Data analysis of P-glycoprotein transport activity with rhodamine123

Rho123 fluorescence intensity inside and outside of giant proteoliposomes was analyzed using ImageJ. Liposome diameters were also measured using ImageJ based on the rho-PE signal outlining the liposomal membrane. Background Rho123 intensity inside the liposome at time 0 (I_{inside t = 0}) was subtracted from all the measured Rho123 intensities for time ≥ 0 (i.e. I_inside and I_outside) in order to take account for Rho123 signal from the out of focus planes. In cases where the data collection started after time 0, the linear regression was performed to estimate Rho123 intensity inside the liposome at t = 0 assuming that the rate of change in fluorescence intensity was constant for early time points. A standard curve of Rho123 intensity as a function of its concentration was confirmed the linear correlation within the concentration range used. This standard curve was used to convert the measured fluorescence intensity values to the Rho123 concentration inside the giant liposomes. The concentration of Rho123 inside the liposome (C_i) was then normalized to its outside concentration (C_o) at each time point in order to account for possible signal fluctuations during time-lapse imaging. The described background subtraction and normalization for each time point is summarized in Eq 1 below.

The normalized Rho123 concentration (C_i/C_o) inside each liposome was plotted against time for further assessment of Rho123 influx rate. The slope of the linear portion of this curve was determined and used as the rate of Rho123 influx (1/s) for individual vesicles. The collected transport rates were plotted as a function of ATP concentration, and K_m value was estimated by fitting the data into Michaelis-Menton (M-M) equation using Prism® (Graphpad Software, La Jolla, CA). The transport rate was also plotted against inhibitor concentrations, and the half maximal inhibitory concentration (IC₅₀) of each inhibitor was determined by fitting the data to a one-phase exponential decay model.

In order to further break down the Pgp transport activity, and to ensure that the passive diffusion of substrate across the membrane did not significantly contribute to the observed influx, we fitted C_i/C_o over time into a model, adapted from the previous work of Horger et al [38], which allowed for dissection of the Pgp-mediated transport and diffusion. By fitting the data into this model using a least-squares fit algorithm in MATLAB^®, we retrieved two transport kinetics parameters of membrane permeability coefficient, P_s, and transport rate constant, k, from the change in Rho123 concentration recorded during the Pgp transport activity in individual liposomes. Transport rate constants plotted against ATP concentration were fitted to Michaelis-Menten model to determine K_m. Also, transport rate constants as a function of Pgp inhibitor concentration (i.e. verapamil, colchicine, and cyclosporin A) were fitted to a one-phase exponential decay model to determine IC₅₀ values for each of these inhibitors.