adhesive biomaterial. Therefore, the introduction of histidine-rich domains into MAPs may be applied in various studies to formulate mussel-inspired adhesives with self-healing properties and to fully utilize the adhesive ability of DOPA.

Tuning Phase Transition of Molecular Self-Assembly by Artificial Chaperones through Aromatic-Aromatic Interactions

The molecular chaperones are essential and play significant roles in controlling the protein phase transition and maintaining physiological homeostasis. However, manipulating phase transformation in biomimetic peptide self-assembly is still challenging. This work shows that an artificial chaperone modulates the energy landscape of supramolecular polymerization, thus controlling the phase transition of amyloid-like assemblies from crystals to hydrogels to solution. The absence of a chaperone allows the NapP to form crystals, while the presence of the chaperone biases the pathway to form nanofibrous hydrogels to soluble oligomers by adjusting the chaperone ratios. Mechanistic studies reveal that the aromatic-aromatic interaction is the key to trapping the molecules in a higher energy fold. Adding the chaperone relieves this restriction, lowers the energy barrier, and transforms the crystal into a hydrogel. This phase transformation can also be achieved in the macromolecular crowding environment, thus providing new insights into understanding molecular self-assembly in multiple component systems.

Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy

Preeclampsia is a life-threatening pregnancy disorder. Current clinical assays cannot predict the onset of preeclampsia until the late 2nd trimester, which often leads to poor maternal and neonatal outcomes. Here we show that Raman spectroscopy combined with machine learning in pregnant patient plasma enables rapid, highly sensitive maternal metabolome screening that predicts preeclampsia as early as the 1st trimester with >82% accuracy. We identified 12, 15 and 17 statistically significant metabolites in the 1st, 2nd and 3rd trimesters, respectively. Metabolic pathway analysis shows multiple pathways corresponding to amino acids, fatty acids, retinol, and sugars are enriched in the preeclamptic cohort relative to a healthy pregnancy. Leveraging Pearson's correlation analysis, we show for the first time with Raman Spectroscopy that metabolites are associated with several clinical factors, including patients' body mass index, gestational age at delivery, history of preeclampsia, and severity of preeclampsia. We also show that protein quantification alone of proinflammatory cytokines and clinically relevant angiogenic markers are inadequate in identifying at-risk patients. Our findings demonstrate that Raman spectroscopy is a powerful tool that may complement current clinical assays in early diagnosis and in the prognosis of the severity of preeclampsia to ultimately enable comprehensive prenatal care for all patients.

Cancer Cell Targeting, Magnetic Sorting, and SERS Detection through Cell Surface Receptors

Integrins are cellular surface receptors responsible for the activation of many cellular pathways in cancer. These integrin proteins can be specifically targeted by small peptide sequences that offer the potential for the differentiation of cellular subpopulations by using magnetically assisted cellular sorting techniques. By adding a gold shell to the magnetic nanoparticles, these integrin-peptide interactions can be differentiated by surface-enhanced Raman spectroscopy (SERS), providing a quick and reliable method for on-target binding. In this paper, we demonstrate the ability to differentiate the peptide-protein interactions of the small peptides CDPGYIGSR and cyclic RGDfC functionalized on gold-coated magnetic nanoparticles with the integrins they are known to bind to using their SERS signal. SW480 and SW620 colorectal cancer cells known to have the integrins of interest were then magnetically sorted using these functionalized nanoparticles, suggesting differentiation between the sorted populations and integrin populations among the two cell lines.

Direct observation of the effects of chemical fixation in MNT-1 cells: A SE-ADM and Raman study

Aldehydes fixation was accidentally discovered in the early 20th century and soon became a widely adopted practice in the histological field, due to an excellent staining enhancement in tissues imaging. However, the fixation process itself entails cell proteins denaturation and crosslinking. The possible presence of artifacts, that depends on the specific system under observation, must therefore be considered to avoid data misinterpretation. This contribution takes advantage of scanning electron assisted-dielectric microscopy (SE-ADM) and Raman 2D imaging to reveal the possible presence and the nature of artifacts in unstained, and paraformldehyde, PFA, fixed MNT-1 cells. The high resolution of the innovative SE-ADM technique allowed the identification of globular protein clusters in the cell cytoplasm, formed after protein denaturation and crosslinking. Concurrently, SE-ADM images showed a preferential melanosome adsorption on the cluster's outer surface. The micron-sized aggregates were discernible in Raman 2D images, as the melanosomes signal, extracted through 2D principal component analysis, unequivocally mapped their location and distribution within the cells, appearing randomly distributed in the cytoplasm. Protein clusters were not observed in living MNT-1 cells. In this case, mature melanosomes accumulate preferentially at the cell periphery and are more closely packed than in fixed cells. Our results show that, although PFA does not affect the melanin structure, it disrupts melanosome distribution within the cells. Proteins secondary structure, conversely, is partially lost, as shown by the Raman signals related to α-helix, β-sheets, and specific amino acids that significantly decrease after the PFA treatment.

Raman Fingerprints of SARS-CoV-2 Omicron Subvariants: Molecular Roots of Virological Characteristics and Evolutionary Directions

The latest RNA genomic mutation of SARS-CoV-2 virus, termed the Omicron variant, has generated a stream of highly contagious and antibody-resistant strains, which in turn led to classifying Omicron as a variant of concern. We systematically collected Raman spectra from six Omicron subvariants available in Japan (i.e., BA.1.18, BA.2, BA.4, BA.5, XE, and BA.2.75) and applied machine-learning algorithms to decrypt their structural characteristics at the molecular scale. Unique Raman fingerprints of sulfur-containing amino acid rotamers, RNA purines and pyrimidines, tyrosine phenol ring configurations, and secondary protein structures clearly differentiated the six Omicron subvariants. These spectral characteristics, which were linked to infectiousness, transmissibility, and propensity for immune evasion, revealed evolutionary motifs to be compared with the outputs of genomic studies. The availability of a Raman "metabolomic snapshot", which was then translated into a barcode to enable a prompt subvariant identification, opened the way to rationalize in real-time SARS-CoV-2 activity and variability. As a proof of concept, we applied the Raman barcode procedure to a nasal swab sample retrieved from a SARS-CoV-2 patient and identified its Omicron subvariant by coupling a commercially available magnetic bead technology with our newly developed Raman analyses.

Fatty Acid-Binding Proteins Identification during the Evolution of Metabolic Syndrome: A Raman Spectroscopy-Based Approach

Excess fat in abdominal deposits is a risk factor for multiple conditions, including metabolic syndrome (MetS); lipid metabolism plays an essential role in these pathologies; fatty acid-binding proteins (FABPs) are dedicated to the cytosolic transport of fat. FABP4, whose primary source is adipose tissue, is released into the circulation, acting as an adipokine, while FABP5 also accompanies the adverse effects of MetS. FABP4 and 5 are potential biomarkers of MetS, but their behavior during syndrome evolution has not been determined. Raman spectroscopy has been applied as an alternative method to disease biomarker detection. In this work, we detected spectral changes related to FABP4 and 5 in the serum at different points of time, using an animal model of a high-fat diet-induced MetS. FABP4 and 5 spectral changes show a contribution during the evolution of MetS, which indicates alteration to a molecular level that predisposes to established MetS. These findings place FABPs as potential biomarkers of MetS and Raman spectroscopy as an alternative method for MetS assessment.

Automated calibration and in-line measurement of product quality during therapeutic monoclonal antibody purification using Raman spectroscopy

Current manufacturing and development processes for therapeutic monoclonal antibodies demand increasing volumes of analytical testing for both real-time process controls and high-throughput process development. The feasibility of using Raman spectroscopy as an in-line product quality measuring tool has been recently demonstrated and promises to relieve this analytical bottleneck. Here, we resolve time-consuming calibration process that requires fractionation and preparative experiments covering variations of product quality attributes (PQAs) by engineering an automation system capable of collecting Raman spectra on the order of hundreds of calibration points from two to three stock seed solutions differing in protein concentration and aggregate level using controlled mixing. We used this automated system to calibrate multi-PQA models that accurately measured product concentration and aggregation every 9.3 s using an in-line flow-cell. We demonstrate the application of a nonlinear calibration model for monitoring product quality in real-time during a biopharmaceutical purification process intended for clinical and commercial manufacturing. These results demonstrate potential feasibility to implement quality monitoring during GGMP manufacturing as well as to increase chemistry, manufacturing, and controls understanding during process development, ultimately leading to more robust and controlled manufacturing processes.

Machine Learning Analysis of Raman Spectra To Quantify the Organic Constituents in Complex Organic-Mineral Mixtures

Important decisions in local agricultural policy and practice often hinge on the soil's chemical composition. Raman spectroscopy offers a rapid noninvasive means to quantify the constituents of complex organic systems. But the application of Raman spectroscopy to soils presents a multifaceted challenge due to organic/mineral compositional complexity and spectral interference arising from overwhelming fluorescence. The present work compares methodologies with the capacity to help overcome common obstacles that arise in the analysis of soils. We created conditions representative of these challenges by combining varying proportions of six amino acids commonly found in soils with fluorescent bentonite clay and coarse mineral components. Referring to an extensive data set of Raman spectra, we compare the performance of the convolutional neural network (CNN) and partial least-squares regression (PLSR) multivariate models for amino acid composition. Strategies employing volume-averaged spectral sampling and data preprocessing algorithms improve the predictive power of these models. Our average test R2 for PLSR models exceeds 0.89 and approaches 0.98, depending on the complexity of the matrix, whereas CNN yields an R2 range from 0.91 to 0.97, demonstrating that classic PLSR and CNN perform comparably, except in cases where the signal-to-noise ratio of the organic component is very low, whereupon CNN models outperform. Artificially isolating two of the most prevalent obstacles in evaluating the Raman spectra of soils, we have characterized the effect of each obstacle on the performance of machine learning models in the absence of other complexities. These results highlight important considerations and modeling strategies necessary to improve the Raman analysis of organic compounds in complex mixtures in the presence of mineral spectral components and significant fluorescence.

Raman Multi-Omic Snapshot and Statistical Validation of Structural Differences between Herpes Simplex Type I and Epstein-Barr Viruses

Raman spectroscopy was applied to study the structural differences between herpes simplex virus Type I (HSV-1) and Epstein-Barr virus (EBV). Raman spectra were first collected with statistical validity on clusters of the respective virions and analyzed according to principal component analysis (PCA). Then, average spectra were computed and a machine-learning approach applied to deconvolute them into sub-band components in order to perform comparative analyses. The Raman results revealed marked structural differences between the two viral strains, which could mainly be traced back to the massive presence of carbohydrates in the glycoproteins of EBV virions. Clear differences could also be recorded for selected tyrosine and tryptophan Raman bands sensitive to pH at the virion/environment interface. According to the observed spectral differences, Raman signatures of known biomolecules were interpreted to link structural differences with the viral functions of the two strains. The present study confirms the unique ability of Raman spectroscopy for answering structural questions at the molecular level in virology and, despite the structural complexity of viral structures, its capacity to readily and reliably differentiate between different virus types and strains.

Locality in amino-acid based imidazolium ionic liquids

Several amino-acid based imidazolium ILs are investigated through the use of ab initio molecular dynamics (AIMD), which includes full polarization. The electric dipole moment distribution and polarization is used as a means of characterizing and understanding these complex systems. Various charge scheme methods were analyzed (Wannier function, Blöchl, Löwdin and Mulliken charge schemes and Voronoi tessellation) to determine their ability to predict dipole moments. These results and the following comparison of methods further deepen the knowledge of polarization by highlighting the importance of the anion and cation separately on polarizability contribution and the need to select a suitable method to predict these. The angular probability distribution is utilized to measure the degree of locality in monopole-dipole electrostatic interactions, which showed no preferential alignment over 700 pm. In addition, the IR and Raman spectra from Voronoi tessellation of [C2C1Im][ala] were analyzed. In these, the strongest signalling peaks showed consistency with experiment and the ability to differentiate between anion and cation components of the IL.

Goldilocks Energy Minimum: Peptide-Based Reversible Aggregation and Biosensing

Colorimetric biosensors based on gold nanoparticle (AuNP) aggregation are often challenged by matrix interference in biofluids, poor specificity, and limited utility with clinical samples. Here, we propose a peptide-driven nanoscale disassembly approach, where AuNP aggregates induced by electrostatic attractions are dissociated in response to proteolytic cleavage. Initially, citrate-coated AuNPs were assembled via a short cationic peptide (RRK) and characterized by experiments and simulations. The dissociation peptides were then used to reversibly dissociate the AuNP aggregates as a function of target protease detection, i.e., main protease (Mpro), a biomarker for severe acute respiratory syndrome coronavirus 2. The dissociation propensity depends on peptide length, hydrophilicity, charge, and ligand architecture. Finally, our dissociation strategy provides a rapid and distinct optical signal through Mpro cleavage with a detection limit of 12.3 nM in saliva. Our dissociation peptide effectively dissociates plasmonic assemblies in diverse matrices including 100% human saliva, urine, plasma, and seawater, as well as other types of plasmonic nanoparticles such as silver. Our peptide-enabled dissociation platform provides a simple, matrix-insensitive, and versatile method for protease sensing.

Monitoring Metabolic Changes in Response to Chemotherapies in Cancer with Raman Spectroscopy and Metabolomics

Resistance to clinical therapies remains a major barrier in cancer management. There is a critical need for rapid and highly sensitive diagnostic tools that enable early prediction of treatment response to allow accurate clinical decisions. Here, Raman spectroscopy was employed to monitor changes in key metabolites as early predictors of response in KRAS-mutant colorectal cancer (CRC) cells, HCT116, treated with chemotherapies. We show at the single cell level that HCT116 is resistant to cetuximab (CTX), the first-line treatment in CRC, but this resistance can be overcome with pre-sensitization of cells with oxaliplatin (OX). In combination treatment of CTX + OX, sequential delivery of OX followed by CTX rather than simultaneous administration of drugs was observed to be critical for effective therapy. Our results demonstrated that metabolic changes are well aligned to cellular mechanical changes where Young's modulus decreased after effective treatment, indicating that both changes in mechanical properties and metabolism in cells are likely responsible for cancer proliferation. Raman findings were verified with mass spectrometry (MS) metabolomics, and both platforms showed changes in lipids, nucleic acids, and amino acids as predictors of resistance/response. Finally, key metabolic pathways enriched were identified when cells are resistant to CTX but downregulated with effective treatment. This study highlights that drug-induced metabolic changes both at the single cell level (Raman) and ensemble level (MS) have the potential to identify mechanisms of response to clinical cancer therapies.

In Situ Raman Study of Neurodegenerated Human Neuroblastoma Cells Exposed to Outer-Membrane Vesicles Isolated from Porphyromonas gingivalis

The aim of this study was to elucidate the chemistry of cellular degeneration in human neuroblastoma cells upon exposure to outer-membrane vesicles (OMVs) produced by Porphyromonas gingivalis (Pg) oral bacteria by monitoring their metabolomic evolution using in situ Raman spectroscopy. Pg-OMVs are a key factor in Alzheimer's disease (AD) pathogenesis, as they act as efficient vectors for the delivery of toxins promoting neuronal damage. However, the chemical mechanisms underlying the direct impact of Pg-OMVs on cell metabolites at the molecular scale still remain conspicuously unclear. A widely used in vitro model employing neuroblastoma SH-SY5Y cells (a sub-line of the SK-N-SH cell line) was spectroscopically analyzed in situ before and 6 h after Pg-OMV contamination. Concurrently, Raman characterizations were also performed on isolated Pg-OMVs, which included phosphorylated dihydroceramide (PDHC) lipids and lipopolysaccharide (LPS), the latter in turn being contaminated with a highly pathogenic class of cysteine proteases, a key factor in neuronal cell degradation. Raman characterizations located lipopolysaccharide fingerprints in the vesicle structure and unveiled so far unproved aspects of the chemistry behind protein degradation induced by Pg-OMV contamination of SH-SY5Y cells. The observed alterations of cells' Raman profiles were then discussed in view of key factors including the formation of amyloid β (Aβ) plaques and hyperphosphorylated Tau neurofibrillary tangles, and the formation of cholesterol agglomerates that exacerbate AD pathologies.

Opportunities in optical and electrical single-cell technologies to study microbial ecosystems

New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.

First Trimester Prediction of Preterm Birth in Patient Plasma with Machine-Learning-Guided Raman Spectroscopy and Metabolomics

Preterm birth (PTB) is the leading cause of infant deaths globally. Current clinical measures often fail to identify women who may deliver preterm. Therefore, accurate screening tools are imperative for early prediction of PTB. Here, we show that Raman spectroscopy is a promising tool for studying biological interfaces, and we examine differences in the maternal metabolome of the first trimester plasma of PTB patients and those that delivered at term (healthy). We identified fifteen statistically significant metabolites that are predictive of the onset of PTB. Mass spectrometry metabolomics validates the Raman findings identifying key metabolic pathways that are enriched in PTB. We also show that patient clinical information alone and protein quantification of standard inflammatory cytokines both fail to identify PTB patients. We show for the first time that synergistic integration of Raman and clinical data guided with machine learning results in an unprecedented 85.1% accuracy of risk stratification of PTB in the first trimester that is currently not possible clinically. Correlations between metabolites and clinical features highlight the body mass index and maternal age as contributors of metabolic rewiring. Our findings show that Raman spectral screening may complement current prenatal care for early prediction of PTB, and our approach can be translated to other patient-specific biological interfaces.

Investigating on sensing mechanism of MoS2-FET biosensors in response to proteins

Field-effect transistor (FET) biosensors based on two-dimensional materials have gained extensive attention due to their high sensitivity, label-free detection capability, and fast response. Molybdenum disulfide (MoS2), with tunable bandgap, high surface-to-volume ratio, and smooth surface without dangling bonds, is a promising material for FET biosensors. Previous reports have demonstrated the fabrication of MoS2-FET biosensors and their high sensitivity detection of proteins. However, most prior research has focused on the realization of MoS2-FETs for detecting different kinds of proteins or molecules, while comprehensive analysis of the sensing mechanism and dominant device factors of MoS2-FETs in response to proteins is yet to investigate. In this study, we first fabricated MoS2-FET biosensor and detected different types of proteins (immunoglobulin G (IgG),β-actin, and prostate-specific antigen (PSA)). Secondly, we built the model of the device and analyzed the sensing mechanism of MoS2-FETs in response to proteins. Experimental and modeling results showed that the induced doping effect and gating effect caused by the target protein binding to the device surface were the major influential factors. Specifically, the channel doping concentration and gate voltage (Vg) offset exhibited monotonic change as the concentration of the protein solution increases. For example, the channel doping concentration increased up to ∼37.9% and theVgoffset was ∼-1.3 V with 10-7μgμl-1IgG. The change was less affected by the device size. We also investigated the effects of proteins with opposite acid-base properties (β-actin and PSA) to IgG on the device sensing mechanism.β-actin and PSA exhibited behavior opposite to that of IgG. Additionally, we studied the response behavior of MoS2-FETs with different dimensions and dielectric materials (channel length, MoS2thickness, dielectric layer thickness, dielectric layer material) to proteins. The underlying mechanisms were discussed in details. This study provides valuable guidelines for the design and application of MoS2-FET biosensors.

Heterogeneous-Driven Glutathione Oxidation: Defining the Catalytic Role of Chalcopyrite Nanoparticles

Transition-metal nanocatalysis represents a novel alternative currently experiencing flourishing progress to tackle the tumor microenvironment (TME) in cancer therapy. These nanomaterials aim at attacking tumor cells using the intrinsic selectivity of inorganic catalysts. In addition, special attention to tune and control the release of these transition metals is also required. Understanding the chemical reactions behind the catalytic action of the transition-metal nanocatalysts and preventing potential undesired side reactions caused by acute cytotoxicity of the released ionic species represent another important field of research. Specifically, copper-based oxides may suffer from acute leaching that potentially may induce toxicity not only to target cancer cells but also to nearby cells and tissues. In this work, we propose the synthesis of chalcopyrite (CuFeS₂) nanostructures capable of triggering two key reactions for an effective chemodynamic therapy (CDT) in the heterogeneous phase: (i) glutathione (GSH) oxidation and (ii) oxidation of organic substrates using H₂O₂, with negligible leaching of metals under TME-like conditions. This represents an appealing alternative toward the development of safer copper-iron-based nanocatalytic materials with an active catalytic response without incurring leaching side phenomena.

Central and Peripheral Fatigue Evaluation during Physical Exercise in Athletic Horses by Means of Raman Spectroscopy

The evaluation of the performance levels in athletic horses is of major importance to prevent sports injuries. Raman spectroscopy is an innovative technique that allows for a rapid evaluation of biomolecules in biological fluids. It also permits qualitative and quantitative sample analyses, which lead to the simultaneous determination of the components of the examined biological fluids. On the basis of this, the Raman spectroscopy technique was applied on serum samples collected from five Italian Saddle horses subjected to a standardized obstacle course preceded by a warm-up to evaluate the applicability of this technique for the assessment of central and peripheral fatigue in athletic horses. Blood samples were collected via jugular venipuncture in a vacutainer tube with a clot activator before exercise, immediately after exercise, and 30 min and 1 h after the end of the obstacle course. Observing the obtained Raman spectra, the major changes due to the experimental conditions appeared in the (1300-1360) cm^-1 and (1385-1520) cm^-1 bands. In the (1300-1360) cm^-1 band, lipids and tryptophan were identified; in the (1385-1520) cm^-1 band, leucine, glycine, isoleucine, lactic acid, tripeptide, adenosine, and beta carotene were identified. A significant effect of exercise was recorded on all the sub-bands. In particular, a change immediately after exercise versus before exercise was found. Moreover, the mean lactic concentration was positively correlated with the Raman area of the sub-band assigned to lactic acid. In this context, the application of Raman spectroscopy on blood serum samples represents a useful technique for secondary-structure protein identification to investigate the metabolic changes that occur in athletic horses during physical exercise.

Uncovering the Molecular Mechanisms of Cosolvency and Predicting the Cosolvency Phenomenon by Molecular Simulations: A Case Study of Amino Acids

Cosolvency is an intriguing thermodynamic phenomenon. However, the lack of theoretical research restricts its development and its further applications. In this work, l-alanine, l-phenylalanine, and L-tryptophan were selected as model substances to investigate the mechanism of cosolvency at the molecular level. First, the dissolution behaviors of three amino acids were characterized to determine the solvent ratios at the occurrence of the cosolvency phenomenon. Furthermore, amino acid molecules undergo a shift in molecular conformation, which leads to changes in inter/intramolecular interactions. A molecular dynamics simulation method was proposed to calculate the trends of inter/intramolecular interactions, demonstrating that the maximum point of the inter/intramolecular interaction ratio exactly corresponds to the occurrence of the cosolvency. The cosolvency phenomenon of l-proline and l-threonine was predicted successfully based on this simulation method. These results are likely to provide in-depth comprehension and guidance for predicting the cosolvency phenomenon of amino-acid-like substances.

Plasma Exosome Analysis for Protein Mutation Identification Using a Combination of Raman Spectroscopy and Deep Learning

Protein mutation detection using liquid biopsy can be simply performed periodically, making it easy to detect the occurrence of newly emerging mutations rapidly. However, it has low diagnostic accuracy since there are more normal proteins than mutated proteins in body fluids. To increase the diagnostic accuracy, we analyzed plasma exosomes using nanoplasmonic spectra and deep learning. Exosomes, a promising biomarker, are abundant in plasma and stably carry intact proteins originating from mother cells. However, the mutated exosomal proteins cannot be detected sensitively because of the subtle changes in their structure. Therefore, we obtained Raman spectra that provide molecular information about structural changes in mutated proteins. To extract the unique features of the protein from complex Raman spectra, we developed a deep-learning classification algorithm with two deep-learning models. Consequently, controls with wild-type proteins and patients with mutated proteins were classified with high accuracy. As a proof of concept, we discriminated the lung cancer patients with mutations in the epidermal growth factor receptor (EGFR), L858R, E19del, L858R + T790M, and E19del + T790M, from controls with an accuracy of 0.93. Moreover, the protein mutation status of the patients with primary (E19del, L858R) and secondary (+T790M) mutations was clearly monitored. Overall, our technique is expected to be applied as a novel method for companion diagnostic and treatment monitoring.

Proteolytically Resistant Bioactive Peptide-Grafted Sr/Mg-Doped Hardystonite Foams: Comparison of Two Covalent Functionalization Strategies

Hardystonite-based (HT) bioceramic foams were easily obtained via thermal treatment of silicone resins and reactive oxide fillers in air. By using a commercial silicone, incorporating strontium oxide and magnesium oxide precursors (as well as CaO and ZnO), and treating it at 1100 °C, a complex solid solution (Ca_1.4Sr_0.6Zn_0.85Mg_0.15Si₂O₇) that has superior biocompatibility and bioactivity properties compared to pure hardystonite (Ca₂ZnSi₂O₇) can be obtained. Proteolytic-resistant adhesive peptide mapped on vitronectin (D2HVP), was selectively grafted to Sr/Mg-doped HT foams using two different strategies. Unfortunately, the first method (via protected peptide) was unsuitable for acid-sensitive materials such as Sr/Mg-doped HT, resulting in the release of cytotoxic levels of Zinc over time, with consequent negative cellular response. To overcome this unexpected result, a novel functionalization strategy requiring aqueous solution and mild conditions was designed. Sr/Mg-doped HT functionalized with this second strategy (via aldehyde peptide) showed a dramatic increase in human osteoblast proliferation at 6 days compared to only silanized or non-functionalized samples. Furthermore, we demonstrated that the functionalization treatment does not induce any cytotoxicity. Functionalized foams enhanced mRNA-specific transcript levels coding IBSP, VTN, RUNX2, and SPP1 at 2 days post-seeding. In conclusion, the second functionalization strategy proved to be appropriate for this specific biomaterial and was effective at enhancing the material's bioactivity.

The Use of Raman Spectroscopy to Monitor Changes in the Intensity of Ratio of Integral Unsaturated Bands in Bio-Greases

Bio-greases were developed on the basis of vegetable oil obtained from Crambe Abyssinic seeds. An important aspect of this research is to monitor changes in their quality taking place under the influence of external factors. Raman spectroscopy was used to identify changes taking place in the bio-lubricant under the influence of mechanical and thermal forces. The performed tests reflected the operating temperature and friction load that may occur during actual operating conditions for the lubricated friction systems. The Raman spectra provided information on qualitative changes in the structure of the tested bio-lubricants at the molecular level. The integral intensity of the bands used to assess the degree of lipid unsaturation was adopted as the evaluation criterion. The influence of the oxidation process under the PetroOxy and wear test conditions on changes in the structure of the bio-lubricants was assessed. Variation in the integral intensity of the bands (I1655/I1440) proves that the structure of vegetable lubricants changes under the influence of the tests performed. Thermal and mechanical forces influence, the bands originating in unsaturated and result in a decrease in the oxidation resistance of vegetable lubricants.

Preliminary investigation into the prediction of indicators of beef spoilage using Raman and Fourier transform infrared spectroscopy

The objective of this study was to assess the potential to predict the microbial beef spoilage indicators by Raman and Fourier transform infrared (FT-IR) spectroscopies. Vacuum skin packaged (VSP) beef steaks were stored at 0 °C, 4 °C, 8 °C and under a dynamic temperature condition (0 °C ∼ 4 °C ∼ 8 °C, for 36 d). Total viable count (TVC) and total volatile basic nitrogen (TVB-N) were obtained during the storage period along with spectroscopic data. The Raman and FTIR spectra were baseline corrected, pre-processed using Savitzky-Golay smoothing and normalized. Subsequently partial least squares regression (PLSR) models of TVC and TVB-N were developed and evaluated. The root mean squared error (RMSE) ranged from 0.81 to1.59 (log CFU/g or mg/100 g) and the determination coefficient (R²) from 0.54 to 0.75. The performance of PLSR model based on data fusion (combination of Raman and FT-IR data) is better than that based on Raman spectra and similar to that of FT-IR. Overall, Raman spectroscopy, FT-IR spectroscopy, and a combination of both exhibited a potential for the prediction of the beef spoilage.

Spectroscopic signature of ZnO NP-induced cell death modalities assessed by non-negative PCA

Selective cytotoxicity of ZnO nanoparticles among different cell types and cancer and non-cancerous cells has been demonstrated earlier. In the view of anticancer potential of ZnO nanoparticles and their presence in numerous industrial products, it is of great importance to carefully evaluate their effects and mechanisms of action in both cancerous and healthy cells. In this paper, the effects of ZnO nanoparticles on cancerous HeLa and non-cancerous MRC-5 cells are investigated by studying the changes in the vibrational properties of the cells using Raman spectroscopy. Both types of cells were incubated with ZnO nanoparticles of average size 40 nm in the doses from the range 10-40 µg/ml for the period of 48 h, after which Raman spectra were collected. Raman modes' intensity ratios I₁₆₅₉/I₁₄₄₄, I₂₈₅₅/I₂₉₃₃ and I₁₃₃₇/I₁₃₀₅ were determined as spectral markers of the cytotoxic effect of ZnO in both cell types. Non-negative principal component analysis was used instead of standard one for analysis and detection of spectral features characteristic for nanoparticle-treated cells. The first several non-negative loading vectors obtained in this analysis coincided remarkably well with the Raman spectra of particular biomolecules, showing increase of lipid and decrease of nucleic acids and protein content. Our study pointed out that Raman spectral markers of lipid unsaturation, especially I₁₂₇₀/I₁₃₀₀, are relevant for tracing the cytotoxic effect of ZnO nanoparticles on both cancerous and non-cancerous cells. The change of these spectral markers is correlated to the dose of applied nanoparticles and to the degree of cellular damage. Furthermore, great similarity of spectral features of increasing lipids to spectral features of phosphatidylserine, one of the main apoptotic markers, was recognized in treated cells. Finally, the results strongly indicated that the degree of lipid saturation, presented in the cells, plays an important role in the interaction of cells with nanoparticles.