This work presents a new numerical method for processing atomic force microscopy (AFM) data to determine the elasticity of cultured adherent biological cells. Raw AFM force-indentation data is commonly interpreted using the Hertz and Sneddon contact mechanics models to fit a Young’s modulus or apparent cell elasticity. This apparent cell elasticity is highly dependent on the method used to identify the first point of contact between the AFM probe and the cell surface. In this work, an automated MATLAB-based data processing algorithm was developed to detect the point of probe-cell contact in the force-indentation curve. The method handles the difficulties associated with finding the contact point using moving averages, thresholds, and mean squared errors. Implementation validation shows that contact point detection accuracy is critical, with seemingly small errors producing up to 250% changes in reported elasticity within a single experiment.

The newly developed method was applied to analyze a large experimental data series with human pancreatic adenocarcinoma (AsPC-1) cells. The results from this test series show that pyramidal AFM probes systematically measure elasticities that are a factor of three greater than those measured by spherical probes. Across a range of typically used probe forces, increasing the indentation force results in a 100% increase in apparent elasticity. Finally, the results of the new data processing method show that accurate contact point detection and data quality checking eliminates the log-normal distribution of elasticity values that is often reported in experimental AFM studies with biological cells. These findings showcase the importance of including detailed descriptions of data processing methods and the need for robust analysis algorithms in AFM research.