SUMMARY
Article Details: Received: 2020-04-07 | Accepted: 2021-01-25 | Available online: 2021-06-30https://doi.org/10.15414/afz.2021.24.02.141-146In biological sciences, western blotting technique is widely used to quantify the expression of proteins in a given sample. However, there is no unified method for quantifying the expression of proteins. As a consequence, quantitative analysis of expression of protein through western blotting often suffers from data inconsistency. At the same time, extraction of the poor sample size (n=3/5/7) turns such analysis non-Gaussian and less robust to statistical errors. In present study, we attempt a noble approach while analyzing an image from western blotting using Gaussian blur as filter and thereby generating data in order to perform meaningful statistical analysis. The differences among various blots that correspond to the expressed target proteins are tested viably using appropriate statistical tools. This procedure of quantifying western blotting is comprehensive, simple and can be applied to collect data in compliance with statistical norms. Furthermore, repeating western blotting on a set of particular proteins may improve the analysis part as well.Keywords: blotting, statistical analysis, non-parametric, densitometryReferencesAldridge, G.M. et al. (2008). The use of total protein stains as loading controls: An alternative to high-abundance singleprotein controls in semiquantitative immunoblotting. Journal of Neuroscience Methods, 172(2), 250–254.Butler, T.A.J. et al. (2019). Misleading westerns: common quantification mistakes in western blot densitometry and proposed corrective measures. BioMedical Research, ID 5214821. https://doi.org/10.1155/2019/5214821Eaton, S.L. et al. (2013). Total Protein Analysis as a Reliable Loading Control for Quantitative Fluorescent Western Blotting. PLoS ONE, 8(8), e72457. https://doi.org/10.1371/journal.pone.0072457Ghosh, R., Gilda, J.E. & Gomes, A.V. (2014). The necessity of and strategies for improving confidence in the accuracy of western blots. Expert Review of Proteomics, 11, 549–560.Graham, B. (2016). Quantifying western blots: none more black. BMC Biology, 14, 116. https://doi.org/10.1186/s12915-016-0339-1Kreutz, C. et al. (2007). An error model for protein quantification. Bioinformatics, 23 (20), 2747–2753, https://doi.org/10.1093/bioinformatics/btm397Maleki, F. et al. (2019). Size matters: how sample size affects the reproducibility and specificity of gene set analysis. Hum Genomics, 13, 42. https://doi.org/10.1186/s40246-019-0226-2Taylor, S.C. et al. (2013). A defined methodology for reliable quantification of Western blot data. Molecular Biotechnology, 55(3), 217–226. https://doi.org/10.1007/s12033-013-9672-6Towbin, H., Staehelin, T. & Gordon, J. (1979). Electrophoretic transfer of proteins from polyacrylamide gels to nitrocellulose sheets: procedure and some applications. Proceedings of the National Academy of Sciences, 76(9), 4350–4354.Welinder, C. & Ekblad, L. (2011). Coomassie staining as loading control in Western blot analysis. Journal of Proteome Research, 10(3), 1416–1419