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The mechanical phenotype of a cell is an inherent biophysical marker of its state and function, with many applications in basic and applied biological research. Microfluidics-based methods have enabled single-cell mechanophenotyping at throughputs comparable to those of flow cytometry. Here, we present a standardized cross-laboratory study comparing three microfluidics-based approaches for measuring cell mechanical phenotype: constriction-based deformability cytometry (cDC), shear flow deformability cytometry (sDC) and extensional flow deformability cytometry (xDC). All three methods detect cell deformability changes induced by exposure to altered osmolarity. However, a dose-dependent deformability increase upon latrunculin B-induced actin disassembly was detected only with cDC and sDC, which suggests that when exposing cells to the higher strain rate imposed by xDC, cellular components other than the actin cytoskeleton dominate the response. The direct comparison presented here furthers our understanding of the applicability of the different deformability cytometry methods and provides context for the interpretation of deformability measurements performed using different platforms. This Analysis compares microfluidics-based methods for assessing mechanical properties of cells in high throughput.

During osmotic changes of their environment, cells actively regulate their volume and plasma membrane tension that can passively change through osmosis. How tension and volume are coupled during osmotic adaptation remains unknown, as their quantitative characterization is lacking. Here, we performed dynamic membrane tension and cell volume measurements during osmotic shocks. During the first few seconds following the shock, cell volume varied to equilibrate osmotic pressures inside and outside the cell, and membrane tension dynamically followed these changes. A theoretical model based on the passive, reversible unfolding of the membrane as it detaches from the actin cortex during volume increase quantitatively describes our data. After the initial response, tension and volume recovered from hypoosmotic shocks but not from hyperosmotic shocks. Using a fluorescent membrane tension probe (fluorescent lipid tension reporter [Flipper-TR]), we investigated the coupling between tension and volume during these asymmetric recoveries. Caveolae depletion and pharmacological inhibition of ion transporters and channels, mTORCs, and the cytoskeleton all affected tension and volume responses. Treatments targeting mTORC2 and specific downstream effectors caused identical changes to both tension and volume responses, their coupling remaining the same. This supports that the coupling of tension and volume responses to osmotic shocks is primarily regulated by mTORC2.

The nutrient-activated mTORC1 (mechanistic target of rapamycin kinase complex 1) signaling pathway determines cell size by controlling mRNA translation, ribosome biogenesis, protein synthesis, and autophagy. Here, we show that vimentin, a cytoskeletal intermediate filament protein that we have known to be important for wound healing and cancer progression, determines cell size through mTORC1 signaling, an effect that is also manifested at the organism level in mice. This vimentin-mediated regulation is manifested at all levels of mTOR downstream target activation and protein synthesis. We found that vimentin maintains normal cell size by supporting mTORC1 translocation and activation by regulating the activity of amino acid sensing Rag GTPase. We also show that vimentin inhibits the autophagic flux in the absence of growth factors and/or critical nutrients, demonstrating growth factor-independent inhibition of autophagy at the level of mTORC1. Our findings establish that vimentin couples cell size and autophagy through modulating Rag GTPase activity of the mTORC1 signaling pathway.

Although label-free cell sorting is desirable for providing pristine cells for further analysis or use, current approaches lack molecular specificity and speed. Here, we combine real-time fluorescence and deformability cytometry with sorting based on standing surface acoustic waves and transfer molecular specificity to image-based sorting using an efficient deep neural network. In addition to general performance, we demonstrate the utility of this method by sorting neutrophils from whole blood without labels. Sorting RT-FDC combines real-time fluorescence and deformability cytometry with sorting based on standing surface acoustic waves to transfer molecular specificity to label-free, image-based cell sorting using an efficient deep neural network.

Cell mechanical properties determine many physiological functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors that govern the mechanical properties is therefore a subject of great interest. Here, we present a mechanomics approach for establishing links between single-cell mechanical phenotype changes and the genes involved in driving them. We combine mechanical characterization of cells across a variety of mouse and human systems with machine learning-based discriminative network analysis of associated transcriptomic profiles to infer a conserved network module of five genes with putative roles in cell mechanics regulation. We validate in silico that the identified gene markers are universal, trustworthy, and specific to the mechanical phenotype across the studied mouse and human systems, and demonstrate experimentally that a selected target, CAV1 , changes the mechanical phenotype of cells accordingly when silenced or overexpressed. Our data-driven approach paves the way toward engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.

Genomic alterations are the driving force behind pancreatic cancer (PC) tumorigenesis, but they do not fully account for its diverse phenotypes. Investigating the epigenetic landscapes of PC offers a more comprehensive understanding and could identify targeted therapies that enhance patient survival. In this study, we have developed a new promising methodology of spatial epigenomics that integrates multiplexed molecular imaging with convolutional neural networks. Then, we used it to map epigenetic modification levels in the six most prevalent PC subtypes. We analyzed and semi-quantified the resulting molecular data, revealing significant variability in their epigenomes. DNA and histone modifications, specifically methylation and acetylation, were investigated. Using the same technique, we examined DNA conformational changes to further elucidate the transcriptional regulatory mechanisms involved in PC differentiation. Our results revealed that the foamy-gland and squamous-differentiated subtypes exhibited significantly increased global levels of epigenetic modifications and elevated Z-DNA ratios. Overall, our findings may suggest a potentially reduced efficacy of therapeutics targeting epigenetic regulators for these subtypes. Conversely, the conventional ductal PC subtype has emerged as a promising candidate for treatment with epigenetic modulators.

Cell mechanical properties determine many physiological functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors that govern the mechanical properties is therefore a subject of great interest. Here, we present a mechanomics approach for establishing links between single-cell mechanical phenotype changes and the genes involved in driving them. We combine mechanical characterization of cells across a variety of mouse and human systems with machine learning-based discriminative network analysis of associated transcriptomic profiles to infer a conserved network module of five genes with putative roles in cell mechanics regulation. We validate in silico that the identified gene markers are universal, trustworthy, and specific to the mechanical phenotype across the studied mouse and human systems, and demonstrate experimentally that a selected target, CAV1 , changes the mechanical phenotype of cells accordingly when silenced or overexpressed. Our data-driven approach paves the way toward engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.

Introduction. Xanthine oxidoreductase (XOR) is an enzyme belonging to the class of hydroxylases. XOR is stated, inter alia, in the kidneys, liver, and small intestine as well as in leukocytes and platelets and endothelial cells of capillaries. Its main role is to participate in the conversion of hypoxanthine to xanthine and the uric acid. It occurs in two isoforms: dehydrogenase (XD) and oxidase (XO), which is considered one of the sources of reactive oxygen species. Aim of the Study. Determination of reference values of xanthine oxidoreductase activity in PPP and platelets. Materials and Methods. Study group consisted of 70 healthy volunteers. The isoform activities of xanthine oxidoreductase were determined by kinetic spectrophotometry. Results. A statistically significant difference between the activity of the XOR in PPP and platelets ( P < 0.001 ). The highest activity of XO was found in both PPP and blood platelets. Significant differences between the activity of the various isoforms in PPP ( P = 0.0032 ) and platelets ( P < 0.001 ) were also found. Conclusions. The healthy volunteers showed the highest activity XO (prooxidant) and the lowest XD (antioxidant), which indicates a slight oxidative stress and confirmed physiological effects of XOR.

Cell mechanical properties determine many physiological functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors that govern the mechanical properties is therefore a subject of great interest. Here we present a mechanomics approach for establishing links between single-cell mechanical phenotype changes and the genes involved in driving them. We combine mechanical characterization of cells across a variety of mouse and human systems with machine learning-based discriminative network analysis of associated transcriptomic profiles to infer a conserved network module of five genes with putative roles in cell mechanics regulation. We validate in silico that the identified gene markers are universal, trustworthy and specific to the mechanical phenotype across the studied mouse and human systems, and demonstrate experimentally that a selected target, CAV1, changes the mechanical phenotype of cells accordingly when silenced or overexpressed. Our data driven approach paves the way towards engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.Competing Interest StatementS.A., M.K., and J.G. are co-founders and shareholders of the company Rivercyte GmbH that is commercializing deformability cytometry technology. The remaining authors declare no competing interests.Footnotes* (1) Added the phrase: across the studied mouse and human systems, to the statement: the identified gene markers are universal, trustworthy and specific to the mechanical phenotype - in abstract, introduction, and discussion. (2) In the captions of Figures: 2, 3, 5, 6, S2, S9, and S11, clarified the meaning of symbol shapes. (3) Added a clarifying sentence explaining how PC-corr is derived at the first time of appearance in the results section. - Introduced a new paragraph in the results section discussing the absolute values of measured Youngs moduli in relation to probing frequencies, accompanied by two new display items in the supplementary materials: Fig. S10 and Table S9. (4) Added three new supplementary figures (Fig. S4-S6) to display the expression matrices for the genes from the identified modules in carcinoma datasets used for validation. (5) At the end of the first paragraph of the discussion, we have added a statement indicating open ends of the current study. (6) Introduced a new paragraph in the discussion section to indicate the known intracellular origins of resistance to deformation and the dominance of the actin cortex at low deformations. (7) Cited Swift et al. 2013, which identifies PTRF, another caveolar component, as being associated with tissue stiffness. (8) Updated Figure S7 to include additional regression lines in each panel, representing origin-grouped sub-selections of data. (9) Made further minor edits to the text and figure captions to correct typos and improve readability.* https://doi.org/10.6084/m9.figshare.c.5399826* https://doi.org/10.6084/m9.figshare.20123159* https://github.com/biomedical-cybernetics/Joint-View-trustworthiness-JVT

The nutrient-activated mTORC1 (mechanistic target of rapamycin kinase complex 1) signaling pathway determines cell size by controlling mRNA translation, ribosome biogenesis, protein synthesis, and autophagy. Here we show that vimentin, a cytoskeletal intermediate filament protein that we know to be important for wound healing and cancer progression, determines cell size through mTORC1 signaling, an effect that is also manifested at the organism level in mice. We found that vimentin maintains normal cell size by supporting mTORC1 activation and through inhibition of autophagic flux. This regulation is manifested at all levels of downstream target activation and regulation of protein synthesis. We show that vimentin controls mTORC1 mobility by allowing access to lysosomes. Vimentin inhibits the autophagic flux in normal fibroblasts even under starved conditions, indicating a growth factor-independent inhibition of autophagy at the level of mTORC1. Our findings demonstrate that vimentin couples cell size signaling and autophagy with the biomechanic, sensing, and kinetic functions of the cytoskeleton.