Development of algorithms for the automated quantification of biological imaging data

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Wen-Chieh and McKeith Pearson are graduate students working in the Aguilar Lab in the Department of Biological Sciences at Purdue University

Abstract:
The initial step for creating effective therapeutic approaches is to understand the disease-causing mechanism in detail, which in many cases involves acquiring and analyzing massive amounts of biological data. However, conventional, human-involved analysis methods present serious issues when processing high-information content image data, such as user-introduced error/biases and low throughput. For instance, examining protein subcellular localization and cell morphology typically requires quantifying hundreds of cells per experiment through visual inspection and manually outlining areas within microscopy images collected. The procedure is not only laborious but also imprecise restricting the efficiency of the data analysis process, and more importantly, affecting the accuracy of the interpretation after the data analysis.

To address such limitations of current methods, we are developing algorithms to extract and quantitatively analyze biological information from microscopy images. Specifically, the algorithms were designed to quantitatively analyze the distribution of “green fluorescent protein” (GFP) tagged membrane proteins or morphological differences of cells. Importantly, we envision these algorithms will contribute to the development of therapeutic approaches against diseases.