Bruker (Germany)

Per- and polyfluoroalkyl substances (PFAS) form a large chemical group, but routine monitoring targets only a few well-known compounds such as perfluorooctane sulfonic acid (PFOS). To better understand the widespread occurrence of PFAS in Dutch surface waters, we performed target analysis using an expanded list of compounds, combined with extensive suspect and de novo non-target screening (SNTS) to explore unreported PFAS beyond the scope of conventional monitoring. Analyses were performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and liquid chromatography-trapped ion-mobility time-of-flight mass spectrometry (LC-timsTOF-MS). Target analysis focused on 24 previously underreported sulfonamide-based PFASs, enabled by newly available analytical standards and 34 common PFASs, representing 58 compounds. Several N-alkyl perfluoroalkane sulfonamidoacetic acids, including MeFBSAA (up to 50ng/L), were found. SPr-FHxSA (N-sulfo propyl perfluorohexane sulfonamide), associated with aqueous film-forming foams (AFFFs), was detected in five samples (0.14-1.99ng/L). In total, 38 PFAS were identified by target analysis. Collision cross-section (CCS) values of analytical standards were measured prior to SNTS. In SNTS, nineteen compounds, mainly fluorinated pharmaceuticals and pesticides, were identified at confidence level 2a using the MassBank library. Another 22 candidates were tentatively classified at levels 2b or 3, including an unsaturated PFOS analogue and a fluazinam transformation product, highlighting the ability of de novo SNTS to uncover unexpected PFAS. Finally, 6:2 fluorotelomer sulfonyl propanoamido-dimethylethyl sulfonate (6:2 FtSO2AoS) was confirmed at level 1a. Overall, integrating new PFAS standards with SNTS provided a far more complete view of PFAS presence in Dutch waters than target monitoring.

Abstract Bony materials are biogenic composites of protein fibers and mineral that create hierarchical structures. In the case of teeth, dentin is the main component and similar to other bones, it contains porosity at multiple length scales. It is traversed by micron‐sized hollow channels known as dentinal tubules, essential for temperature and pain sensation. Tubule density and thus porosity vary throughout the macroscopic three‐dimensional (3D) structure, with porosity increasing toward the pulp. The different densities in teeth are easily revealed non‐destructively in 3D by X‐ray imaging using computer tomography (CT). Yet elemental composition analysis is more difficult to obtain from within the centimeter‐sized heterogeneous bulk material. We describe an approach of merging CT measurements of healthy, intact bovine teeth with micro‐X‐ray fluorescence (micro‐XRF) images of matching serially sectioned slices. Through the combination of multi‐resolution quantitative CT measurements with elemental mass fraction derivation, gradients in density and element distributions such as calcium (Ca), phosphorus (P), and zinc (Zn) are revealed across entire teeth in 3D. While the main constituents (Ca and P) are homogeneously distributed in the matrix, Zn concentration increases significantly and exponentially toward the pulp. We find an inverse association between dentin tissue density and Zn concentration localizing this element in or around tubules. Our data serve as a quantitative reference for density and Zn mass fractions in healthy, neither carious nor hypermineralized dentin, as a basis for comparisons across species in health and disease states.

ABSTRACT The analytical comparability of biologic products and their biosimilars, including higher-order structure (HOS) assessment, ensures product quality and is required for regulatory approval. In this study, nuclear magnetic resonance (NMR) spectroscopy was used to evaluate the HOS of Humira (adalimumab) and its biosimilars under normal and photo-stressed conditions. Under normal conditions, 1D and 2D NMR spectra showed strong structural similarity among all products. However, photo-stressed samples exhibited distinct structural differences, including increased methionine oxidation, and localized conformational changes, most notably in the reference product. These changes correlated with findings from size-exclusion chromatography, capillary isoelectric focusing, and mass spectrometry (MS), which revealed size and charge heterogeneity, as well as site-specific methionine oxidation in the heavy chains. The differences in photostability were found to be influenced by container closure systems (CCSs) and formulations. In contrast, circular dichroism spectral analysis showed minimal variation in secondary structures among stressed and unstressed samples. These results underscore the utility of NMR as a sensitive tool for comparative structural analysis of monoclonal antibodies and their biosimilars, particularly under stress conditions, and highlight the impact of formulation and CCS on product stability.

Analyzing microbial colonies on agar plates is a critical task in microbiology, with applications spanning research, diagnostics, and industry. Despite advances in automated systems, challenges remain in accurately segmenting and clustering colonies due to variability in their appearance and distribution. To address these challenges, we present a fully automated modular system for colony segmentation and clustering, combining state-of-the-art deep learning for segmentation and machine learning for cluster count prediction and clustering. The main contribution of this work is the proposition, development and evaluation of a novel system, which achieved a V-measure of 0.532 under real-world conditions, improving to 0.727 with ideal segmentation and cluster counts, setting a new benchmark for microbiological analysis. At the core of the system we propose AgarNet, an Attention U-Net-based architecture combined with an EfficientNetB4 backbone, which achieved an F1-score of 0.906 for segmentation. We also introduce a new BRUKERCLUSTER dataset, which is one of the largest and most diverse annotated resources of its kind, featuring expert-annotated images from a controlled cultivation. By combining the dataset, robust segmentation, accurate cluster count prediction, and effective clustering, the proposed system delivers a scalable solution for advancing automated microbial colony analysis. These results establish a new benchmark for automated colony clustering in microbiology, and by releasing the unique BRUKERCLUSTER dataset, we provide a valuable tool for future advancements in automated colony analysis.

Merkel cell carcinomas (MCCs) are rare, highly aggressive skin cancers with poor outcome due to early lymphatic tumor spread and frequent recurrences. MCCs mostly occur in the head and neck and are mainly caused by an infection with the Merkel cell polyomavirus (MCPyV). Increasing evidence suggests that Piwil-2 and small non-coding PIWI-interacting RNAs (piRNAs) play an important role in solid malignancies and we thought that this might also be the case for MCCs. Therefore, Piwil-2 expression was first evaluated in 27 MCC specimens and correlated with oncological outcome. We found an association with high Piwil-2 expression and advanced tumor stage, MCPyV positivity and poor outcome. Next, we utilized siRNAs for Piwil-2 knock-down in MCC cells. Downregulation of Piwil-2 caused a significant change of 202 different piRNAs and 419 proteins. Interestingly, proteins related to viral driven MCC pathways (TRRAP, BRD8, PRIM2, ORC4) were significantly downregulated. Moreover, there was a moderate cell cycle arrest of cells in the G0/G1-phase, as well as a significant upregulation of SOX-2, a key regulator of Merkel cells. Altogether, Piwil-2 poses a poor prognosticator in MCCs, which is linked to MCC oncogenesis and SOX-2. Further research is needed to better understand underlying mechanisms and to prove their clinical relevance.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-06247-8.

Spatial biology pursues the analysis of cells in their native microenvironment, often with regard to morphology and gene- or protein expression. The spatial analysis of lipid and metabolic profiles by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can add another layer of molecular information. A seamless integration of MSI data with other (multimodal) methods at a single-cell level is often hampered by insufficient co-registration and resolution. Here, we introduce a MALDI-MSI based method that integrates in-source bright-field and fluorescence microscopy, allowing for a coupled (sub-)cellular investigation of the same sample in both modalities. Presented examples from cell culture and tissue analysis include the visualization of intracellular lipid distributions in macrophages during phagocytosis, and the heterogeneity of lipid profiles of tumor infiltrating neutrophils correlated to their individual microenvironments. The achieved combination of lipid profiling with morphological features and protein expression on the single-cell level constitutes a powerful method for cell biology.

Bony materials are biogenic composites of protein fibers and mineral that create hierarchical structures. In the case of teeth, dentin is the main component and similar to other bones, it contains porosity at multiple length scales. It is traversed by

The nutritional benefits of Extra Virgin Olive Oils (EVOOs) depend on their chemical composition. Currently, there is no simple way to compare the health benefits of different EVOOs based on their fat profiles. Samples from Australia and four Mediterranean countries (N=423) were analyzed using proton nuclear magnetic resonance spectroscopy to screen for six quality parameters (free acidity, peroxides, K270, K232, Delta K, wax) and to measure fat compositions. These fat compositions were compared against healthy eating guidelines to produce five binary descriptors, which were weighted by evidence to create an accessible Fatty Acid based Nutrition Index (FNI). EVOOs were differentiated by saturated fat and balance between monounsaturated (MUFA) and polyunsaturated fat (PUFA). Most samples (56.4%) showed poor SFA, poor PUFA and good MUFA (NI=56), 21% had good SFA, poor PUFA and good MUFA (NI=81), and 19.4% exhibited poor SFA, good PUFA and poor MUFA (NI=64). The FNI identifies EVOOs with superior nutritional value, enabling informed consumer choices.

Candida (Candidozyma) auris has distinct genetic clades. Clade distinction is relevant for infection control and epidemiological purposes. State-of-the-art typing methodologies for clade distinction are based on genomic approaches, such as short tandem repeat (STR) analysis and whole-genome sequencing (WGS). However, they are time-consuming and expensive. Fourier transform infrared spectroscopy (FT-IR) is an alternative tool for strain typing based on their unique biochemical spectral profiles. To apply FT-IR to differentiate C. auris clades and evaluate epidemiological relationships based on biochemical data among isolates from a multicenter C. auris outbreak in the state of Pernambuco, northeastern Brazil. Sixty-nine C. auris strains from clades I, II, III, and IV were analysed. Fifty-nine were clade IV strains obtained from three outbreaks that took place in Pernambuco state, northeastern Brazil. An adjusted FT-IR spectroscopy protocol was applied to obtain carbohydrates and lipid fingerprints. Short Tandem Repeat (STR) analysis was used in order to validate the spectroscopy approach. The adjusted preparation protocol for FT-IR analysis improved the spectral quality by 31.42% compared to standard protocol. FT-IR allowed us to discriminate C. auris clades I to IV. Moreover, important similarities were observed in C. auris clade IV strains obtained from two of the three hospitals, based on polysaccharides (1300-800 cm-1) plus lipids (3000-2800 cm-1 and 1500-1400 cm-1) spectra. STR confirmed the similarity results obtained by FT-IR, clustering the strains from two different hospitals. The IR Biotyper is fast, easy-to-use, and a promising alternative for moderate-to-high-complexity laboratories to differentiate C. auris clades. Furthermore, this technique has the potential for isolate-level source tracking, which could be valuable for monitoring transmission routes in clinical settings.