Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-26T03:01:35.479Z Has data issue: false hasContentIssue false
  • English
  • Français

Characterization of the Distance Relationship Between Localized Serotonin Receptors and Glia Cells on Fluorescence Microscopy Images of Brain Tissue

Published online by Cambridge University Press:  15 July 2015

Jaroslaw Jacak ,
Susanne Schaller ,
Daniela Borgmann  and
Stephan M. Winkler
Jaroslaw Jacak
Affiliation:
Department of Medical Engineering, University of Applied Sciences Upper Austria, Garnisonstraße 21, 4020 Linz, Austria Institute of Applied Physics, Johannes Kepler University Linz, Altenberger Straße 69, 4040 Linz, Austria
Susanne Schaller
Affiliation:
Bioinformatics Research Group, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria
Daniela Borgmann
Affiliation:
Bioinformatics Research Group, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria
Stephan M. Winkler*
Affiliation:
Bioinformatics Research Group, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria
*
*Corresponding author.[email protected]
Get access

Abstract

We here present two new methods for the characterization of fluorescent localization microscopy images obtained from immunostained brain tissue sections. Direct stochastic optical reconstruction microscopy images of 5-HT1A serotonin receptors and glial fibrillary acidic proteins in healthy cryopreserved brain tissues are analyzed. In detail, we here present two image processing methods for characterizing differences in receptor distribution on glial cells and their distribution on neural cells: One variant relies on skeleton extraction and adaptive thresholding, the other on k-means based discrete layer segmentation. Experimental results show that both methods can be applied for distinguishing classes of images with respect to serotonin receptor distribution. Quantification of nanoscopic changes in relative protein expression on particular cell types can be used to analyze degeneration in tissues caused by diseases or medical treatment.

Keywords

super-resolution microscopyimage processingserotonin receptorsbrain tissuespatial relationshipclustering
Type
Biological Applications and Techniques
Information
Microscopy and Microanalysis , Volume 21 , Supplement 4 , August 2015 , pp. 826 - 836
Copyright
© Microscopy Society of America 2015 

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.)

References

Albert, P.R. & Lemonde, S. (2004). 5-HT1A receptors, gene repression, and depression: Guilt by association. Neuroscientist 10(6), 575–593.Google Scholar
Arcelli, C. & Sanniti di Baja, G. (1993). Euclidean skeleton via centre-of-maximal-disc extraction. Image Vision Comput 11(3), 163–173.Google Scholar
Babcock, H., Sigal, Y.M. & Zhuang, X. (2012). A high-density 3D localization algorithm for stochastic optical reconstruction microscopy. Optical Nanosc 1, 6.Google Scholar
Bao, M., Guo, S., Tang, Q. & Zhang, F. (2009). Optimization of the bwmorph Function in the MATLAB Image Processing Toolbox for Binary Skeleton Computation. International Conference on Computational Intelligence and Natural Computing (CINC '09), Vol. 2.Google Scholar
Baune, B.T., Hohoff, C., Mortensen, L.S., Deckert, J., Arolt, V. & Domschke, K. (2008). Serotonin transporter polymorphism (5-HTTLPR) association with melancholic depression: A female specific effect? Depress Anxiety 25(11), 920–925.CrossRefGoogle ScholarPubMed
Benjamini, Y. & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. J Royal Statistical Soc 57(1), 289–300.Google Scholar
Betzig, E., Patterson, G.H., Sougrat, R., Lindwasser, O.W., Olenych, S., Bonifacino, J.S., Davidson, M.W., Lippincott-Schwartz, J. & Hess, H.F. (2006). Imaging intracellular fluorescent proteins at nanometer resolution. Science 313(5793), 1642–1645.Google Scholar
Cho, W., Lam, K. & Siu, W. (2003). Extraction of the Euclidean skeleton based on a connectivity criterion. Image Vision Comput 36(3), 721–729.Google Scholar
Dani, A., Huang, B., Bergan, J., Dulac, C. & Zhuang, X. (2010). Superresolution imaging of chemical synapses in the brain. Neuron 68(5), 843–856.Google Scholar
Davies, D.L. & Bouldin, D.W. (1979). A Cluster Separation Measure. IEEE Trans Pattern Anal Mach Intell 1(2), 224–227.Google Scholar
Epanechnikov, V.A. (1969). Non-parametric estimation of a multivariate probability density. Theory Probability Applications 14, 153–158.Google Scholar
Haralick, R.M. & Shapiro, L.G. (1993). Computer and Robot Vision. Addison-Wesley, 650pp.Google Scholar
Hess, S.T., Girirajan, T.P. & Mason, M.D. (2006). Ultra-high resolution imaging by fluorescence photoactivation localization microscopy. Biophys J 91(11), 4258–4272.Google Scholar
Hesse, J., Jacak, J., Kasper, M., Regl, G., Eichberger, T., Winklmayr, M., Aberger, F., Sonnleitner, M., Schlapak, R., Howorka, S., Muresan, L., Frischauf, A.M. & Schütz, G.J. (2006). RNA expression profiling at the single molecule level. Genome Res 16(8), 1041–1045.CrossRefGoogle ScholarPubMed
Huang, F., Hartwich, T.M., Rivera-Molina, F.E., Lin, Y., Duim, W.C., Long, J.J., Uchil, P.D., Myers, J.R., Baird, M.A., Mothes, W., Davidson, M.W., Toomre, D. & Bewersdorf, J. (2013). Video-rate nanoscopy using sCMOS camera-specific single-molecule localization algorithms. Nat Methods 10(7), 653–658.Google Scholar
Jacak, J., Schnidar, H., Muresan, L., Regl, G., Frischauf, A., Aberger, F., Schütz, G.J. & Hesse, J. (2013). Expression analysis of multiple myeloma CD138 negative progenitor cells using single molecule microarray readout. J Biotechnol 164(4), 525–530.Google Scholar
Jaccard, P. (1901). Etude comparative de la distribution flora le dans une portion des Alpes et des Jura. Bull Soc Vaud Sci Naturel 37(1), 547–579.Google Scholar
Kay, K.R., Smith, C., Wright, A.K., Serrano-Pozo, A., Pooler, A.M., Koffie, R., Bastin, M.E., Bak, T.H., Abrahams, S., Kopeikina, K.J., McGuone, D., Frosch, M.P., Gillingwater, T.H., Hyman, B.T. & Spires-Jones, T.L. (2013). Studying synapses in human brain with array tomography and electron microscopy. Nat Protoc 8(7), 1366–1380.Google Scholar
Kechkar, A., Nair, D., Heilemann, M., Choquet, D. & Sibarita, J.B. (2013). Real-time analysis and visualization for single-molecule based super-resolution microscopy. PLoS One 8(4), e62918.CrossRefGoogle ScholarPubMed
Kong, T.Y. & Rosenfeld, A. (1996). Topological Algorithms for Digital Image Processing. Elsevier Science Inc., 302pp.Google Scholar
Lakadamyali, M. (2014). Super-resolution microscopy: Going live and going fast. Chemphyschem 15(4), 630–636.Google Scholar
Lam, L., Lee, S.W. & Suen, C.Y. (1992). Thinning methodologies – A Comprehensive survey. IEEE Trans Pattern Anal Mach Intell 14(9), 879.Google Scholar
Lambe, E.K., Fillman, S.G., Webster, M.J. & Shannon Weickert, C. (2011). Serotonin receptor expression in human prefrontal cortex: Balancing excitation and inhibition across postnatal development. PLoS One 6(7), e22799.Google Scholar
Mortensen, K.I., Churchman, L.S., Spudich, J.A. & Flyvbjerg, H. (2010). Optimized localization analysis for single-molecule tracking and super-resolution microscopy. Nat Methods 7(5), 377–381.Google Scholar
Muresan, L., Jacak, J., Klement, E.P., Hesse, J. & Schütz, G.J. (2010). Microarray analysis at single-molecule resolution. IEEE Trans Nanobiosci 9(1), 51–58.Google Scholar
Nanguneri, S., Flottmann, B., Horstmann, H., Heilemann, M. & Kuner, T. (2012). Three-dimensional, tomographic super-resolution fluorescence imaging of serially Sectioned thick samples. PLoS One 7(5), e38098.CrossRefGoogle ScholarPubMed
Olivo-Marin, J.C. (2002). Extraction of spots in biological images using multiscale products. Pattern Recognit 35(9), 1989–1996.CrossRefGoogle Scholar
Otsu, N. (1975). A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1), 62–66.Google Scholar
Parzen, E. (1962). On estimation of a probability density function and mode. Ann Mathematical Stat 33(3), 1065.Google Scholar
Pratt, W.K. (1991). Digital Image Processing: PIKS Inside, 3rd edition. John Wiley & Sons.Google Scholar
Qian, H., Sheetz, M.P. & Elson, E.L. (1991). Single particle tracking. Analysis of diffusion and flow in two-dimensional systems. Biophys J 60(4), 910–921.Google Scholar
Quan, T., Li, P., Long, F., Zeng, S., Luo, Q., Hedde, P.N., Nienhaus, G.U. & Huang, Z.L. (2010). Ultra-fast, high-precision image analysis for localization-based super resolution microscopy. Opt Express 18(11), 11867–11876.Google Scholar
Rees, E.J., Erdelyi, M., Pinotsi, D., Knight, A., Metcalf, D. & Kaminski, C.F. (2012). Blind assessment of localization microscope image resolution. Optical Nanosc 1(12), 1–10.CrossRefGoogle Scholar
Rust, M.J., Bates, M. & Zhuang, X. (2006). Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat Methods 3(10), 793–795.Google Scholar
Sams, M., Silye, R., Gohring, J., Muresan, L., Schilcher, K. & Jacak, J. (2014). Spatial cluster analysis of nanoscopically mapped serotonin receptors for classification of fixed brain tissue. J Biomed Opt 19(1), 011021.Google Scholar
Savitz, J., Lucki, I. & Drevets, W.C. (2009). 5-HT(1A) receptor function in major depressive disorder. Prog Neurobiol 88(1), 17–31.Google Scholar
Saxton, M.J. (1994). Single-particle tracking: models of directed transport. Biophys J 67(5), 2110–2119.Google Scholar
Schoen, I., Ries, J., Klotzsch, E., Ewers, H. & Vogel, V. (2011). Binding-activated localization microscopy of DNA structures. Nano Lett 11(9), 4008–4011.Google Scholar
Schütz, G.J., Schindler, H. & Schmidt, T. (1997). Single-molecule microscopy on model membranes reveals anomalous diffusion. Biophys J 73(2), 1073–1080.Google Scholar
Shim, S.H., Xia, C., Zhong, G., Babcock, H.P., Vaughan, J.C., Huang, B., Wang, X., Xu, C., Bi, G.Q. & Zhuang, X. (2012). Super-resolution fluorescence imaging of organelles in live cells with photoswitchable membrane probes. Proc Natl Acad Sci USA 109(35), 13978–13983.Google Scholar
Shroff, H., Galbraith, C.G., Galbraith, J.A. & Betzig, E. (2008 a). Live-cell photoactivated localization microscopy of nanoscale adhesion dynamics. Nat Methods 5(5), 417–423.Google Scholar
Shroff, H., White, H. & Betzig, E. (2008 b). Photoactivated localization microscopy (PALM) of adhesion complexes, Chapter 4, Curr Protoc Cell Biol, Unit 4, p. 21.Google Scholar
Sinko, J., Kakonyi, R., Rees, E., Metcalf, D., Knight, A.E., Kaminski, C.F., Szabo, G. & Erdelyi, M. (2014). TestSTORM: Simulator for optimizing sample labeling and image acquisition in localization based super-resolution microscopy. Biomed Opt Express 5(3), 778–787.Google Scholar
Starck, J.L., Fadili, J. & Murtagh, F. (2007). The undecimated wavelet decomposition and its reconstruction. IEEE Trans Image Process 16(2), 297–309.Google Scholar
Thompson, R.E., Larson, D.R. & Webb, W.W. (2002). Precise nanometer localization analysis for individual fluorescent probes. Biophys J 82(5), 2775–2783.Google Scholar
Tokunaga, M., Imamoto, N. & Sakata-Sogawa, K. (2008). Highly inclined thin illumination enables clear single-molecule imaging in cells. Nat Methods 5(2), 159–161.Google Scholar
van de Linde, S., Sauer, M. & Heilemann, M. (2008). Subdiffraction-resolution fluorescence imaging of proteins in the mitochondrial inner membrane with photoswitchable fluorophores. J Struct Biol 164(3), 250–254.Google Scholar
Wieser, S. & Schütz, G.J. (2008). Tracking single molecules in the live cell plasma membrane – Do’s and Don’t’s. Methods 46(2), 131–140.Google Scholar
Supplementary material: Image

Jacak supplementary material

Figure S1

Download Jacak supplementary material(Image)
Image 22.7 MB
Supplementary material: Image

Jacak supplementary material

Figure S2

Download Jacak supplementary material(Image)
Image 1.2 MB