Abstract
Imaging applications often produce large numbers of data sets, which need to be processed in a uniform and unbiased manner to obtain precise information about the number and size of cells or cell densities in different regions of the brain. Machine learning is a novel method here introduced to adjust algorithms to the biological requirements and to evaluate cellular features of tissue samples in an automated manner. In this chapter we describe methods to prepare mouse brain tissue for subsequent image processing and data evaluation. We give information in a step-by-step manner how to choose and perform appropriate fixation protocols, decide for suitable sectioning, and give hints what to consider when performing immunofluorescence stainings. Furthermore, we introduce the Machine Learning-Based Image Segmentation (MLBIS) to determine neuronal cell density in brain slices.
Key words
- Machine learning
- Neuron density
- Mouse brain
- Microtome sectioning
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References
Kemper TL (1994) Neuroanatomical and neuropathological changes during aging and in dementia. In: Albert ML, Knoepfel EJE (eds) Clinical neurology of aging, 2nd edn. Oxford University Press, New York, pp 3–67
Salat DH, Buckner RL, Snyder AZ, Greve DN, Desikan RS, Busa E, Morris JC, Dale AM, Fischl B (2004) Thinning of the cerebral cortex in aging. Cereb Cortex 14:721–730
Freeman SH, Kandel R, Cruz L, Rozkalne A, Newell K, Frosch MP, Hedley-Whyte ET, Locascio JJ, Lipsitz LA, Hyman BT (2008) Preservation of neuronal number despite age-related cortical brain atrophy in elderly subjects without Alzheimer disease. J Neuropathol Exp Neurol 67:1205–1212
Padurariu M, Ciobica A, Mavroudis I, Fotiou D, Baloyannis S (2012) Hippocampal neuronal loss in the CA1 and CA3 areas of Alzheimer’s disease patients. Psychiatr Danub 24:152–8
Sabuncu MR, Desikan RS, Sepulcre J, Yeo BT, Liu H, Schmansky NJ, Reuter M, Weiner MW, Buckner RL, Sperling RA, Fischl B (2011) The dynamics of cortical and hippocampal atrophy in Alzheimer disease. Arch Neurol 68:1040–8
Bancroft J, Stevens A (eds) (1996) Theory and practice of histological techniques. Churchill Livingstone, New York
Renshaw S (2007) Immunochemical staining techniques. In: Immunohistochemistry: Methods express series, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY
Krenacs L, Krenacs T, Raffeld M (1999) Antigen retrieval for immunohistochemical reactions in routinely processed paraffin sections. Methods Mol Biol 115:85–93
D’Amico F, Skarmoutsou E, Stivala F (2008) State of the art in antigen retrieval for immunohistochemistry. J Immunol Methods 341:1–18
Pileri SA, Roncador G, Ceccarelli C, Piccioli M, Briskomatis A, Sabattini E, Ascani S, Santini D, Piccaluga PP, Leone O, Damiani S, Ercolessi C, Sandri F, Pieri F, Leoncini L, Falini B (1997) Antigen retrieval techniques in immunohistochemistry: a comparison of different methods. J Pathol 183:116–23
http://www.neurobiologie.uniosnabrueck.de/index.php?cat=Research&page=Ressources%20and%20Materials
http://fiji.sc/wiki/index.php/Rolling_Ball_Background_Subtraction
http://rsbweb.nih.gov/ij/plugins/anisotropic-diffusion-2d.html
http://fiji.sc/wiki/index.php/Enhance_Local_Contrast_(CLAHE)
Zuiderveld K (1994) Contrast limited adaptive histogram equalization, Graphics gems IV. Academic, London, pp 474–485
http://occm.otago.ac.nz/resources/Spatial-Calibration-of-an-Image.pdf
Acknowledgements
We thank Clemens Thölken and Jens Klinzing for early work on developing a processing pipeline and establishing machine learning algorithms in our lab. We also appreciate the technical help of Prof. Günter Purschke and Werner Mangerich regarding tissue preparation. The work was supported by the Deutsche Forschungsgemeinschaft (DFG grant BR1192/11-2) to R.B. and a Lichtenberg Fellowship of the state of Lower Saxony (to F.S.).
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Penazzi, L., Sündermann, F., Bakota, L., Brandt, R. (2014). Machine Learning to Evaluate Neuron Density in Brain Sections. In: Bakota, L., Brandt, R. (eds) Laser Scanning Microscopy and Quantitative Image Analysis of Neuronal Tissue. Neuromethods, vol 87. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0381-8_13
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DOI: https://doi.org/10.1007/978-1-4939-0381-8_13
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Publisher Name: Humana Press, New York, NY
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