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Abstract
Atomic force microscopy (AFM) is an attractive technique for studying biomechanical and morphological changes in live cells. Using real-time AFM monitoring of cellular mechanical properties, spontaneous oscillations in cell stiffness and cell adhesion to the extracellular matrix (ECM) have been found. However, the lack of automated analytical approaches to systematically extract oscillatory signals, and noise filtering from a large set of AFM data, is a significant obstacle when quantifying and interpreting the dynamic characteristics of live cells. Here we demonstrate a method that extends the usage of AFM to quantitatively investigate live cell dynamics. Approaches such as singular spectrum analysis (SSA), and fast Fourier transform (FFT) were introduced to analyze a real-time recording of cell stiffness and the unbinding force between the ECM protein-decorated AFM probe and vascular smooth muscle cells (VSMCs). The time series cell adhesion and stiffness data were first filtered with SSA and the principal oscillatory components were isolated from the noise floor with the computed eigenvalue from the lagged-covariance matrix. Following the SSA, the oscillatory parameters were detected by FFT from the noise-reduced time series data sets and the sinusoidal oscillatory components were constructed with the parameters obtained by FFT.
Highlights
Atomic force microscopy (AFM) is a powerful technique with a broad range of biological applications in morphological and mechanical characterization at the molecular[1], cellular[2,3,4,5,6,7], and tissue level[8,9,10]
The results showed most of the background noise had been removed after singular spectrum analysis (SSA) filtering, and it was easy to detect the main leading oscillatory components of the data set, which were the potential candidates for the real biological signals
Periodic oscillations in cell adhesion and stiffness have been reported previously by our laboratory[2,13,14] and others[11,12]. These highly dynamic characteristics of live cells are believed to be associated with the biological functions within the cell
Summary
Atomic force microscopy (AFM) is a powerful technique with a broad range of biological applications in morphological and mechanical characterization at the molecular[1], cellular[2,3,4,5,6,7], and tissue level[8,9,10]. Based on the size and complexity of these data sets, an automated analytical approach is required to systematically extract periodic signals from a large set of time series AFM data This will provide a more unbiased approach to quantitatively investigate and interpret the dynamic live cell mechanics. The method presented here is an automated analytical tool based on the theory of time series data analysis described by Elsner et al.[27] and Golyandina et al.[28] It includes uneven time series cell adhesion and cell stiffness data interpolation, SSA filtering, and FFT prediction. This type of analysis is of fundamental importance for studying these oscillatory events and will help reveal their physiological function and clinical relevance
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