Calcium Signaling in Interstitial Cells: Focus on Telocytes.

In this review, we describe the current knowledge on calcium signaling pathways in interstitial cells with a special focus on interstitial cells of Cajal (ICCs), interstitial Cajal-like cells (ICLCs), and telocytes. In detail, we present the generation of Ca2+ oscillations, the inositol triphosphate (IP3)/Ca2+ signaling pathway and modulation exerted by cytokines and vasoactive agents on calcium signaling in interstitial cells. We discuss the physiology and alterations of calcium signaling in interstitial cells, and in particular in telocytes. We describe the physiological contribution of calcium signaling in interstitial cells to the pacemaking activity (e.g., intestinal, urinary, uterine or vascular pacemaking activity) and to the reproductive function. We also present the pathological contribution of calcium signaling in interstitial cells to the aortic valve calcification or intestinal inflammation. Moreover, we summarize the current knowledge of the role played by calcium signaling in telocytes in the uterine, cardiac and urinary physiology, and also in various pathologies, including immune response, uterine and cardiac pathologies.

Integrating semantic annotation and information visualization for the analysis of multichannel fluorescence micrographs from pancreatic tissue.

The challenging problem of computational bioimage analysis receives growing attention from life sciences. Fluorescence microscopy is capable of simultaneously visualizing multiple molecules by staining with different fluorescent dyes. In the analysis of the result multichannel images, segmentation of ROIs resembles only a first step which must be followed by a second step towards the analysis of the ROI's signals in the different channels. In this paper we present a system that combines image segmentation and information visualization principles for an integrated analysis of fluorescence micrographs of tissue samples. The analysis aims at the detection and annotation of cells of the Islets of Langerhans and the whole pancreas, which is of great importance in diabetes studies and in the search for new anti-diabetes treatments. The system operates with two modules. The automatic annotation module applies supervised machine learning for cell detection and segmentation. The second information visualization module can be used for an interactive classification and visualization of cell types following the link-and-brush principle for filtering. We can compare the results obtained with our system with results obtained manually by an expert, who evaluated a set of example images three times to account for his intra-observer variance. The comparison shows that using our system the images can be evaluated with high accuracy which allows a considerable speed up of the time-consuming evaluation process.

About the presence of interstitial cells of Cajal outside the musculature of the gastrointestinal tract.

Santiago Ramon y Cajal observed a special cell type that appeared to function as endstructures of the intrinsic nervous system in several organs. These cells were structurally and functionally further characterized in the gut musculature and named interstitial cells of Cajal (ICC). In recent years, interstitial cells have been identified in the vasculature, urinary tract, glands and other organs. Their morphologies and functions are just beginning to be clarified. It is likely that amongst them, subtypes will be discovered that warrant the classification of interstitial cells of Cajal. This "point of view" continues the discussion on the criteria that should be used to identify ICC outside the musculature of the gut.

Optimal molecular profiling of tissue and tissue components: defining the best processing and microdissection methods for biomedical applications.

Isolation of well-preserved pure cell populations is a prerequisite for sound studies of the molecular basis of any tissue-based biological phenomenon. This article reviews current methods for obtaining anatomically specific signals from molecules isolated from tissues, a basic requirement for productive linking of phenotype and genotype. The quality of samples isolated from tissue and used for molecular analysis is often glossed over or omitted from publications, making interpretation and replication of data difficult or impossible. Fortunately, recently developed techniques allow life scientists to better document and control the quality of samples used for a given assay, creating a foundation for improvement in this area. Tissue processing for molecular studies usually involves some or all of the following steps: tissue collection, gross dissection/identification, fixation, processing/embedding, storage/archiving, sectioning, staining, microdissection/annotation, and pure analyte labeling/identification and quantification. We provide a detailed comparison of some current tissue microdissection technologies, and provide detailed example protocols for tissue component handling upstream and downstream from microdissection. We also discuss some of the physical and chemical issues related to optimal tissue processing, and include methods specific to cytology specimens. We encourage each laboratory to use these as a starting point for optimization of their overall process of moving from collected tissue to high quality, appropriately anatomically tagged scientific results. In optimized protocols is a source of inefficiency in current life science research. Improvement in this area will significantly increase life science quality and productivity. The article is divided into introduction, materials, protocols, and notes sections. Because many protocols are covered in each of these sections, information relating to a single protocol is not contiguous. To get the greatest benefit from this article, readers are advised to read through the entire article first, identify protocols appropriate to their laboratory for each step in their workflow, and then reread entries in each section pertaining to each of these single protocols.

A mathematical programming approach for gene selection and tissue classification.

MOTIVATION: Extracting useful information from expression levels of thousands of genes generated with microarray technology needs a variety of analytical techniques. Mathematical programming approaches for classification analysis outperform parametric methods when the data depart from assumptions underlying these methods. Therefore, a mathematical programming approach is developed for gene selection and tissue classification using gene expression profiles. RESULTS: A new mixed integer programming model is formulated for this purpose. The mixed integer programming model simultaneously selects genes and constructs a classification model to classify two groups of tissue samples as accurately as possible. Very encouraging results were obtained with two data sets from the literature as examples. These results show that the mathematical programming approach can rival or outperform traditional classification methods.