Abstract-Location proteomics is concerned with the systematic analysis of the subcellular location of proteins. In order to perform high-resolution, high-throughput analysis of all protein location patterns, automated methods are needed. Here we describe the use of such methods on a large collection of images obtained by automated microscopy to perform high-throughput analysis of endogenous proteins randomly-tagged with a fluorescent protein in NIH 3T3 cells. Cluster analysis was performed to identify the statistically significant location patterns in these images. This allowed us to assign a location pattern to each tagged protein without specifying what patterns are possible. To choose the best feature set for this clustering, we have used a novel method that determines which features do not artificially discriminate between control wells on different plates and uses Stepwise Discriminant Analysis (SDA) to determine which features do discriminate as much as possible among the randomly-tagged wells. Combining this feature set with consensus clustering methods resulted in 35 clusters among the first 188 clones we obtained. This approach represents a powerful automated solution to the problem of identifying subcellular locations on a proteome-wide basis for many different cell types.

We present an algorithm for the segmentation of multicell fluorescence microscopy images. Such images abound and a segmentation algorithm robust to different experimental conditions as well as cell types is becoming a necessity. In cellular imaging, among the most often used segmentation algorithms is seeded watershed. One of its features is that it tends to oversegment, splitting the cells, as well as create segmented regions much larger than a true cell. This can be an advantage (the entire cell is within the region) as well as a disadvantage (a large amount of background noise is included). We present an algorithm which segments with tight contours by building upon an active contour algorithm-STACS, by Pluempitiwiriyawej et al. We adapt the algorithm to suit the needs of our data and use another technique, topology preservation by Han et al., to build our topology preserving STACS (TPSTACS). Our algorithm significantly outperforms the seeded watershed both visually as well as by standard measures of segmentation quality: recall/precision, area similarity and area overlap. SEGMENTATION OF FLUORESCENCE MICROSCOPY IMAGESFluorescence microscopy is one of the main ways for biologists to observe processes in a live cell. As collection of fluorescence microscopy data sets continues, automated and robust processing methods are becoming increasingly important. One common task in such systems is segmentation when acquired images contain more than one cell. This is a basic (and very hard) problem in image processing. It aims to separate an object of interest from other objects and the background. Its result is a closed curve around the object of interest called a contour.An example of automated processing mentioned above is the system for classification of proteins based on fluorescence microscopy images of their subcellular locations (spatial distributions within the cell) [1]. The data set contained parallel images for a specific protein, total protein and total DNA. Segmentation was performed using the seeded watershed algorithm on the total protein channel using the nuclei as seeds [2], and was modified to exclude partial cells on the boundaries [1]. In this paper, we use the same data set and compare the results of our algorithm to those obtained by seeded watershed (SW) in [1].

We present an algorithm for efficient acquisition of fluorescence microscopy data sets, a problem not addressed until now in the literature. We do this as part of a larger system for protein classification based on their subcellular location patterns, and thus strive to maintain the achieved level of classification accuracy as much as possible. This problem is similar to image compression but unique due to additional restrictions, namely causality; we have access only to the information that has been scanned up to that point. While we do want to acquire fewer samples with as low distortion as possible to achieve compression, our goal is to do so while affecting the overall classification accuracy as little as possible. We achieve this by using an adaptive multiresolution scanning scheme which samples the regions of the image area that hold the most pertinent information. Our results show that we can achieve significant compression which we can then use to increase either time of space resolution of our data set, all while minimally affecting the classification accuracy of the entire system.

He also worked for CarboMedics Inc. in Austin, TX, in the research and development of prosthetic heart valves. Dr. Zapanta's primary teaching responsibility is to develop laboratory classes for undergraduates in the Department of Biomedical Engineering. Additional teaching interests include medical device design education, biomedical engineering design, and professional issues in biomedical engineering. Dr. Zapanta's responsibilities as Associate Department head include coordination of undergraduate curriculum, undergraduate student advising, and class scheduling. Dr. Zapanta's research interests are in developing medical devices to treat cardiovascular disease, focusing on the areas of cardiac assist devices and prosthetic heart valves.

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