Microorganisms often form multicellular structures such as biofilms and structured colonies

Microorganisms often form multicellular structures such as biofilms and structured colonies that can influence the organism’s virulence drug resistance and adherence to UNC-1999 medical devices. time point or over a series of time-lapse images as well as the classification of unique colony shapes based on image-derived features. Phenotypic changes in colony morphology can be expressed as changes in feature space trajectories over time thereby enabling the visualization and quantitative analysis of morphological development. To facilitate data exploration results are plotted dynamically through an interactive Yeast Image Analysis web application (YIMAA; http://yimaa.cs.tut.fi) that integrates the natural and processed images across all time points allowing exploration of the image-based features and principal components associated with morphological development. a stylish organism in which to study the development of complex morphologies with the goal of ultimately uncovering the molecular mechanisms underlying biofilm formation (11). While studies aimed UNC-1999 at characterizing the variance in colony morphology in have been as objective as you possibly can qualitative classification techniques such as having a single investigator categorize colonies by vision are still widely used (12-14). Image analysis tools have also been applied to the automated analysis of yeast colonies. The image analysis platform ImageJ (15) offers tools for processing and quantifying colony images (16) and the image analysis tool CellProfiler (17) has been used to segment colonies on agar plates and group them based on shape size and color. Methods and software for quantifying colony growth combined with statistical analysis have also been offered in the literature (18 19 Other model organisms have also been subjected to quantitative image-based characterization and morphological classification. For example image analysis has been applied to the automated screening of a variety of phenotypes (including morphology) in (20) and recently an application much like ours was applied to the study of filamentous fungi using a set of over 30 morphological features (21). Here we describe an automated image analysis pipeline (Physique 1) that facilitates the quantitative study of colony morphology dynamics in large time-lapse data units. We start with automated image processing and then extract a large generic set of quantitative descriptors. The combination of high-dimensional feature representation together with a sparse supervised logistic regression-based classification model is usually a powerful platform for the analysis of colony morphology. We have also built a web-based application to facilitate the intuitive exploration of the original natural and segmented time series images the results of Principal Component Analysis (PCA) and hundreds of individual quantitative features. We test the accuracy of our method by using it to computationally distinguish the complex (fluffy) and unstructured (easy) colony phenotypes (6 22 based on image data from both single time points and fine resolution time-lapses. Physique 1 The components of the platform for automated quantitative analysis of yeast colonies Materials and methods Yeast strains and growth conditions Standard media and methods UNC-1999 were utilized for the growth and genetic manipulation of (23). All colonies were produced and imaged in a 30°C warm room on YPD (2% glucose) agar plates. Strains used in this study are explained in Table 1. Table 1 strains used in this study. Colony imaging Colonies used to distinguish the fluffy and easy phenotype based on a single time point were generated by manually micro-manipulating individual cells into a gridded pattern separated by 10 mm in both the x- and y-axis. Colonies were imaged after five days of growth using a Mouse monoclonal to CD11b.4AM216 reacts with CD11b, a member of the integrin a chain family with 165 kDa MW. which is expressed on NK cells, monocytes, granulocytes and subsets of T and B cells. It associates with CD18 to form CD11b/CD18 complex.The cellular function of CD11b is on neutrophil and monocyte interactions with stimulated endothelium; Phagocytosis of iC3b or IgG coated particles as a receptor; Chemotaxis and apoptosis. PowerShot SX10IS video camera outfitted with a Raynox DCR-250 macro lens (Yoshida Industry Co. Ltd. Tokyo Japan). Colonies used for automated time-lapse imaging were generated by depositing single cells 12.7 mm apart in a checkerboard pattern with a FACSAria II cell sorter (BD Biosciences Franklin Lakes NJ) UNC-1999 (Supplementary Materials). These colonies were imaged every 14 min for 5 days using a 5d Mark II camera UNC-1999 outfitted with a MP-E 65mm 1-5x macro lens (Cannon Tokyo Japan). The camera was attached to a custom built 2-axis gantry that UNC-1999 moves the camera over the entire set of plates (Supplementary Materials). Camera settings were held constant at an exposure time of 0.2 s and aperture of can be classified based on the conditional probability of belonging to the fluffy class given by the logistic.