Intact cell mass spectrometry coupled with machine learning reveals minute changes induced by single gene silencing.

Intact (whole) cell MALDI TOF mass spectrometry is a commonly used tool in clinical microbiology for several decades. Recently it was introduced to analysis of eukaryotic cells, including cancer and stem cells. Besides targeted metabolomic and proteomic applications, the intact cell MALDI TOF mass spectrometry provides a sufficient sensitivity and specificity to discriminate cell types, isogenous cell lines or even the metabolic states. This makes the intact cell MALDI TOF mass spectrometry a promising tool for quality control in advanced cell cultures with a potential to reveal batch-to-batch variation, aberrant clones, or unwanted shifts in cell phenotype. However, cellular alterations induced by change in expression of a single gene has not been addressed by intact cell mass spectrometry yet. In this work we used a well-characterized human ovarian cancer cell line SKOV3 with silenced expression of a tumor suppressor candidate 3 gene (TUSC3). TUSC3 is involved in co-translational N-glycosylation of proteins with well-known global impact on cell phenotype. Altogether, this experimental design represents a highly suitable model for optimization of intact cell mass spectrometry and analysis of spectral data. Here we investigated five machine learning algorithms (k-nearest neighbors, decision tree, random forest, partial least squares discrimination, and artificial neural network) and optimized their performance either in pure populations or in two-component mixtures composed of cells with normal or silenced expression of TUSC3. All five algorithms reached accuracy over 90 % and were able to reveal even subtle changes in mass spectra corresponding to alterations of TUSC3 expression. In summary, we demonstrate that spectral fingerprints generated by intact cell MALDI-TOF mass spectrometry coupled to a machine learning classifier can reveal minute changes induced by alteration of a single gene, and therefore contribute to the portfolio of quality control applications in routine cell and tissue cultures.

Persisting IL-18 levels after COVID-19 correlate with markers of cardiovascular inflammation reflecting potential risk of CVDs development.

COVID-19 manifestation is associated with a strong immune system activation leading to inflammation and subsequently affecting the cardiovascular system. The objective of the study was to reveal possible interconnection between prolongated inflammation and the development or exacerbation of long-term cardiovascular complications after COVID-19. We investigated correlations between humoral and cellular immune system markers together with markers of cardiovascular inflammation/dysfunction during COVID-19 onset and subsequent recovery. We analyzed 22 hospitalized patients with severe COVID-19 within three timepoints (acute, 1 and 6 months after COVID-19) in order to track the impact of COVID-19 on the long-term decline of the cardiovascular system fitness and eventual development of CVDs. Among the cytokines dysregulated during COVID-19 changes, we showed significant correlations of IL-18 as a key driver of several pathophysiological changes with markers of cardiovascular inflammation/dysfunction. Our findings established novel immune-related markers, which can be used for the stratification of patients at high risk of CVDs for further therapy.

Improved Screening of Monoclonal Gammopathy Patients by MALDI-TOF Mass Spectrometry.

Monoclonal gammopathies are a group of blood diseases characterized by presence of abnormal immunoglobulins in peripheral blood and/or urine of patients. Multiple myeloma and plasma cell leukemia are monoclonal gammopathies with unclear etiology, caused by malignant transformation of bone marrow plasma cells. Mass spectrometry with matrix-assisted laser desorption/ionization and time-of-flight detection is commonly used for investigation of the peptidome and small proteome of blood plasma with high accuracy, robustness, and cost-effectivity. In addition, mass spectrometry coupled with advanced statistics can be used for molecular profiling, classification, and diagnosis of liquid biopsies and tissue specimens in various malignancies. Despite the fact there have been fully optimized protocols for mass spectrometry of normal blood plasma available for decades, in monoclonal gammopathy patients, the massive alterations of biophysical and biochemical parameters of peripheral blood plasma often limit the mass spectrometry measurements. In this paper, we present a new two-step extraction protocol and demonstrated the enhanced resolution and intensity (>50×) of mass spectra obtained from extracts of peripheral blood plasma from monoclonal gammopathy patients. When coupled with advanced statistics and machine learning, the mass spectra profiles enabled the direct identification, classification, and discrimination of multiple myeloma and plasma cell leukemia patients with high accuracy and precision. A model based on PLS-DA achieved the best performance with 71.5% accuracy (95% confidence interval, CI = 57.1-83.3%) when the 10× repeated 5-fold CV was performed. In summary, the two-step extraction protocol improved the analysis of monoclonal gammopathy peripheral blood plasma samples by mass spectrometry and provided a tool for addressing the complex molecular etiology of monoclonal gammopathies.

Association of anthropometric and body composition parameters with the presence of hypertension in the Central European population: results from KardioVize 2030 study.

BACKGROUND: Hypertension is one of the most prevalent chronic non-communicable diseases and affects more than 60% of individuals over 60 years of age. Additionally, hypertension is a prominent risk factor for the development of cardiovascular diseases (CVDs). Human body composition is both the result and predictor of an individual's health status, and hypertension has consistently been shown to be more prevalent among obese individuals. In the current study, we focussed on the association between body composition parameters and hypertension occurrence. METHODS: Data from KardioVize 2030, a population-based study (n = 1988), was used to determine the association between the body composition parameters related to both fat and water content with hypertension. Body composition was assessed using the direct segmental multi-frequency bioelectrical impedance analysis method (DSM-BIA). RESULTS: Using logistic regression modelling we found that the majority of hypertension incidence could be determined by body fat and water content, as hypertension occurrence was positively correlated with increased fat-related body composition parameters and water content. Specifically, results from this study demonstrate that increased intracellular fluid was positively associated with higher hypertension incidence in men (14%) and women (16%). CONCLUSION: Body composition reflects the occurrence of hypertension and may serve as a novel therapeutic goal that can be easily implemented in the clinical setting using DSM-BIA.