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.