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

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.

Fast whole-body motor neuron calcium imaging of freely moving Caenorhabditis elegans without coverslip pressed.

Caenorhabditis elegans (C. elegans) is an ideal model organism for studying neuronal functions at the system level. This article develops a customized system for whole-body motor neuron calcium imaging of freely moving C. elegans without the coverslip pressed. Firstly, we proposed a fast centerline localization algorithm that could deal with most topology-variant cases costing only 6 ms for one frame, not only benefits for real-time localization but also for post-analysis. Secondly, we implemented a full-time two-axis synchronized motion strategy by adaptively adjusting the motion parameters of two motors in every short-term motion step (~50 ms). Following the above motion tracking configuration, the tracking performance of our system has been demonstrated to completely support the high spatiotemporal resolution calcium imaging on whole-body motor neurons of wild-type (N2) worms as well as two mutants (unc-2, unc-9), even the instantaneous speed of worm moving without coverslip pressed was extremely up to 400 µm/s.

High cost-efficient and computational gigapixel video camera based on commercial lenses and CMOS chips.

The state-of-the-art commercial telephoto lens has already provided us almost one giga space-bandwidth product. Since the single-image sensor cannot take such sampling capacity, we implement a four-parallel-boresight imaging system by using four such lenses and use 64 image sensors to complete full field of view (FOV) imaging for achieving 0.8 gigapixel over 15.6°×10.5°. Multiple sensors mosaicking can make most online computation and data transfer in parallel, and help us to realize a gigapixel video camera. Meanwhile, according to the four-parallel-boresight configuration, the flat image plane simplifies the image registration and image stitching, and allows us to keep high imaging performance in full frame following geometric and optical calibration and correction. Furthermore, considering that working distance changes do bring additional x/y offsets between sensor arrays, we propose a computation-based method and introduce an eight-axis automatic motion mechanism into the system to perform the online active displacement. Our prototype camera using 16 sensors has been validated in 50 m indoor conditions and 145 m outdoor condition experiments, respectively. The effective angular resolution under the 0.2 giga 24 Hz video output is 18 µrad, which is two times the lens instantaneous FOV. Compared with other gigapixel cameras, it is superior in terms of optical system simplicity and cost efficiency, which would potentially benefit numerous unmanned aerial vehicle photogrammetric applications that pursue high angular resolution over moderate FOV.

Temporal multiplexing of the scientific grade camera for hyper-frame-rate imaging.

In order to maximize the spatio-temporal resolution of the scientific grade camera at width-limited ROI, this paper proposes a new hyper-frame-rate imaging method by temporal multiplexing the sub-region of the image sensor. In the system, a dual-axis scanning galvanometer is localized at the relay pupil plane and a high quality scan lens is utilized to form an image-side telecentric path. Following this path can overcome bandwidth waste in the conventional exposure and readout mode, and maintain other performances of image sensors. As a result, the sCMOS camera has performed 432fps over 820 × 700 pixel arrays to record the dynamic heartbeat of zebrafish larvae.