Wavelet transform-based de-noising for two-photon imaging of synaptic Ca2+ transients

摘要

Postsynaptic Ca2+ transients triggered by neurotransmission at excitatory synapses are a key signaling step for the induction of synaptic plasticity and are typically recorded in tissue slices using two-photon fluorescence imaging with Ca2+-sensitive dyes. The signals generated are small with very low peak signaloise ratios (pSNRs) that make detailed analysis problematic. Here, we implement a wavelet-based de-noising algorithm (PURE-LET) to enhance signaloise ratio for Ca2+ fluorescence transients evoked by single synaptic events under physiological conditions. Using simulated Ca2+ transients with defined noise levels, we analyzed the ability of the PURE-LET algorithm to retrieve the underlying signal. Fitting single Ca2+ transients with an exponential rise and decay model revealed a distortion of τrise but improved accuracy and reliability of τdecay and peak amplitude after PURE-LET de-noising compared to raw signals. The PURE-LET de-noising algorithm also provided a ～30-dB gain in pSNR compared to ～16-dB pSNR gain after an optimized binomial filter. The higher pSNR provided by PURE-LET de-noising increased discrimination accuracy between successes and failures of synaptic transmission as measured by the occurrence of synaptic Ca2+ transients by ～20% relative to an optimized binomial filter. Furthermore, in comparison to binomial filter, no optimization of PURE-LET de-noising was required for reducing arbitrary bias. In conclusion, the de-noising of fluorescent Ca2+ transients using PURE-LET enhances detection and characterization of Ca2+ responses at central excitatory synapses.