BACKGROUND: Lipopolysaccharide (LPS)-induced inflammation of lung tissues triggers irreversible alterations in the lung parenchyma, leading to fibrosis and pulmonary dysfunction. While the molecular and cellular responses of immune and connective tissue cells in the lungs are well characterized, the specific epithelial response remains unclear due to the lack of representative cell models. Recently, we introduced human embryonic stem cell-derived expandable lung epithelial (ELEP) cells as a novel model for studying lung injury and regeneration. METHODS: ELEPs were derived from the CCTL 14 human embryonic stem cell line through activin A-mediated endoderm specification, followed by further induction toward pulmonary epithelium using FGF2 and EGF. ELEPs exhibit a high proliferation rate and express key structural and molecular markers of alveolar progenitors, such as NKX2-1. The effects of Escherichia coli LPS serotype O55:B5 on the phenotype and molecular signaling of ELEPs were analyzed using viability and migration assays, mRNA and protein levels were determined by qRT-PCR, western blotting, and immunofluorescent microscopy. RESULTS: We demonstrated that purified LPS induces features of a hybrid epithelial-to-mesenchymal transition in pluripotent stem cell-derived ELEPs, triggers the unfolded protein response, and upregulates intracellular β-catenin level through retention of E-cadherin within the endoplasmic reticulum. CONCLUSIONS: Human embryonic stem cell-derived ELEPs provide a biologically relevant, non-cancerous lung cell model to investigate molecular responses to inflammatory stimuli and address epithelial plasticity. This approach offers novel insights into the fine molecular processes underlying lung injury and repair.

• Klíčová slova
• Epithelial-to-mesenchymal transition, Expandable lung epithelium, Lipopolysaccharide, Unfolded protein response,
• MeSH
• buněčné linie MeSH
• CD antigeny metabolismus   MeSH
• endoplazmatické retikulum * metabolismus   účinky léků   MeSH
• epitelo-mezenchymální tranzice * účinky léků   MeSH
• epitelové buňky * účinky léků   metabolismus   cytologie   MeSH
• kadheriny * metabolismus   MeSH
• lidé MeSH
• lidské embryonální kmenové buňky * cytologie   MeSH
• lipopolysacharidy * farmakologie   MeSH
• plíce * cytologie   MeSH
• tyreoidální jaderný faktor 1 MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• CD antigeny MeSH
• CDH1 protein, human MeSH Prohlížeč
• kadheriny * MeSH
• lipopolysacharidy * MeSH
• NKX2-1 protein, human MeSH Prohlížeč
• tyreoidální jaderný faktor 1 MeSH

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.

• Klíčová slova
• Bioinformatics, Biotyping, Cell culture, Intact cell MALDI TOF MS, Machine learning, Quality control, R programming language, TUSC3,
• Publikační typ
• časopisecké články MeSH