SERS of cells: What can we learn from cell lysates?

Raman-based PAT for VLP precipitation: systematic data diversification and preprocessing pipeline identification

Virus-like particles (VLPs) are a promising class of biopharmaceuticals for vaccines and targeted delivery. Starting from clarified lysate, VLPs are typically captured by selective precipitation. While VLP precipitation is induced by step-wise or continuous precipitant addition, current monitoring approaches do not support the direct product quantification, and analytical methods usually require various, time-consuming processing and sample preparation steps. Here, the application of Raman spectroscopy combined with chemometric methods may allow the simultaneous quantification of the precipitated VLPs and precipitant owing to its demonstrated advantages in analyzing crude, complex mixtures. In this study, we present a Raman spectroscopy-based Process Analytical Technology (PAT) tool developed on batch and fed-batch precipitation experiments of Hepatitis B core Antigen VLPs. We conducted small-scale precipitation experiments providing a diversified data set with varying precipitation dynamics and backgrounds induced by initial dilution or spiking of clarified Escherichia coli-derived lysates. For the Raman spectroscopy data, various preprocessing operations were systematically combined allowing the identification of a preprocessing pipeline, which proved to effectively eliminate initial lysate composition variations as well as most interferences attributed to precipitates and the precipitant present in solution. The calibrated partial least squares models seamlessly predicted the precipitant concentration with R 2 of 0.98 and 0.97 in batch and fed-batch experiments, respectively, and captured the observed precipitation trends with R 2 of 0.74 and 0.64. Although the resolution of fine differences between experiments was limited due to the observed non-linear relationship between spectral data and the VLP concentration, this study provides a foundation for employing Raman spectroscopy as a PAT sensor for monitoring VLP precipitation processes with the potential to extend its applicability to other phase-behavior dependent processes or molecules.

Label-free screening of common urinary system tumors from blood plasma based on surface-enhanced Raman spectroscopy

BACKGROUND

The incidence of common urinary system tumors has been rising rapidly in recent years, and most urinary system-derived tumors lack specific biomarkers.

OBJECTIVES

To explore the efficacy of surface-enhanced Raman spectroscopy (SERS) of blood plasma in screening three common urinary system tumors, including bladder cancer (BC), prostate cancer (PCa), and renal cell carcinoma (RCC).

METHODS

SERS plasma spectra from 125 plasma samples, including 25 PCa, 38 RCC, 24 BC patients, and 38 normal volunteers, were collected. All candidates had no other comorbidities. The Diagnosis was based on the combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and the effectiveness of the diagnostic algorithms was verified using the Receiver Operating Characteristic Curve (ROC).

RESULTS

There are significant differences in SERS signals between PCa, BC, RCC, and normal plasma, especially at 639, 889, 1010, 1136, and 1205 cm-1. The PCA-LDA results show that high sensitivity (100 %), specificity (100 %), and accuracy (100 %) could be achieved for screening the PCa, RCC, BC group vs. the normal group, the PCa group vs. the BC and RCC group, respectively. The diagnostic sensitivity, specificity, and accuracy for the BC group vs. the RCC group are 79.2 %, 71.1 %, and 75.15 %, respectively. The integrated area under the ROC curve (AUC) is 1.0, 1.0, and 1.0 for the PCa, RCC, and BC group vs. the normal group, respectively. The AUC of the PCa group vs. the BC group and RCC group and the BC group vs. the RCC group are 1.0, 1.0, and 0.842, respectively.

CONCLUSIONS

Label-free plasma-SERS technology with PCA-LDA analysis could be a useful screening method for detecting urinary system tumors (PCa, RCC, and BC) in this exploratory study.

SERS spectroscopy as a tool for the study of thiopurine drug pharmacokinetics in a model of human B leukemia cells

Thiopurine drugs are immunomodulatory antimetabolites relevant for pediatric patients characterized by dose-dependent adverse effects such as myelosuppression and hepatotoxicity, often related to inter-individual differences, involving the activity of important enzymes at the basis of their biotransformation, such as thiopurine S-methyltransferase (TPMT). Surface Enhanced Raman Scattering (SERS) spectroscopy is emerging as a bioanalytical tool and represents a valid alternative in terms of affordable costs, shorter analysis time and easier sample preparation in comparison to the most employed methods for pharmacokinetic analysis of drugs. The aim of this study is to investigate mercaptopurine and thioguanine pharmacokinetics by SERS in cell lysates of a B-lymphoblastoid cell line (NALM-6), that did (TPMT*1) or did not (MOCK) overexpress the wild-type form of TPMT as an in vitro cellular lymphocyte model to discriminate between cells with different levels of TPMT activity on the base of the amount of thioguanosine nucleotides (TGN) metabolites formed. SERS analysis of the cell lysates was carried out using SERS substrates constituted by Ag nanoparticles deposited on paper and parallel samples were used for quantification of thiopurine nucleotides with liquid chromatography-tandem mass spectrometry (LC-MS/MS). A direct SERS detection method has been set up that could be a tool to study thiopurine drug pharmacokinetics in in vitro cellular models to qualitatively discriminate between cells that do and do not overexpress the TPMT enzyme, as an alternative to other more laborious techniques. Results underlined decreased levels of TGN and increased levels of methylated metabolites when TPMT was overexpressed, both after mercaptopurine and thioguanine treatments. A strong positive correlation (Spearman's rank correlation coefficient rho = 0.96) exists between absolute quantification of TGMP (pmol/1 x 106 cells), obtained by LC-MS/MS, and SERS signal (intensity of TGN at 915 cm-1). In future studies, we aim to apply this method to investigate TPMT activity in pediatric patients' leukocytes.

Machine Learning-Assisted High-Throughput SERS Classification of Cell Secretomes

During the response to different stress conditions, damaged cells react in multiple ways, including the release of a diverse cocktail of metabolites. Moreover, secretomes from dying cells can contribute to the effectiveness of anticancer therapies and can be exploited as predictive biomarkers. The nature of the stress and the resulting intracellular responses are key determinants of the secretome composition, but monitoring such processes remains technically arduous. Hence, there is growing interest in developing tools for noninvasive secretome screening. In this regard, it has been previously shown that the relative concentrations of relevant metabolites can be traced by surface-enhanced Raman scattering (SERS), thereby allowing label-free biofluid interrogation. However, conventional SERS approaches are insufficient to tackle the requirements imposed by high-throughput modalities, namely fast data acquisition and automatized analysis. Therefore, machine learning methods were implemented to identify cell secretome variations while extracting standard features for cell death classification. To this end, ad hoc microfluidic chips were devised, to readily conduct SERS measurements through a prototype relying on capillary pumps made of filter paper, which eventually would function as the SERS substrates. The developed strategy may pave the way toward a faster implementation of SERS into cell secretome classification, which can be extended even to laboratories lacking highly specialized facilities.

Intracellular metabolic profiling of drug resistant cells by surface enhanced Raman scattering

BACKGROUND

Intracellular metabolic profiling reveals real-time metabolic information useful for the study of underlying mechanisms of cells in particular conditions such as drug resistance. However, mass spectrometry (MS), one of the leading metabolomics technologies, usually requires a large number of cells and complex pretreatments. Surface enhanced Raman scattering (SERS) has an ultrahigh detection sensitivity and specificity, favorable for metabolomics analysis. However, some targeted SERS methods focus on very limited metabolite without global bioprofiling, and some label-free approaches try to fingerprint the metabolic response based on whole SERS spectral classification, but comprehensive interpretation of biological mechanisms was lacking. (95) RESULTS: We proposed a label-free SERS technique for intracellular metabolic profiling in complex cellular lysates within 3 min. We first compared three kinds of cellular lysis methods and sonication lysis shows the highest extraction efficiency of metabolites. To obtain comprehensive metabolic information, we collected a spectral set for each sample and further qualified them by the Pearson correlation coefficient (PCC) to calculate how many spectra should be acquired at least to gain the adequate information from a statistical and global view. In addition, according to our measurements with 10 pure metabolites, we can understand the spectra acquired from complex cellular lysates of different cell lines more precisely. Finally, we further disclosed the variations of 22 SERS bands in enzalutamide-resistant prostate cancer cells and some are associated with the androgen receptor signaling activity and the methionine salvage pathway in the drug resistance process, which shows the same metabolic trends as MS. (149) SIGNIFICANCE: Our technique has the capability to capture the intracellular metabolic fingerprinting with the optimized lysis approach and spectral set collection, showing high potential in rapid, sensitive and global metabolic profiling in complex biosamples and clinical liquid biopsy. This gives a new perspective to the study of SERS in insightful understanding of relevant biological mechanisms. (54).

Acoustofluidic lysis of cancer cells and Raman spectrum profiling

The lysis of cancer cells inside a sessile droplet was performed using traveling surface acoustic waves (SAWs) without any chemical reagents. Raman spectrum profiling was then carried out to explore detailed cell-derived data. The Rayleigh waves formed by an interdigital transducer were made to propagate along the surface of an LiNbO3 substrate. Polystyrene microparticles (PSMPs) were used to establish mechanical cell lysis effectively, and gold nanoparticles (AuNPs) were added to enhance the Raman signals from the lysed cells by SAWs. The lysis efficiency was evaluated according to the size and concentration of the PSMPs in experiments where the frequency was varied. Lysis occurred mainly by mechanical collision using PSMPs in a high-frequency domain, and the lysis efficiency was improved by increasing the application time and the energy density of the SAWs. Raman signals from the lysed cells were greatly enhanced by nanogaps formed by the AuNPs, which were evenly distributed irrespective of the SAWs through the frequency-independent behavior of the AuNPs. Finally, detailed Raman spectra of MDA-MB-231, malignant breast cancer cells, were acquired, and various organic matter-derived peaks were observed. The 95% confidence region for cells subjected to lysis was more widely distributed than that of cells not subjected to lysis. The proposed SAW platform is expected to facilitate the detection of small quantities and to be applied in biomedical applications.

Label-Free Plasmon-Enhanced Spectroscopic HER2 Detection for Dynamic Therapeutic Surveillance of Breast Cancer

The expression of human epidermal growth factor receptor-2 (HER2) has important implications for pathogenesis, progression, and therapeutic efficacy of breast cancer. The detection of its variation during the treatment is crucial for therapeutic decision-making but remains a grand challenge, especially at the cellular level. Here, we develop a machine learning-driven surface-enhanced Raman spectroscopy (SERS)-integrated strategy for label-free detection of cellular HER2. Specifically, our method allows the extraction of cell-rich spectral signatures utilized for identification and classification of cancer cells with distinct HER2 expression with a high accuracy of 99.6%. By combining label-free SERS detection and machine learning-driven chemometric analysis, we are able to perform longitudinal monitoring of therapeutic efficacy at the cellular level during the treatment of HER2+ breast cancer, which aids in the subsequent decision-making and management. This work provides a promising technique capable of performing dynamic label-free spectroscopic detection for therapeutic surveillance of diseases.

Facile Microwave Assisted Synthesis of Silver Nanostars for Ultrasensitive Detection of Biological Analytes by SERS

We report a very simple, rapid and reproducible method for the fabrication of anisotropic silver nanostars (AgNS) that can be successfully used as highly efficient SERS substrates for different bioanalytes, even in the case of a near-infra-red (NIR) excitation laser. The nanostars have been synthesized using the chemical reduction of Ag+ ions by trisodium citrate. This is the first research reporting the synthesis of AgNS using only trisodium citrate as a reducing and stabilizing agent. The key elements of this original synthesis procedure are rapid hydrothermal synthesis of silver nanostars followed by a cooling down procedure by immersion in a water bath. The synthesis was performed in a sealed bottom flask homogenously heated and brought to a boil in a microwave oven. After 60 s, the colloidal solution was cooled down to room temperature by immersion in a water bath at 35 °C. The as-synthesized AgNS were washed by centrifugation and used for SERS analysis of test molecules (methylene blue) as well as biological analytes: pharmaceutical compounds with various Raman cross sections (doxorubicin, atenolol & metoprolol), cell lysates and amino acids (methionine & cysteine). UV-Vis absorption spectroscopy, (Scanning) Transmission Electron Microscopy ((S)TEM) and Atomic Force Microscopy (AFM) have been employed for investigating nanostars' physical properties.

Lung Cancer: Spectral and Numerical Differentiation among Benign and Malignant Pleural Effusions Based on the Surface-Enhanced Raman Spectroscopy

We present here that the surface-enhanced Raman spectroscopy (SERS) technique in conjunction with the partial least squares analysis is as a potential tool for the differentiation of pleural effusion in the course of the cancerous disease and a tool for faster diagnosis of lung cancer. Pleural effusion occurs mainly in cancer patients due to the spread of the tumor, usually caused by lung cancer. Furthermore, it can also be initiated by non-neoplastic diseases, such as chronic inflammatory infection (the most common reason for histopathological examination of the exudate). The correlation between pleural effusion induced by tumor and non-cancerous diseases were found using surface-enhanced Raman spectroscopy combined with principal component regression (PCR) and partial least squares (PLS) multivariate analysis method. The PCR predicts 96% variance for the division of neoplastic and non-neoplastic samples in 13 principal components while PLS 95% in only 10 factors. Similarly, when analyzing the SERS data to differentiate the type of tumor (squamous cell vs. adenocarcinoma), PLS gives more satisfactory results. This is evidenced by the calculated values of the root mean square errors of calibration and prediction but also the coefficients of calibration determination and prediction (R2C = 0.9570 and R2C = 0.7968), which are more robust and rugged compared to those calculated for PCR. In addition, the relationship between cancerous and non-cancerous samples in the dependence on the gender of the studied patients is presented.

Surface-enhanced Raman probes based on gold nanomaterials for in vivo diagnosis and imaging

Surface-enhanced Raman scattering (SERS) has received considerable attention from researchers due to its high molecular specificity, high sensitivity, non-invasive and multiplexing. Recently, various metal substrates have been exploited for SERS analysis and imaging. Among them, gold nanomaterials are important SERS substrates with outstanding surface plasmon resonance effects, structural adjustability and good biocompatibility, making them widely used in biomedical diagnosis and clinical fields. In this minireview, we discuss the latest progress about the application of gold-based nanomaterials as SERS probes in biomedical research, primarily for in vivo disease diagnosis and imaging. This review mainly includes the basic shapes and morphologies of gold based SERS probes, such as gold nanoparticles (AuNPs), gold nanorods (AuNRs), gold nanostars (AuNSs), as well as other gold nanostructures. Finally, a brief outlook for the future development of SERS technique in the context of efficient diagnostics and therapy guidance is provided. We hope that this minireview will facilitate the design and future development of Surface-enhanced Raman probes based on gold nanomaterials.

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.

Analysis of biomolecules in cochineal dyed archaeological textiles by surface-enhanced Raman spectroscopy

SERS spectroscopy is successfully employed in this work to reveal different components integrating the cochineal colorant employed for dying archaeological textile samples from the Arica Region in North Chile. This analysis was done by in-situ experiments that does not imply the material (colorant and biomolecules) extraction. The spectroscopic analysis of the archaeological textiles by SERS reveals the presence of bands attributed to carminic acid and nucleobases, mainly adenine and guanine. The identification of these biomolecules was also verified in raw cochineal extract and in cochineal dyed replica wool fibers fabricated by us following ancient receipts. The effect of Al on the complexation of carminic acid and other biomolecules was also tested in order to understand the changes induced by the metal interaction on the colorant structure. This study revealed that Al can also complex biomolecules existing in the cochineal extract. In particular, guanine residue seems to interact strongly with the metal, since SERS bands of this residue are enhanced. Furthermore, a theoretical analysis on the interaction of carminic acid and a silver surface was also performed in order to better understand the interaction mechanism between carminic acid and a metal surface that leads to the final SERS spectrum. The results of the present work will be very useful in the identification of different molecules and metal complexes that may be forming part of the cochineal colorant found in archaeological materials.

Probiotics in Cosmetic and Personal Care Products: Trends and Challenges

Probiotics, defined as "live microorganisms that, when administered in adequate amounts, confer a health benefit on the host," are becoming increasingly popular and marketable. However, too many of the products currently labelled as probiotics fail to comply with the defining characteristics. In recent years, the cosmetic industry has increased the number of products classified as probiotics. While there are several potential applications for probiotics in personal care products, specifically for oral, skin, and intimate care, proper regulation of the labelling and marketing standards is still required to guarantee that consumers are indeed purchasing a probiotic product. This review explores the current market, regulatory aspects, and potential applications of probiotics in the personal care industry.

Repeated double cross-validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients

Intense label-free surface-enhanced Raman scattering (SERS) spectra of serum samples were rapidly obtained on Ag plasmonic paper substrates upon 785 nm excitation. Spectra from the hepatocellular carcinoma (HCC) patients showed consistent differences with respect to those of the control group. In particular, uric acid was found to be relatively more abundant in patients, while hypoxanthine, ergothioneine, and glutathione were found as relatively more abundant in the control group. A repeated double cross-validation (RDCV) strategy was applied to optimize and validate principal component analysis-linear discriminant analysis (PCA-LDA) models. An analysis of the RDCV results indicated that a PCA-LDA model using up to the first four principal components has a good classification performance (average accuracy was 81%). The analysis also allowed confidence intervals to be calculated for the figures of merit, and the principal components used by the LDA to be interpreted in terms of metabolites, confirming that bands of uric acid, hypoxanthine, ergothioneine, and glutathione were indeed used by the PCA-LDA algorithm to classify the spectra.

Improvement of Surface-Enhanced Raman Scattering Method for Single Bacterial Cell Analysis

Surface-enhanced Raman scattering (SERS) is a useful tool for label-free analysis of bacteria at the single cell level. However, low reproducibility limits the use of SERS. In this study, for the sake of sensitive and reproducible Raman spectra, we optimized the methods for preparing silver nanoparticles (AgNPs) and depositing AgNPs onto a cell surface. We found that fast dropwise addition of AgNO₃ into the reductant produced smaller and more stable AgNPs, with an average diameter of 45 ± 4 nm. Compared with that observed after simply mixing the bacterial cells with AgNPs, the SERS signal was significantly improved after centrifugation. To optimize the SERS enhancement method, the centrifugal force, method for preparing AgNPs, concentration of AgNPs, ionic strength of the solution used to suspend the cells, and density of the cells were chosen as impact factors and optimized through orthogonal experiments. Finally, the improved method could generate sensitive and reproducible SERS spectra from single Escherichia coli cells, and the SERS signals primarily arose from the cell envelope. We further verified that this optimal method was feasible for the detection of low to 25% incorporation of ¹³C isotopes by the cells and the discrimination of different bacterial species. Our work provides an improved method for generating sensitive and reproducible SERS spectra.

Study on the precise mechanism of Mitoxantrone-induced Jurkat cell apoptosis using surface enhanced Raman scattering

Mitoxantrone (MTX), one representative of anthraquinone ring anticancer drugs, reveals excellent anticancer effects in acute leukemia. Though current studies have shown that MTX-induced acute leukemia cell apoptosis is implemented by inserting into DNA, and then leading to DNA breakage and the subsequent transcription termination, but the specific location information of MTX embedded in DNA remains unknown. In this study, combining surface enhanced Raman scattering (SERS) and principal component analysis (PCA), we achieve the biochemical changes of MTX-induced Jurkat cell apoptosis and the location information of MTX embedded in DNA. In contrast, we also present the corresponding result of Daunorubicin (DNR)-induced Jurkat cell apoptosis. It is found that the location of MTX embedded in DNA of Jurkat cell is different from DNR, in which the action site of MTX is mainly implemented by blocking and destroying AT base pairs while DNR is performed by embedding and destroying GC base pairs and then the base A. Clearly, this achieved information is very useful for the designing and modification of anthraquinone ring anticancer drugs.

Characterization of white wines from north-eastern Italy with surface-enhanced Raman spectroscopy

In the present study, label-free SERS spectroscopy is applied as a useful analytical technique for white wine characterization. 180 samples of three white wines varieties from northeastern Italy, Sauvignon Blanc, Ribolla Gialla and Friulano, collected from three different Italian producers from 2016 vintage, have been analyzed using Ag citrate-reduced colloids and a portable Raman instrument with a 785 nm laser. A PCA of SERS spectra showed that discrimination between wines and wineries is possible. Main spectral differences are due to adenine, carboxylic acids and glutathione, with their ratio changing among different wine types and producers. A robust version of the Soft Independent Modelling of Class Analogy (SIMCA) method was used to model the class space of each wine and to perform the classification among the different categories, yielding overall efficiencies between 87 and 93%. These results are extremely encouraging and open the way to the application of this SERS protocol as a wine identification assay.

Sensors Based on Bio and Biomimetic Receptors in Medical Diagnostic, Environment, and Food Analysis

Analytical chemistry is now developing mainly in two areas: automation and the creation of complexes that allow, on the one hand, for simultaneously analyzing a large number of samples without the participation of an operator, and on the other, the development of portable miniature devices for personalized medicine and the monitoring of a human habitat. The sensor devices, the great majority of which are biosensors and chemical sensors, perform the role of the latter. That last line is considered in the proposed review. Attention is paid to transducers, receptors, techniques of immobilization of the receptor layer on the transducer surface, processes of signal generation and detection, and methods for increasing sensitivity and accuracy. The features of sensors based on synthetic receptors and additional components (aptamers, molecular imprinted polymers, biomimetics) are discussed. Examples of bio- and chemical sensors' application are given. Miniaturization paths, new power supply means, and wearable and printed sensors are described. Progress in this area opens a revolutionary era in the development of methods of on-site and in-situ monitoring, that is, paving the way from the "test-tube to the smartphone".