This research is aimed at developing and testing methods that will enable proteome-wide high-throughput studies of the subcellular distributions of proteins that form networks (such as microtubules), and for identifying proteins whose distributions are related to these. More specifically we will test and further refine methods that we have developed to estimate the average properties (characterize the statistical variation) of the filament distributions for a large number of cells.
Our aim i s to use the power afforded by the availability of data arising from many (possibly thousands) of cells to estimate what is really at the heart of the question for high-throughput studies of proteins of this type: what is the overall average effect of some independent variable (drug or other experimental condition) on the network filament distribution of interest. Preliminary work has established the feasibility of this approach, and we propose to test it extensively with real data for microtubules in a variety of cell types under a variety of conditions that perturb distributions in known ways. The results will establish whether a single microtubule growth model (with changes in parameters) is valid for many cell types (i.e., to remove variation due solely to cell size and shape). We will also extend this method to determine the correlation (affinity) of many unknown proteins to filament networks for several proteome wide studies currently generating such data. The methods will be used to analyze images for thousands of proteins from existing and ongoing proteome-scale studies. The identification solely on the basis of image-based modeling of specific proteins as likely to be microtubule-associated will be tested for selected examples by comparison with information in existing protein databases and literature and by additional experimentation. The successful completion of this study would not only provide important new information about the location of many proteins, but will fill a current void in modeling approaches for proteome-wide studies and facilitate the mechanistic quantification of effects of different drugs, siRNAs or mutations in high throughput screening experiments.

Public Health Relevance

This research is aimed at enabling fundamental understanding of subcellular filament-type protein structure and distribution through generative modeling approaches. The successful completion of this study would enable the development of modeling approaches for similar proteins and subcellular structures and facilitate the mechanistic quantification of effects of different drugs, siRNAs or mutations in high throughput screening experiments.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM090033-04
Application #
8449158
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Brazhnik, Paul
Project Start
2010-04-01
Project End
2014-03-31
Budget Start
2013-04-01
Budget End
2014-03-31
Support Year
4
Fiscal Year
2013
Total Cost
$286,040
Indirect Cost
$94,970
Name
Carnegie-Mellon University
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
052184116
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
Huang, Hu; Tosun, Akif Burak; Guo, Jia et al. (2014) Cancer diagnosis by nuclear morphometry using spatial information (.) Pattern Recognit Lett 42:115-121
Basu, Saurav; Kolouri, Soheil; Rohde, Gustavo K (2014) Detecting and visualizing cell phenotype differences from microscopy images using transport-based morphometry. Proc Natl Acad Sci U S A 111:3448-53
Ozolek, John A; Tosun, Akif Burak; Wang, Wei et al. (2014) Accurate diagnosis of thyroid follicular lesions from nuclear morphology using supervised learning. Med Image Anal 18:772-80
Chen, Cheng; Wang, Wei; Ozolek, John A et al. (2013) A flexible and robust approach for segmenting cell nuclei from 2D microscopy images using supervised learning and template matching. Cytometry A 83:495-507
Basu, S; Dahl, K N; Rohde, G K (2013) Localizing and extracting filament distributions from microscopy images. J Microsc 250:57-67
Wang, Wei; Slepcev, Dejan; Basu, Saurav et al. (2013) A linear optimal transportation framework for quantifying and visualizing variations in sets of images. Int J Comput Vis 101:254-269
Murphy, Robert F (2012) CellOrganizer: Image-derived models of subcellular organization and protein distribution. Methods Cell Biol 110:179-93
Shariff, Aabid; Murphy, Robert F; Rohde, Gustavo K (2011) AUTOMATED ESTIMATION OF MICROTUBULE MODEL PARAMETERS FROM 3-D LIVE CELL MICROSCOPY IMAGES. Proc IEEE Int Symp Biomed Imaging 2011:1330-1333
Shariff, Aabid; Murphy, Robert F; Rohde, Gustavo K (2010) A generative model of microtubule distributions, and indirect estimation of its parameters from fluorescence microscopy images. Cytometry A 77:457-66