This focus article introduces the concept of NutriChip, an integrated microfluidic platform for investigating the potential of the immuno-modulatory function of dairy food. The core component of the NutriChip is a miniaturized artificial human gastrointestinal tract (GIT), which consists of a confluent layer of epithelial cells separated from a co-culture of immune cells by a permeable membrane. This setting creates conditions mimicking the human GIT and allows studying processes that characterize the passage of nutrients though the human GIT, including the activation of immune cells in response to the transfer of nutrients across the epithelial layer. The NutriChip project started by developing a biologically active in vitro cellular system in a commercial Transwell co-culture system. This Transwell system serves as a reference for the micro-scale device which is being developed. The microfluidic setup of NutriChip allows monitoring of the response of immune cells to pro-inflammatory stimuli, such as lipid polysaccharide (LPS), and to the application of potentially anti-inflammatory dairy food. This differential response will be quantified by measuring the variation in expression of pro-inflammatory cytokines, including interleukin 1 (IL-1) and interleukin 6 (IL-6), secreted by the immune cells, and this is achieved by using a dedicated optical imager. A series of dairy products will be screened for their anti-inflammatory properties using the NutriChip system and, finally, the outcome of the NutriChip will be validated by a human nutrition trial.Therefore, the NutriChip platform offers a new option to evaluate the influence of food quality on health, by monitoring the expression of relevant immune cell biomarkers.

s 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. © 2013 International Society for Advancement of Cytometry

Digital image processing and epifluorescence microscopy provide one of the main and basic tools for living biological cell analysis and studying. Developing, testing, and comparing those image processing methods properly is eased by the use of a controlled environment. Taking advantage of an existing database of verified and trustworthy images and metadata helps controlling the validity of the processing results. Manually generating that golden database is a long process involving specialists being able to apprehend and extract useful data out of fluorescent images. Having enough cases in the database to challenge the processing methods and gain trust in them can only be achieved manually through time-consuming, prone to human-error processes. More and more we need to automate this process. This paper presents a framework implementing a novel approach to generate synthetic fluorescent images of fluorescently stained cell populations by simulating the imaging process of fluorescent molecules. Ultimately, the proposed simulator allows us to generate images and golden data to populate

Abstract-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 labon-chip systems.

Abstract-In biological applications and systems where even the smallest details have a meaning, CCD cameras are mostly preferred and they hold most of the market share despite their high costs. In this paper, we propose a custom-designed CMOS camera to compete with the default CCD camera of an inverted microscope for fluorescence imaging. The custom-designed camera includes a commercially available mid-performance CMOS image sensor and a Field-Programmable Gate Array (FPGA) based hardware platform (FPGA4U). The high cost CCD camera of the microscope is replaced by the custom-designed CMOS camera and the two are quantitatively compared for a specific application where an Estrogen Reception (ER) expression in breast cancer diagnostic samples that emits light at 665nm has been imaged by both cameras. The gray-scale images collected by both cameras show a very similar intensity distribution. In addition, normalized white pixels after thresholding resulted in 4.96% for CCD and 3.38% for CMOS. The results and images after thresholding show that depending on the application even a mid-performance CMOS camera can provide enough image quality when the target is localization of fluorescent stained biological details. Therefore the cost of the cameras can be drastically reduced while benefiting from the inherent advantages of CMOS devices plus adding more features and flexibility to the camera systems with FPGAs.