I've developed a python program for circadian analysis at pixel resolution in tissue recordings. Happy to share it. We have used it in this work -
www.pnas.org/doi/10.1073/...

I've had this discussion with Lance Riley :) It is a very interesting system! If you can extract time series/pixel intensities from each nucleus, inter-nucleus communication can be mapped with this method.

In conclusion, we reveal that robust circadian timekeeping in mammalian SCN arises from functionally heterogeneous cell types, organized into two cellular networks and driven by a subset of VIP hubs neurons with unique connectivity topology. end!

Network wiring shapes SCN emergent properties: Simulated SCN cell behavior showed a strong correlation with their corresponding experimentally recorded explants. In silico ventral SCN synchronized faster and also recapitulated the characteristic wave of PER2 expression. (12/n)

All signals converged on the dorsal 'sink' cells (including the AVP cells) which were not essential for driving SCN synchrony. We also identified 'bridge' cells in the central SCN that couple information flow across the modules & likely underlie seasonal SCN reorganization (11/n)

VIP hubs drive SCN circadian synchrony: We found a topologically unique group of VIP hub neurons that generate and broadcast signals with their extensive and long-range connections. SCN networks did not synchronize on ablating the VIP hubs but not AVP cells. (10/n)

To identify key hub cells driving SCN network synchrony, we simulated the experimentally inferred SCN maps in 6 mice and tested the impact of ablating various cell types on networks' ability to synchronize. (9/n)

We identified four functional cell types that mediate circadian signaling across the SCN neural network, acting as circadian signal 'generators,' 'broadcasters,' 'bridges,' or 'sinks.' (8/n)

The SCN exhibited strong ventral-to-dorsal signaling, with over 60% of all connections originating from ventral cells. Accordingly, ventral module cells synchronize their rhythms first, driving the rest of the network. (7/n)

Two asymmetrically coupled cellular networks drive SCN synchrony: Within each explant, we found dorsal and ventral cellular modules resembling the anatomical shell-core regions. Notably, these were absent in explants that failed to synchronize during our experiments. (6/n)

While SCN cells connect as a #smallworld network with most cells signaling proximally; intriguingly, we found a few long-range connections from ventral cells that project halfway > across the SCN, short-circuiting the network to enhance signal transmission. (5/n)

Mammalian clock is efficiently wired: SCN connectivity patterns were highly conserved, both bilaterally and across mice. SCN is sparsely connected compared to other brain areas (~720 connections/cell), yet signals reached any other SCN cell via just 3–4 intermediaries! (4/n)

We developed an #informationtheory method, MITE, to identify functional cell-cell connections by observing the coevolution of circadian gene expression as the cells synchronized. This framework can be used for inferring connectivity in any cellular network. (3/n)

Preprint: www.biorxiv.org/content/10.1... (2/n)