Identification and quantification of the selenium species in biological tissues is imperative, considering the need to properly understand its metabolism and its importance in various field of sciences, especially nutrition science. Although a number of studies deals with the speciation of selenium, speciation analysis is still far from being a routine task, and so far strongly depends on the type of the samples. We present a study aimed to examine speciation analysis of Se in tissues of livers, muscles, and hearts obtained from lambs, namely in liver, muscle, and heart. The studied lambs were fed with the diet enriched with an inorganic (as sodium selenate) and organic chemical form of Se (as Se-enriched yeast) compounds with simultaneous addition of fish oil (FO) and carnosic acid (CA). The first part of the work was focused on the optimization of the extraction procedure of selenium compounds from tissues. Next, hyphenated high performance liquid chromatography and inductively coupled plasma mass spectrometry (HPLC–ICP–MS) was used for the identification of five seleno-compounds—Se-methionine (SeMet), Se-cystine (SeCys2), Se-methyl-Se-cysteine (SeMetSeCys), and Se(IV) and Se(VI). Verification of the identified seleno-compounds was achieved using triple-quadrupole mass spectrometer coupled to high performance liquid chromatography (HPLC–ESI–MS/MS). The applied procedure allowed for quantitative analysis of SeMet, SeCys2, and SeMetSeCys, in biological tissues. The developed analytical protocol is feasible for speciation analysis of small molecular seleno-compounds in animals samples.

Nowadays growing attention is paid to the control of fluorine content in samples of biological origin as it is present in the form of various biologically active organic compounds. Due to the chemically-rich matrix of biological tissues, the determination of fluorine becomes a very difficult task. Furthermore, a required complex sample preparation procedure makes the determination of the low contents of F by ion chromatography UV-Vis or ion-selective electrodes not possible. High-resolution continuum source graphite furnace molecular absorption spectrometry (HR-CS GF MAS) seems to be the best option for this purpose due to its high robustness to matrix interferences, especially in the presence of carefully selected modifiers. In this work the possibility of quantitative F determination in water and animal tissues was examined by measuring the molecular absorption of gallium monofluoride (GaF) at 211.248 nm with the use of a commercially available HR-CS GF MAS system. Experimental conditions for the sensitive and precise determination of fluorine were optimized, including the time/temperature program as well as addition of gallium and modifier mixture in combined mode. Under these conditions the fluoride present in the sample was stabilized up to 600 °C, and the optimum vaporization temperature for GaF was 1540 °C. Palladium and zirconium deposited onto the graphite surface served as solid modifiers; sodium acetate and ruthenium modifiers were added directly to the sample. The limit of detection and the characteristic mass of the method were 0.43 μg/L and 8.7 pg, respectively. The proposed procedure was validated by the use of certified reference materials (CRMs) of lake water and animal tissue; the acceptable recovery was obtained, proving that it can be applied for samples with a similar matrix.

Significant advances in the technological development of mass spectrometry in the field of proteomics and the generation of extremely large amounts of data require a very critical approach to assure the validity of results. Commonly used procedures involved liquid chromatography followed by high-resolution mass spectrometry measurements. Proteomics analysis is used in many fields including the investigation of the metabolism of biologically active substances in organisms. Thus, there is a need to care about the validity of the obtained results. In this work, we proposed a standardized protocol for proteomic analysis using liquid chromatography–high-resolution mass spectrometry, which covers all of these analytical steps to ensure the validity of the results. For this purpose, we explored the requirements of the ISO/IEC 17025:2017 standard as a reference document for quality control in biochemistry research-based mass spectrometry.

Mass-spectrometry-based proteomics frequently utilizes label-free quantification strategies due to their cost-effectiveness, methodological simplicity, and capability to identify large numbers of proteins within a single analytical run. Despite these advantages, the prevalence of missing values (MV), which can impact up to 50% of the data matrix, poses a significant challenge by reducing the accuracy, reproducibility, and interpretability of the results. Consequently, effective handling of missing values is crucial for reliable quantitative analysis in proteomic studies. This study systematically evaluated the performance of selected imputation methods for addressing missing values in proteomic dataset. Two protein identification algorithms, FragPipe and MaxQuant, were employed to generate datasets, enabling an assessment of their influence on im-putation efficacy. Ten imputation methods, representing three methodological categories—single-value (LOD, ND, SampMin), local-similarity (kNN, LLS, RF), and global-similarity approaches (LSA, BPCA, PPCA, SVD)—were analyzed. The study also investigated the impact of data logarithmization on imputation performance. The evaluation process was conducted in two stages. First, performance metrics including normalized root mean square error (NRMSE) and the area under the receiver operating characteristic (ROC) curve (AUC) were applied to datasets with artificially introduced missing values. The datasets were designed to mimic varying MV rates (10%, 25%, 50%) and proportions of values missing not at random (MNAR) (0%, 20%, 40%, 80%, 100%). This step enabled the assessment of data characteristics on the relative effectiveness of the imputation methods. Second, the imputation strategies were applied to real proteomic datasets containing natural missing values, focusing on the true-positive (TP) classification of proteins to evaluate their practical utility. The findings highlight that local-similarity-based methods, particularly random forest (RF) and local least-squares (LLS), consistently exhibit robust performance across varying MV scenarios. Furthermore, data logarithmization significantly enhances the effectiveness of global-similarity methods, suggesting it as a beneficial preprocessing step prior to imputation. The study underscores the importance of tailoring imputation strategies to the specific characteristics of the data to maximize the reliability of label-free quantitative proteomics. Interestingly, while the choice of protein identification algorithm (FragPipe vs. MaxQuant) had minimal influence on the overall imputation error, differences in the number of proteins classified as true positives revealed more nuanced effects, emphasizing the interplay between imputation strategies and downstream analysis outcomes. These findings provide a comprehensive framework for improving the accuracy and reproducibility of proteomic analyses through an informed selection of imputation approaches.

Although several analytical techniques are known for their ability to detect fluorine and its compounds, the investigation of its biotransformation in living organisms is not a trivial task. Therefore, much attention is given to the development of new analytical strategies that can trace the metabolism of fluorine-containing compounds. Molecular absorption and mass spectrometry were used as tools to collect complementary information for the investigation of the biotransformation of the fluorine-containing compounds in animal model organisms. The study aimed to examine the effectiveness of the adsorption of fluorine by animal tissues, which was done by measuring the molecular absorption of gallium monofluoride (GaF) using high-resolution continuum source graphite furnace absorption spectrometry (HR-CS GF MAS). The limit of detection (LOD) was 6 μg L⁻¹. Then, the distribution of fluorine-containing compunds was obtained by measuring the studied drug concentrations in animal tissues using high-performance liquid chromatography with electrospray ionisation (ESI) tandem mass spectrometry (HPLC-ESI-MS/MS). The LOD for compounds containing fluorine was 0.05 μg kg⁻¹. Finally, proteins containing fluorine modifications bound in a covalent way were studied using ultra-high performance liquid chromatography coupled to an electrospray mass spectrometer equipped with a high resolution Orbitrap mass analyzer (nano-UHPLC-ESI-(Orbitrap)-MS/MS). The developed analytical protocol contributes to considerable progress in understanding the molecular basis of the influence of fluorine-containing compounds on aminal organisms.