Digital processing of in situ hybridization images: identification and spatial allocation of specific labels
Keywords:Segmentation, Digital image processing, In situ hybridization, Developmental Biology
In situ hybridization (ISH) method allows to reveal specific genes expression, identify specific cell types and detect areas or tissues, displaying differential gene expression. This work describes a standardized procedure of digital image processing that allows detailed analyses of ISH preparations. We have developed a software that allows through a graphical interface (a) to reliably identify and quantify ISH labels, (b) to locate each label within the image reference system (c) to assemble the total series of images obtained from a complete histological sections of a biological structure, and d) to locate all the labels within a unique reference system that corresponds to the complete biological structure. As each ISH label is positioned within a spatial coordinates system coinciding with an intrinsic biological reference axis, this software can be used to analyze the spatial pattern of distribution of specific genes expression during the embryonic development. The software allows the construction of numerical space series that can be used to analyze the variability and the dynamics of genes expression as a function of space and/or time during the embryonic development.
 D. J. Rodrıguez Gil, M.Vacotto, M. Rapacioli, G.Scicolone, V. Flores and S. Fiszer de Plazas. “Development and Localisation of GABAA Receptor a1, a2, b2 and c2 Subunit mRNA in the Chick Optic Tectum”. Journal of Neuroscience Research, Vol. 81, 2005, pp. 469–480
 W. H. Schuette, S. Chen, S. J. Occhipinti, H. S. Mujagic, S. E. Shackney, “Automated radioautographic grain counting”, Cell Tissue Kinet, Vol. 3, 1983, pp. 221-7.
 M. Masseroli, A. Bollea, C. Bendotti, G. Forloni, “In situ hybridization histochemistry quantification: automatic count on single cell in digital image”, J Neurosci Methods, Vol. 2, 1993, pp. 93-103.
 R. Mize, C. Thouron., L. Lucas, R. Harlan, “Semiautomatic image analysis for grain counting in situ hybridization experiments”, Neuroimage, Vol. 3, 1994, pp. 163-172.
 W. Stolz, K. Scharffetter, W. Abmayr, W. Koditz, T. Krieg, "An automatic analysis method for in situ hybridization using high-resolution image analysis”, Arch Dermatol Res., Vol. 5, 1989, pp. 336-341.
 A. Ambesi-Impiombato, G. D'Urso, G. Muscettola, A. de Bartolomeis, “Method for quantitative in situ hybridization histochemistry and image analysis applied for Homer1a gene expression in rat brain”, Brain Res Brain Res Protoc., Vol. 3, , 2003, pp. 189-196.
 W. Zhao, J. Truettner, R. Schmidt-Kastner, L. Belayev, M. D Ginsberg, “Quantitation of multiple gene expression by in situ hybridization autoradiography: accurate normalization using Bayes classifier.”., J Neurosci Methods, Vol. 1, 1999, pp. 63-70.
 H.M. Deitel, P.J. Deitel, Como programar en C++, Prentice hall, 1994.
 C. Russ, The Image Processing Handbook, CRC Press, 1992.
 R. González, R. Woods, Tratamiento Digital de imágenes, Addison Wesley, 1996.