Data and Statistical Analysis
Statistical analysis was conducted with GraphPad Prism, following the recommendations of the British Journal of Pharmacology on experimental design and analysis in pharmacology (Curtis, Alexander et al ., 2018). Data analysis was not blinded. However, the order of the application of the compounds was varied in each protocol to minimize potential errors. Data are given as mean ± SEM for the number (n) of cells. The normality test Kolmogorov–Smirnov was first performed, and then data were compared using the corresponding two tailed paired test. In some experiments, each drug value was normalized with respect to the corresponding matched control value due to the high variability of the responses. In this case, data are given as X-fold matched control. Data were compared using the non-parametric Wilcoxon test for matched pairs. Data were determined to be statistically significant when p value was ≤ 0.05 (*p).
Antagonist concentration-response curves were generated according to the following procedures: the peak amplitudes of three control responses were averaged and the level of inhibition by the antagonist was determined by dividing the peak amplitude of the response in the presence of the antagonist by the averaged control response to obtain a percent response value. In the case of the agonists, EC50 and efficacy values were obtained as follows: for the ACh curves, the current amplitudes for ACh-evoked responses for each individual cell were fit to the Hill equation to obtain the calculated plateau value for activation. The observed ACh-evoked responses were then normalized to the calculated plateau value to obtain a percent response. All other non ACh agonist responses were normalized to those obtained by 300 μM ACh in the same cell. Data used to obtain concentration-response relationships for agonists were analyzed using GraphPad Prism 8.0 (GraphPad Software, San Diego, CA, USA) and IgorPro 4.08 (Wavemetrics, Lake Oswego, OR, USA).
In the amperometric experiments, data came from a single donor due to the low number of transplants performed during to the coronavirus pandemic situation. In this case, no statistical analysis was performed, and data are reported as tendencies. The analysis of the amperometric data was performed using IGOR Pro software and macros that allow the analysis of single events and the rejection of overlapping spikes (Mosharov & Sulzer, 2005). A threshold of 4.5 times the first derivative of the noise standard deviation was calculated to clearly detect amperometric events. Among the events whose first derivative was above this threshold, only those showing one peak and one rising and falling phase were considered as single spikes. To minimize variability among cells, the overall mean of average spike values recorded in several single cells was used.