Improved spike inference accuracy by estimating the peak amplitude of unitary [Ca²⁺] transients in weakly GCaMP6f‐expressing hippocampal pyramidal cells

Abstract

Key points The amplitude of unitary, single action potential‐evoked [Ca2+] 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 [Ca2+] 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 Ca2+ indicators in behaving animals is hampered by inaccuracies in spike inference from fluorescent tracers. Here we combine two‐photon [Ca2+] 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 [Ca2+] transients in mouse hippocampal pyramidal cells (PCs). The amplitude of unitary [Ca2+] 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 [Ca2+] transients in individual weakly GCaMP6f‐expressing PCs, with which we achieve spike inference error rates of ∼5%.

Tonotopic and non-auditory organization of the mouse dorsal inferior colliculus revealed by two-photon imaging

“…In our hands, these two transgenic lines had the advantage that the kinetics of the responses were relatively homogeneous and well approximated by a linear model (Figure 5B), suggesting that the expression levels, which are a major determinant of the Ca 2+ kinetics (e.g. Éltes et al, 2019), were similar across cells. Moreover, in contrast to pilot experiments in which we expressed GCaMP6 using adeno-associated viruses, we found little evidence for toxicity.…”

Single-neuron representation of learned complex sounds in the auditory cortex

“…Concerns on toxicity 29 , 30 and cell-by-cell variation in Ca 2+ sensing 31 caused by GCaMP6f expression may have undermined the finding of sparse highly Ca 2+ -responsive cells in trained animals. Thus, we performed a new set of experiments by applying single-cell loose-patch recordings 23 , 32 , 33 in highly Ca 2+ -responsive cells identified with a chemical Ca 2+ dye, Cal-520 24 , 27 , 34 (for example, see Supplementary Movie 5 ), with lesser concerns for toxicity and inhomogeneity in cell-by-cell Ca 2+ sensing.…”

Database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging

“…Deeper convolutional network (5 CNN layers). Consisting of 5 convolutional layers with filter sizes (11,9,7,5,3) and filter numbers (20,30,40,40,40), with three max pooling layers after the second, fourth and fifth convolutional layers, and a final dense layer expansion of 10 neurons. The reduction of the filter sizes compared with the default network is necessary since no zeropadding was applied, resulting in a decrease of the size of the 1D trace with increasing layer depth.…”

“…Deeper convolutional network (7 CNN layers). Consisting of 7 convolutional layers with filter sizes (7,6,5,4,3,3,3) and filter numbers (20,30,40,40,40,40,40), with three max pooling layers after the second, fifth and seventh convolutional layers, and a final dense layer expansion of 10 neurons. Total of 31'221 trainable parameters.…”

“…As a consequence, experimental conditions of novel datasets are often not well matched to those of available ground truth data. It is therefore not clear how an algorithm based on a specific ground truth dataset generalizes to other datasets, which causes problems for the inference of spike rates from calcium imaging data under most experimental conditions 12,13,20,21 .…”