Imaging whole nervous systems: insights into behavior from worms to fish

Statistically unbiased prediction enables accurate denoising of voltage imaging data

Here we report SUPPORT (statistically unbiased prediction utilizing spatiotemporal information in imaging data), a self-supervised learning method for removing Poisson-Gaussian noise in voltage imaging data. SUPPORT is based on the insight that a pixel value in voltage imaging data is highly dependent on its spatiotemporal neighboring pixels, even when its temporally adjacent frames alone do not provide useful information for statistical prediction. Such dependency is captured and used by a convolutional neural network with a spatiotemporal blind spot to accurately denoise voltage imaging data in which the existence of the action potential in a time frame cannot be inferred by the information in other frames. Through simulations and experiments, we show that SUPPORT enables precise denoising of voltage imaging data and other types of microscopy image while preserving the underlying dynamics within the scene.

Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising

Calcium imaging has transformed neuroscience research by providing a methodology for monitoring the activity of neural circuits with single-cell resolution. However, calcium imaging is inherently susceptible to detection noise, especially when imaging with high frame rate or under low excitation dosage. Here we developed DeepCAD, a self-supervised deep-learning method for spatiotemporal enhancement of calcium imaging data that does not require any high signal-to-noise ratio (SNR) observations. DeepCAD suppresses detection noise and improves the SNR more than tenfold, which reinforces the accuracy of neuron extraction and spike inference and facilitates the functional analysis of neural circuits.

Light-field microscopy for fast volumetric brain imaging

Recording neural activities over large populations is critical for a better understanding of the functional mechanisms of animal brains. Traditional optical imaging technologies for in vivo neural activity recording are usually limited in throughput and cannot cover a large imaging volume at high speed. Light-field microscopy features a highly parallelized imaging collection mechanism and can simultaneously record optical signals from different depths. Therefore, it can potentially increase the imaging throughput substantially. Furthermore, its unique instantaneous volumetric imaging capability enables the capture of highly dynamic processes, such as recording whole-animal neural activities in freely moving Caenorhabditis elegans and whole-brain neural activity in freely swimming larval zebrafish during prey capture. Here, we summarize the principles of and considerations in the practical implementation of light-field microscopy as currently applied in biological imaging experiments. We also discuss the strategies that light-field microscopy can employ when imaging thick tissues in the presence of scattering and background interference. Finally, we present a few examples of applying light-field microscopy in neuroscientific studies in several important animal models.

Theoretical principles for illuminating sensorimotor processing with brain-wide neuronal recordings

Modern recording techniques now permit brain-wide sensorimotor circuits to be observed at single neuron resolution in small animals. Extracting theoretical understanding from these recordings requires principles that organize findings and guide future experiments. Here we review theoretical principles that shed light onto brain-wide sensorimotor processing. We begin with an analogy that conceptualizes principles as streetlamps that illuminate the empirical terrain, and we illustrate the analogy by showing how two familiar principles apply in new ways to brain-wide phenomena. We then focus the bulk of the review on describing three more principles that have wide utility for mapping brain-wide neural activity, making testable predictions from highly parameterized mechanistic models, and investigating the computational determinants of neuronal response patterns across the brain.

Aptamers, the Nucleic Acid Antibodies, in Cancer Therapy

The arrival of the monoclonal antibody (mAb) technology in the 1970s brought with it the hope of conquering cancers to the medical community. However, mAbs, on the whole, did not achieve the expected wonder in cancer therapy although they do have demonstrated successfulness in the treatment of a few types of cancers. In 1990, another technology of making biomolecules capable of specific binding appeared. This technique, systematic evolution of ligands by exponential enrichment (SELEX), can make aptamers, single-stranded DNAs or RNAs that bind targets with high specificity and affinity. Aptamers have some advantages over mAbs in therapeutic uses particularly because they have little or no immunogenicity, which means the feasibility of repeated use and fewer side effects. In this review, the general properties of the aptamer, the advantages and limitations of aptamers, the principle and procedure of aptamer production with SELEX, particularly the undergoing studies in aptamers for cancer therapy, and selected anticancer aptamers that have entered clinical trials or are under active investigations are summarized.

Dynamics of pattern formation and emergence of swarming in Caenorhabditis elegans

Many animals collectively form complex patterns to tackle environmental difficulties. Several biological and physical factors, such as animal motility, population densities, and chemical cues, play significant roles in this process. However, very little is known about how sensory information interplays with these factors and controls the dynamics of pattern formation. Here, we study the direct relation between oxygen sensing, pattern formation, and emergence of swarming in active Caenorhabditis elegans aggregates. We find that when thousands of animals gather on food, bacteria-mediated decrease in oxygen level slows down the animals and triggers motility-induced phase separation. Three coupled factors—bacterial accumulation, aerotaxis, and population density—act together and control the entire dynamics. Furthermore, we find that biofilm-forming bacterial lawns including Bacillus subtilis and Pseudomonas aeruginosa strongly alter the collective dynamics due to the limited diffusibility of bacteria. Additionally, our theoretical model captures behavioral differences resulting from genetic variations and oxygen sensitivity.

Two-photon Bessel beam tomography for fast volume imaging

Light microscopy on dynamic samples, for example neural activity in the brain, often requires imaging volumes that extend over several 100 µm in axial direction at a rate of at least several tens of Hertz. Here, we develop a tomography approach for scanning fluorescence microscopy which allows recording a volume image in a single frame scan. Volumes are imaged by simultaneously recording four independent projections at different angles using temporally multiplexed, tilted Bessel beams. From the resulting projections, three-dimensional images are reconstructed using inverse Radon transforms combined with convolutional neural networks (U-net).