A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells

TLDR

Assessment of protein subcellular location is crucial to proteomics efforts because localization information provides context for a protein’s sequence, structure, and function. The study aims to quantitatively and comprehensively address subcellular protein localization for the first time. The authors extracted numeric features—including Zernike moments, Haralick texture, and novel descriptors—from fluorescence images, trained a neural‑network classifier, and made the implementation and source code available in Matlab, S‑Plus, SAS, and C at the provided URL. The classifier achieved an average 83 % accuracy on unseen cells across ten subcellular patterns and 98 % accuracy on unseen homogeneously prepared cell sets. Correspondence should be addressed to [e-mail].

Abstract

Abstract Motivation: Assessment of protein subcellular location is crucial to proteomics efforts since localization information provides a context for a protein’s sequence, structure, and function. The work described below is the first to address the subcellular localization of proteins in a quantitative, comprehensive manner. Results: Images for ten different subcellular patterns (including all major organelles) were collected using fluorescence microscopy. The patterns were described using a variety of numeric features, including Zernike moments, Haralick texture features, and a set of new features developed specifically for this purpose. To test the usefulness of these features, they were used to train a neural network classifier. The classifier was able to correctly recognize an average of 83% of previously unseen cells showing one of the ten patterns. The same classifier was then used to recognize previously unseen sets of homogeneously prepared cells with 98% accuracy. Availability: Algorithms were implemented using the commercial products Matlab, S-Plus, and SAS, as well as some functions written in C. The scripts and source code generated for this work are available at http://murphylab.web.cmu.edu/software. Contact: [e-mail] * To whom correspondence should be addressed.