TL;DR
This paper introduces a new quantification method for scratch assay data that improves accuracy and robustness, especially with irregular and low-quality images, facilitating better analysis of cell migration.
Contribution
The authors present a novel quantification approach that handles irregularities in scratch assay images, outperforming existing methods in classifying migration rates.
Findings
More accurate classification of migration rates.
Effective analysis of low-quality and irregular data.
Enhanced reproducibility of scratch assay quantification.
Abstract
Motivation: The scratch assay is a standard experimental protocol used to characterize cell migration. It can be used to identify genes that regulate migration and evaluate the efficacy of potential drugs that inhibit cancer invasion. In these experiments, a scratch is made on a cell monolayer and recolonisation of the scratched region is imaged to quantify cell migration rates. A drawback of this methodology is the lack of its reproducibility resulting in irregular cell-free areas with crooked leading edges. Existing quantification methods deal poorly with such resulting irregularities present in the data. Results: We introduce a new quantification method that can analyse low quality experimental data. By considering in-silico and in-vitro data, we show that the method provides a more accurate statistical classification of the migration rates than two established quantification…
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