Histochemistry and Cell Biology

, Volume 134, Issue 3, pp 307–317 | Cite as

Development of image analysis tool for the classification of muscle fibre type using immunohistochemical staining

  • Bruno Meunier
  • Brigitte Picard
  • Thierry Astruc
  • Roland Labas
Original Paper

Abstract

An accurate characterisation of muscle fibres is essential for studying muscle plasticity. During some transient events such as ageing, myogenesis, physical activity or conversion of muscle to meat, the morphological parameters and/or the fibre type distribution may change. Nowadays, this information is generally obtained using immunohistology techniques, but these analyses are acknowledged to be laborious and time-consuming. In fact, each myofibre, from thousands, must be measured individually and its expression profile in response to different anti-myosin antibodies must be established step by step. In this paper, we describe a new histological approach using double-labelling (laminin, myosin) serial sections, fluorescence microscopy visualisation and, finally, semi-automatic image analysis. The goal of the study was to propose a tool allowing faster fibre type characterisation, including the identification of hybrid fibres from pure ones. The steps in the image processing prone to subjectivity have been fully automated. On the other hand, the expert retained control of all image analysis procedures requiring visual diagnosis. The tool that we developed with the Visilog software allowed a rapid and objective fibre typing and morphometric characterisation of two different bovine muscles. The results were in agreement with our previous histological and densitometric assays. The method and the tool proved to be potentially more efficient than other techniques used in our institute or described in the literature. A more global evaluation will be considered in other laboratories as well as on other animal species.

Keywords

Muscle fibre types Image analysis Myosin heavy chain Immunohistochemistry 

References

  1. Brooke MH, Kaiser KK (1970) Muscle fiber types: how many and what kind? Arch Neurol 23:369–379PubMedGoogle Scholar
  2. Buche P, Mauron D (1997) Quantitative characterization of muscle fiber by image analysis. Comput Electron Agr 16:189–217CrossRefGoogle Scholar
  3. Cerisuelo A, Sala R, Nürnberg G, Baucells M, Rehfeldt C (2007) How many muscle samples are required to obtain reliable estimations of muscle fibre characteristics from pig longissimus muscle? Meat Sci 76:583–587CrossRefGoogle Scholar
  4. Duris MP, Picard B, Geay Y (2000) Specificity of different anti-myosin heavy chain antibodies in bovine muscle. Meat Sci 55:67–78CrossRefGoogle Scholar
  5. Guth L, Samaha FJ (1969) Qualitative differences between actomyosin ATPase of slow and fast mammalian muscle. Exp Neurol 25:138–152CrossRefPubMedGoogle Scholar
  6. Hemmings KM, Parr T, Daniel ZCTR, Picard B, Buttery PJ, Brameld JM (2009) Examination of myosin heavy chain isoform expression in ovine skeletal muscles. J Anim Sci 87:3915–3922CrossRefPubMedGoogle Scholar
  7. Henckel P, Ducro B, Oksbjerg N, Hassing L (1998) Objectivity of two methods of differentiating fibre types and repeatability of measurements by application of the TEMA image analysing system. Eur J Histochem 42:49–62PubMedGoogle Scholar
  8. Jurie C, Picard B, Hocquette JF, Dransfield E, Micol D, Listrat A (2007) Muscle and meat quality characteristics of Holstein and Salers cull cows. Meat Sci 77:459–466CrossRefGoogle Scholar
  9. Karen P, Stevanec M, Smerdu V, Cvetko E, Kubínová L, Erzen I (2009) Software for muscle fibre type classification and analysis. Eur J Histochem 53:87–95PubMedGoogle Scholar
  10. Lefaucheur L, Buche P, Ecolan P, Lemoing M (1992) Classification of pig myofibres and assessment of post-mortem glycogen depletion according to fibre type by computerized image analysis. Meat Sci 32:267–278CrossRefGoogle Scholar
  11. Listrat A, Picard B, Jailler R, Collignon H, Peccatte JR, Micol D, Geay Y, Dozias D (2001) Grass valorisation and muscular characteristics of blonde d’Aquitaine steers. Anim Res 50:105–118CrossRefGoogle Scholar
  12. Luthon F, Liévin M, Faux F (2004) On the use of entropy power for threshold selection. Signal Process 84:1789–1804CrossRefGoogle Scholar
  13. Miller GR, Stauber WT (1994) Use of computer-assisted analysis for myofiber size measurements of rat soleus muscles from photographed images. J Histochem Cytochem 42:377–382PubMedGoogle Scholar
  14. Model MA, Burkhardt JK (2001) A standard for calibration and shading correction of a fluorescence microscope. Cytometry 44:309–316CrossRefPubMedGoogle Scholar
  15. Noesis (2010) Visilog 6 Programming Guide. http://www.noesis.fr/fr/download.html. Accessed 26 April 2010
  16. Peter JB, Barnard RJ, Edgerton UR, Gillespie A, Stempel KE (1972) Metabolic profiles of three fibre types of skeletal muscle in guinea pigs and rabbits. Biochemistry 11:2627–2633CrossRefPubMedGoogle Scholar
  17. Pette D, Staron RS (1990) Cellular and molecular diversities of mammalian skeletal muscle fibers. Rev Physiol Biochem Pharmacol 116:1–76PubMedGoogle Scholar
  18. Picard B, Cassar-Malek I (2009) Evidence for expression of IIb myosin heavy chain isoform in some skeletal muscles of Blonde d’Aquitaine bulls. Meat Sci 82:30–36CrossRefGoogle Scholar
  19. Picard B, Duris MP, Jurie C (1998) Classification of bovine muscle fibres by different histochemical techniques. Histochem J 30:473–479CrossRefPubMedGoogle Scholar
  20. Raats JMH, Hof D (2005) Recombinant antibody expression vectors enabling double and triple immunostaining of tissue culture cells using monoclonal antibodies. Eur J Cell Biol 84:517–521CrossRefPubMedGoogle Scholar
  21. Raheem O, Huovinen S, Suominen T, Haapasalo H, Udd B (2010) Novel myosin heavy chain immunohistochemical double staining developed for the routine diagnostic separation of I, IIA and IIX fibers. Acta Neuropathol 119:495–500CrossRefPubMedGoogle Scholar
  22. Rivero JLL, Talmadge RJ, Edgerton VR (1997) A sensitive electrophoretic method for the quantification of myosin heavy chain isoforms in horse skeletal muscle: Histochemical and immunocytochemical verifications. Electrophoresis 18:1967–1972CrossRefPubMedGoogle Scholar
  23. Sabri S, Richelme F, Pierres A, Benoliel AM, Bongrand P (1997) Interest of image processing in cell biology and immunology. J Immunol Methods 208:1–27CrossRefPubMedGoogle Scholar
  24. Schiaffino S, Gorza L, Sartore S, Saggin L, Ausoni S, Vianello M, Gundersen K, Lomo T (1989) Three myosin heavy chain isoforms in type 2 skeletal muscle fibres. J Muscle Res Cell Motil 10:197–205CrossRefPubMedGoogle Scholar
  25. Sifre L, Berge P, Engel E, Martin JF, Bonny JM, Listrat A, Taylor R, Culioli J (2005) Influence of the spatial organization of the perimysium on beef tenderness. J Agric Food Chem 53:8390–8399CrossRefPubMedGoogle Scholar
  26. Tomazevic D, Likar B, Pernus F (2002) Comparative evaluation of retrospective shading correction methods. J Microsc 208:212–223CrossRefPubMedGoogle Scholar
  27. Verveer PJ, Bastiaens PIH (2008) Quantitative microscopy and systems biology: seeing the whole picture. Histochem Cell Biol 130:833–843CrossRefPubMedGoogle Scholar
  28. Zitová B, Flusser J (2003) Image registration methods: a survey. Image Vision Comput 21:977–1000CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Bruno Meunier
    • 1
  • Brigitte Picard
    • 1
  • Thierry Astruc
    • 2
  • Roland Labas
    • 2
  1. 1.INRA, UR1213 HerbivoresSaint Genès ChampanelleFrance
  2. 2.INRA, UR370 Qualité des Produits AnimauxSaint Genès ChampanelleFrance

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