Sub-cellular feature detection and automated extraction of collocalized actin and myosin regions

We describe a new distance-based metric to measure the strength of collocalization in multi-color microscopy images for user-selected regions. This metric helps to standardize, objectify, quantify, and even automate light microscopy observations. Our new algorithm uses this metric to automatically identify and annotate a donut shaped actomyosin stress fiber bundle evident in vascular smooth muscle cells on certain types of surfaces. Both the metric and the algorithm have been implemented as an open source plugin for the popular ImageJ toolkit. They are available for download at http://code.google.com/p/actin-myosin-plugin/. Using cells stained for the cytoskeletal proteins actin and myosin, we show how characteristics of the identified stress fiber bundle are indicative of the kind of surface the cell is placed upon, and prove that weak spots in this structure are correlated with local membrane extensions. Given the relationship between membrane extension, cell migration, vascular disease, embryonic development, and cancer metastasis we provide that these tools to enable biological research that could improve our quality of life.

[1]  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..

[2]  Yelena Yesha,et al.  Image classification of vascular smooth muscle cells , 2010, IHI.

[3]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[4]  Ari Visa,et al.  Binary Histogram in Image Classification for Retrieval Purposes , 2003, WSCG.

[5]  Anne L Plant,et al.  Nanomechanical properties of thin films of type I collagen fibrils. , 2010, Langmuir : the ACS journal of surfaces and colloids.

[6]  Anant Madabhushi,et al.  Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[7]  John T Elliott,et al.  Comparison of reagents for shape analysis of fixed cells by automated fluorescence microscopy , 2003, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[8]  R. Eils,et al.  Computational imaging in cell biology , 2003, The Journal of cell biology.

[9]  Manuel Théry,et al.  Anisotropy of cell adhesive microenvironment governs cell internal organization and orientation of polarity , 2006, Proceedings of the National Academy of Sciences.

[10]  John T Elliott,et al.  Quantifying myosin light chain phosphorylation in single adherent cells with automated fluorescence microscopy , 2007, BMC Cell Biology.

[11]  John T Elliott,et al.  The stiffness of collagen fibrils influences vascular smooth muscle cell phenotype. , 2007, Biophysical journal.