Difference in biophysical properties of cancer-initiating cells in melanoma mutated zebrafish

Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning

The development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by analyzing cells extracted from urine. However, these promising findings were limited by a relatively small patient cohort, resulting in modest statistical significance. In this study, we corroborated the AFM technique's capability to identify bladder cancer cells with high accuracy using a controlled model system of genetically purified human bladder epithelial cell lines, comparing cancerous cells with nonmalignant controls. By processing AFM adhesion maps through machine learning algorithms, following previously established methods, we achieved an area under the ROC curve (AUC) of 0.97, with 91% accuracy in cancer cell identification. Furthermore, we enhanced cancer detection by incorporating multiple imaging channels recorded with AFM operating in Ringing mode, achieving an AUC of 0.99 and 93% accuracy. These results demonstrated strong statistical significance (p < 0.0001) in this well-defined model system. While this controlled study does not capture the biological variation present in clinical settings, it provides independent support for AFM-based detection methods and establishes a rigorous technical foundation for further clinical development of AFM imaging-based methods for bladder cancer detection.

Correlation of cell mechanics with the resistance to malignant transformation in naked mole rat fibroblasts

Naked mole rats (NMRs) demonstrate exceptional longevity and resistance to cancer. Using a biochemical approach, it was previously shown that the treatment of mouse fibroblast cells with RasV12 oncogene and SV40 Large T antigen (viral oncoprotein) led to malignant transformations of cells. In contrast, NMR fibroblasts were resistant to malignant transformations upon this treatment. Here we demonstrate that atomic force microscopy (AFM) can provide information which is in agreement with the above finding, and further, adds unique information about the physical properties of cells that is impossible to obtain by other existing techniques. AFM indentation data were collected from individual cells and subsequently processed through the brush model to obtain information about the mechanics of the cell body (absolute values of the effective Young's moduli). Furthermore, information about the physical properties of the pericellular layer surrounding the cells was obtained. We found a statistically significant decrease in the rigidity of mouse cells after the treatment, whereas there was no significant change found in the rigidity of NMR cells upon the treatment. We also found that the treatment caused a substantial increase in a long part of the pericellular layer in NMR cells only (the long brush was defined as having a size of >10 microns). The mouse cells and smaller brush did not show statistically significant changes upon treatment. The observed change in cell mechanics is in agreement with the frequently observed decrease in cell rigidity during progression towards cancer. The change in the pericellular layer due to the malignant transformation of fibroblast cells has practically not been studied, though it was shown that the removal of part of the pericellular layer of NMR fibroblasts made the cells susceptible to malignant transformation. Although it is plausible to speculate that the observed increase in the long part of the brush layer of NMR cells might help cells to resist malignant transformations, the significance of the observed change in the pericellular layer is yet to be understood. As of now, we can conclude that changes in cell mechanics might be used as an indication of the resistance of NMR cells to malignant transformations.

Cell mechanics can be robustly derived from AFM indentation data using the brush model: error analysis

The brush model was introduced to interpret AFM indentation data collected on biological cells in a more consistent way compared just to the traditional Hertz model. It takes into account the presence of non-Hertzian deformation of the pericellular brush-like layer surrounding cells (a mix of glycocalyx molecules and microvilli/microridges). The model allows finding the effective Young's modulus of the cell body in a less depth-dependent manner. In addition, it allows finding the force due to the pericellular brush layer. Compared to simple mechanical models used to interpret the indentation experiments, the brush model has additional complexity. It raises the concern about the possible unambiguity of separation of mechanical properties of the cell body and pericellular layer. Here we present the analysis of the robustness of the brush model and demonstrate a weak dependence of the obtained results on the uncertainties within the model and experimental data. We critically analyzed the use of the brush model on a variety of AFM force curves collected on rather distinct cell types: human cervical epithelial cells, rat neurons, and zebrafish melanocytes. We conclude that the brush model is robust; the errors in the definition of the effective Young's modulus due to possible uncertainties of the model and experimental data are within 4%, which is less than the error, for example, due to a typical uncertainty in the spring constant of the AFM cantilever. We also discuss the errors of parameterization of the force due to the pericellular brush layer.

Recent Advances on the Model, Measurement Technique, and Application of Single Cell Mechanics

Since the cell was discovered by humans, it has been an important research subject for researchers. The mechanical response of cells to external stimuli and the biomechanical response inside cells are of great significance for maintaining the life activities of cells. These biomechanical behaviors have wide applications in the fields of disease research and micromanipulation. In order to study the mechanical behavior of single cells, various cell mechanics models have been proposed. In addition, the measurement technologies of single cells have been greatly developed. These models, combined with experimental techniques, can effectively explain the biomechanical behavior and reaction mechanism of cells. In this review, we first introduce the basic concept and biomechanical background of cells, then summarize the research progress of internal force models and experimental techniques in the field of cell mechanics and discuss the latest mechanical models and experimental methods. We summarize the application directions of cell mechanics and put forward the future perspectives of a cell mechanics model.