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Automating Quantitative Confocal Microscopy Analysis

Mark E. Fenner
Coveros, Inc., Fairfax, VA

Barbara M. Fenner
King's College, Wilkes-Barre, PA

Abstract

Quantitative confocal microscopy is a powerful analytical tool used to visualize the associations between cellular processes and anatomical structures. In our biological experiments, we use quantitative confocal microscopy to study the association of three cellular components: binding proteins, receptors, and organelles. We propose an automated method that will (1) reduce the time consuming effort of manual background correction and (2) compute numerical coefficients to associate cellular process with structure. The project is implemented, end-to-end, in Python. Pure Python is used for managing file access, input parameters, and initial processing of the repository of 933 images. NumPy is used to apply manual background correction, to compute the automated background corrections, and to calculate the domain specific coefficients. We visualize the raw intensity values and computed coefficient values with Tufte-style panel plots created in matplotlib. A longer term goal of this work is to explore plausible extensions of our automated methods to triple-label coefficients.

Keywords

confocal microscopy, immunofluorescence, thresholding, colocalization coefficients

Bibtex entry

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