Particle retracking algorithm capable of quantifying large, local matrix deformation for traction force microscopy

Samuel E Haarman, Sue Y Kim, Tadamoto Isogai, Kevin M Dean and Sangyoon J Han

PLOS ONE, 2022, vol. 17, issue 6, 1-18

Abstract: Deformation measurement is a key process in traction force microscopy (TFM). Conventionally, particle image velocimetry (PIV) or correlation-based particle tracking velocimetry (cPTV) have been used for such a purpose. Using simulated bead images, we show that those methods fail to capture large displacement vectors and that it is due to a poor cross-correlation. Here, to redeem the potential large vectors, we propose a two-step deformation tracking algorithm that combines cPTV, which performs better for small displacements than PIV methods, and newly-designed retracking algorithm that exploits statistically confident vectors from the initial cPTV to guide the selection of correlation peak which are not necessarily the global maximum. As a result, the new method, named ‘cPTV-Retracking’, or cPTVR, was able to track more than 92% of large vectors whereas conventional methods could track 43–77% of those. Correspondingly, traction force reconstructed from cPTVR showed better recovery of large traction than the old methods. cPTVR applied on the experimental bead images has shown a better resolving power of the traction with different-sized cell-matrix adhesions than conventional methods. Altogether, cPTVR method enhances the accuracy of TFM in the case of large deformations present in soft substrates. We share this advance via our TFMPackage software.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0268614

DOI: 10.1371/journal.pone.0268614

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