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System-level time computation and representation in the suprachiasmatic nucleus revealed by large-scale calcium imaging and machine learning

Zichen Wang^1,2,† , Jing Yu^1,2,† , Muyue Zhai¹ , Zehua Wang^3,4 , Kaiwen Sheng^5,8 , Yu Zhu⁵ , Tianyu Wang¹ , Mianzhi Liu¹ , Lu Wang¹ , Miao Yan¹ , Jue Zhang^4,6 , Ying Xu⁷ , Xianhua Wang^1,2 , Lei Ma^1,5,* , Wei Hu^3,* , Heping Cheng^1,2,*

¹National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China
²Research Unit of Mitochondria in Brain Diseases, Chinese Academy of Medical Sciences, PKU-Nanjing Institute of Translational Medicine, Nanjing, Jiangsu, China
³Wangxuan Institute of Computer Technology, Peking University, Beijing, China
⁴Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
⁵Beijing Academy of Artificial Intelligence, Beijing, China
⁶College of Engineering, Peking University, Beijing, China
⁷Jiangsu Key Laboratory of Neuropsychiatric Diseases and Cambridge-Su Genomic Resource Center, Medical School of Soochow University, Suzhou, Jiangsu, China
⁸Present address: Department of Bioengineering, Stanford University, Stanford, CA, USA
^†These authors contributed equally: Zichen Wang, Jing Yu
* Correspondence: Lei Ma(lei.ma@pku.edu.cn)Wei Hu(forhuwei@pku.edu.cn)Heping Cheng(chengp@pku.edu.cn)

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.

https://doi.org/10.1038/s41422-024-00956-x

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