Large-Scale Automated Analysis of Location Patterns in Randomly Tagged 3T3 Cells

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.

[1]  Anne E Carpenter,et al.  Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins , 2006, Nature Methods.

[2]  John C Reed,et al.  Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools , 2002, Journal of cellular biochemistry. Supplement.

[3]  A. Poustka,et al.  Systematic subcellular localization of novel proteins identified by large‐scale cDNA sequencing , 2000, EMBO reports.

[4]  Kai Huang,et al.  Boosting accuracy of automated classification of fluorescence microscope images for location proteomics , 2004, BMC Bioinformatics.

[5]  Minoru Kanehisa,et al.  Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs , 2003, Bioinform..

[6]  M. Markey,et al.  Classification of protein localization patterns obtained via fluorescence light microscopy , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[7]  A. Danckaert,et al.  Automated Recognition of Intracellular Organelles in Confocal Microscope Images , 2002, Traffic.

[8]  Zhiyong Lu,et al.  Predicting subcellular localization of proteins using machine-learned classifiers , 2004, Bioinform..

[9]  L Hennen,et al.  In vivo functional proteomics: mammalian genome annotation using CD-tagging. , 2002, BioTechniques.

[10]  D L Taylor,et al.  Real-time molecular and cellular analysis: the new frontier of drug discovery. , 2001, Current opinion in biotechnology.

[11]  J. Jarvik,et al.  CD-tagging: a new approach to gene and protein discovery and analysis. , 1996, BioTechniques.

[12]  E. O’Shea,et al.  Global analysis of protein localization in budding yeast , 2003, Nature.

[13]  Kai Huang,et al.  Automated classification of subcellular patterns in multicell images without segmentation into single cells , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[14]  R. Murphy,et al.  Objective Clustering of Proteins Based on Subcellular Location Patterns , 2005, Journal of biomedicine & biotechnology.

[15]  Eoin Fahy,et al.  MITOPRED: a genome-scale method for prediction of nucleus-encoded mitochondrial proteins , 2004, Bioinform..

[16]  Robert F. Murphy,et al.  Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images , 2003, J. VLSI Signal Process..

[17]  Stephen S. Taylor,et al.  A Visual Screen of a Gfp-Fusion Library Identifies a New Type of Nuclear Envelope Membrane Protein , 1999, The Journal of cell biology.

[18]  C. Conrad,et al.  Automatic identification of subcellular phenotypes on human cell arrays. , 2004, Genome research.

[19]  K. Nakai.  Protein sorting signals and prediction of subcellular localization. , 2000, Advances in protein chemistry.

[20]  Robert F. Murphy,et al.  A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells , 2001, Bioinform..

[21]  M V Boland,et al.  Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images. , 1998, Cytometry.

[22]  Kuo-Chen Chou,et al.  Prediction and classification of protein subcellular location—sequence‐order effect and pseudo amino acid composition , 2003, Journal of cellular biochemistry.

[23]  M V Boland,et al.  Toward objective selection of representative microscope images. , 1999, Biophysical journal.