Robust quantitative scratch assay

Andrea Vargas; Marc Angeli; Chiara Pastrello; Rosanne McQuaid; Han Li; Andrea Jurisicova; Igor Jurisica

doi:10.1093/bioinformatics/btv746

Robust quantitative scratch assay

Bioinformatics. 2016 May 1;32(9):1439-40. doi: 10.1093/bioinformatics/btv746. Epub 2015 Dec 31.

Authors

Andrea Vargas¹, Marc Angeli², Chiara Pastrello², Rosanne McQuaid³, Han Li⁴, Andrea Jurisicova⁵, Igor Jurisica⁶

Affiliations

¹ Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
² Princess Margaret Cancer Centre and Techna, University Health Network, Toronto, ON, Canada.
³ Department of Physiology, University of Toronto, Toronto, ON, Canada, Lunenfeld Tanenbaum Research Institute, Toronto, ON, Canada.
⁴ Lunenfeld Tanenbaum Research Institute, Toronto, ON, Canada.
⁵ Department of Physiology, University of Toronto, Toronto, ON, Canada, Lunenfeld Tanenbaum Research Institute, Toronto, ON, Canada, Department of Obstetrics and Gynecology and.
⁶ Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada, Princess Margaret Cancer Centre and Techna, University Health Network, Toronto, ON, Canada, Department of Computer Science, University of Toronto, Toronto, ON, Canada.

Abstract

The wound healing assay (or scratch assay) is a technique frequently used to quantify the dependence of cell motility-a central process in tissue repair and evolution of disease-subject to various treatments conditions. However processing the resulting data is a laborious task due its high throughput and variability across images. This Robust Quantitative Scratch Assay algorithm introduced statistical outputs where migration rates are estimated, cellular behaviour is distinguished and outliers are identified among groups of unique experimental conditions. Furthermore, the RQSA decreased measurement errors and increased accuracy in the wound boundary at comparable processing times compared to previously developed method (TScratch).

Availability and implementation: The RQSA is freely available at: http://ophid.utoronto.ca/RQSA/RQSA_Scripts.zip The image sets used for training and validation and results are available at: (http://ophid.utoronto.ca/RQSA/trainingSet.zip, http://ophid.utoronto.ca/RQSA/validationSet.zip, http://ophid.utoronto.ca/RQSA/ValidationSetResults.zip, http://ophid.utoronto.ca/RQSA/ValidationSet_H1975.zip, http://ophid.utoronto.ca/RQSA/ValidationSet_H1975Results.zip, http://ophid.utoronto.ca/RQSA/RobustnessSet.zip, http://ophid.utoronto.ca/RQSA/RobustnessSet.zip). Supplementary Material is provided for detailed description of the development of the RQSA.

Contact: juris@ai.utoronto.ca

Supplementary information: Supplementary data are available at Bioinformatics online.

MeSH terms

Algorithms*
Animals
Cell Migration Assays*
Humans
Wound Healing