Image thresholding techniques for localization of sub-resolution fluorescent biomarkers.

In this article, we explore adaptive global and local segmentation techniques for a lab-on-chip nutrition monitoring system (NutriChip). The experimental setup consists of Caco-2 intestinal cells that can be artificially stimulated to trigger an immune response. The eventual response is optically monitored using immunofluoresence techniques targeting toll-like receptor 2 (TLR2). Two problems of interest need to be addressed by means of image processing. First, a new cell sample must be properly classified as stimulated or not. Second, the location of the stained TLR2 must be recovered in case the sample has been stimulated. The algorithmic approach to solving these problems is based on the ability of a segmentation technique to properly segment fluorescent spots. The sample classification is based on the amount and intensity of the segmented pixels, while the various segmenting blobs provide an approximate localization of TLR2. A novel local thresholding algorithm and three well-known spot segmentation techniques are compared in this study. Quantitative assessment of these techniques based on real and synthesized data demonstrates the improved segmentation capabilities of the proposed algorithm.

The NutriChip project--translating technology into nutritional knowledge.

Advances in food transformation have dramatically increased the diversity of products on the market and, consequently, exposed consumers to a complex spectrum of bioactive nutrients whose potential risks and benefits have mostly not been confidently demonstrated. Therefore, tools are needed to efficiently screen products for selected physiological properties before they enter the market. NutriChip is an interdisciplinary modular project funded by the Swiss programme Nano-Tera, which groups scientists from several areas of research with the aim of developing analytical strategies that will enable functional screening of foods. The project focuses on postprandial inflammatory stress, which potentially contributes to the development of chronic inflammatory diseases. The first module of the NutriChip project is composed of three in vitro biochemical steps that mimic the digestion process, intestinal absorption, and subsequent modulation of immune cells by the bioavailable nutrients. The second module is a miniaturised form of the first module (gut-on-a-chip) that integrates a microfluidic-based cell co-culture system and super-resolution imaging technologies to provide a physiologically relevant fluid flow environment and allows sensitive real-time analysis of the products screened in vitro. The third module aims at validating the in vitro screening model by assessing the nutritional properties of selected food products in humans. Because of the immunomodulatory properties of milk as well as its amenability to technological transformation, dairy products have been selected as model foods. The NutriChip project reflects the opening of food and nutrition sciences to state-of-the-art technologies, a key step in the translation of transdisciplinary knowledge into nutritional advice.