Abstract
Calcium imaging has gained substantial popularity as a tool to profile the activity of multiple simultaneously active cells at high spatiotemporal resolution. Among the diverse approaches to processing of Ca2+ imaging data is an often subjective decision of how to quantify baseline fluorescence or F0. We examine the effect of popular F0 determination methods on the interpretation of neuronal and astrocyte activity in a single dataset of rats trained to self-administer intravenous infusions of cocaine and compare them with an F0-independent wavelet ridgewalking event detection approach. We find that the choice of the processing method has a profound impact on the interpretation of widefield imaging results. All of the dF/F0 thresholding methods tended to introduce spurious events and fragment individual transients, leading to smaller calculated event durations and larger event frequencies. Analysis of simulated datasets confirmed these observations and indicated substantial intermethod variability as to the events classified as significant. Additionally, most dF/F0 methods on their own were unable to adequately account for bleaching of fluorescence, although the F0 smooth approach and the wavelet ridgewalking algorithm both did so. In general, the choice of the processing method led to dramatically different quantitative and sometimes opposing qualitative interpretations of the effects of cocaine self-administration both at the level of individual cells and at the level of cell networks. Significantly different distributions of event duration, amplitude, frequency, and network measures were found across the majority of dF/F0 approaches. The wavelet ridgewalking algorithm broadly outperformed dF/F0-based methods for both neuron and astrocyte recordings. These results indicate the need for heightened awareness of the limitations and tendencies associated with decisions to use particular Ca2+ image processing pipelines. Both quantification and interpretation of the effects of experimental manipulations are strongly sensitive to such decisions.
Highlights
Development of genetically encoded calcium indicators (GECIs) has encouraged a bloom of research to capture the activity of large cell populations at high spatiotemporal resolution
We reasoned that for spontaneous neuronal activity, this probability should be stable across time, and that deviations are likely caused by erroneous event identification
Widefield imaging is vulnerable to contamination by signals from neighboring areas within the field of view as well as by signals originating both below and above the optical imaging plane
Summary
Development of genetically encoded calcium indicators (GECIs) has encouraged a bloom of research to capture the activity of large cell populations at high spatiotemporal resolution. The activity of diverse cell types can be examined using Ca2+ imaging approaches. Amplitude and duration of neuronal Ca2+ transients are typically interpreted as proxies for action potentials (Ali and Kwan, 2020), and diverse features of astrocytic Ca2+ have been proposed to play functional roles in neural circuit regulation, behavior, and information processing (Guerra-Gomes et al, 2017). A variety of analytical approaches to process large amounts of imaging data are available, but the extent to which the choice of the analysis pipeline may impact interpretation of the underlying data remains unclear. Most “traditional” event identification methods are based on transforming a fluorescence signal into dF/F0 and applying a threshold to find significant deviations from the presumed background noise. We implement three common F0 definitions: an initial segment of the fluorescence trace (F0 initial), often used in recordings involving extracellular stimulations (e.g., Ellefsen et al, 2014; Lock et al, 2015; Rahmati et al, 2016); a minimally variable and dim segment (F0 minimal); and as a fit to the background in a sliding window throughout the trace (F0 smooth), such as implemented in a toolbox by Romano et al (2017), SICT (Mancini et al, 2018), or FluoroSNNAP (Patel et al, 2015)
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