The alveolar-capillary interface is the key functional element of gas exchange in the human lung, and disruptions to this interface can lead to significant medical complications. However, it is currently challenging to adequately model this interface in vitro, as it requires not only the co-culture of human alveolar epithelial and endothelial cells but mainly the preparation of a biocompatible scaffold that mimics the basement membrane. This scaffold should support cell seeding from both sides, and maintain optimal cell adhesion, growth, and differentiation conditions. Our study investigates the use of polycaprolactone (PCL) nanofibers as a versatile substrate for such cell cultures, aiming to model the alveolar-capillary interface more accurately. We optimized nanofiber production parameters, utilized polyamide mesh UHELON as a mechanical support for scaffold handling, and created 3D-printed inserts for specialized co-cultures. Our findings confirm that PCL nanofibrous scaffolds are manageable and support the co-culture of diverse cell types, effectively enabling cell attachment, proliferation, and differentiation. Our research establishes a proof-of-concept model for the alveolar-capillary interface, offering significant potential for enhancing cell-based testing and advancing tissue-engineering applications that require specific nanofibrous matrices.
Pancreas is a vital gland of gastrointestinal system with exocrine and endocrine secretory functions, interweaved into essential metabolic circuitries of the human body. Pancreatic ductal adenocarcinoma (PDAC) represents one of the most lethal malignancies, with a 5-year survival rate of 11%. This poor prognosis is primarily attributed to the absence of early symptoms, rapid metastatic dissemination, and the limited efficacy of current therapeutic interventions. Despite recent advancements in understanding the etiopathogenesis and treatment of PDAC, there remains a pressing need for improved individualized models, identification of novel molecular targets, and development of unbiased predictors of disease progression. Here we aim to explore the concept of precision medicine utilizing 3-dimensional, patient-specific cellular models of pancreatic tumors and discuss their potential applications in uncovering novel druggable molecular targets and predicting clinical parameters for individual patients.
- MeSH
- duktální karcinom slinivky břišní * patologie genetika metabolismus MeSH
- individualizovaná medicína * metody MeSH
- lidé MeSH
- nádory slinivky břišní * patologie genetika MeSH
- techniky 3D buněčné kultury metody MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Spinal cord injury (SCI) often leads to central neuropathic pain, a condition associated with significant morbidity and is challenging in terms of the clinical management. Despite extensive efforts, identifying effective biomarkers for neuropathic pain remains elusive. Here we propose a novel approach combining matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with artificial neural networks (ANNs) to discriminate between mass spectral profiles associated with chronic neuropathic pain induced by SCI in female mice. Functional evaluations revealed persistent chronic neuropathic pain following mild SCI as well as minor locomotor disruptions, confirming the value of collecting serum samples. Mass spectra analysis revealed distinct profiles between chronic SCI and sham controls. On applying ANNs, 100% success was achieved in distinguishing between the two groups through the intensities of m/z peaks. Additionally, the ANNs also successfully discriminated between chronic and acute SCI phases. When reflexive pain response data was integrated with mass spectra, there was no improvement in the classification. These findings offer insights into neuropathic pain pathophysiology and underscore the potential of MALDI-TOF MS coupled with ANNs as a diagnostic tool for chronic neuropathic pain, potentially guiding attempts to discover biomarkers and develop treatments.
- MeSH
- biologické markery krev MeSH
- chronická bolest krev diagnóza etiologie MeSH
- myši inbrední C57BL MeSH
- myši MeSH
- neuralgie * krev diagnóza etiologie MeSH
- neuronové sítě * MeSH
- poranění míchy * komplikace krev MeSH
- spektrometrie hmotnostní - ionizace laserem za účasti matrice * metody MeSH
- zvířata MeSH
- Check Tag
- myši MeSH
- ženské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND: High-grade serous ovarian carcinoma (HGSOC) is the most common and aggressive subtype of epithelial ovarian carcinoma. It is primarily diagnosed at stage III or IV when the 5-year survival rate ranges between 20% and 40%. Here, we aimed to validate the hypothesis, based on HGSOC cell lines, that proposed the existence of two distinct groups of HGSOC cells with high and low oxidative phosphorylation (OXPHOS) metabolism, respectively, which are associated with their responses to glucose and glutamine withdrawal. METHODS: We isolated and cultivated primary cancer cell cultures from HGSOC and nontransformed ovarian fibroblasts from the surrounding ovarium of 45 HGSOC patients. We tested the metabolic flexibility of the primary cells, particularly in response to glucose and glutamine depletion, analyzed and modulated endoplasmic reticulum stress, and searched for indices of the existence of previously reported groups of HGSOC cells with high and low OXPHOS metabolism. RESULTS: The primary HGSOC cells did not form two groups with high and low OXPHOS that responded differently to glucose and glutamine availabilities in the cell culture medium. Instead, they exhibited a continuum of OXPHOS phenotypes. In most tumor cell isolates, the responses to glucose or glutamine withdrawal were mild and surprisingly correlated with those of nontransformed ovarian fibroblasts from the same patients. The growth of tumor-derived cells in the absence of glucose was positively correlated with the lipid trafficking regulator FABP4 and was negatively correlated with the expression levels of HK2 and HK1. The correlations between the expression of electron transport chain (ETC) proteins and the oxygen consumption rates or extracellular acidification rates were weak. ER stress markers were strongly expressed in all the analyzed tumors. ER stress was further potentiated by tunicamycin but not by the recently proposed ER stress inducers based on copper(II)-phenanthroline complexes. ER stress modulation increased autophagy in tumor cell isolates but not in nontransformed ovarian fibroblasts. CONCLUSIONS: Analysis of the metabolism of primary HGSOC cells rejects the previously proposed hypothesis that there are distinct groups of HGSOC cells with high and low OXPHOS metabolism that respond differently to glutamine or glucose withdrawal and are characterized by ETC protein levels.
- Publikační typ
- časopisecké články MeSH
Multiple myeloma (MM) is the second most prevalent hematological malignancy, characterized by infiltration of the bone marrow by malignant plasma cells. Extramedullary disease (EMD) represents a more aggressive condition involving the migration of a subclone of plasma cells to paraskeletal or extraskeletal sites. Liquid biopsies could improve and speed diagnosis, as they can better capture the disease heterogeneity while lowering patients' discomfort due to minimal invasiveness. Recent studies have confirmed alterations in the proteome across various malignancies, suggesting specific changes in protein classes. In this study, we show that MALDI-TOF mass spectrometry fingerprinting of peripheral blood can differentiate between MM and primary EMD patients. We constructed a predictive model using a supervised learning method, partial least squares-discriminant analysis (PLS-DA) and evaluated its generalization performance on a test dataset. The outcome of this analysis is a method that predicts specifically primary EMD with high sensitivity (86.4%), accuracy (78.4%), and specificity (72.4%). Given the simplicity of this approach and its minimally invasive character, this method provides rapid identification of primary EMD and could prove helpful in clinical practice.
- MeSH
- lidé středního věku MeSH
- lidé MeSH
- mnohočetný myelom * krev diagnóza MeSH
- nádorové biomarkery krev MeSH
- senioři MeSH
- spektrometrie hmotnostní - ionizace laserem za účasti matrice metody MeSH
- tekutá biopsie metody MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Pathological pain subtypes can be classified as either neuropathic pain, caused by a somatosensory nervous system lesion or disease, or nociplastic pain, which develops without evidence of somatosensory system damage. Since there is no gold standard for the diagnosis of pathological pain subtypes, the proper classification of individual patients is currently an unmet challenge for clinicians. While the determination of specific biomarkers for each condition by current biochemical techniques is a complex task, the use of multimolecular techniques, such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), combined with artificial intelligence allows specific fingerprints for pathological pain-subtypes to be obtained, which may be useful for diagnosis. We analyzed whether the information provided by the mass spectra of serum samples of four experimental models of neuropathic and nociplastic pain combined with their functional pain outcomes could enable pathological pain subtype classification by artificial neural networks. As a result, a simple and innovative clinical decision support method has been developed that combines MALDI-TOF MS serum spectra and pain evaluation with its subsequent data analysis by artificial neural networks and allows the identification and classification of pathological pain subtypes in experimental models with a high level of specificity.
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.
