![]() Īlthough technical aspects of Western blotting have improved over the years, for example by extending the linear range of detection, it is not yet clear how much quantitative information can be obtained and in which settings. Two main applications are the parameterisation and validation of mathematical models of biological systems and the testing of statistical significance between two or more experimental conditions or treatments. Although originally a qualitative or at best a semi-quantitative method, with the rise of computational systems biology, Western blotting has become increasingly important for fully quantitative applications. Western blotting or protein immunoblotting, was introduced at the end of the 1970s to enable the detection of specific proteins. These results will aid users of Western blotting to choose a suitable normalisation strategy and also understand the implications of this normalisation for subsequent hypothesis testing. This causes the effect of normalisations by sum or optimal alignment on hypothesis testing to depend on the mean of the data tested for high intensity points, false positives are increased and false negatives are decreased, while for low intensity points, false positives are decreased and false negatives are increased. Normalisation by sum or by optimal alignment redistributes the raw data uncertainty in a mean-dependent manner, reducing the CV of high intensity points and increasing the CV of low intensity points. Analysis of published experimental data shows that choosing normalisation points with low quantified intensities results in a high normalised data CV and should thus be avoided. Thus, in the context of hypothesis testing, normalisation by fixed point reduces false positives and increases false negatives. Normalisation by fixed point tends to increase the mean CV of normalised data in a manner that naturally depends on the choice of the normalisation point. We consider how these different strategies affect the coefficient of variation (CV) and the results of hypothesis testing with the normalised data. Here we evaluate three commonly used normalisation strategies: (i) by fixed normalisation point or control (ii) by sum of all data points in a replicate and (iii) by optimal alignment of the replicates. To ensure accurate quantitation and comparability between experiments, Western blot replicates must be normalised, but it is unclear how the available methods affect statistical properties of the data. Since the area intensity is in arbitrary unit, it can also be normalised to the BCA assay measurement, DNA content or any other number chosen.Western blot data are widely used in quantitative applications such as statistical testing and mathematical modelling. To normalise the intensity of the area underneath the peak to the Ponceau staining, measure the intensity of 3 randomly chosen peaks on the Ponceau image, average the measurements and use that value to normalise the data against. The report will automatically pop up on the side. Go to: Analyse→Gels→Label Peaks to get the report.Īlternatively, use the magic wand tool to highlight the area underneath the peak for each lane. Draw the line at where the peak begins and ends (bend in the line) for each peak. Use the line tool to draw the lines to eliminate the lane background from the calculations. Continue this for the subsequent lanes (pressing Crtl and 2 every time).įor the last lane, repeat the procedure but press Ctrl and 3 to set the last lane. Press Ctrl and 1 to set first lane (Command and 1 on the Mac).Ĭlick the centre of the square and drag it across to the next lane. Use the square selection tool to highlight the first lane. Convert the image to 8-bit using ImageJ function (Image→Type→8-bit).
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |