Quantitative Estimation of Biological Cell Surface Receptors by Segmenting Conventional Fluorescence Microscopy Images

State-of-the-art techniques for measuring and monitoring gene level expression rely on messenger RNA (mRNA) extraction and quantification, usually based on the concept of reverse transcription polymerase chain reaction. In this paper, we take advantage of capabilities of image segmentation algorithms for monitoring target cell surface biomarkers using immunofluorescence microscopy. As a case study, we are looking at the expression level of toll-like receptor 2 (TLR2) proteins on Caco-2 intestinal cells after stimulation with lipopolysaccharide. The goal of this paper is to identify the segmentation algorithm which provides the best correlation between the pixel intensities of fluorescent images and quantified TLR2 mRNA. Three image segmentation algorithms are considered in this study for processing the fluorescent images acquired using a low-cost CMOS sensor. We conclusively show the existence of a proper segmentation algorithm from which we can extract results that are heavily correlated with TLR2 mRNA quantifications. The obtained results open possibilities for cost-effective and real-time monitoring of biomarkers with applications in embedded or lab-on-chip systems.