Assessing CaMPARI as new approach methodology for evaluating neurotoxicity.

Developmental exposure to environmental toxicants has been linked to the onset of neurological disorders and diseases. Despite substantial advances in the field of neurotoxicology, there remain significant knowledge gaps in our understanding of cellular targets and molecular mechanisms that mediate the neurotoxicological endpoints associated with exposure to both legacy contaminants and emerging contaminants of concern. Zebrafish are a powerful neurotoxicological model given their high degree sequence conservation with humans and the similarities they share with mammals in micro- and macro-level brain structures. Many zebrafish studies have effectively utilized behavioral assays to predict the neurotoxic potential of different compounds, but behavioral phenotypes are rarely able to predict the brain structures, cell types, or mechanisms affected by chemical exposures. Calcium-modulated photoactivatable ratiometric integrator (CaMPARI), a recently developed genetically-encoded calcium indicator, undergoes a permanent green to red switch in the presence of elevated intracellular Ca2+ concentrations and 405-nm light, which allows for a "snapshot" of brain activity in freely-swimming larvae. To determine whether behavioral results are predictive of patterns of neuronal activity, we assessed the effects of three common neurotoxicants, ethanol, 2,2',3,5',6-pentachlorobiphenyl (PCB 95), and monoethylhexyl phthalate (MEHP), on both brain activity and behavior by combining the behavioral light/dark assay with CaMPARI imaging. We demonstrate that brain activity profiles and behavioral phenotypes are not always concordant and, therefore, behavior alone is not sufficient to understand how toxicant exposure affects neural development and network dynamics. We conclude that pairing behavioral assays with functional neuroimaging tools such as CaMPARI provides a more comprehensive understanding of the neurotoxic endpoints of compounds while still offering a relatively high throughput approach to toxicity testing.

Iterative Non-Negative Matrix Factorization Filter for Blind Deconvolution in Photon/Ion Counting.

A digital filter based on non-negative matrix factorization (NMF) enables blind deconvolution of temporal information from large data sets, simultaneously recovering both photon arrival times and the instrument impulse response function (IRF). In general, the measured digital signals produced by modern analytical instrumentation are convolved by the corresponding IRFs, which can complicate quantitative analyses. Common examples include photon counting (PC), chromatography, super resolution imaging, fluorescence imaging, and mass spectrometry. Scintillation counting, in particular, provides a signal-to-noise advantage in measurements of low intensity signals, but has a limited dynamic range due to pulse overlap. This limitation can complicate the interpretation of data by masking temporal and amplitude information on the underlying detected signal. Typical methods for deconvolution of the photon events require advanced knowledge of the IRF, which is not generally trivial to obtain. In this work, a sliding window approach was developed to perform NMF one pixel at a time on short segments of large (e.g., 25 million point) data sets. Using random initial guesses for the IRF, the NMF filter simultaneously recovered both the deconvolved photon arrival times and the IRF. Applying the NMF filter to the analysis of triboluminescence (TL) data traces of active pharmaceutical ingredients enabled discrimination between different hypothesized physical origins of the signal.