Genetically encodable calcium ion (Ca) indicators (GECIs) based on green fluorescent proteins (GFP) are powerful tools for imaging of cell signaling and neural activity in model organisms. Following almost 2 decades of steady improvements in the GFP-based GCaMP series of GECIs, the performance of the most recent generation (i.e., jGCaMP7) may have reached its practical limit due to the inherent properties of GFP. In an effort to sustain the steady progression toward ever-improved GECIs, we undertook the development of a new GECI based on the bright monomeric GFP, mNeonGreen (mNG). The resulting indicator, mNG-GECO1, is 60% brighter than GCaMP6s in vitro and provides comparable performance as demonstrated by imaging Ca dynamics in cultured cells, primary neurons, and in vivo in larval zebrafish. These results suggest that mNG-GECO1 is a promising next-generation GECI that could inherit the mantle of GCaMP and allow the steady improvement of GECIs to continue for generations to come.

Imaging changes in membrane potential using genetically encoded fluorescent voltage indicators (GEVIs) has great potential for monitoring neuronal activity with high spatial and temporal resolution. Brightness and photostability of fluorescent proteins and rhodopsins have limited the utility of existing GEVIs. We engineered a novel GEVI, "Voltron", that utilizes bright and photostable synthetic dyes instead of protein-based fluorophores, extending the combined duration of imaging and number of neurons imaged simultaneously by more than tenfold relative to existing GEVIs. We used Voltron for in vivo voltage imaging in mice, zebrafish, and fruit flies. In mouse cortex, Voltron allowed single-trial recording of spikes and subthreshold voltage signals from dozens of neurons simultaneously, over 15 min of continuous imaging. In larval zebrafish, Voltron enabled the precise correlation of spike timing with behavior.

Increasing the volumetric imaging speed of light-sheet microscopy will improve its ability to detect fast changes in neural activity. Here, a system is introduced for brain-wide imaging of neural activity in the larval zebrafish by coupling structured illumination with cubic phase extended depth-of-field (EDoF) pupil encoding. This microscope enables faster light-sheet imaging and facilitates arbitrary plane scanning—removing constraints on acquisition speed, alignment tolerances, and physical motion near the sample. The usefulness of this method is demonstrated by performing multi-plane calcium imaging in the fish brain with a 416×832×160  μm field of view at 33 Hz. The optomotor response behavior of the zebrafish is monitored at high speeds, and time-locked correlations of neuronal activity are resolved across its brain.

The small size and translucency of larval zebrafish () have made it a unique experimental system to investigate whole-brain neural circuit structure and function. Still, the connectivity patterns between most neuronal types remain mostly unknown. This gap in knowledge underscores the critical need for effective neural circuit mapping tools, especially ones that can integrate structural and functional analyses. To address this, we previously developed a vesicular stomatitis virus (VSV) based approach called Tracer with Restricted Anterograde Spread (TRAS). TRAS utilizes lentivirus to complement replication-incompetent VSV (VSVΔG) to allow restricted (monosynaptic) anterograde labeling from projection neurons to their target cells in the brain. Here, we report the second generation of TRAS (TRAS-M51R), which utilizes a mutant variant of VSVΔG [VSV(M51R)ΔG] with reduced cytotoxicity. Within the primary visual pathway, we found that TRAS-M51R significantly improved long-term viability of transsynaptic labeling (compared to TRAS) while maintaining anterograde spread activity. By using Cre-expressing VSV(M51R)ΔG, TRAS-M51R could selectively label excitatory ( positive) and inhibitory ( positive) retinorecipient neurons. We further show that these labeled excitatory and inhibitory retinorecipient neurons retained neuronal excitability upon visual stimulation at 5-8 days post fertilization (2-5 days post-infection). Together, these findings show that TRAS-M51R is suitable for neural circuit studies that integrate structural connectivity, cell-type identity, and neurophysiology.

Calcium imaging has been widely adopted for its ability to record from large neuronal populations. To summarize the time course of neural activity, dimensionality reduction methods, which have been applied extensively to population spiking activity, may be particularly useful. However, it is unclear if the dimensionality reduction methods applied to spiking activity are appropriate for calcium imaging. We thus carried out a systematic study of design choices based on standard dimensionality reduction methods. We also developed a novel method to perform deconvolution and dimensionality reduction simultaneously (termed CILDS). CILDS most accurately recovered the single-trial, low-dimensional time courses from calcium imaging that would have been recovered from spiking activity. CILDS also outperformed the other methods on calcium imaging recordings from larval zebrafish and mice. More broadly, this study represents a foundation for summarizing calcium imaging recordings of large neuronal populations using dimensionality reduction in diverse experimental settings.

Calcium imaging with protein-based indicators is widely used to follow neural activity in intact nervous systems, but current protein sensors report neural activity at timescales much slower than electrical signalling and are limited by trade-offs between sensitivity and kinetics. Here we used large-scale screening and structure-guided mutagenesis to develop and optimize several fast and sensitive GCaMP-type indicators. The resulting 'jGCaMP8' sensors, based on the calcium-binding protein calmodulin and a fragment of endothelial nitric oxide synthase, have ultra-fast kinetics (half-rise times of 2 ms) and the highest sensitivity for neural activity reported for a protein-based calcium sensor. jGCaMP8 sensors will allow tracking of large populations of neurons on timescales relevant to neural computation.

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful patterns embedded within complex and rich data sources. Here, we introduce Activity Quantification and Analysis (AQuA2), a fast, accurate and versatile data analysis platform built upon advanced machine learning techniques. It decomposes complex live imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a range of biosensors (calcium, norepinephrine, ATP, acetylcholine, dopamine), cell types (astrocytes, oligodendrocytes, microglia, neurons), organs (brains and spinal cords), animal models (zebrafish and mouse), and imaging modalities (confocal, two-photon, light sheet). As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, and distinct sensorimotor signal propagation patterns in the mouse spinal cord.

Differentiable simulations of optical systems can be combined with deep learning-based reconstruction networks to enable high performance computational imaging via end-to-end (E2E) optimization of both the optical encoder and the deep decoder. This has enabled imaging applications such as 3D localization microscopy, depth estimation, and lensless photography via the optimization of local optical encoders. More challenging computational imaging applications, such as 3D snapshot microscopy which compresses 3D volumes into single 2D images, require a highly non-local optical encoder. We show that existing deep network decoders have a locality bias which prevents the optimization of such highly non-local optical encoders. We address this with a decoder based on a shallow neural network architecture using global kernel Fourier convolutional neural networks (FourierNets). We show that FourierNets surpass existing deep network based decoders at reconstructing photographs captured by the highly non-local DiffuserCam optical encoder. Further, we show that FourierNets enable E2E optimization of highly non-local optical encoders for 3D snapshot microscopy. By combining FourierNets with a large-scale multi-GPU differentiable optical simulation, we are able to optimize non-local optical encoders 170× to 7372× larger than prior state of the art, and demonstrate the potential for ROI-type specific optical encoding with a programmable microscope.

When a behavior repeatedly fails to achieve its goal, animals often give up and become passive, which can be strategic for preserving energy or regrouping between attempts. It is unknown how the brain identifies behavioral failures and mediates this behavioral-state switch. In larval zebrafish swimming in virtual reality, visual feedback can be withheld so that swim attempts fail to trigger expected visual flow. After tens of seconds of such motor futility, animals became passive for similar durations. Whole-brain calcium imaging revealed noradrenergic neurons that responded specifically to failed swim attempts and radial astrocytes whose calcium levels accumulated with increasing numbers of failed attempts. Using cell ablation and optogenetic or chemogenetic activation, we found that noradrenergic neurons progressively activated brainstem radial astrocytes, which then suppressed swimming. Thus, radial astrocytes perform a computation critical for behavior: they accumulate evidence that current actions are ineffective and consequently drive changes in behavioral states.

The ability to measure synaptic connectivity and properties is essential for understanding neuronal circuits. However, existing methods that allow such measurements at cellular resolution are laborious and technically demanding. Here, we describe a system that allows such measurements in a high-throughput way by combining two-photon optogenetics and volumetric Ca2+ imaging with whole-cell recording. We reveal a circuit motif for generating fast undulatory locomotion in zebrafish.