Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer.

BACKGROUND: Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine. METHODS: Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naïve Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types. RESULTS: Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 ± 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 ± 0.03) or SERS data (AUC = 0.84 ± 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 ± 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 ± 0.04) or SERS (AUC = 0.92 ± 0.05) individually, although SERS alone performed better in terms of classification accuracy. CONCLUSION: miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged.

Fermi Level Equilibration at the Metal-Molecule Interface in Plasmonic Systems.

We highlight a new metal-molecule charge transfer process by tuning the Fermi energy of plasmonic silver nanoparticles (AgNPs) in situ. The strong adsorption of halide ions upshifts the Fermi level of AgNPs by up to ∼0.3 eV in the order Cl- < Br- < I-, favoring the spontaneous charge transfer to aligned molecular acceptor orbitals until charge neutrality across the interface is achieved. By carefully quantifying, experimentally and theoretically, the Fermi level upshift, we show for the first time that this effect is comparable in energy to different plasmonic effects such as the plasmoelectric effect or hot-carriers production. Moreover, by monitoring in situ the adsorption dynamic of halide ions in different AgNP-molecule systems, we show for the first time that the catalytic role of halide ions in plasmonic nanostructures depends on the surface affinity of halide ions compared to that of the target molecule.

Adduct of Aquacobalamin with Hydrogen Peroxide.

Aquacobalamin binds hydrogen peroxide reversibly to form a cobalt(III) hydroperoxo adduct with a 0.25 mM dissociation constant, as evidenced by UV-vis absorption spectroscopy and corroborated by NMR, Raman spectroscopy, stopped-flow UV-vis measurements, and density functional theory calculations.

Cellular Internalization of Beta-Carotene Loaded Polyelectrolyte Multilayer Capsules by Raman Mapping.

Raman mapping is becoming a very useful tool in investigating cells and cellular components, as well as bioactive molecules intracellularly. In this study, we have encapsulated beta-carotene using a layer-by-layer technique, as a way to enhance its stability and bioavailability. Further, we have used Raman mapping to characterize the as-obtained capsules and monitor their uptake by the human retinal epithelial D407 cells. We were able to successfully map the beta-carotene distribution inside the capsules, to localize the capsules intracellularly, and distinguish between capsules and other cellular components.

SERS-based liquid biopsy of saliva and serum from patients with Sjögren's syndrome.

In this preliminary study, we employed surface-enhanced Raman scattering (SERS) of saliva and serum samples for diagnosing Sjogren's syndrome (SjS), a systemic autoimmune disease characterized by dryness of the mouth and eyes. The saliva and serum samples from n = 29 patients with SjS and n = 21 controls were deproteinized with methanol and then the SERS spectra were acquired using silver nanoparticles synthesized by reduction with hydroxylamine hydrochloride. In the case of both saliva and serum, the SERS spectra were dominated by similar bands attributed to purine metabolites such as uric acid, xanthine, and hypoxanthine. Principal component analysis-linear discriminant analysis (PCA-LDA) models built from SERS spectra of saliva and serum yielded an overall classification accuracy of 94% and 98%, respectively. These results suggest that the SERS analysis of saliva and serum is able to capture the complex biochemical perturbations that accompany the onset of SjS, a strategy which could be translated in the future into a novel point-of-care diagnosis method. Graphical abstract.

SERS assessment of the cancer-specific methylation pattern of genomic DNA: towards the detection of acute myeloid leukemia in patients undergoing hematopoietic stem cell transplantation.

In this label-free surface-enhanced Raman scattering (SERS) study of genomic DNA, we demonstrate that the cancer-specific DNA methylation pattern translates into specific spectral differences. Thus, DNA extracted from an acute myeloid leukemia (AML) cell line presented a decreased intensity of the 1005 cm-1 band of 5-methylcytosine compared to normal DNA, in line with the well-described hypomethylation of cancer DNA. The unique methylation pattern of cancer DNA also influences the DNA adsorption geometry, resulting in higher adenine SERS intensities for cancer DNA. The possibility of detecting cancer DNA based on its SERS spectrum was validated on peripheral blood genomic DNA samples from n = 17 AML patients and n = 17 control samples, yielding an overall classification of 82% based on the 1005 cm-1 band of 5-methylcytosine. By demonstrating the potential of SERS in assessing the methylation status in the case of real-life DNA samples, the study paves the way for novel methods of diagnosing cancer. Graphical abstract.

Knee osteoarthritis grading by resonant Raman and surface-enhanced Raman scattering (SERS) analysis of synovial fluid.

In this preliminary study on synovial fluid (SF), knee osteoarthritis (OA) grading of n = 23 patients was accomplished by combining two methods: resonant Raman spectroscopy, and surface-enhanced Raman scattering (SERS) of native proteins acquired with iodide-modified silver nanoparticles and a laser emitting at 633 nm. Based on principal component analysis-linear discriminant analysis (PCA-LDA), the SERS spectra of proteins enabled the classification of low-grade and high-grade OA groups with an accuracy of 91%. Resonant Raman spectra of SF, recorded with laser excitation at 532 nm, exhibited carotenoid-associated bands that were less intense in the case of high-grade knee OA patients. Based on the resonant Raman spectra, the grading of OA patients was accomplished with an accuracy of 74%. Concatenating SERS and Raman spectral information increased the classification accuracy between the two groups to 100%. These results demonstrate the potential of Raman and SERS as a point-of-care method for aiding OA grading.

Raman spectroscopy applications in rheumatology.

Raman spectroscopy is a type of vibrational spectroscopy based on the inelastic scattering of photons, which has attracted much attention due to its potential clinical application in rheumatology. In this review, we discuss the typical spectral features of cartilage, bone, synovial fluid, and pathologic crystal deposits, as well as methods of amplifying the Raman signal of biofluids such as drop-coating deposition Raman spectroscopy. Further, applications of Raman and drop-coating deposition Raman spectroscopy in osteoarthritis are described, highlighting the clinical potential of these methods. We also discuss the role of Raman and related techniques in analyzing pathologic crystals such as monosodium urate, calcium pyrophosphate dihydrate, and hydroxyapatite. The results presented in this review demonstrate that Raman spectroscopy has grown past the stage of proof-of-concept, especially in the case of pathologies involving crystal depositions such as gout and calcium pyrophosphate deposition disease , for which the method has been validated on large number of samples. As the medical community becomes more and more aware of Raman spectroscopy, it is envisioned that it will become a standard technique in the near future.

Warfarin-Capped Gold Nanoparticles: Synthesis, Cytotoxicity, and Cellular Uptake.

Currently, research studies on nanoparticle cytotoxicity, uptake or internalization into the body's cells are of great interest for the improvement of diagnostic and therapeutic applications. We report here the synthesis and characterization of very stable novel warfarin-capped gold nanoparticles with an average diameter of 54 ± 10 nm which were prepared using sodium warfarin as a reducing agent. The nanoparticles were tested in terms of cytotoxicity and cellular internalization in vitro on two cell lines: normal lung fibroblast HFL-1 and human retinal pigment epithelial D407 cells. Our results showed that the normal lung fibroblast HFL-1 cells were more sensitive to the nanoparticle treatment compared to the human retinal pigment epithelial D407 cells. Moreover, any signs of potential cytotoxicity occurred during the first 24 h of treatment, the cellular viability remaining largely unchanged for longer exposure times. Transmission electron microscopy and dark field hyperspectral imaging revealed that the nanoparticles were effectively delivered and released to the HFL-1 and D407 cells' cytoplasm. Our results provide valuable information to further investigate sodium warfarin-capped gold nanoparticles for possible biological applications.

Exosome-carried microRNA-based signature as a cellular trigger for the evolution of chronic lymphocytic leukemia into Richter syndrome.

Even if considered a cumulative and not a proliferative CD5+ B-cell neoplasm, chronic lymphocytic leukemia (CLL) has a proliferation rate higher than that recognized earlier, especially in the lymphoid tissues. Some patients with CLL develop a clinical syndrome entitled Richter syndrome (RS). Understanding CLL genetics and epigenetics may help to elucidate the molecular basics of the clinical heterogeneity of this type of malignancy. In the present project we aimed to identify a microRNA species that can predict the evolution of therapy-resistant CLL towards RS. In the first phase of our study, microRNA-19b was identified as a possible target, and in the second phase, we transfected three different CLL cell lines with microRNA-19b mimic and inhibitor and assessed the potential role on leukemia cells in vitro. The mechanism by which miR-19b acts were identified as the upregulation of Ki67 and downregulation of p53. This was further supported through RT-PCR and western blotting on CLL cell lines, as well as by next generation sequencing on two patients diagnosed with CLL that evolved into RS.

SERS-based quantification of albuminuria in the normal-to-mildly increased range.

The lack of an accurate point-of-care detection system for microalbuminuria represents an important unmet medical need that contributes to the morbidity and mortality of patients with kidney diseases. In this proof-of-concept study, we used SERS spectroscopy to detect urinary albumin concentrations in the normal-to-mildly increased albuminuria range, a strategy that could be useful for the early diagnosis of renal impairment due to uncontrolled hypertension, cardiovascular disease or diabetes. We analyzed 27 urine samples by SERS, using iodide-modified silver nanoparticles and we could discriminate between groups with high and low albumin concentrations with an overall accuracy of 89%, 93% and 89%, using principal component analysis-linear discriminant analysis and cut-off values of 3, 6 and 10 µg mL-1 for urinary albumin concentrations, respectively. We achieved a detection limit of 3 µg mL-1 for human serum albumin based on the 1002 cm-1 SERS band, attributed to the ring breathing vibration of phenylalanine. Our detection limit is similar to that of the immunoturbidimetric assays and around one order of magnitude below the detection limit of urinary dipsticks used to detect microalbuminuria. We used principal least squares regression for building a spectral model for quantifying albumin. Using an independent prediction set, the R2 and root mean squared error of prediction between predicted and reference values of human serum albumin concentrations were 0.982 and 2.82, respectively. Here, we show that direct SERS spectroscopy has the sensitivity required for detecting clinically relevant concentrations of urinary albumin, a strategy that could be used in the future for the point-of-care screening of microalbuminuria.

Subpicosecond surface dynamics in genomic DNA from in vitro-grown plant species: a SERS assessment.

In this work the surface-enhanced Raman total half band widths of seven genomic DNAs from leaves of chrysanthemum (Dendranthema grandiflora Ramat.), common sundew (Drosera rotundifolia L.), edelweiss (Leontopodium alpinum Cass), Epilobium hirsutum L., Hypericum richeri ssp. transsilvanicum (Celak) Ciocârlan, rose (Rosa x hybrida L.) and redwood (Sequoia sempervirens D. Don. Endl.) have been measured. We have shown that surface-enhanced Raman spectroscopy (SERS) can be used to study the fast subpicosecond dynamics of DNA in the proximity of a metallic surface. The dependencies of the total half band widths and the global relaxation times, on the DNA molecular subgroup structure and on the type of genomic DNA, are reported. In our study, the full widths at half-maximum (FWHMs) for the SERS bands of genomic DNAs from different leaf tissues are typically in the wavenumber range from 15 to 55 cm(-1). Besides, it can be observed that molecular relaxation processes studied in this work have a global relaxation time smaller than 0.71 ps and larger than 0.19 ps. A comparison between different ranges of FT-Raman and SERS band parameters, respectively, corresponding to DNA extracted from leaf tissues is given. It is shown that the interaction between DNA and a metallic surface has the potential to lead to a shortening of the global relaxation times, as compared with molecular dynamics in solution. We have found that the surface dynamics of molecular subgroups in plant DNA is, in some cases, about two times faster than the solution dynamics of nucleic acids. This can be rationalized in a qualitative manner by invoking the complex landscape of the interaction energy between the molecule and the silver surface.

Towards a receptor-free immobilization and SERS detection of urinary tract infections causative pathogens.

Based on molecular-specific surface-enhanced Raman scattering (SERS) spectroscopy we were able to discriminate between rough and smooth strains of Escherichia coli and Proteus mirabilis bacteria. For this purpose, bacteria have been immobilized through electrostatic forces by inducing a positive charge on the glass slide. This way, SERS spectra on bacterial biomass and also on single bacteria could be recorded in less than 2 h, by using concentrated silver nanoparticles as SERS-active substrate. Single-bacterium SERS spectral fingerprints showed to be sensitive to the presence of the O-antigen at strain level and to the microorganisms growth phase. By using principal component analysis (PCA) on the SERS spectra recorded from E. coli and P. mirabilis, these two uropathogens could be fairly discriminated.

Weakly bound PTCDI and PTCDA dimers studied by using MP2 and DFT methods with dispersion correction.

Potential energy curves along the inter-planar coordinates have been calculated for the dimers of two perylene derivatives (PTCDI and PTCDA) by using MP2 and dispersion corrected DFT (DFT-D) methods with B3LYP, B97 and PBE0 density functionals. The performance of dispersion-correcting potentials (DCPs) for describing intermolecular van der Waals interactions was also tested in conjunction with PBE0 and B971 functionals. Analytical potential energy curves were derived at different levels of theory by fitting the calculated data to modified Morse, Murrell-Sorbie, Buckingham and Lennard-Jones potentials. Potential energy surfaces for the two types of dimers were explored at the PBE0-DCP/6-31+G(d,p) level of theory in order to assess the effects of geometrical perturbations (displacements and/or rotations) on the stability of the dimers. Two minima were located for each perylene derivative, depending on the starting geometries of the dimers. Inter-monomer geometrical parameters of fully and partially optimized dimeric structures, as well as their relative stability, are discussed in comparison to available experimental data and other theoretical results on these or similar compounds.

Silver Nanostar-Based SERS for the Discrimination of Clinically Relevant Acinetobacter baumannii and Klebsiella pneumoniae Species and Clones.

The development of rapid, reliable, and low-cost methods that enable discrimination among clinically relevant bacteria is crucial, with emphasis on those listed as WHO Global Priority 1 Critical Pathogens, such as carbapenem-resistant Acinetobacter baumannii and carbapenem-resistant or ESBL-producing Klebsiella pneumoniae. To address this problem, we developed and validated a protocol of surface-enhanced Raman spectroscopy (SERS) with silver nanostars for the discrimination of A. baumannii and K. pneumoniae species, and their globally disseminated and clinically relevant antibiotic resistant clones. Isolates were characterized by mixing bacterial colonies with silver nanostars, followed by deposition on filter paper for SERS spectrum acquisition. Spectral data were processed with unsupervised and supervised multivariate data analysis methods, including principal component analysis (PCA) and partial least-squares discriminant analysis (PLSDA), respectively. Our proposed SERS procedure using silver nanostars adsorbed to the bacteria, followed by multivariate data analysis, enabled differentiation between and within species. This pilot study demonstrates the potential of SERS for the rapid discrimination of clinically relevant A. baumannii and K. pneumoniae species and clones, displaying several advantages such as the ease of silver nanostars synthesis and the possible use of a handheld spectrometer, which makes this approach ideal for point-of-care applications.

Effects of polystyrene nanoplastics exposure on in vitro-grown Stevia rebaudiana plants.

Nanoplastics (NPs) as environmental contaminants have received increased attention in recent years. Numerous studies have suggested possible negative effects of plants exposure to NPs, but more data are needed with various plants under different exposure conditions to clarify the underlying phytotoxicity mechanisms. In this study, we investigated the effect of polystyrene nanoplastics (PSNPs; 28.65 nm average diameter) exposure (10, 100 and 250 mg/L) on plant morphology and production of relevant metabolites (steviol glycosides, chlorophylls, carotenoids, and vitamins) of in vitro-grown Stevia rebaudiana plantlets. Additionally, we used dark field microscopy combined with fluorescence hyperspectral imaging for the visualization of internalized PSNPs inside plant tissues. At higher concentrations (>100 mg/L), PSNPs were shown to aggregate in roots and to be transported to leaves, having a significantly negative impact on plant growth (reduced size and biomass), while increasing the production of metabolites compared to controls, most probably because of response to stress. The production of steviol glycosides presented a biphasic dose-response suggestive of hormesis, with the highest values at 10 mg/L PSNPs (1.5-2.2-fold increase compared to controls), followed by a decline in production at higher concentrations (100 and 250 mg/L), but with values comparable to controls. These results are promising for future in vivo studies evaluating the effect of NP exposure on the production of steviol glycosides, the natural sweeteners from stevia.

Interface-Dependent Selectivity in Plasmon-Driven Chemical Reactions.

Plasmonic nanoparticles can drive chemical reactions powered by sunlight. These processes involve the excitation of surface plasmon resonances (SPR) and the subsequent charge transfer to adsorbed molecular orbitals. Nonetheless, controlling the flow of energy and charge from SPR to adsorbed molecules is still difficult to predict or tune. Here, we show the crucial role of halide ions in modifying the energy landscape of a plasmon-driven chemical reaction by carefully engineering the nanoparticle-molecule interface. By doing so, the selectivity of plasmon-driven chemical reactions can be controlled, either enhancing or inhibiting the metal-molecule charge and energy transfer or by regulating the vibrational pumping rate. These results provide an elegant method for controlling the energy flow from plasmonic nanoparticles to adsorbed molecules, in situ, and selectively targeting chemical bonds by changing the chemical nature of the metal-molecule interface.

Stage related metabolic profile of the synovial fluid in patients with acute flares of knee osteoarthritis.

Background and aim: Osteoarthritis (OA) is the most common joint condition and the leading cause of pain and disability in elderly patients. Currently, there is no biomarker available for the early diagnosis of OA, and limited data is available regarding the molecular basis of progression for OA. For this reason, this study aimed to identify the metabolomic profile of early and late OA using high-performance liquid chromatography coupled with untargeted mass spectrometry (LC-MS). Methods: 31 patients with knee OA and joint effusion were enrolled. Based on Kellgren/Laurence scale, 12 patients were classified as early OA (eOA) and 19 as late OA (lOA). The synovial fluid (SF) was collected and characterized by untargeted LC-MS. Only the metabolites identified in more than 25% of each group were kept for further analysis. Principal component analysis (PCA) enabled the unsupervised clustering of the eOA and lOA groups. Further, for classification, the best three principal components (PCs) were used as input for two machine learning algorithms (random forest and naïve Bayes), which were trained to discriminate between the eOA and lOA groups. Results: 43 metabolites were identified in both eOA and lOA, but after selecting the metabolites present in at least 25% of the patients in each group, the metabolomics analysis yielded a panel of only nine metabolites: four metabolites related to phospholipids (phosphatidylcholine 20:0/18:2 and 18:0/20:2, sphingomyelin, and ceramide), three metabolites belonging to purine metabolites (inosine 5'-phosphate, adenosine thiamine diphosphate, and diadenosine 5',5'-diphosphate), one metabolite was a gonadal steroid hormone (estrone 3-sulfate), and one metabolite represented by heme, with all but ceramide (d18:1/20:0) being enriched in the lOA group. By using as features the best three PCs (PC2, PC8 and PC9), random forest and naïve Bayes machine learning algorithms yielded a classification accuracy of 0.81 and 0.78, respectively. Conclusion: Our LC-MS analysis of SF from patients with eOA and lOA indicates stage-dependent differences, lOA being associated with a perturbed metabolome of phospholipids, purine metabolites, gonadal steroid hormones (estrone 3-sulfate) and a heme molecule. Specific questions need to be answered regarding the biosynthesis and function of these metabolites in osteoarthritic joints, with the aim of developing new relevant biomarkers and therapeutic strategies.

SERS Liquid Biopsy Profiling of Serum for the Diagnosis of Kidney Cancer.

Renal cancer (RC) represents 3% of all cancers, with a 2% annual increase in incidence worldwide, opening the discussion about the need for screening. However, no established screening tool currently exists for RC. To tackle this issue, we assessed surface-enhanced Raman scattering (SERS) profiling of serum as a liquid biopsy strategy to detect renal cell carcinoma (RCC), the most prevalent histologic subtype of RC. Thus, serum samples were collected from 23 patients with RCC and 27 controls (CTRL) presenting with a benign urological pathology such as lithiasis or benign prostatic hypertrophy. SERS profiling of deproteinized serum yielded SERS band spectra attributed mainly to purine metabolites, which exhibited higher intensities in the RCC group, and Raman bands of carotenoids, which exhibited lower intensities in the RCC group. Principal component analysis (PCA) of the SERS spectra showed a tendency for the unsupervised clustering of the two groups. Next, three machine learning algorithms (random forest, kNN, naïve Bayes) were implemented as supervised classification algorithms for achieving discrimination between the RCC and CTRL groups, yielding an AUC of 0.78 for random forest, 0.78 for kNN, and 0.76 for naïve Bayes (average AUC 0.77 ± 0.01). The present study highlights the potential of SERS liquid biopsy as a diagnostic and screening strategy for RCC. Further studies involving large cohorts and other urologic malignancies as controls are needed to validate the proposed SERS approach.

SERS liquid biopsy in breast cancer. What can we learn from SERS on serum and urine?

SERS analysis of biofluids, coupled with classification algorithms, has recently emerged as a candidate for point-of-care medical diagnosis. Nonetheless, despite the impressive results reported in the literature, there are still gaps in our knowledge of the biochemical information provided by the SERS analysis of biofluids. Therefore, by a critical assignment of the SERS bands, our work aims to provide a systematic analysis of the molecular information that can be achieved from the SERS analysis of serum and urine obtained from breast cancer patients and controls. Further, we compared the relative performance of five different machine learning algorithms for breast cancer and control samples classification based on the serum and urine SERS datasets, and found comparable classification accuracies in the range of 61-89%. This result is not surprising since both biofluids show striking similarities in their SERS spectra providing similar metabolic information, related to purine metabolites. Lastly, by carefully comparing the two datasets (i.e., serum and urine) we show that it is possible to link the misclassified samples to specific metabolic imbalances, such as carotenoid levels, or variations in the creatinine concentration.