Using Adhesive Micropatterns and AFM to Assess Cancer Cell Morphology and Mechanics

Single-Cell Fluidic Force Spectroscopy Reveals Dynamic Mechanical Fingerprints of Malignancy in Breast Cancer

The interplay between cancer cell physical characteristics and metastatic potential highlights the significance of cancer cell mechanobiology. Using fluidic-based single-cell force spectroscopy (SCFS), quartz crystal microbalance with dissipation (QCM-D), and a model of cells with a spectrum of metastatic potential, we track the progression of biomechanics across the metastatic states by measuring cell-substrate and cell-to-cell adhesion forces, cell spring constant, cell height, and cell viscoelasticity. Compared to highly metastatic cells, cells in the lower spectrum of metastatic ability are found to be systematically stiffer, less viscoelastic, and larger. These mechanical transformations in cells within a cluster correlate with cells' metastatic potential but are significantly absent in single cells. Additionally, the response to chemotherapy is found to be highly dependent on cell viscoelastic properties in terms of both response time and magnitude. Shifts in cell softness and elasticity might serve as mechanoadaptive mechanisms during cancer cell metastasis, contributing to our understanding of metastasis and the effectiveness of potential therapeutic interventions.

Cell recognition based on features extracted by AFM and parameter optimization classifiers

Intelligent technology can assist in the diagnosis and treatment of disease, which would pave the way towards precision medicine in the coming decade. As a key focus of medical research, the diagnosis and prognosis of cancer play an important role in the future survival of patients. In this work, a diagnostic method based on nano-resolution imaging was proposed to meet the demand for precise detection methods in medicine and scientific research. The cell images scanned by AFM were recognized by cell feature engineering and machine learning classifiers. A feature ranking method based on the importance of features to responses was used to screen features closely related to categorization and optimization of feature combinations, which helps to understand the feature differences between cell types at the micro level. The results showed that the Bayesian optimized back propagation neural network has accuracy rates of 90.37% and 92.68% on two cell datasets (HL-7702 & SMMC-7721 and GES-1 & SGC-7901), respectively. This provides an automatic analysis method for identifying cancer cells or abnormal cells, which can help to reduce the burden of medical or scientific research, decrease misjudgment and promote precise medical care for the whole society.

Biomechanical Characterization of Retinal Pigment Epitheliums Derived from hPSCs Using Atomic Force Microscopy

The retinal pigment epithelium (RPE), a multifunctional cell monolayer located at the back of the eye, plays a crucial role in the survival and homeostasis of photoreceptors. Dysfunction or death of RPE cells leads to retinal degeneration and subsequent vision loss, such as in Age-related macular degeneration and some forms of Retinitis Pigmentosa. Therefore, regenerative medicine that aims to replace RPE cells by new cells obtained from the differentiation of human pluripotent stem cells, is the focus of intensive research. However, despite their critical interest in therapy, there is a lack of biomechanical RPE surface description. Such biomechanical properties are tightly related to their functions. Herein, we used atomic force microscopy (AFM) to analyze both the structural and mechanical properties of RPEs obtained from four cell lines and at different stages of epithelial formation. To characterize epitheliums, we used apical markers in immunofluorescence and showed the increase of transepithelial resistance, as well as the ability to secrete cytokines with an apico-basal polarity. Then, we used AFM to scan the apical surface of living or fixed RPE cells. We show that RPE monolayers underwent softening of apical cell center as well as stiffening of cell borders over epithelial formation. We also observed apical protrusions that depend on actin network, suggesting the formation of microvilli at the surface of RPE epitheliums. These RPE cell characteristics are essential for their functions into the retina and AFM studies may improve the characterization of the RPE epithelium suitable for cell therapy.

Dynamically Mapping the Topography and Stiffness of the Leading Edge of Migrating Cells Using AFM in Fast-QI Mode

Cell migration profoundly influences cellular function, often resulting in adverse effects in various pathologies including cancer metastasis. Directly assessing and quantifying the nanoscale dynamics of living cell structure and mechanics has remained a challenge. At the forefront of cell movement, the flat actin modules─the lamellipodium and the lamellum─interact to propel cell migration. The lamellipodium extends from the lamellum and undergoes rapid changes within seconds, making measurement of its stiffness a persistent hurdle. In this study, we introduce the fast-quantitative imaging (fast-QI) mode, demonstrating its capability to simultaneously map both the lamellipodium and the lamellum with enhanced spatiotemporal resolution compared with the classic quantitative imaging (QI) mode. Specifically, our findings reveal nanoscale stiffness gradients in the lamellipodium at the leading edge, where it appears to be slightly thinner and significantly softer than the lamellum. Additionally, we illustrate the fast-QI mode's accuracy in generating maps of height and effective stiffness through a streamlined and efficient processing of force-distance curves. These results underscore the potential of the fast-QI mode for investigating the role of motile cell structures in mechanosensing.

Cell Surface Parameters for Accessing Neutrophil Activation Level with Atomic Force Microscopy

In this study, we examine the topography and adhesion images of the cell surface of neutrophils during the activation process. Our analysis of cell surface parameters indicates that the most significant changes in neutrophils occur within the first 30 min of activation, suggesting that reactive oxygen species may require approximately this amount of time to activate the cells. Interestingly, we observed surface granular structure as early as 10 min after neutrophil activation when examining atomic force microscopy images. This finding aligns with the reorganization observed within the cells under confocal laser scanning microscopy. By analyzing the cell surface images of adhesion, we identified three spatial surface parameters that correlate with the activation time. This finding enables us to estimate the degree of activation by using atomic force microscopy maps of the cell surface.