Article

Automated learning of generative models for subcellular location: building blocks for systems biology.

Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.
Cytometry Part A (Impact Factor: 3.71). 01/2008; 71(12):978-90. DOI: 10.1002/cyto.a.20487
Source: PubMed

ABSTRACT The goal of location proteomics is the systematic and comprehensive study of protein subcellular location. We have previously developed automated, quantitative methods to identify protein subcellular location families, but there have been no effective means of communicating their patterns to integrate them with other information for building cell models. We built generative models of subcellular location that are learned from a collection of images so that they not only represent the pattern, but also capture its variation from cell to cell. Our models contain three components: a nuclear model, a cell shape model and a protein-containing object model. We built models for six patterns that consist primarily of discrete structures. To validate the generated images, we showed that they are recognized with reasonable accuracy by a classifier trained on real images. We also showed that the model parameters themselves can be used as features to discriminate the classes. The models allow the synthesis of images with the expectation that they are drawn from the same underlying statistical distribution as the images used to train them. They can potentially be combined for many proteins to yield a high resolution location map in support of systems biology.

0 Bookmarks
 · 
96 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Cytomics is the high-content analysis of cell-systems [6, 78]. The area of Cytomics and Systems Biology received great attention during the last years as it harbours the promise to substantially impact on various fields of biomedicine, drug discovery, predictive medicine [6] and may have major potential for regenerative medicine. In regenerative medicine Cytomics includes process control of cell preparation and culturing using non-invasive detection techniques, quality control and standardization for GMP and GLP conformity and even prediction of cell fate based on sophisticated data analysis. Cytomics requires quantitative and stoichiometric single cell analysis. In some areas the leading cytometric techniques represent the cutting edge today. Many different applications/variations of multicolour staining were developed for flow- or slide-based cytometry (SBC) analysis of suspensions and sections to whole animal analysis [78]. SBC has become an important analytical technology in drug discovery, diagnosis and research and is an emerging technology for systems analysis [78]. It enables high-content high-throughput measurement of cell suspensions, cell cultures and tissues. In the last years various commercial SBC instruments were launched principally enabling to perform similar tasks. Standardisation as well as comparability of different instruments is a major challenge. Hyperspectral optical imaging may be implemented in SBC analysis for label free cell detection based on cellular autofluorescence [3]. All of these developments push the systemic approach of the analysis of biological specimens to enhance the outcome of regenerative medicine.
    Proceedings of SPIE - The International Society for Optical Engineering 03/2008; · 0.20 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: We propose a mathematical framework and algorithms both to build accurate models of fluorescence microscope time series, as well as to design intelligent acquisition systems based on these models. Model building allows the information contained in the 2-D and 3-D time series to be presented in a more useful and concise form than the raw image data. This is particularly relevant as the trend in biology tends more and more towards high-throughput applications, and the resulting increase in the amount of acquired image data makes visual inspection impractical. The intelligent acquisition system uses an active learning approach to choose the acquisition regions that let us build our model most efficiently, resulting in a shorter acquisition time, as well as a reduction of the amount of photobleaching and phototoxicity incurred during acquisition. We validate our methodology by modeling object motion within a cell. For intelligent acquisition, we propose a set of algorithms to evaluate the information contained in a given acquisition region, as well as the costs associated with acquiring this region in terms of the resulting photobleaching and phototoxicity and the amount of time taken for acquisition. We use these algorithms to determine an acquisition strategy: where and when to acquire, as well as when to stop acquiring. Results, both on synthetic as well as real data, demonstrate accurate model building and large efficiency gains during acquisition.
    IEEE Transactions on Image Processing 09/2009; 18(9):2071-2084. · 3.11 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Stereologic cell counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic cell counting is that the user must manually decide whether or not each cell is counted according to three-dimensional (3D) stereologic counting rules by visual inspection within hundreds of microscopic fields-of-view per investigated brain or brain region. Reliance on visual inspection forces stereologic cell counting to be very labor-intensive and time-consuming, and is the main reason why biased, non-stereologic two-dimensional (2D) "cell counting" approaches have remained in widespread use. We present an evaluation of the performance of modern automated cell detection and segmentation algorithms as a potential alternative to the manual approach in stereologic cell counting. The image data used in this study were 3D microscopic images of thick brain tissue sections prepared with a variety of commonly used nuclear and cytoplasmic stains. The evaluation compared the numbers and locations of cells identified unambiguously and counted exhaustively by an expert observer with those found by three automated 3D cell detection algorithms: nuclei segmentation from the FARSIGHT toolkit, nuclei segmentation by 3D multiple level set methods, and the 3D object counter plug-in for ImageJ. Of these methods, FARSIGHT performed best, with true-positive detection rates between 38 and 99% and false-positive rates from 3.6 to 82%. The results demonstrate that the current automated methods suffer from lower detection rates and higher false-positive rates than are acceptable for obtaining valid estimates of cell numbers. Thus, at present, stereologic cell counting with manual decision for object inclusion according to unbiased stereologic counting rules remains the only adequate method for unbiased cell quantification in histologic tissue sections.
    Frontiers in Neuroanatomy 05/2014; 8:27. · 4.18 Impact Factor

Full-text (2 Sources)

Download
82 Downloads
Available from
May 21, 2014