High Throughput Phenotypic Profiling of Root Anatomical Traits in Maize Amy Burton, Michael Williams, Jonathan Lynch, Kathleen Brown Department of Horticulture, The Pennsylvania State University, University Park, PA PHASE I: CROSS SECTION ISOLATION PHASE II: STELE SEPARATION ANALYTICAL COMPARISON ANALYTICAL COMPARISON Project Objective RootScan is a program we have developed using Matlab to evaluate anatomical traits in crop plants. METHODS Two hundred genotypes from a recombinant inbred population of Zea mays (Intermated B73 x Mo17) were grown to four weeks (V6 stage) in a greenhouse. Tissue segments were collected from the basal portion of a second whorl crown root for free hand sectioning. Black and white images of root cross-sections were analyzed using RootScan, a semi- automated image analysis program operating in Matlab. The program is divided into two phases: Phase I in which the cross-section is isolated, and Phase II in which the stele and cortex are separated from one another. Data collection occurs automatically at the conclusion of Phase II. Conclusions We have developed a semi-automated image analysis program, RootScan, for high throughput phenotype profiling of anatomical root traits. This method is fast, powerful and accurate. Analysis of anatomical traits in the IBM population shows genetic variation, indicating potential to breeding and selection of traits affecting performance. ACKNOWLEDGEMENTS We would like to thank Shawn Kaeppler for supplying seed. We thank Maria Baeza, Lauren Gelesh, Gina Marchini, Johanna Mirenda and Bob Snyder for assistance in the greenhouse and for tissue sectioning. We thank Chinmay Rao for work on the Matlab programming. Funding for this project was provided through a grant from the United State Department of Agriculture, National Research Initiative (Grant # ). In Phase I of the image analysis, a graphical user interface (GUI) is used for contrast adjustment (left), followed by a series of steps to isolate the cross-section from any debris and from the background of the image (right). Contrast adjustment minimizes potential issues that may arise later due to variation in image quality. In Phase II, a series of steps isolates the stele (A8) from the cortex (A9), allowing for separate analyses of these areas. If the program cannot properly perform the function, a GUI offers the option of manual stele selection (B). Additional GUI windows allow for lateral removal (C), aerenchyma (D) and xylem vessel (F) detection. Additional windows show color-coded cell size zones (E) and the final tissue selection map (F). Data collection includes area measurement, counting and identification of relative locations of objects. Correct Identification of these features is based on the number of pixels and value thresholding for edge detection. A tissue segment showing longitudinal distribution of aerenchyma lacunae along the root axis. A CBD F G E Previous methods of image analysis used time-consuming manual selection of tissue features in Photoshop. Data entry was user-directed, and required further calculations after data collection. Overall, the process was subject to user variability. At its present level of efficiency, RootScan has increased the rate of image analysis, and amount of data taken per image. With Photoshop, 3 variables were collected per image, on 30 images/hour. With RootScan, 23 variables are collected on an image at 60 images/hour. Series progression with corresponding data show the process of data collection (right). Correlations between RootScan and the manual selection method in Photoshop shown below. Original Image Contrast Adjustment Stele Segmentation Final Image Cortex Segmentation Variation exists for traits measured in this population, and is important in evaluating breeding potential.