Cellular Traction Force Reconstruction Based on a Self-adaptive Filtering Scheme

As an emerging measurement technique in cell mechanics, cellular traction force microscopy has begun to be applied to monitor spatiotemporal dynamics of cell-substrate interactions, in which Fourier transform traction cytometry (FTTC) can be utilized to reconstruct cellular traction fields from substrate displacement knowledge in a computationally cheap way. Owing to the intrinsic ill-posed property, cellular traction recovery founded on FTTC is extremely susceptible to measurement noise so that it is very likely to produce unreliable traction results. This paper investigates the nature of noise amplification during the process of deconvolution and accordingly proposes a set of filtering algorithms to evaluate cellular tractions. By self-adaptively filtering out the noise components in the two-dimensional Fourier space, high-resolution traction fields can thus be recovered in a relatively efficient manner. The feasibility and effectiveness of the set of algorithms are verified by both systematic simulation analyses and actual cellular traction reconstructions.