Simultaneous profiling of activity patterns in multiple neuronal subclasses.

Background: Neuronal networks typically comprise heterogeneous populations of neurons. A core objective when seeking to understand such networks, therefore, is to identify what roles these different neuronal classes play. Acquiring single cell electrophysiology data for multiple cell classes can prove to be a large and daunting task. Alternatively, Ca network imaging provides activity profiles of large numbers of neurons simultaneously, but without distinguishing between cell classes.

New Method: We therefore developed a strategy for combining cellular electrophysiology, Ca network imaging, and immunohistochemistry to provide activity profiles for multiple cell classes at once. This involves cross-referencing easily identifiable landmarks between imaging of the live and fixed tissue, and then using custom MATLAB functions to realign the two imaging data sets, to correct for distortions of the tissue introduced by the fixation or immunohistochemical processing.

Results: We illustrate the methodology for analyses of activity profiles during epileptiform events recorded in mouse brain slices. We further demonstrate the activity profile of a population of parvalbumin-positive interneurons prior, during, and following a seizure-like event.

Comparison With Existing Methods: Current approaches to Ca network imaging analyses are severely limited in their ability to subclassify neurons, and often rely on transgenic approaches to identify cell classes. In contrast, our methodology is a generic, affordable, and flexible technique to characterize neuronal behaviour with respect to classification based on morphological and neurochemical identity.

Conclusions: We present a new approach for analysing Ca network imaging datasets, and use this to explore the parvalbumin-positive interneuron activity during epileptiform events.