A Matlab-Based Approach for Estimating the Area Taken Up by Cells Attached to Micropatterned Optically Opaque Surfaces

Proceedings of the Latvian Academy of Sciences, Section B: Natural, Exact, and Applied Sciences 2023
Jurijs Dehtjars, Hermanis Sorokins, Sabīne Teifurova

Cell attachment is of paramount importance in implant design, bioreactor design, tissue engineering and the design of non-fouling surfaces. Surface roughness is a significant factor that affects cell attachment. To explore the impact of roughness characteristics, micromachining approaches can be used to fabricate surfaces with controlled microscale topography. When optical microscopy is employed to study cell attachment to optically opaque micropatterned surfaces, one needs to separate the area of an image coated with cells from the background. Manual cell counting can be used to assess the amount of attached cells. However, this process is very time consuming, when the studied surface is larger than several square millimeters. This paper describes an approach for the automatic estimation of the area of cells attached to the surfaces of micro-patterned optically opaque platforms. Saccharomyces cerevisiae yeast cells were used to test the developed approach. The approach uses image registration and segmentation tools available in MathWorks MATLAB R2020b Image Processing Toolbox. The factors that affect the accuracy of the developed approach (magnification, contrast and focus) as well as the ways of improving the results are discussed.

Keywords
attached cell area | cell attachment analysis | image processing | MATLAB | micropatterned surfaces
DOI
10.2478/prolas-2023-0016
Hyperlink
https://sciendo.com/article/10.2478/prolas-2023-0016

Dehtjars, J., Sorokins, H., Teifurova, S. A Matlab-Based Approach for Estimating the Area Taken Up by Cells Attached to Micropatterned Optically Opaque Surfaces. Proceedings of the Latvian Academy of Sciences, Section B: Natural, Exact, and Applied Sciences, 2023, Vol. 77, No. 2, pp. 109-116. ISSN 1407-009X. Available from: doi:10.2478/prolas-2023-0016

Publication language
English (en)