Discovery of calcite as a new pro-inflammatory calcium-containing crystal in human osteoarthritic synovial fluid.

OBJECTIVE: We aimed to characterize calcium-containing crystals present in synovial fluid from patients with knee osteoarthritis (OA) using Raman spectroscopy, and specifically investigate the biological effects of calcite crystals. DESIGN: Thirty-two synovial fluid samples were collected pre-operatively from knee OA patients undergoing total joint arthroplasty. An integrated Raman polarized light microscope was used for identification of crystals in synovial fluid. Human peripheral blood mononuclear cells (PBMC's), human OA articular chondrocytes (HACs) and fibroblast-like synoviocytes (FLSs) were exposed to calcite crystals. Expression of relevant cytokines and inflammatory genes were measured using ELISA and real-time PCR. RESULTS: Various calcium-containing crystals were identified, including calcium pyrophosphate (37.5 %) and basic calcium phosphate (21.8 %), but they were never found simultaneously in the same OA synovial fluid sample. For the first time, we discovered the presence of calcite crystals in 93.8 % of the samples, while dolomite was detected in 25 % of the cases. Characterization of the cellular response to calcite crystal exposure revealed increased production of innate immune-derived cytokines by PBMC's, when co-stimulated with lipopolysaccharide (LPS). Additionally, calcite crystal stimulation of HACs and FLSs resulted in enhanced secretion of pro-inflammatory molecules and alterations in the expression of extracellular matrix remodeling enzymes. CONCLUSIONS: This study highlights the unique role of Raman spectroscopy in OA crystal research and identified calcite as a novel pro-inflammatory crystal type in OA synovial fluid. Understanding the role of specific crystal species in the OA joint may open new avenues for pharmacological interventions and personalized approaches to treating OA.

High-Resolution Raman Imaging of >300 Patient-Derived Cells from Nine Different Leukemia Subtypes: A Global Clustering Approach.

Leukemia comprises a diverse group of bone marrow tumors marked by cell proliferation. Current diagnosis involves identifying leukemia subtypes through visual assessment of blood and bone marrow smears, a subjective and time-consuming method. Our study introduces the characterization of different leukemia subtypes using a global clustering approach of Raman hyperspectral maps of cells. We analyzed bone marrow samples from 19 patients, each presenting one of nine distinct leukemia subtypes, by conducting high spatial resolution Raman imaging on 319 cells, generating over 1.3 million spectra in total. An automated preprocessing pipeline followed by a single-step global clustering approach performed over the entire data set identified relevant cellular components (cytoplasm, nucleus, carotenoids, myeloperoxidase (MPO), and hemoglobin (HB)) enabling the unsupervised creation of high-quality pseudostained images at the single-cell level. Furthermore, this approach provided a semiquantitative analysis of cellular component distribution, and multivariate analysis of clustering results revealed the potential of Raman imaging in leukemia research, highlighting both advantages and challenges associated with global clustering.

Diagnostic Accuracy of Raman Spectroscopy Integrated With Polarized Light Microscopy for Calcium Pyrophosphate-Associated Arthritis.

OBJECTIVE: We studied the performance of integrated Raman polarized light microscopy (iRPolM) for the identification of calcium pyrophosphate (CPP)-associated arthritis (CPPD). METHODS: This is a diagnostic accuracy study including 400 consecutive synovial fluid samples from a single hospital in the Netherlands. Accuracy measures were calculated against polarized light microscopy (PLM) and the 2023 American College of Rheumatology (ACR)/EULAR criteria set for CPPD. RESULTS: The interrater reliability between iRPolM and the 2023 ACR/EULAR criteria set for CPPD was strong (κ = 0.88). The diagnostic performance of iRPolM compared to the 2023 ACR/EULAR criteria set was sensitivity 86.0% (95% confidence interval [CI] 73.3-94.2), specificity 99.1% (95% CI 97.5-99.8), positive likelihood ratio 100.33 (95% CI 32.3-311.3), negative likelihood ratio 0.14 (95% CI 0.07-0.28), positive predictive value 93.5% (95% CI 82.2-97.8), negative predictive value 98.0% (95% CI 82.2-97.8), and accuracy 97.5% (95% CI 95.5-98.8). We allowed rheumatologists to rate the certainty of their microscopic identification of CPP and found a large correspondence between iRPolM and a certain identification (κ = 0.87), whereas only 10% of the uncertain CPP identifications could be confirmed with iRPolM. We identified several novel particle types in synovial fluid analysis, including calcium carbonate crystals, deposited carotenoids, microplastics, and three types of Maltese cross birefringent objects. CONCLUSION: iRPolM can easily identify CPP crystals with a strong diagnostic performance. PLM alone is not specific enough to reliably resolve complicated cases, and the implementation of Raman spectroscopy in rheumatology practice can be of benefit to patients with suspected CPPD.

Identification of extracellular vesicles from their Raman spectra via self-supervised learning.

Extracellular vesicles (EVs) released from cells attract interest for their possible role in health and diseases. The detection and characterization of EVs is challenging due to the lack of specialized methodologies. Raman spectroscopy, however, has been suggested as a novel approach for biochemical analysis of EVs. To extract information from the spectra, a novel deep learning architecture is explored as a versatile variant of autoencoders. The proposed architecture considers the frequency range separately from the intensity of the spectra. This enables the model to adapt to the frequency range, rather than requiring that all spectra be pre-processed to the same frequency range as it was trained on. It is demonstrated that the proposed architecture accepts Raman spectra of EVs and lipoproteins from 13 biological sources and from two laboratories. High reconstruction accuracy is maintained despite large variances in frequency range and noise level. It is also shown that the architecture is able to cluster the biological nanoparticles by their Raman spectra and differentiate them by their origin without pre-processing of the spectra or supervision during learning. The model performs label-free differentiation, including separating EVs from activated vs. non-activated blood platelets and EVs/lipoproteins from prostate cancer patients versus non-cancer controls. The differentiation is evaluated by creating a neural network classifier that observes the features extracted by the model to classify the spectra according to their sample origin. The classification reveals a test sensitivity of 92.2 % and selectivity of 92.3 % over 769 measurements from two labs that have different measurement configurations.

Physical association of low density lipoprotein particles and extracellular vesicles unveiled by single particle analysis.

Extracellular vesicles (EVs) in blood plasma are recognized as potential biomarkers for disease. Although blood plasma is easily obtainable, analysis of EVs at the single particle level is still challenging due to the biological complexity of this body fluid. Besides EVs, plasma contains different types of lipoproteins particles (LPPs), that outnumber EVs by orders of magnitude and which partially overlap in biophysical properties such as size, density and molecular makeup. Consequently, during EV isolation LPPs are often co-isolated. Furthermore, physical EV-LPP complexes have been observed in purified EV preparations. Since co-isolation or association of LPPs can impact EV-based analysis and biomarker profiling, we investigated the presence and formation of EV-LPP complexes in biological samples by using label-free atomic force microscopy, cryo-electron tomography and synchronous Rayleigh and Raman scattering analysis of optically trapped particles and fluorescence-based high sensitivity single particle flow cytometry. Furthermore, we evaluated the impact on flow cytometric analysis in the presence of LPPs using in vitro spike-in experiments of purified tumour cell line-derived EVs in different classes of purified human LPPs. Based on orthogonal single-particle analysis techniques we demonstrate that EV-LPP complexes can form under physiological conditions. Furthermore, we show that in fluorescence-based flow cytometric EV analysis staining of LPPs, as well as EV-LPP associations, can influence quantitative and qualitative EV analysis. Lastly, we demonstrate that the colloidal matrix of the biofluid in which EVs reside impacts their buoyant density, size and/or refractive index (RI), which may have consequences for down-stream EV analysis and EV biomarker profiling.

Test characteristics of Raman spectroscopy integrated with polarized light microscopy for the diagnosis of acute gouty arthritis.

OBJECTIVES: We studied the performance of Raman spectroscopy integrated with polarized light microscopy (iRPolM) as a next-generation technique for synovial fluid analysis in gout. METHODS: This is a prospective study, including consecutive synovial fluid samples drawn from any peripheral swollen joint. Diagnostic accuracy was compared to the 2015 ACR/EULAR Gout classification criteria as a reference test and to polarized light microscopy (PLM) analysis by a rheumatologist. Synovial fluid was analysed with iRPolM after unblinding the PLM results. RESULTS: Two hundred unselected consecutive patient samples were included in this study. Validation against clinical criteria: 67 patients were classified as gout according to 2015 ACR/EULAR classification criteria. Compared to the 2015 ACR/EULAR gout classification criteria, iRPolM had a sensitivity of 77.6% (95% CI: 65.8-86.9), specificity of 97.7% (95% CI: 93.5-99.5), positive predictive value (PPV) of 94.5% (95% CI: 84.9-98.2), negative predictive value (NPV) of 89.7% (95% CI: 84.7-93.1), an accuracy of 91.0% (95% CI: 86.2-94.6), a positive likelihood ratio of 34.4 (95% CI: 11.16-106.10) and a negative likelihood ratio of 0.23 (95% CI: 0.15-0.36). Validation against PLM: 55 samples were positive for MSU according to PLM. The interrater agreement between PLM and iRPolM was near perfect (к=0.90). The sensitivity of iRPolM to identify MSU in PLM-positive samples was 91.2% (95% CI: 80.7-97.1), the specificity was 97.6% (95% CI: 93.0-99.5), the PPV was 94.6% (95% CI: 85.0-98.2), NPV was 96.0% (95% CI: 91.2-98.2) and the accuracy was 95.6% (95% CI: 91.4-98.2). The positive likelihood ratio was 37.4 (95% CI: 12.20-114.71), and the negative likelihood ratio was 0.09 (95% CI: 0.04-0.21). CONCLUSION: iRPolM is a promising next-generation diagnostic tool for rheumatology by diagnosing gout with high specificity, increased objectivity, and a sensitivity comparable to PLM.

Extracellular Vesicles: A New Source of Biomarkers in Pediatric Solid Tumors? A Systematic Review.

Virtually every cell in the body releases extracellular vesicles (EVs), the contents of which can provide a "fingerprint" of their cellular origin. EVs are present in all bodily fluids and can be obtained using minimally invasive techniques. Thus, EVs can provide a promising source of diagnostic, prognostic, and predictive biomarkers, particularly in the context of cancer. Despite advances using EVs as biomarkers in adult cancers, little is known regarding their use in pediatric cancers. In this review, we provide an overview of published clinical and in vitro studies in order to assess the potential of using EV-derived biomarkers in pediatric solid tumors. We performed a systematic literature search, which yielded studies regarding desmoplastic small round cell tumor, hepatoblastoma, neuroblastoma, osteosarcoma, and rhabdomyosarcoma. We then determined the extent to which the in vivo findings are supported by in vitro data, and vice versa. We also critically evaluated the clinical studies using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) system, and we evaluated the purification and characterization of EVs in both the in vivo and in vitro studies in accordance with MISEV guidelines, yielding EV-TRACK and PedEV scores. We found that several studies identified similar miRNAs in overlapping and distinct tumor entities, indicating the potential for EV-derived biomarkers. However, most studies regarding EV-based biomarkers in pediatric solid tumors lack a standardized system of reporting their EV purification and characterization methods, as well as validation in an independent cohort, which are needed in order to bring EV-based biomarkers to the clinic.

Ultrasensitive Detection and In Situ Imaging of Analytes on Graphene Oxide Analogues Using Enhanced Raman Spectroscopy.

We demonstrate how algorithm-improved confocal Raman microscopy (ai-CRM), in combination with chemical enhancement by two-dimensional substrates, can be used as an ultrasensitive detection method for rhodamine (R6G) molecules adsorbed from aqueous solutions. After developing a protocol for laser-induced reduction of graphene oxide, followed by noninvasive Raman imaging, a limit of detection (LOD) of 5 × 10-10 M R6G was achieved using ai-CRM. An equivalent subnanomolar LOD was also achieved on another graphene oxide analogue -UV/ozone-oxidized graphene. These record-breaking detection capabilities also enabled us to study the adsorption kinetics and image the spatial distribution of the adsorbed R6G. These findings indicate a strong potential for algorithm-improved graphene-enhanced Raman spectroscopy as a facile method for detecting, imaging, and quantifying trace amounts of adsorbing molecules on a variety of 2D substrates.

Evaluation of the changes in human milk lipid composition and conformational state with Raman spectroscopy during a breastfeed.

Human milk fat forms the main energy source for breastfed infants, and is highly variable in terms of concentration and composition. Understanding the changes in human milk lipid composition and conformational state during a breastfeed can provide insight into lipid synthesis and secretion in the mammary gland. Therefore, the aim of this study was to evaluate human milk fatty acid length, degree of unsaturation (lipid composition) and lipid phase (lipid conformational state) at different stages during a single breastfeed (fore-, bulk- and hindmilk). A total of 48 samples from 16 lactating subjects were investigated with confocal Raman spectroscopy. We did not observe any significant changes in lipid composition between fore-, bulk and hindmilk. A new finding from this study is that lipid conformational state at room temperature changed significantly during a breastfeed, from almost crystalline to almost liquid. This observation suggests that lipid synthesis in the mammary gland changes during a single breastfeed.

Organosilicon uptake by biological membranes.

Organosilicon compounds are ubiquitous in everyday use. Application of some of these compounds in food, cosmetics and pharmaceuticals is widespread on the assumption that these materials are not systemically absorbed. Here the interactions of various organosilicon compounds (simeticone, hexamethyldisilazane and polydimethylsiloxane) with cell membranes and models thereof were characterized with a range of analytical techniques, demonstrating that these compounds were retained in or on the cell membrane. The increasing application of organosilicon compounds as replacement of other plastics calls for a better awareness and understanding of these interactions. Moreover, with many developments in biotechnology relying on organosilicon materials, it becomes important to scrutinize the potential effect that silicone leaching may have on biological systems.

PDMS Curing Inhibition on 3D-Printed Molds: Why? Also, How to Avoid It?

Three-dimensional (3D)-printing techniques such as stereolithography (SLA) are currently gaining momentum for the production of miniaturized analytical devices and molds for soft lithography. However, most commercially available SLA resins inhibit polydimethylsiloxane (PDMS) curing, impeding reliable replication of the 3D-printed structures in this elastomeric material. Here, we report a systematic study, using 16 commercial resins, to identify a fast and straightforward treatment of 3D-printed structures and to support accurate PDMS replication using UV and/or thermal post-curing. In-depth analysis using Raman spectroscopy, nuclear magnetic resonance, and high-resolution mass spectrometry revealed that phosphine oxide-based photo-initiators, leaching out of the 3D-printed structures, are poisoning the Pt-based PDMS catalyst. Yet, upon UV and/or thermal treatments, photo-initiators were both eliminated and recombined into high molecular weight species that were sequestered in the molds.

In-situ observation of reactive wettability alteration using algorithm-improved confocal Raman microscopy.

HYPOTHESIS: The wettability of complex fluids on surfaces usually depends on the adsorption of solutes to any of the constituting interfaces. Controlling such interfacial processes by varying the composition of a phase enables the design of smart responsive systems. Our goal is to demonstrate that 3D Confocal Raman Microscopy (CRM) can reveal the mechanistic details of such processes by allowing to simultaneously monitor the contact angle variation and redistribution of the chemical species involved. EXPERIMENTS: Motivated by the enhanced oil recovery process of low salinity water flooding, we studied the response of picolitre oil drops on mineral substrates upon varying the ambient brine salinity. The substrates were pre-coated with thin layers of deuterated-stearic acid (surfactant) that display salinity-dependent stability. FINDINGS: 3D CRM imaging using a recently proposed faster 'ai' (algorithm-improved) mode reveals that the surfactant layer is stable at high salinities, leading to preferential oil wetting. Upon reducing the ambient brine salinity, this layer decomposes and the investigated surfaces of mica and - somewhat less pronounced - silica become more water wet. Eventually, the surfactant is found to partly dissolve in the oil and partly precipitate at the oil-water interface. We anticipate that ai-3D-CRM will prove useful to holistically study similar systems displaying reactive wetting.

Label-free identification and chemical characterisation of single extracellular vesicles and lipoproteins by synchronous Rayleigh and Raman scattering.

Extracellular vesicles (EVs) present in blood originate from cells of different origins such as red blood cells (RBCs), platelets and leukocytes. In patients with cancer, a small portion of EVs originate from tumour cells and their load is associated with poor clinical outcome. Identification of these tumour-derived extracellular vesicles (tdEVs) is difficult as they are outnumbered by EVs of different tissue of origin as well a large number of lipoproteins (LPs) that are in the same size range. In order to detect tdEVs from the abundant presence of other particles, single-particle techniques are necessary. Here, synchronous Rayleigh and Raman scattering is used for that purpose. This combination of light scattering techniques identifies optically trapped single particles based on Rayleigh scattering and distinguishes differences in chemical composition of particle populations based on Raman scattering. Here, we show that tdEVs can be distinguished from RBC EVs and LPs in a label-free manner and directly in suspension.

Algorithm-improved high-speed and non-invasive confocal Raman imaging of 2D materials.

Confocal Raman microscopy is important for characterizing 2D materials, but its low throughput significantly hinders its applications. For metastable materials such as graphene oxide (GO), the low throughput is aggravated by the requirement of extremely low laser dose to avoid sample damage. Here we introduce algorithm-improved confocal Raman microscopy (ai-CRM), which increases the Raman scanning rate by one to two orders of magnitude with respect to state-of-the-art works for a variety of 2D materials. Meanwhile, GO can be imaged at a laser dose that is two to three orders of magnitude lower than previously reported, such that laser-induced variations of the material properties can be avoided. ai-CRM also enables fast and spatially resolved quantitative analysis, and is readily extended to 3D mapping of composite materials. Since ai-CRM is based on general mathematical principles, it is cost-effective, facile to implement and universally applicable to other hyperspectral imaging methods.

Silver Nanocubes Coated in Ceria: Core/Shell Size Effects on Light-Induced Charge Transfer.

Plasmonic sensitization of semiconductors is an attractive approach to increase light-induced photocatalytic performance; one method is to use plasmonic nanostructures in core@shell geometry. The occurrence and mechanism of synergetic effects in photocatalysis of such geometries are under intense debate and proposed to occur either through light-induced charge transfer (CT) or through thermal effects. This study focuses on the relation between the dimensions of Ag@CeO2 nanocubes, the wavelength-dependent efficiency, and the mechanism of light-induced direct CT. A 4-mercaptobenzoic acid (4-MBA) linker between core and shell acts as a Raman probe for CT. For all Ag@CeO2 nanocubes, CT increases with decreasing excitation wavelength, with notable increase at and below 514 nm. This is fully explainable by CT from silver to the 4-MBA LUMO, with the increase for excitation wavelengths that exceed the Ag/4-MBA LUMO gap of 2.28 eV (543 nm). A second general trend observed is an increase in CT yield with ceria shell thickness, which is assigned to relaxation of the excited electron further into the ceria conduction band, potentially producing defects.