Toward a Confocal Subcellular Atlas of the Human Proteome

Department of Biotechnology, AlbaNova University Center, Royal Institute of Technology, SE-106 91 Stockholm, Sweden.
Molecular &amp Cellular Proteomics (Impact Factor: 6.56). 04/2008; 7(3):499-508. DOI: 10.1074/mcp.M700325-MCP200
Source: PubMed


Information on protein localization on the subcellular level is important to map and characterize the proteome and to better understand cellular functions of proteins. Here we report on a pilot study of 466 proteins in three human cell lines aimed to allow large scale confocal microscopy analysis using protein-specific antibodies. Approximately 3000 high resolution images were generated, and more than 80% of the analyzed proteins could be classified in one or multiple subcellular compartment(s). The localizations of the proteins showed, in many cases, good agreement with the Gene Ontology localization prediction model. This is the first large scale antibody-based study to localize proteins into subcellular compartments using antibodies and confocal microscopy. The results suggest that this approach might be a valuable tool in conjunction with predictive models for protein localization.

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    • "Furthermore, ppp1r12a (also known as MYPT1 (Entrez Gene ID: 4659)) is a myosin phosphatase. Smarca2, another family member of smarca4, interacts with the intermediate filament [58]( ENSG00000080503). Magi2 has been shown to be a component of tight junction [59] and is regulated by the planar cell polarity pathway in glomerular podocytes [60]. "
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    • "Table 2. Dataset statistics Name Number of images Number of classes Reference RT-widefield 1382 10 RT-confocal 304 10 HeLa2D 862 10 Boland and Murphy, 2001 LOCATE-transfected 553 11 Hamilton et al., 2007 LOCATE-endogenous 502 10 Hamilton et al., 2007 Binucleate 41 2 Shamir et al., 2008a CHO 327 5 Shamir et al., 2008a Terminalbulb 970 7 Shamir et al., 2008a RNAi 200 10 Shamir et al., 2008a HPA 1842 13 Barbe et al., 2008 "
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    ABSTRACT: Evaluation of previous systems for automated determination of subcellular location from microscope images has been done using datasets in which each location class consisted of multiple images of the same representative protein. Here, we frame a more challenging and useful problem where previously unseen proteins are to be classified. Using CD-tagging, we generated two new image datasets for evaluation of this problem, which contain several different proteins for each location class. Evaluation of previous methods on these new datasets showed that it is much harder to train a classifier that generalizes across different proteins than one that simply recognizes a protein it was trained on.We therefore developed and evaluated additional approaches, incorporating novel modifications of local features techniques. These extended the notion of local features to exploit both the protein image and any reference markers that were imaged in parallel. With these, we obtained a large accuracy improvement in our new datasets over existing methods. Additionally, these features help achieve classification improvements for other previously studied datasets. The datasets are available for download at The software was written in Python and C++ and is available under an open-source license at The code is split into a library which can be easily reused for other data and a small driver script for reproducing all results presented here. A step-by-step tutorial on applying the methods to new datasets is also available at that address.
    Bioinformatics 07/2013; 29(18). DOI:10.1093/bioinformatics/btt392 · 4.98 Impact Factor
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    • "We next assessed which of the proteins encoded by genes that were upregulated in our ciliated MTEC datasets can be detected at the cilia or the basal bodies of multiciliated cells. To do so, we scanned immunohistochemistry data from the Human Protein Atlas (HPA), an online compendium of tissue microarray data from normal tissues, pathology specimens and cell lines [51]. The HPA dataset consists of tissue sections stained with antibodies raised against human proteins. "
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