Abstract
The suprachiasmatic nucleus (SCN), locus of a circadian clock, is a small nucleus of approximately 20,000 neurons that oscillate with a period of about 24 hours. While individual neurons produce circadian oscillations even when dispersed in culture, the coherence and robustness of oscillation of the SCN as a whole is dependent on its circuitry. Surprisingly, the individual neurons of the intact SCN do not all oscillate in phase with each other. To understand the oscillatory dynamics across the intact nucleus, we develop a model of the relation of the phase of neurons to their PER2 expression at a particular subjective time (CT1900) using time series data from SCN slice preparations. Next, we use the model, which produces a surprisingly good fit in the SCN slice data, to estimate oscillator phase at a single time point (CT1900) in snapshot data from PER2 expression measurements in intact, unsliced SCN-wide tissue. To monitor temporal changes in phase in time series data, we use PER2::LUC imaging in an ex vivo SCN slice preparation. To study phase in the intact SCN at a fixed time point we use data generated by PER2 staining and a tissue clearing protocol. Because PER2 expression, as measured in the time series slices and the snapshot intact SCN are not directly comparable, the model estimated from time series slices to the snapshot intact SCN data requires a calibrating constant. The results indicate that our model provides a surprisingly good fit to the SCN slice data and is therefore a meaningful method for estimating phase in the intact SCN snapshot data, permitting the study of virtual interventions such as virtual tissue slicing. We next compare oscillation in circuits in the SCN-wide tissue to those that have been disrupted by virtual slicing using a Kuramoto model to simulate the dynamics. The results support prior evidence that the damage done by coronal slicing has the most disruptive impact on SCN oscillation, while horizontal slicing has the least damage. The results point to the importance of connectivity along the caudal-to-rostral axis and indicate that SCN circuit organization depends on the caudal-to-rostral flow of information. In summary, the construction of this model is a major finding of the paper. Our modeling allows us to perform the previously impossible analysis of oscillatory dynamics in static data in an intact SCN captured at a single time point.