Fast, Automated Implementation of Temporally Precise Blind Deconvolution of Multiphasic Excitatory Postsynaptic Currents

Abstract

Records of excitatory postsynaptic currents (EPSCs) are often complex, with overlapping signals that display a large range of amplitudes. Statistical analysis of the kinetics and amplitudes of such complex EPSCs is nonetheless essential to the understanding of transmitter release. We therefore developed a maximum-likelihood blind deconvolution algorithm to detect exocytotic events in complex EPSC records. The algorithm is capable of characterizing the kinetics of the prototypical EPSC as well as delineating individual release events at higher temporal resolution than other extant methods. The approach also accommodates data with low signal-to-noise ratios and those with substantial overlaps between events. We demonstrated the algorithm’s efficacy on paired whole-cell electrode recordings and synthetic data of high complexity. Using the algorithm to align EPSCs, we characterized their kinetics in a parameter-free way. Combining this approach with maximum-entropy deconvolution, we were able to identify independent release events in complex records at a temporal resolution of less than 250 µs. We determined that the increase in total postsynaptic current associated with depolarization of the presynaptic cell stems primarily from an increase in the rate of EPSCs rather than an increase in their amplitude. Finally, we found that fluctuations owing to postsynaptic receptor kinetics and experimental noise, as well as the model dependence of the deconvolution process, explain our inability to observe quantized peaks in histograms of EPSC amplitudes from physiological recordings.

What We Can Learn From Cumulative Numbers of Vesicular Release Events

“…Fitting the deconvolved trace with vertically scaled spikes allowed us to detect vesicular release events in the trace. Decomposition of postsynaptic current into the contributions of individual vesicle events by methods including our method is based on the assumption that each vesicular event is a scaled version of a fixed template having the shape of mEPSCs (Clements and Bekkers, 1997; Andor-Ardó et al, 2012; Pernía-Andrade et al, 2012). After converting synaptic responses to vesicular release events by the decomposition of EPSCs, variance-mean analysis of the events reveals 3–10 DSs at one synapse (Malagon et al, 2016), a value comparable to that obtained by EPSC fluctuation analysis (Schmidt et al, 2013; Ishiyama et al, 2014).…”

Uniquantal Release through a Dynamic Fusion Pore Is a Candidate Mechanism of Hair Cell Exocytosis

“…Here, for UQR through a dynamic fusion pore, we aimed at revealing the times and nonquantized amounts of glutamate released from a single SV (i.e., in individual EPSCs). Therefore, we only fixed the eEPSC shape to an ''ideal EPSC,'' obtained by normalizing, aligning, and averaging the fastest compact EPSCs for each SGN ( Figure 4A), and allowed its size to vary for the deconvolution (e.g., Andor-Ardó et al, 2012). Applying the iterative fitting/deconvolution algorithm on all EPSCs ( Figure 4B), we obtained the estimated time and amplitude of all underlying eEPSCs.…”

Impaired Light Adaptation of ON-Sustained Ganglion Cells in Early Diabetes Is Attributable to Diminished Response to Dopamine D4 Receptor Activation

“…Spontaneous events were analyzed in the baseline after a light response up to 1 second before the subsequent light stimulus. Events were identified via the MATLAB code and methodology outlined in Andor-Ardo et al 34 Frequency, amplitude, interevent interval (IEI), and decay τ (single exponent fit) for identified sEPSCs were calculated using custom-written MATLAB (MathWorks, Natick, MA, USA) scripts. Effects of treatment on sEPSCs were analyzed at the single cell level with Kolmogorov–Smirnov (K-S) tests.…”