Dystrophin quantification: Biological and translational research implications

Abstract

Objective: We formed a multi-institution collaboration in order to compare dystrophin quantification methods, reach a consensus on the most reliable method, and report its biological significance in the context of clinical trials.

Methods: Five laboratories with expertise in dystrophin quantification performed a data-driven comparative analysis of a single reference set of normal and dystrophinopathy muscle biopsies using quantitative immunohistochemistry and Western blotting. We developed standardized protocols and assessed inter- and intralaboratory variability over a wide range of dystrophin expression levels.

Results: Results from the different laboratories were highly concordant with minimal inter- and intralaboratory variability, particularly with quantitative immunohistochemistry. There was a good level of agreement between data generated by immunohistochemistry and Western blotting, although immunohistochemistry was more sensitive. Furthermore, mean dystrophin levels determined by alternative quantitative immunohistochemistry methods were highly comparable.

Conclusions: Considering the biological function of dystrophin at the sarcolemma, our data indicate that the combined use of quantitative immunohistochemistry and Western blotting are reliable biochemical outcome measures for Duchenne muscular dystrophy clinical trials, and that standardized protocols can be comparable between competent laboratories. The methodology validated in our study will facilitate the development of experimental therapies focused on dystrophin production and their regulatory approval.

© 2014 American Academy of Neurology.

Figures

Figure 1. Inter- and intralaboratory variability of dystrophin quantification using immunohistochemistry

Five laboratories each quantified the level of dystrophin expression in the same 6 biopsies using a standardized immunohistochemistry protocol; data were analyzed using the Arechavala-Gomeza method. To assess interlaboratory variability, the mean ± SD for each biopsy was calculated as well as the coefficient of variation (CV). Note how this variation is higher for those samples containing less dystrophin (E and B). To assess intraassay precision within each laboratory, the mean ± SD for each laboratory per sample was calculated as well as the average CV per laboratory. Laboratories are unidentified.

Figure 2. Assessing the agreement between different methods of immunohistochemical dystrophin measurement

The mean data from each method were compared in a bar chart ± SD (A) and plotted with a regression line (B). The difference between the Arechavala-Gomeza and Taylor methods was plotted against their mean in a Bland-Altman plot (C) where the mean of the differences between the methods represents the bias (i.e., the value determined by one method minus the value determined by the other method) and the upper and lower 95% confidence limits represent the upper and lower limits of agreement, respectively (the difference between the 2 methods should lie within these bounds on 95% of occasions).

Figure 3. Inter- and intralaboratory variability of dystrophin quantification using Western blotting

Five laboratories each quantified the level of dystrophin expression in the same 6 biopsies using a standardized Western blotting protocol. To assess interlaboratory variability, the mean ± SD for each laboratory and biopsy was plotted on a bar chart and the average coefficient of variation (CV) per laboratory calculated. To assess intralaboratory variation, the mean ± SD for each laboratory per sample was calculated as well as the average CV per laboratory. Laboratories are unidentified.

Figure 4. Assessing the agreement between immunohistochemistry and Western blotting for dystrophin quantification

The mean immunohistochemistry and Western blotting data for each biopsy were compared in a bar chart ± SD (A) and plotted with a regression line (B). The difference between the methods was plotted against their mean in a Bland-Altman plot (C) where the mean of the differences between the methods represents the bias (i.e., the value determined by one method minus the value determined by the other method) and the upper and lower 95% confidence limits represent the upper and lower limits of agreement, respectively (the difference between the 2 methods should lie within these bounds on 95% of occasions).