Literature DB >> 27327612

Point process models for localization and interdependence of punctate cellular structures.

Ying Li1,2, Timothy D Majarian2,3, Armaghan W Naik2, Gregory R Johnson2, Robert F Murphy2,3,4,5.   

Abstract

Accurate representations of cellular organization for multiple eukaryotic cell types are required for creating predictive models of dynamic cellular function. To this end, we have previously developed the CellOrganizer platform, an open source system for generative modeling of cellular components from microscopy images. CellOrganizer models capture the inherent heterogeneity in the spatial distribution, size, and quantity of different components among a cell population. Furthermore, CellOrganizer can generate quantitatively realistic synthetic images that reflect the underlying cell population. A current focus of the project is to model the complex, interdependent nature of organelle localization. We built upon previous work on developing multiple non-parametric models of organelles or structures that show punctate patterns. The previous models described the relationships between the subcellular localization of puncta and the positions of cell and nuclear membranes and microtubules. We extend these models to consider the relationship to the endoplasmic reticulum (ER), and to consider the relationship between the positions of different puncta of the same type. Our results do not suggest that the punctate patterns we examined are dependent on ER position or inter- and intra-class proximity. With these results, we built classifiers to update previous assignments of proteins to one of 11 patterns in three distinct cell lines. Our generative models demonstrate the ability to construct statistically accurate representations of puncta localization from simple cellular markers in distinct cell types, capturing the complex phenomena of cellular structure interaction with little human input. This protocol represents a novel approach to vesicular protein annotation, a field that is often neglected in high-throughput microscopy. These results suggest that spatial point process models provide useful insight with respect to the spatial dependence between cellular structures.
© 2016 International Society for Advancement of Cytometry. © 2016 International Society for Advancement of Cytometry.

Entities:  

Keywords:  generative models; pattern recognition; spatial point processes; subcellular location; systems biology

Mesh:

Year:  2016        PMID: 27327612      PMCID: PMC5308220          DOI: 10.1002/cyto.a.22873

Source DB:  PubMed          Journal:  Cytometry A        ISSN: 1552-4922            Impact factor:   4.355


  19 in total

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Authors:  Hiroaki Kitano
Journal:  Nature       Date:  2002-11-14       Impact factor: 49.962

2.  CellOrganizer: Image-derived models of subcellular organization and protein distribution.

Authors:  Robert F Murphy
Journal:  Methods Cell Biol       Date:  2012       Impact factor: 1.441

3.  Spatio-temporal analysis of constitutive exocytosis in epithelial cells.

Authors:  Rafael Sebastian; María-Elena Diaz; Guillermo Ayala; Kresimir Letinic; Jose Moncho-Bogani; Derek Toomre
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2006 Jan-Mar       Impact factor: 3.710

4.  Image-derived, three-dimensional generative models of cellular organization.

Authors:  Tao Peng; Robert F Murphy
Journal:  Cytometry A       Date:  2011-04-06       Impact factor: 4.355

5.  Movements of vesicles on microtubules.

Authors:  M P Sheetz; R Vale; B Schnapp; T Schroer; T Reese
Journal:  Ann N Y Acad Sci       Date:  1987       Impact factor: 5.691

6.  Instance-Based Generative Biological Shape Modeling.

Authors:  Tao Peng; Wei Wang; Gustavo K Rohde; Robert F Murphy
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2009

7.  Detailed simulations of cell biology with Smoldyn 2.1.

Authors:  Steven S Andrews; Nathan J Addy; Roger Brent; Adam P Arkin
Journal:  PLoS Comput Biol       Date:  2010-03-12       Impact factor: 4.475

8.  Spatial modeling of vesicle transport and the cytoskeleton: the challenge of hitting the right road.

Authors:  Michael Klann; Heinz Koeppl; Matthias Reuss
Journal:  PLoS One       Date:  2012-01-12       Impact factor: 3.240

9.  Key role of local regulation in chemosensing revealed by a new molecular interaction-based modeling method.

Authors:  Martin Meier-Schellersheim; Xuehua Xu; Bastian Angermann; Eric J Kunkel; Tian Jin; Ronald N Germain
Journal:  PLoS Comput Biol       Date:  2006-05-25       Impact factor: 4.475

10.  Automated Learning of Subcellular Variation among Punctate Protein Patterns and a Generative Model of Their Relation to Microtubules.

Authors:  Gregory R Johnson; Jieyue Li; Aabid Shariff; Gustavo K Rohde; Robert F Murphy
Journal:  PLoS Comput Biol       Date:  2015-12-01       Impact factor: 4.475

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  3 in total

1.  Objective quantification of nanoscale protein distributions.

Authors:  Miklos Szoboszlay; Tekla Kirizs; Zoltan Nusser
Journal:  Sci Rep       Date:  2017-11-10       Impact factor: 4.379

2.  Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting.

Authors:  Alex X Lu; Oren Z Kraus; Sam Cooper; Alan M Moses
Journal:  PLoS Comput Biol       Date:  2019-09-03       Impact factor: 4.475

3.  Learning the sequence of influenza A genome assembly during viral replication using point process models and fluorescence in situ hybridization.

Authors:  Timothy D Majarian; Robert F Murphy; Seema S Lakdawala
Journal:  PLoS Comput Biol       Date:  2019-01-28       Impact factor: 4.475

  3 in total

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