Hostname: page-component-5c6d5d7d68-xq9c7 Total loading time: 0 Render date: 2024-08-14T22:46:26.224Z Has data issue: false hasContentIssue false
  • English
  • Français

Automatic Counting of Intra-Cellular Ribonucleo-Protein Aggregates in Saccharomyces cerevisiae Using a Textural Approach

Published online by Cambridge University Press:  13 February 2019

Ambroise Marin ,
Emmanuel Denimal ,
Lucie Bertheau ,
Stéphane Guyot ,
Ludovic Journaux  and
Paul Molin
Ambroise Marin*
Affiliation:
Agrosup, 26 boulevard docteur, petitjean, Dijon, Bourgogne, 21000, France
Emmanuel Denimal
Affiliation:
Agrosup, 26 boulevard docteur, petitjean, Dijon, Bourgogne, 21000, France
Lucie Bertheau
Affiliation:
Agrosup, 26 boulevard docteur, petitjean, Dijon, Bourgogne, 21000, France
Stéphane Guyot
Affiliation:
UMR A 02.102 Procédés Alimentaires et Microbiologiques, équipe Procédés Microbiologiques et Biotechnologiques, Agrosup Dijon/Université de Bourgogne, 1, esplanade Erasme, Dijon, Bourgogne 21000, France
Ludovic Journaux
Affiliation:
Agrosup, 26 boulevard docteur, petitjean, Dijon, Bourgogne, 21000, France Laboratoire d'Informatique de Bourgogne, EA7534, Université de Bourgogne, UFR Sciences et Techniques, allée Alain Savary, Dijon, Bourgogne, 21000, France
Paul Molin
Affiliation:
UMR A 02.102 Procédés Alimentaires et Microbiologiques, équipe Procédés Microbiologiques et Biotechnologiques, Agrosup Dijon/Université de Bourgogne, 1, esplanade Erasme, Dijon, Bourgogne, France
*
*Author for correspondence: Ambroise Marin, E-mail: ambroise.marin@agrosupdijon.fr
Get access

Abstract

In the context of microbiology, recent studies show the importance of ribonucleo-protein aggregates (RNPs) for the understanding of mechanisms involved in cell responses to specific environmental conditions. The assembly and disassembly of aggregates is a dynamic process, the characterization of the stage of their evolution can be performed by the evaluation of their number. The aim of this study is to propose a method to automatically determine the count of RNPs. We show that the determination of a precise count is an issue by itself and hence, we propose three textural approaches: a classical point of view using Haralick features, a frequency point of view with generalized Fourier descriptors, and a structural point of view with Zernike moment descriptors (ZMD). These parameters are then used as inputs for a supervised classification in order to determine the most relevant. An experiment using a specific Saccharomyces cerevisiae strain presenting a fusion between a protein found in RNPs (PAB1) and the green fluorescent protein was performed to benchmark this approach. The fluorescence was observed with two-photon fluorescence microscopy. Results show that the textural approach, by mixing ZMD with Haralick features, allows for the characterization of the number of RNPs.

Keywords

Generalized Fourier descriptorsHaralickribonucleo-protein aggregatessub-cellular structuresZernike
Type
Biological Science Applications
Information
Microscopy and Microanalysis , Volume 25 , Issue 1 , February 2019 , pp. 164 - 179
Copyright
Copyright © Microscopy Society of America 2019 

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

Bishop, CM (1995). Neural Networks for Pattern Recognition. Oxford: Oxford University Press.Google Scholar
Buchan, JR, Yoon, J-H & Parker, R (2010). Stress-specific composition, assembly and kinetics of stress granules in Saccharomyces cerevisiae. J Cell Sci 15, 228–39.Google Scholar
Cunningham, P & Delany, SJ (2007). k-Nearest neighbour classifiers. Mult Classifier Syst 34, 1–17.Google Scholar
Denimal, E, Marin, A, Guyot, S, Journaux, L & Molin, P (2017). Automatic biological cell counting using a modified gradient Hough transform. Microsc Microanal 23, 11–21.Google Scholar
Duda, RO, Hart, PE & Stork, DG (2001). Pattern Classification, 2nd ed. New York: John Wiley & Sons.Google Scholar
Fleet, BD, Yan, J, Knoester, DB, Yao, M, Deller, JR & Goodman, ED (2014). Breast cancer detection using Haralick features of images reconstructed from ultra wideband microwave scans. In Workshop on Clinical Image-Based Procedures, pp. 9–16. Springer.Google Scholar
Gietz, RD, Schiestl, RH, Willems, AR & Woods, RA (1995). Studies on the transformation of intact yeast cells by the LiAc/SS-DNA/PEG procedure. Yeast 11, 355–360.Google Scholar
Groušl, T, Ivanov, P, Frydlová, I, Vašicová, P, Janda, F, Vojtová, J, Malínská, K, Malcová, I, Nováková, L, Janošková, D, Valášek, L & Hašek, J (2009). Robust heat shock induces eIF2α-phosphorylation-independent assembly of stress granules containing eIF3 and 40S ribosomal subunits in budding yeast, Saccharomyces cerevisiae. J Cell Sci 122, 2078–2088.Google Scholar
Guyot, S, Gervais, P, Young, M, Winckler, P, Dumont, J & Davey, HM (2015). Surviving the heat: Heterogeneity of response in Saccharomyces cerevisiae provides insight into thermal damage to the membrane. Environ Microbiol 17, 2982–2992.Google Scholar
Haralick, RM (1979). Statistical and structural approaches to texture. Proc IEEE 67, 786–804.Google Scholar
Kovesi, P (1999). Phase preserving denoising of images. In Proceeding's of the 5th International/National Biennial Conference on Digital Image Computing, Techniques and Applications, The Australian Pattern Recognition Society Conference, pp. 212–217. Perth, Australia.Google Scholar
McLachlan, G (2004). Discriminant Analysis and Statistical Pattern Recognition. New York: John Wiley & Sons.Google Scholar
Otsu, N (1979). A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern B Cybern 9, 62–66.Google Scholar
Protter, DS & Parker, R (2016). Principles and properties of stress granules. Trends Cell Biol 26, 668–679.Google Scholar
Quinlan, JR (1986). Induction of decision trees. Mach Learn 1, 81–106.Google Scholar
Ryu, S-J, Kirchner, M, Lee, M-J & Lee, H-K (2013). Rotation invariant localization of duplicated image regions based on Zernike moments. IEEE Trans Inf Forensics Secur 8, 1355–1370.Google Scholar
Saporta, G. (2006). Probabilités, analyse des données et statistique. Paris: Editions Technip.Google Scholar
Smach, F, Lemaître, C, Gauthier, J-P, Miteran, J & Atri, M (2008). Generalized Fourier descriptors with applications to objects recognition in SVM context. J Math Imaging Vis 30, 43–71.Google Scholar
Vapnik, V (1998). Statistical Learning Theory. New York: Wiley.Google Scholar
Vincent, L & Soille, P (1991). Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13, 583–598.Google Scholar