Improved spike inference accuracy by estimating the peak amplitude of unitary [Ca ] transients in weakly GCaMP6f-expressing hippocampal pyramidal cells.

Key Points: The amplitude of unitary, single action potential-evoked [Ca ] transients negatively correlates with GCaMP6f expression, but displays large variability among hippocampal pyramidal cells with similarly low expression levels. The summation of fluorescence signals is frequency-dependent, supralinear and also shows remarkable cell-to-cell variability. The main source of spike inference error is variability in the peak amplitude, and not in the decay or supralinearity. We developed two procedures to estimate the peak amplitudes of unitary [Ca ] transients and show that spike inference performed with MLspike using these unitary amplitude estimates in weakly GCaMP6f-expressing cells results in error rates of ∼5%.

Abstract: Investigating neuronal activity using genetically encoded Ca indicators in behaving animals is hampered by inaccuracies in spike inference from fluorescent tracers. Here we combine two-photon [Ca ] imaging with cell-attached recordings, followed by post hoc determination of the expression level of GCaMP6f, to explore how it affects the amplitude, kinetics and temporal summation of somatic [Ca ] transients in mouse hippocampal pyramidal cells (PCs). The amplitude of unitary [Ca ] transients (evoked by a single action potential) negatively correlates with GCaMP6f expression, but displays large variability even among PCs with similarly low expression levels. The summation of fluorescence signals is frequency-dependent, supralinear and also shows remarkable cell-to-cell variability. We performed experimental data-based simulations and found that spike inference error rates using MLspike depend strongly on unitary peak amplitudes and GCaMP6f expression levels. We provide simple methods for estimating the unitary [Ca ] transients in individual weakly GCaMP6f-expressing PCs, with which we achieve spike inference error rates of ∼5%.