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.