1. System-level time computation and representation in the suprachiasmatic nucleus revealed by large-scale calcium imaging and machine learning.
- Author
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Wang, Zichen, Yu, Jing, Zhai, Muyue, Wang, Zehua, Sheng, Kaiwen, Zhu, Yu, Wang, Tianyu, Liu, Mianzhi, Wang, Lu, Yan, Miao, Zhang, Jue, Xu, Ying, Wang, Xianhua, Ma, Lei, Hu, Wei, and Cheng, Heping
- Subjects
Machine Learning ,Suprachiasmatic Nucleus ,Animals ,Calcium ,Mice ,Male ,Calcium Signaling ,Circadian Rhythm ,Mice ,Inbred C57BL ,GABAergic Neurons ,Circadian Clocks ,Neurons - Abstract
The suprachiasmatic nucleus (SCN) is the mammalian central circadian pacemaker with heterogeneous neurons acting in concert while each neuron harbors a self-sustained molecular clockwork. Nevertheless, how system-level SCN signals encode time of the day remains enigmatic. Here we show that population-level Ca2+ signals predict hourly time, via a group decision-making mechanism coupled with a spatially modular time feature representation in the SCN. Specifically, we developed a high-speed dual-view two-photon microscope for volumetric Ca2+ imaging of up to 9000 GABAergic neurons in adult SCN slices, and leveraged machine learning methods to capture emergent properties from multiscale Ca2+ signals as a whole. We achieved hourly time prediction by polling random cohorts of SCN neurons, reaching 99.0% accuracy at a cohort size of 900. Further, we revealed that functional neuron subtypes identified by contrastive learning tend to aggregate separately in the SCN space, giving rise to bilaterally symmetrical ripple-like modular patterns. Individual modules represent distinctive time features, such that a module-specifically learned time predictor can also accurately decode hourly time from random polling of the same module. These findings open a new paradigm in deciphering the design principle of the biological clock at the system level.
- Published
- 2024