AFM force indentation analysis on leukemia cells

Abstract

A significant body of literature has reported strategies and techniques to assess the mechanical properties of biological samples such as proteins, cellular and tissue systems. Atomic force microscopy (AFM) has been used to detect elasticity changes of cancer cells. However, only a few studies have provided a detailed and complete protocol of the experimental procedures and data analysis methods for non-adherent blood cancer cells. In this work, the elasticity of NB4 cells derived from acute promyelocytic leukemia (APL) was probed by AFM indentation measurements to investigate the effects of the disease on cellular biomechanics. Understanding how leukemia influences the nanomechanical properties of cells is expected to provide a better understanding of the cellular mechanisms associated with cancer, and promises to become a valuable new tool for cancer detection and staging. In this context, the quantification of the mechanical properties of APL cells requires a systematic and optimized approach for data collection and analysis in order to generate reproducible and comparative data. This report elucidates the automated data analysis process that integrates programming, force curve collection and analysis optimization to assess variations of cell elasticity in response to processing criteria. A processing algorithm was developed to automatically analyze large numbers of AFM datasets in an efficient and accurate manner. In fact, since the analysis involves multiple steps that must be repeated for many individual cells, an automated and unbiased processing approach is essential to precisely determine cell elasticity. Different fitting models for extracting the Young’s modulus have been systematically applied to validate the process, and the best fitting criteria, such as the contact point location and indentation length, have been determined in order to obtain consistent results. The designed automated processing code described in this report not only permits us to correlate alterations in cellular biomechanics to cancer cell maturity, but also to assess drug-induced changes in cell elasticity for drug screening purposes.