Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-24T07:19:25.249Z Has data issue: false hasContentIssue false

Effects of Noninhibitory Serpin Maspin on the Actin Cytoskeleton: A Quantitative Image Modeling Approach

Published online by Cambridge University Press:  24 February 2016

Mohammed Al-Mamun
Affiliation:
Computational Intelligence Group, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK Department of Population Medicine & Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14850, USA
Lorna Ravenhill
Affiliation:
School of Biological Sciences, University of East Anglia, Norwich, Norfolk, NR4 7TJ, UK
Worawut Srisukkham
Affiliation:
Computational Intelligence Group, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
Alamgir Hossain
Affiliation:
Computational Intelligence Group, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK Anglia Ruskin IT Research Institute (ARITI), Anglia Ruskin University, Cambridge CB1 1PT, UK
Charles Fall
Affiliation:
Computational Intelligence Group, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
Vincent Ellis
Affiliation:
School of Biological Sciences, University of East Anglia, Norwich, Norfolk, NR4 7TJ, UK
Rosemary Bass*
Affiliation:
Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
*
*Corresponding author. [email protected]
Get access

Abstract

Recent developments in quantitative image analysis allow us to interrogate confocal microscopy images to answer biological questions. Clumped and layered cell nuclei and cytoplasm in confocal images challenges the ability to identify subcellular compartments. To date, there is no perfect image analysis method to identify cytoskeletal changes in confocal images. Here, we present a multidisciplinary study where an image analysis model was developed to allow quantitative measurements of changes in the cytoskeleton of cells with different maspin exposure. Maspin, a noninhibitory serpin influences cell migration, adhesion, invasion, proliferation, and apoptosis in ways that are consistent with its identification as a tumor metastasis suppressor. Using different cell types, we tested the hypothesis that reduction in cell migration by maspin would be reflected in the architecture of the actin cytoskeleton. A hybrid marker-controlled watershed segmentation technique was used to segment the nuclei, cytoplasm, and ruffling regions before measuring cytoskeletal changes. This was informed by immunohistochemical staining of cells transfected stably or transiently with maspin proteins, or with added bioactive peptides or protein. Image analysis results showed that the effects of maspin were mirrored by effects on cell architecture, in a way that could be described quantitatively.

Type
Biological Applications
Copyright
© Microscopy Society of America 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

a

These authors contributed equally.

References

Al-Mamun, M.A., Brown, L.J., Hossain, M.A., Fall, C., Wagstaff, L. & Bass, R. (2013). A hybrid computational model for the effects of maspin on cancer cell dynamics. J Theor Biol 337, 150160.CrossRefGoogle ScholarPubMed
Bass, R., Moreno Fernandez, A.M. & Ellis, V. (2002). Maspin inhibits cell migration in the absence of protease inhibitory activity. J Biol Chem 277(49), 4684546848.CrossRefGoogle Scholar
Bass, R., Wagstaff, L., Ravenhill, L. & Ellis, V. (2009). Binding of extracellular maspin to beta1 integrins inhibits vascular smooth muscle cell migration. J Biol Chem 284(40), 2771227720.CrossRefGoogle ScholarPubMed
Bernardo, V., Lourenço, S.Q., Cruz, R., Monteiro-Leal, L.H., Silva, L.E., Camisasca, D.R., Farina, M. & Lins, U. (2009). Reproducibility of immunostaining quantification and description of a new digital image processing procedure for quantitative evaluation of immunohistochemistry in pathology. Microsc Microanal 15(4), 353365.CrossRefGoogle Scholar
Bodenstine, T.M., Seftor, R.E., Khalkhali-Ellis, Z., Seftor, E.A., Pemberton, P.A. & Hendrix, M.J. (2012). Maspin: Molecular mechanisms and therapeutic implications. Cancer Metastasis Rev 31(3–4), 529551.CrossRefGoogle ScholarPubMed
Breu, H., Gil, J., Kirkpatrick, D. & Werman, M. (1995). Linear time Euclidean distance transform algorithms. IEEE Trans Pattern Anal Mach Intell 17, 529533.CrossRefGoogle Scholar
Cates, J.E., Whitaker, R.T. & Jones, G.M. (2005). Case study: an evaluation of user-assisted hierarchical watershed segmentation. Med Image Anal 9(6), 566578.CrossRefGoogle ScholarPubMed
Cella, N., Contreras, A., Latha, K., Rosen, J.M. & Zhang, M. (2006). Maspin is physically associated with (beta)1 integrin regulating cell adhesion in mammary epithelial cells. FASEB J 20(9), 15101512.CrossRefGoogle ScholarPubMed
Chen, E.I., Florens, L., Axelrod, F.T., Monosov, E., Barbas, C.F. III, Yates, J.R. III, Felding-Habermann, B. & Smith, J.W. (2005). Maspin alters the carcinoma proteome. FASEB J 19, 11231124.CrossRefGoogle ScholarPubMed
Cheng, J. & Rajapakse, J.C. (2009). Segmentation of clustered nuclei with shape markers and marking function. IEEE Trans Biomed Eng 56(3), 741748.CrossRefGoogle ScholarPubMed
Deshpande, M., Notari, L., Subramanian, P., Notario, V. & Becerra, S.P. (2012). Inhibition of tumor cell surface ATP synthesis by pigment epithelium-derived factor: implications for antitumor activity. Int J Oncol 41(1), 219227.Google ScholarPubMed
Fatima, M.M. & Seenivasagam, V. (2011). A fast fuzzy-c means based marker controlled watershed segmentation of clustered nuclei. In Proceedings of International Conference on Computer, Communication and Electrical Technology, 15–17 September 2011, Adriatic Islands, Split, Croatia.Google Scholar
Fouard, C. & Gedda, M. (2006). An objective comparison between grey weighted distance transforms and weighted distance transforms on curved spaces. In Discrete Geometry for Computer Imagery, Hutchison, D., Kanade, T., Kittler, J., et al. (Eds.), pp. 259–270. Lecture Notes in Computer Science, Springer.CrossRefGoogle Scholar
Fuseler, J.W., Bedenbaugh, A., Yekkala, K. & Baudino, T.A. (2010). Fractal and image analysis of the microvasculature in normal intestinal submucosa and intestinal polyps in Apc(Min/+) mice. Microsc Microanal 16(1), 7379.CrossRefGoogle ScholarPubMed
Fuseler, J.W., Millette, C.F., Davis, J.M. & Carver, W. (2007). Fractal and image analysis of morphological changes in the actin cytoskeleton of neonatal cardiac fibroblasts in response to mechanical stretch. Microsc Microanal 13, 133143.CrossRefGoogle ScholarPubMed
Ghosh, M., Das, D., Chakraborty, C. & Ray, A.K. (2010). Automated leukocyte recognition using fuzzy divergence. Micron 41, 840846.CrossRefGoogle ScholarPubMed
Gonzalez, R.C. & Woods, R.E. (1992). Digital imaging processing, Massachusetts: Addison-Wesley.Google Scholar
Jordan, P.A. & Gibbins, J.M. (2006). Extracellular disulfide exchange and the regulation of cellular function. Antioxid Redox Signal 8(3–4), 312324.CrossRefGoogle ScholarPubMed
Jung, C. & Kim, C. (2010). Segmenting clustered nuclei using h-minima transform-based marker extraction and contour parameterization. IEEE Trans Biomed Eng 57(10), 26002604.CrossRefGoogle ScholarPubMed
Ko, B.C., Gim, J. & Nam, J. (2011). Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake. Micron 42, 695705.CrossRefGoogle ScholarPubMed
Koyuncu, C.F., Arslan, S., Durmaz, I., Cetin-Atalay, R. & Gunduz-Demir, C. (2012). Smart markers for watershed-based cell segmentation. PLoS One 7(11), e48664.CrossRefGoogle ScholarPubMed
Lara, H., Wang, Y., Beltran, A.S., Juárez-Moreno, K., Yuan, X., Kato, S., Leisewitz, A.V., Cuello Fredes, M., Licea, A.F., Connolly, D.C., Huang, L. & Blancafort, P. (2012). Targeting serous epithelial ovarian cancer with designer zinc finger transcription factors. J Biol Chem 287(35), 2987329886.CrossRefGoogle ScholarPubMed
Liao, X.H., Li, Y.Q., Wang, N., Zheng, L., Xing, W.J., Zhao, D.W., Yan, T.B., Wang, Y., Liu, L.Y., Sun, X.G., Hu, P., Zhou, H. & Zhang, T.C. (2014). Re-expression and epigenetic modification of maspin induced apoptosis in MCF-7 cells mediated by myocardin. Cell Signal 26(6), 13351346.CrossRefGoogle ScholarPubMed
Lindblad, J., Wählby, C., Bengtsson, E. & Zaltsman, A. (2004). Image analysis for automatic segmentation of cytoplasms and classification of Rac1 activation. Cytometry A 57(1), 2233.CrossRefGoogle ScholarPubMed
Meddens, M.B., Rieger, B., Figdor, C.G., Cambi, A. & van den Dries, K. (2013). Automated podosome identification and characterization in fluorescence microscopy images. Microsc Microanal 19(1), 180189.CrossRefGoogle ScholarPubMed
Mohapatra, S., Patra, D. & Satpathy, S. (2011). Automated leukemia detection in blood microscopic images using statistical texture analysis. In Proceedings of International Conference on Communication, Computing; Security, 12–14 February 2011, National Institute of Technology Rourkela, Odisha, India.CrossRefGoogle Scholar
Mueller, J.L., Harmany, Z.T., Mito, J.K., Kennedy, S.A., Kim, Y., Dodd, L., Geradts, J., Kirsch, D.G., Willett, R.M., Brown, J.Q. & Ramanujam, N. (2013). Quantitative segmentation of fluorescence microscopy images of heterogeneous tissue: Application to the detection of residual disease in tumor margins. PLoS One 8(6), e66198.CrossRefGoogle Scholar
Ng, H.P., Ong, S.H., Foong, K.W., Goh, P.S. & Nowinski, W.L. (2008). Masseter segmentation using an improved watershed algorithm with unsupervised classification. Comput Biol Med 38(2), 171184.CrossRefGoogle ScholarPubMed
Notari, L., Arakaki, N., Mueller, D., Meier, S., Amaral, J. & Becerra, S.P. (2010). Pigment epithelium-derived factor binds to cell-surface F(1)-ATP synthase. FEBS J 277(9), 21922205.CrossRefGoogle ScholarPubMed
Nyirenda, N., Farkas, D.L. & Ramanujan, V.K. (2011). Preclinical evaluation of nuclear morphometry and tissue topology for breast carcinoma detection and margin assessment. Breast Cancer Res Treat 126(2), 345354.CrossRefGoogle ScholarPubMed
Odero-Marah, V.A., Khalkhali-Ellis, Z., Chunthapong, J., Amir, S., Seftor, R.E., Seftor, E.A. & Hendrix, M.J. (2003). Maspin regulates different signaling pathways for motility and adhesion in aggressive breast cancer cells. Cancer Biol Ther 2, 398403.CrossRefGoogle ScholarPubMed
Pham, T.D. (2008). Fuzzy fractal analysis of molecular imaging data. Proc IEEE 96(8), 13321347.CrossRefGoogle Scholar
Plissiti, M.E., Nikou, C. & Charchanti, A. (2011). Combining shape, texture and intensity features for cell nuclei extraction in pap smear images. Pattern Recognit Lett 32, 838853.CrossRefGoogle Scholar
Qin, L. & Zhang, M. (2010). Maspin regulates endothelial cell adhesion and migration through an integrin signaling pathway. J Biol Chem 285(42), 3236032369.CrossRefGoogle ScholarPubMed
Ravenhill, L., Wagstaff, L., Edwards, D.R., Ellis, V. & Bass, R. (2010). The G-helix of maspin mediates effects on cell migration and adhesion. J Biol Chem 285(47), 3628536292.CrossRefGoogle ScholarPubMed
Ridley, A.J., Schwartz, M.A., Burridge, K., Firtel, R.A., Ginsberg, M.H., Borisy, G., Parsons, J.T. & Horwitz, A.R. (2003). Cell migration: Integrating signals from front to back. Science 302(5651), 17041709.CrossRefGoogle ScholarPubMed
Rosenfeld, A. & Pfaltz, J.L. (1968). Distance functions on digital pictures. Pattern Recognit 1(1), 3361.CrossRefGoogle Scholar
Schneider, C.A., Rasband, W.S. & Eliceiri, K.W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9(7), 671675.CrossRefGoogle ScholarPubMed
Seftor, R.E., Seftor, E.A., Sheng, S., Pemberton, P.A., Sager, R. & Hendrix, M.J. (1998). Maspin suppresses the invasive phenotype of human breast carcinoma. Cancer Res 58(24), 56815685.Google ScholarPubMed
Sheng, S., Carey, J., Seftor, E.A., Dias, L., Hendrix, M.J. & Sager, R. (1996). Maspin acts at the cell membrane to inhibit invasion and motility of mammary and prostatic cancer cells. Proc Natl Acad Sci USA 93(21), 1166911674.CrossRefGoogle ScholarPubMed
Shi, H.Y., Stafford, L.J., Liu, Z., Liu, M. & Zhang, M. (2007). Maspin controls mammary tumor cell migration through inhibiting Rac1 and Cdc42, but not the RhoA GTPase. Cell Motil Cytoskeleton 64(5), 338346.CrossRefGoogle Scholar
Shi, H.Y., Zhang, W., Liang, R., Abraham, S., Kittrell, F.S., Medina, D. & Zhang, M. (2001). Blocking tumor growth, invasion, and metastasis by maspin in a syngeneic breast cancer model. Cancer Res 61, 69456951.Google Scholar
Sinha, N. & Ramakrishnan, A.G. (2003). Automation of differential blood count. In Proceedings of TENCON Conference on Convergent Technologies for Asia-Pacific Region, 14–17 October 2003, Bangalore, India.CrossRefGoogle Scholar
Srisukkham, W., Lepcha, P., Hossain, M.A., Zhang, L., Jiang, R. & Lim, H.N. (2013). A mobile enabled intelligent scheme to identify blood cancer for remote areas—Cell membrane segmentation using marker controlled watershed segmentation phase. In The 7th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Chiang Mai, Thailand, December 18–20.Google Scholar
Sun, H.Q. & Luo, Y.J. (2009). Adaptive watershed segmentation of binary particle image. J Microsc 233(2), 326330.CrossRefGoogle ScholarPubMed
Teera-Umpon, N. (2005). Patch-based white blood cell nucleus segmentation using fuzzy clustering. ECTI Trans Electr Electron Commun 3, 510.Google Scholar
Teoh, S.S., Vieusseux, J., Prakash, M., Berkowicz, S., Luu, J., Bird, C.H., Law, R.H., Rosado, C., Price, J.T., Whisstock, J.C. & Bird, P.I. (2014). Maspin is not required for embryonic development or tumor suppression. Nat Commun 5, 3164.CrossRefGoogle Scholar
Wählby, C., Sintorn, I.M., Erlandsson, F., Borgefors, G. & Bengtsson, E. (2004). Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections. J Microsc 215(Pt 1), 6776.CrossRefGoogle ScholarPubMed
Wienert, S., Heim, D., Saeger, K., Stenzinger, A., Beil, M., Hufnagl, P., Dietel, M., Denkert, C. & Klauschen, F. (2011). Detection and segmentation of cell nuclei in virtual microscopy images: A minimum-model approach. Sci Rep 2, 503.CrossRefGoogle Scholar
Yang, L., Tuzel, O., Meer, P. & Foran, D. (2008). Automatic image analysis of histopathology specimens using concave vertex graph. In Proceedings of Medical Image Computing and Computer-Assisted Intervention, New York, NY, pp. 833–841.CrossRefGoogle Scholar
Yin, S., Li, X., Meng, Y., Finley, R.L. Jr, Sakr, W., Yang, H., Reddy, N. & Sheng, S. (2005). Tumor-suppressive maspin regulates cell response to oxidative stress by direct interaction with glutathione S-transferase. J Biol Chem 280(41), 3498534996.CrossRefGoogle ScholarPubMed
Zaritsky, A., Natan, S., Horev, J., Hecht, I., Wolf, L., Ben-Jacob, E. & Tsarfaty, I. (2011). Cell motility dynamics: A novel segmentation algorithm to quantify multi-cellular bright field microscopy images. PLoS One 6(11), e27593.CrossRefGoogle ScholarPubMed
Zimmer, C. & Olivo-Marin, J.C. (2005). Coupled parametric active contours. IEEE Trans Pattern Anal Mach Intell 27(11), 18381842.CrossRefGoogle ScholarPubMed
Zou, Z., Anisowicz, A., Hendrix, M.J., Thor, A., Neveu, M., Sheng, S., Rafidi, K., Seftor, E. & Sager, R. (1994). Maspin, a serpin with tumor-suppressing activity in human mammary epithelial cells. Science 263(5146), 526529.CrossRefGoogle ScholarPubMed
Zuiderveld, K. (1994). Contrast limited adaptive histogram equalization. In Graphics Gems IV , Heckbert, P.S. (Ed.), pp. 474485. Pittsburgh: Academic Press Professional, Inc.CrossRefGoogle Scholar
Supplementary material: Image

Al-Mamun supplementary material

Figure S1

Download Al-Mamun supplementary material(Image)
Image 243 KB