Abstract
High content analysis (HCA; also referred to as high content biology) is a quantitative, automated, medium-throughput microscopy approach whereby cell images are segmented into relevant compartments (nuclei, cytoplasm) and the staining in each compartment quantified by computer algorithms. The extraction of quantitative information from the cell image generates a wealth of data which contributes significantly to the acceleration of drug discovery and biological research. Here we have adapted HCA to analyze protein acetylations in the cytoskeleton. This approach yields associative information on the link between acetylation and cytoskeletal organization. The protocol also describes optimization steps for cytoskeletal analysis and its application across different cell types, and HCA platforms. The methods described herein are readily adaptable to non-cytoskeletal acetylations and have been applied to the analysis of transcription factors.
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Acknowledgments
Our work is funded by BBSRC, FSA, University of Sheffield (BC) and by Engineering and Physical Sciences Research Council, ChELSI initiative EP/E036252/1 (CE).
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Corfe, B.M., Kilner, J., Chowdry, J., Benson, R.S.P., Griffiths, G.J., Evans, C.A. (2013). Application of High Content Biology to Yield Quantitative Spatial Proteomic Information on Protein Acetylations. In: Hake, S., Janzen, C. (eds) Protein Acetylation. Methods in Molecular Biology, vol 981. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-305-3_4
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DOI: https://doi.org/10.1007/978-1-62703-305-3_4
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Publisher Name: Humana Press, Totowa, NJ
Print ISBN: 978-1-62703-304-6
Online ISBN: 978-1-62703-305-3
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