Work

CSMA: A Standalone and ImageJ-Compatible Tool for Enhanced Wound Healing Assay Analysis

Year: 2025

Type: article

Abstract: Accurate quantification of wound closure in cell migration assays is crucial yet challenging. Still, existing methods often underperform due to omitting cell detection within the wound area, resulting in biased outcomes. We developed the CSMA standalone and ImageJ-compatible tool, which utilizes advanced image processing techniques, including contrast enhancement, edge detection, and morphological operations, to precisely identify and quantify cells in the wound region. CSMA offers user-friendly features and adjustable parameters to accommodate different imaging conditions, ensuring robust performance across diverse experimental setups. Validation against conventional tools confirms CSMA’s superior ability to delineate wound boundaries and provide accurate estimations of area and width at every time point. As applied to SW480-ADH colon cancer cells treated with various compounds, CSMA proves valuable in biomedical research. CSMA represents a significant advancement in wound healing assay analysis, providing researchers with a simple and reliable tool for studying cell migration dynamics with enhanced precision and reproducibility. CSMA is available as a standalone and ImageJ-compatible tool with its source code at https://github.com/AminaSagymbayeva/CSMA_WoundHealing

Source: IEEE Access

DOI: 10.1109/access.2025.3561607

Authors: Tri Thanh Pham, Amina Sagymbayeva, Timur Elebessov, Zhadyra Onzhanova, Ferdinand Molnár

Institution: Nazarbayev University

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funders: Ministry of Education and Science of the Republic of Kazakhstan, Nazarbayev University