Whole-brain functional imaging at cellular resolution using light-sheet microscopy

Chemical imaging for biological systems: techniques, AI-driven processing, and applications

Visualizing the chemical compositions of biological samples is pivotal to advancing biological sciences, with the past two decades witnessing the emergence of innovative chemical imaging platforms such as single-molecule imaging, coherent Raman scattering microscopy, transient absorption microscopy, photothermal microscopy, ambient ionization mass spectrometry, electrochemical microscopy, and advanced chemical probes. These technologies have enabled significant breakthroughs in diagnosing pathological transitions, designing targeted therapies, and understanding drug resistance mechanisms. Recent advancements in resolution, contrast, sensitivity, and speed have transformed the field, with techniques like fluorescence, infrared absorption, and Raman scattering being widely applied across diverse biological domains. This review provides a comprehensive overview of the evolution and current state of chemical imaging technologies, coupled with systematic analyses of data processing workflows, including pre-processing, machine learning-assisted pattern extraction, and neural network-based predictions. Artificial intelligence (AI) and machine learning-assisted imaging are transforming chemical imaging through key advancements such as improved resolution and sensitivity via noise reduction techniques, enhanced data analysis (e.g., spectral unmixing, pattern recognition), automated feature extraction using neural networks, real-time processing via high-performance cluster, and data fusion across optical platforms. These innovations are significantly advancing both current applications and the future development of chemical imaging techniques in biomedical research. However, several critical challenges remain, including the scarcity of high-quality training datasets, limited generalizability across different instruments and experimental conditions, high computational costs, challenges in output interpretability and trust, and the lack of standardized validation protocols across different approaches. Looking ahead, the integration of bioimaging into cell biology, lipid research, tumor studies, microbiology, neurobiology, and developmental biology is anticipated to expand its impact, aided by interdisciplinary expertise in biochemistry, physics, and optical engineering. These developments promise unprecedented resolution and speed, facilitating high-speed, high-resolution imaging of living systems, with applications leading to discoveries such as biomarkers for cancer aggressiveness and drug resistance. Moreover, the miniaturization and commercialization of imaging platforms are broadening accessibility, enabling on-site clinical investigations and in vivo measurements, underscoring the transformative potential of chemical imaging in advancing biological science and medical research.

Altered locomotion and anxiety after exposure to SiO2 nanoparticles in larval zebrafish

Nanoparticles (NP) have been driving the rapid advancement of nanomedicines in recent decades. However, their wide application also raises safety concerns, particularly their neurotoxicity due to their ability to cross the blood-brain barrier and accumulate in the brain, which remains largely underexplored. Here, we used silica nanoparticles (SiO2 NP) as a model to study the neurotoxicity of nanomedicine, based on their general features and functionalities. Using the light/dark preference behavioral assays of larval zebrafish, we focused on the neurotoxic consequences of exposure to an array of low concentrations of SiO2 NP, which reflected real-world conditions compared to previous studies, and examined the effect of different exposure durations. We observed dose-dependent and temporally sensitive changes in locomotor activities and elevated anxiety-related behaviors after exposure. Strikingly, exposed animals exhibited biphasic alteration: hypo-locomotion after 24-hour exposure and hyper-locomotion after 48-hour exposure. Our work provided real-world relevant behavioral insights, and highlighted the biphasic response and the temporal sensitivity of the SiO2 NP neurotoxicity. These findings underscore the potential neurotoxic risks of nanomedicine applications and emphasize the urgent need for further research into NP-associated neurotoxicity and public awareness.

On analogies in vertebrate and insect visual systems

Despite the large evolutionary distance between vertebrates and insects, the visual systems of these two taxa bear remarkable similarities that have been noted repeatedly, including by pioneering neuroanatomists such as Ramón y Cajal. Fuelled by the advent of transgenic approaches in neuroscience, studies of visual system anatomy and function in both vertebrates and insects have made dramatic progress during the past two decades, revealing even deeper analogies between their visual systems than were noted by earlier observers. Such across-taxa comparisons have tended to focus on either elementary motion detection or relatively peripheral layers of the visual systems. By contrast, the aims of this Review are to expand the scope of this comparison to pathways outside visual motion detection, as well as to deeper visual structures. To achieve these aims, we primarily discuss examples from recent work in larval zebrafish (Danio rerio) and the fruitfly (Drosophila melanogaster), a pair of genetically tractable model organisms with comparatively sized, small brains. In particular, we argue that the brains of both vertebrates and insects are equipped with third-order visual structures that specialize in shared behavioural tasks, including postural and course stabilization, approach and avoidance, and some other behaviours. These wider analogies between the two distant taxa highlight shared behavioural goals and associated evolutionary constraints and suggest that studies on vertebrate and insect vision have a lot to inspire each other.

Spatiotemporal calcium dynamics orchestrate oligodendrocyte development and myelination

Oligodendrocyte lineage cells (OLCs), comprising oligodendrocyte precursor cells (OPCs) and oligodendrocytes, are pivotal in sculpting central nervous system (CNS) architecture and function. OPCs mature into oligodendrocytes, which enwrap axons with myelin sheaths that are critical for enhancing neural transmission. Notably, OLCs actively respond to neuronal activity, modulating neural circuit functions. Understanding neuron-OLC interactions is key to unraveling how OLCs contribute to CNS health and pathology. This review highlights insights from zebrafish and mouse models, revealing how synaptic and extrasynaptic pathways converge to shape spatiotemporal calcium (Ca2+) dynamics within OLCs. We explore how Ca2+ signal integration across spatial and temporal scales acts as a master regulator of OLC fate determination and myelin plasticity.

Seasonal and comparative evidence of adaptive gene expression in mammalian brain size plasticity

Contrasting almost all other mammalian wintering strategies, Eurasian common shrews, Sorex araneus, endure winter by shrinking their brain, skull, and most organs, only to then regrow to breeding size the following spring. How such tiny mammals achieve this unique brain size plasticity while maintaining activity through the winter remains unknown. To discover potential adaptations underlying this trait, we analyzed seasonal differential gene expression in the shrew hypothalamus, a brain region that both regulates metabolic homeostasis and drastically changes size, and compared hypothalamus gene expression across species. We discovered seasonal variation in suites of genes involved in energy homeostasis and apoptosis, shrew-specific upregulation of genes involved in the development of the hypothalamic blood-brain barrier and calcium signaling, as well as overlapping seasonal and comparative gene expression divergence in genes implicated in the development and progression of human neurological and metabolic disorders, including CCDC22. With high metabolic rates and facing harsh winter conditions, S. araneus have evolved both adaptive and plastic mechanisms to sense and regulate their energy budget. Many of these changes mirrored those identified in human neurological and metabolic disease, highlighting the interactions between metabolic homeostasis, brain size plasticity, and longevity.

Scattering Reduction and Axial Resolution Enhancement in Light-Sheet Fluorescence Microscopy

Light-sheet fluorescence microscopy (LSFM) provides an ideal tool for long-term observation of live specimens due to its low photodamage and fast volumetric imaging speed. The wavefront distortions in the illumination path of LSFM will reduce the intensity and broaden the light-sheet thickness, thereby degrading the image quality. We propose to use the wavefront shaping technique to reduce the scattering effect and shrink the light-sheet thickness. Scanning the refocused laser beam to generate LS improves both the fluorescence intensity and the axial resolution. The axial resolution can be further enhanced by subtracting the two images captured via double scanning the samples with the refocused beam and the uncorrected scattered beam for each slice. The axial resolution is improved from 2.2 ± 0.3 to 1.5 ± 0.2 μm across the field of view of 270 μm × 270 μm. The effectiveness of the wavefront shaping subtraction method is demonstrated by imaging fluorescent beads and Aspergillus conidiophores behind a scattering medium.

Brain-Wide Impacts of Sedation on Spontaneous Activity and Auditory Processing in Larval Zebrafish

Despite their widespread use, we have limited knowledge of the mechanisms by which sedatives mediate their effects on brain-wide networks. This is, in part, due to the technical challenge of observing activity across large populations of neurons in normal and sedated brains. In this study, we examined the effects of the sedative dexmedetomidine, and its antagonist atipamezole, on spontaneous brain dynamics and auditory processing in zebrafish larvae, a stage when sex differentiation has not yet occurred. Our brain-wide, cellular-resolution calcium imaging reveals the brain regions involved in these network-scale dynamics and the individual neurons that are affected within those regions. Further analysis reveals a variety of dynamic changes in the brain at baseline, including marked reductions in spontaneous activity, correlation, and variance. The reductions in activity and variance represent a "quieter" brain state during sedation, an effect inducing highly correlated evoked activity in the auditory system to stand out more than it does in unsedated brains. We also observe a reduction in the persistence of auditory information across the brain during sedation, suggesting that the removal of spontaneous activity leaves the core auditory pathway free of impingement from other nonauditory information. Finally, we describe a less dynamic brain-wide network during sedation, with a higher energy barrier and a lower probability of brain state transitions during sedation. Overall, our brain-wide, cellular-resolution analysis shows that sedation leads to a quieter, more stable, and less dynamic brain and, that against this background, responses across the auditory processing pathway become sharper and more prominent.

Imaging the cerebral vasculature at different scales: translational tools to investigate the neurovascular interfaces

The improvements in imaging technology opened up the possibility to investigate the structure and function of cerebral vasculature and the neurovascular unit with unprecedented precision and gaining deep insights not only on the morphology of the vessels but also regarding their function and regulation related to the cerebral activity. In this review, we will dissect the different imaging capabilities regarding the cerebrovascular tree, the neurovascular unit, the haemodynamic response function, and thus, the vascular-neuronal coupling. We will discuss both clinical and preclinical setting, with a final discussion on the current scenery in cerebrovascular imaging where magnetic resonance imaging and multimodal microscopy emerge as the most potent and versatile tools, respectively, in the clinical and preclinical context.

High-Throughput Volumetric Mapping Facilitated by Active Tissue SHRINK

Comprehensive visualization of tissue architecture in large organs such as the brain is crucial for understanding functional relationships across key tissue regions. However, the large size of whole organs makes it challenging to image their entirety with subcellular resolution, often requiring prolonged imaging sessions, volume reconstruction, and compromises in spatial coverage. Here, Scalable Hydrogel-embedded Rapid Imaging of tissue NetworK (SHRINK) is reported to address this challenge through active tissue shrinkage and clearing. Utilizing the identified hydrogel network to preserve the spatial pattern of proteins in situ and remove the uncrosslinked biomolecules to create space, it is shown that SHRINK isotropically drives the reduction of sample sizes down to 16% of their original volume while maintaining high cellular and tissue-level integrity in a reversible manner. The size reduction and the corresponding 3D concentrating of the biomolecules render a more than sixfold enhancement for throughput and signal respectively, which addresses a key bottleneck for the stimulated Raman scattering (SRS) microscopy, ideal for 3D, label-free and super-multiplex tissue mapping. It is further demonstrated that SHRINK-SRS achieves organ-scale mapping of brain, intestine, heart, and kidney tissues. SHRINK offers a powerful approach to overcome traditional imaging barriers, enabling rapid and detailed visualization of large organs.

A sensitive soma-localized red fluorescent calcium indicator for in vivo imaging of neuronal populations at single-cell resolution

Recent advancements in genetically encoded calcium indicators, particularly those based on green fluorescent proteins, have optimized their performance for monitoring neuronal activities in a variety of model organisms. However, progress in developing red-shifted GECIs, despite their advantages over green indicators, has been slower, resulting in fewer options for end users. In this study, we explored topological inversion and soma-targeting strategies, which are complementary to conventional mutagenesis, to re-engineer a red genetically encoded calcium indicator, FRCaMP, for enhanced in vivo performance. The resulting sensors, FRCaMPi and soma-targeted FRCaMPi (SomaFRCaMPi), exhibit up to 2-fold higher dynamic range and peak ΔF/F0 per single AP compared to widely used jRGECO1a in neurons both in culture and in vivo. Compared to jRGECO1a and FRCaMPi, SomaFRCaMPi reduces erroneous correlation of neuronal activity in the brains of mice and zebrafish by two- to 4-fold due to diminished neuropil contamination without compromising the signal-to-noise ratio. Under wide-field imaging in primary somatosensory and visual cortices in mice with high labeling density (80-90%), SomaFRCaMPi exhibits up to 40% higher SNR and decreased artifactual correlation across neurons. Altogether, SomaFRCaMPi improves the accuracy and scale of neuronal activity imaging at single-neuron resolution in densely labeled brain tissues due to a 2-3-fold enhanced automated neuronal segmentation, 50% higher fraction of responsive cells, up to 2-fold higher SNR compared to jRGECO1a. Our findings highlight the potential of SomaFRCaMPi, comparable to the most sensitive soma-targeted GCaMP, for precise spatial recording of neuronal populations using popular imaging modalities in model organisms such as zebrafish and mice.

A multiwell plate approach to increase the sample throughput during tissue clearing

Tissue clearing, coupled with immunostaining, enables the transition from two-dimensional to three-dimensional pathology and has the potential to substantially improve data quality for biomedical diagnostics. Nevertheless, the workflows are limited by the complex sample processing protocols. Approaches for the parallel processing of samples, to include tissue clearing, immunostaining, imaging and analysis can increase three-dimensional pathology throughput. Here we detail a step-by-step approach that combines a tissue clearing device with a six-well multiwell plate to increase the throughput compared with methods using conventional clearing protocols. The six-well multiplate allows for parallel tissue clearing of multiple samples and is compatible with passive tissue clearing methods including Clear, Unobstructed Brain/Body Imaging Cocktails and Computational (CUBIC) analysis. In addition, gel embedding is performed without moving the samples from the wells, and a series of steps such as imaging with a high-speed light-sheet microscope and analysis in the cloud can be performed. Although this procedure slightly extends the overall time required for preparing and analyzing a single sample, it reduces the effort required at each step, such as reagent exchange and gel embedding, which results in an overall reduction in hands-on time due to the parallel sample processing. We describe a series of whole-organ analyses, including high-throughput tissue clearing, staining, gel embedding, imaging and data analysis in the cloud, as a useful platform for cellular biology and pathology. The total process varies depending on the presence or absence of immunostaining, but for some six-well plates, the tissue clearing process, imaging and data analysis can be completed within 10 d.

Modeling conditional distributions of neural and behavioral data with masked variational autoencoders

Extracting the relationship between high-dimensional neural recordings and complex behavior is a ubiquitous problem in neuroscience. Encoding and decoding models target the conditional distribution of neural activity given behavior and vice versa, while dimensionality reduction techniques extract low-dimensional representations thereof. Variational autoencoders (VAEs) are flexible tools for inferring such low-dimensional embeddings but struggle to accurately model arbitrary conditional distributions such as those arising in neural encoding and decoding, let alone simultaneously. Here, we present a VAE-based approach for calculating such conditional distributions. We first validate our approach on a task with known ground truth. Next, we retrieve conditional distributions over masked body parts of walking flies. Finally, we decode motor trajectories from neural activity in a monkey-reach task and query the same VAE for the encoding distribution. Our approach unifies dimensionality reduction and learning conditional distributions, allowing the scaling of common analyses in neuroscience to today's high-dimensional multi-modal datasets.

Deep learning image analysis for continuous single-cell imaging of dynamic processes in Plasmodium falciparum-infected erythrocytes

Continuous high-resolution imaging of the disease-mediating blood stages of the human malaria parasite Plasmodium falciparum faces challenges due to photosensitivity, small parasite size, and the anisotropy and large refractive index of host erythrocytes. Previous studies often relied on snapshot galleries from multiple cells, limiting the investigation of dynamic cellular processes. We present a workflow enabling continuous, single-cell monitoring of live parasites throughout the 48-hour intraerythrocytic life cycle with high spatial and temporal resolution. This approach integrates label-free, three-dimensional differential interference contrast and fluorescence imaging using an Airyscan microscope, automated cell segmentation through pre-trained deep-learning algorithms, and 3D rendering for visualization and time-resolved analyses. As a proof of concept, we applied this workflow to study knob-associated histidine-rich protein (KAHRP) export into the erythrocyte compartment and its clustering beneath the plasma membrane. Our methodology opens avenues for in-depth exploration of dynamic cellular processes in malaria parasites, providing a valuable tool for further investigations.

Development of sensorimotor responses in larval zebrafish: A comparison between wild-type and GCaMP6s transgenic line

During early development, zebrafish larvae exhibit stereotypical behaviors, which rapidly become more complex. Thus, generating mutant transgenic lines that maintain transparency throughout their larval stage and that can be used to record brain activity has offered strategic opportunities to investigate the underlying neural correlates of behavior establishment. However, few studies have documented the sensorimotor profile of these lines during larval development. Here, we set up a behavioral characterization using diverse stimuli (light and vibration) throughout larval development to compare the responses of a transgenic strain expressing a pan-neuronal calcium indicator (GCaMP6s) with that of a wild-type strain. Interestingly, we report a drastic switch in behavioral responses to light transitions at 11 days post-fertilization (dpf) and to vibration stimuli at 14 dpf in both lines. These data highlight a specific time window representing an increase in behavioral complexity. Meanwhile, we found some differences in the maturation of sensorimotor responses between GCaMP6s and wild-type strains. Although some of these differences are minor, they highlight the need for careful attention when using mutant/transgenic lines for behavioral studies. Overall, our results support using GCaMP6s strain in investigating the neural mechanisms underlying the developmental maturation of sensorimotor responses.

Single Objective Light Sheet Microscopy allows high-resolution in vivo brain imaging of Drosophila

In vivo imaging of dynamic sub-cellular brain structures in Drosophila melanogaster is key to understanding several phenomena in neuroscience. However, its implementation has been hindered by a trade-off between spatial resolution, speed, photobleaching, phototoxicity, and setup complexity required to access the specific target regions of the small brain of Drosophila . Here, we present a single objective light-sheet microscope, customized for in vivo imaging of adult flies and optimized for maximum resolution. With it, we imaged the axonal projections of small lateral ventral neurons (known as s-LNvs) in intact adult flies. We imaged the plasma membrane, mitochondria, and dense-core vesicles with high spatial resolution up to 370 nm, ten times lower photobleaching than confocal microscopy, lower invasiveness and complexity in sample mounting than alternative light-sheet technologies, and without relying on phototoxic pulsed infrared lasers. This unique set of features paves the way for new long-term, dynamic studies in the brains of living flies.

A Sensitive Soma-localized Red Fluorescent Calcium Indicator for Multi-Modality Imaging of Neuronal Populations In Vivo

Recent advancements in genetically encoded calcium indicators, particularly those based on green fluorescent proteins, have optimized their performance for monitoring neuronal activities in a variety of model organisms. However, progress in developing red-shifted GECIs, despite their advantages over green indicators, has been slower, resulting in fewer options for end-users. In this study, we explored topological inversion and soma-targeting strategies, which are complementary to conventional mutagenesis, to re-engineer a red genetically encoded calcium indicator, FRCaMP, for enhanced in vivo performance. The resulting sensors, FRCaMPi and soma-targeted FRCaMPi (SomaFRCaMPi), exhibit up to 2-fold higher dynamic range and peak ΔF/F0 per single AP compared to widely used jRGECO1a in neurons in culture and in vivo. Compared to jRGECO1a and FRCaMPi, SomaFRCaMPi reduces erroneous correlation of neuronal activity in the brains of mice and zebrafish by two- to four-fold due to diminished neuropil contamination without compromising the signal-to-noise ratio. Under wide-field imaging in primary somatosensory and visual cortex in mice with high labeling density (80-90%), SomaFRCaMPi exhibits up to 40% higher SNR and decreased artifactual correlation across neurons. Altogether, SomaFRCaMPi improves the accuracy and scale of neuronal activity imaging at single-neuron resolution in densely labeled brain tissues due to a 2-3-fold enhanced automated neuronal segmentation, 50% higher fraction of responsive cells, up to 2-fold higher SNR compared to jRGECO1a. Our findings highlight the potential of SomaFRCaMPi, comparable to the most sensitive soma-targeted GCaMP, for precise spatial recording of neuronal populations using popular imaging modalities in model organisms such as zebrafish and mice.

Fully automated in vivo screening system for multi-organ imaging and pharmaceutical evaluation

Advancements in screening technologies employing small organisms have enabled deep profiling of compounds in vivo. However, current strategies for phenotyping of behaving animals, such as zebrafish, typically involve tedious manipulations. Here, we develop and validate a fully automated in vivo screening system (AISS) that integrates microfluidic technology and computer-vision-based control methods to enable rapid evaluation of biological responses of non-anesthetized zebrafish to molecular gradients. Via precise fluidic control, the AISS allows automatic loading, encapsulation, transportation and immobilization of single-larva in droplets for multi-organ imaging and chemical gradients generation inaccessible in previous systems. Using this platform, we examine the cardiac sensitivity of an antipsychotic drug with multiple concentration gradients, and reveal dramatic diversity and complexity in the accurate chemical regulation of cardiac functions in vivo. This proposed system expands the arsenal of tools available for in vivo screening and facilitates comprehensive profiling of pharmaceuticals.

Unifying community whole-brain imaging datasets enables robust neuron identification and reveals determinants of neuron position in C. elegans

We develop a data harmonization approach for C. elegans volumetric microscopy data, consisting of a standardized format, pre-processing techniques, and human-in-the-loop machine-learning-based analysis tools. Using this approach, we unify a diverse collection of 118 whole-brain neural activity imaging datasets from five labs, storing these and accompanying tools in an online repository WormID (wormid.org). With this repository, we train three existing automated cell-identification algorithms, CPD, StatAtlas, and CRF_ID, to enable accuracy that generalizes across labs, recovering all human-labeled neurons in some cases. We mine this repository to identify factors that influence the developmental positioning of neurons. This growing resource of data, code, apps, and tutorials enables users to (1) study neuroanatomical organization and neural activity across diverse experimental paradigms, (2) develop and benchmark algorithms for automated neuron detection, segmentation, cell identification, tracking, and activity extraction, and (3) share data with the community and comply with data-sharing policies.

Social behavior: A tiny fish with prodigious skills

Identifying new organisms that allow linking brain-wide circuit dynamics to complex adaptive behaviors is an ongoing challenge. A new study has demonstrated that Danionella cerebrum - a miniature teleost fish - is capable of multimodal sensory discrimination and displays oxytocin-dependent social interaction, thus opening the way for a wide range of detailed circuit investigations.

Conserved multisensory integration of social cues in the thalamus

The recognition of conspecifics, animals of the same species, and keeping track of changes in the social environment is essential to all animals. While molecules, circuits, and brain regions that control social behaviors across species are studied in-depth, the neural mechanisms that enable the recognition of social cues are largely obscure. Recent evidence suggests that social cues across sensory modalities converge in a thalamic area conserved across vertebrates. These thalamic neurons control social behavior both via direct synaptic projections to other brain areas relevant for social behavior and by exerting brain-wide neuropeptidergic modulatory influence. Conspecifics are recognized by auditory, visual, and somatosensory cues, as well as mechanosensory inputs. These inputs are mostly processed in the mammalian colliculi and homologous structures in other vertebrates and are subsequently integrated in the posterior thalamus. Increased neuronal activity in this area promotes pro-social behavior across vertebrates. We propose a framework for social cue recognition by conspecific frequency-tuning in the vertebrate thalamus, discuss the potential roles of these conserved social representations and point to open questions.

V-shaped PSF for 3D imaging over an extended depth of field in wide-field microscopy

Single-shot 3D optical microscopy that can capture high-resolution information over a large volume has broad applications in biology. Existing 3D imaging methods using point-spread-function (PSF) engineering often have limited depth of field (DOF) or require custom and often complex design of phase masks. We propose a new, to the best of our knowledge, PSF approach that is easy to implement and offers a large DOF. The PSF appears to be axially V-shaped, engineered by replacing the conventional tube lens with a pair of axicon lenses behind the objective lens of a wide-field microscope. The 3D information can be reconstructed from a single-shot image using a deep neural network. Simulations in a 10× magnification wide-field microscope show the V-shaped PSF offers excellent 3D resolution (<2.5 µm lateral and ∼15 µm axial) over a ∼350 µm DOF at a 550 nm wavelength. Compared to other popular PSFs designed for 3D imaging, the V-shaped PSF is simple to deploy and provides high 3D reconstruction quality over an extended DOF.

Random Illumination Microscopy: faster, thicker, and aberration-insensitive

The Extended Depth of Field (EDF) approach has been combined with Random Illumination Microscopy (RIM) to realize aberration-insensitive, fast super-resolution imaging with extended depth, which is a promising tool for dynamic imaging in larger and thicker live cells and tissues.

BIFROST: A method for registering diverse imaging datasets of the Drosophila brain

Imaging methods that span both functional measures in living tissue and anatomical measures in fixed tissue have played critical roles in advancing our understanding of the brain. However, making direct comparisons between different imaging modalities, particularly spanning living and fixed tissue, has remained challenging. For example, comparing brain-wide neural dynamics across experiments and aligning such data to anatomical resources, such as gene expression patterns or connectomes, requires precise alignment to a common set of anatomical coordinates. However, reaching this goal is difficult because registering in vivo functional imaging data to ex vivo reference atlases requires accommodating differences in imaging modality, microscope specification, and sample preparation. We overcome these challenges in Drosophila by building an in vivo reference atlas from multiphoton-imaged brains, called the Functional Drosophila Atlas. We then develop a registration pipeline, BrIdge For Registering Over Statistical Templates (BIFROST), for transforming neural imaging data into this common space and for importing ex vivo resources such as connectomes. Using genetically labeled cell types as ground truth, we demonstrate registration with a precision of less than 10 microns. Overall, BIFROST provides a pipeline for registering functional imaging datasets in the fly, both within and across experiments.

Analysis methods for large-scale neuronal recordings

Simultaneous recordings from hundreds or thousands of neurons are becoming routine because of innovations in instrumentation, molecular tools, and data processing software. Such recordings can be analyzed with data science methods, but it is not immediately clear what methods to use or how to adapt them for neuroscience applications. We review, categorize, and illustrate diverse analysis methods for neural population recordings and describe how these methods have been used to make progress on longstanding questions in neuroscience. We review a variety of approaches, ranging from the mathematically simple to the complex, from exploratory to hypothesis-driven, and from recently developed to more established methods. We also illustrate some of the common statistical pitfalls in analyzing large-scale neural data.

The recurrent temporal restricted Boltzmann machine captures neural assembly dynamics in whole-brain activity

Animal behaviour alternates between stochastic exploration and goal-directed actions, which are generated by the underlying neural dynamics. Previously, we demonstrated that the compositional Restricted Boltzmann Machine (cRBM) can decompose whole-brain activity of larval zebrafish data at the neural level into a small number (∼100-200) of assemblies that can account for the stochasticity of the neural activity (van der Plas et al., eLife, 2023). Here, we advance this representation by extending to a combined stochastic-dynamical representation to account for both aspects using the recurrent temporal RBM (RTRBM) and transfer-learning based on the cRBM estimate. We demonstrate that the functional advantage of the RTRBM is captured in the temporal weights on the hidden units, representing neural assemblies, for both simulated and experimental data. Our results show that the temporal expansion outperforms the stochastic-only cRBM in terms of generalization error and achieves a more accurate representation of the moments in time. Lastly, we demonstrate that we can identify the original time-scale of assembly dynamics by estimating multiple RTRBMs at different temporal resolutions. Together, we propose that RTRBMs are a valuable tool for capturing the combined stochastic and time-predictive dynamics of large-scale data sets.

Adult zebrafish can learn Morris water maze-like tasks in a two-dimensional virtual reality system

Virtual reality (VR) has emerged as a powerful tool for investigating neural mechanisms of decision-making, spatial cognition, and navigation. In many head-fixed VRs for rodents, animals locomote on spherical treadmills that provide rotation information in two axes to calculate two-dimensional (2D) movement. On the other hand, zebrafish in a submerged head-fixed VR can move their tail to enable movement in 2D VR environment. This motivated us to create a VR system for adult zebrafish to enable 2D movement consisting of forward translation and rotations calculated from tail movement. Besides presenting the VR system, we show that zebrafish can learn a virtual Morris water maze-like (VMWM) task in which finding an invisible safe zone was necessary for the zebrafish to avoid an aversive periodic mild electric shock. Results show high potential for our VR system to be combined with optical imaging for future studies to investigate spatial learning and navigation.

Few-shot meta-learning applied to whole brain activity maps improves systems neuropharmacology and drug discovery

In this study, we present an approach to neuropharmacological research by integrating few-shot meta-learning algorithms with brain activity mapping (BAMing) to enhance the discovery of central nervous system (CNS) therapeutics. By utilizing patterns from previously validated CNS drugs, our approach facilitates the rapid identification and prediction of potential drug candidates from limited datasets, thereby accelerating the drug discovery process. The application of few-shot meta-learning algorithms allows us to adeptly navigate the challenges of limited sample sizes prevalent in neuropharmacology. The study reveals that our meta-learning-based convolutional neural network (Meta-CNN) models demonstrate enhanced stability and improved prediction accuracy over traditional machine-learning methods. Moreover, our BAM library proves instrumental in classifying CNS drugs and aiding in pharmaceutical repurposing and repositioning. Overall, this research not only demonstrates the effectiveness in overcoming data limitations but also highlights the significant potential of combining BAM with advanced meta-learning techniques in CNS drug discovery.

Loss of Stim2 in zebrafish induces glaucoma-like phenotype

Calcium is involved in vision processes in the retina and implicated in various pathologies, including glaucoma. Rod cells rely on store-operated calcium entry (SOCE) to safeguard against the prolonged lowering of intracellular calcium ion concentrations. Zebrafish that lacked the endoplasmic reticulum Ca2+ sensor Stim2 (stim2 knockout [KO]) exhibited impaired vision and lower light perception-related gene expression. We sought to understand mechanisms that are responsible for vision impairment in stim2 KO zebrafish. The single-cell RNA (scRNA) sequencing of neuronal cells from brains of 5 days postfertilization larvae distinguished 27 cell clusters, 10 of which exhibited distinct gene expression patterns, including amacrine and γ-aminobutyric acid (GABA)ergic retinal interneurons and GABAergic optic tectum cells. Five clusters exhibited significant changes in cell proportions between stim2 KO and controls, including GABAergic diencephalon and optic tectum cells. Transmission electron microscopy of stim2 KO zebrafish revealed decreases in width of the inner plexiform layer, ganglion cells, and their dendrites numbers (a hallmark of glaucoma). GABAergic neuron densities in the inner nuclear layer, including amacrine cells, as well as photoreceptors significantly decreased in stim2 KO zebrafish. Our study suggests a novel role for Stim2 in the regulation of neuronal insulin expression and GABAergic-dependent vision causing glaucoma-like retinal pathology.

Model based optimization for refractive index mismatched light sheet imaging

Selective plane illumination microscopy (SPIM) is an optical sectioning imaging approach based on orthogonal light pathways for excitation and detection. The excitation pathway has an inverse relation between the optical sectioning strength and the effective field of view (FOV). Multiple approaches exist to extend the effective FOV, and here we focus on remote focusing to axially scan the light sheet, synchronized with a CMOS camera's rolling shutter. A typical axially scanned SPIM configuration for imaging large samples utilizes a tunable optic for remote focusing, paired with air objectives focused into higher refractive index media. To quantitatively explore the effect of remote focus choices and sample space refractive index mismatch on light sheet intensity distributions, we developed an open-source computational approach for integrating ray tracing and field propagation. We validate our model's performance against experimental light sheet profiles for various SPIM configurations. Our findings indicate that optimizing the position of the sample chamber relative to the excitation optics can enhance image quality by balancing aberrations induced by refractive index mismatch. We validate this prediction using a home-built, large sample axially scanned SPIM configuration and calibration samples. Our open-source, extensible modeling software can easily extend to explore optimal imaging configurations in diverse light sheet imaging settings.

A modular chemigenetic calcium indicator for multiplexed in vivo functional imaging

Genetically encoded fluorescent calcium indicators allow cellular-resolution recording of physiology. However, bright, genetically targetable indicators that can be multiplexed with existing tools in vivo are needed for simultaneous imaging of multiple signals. Here we describe WHaloCaMP, a modular chemigenetic calcium indicator built from bright dye-ligands and protein sensor domains. Fluorescence change in WHaloCaMP results from reversible quenching of the bound dye via a strategically placed tryptophan. WHaloCaMP is compatible with rhodamine dye-ligands that fluoresce from green to near-infrared, including several that efficiently label the brain in animals. When bound to a near-infrared dye-ligand, WHaloCaMP shows a 7× increase in fluorescence intensity and a 2.1-ns increase in fluorescence lifetime upon calcium binding. We use WHaloCaMP1a to image Ca2+ responses in vivo in flies and mice, to perform three-color multiplexed functional imaging of hundreds of neurons and astrocytes in zebrafish larvae and to quantify Ca2+ concentration using fluorescence lifetime imaging microscopy (FLIM).

A population code for spatial representation in the zebrafish telencephalon

Spatial learning in teleost fish requires an intact telencephalon1, a brain region that contains putative analogues to components of the mammalian limbic system (for example, hippocampus)2-4. However, cells fundamental to spatial cognition in mammals-for example, place cells (PCs)5,6-have yet to be established in any fish species. In this study, using tracking microscopy to record brain-wide calcium activity in freely swimming larval zebrafish7, we compute the spatial information content8 of each neuron across the brain. Strikingly, in every recorded animal, cells with the highest spatial specificity were enriched in the zebrafish telencephalon. These PCs form a population code of space from which we can decode the animal's spatial location across time. By continuous recording of population-level activity, we found that the activity manifold of PCs refines and untangles over time. Through systematic manipulation of allothetic and idiothetic cues, we demonstrate that zebrafish PCs integrate multiple sources of information and can flexibly remap to form distinct spatial maps. Using analysis of neighbourhood distance between PCs across environments, we found evidence for a weakly preconfigured network in the telencephalon. The discovery of zebrafish PCs represents a step forward in our understanding of spatial cognition across species and the functional role of the early vertebrate telencephalon.

Spontaneous Brain Activity Emerges from Pairwise Interactions in the Larval Zebrafish Brain

Brain activity is characterized by brainwide spatiotemporal patterns that emerge from synapse-mediated interactions between individual neurons. Calcium imaging provides access to in vivo recordings of whole-brain activity at single-neuron resolution and, therefore, allows the study of how large-scale brain dynamics emerge from local activity. In this study, we use a statistical mechanics approach-the pairwise maximum entropy model-to infer microscopic network features from collective patterns of activity in the larval zebrafish brain and relate these features to the emergence of observed whole-brain dynamics. Our findings indicate that the pairwise interactions between neural populations and their intrinsic activity states are sufficient to explain observed whole-brain dynamics. In fact, the pairwise relationships between neuronal populations estimated with the maximum entropy model strongly correspond to observed structural connectivity patterns. Model simulations also demonstrated how tuning pairwise neuronal interactions drives transitions between observed physiological regimes and pathologically hyperexcitable whole-brain regimes. Finally, we use virtual resection to identify the brain structures that are important for maintaining the brain in a physiological dynamic regime. Together, our results indicate that whole-brain activity emerges from a complex dynamical system that transitions between basins of attraction whose strength and topology depend on the connectivity between brain areas.

T-CLEARE: a pilot community-driven tissue clearing protocol repository

Selecting and implementing a tissue clearing protocol is challenging. Established more than 100 years ago, tissue clearing is still a rapidly evolving field of research. There are currently many published protocols to choose from, and each performs better or worse across a range of key evaluation factors (e.g., speed, cost, tissue stability, fluorescence quenching). Additionally, tissue clearing protocols are often optimized for specific experimental contexts, and applying an existing protocol to a new problem can require a lengthy period of adaptation by trial and error. Although the primary literature and review articles provide a useful starting point for optimization, there is growing recognition that results can vary dramatically with changes to tissue type or antibody used. To help address this issue, we have developed a novel, freely available repository of tissue clearing protocols named T-CLEARE (Tissue CLEAring protocol REpository; https://doryworkspace.org/doryviz). T-CLEARE incorporates community responses to an open survey designed to capture details not commonly found in the scientific literature, including modifications to published protocols required for specific use cases and instances when tissue clearing protocols did not perform well (negative results). The goal of T-CLEARE is to help the community share evaluations and modifications of tissue clearing protocols for various tissue types and potentially identify best-in-class methods for a given application.

Decreased GABA levels during development result in increased connectivity in the larval zebrafish tectum

γ-aminobutyric acid (GABA) is an abundant neurotransmitter that plays multiple roles in the vertebrate central nervous system (CNS). In the early developing CNS, GABAergic signaling acts to depolarize cells. It mediates several aspects of neural development, including cell proliferation, neuronal migration, neurite growth, and synapse formation, as well as the development of critical periods. Later in CNS development, GABAergic signaling acts in an inhibitory manner when it becomes the predominant inhibitory neurotransmitter in the brain. This behavior switch occurs due to changes in chloride/cation transporter expression. Abnormalities of GABAergic signaling appear to underlie several human neurological conditions, including seizure disorders. However, the impact of reduced GABAergic signaling on brain development has been challenging to study in mammals. Here we take advantage of zebrafish and light sheet imaging to assess the impact of reduced GABAergic signaling on the functional circuitry in the larval zebrafish optic tectum. Zebrafish have three gad genes: two gad1 paralogs known as gad1a and gad1b, and gad2. The gad1b and gad2 genes are expressed in the developing optic tectum. Null mutations in gad1b significantly reduce GABA levels in the brain and increase electrophysiological activity in the optic tectum. Fast light sheet imaging of genetically encoded calcium indicator (GCaMP)-expressing gab1b null larval zebrafish revealed patterns of neural activity that were different than either gad1b-normal larvae or gad1b-normal larvae acutely exposed to pentylenetetrazole (PTZ). These results demonstrate that reduced GABAergic signaling during development increases functional connectivity and concomitantly hyper-synchronization of neuronal networks.

A practical guide to light-sheet microscopy for nanoscale imaging: Looking beyond the cell

We present a comprehensive guide to light-sheet microscopy (LSM) to assist scientists in navigating the practical implementation of this microscopy technique. Emphasizing the applicability of LSM to image both static microscale and nanoscale features, as well as diffusion dynamics, we present the fundamental concepts of microscopy, progressing through beam profile considerations, to image reconstruction. We outline key practical decisions in constructing a home-built system and provide insight into the alignment and calibration processes. We briefly discuss the conditions necessary for constructing a continuous 3D image and introduce our home-built code for data analysis. By providing this guide, we aim to alleviate the challenges associated with designing and constructing LSM systems and offer scientists new to LSM a valuable resource in navigating this complex field.

CalciSeg: A versatile approach for unsupervised segmentation of calcium imaging data

Recent advances in calcium imaging, including the development of fast and sensitive genetically encoded indicators, high-resolution camera chips for wide-field imaging, and resonant scanning mirrors in laser scanning microscopy, have notably improved the temporal and spatial resolution of functional imaging analysis. Nonetheless, the variability of imaging approaches and brain structures challenges the development of versatile and reliable segmentation methods. Standard techniques, such as manual selection of regions of interest or machine learning solutions, often fall short due to either user bias, non-transferability among systems, or computational demand. To overcome these issues, we developed CalciSeg, a data-driven and reproducible approach for unsupervised functional calcium imaging data segmentation. CalciSeg addresses the challenges associated with brain structure variability and user bias by offering a computationally efficient solution for automatic image segmentation based on two parameters: regions' size limits and number of refinement iterations. We evaluated CalciSeg efficacy on datasets of varied complexity, different insect species (locusts, bees, and cockroaches), and imaging systems (wide-field, confocal, and multiphoton), showing the robustness and generality of our approach. Finally, the user-friendly nature and open-source availability of CalciSeg facilitate the integration of this algorithm into existing analysis pipelines.

EDTP enhances and protects the fluorescent signal of GFP in cleared and expanded tissues

Advanced 3D high-resolution imaging techniques are essential for investigating biological challenges, such as neural circuit analysis and tumor microenvironment in intact tissues. However, the fluorescence signal emitted by endogenous fluorescent proteins in cleared or expanded biological samples gradually diminishes with repeated irradiation and prolonged imaging, compromising its ability to accurately depict the underlying scientific problem. We have developed a strategy to preserve fluorescence in cleared and expanded tissue samples during prolonged high-resolution three-dimensional imaging. We evaluated various compounds at different concentrations to determine their ability to enhance fluorescence intensity and resistance to photobleaching while maintaining the structural integrity of the tissue. Specifically, we investigated the impact of EDTP utilization on GFP, as it has been observed to significantly improve fluorescence intensity, resistance to photobleaching, and maintain fluorescence during extended room temperature storage. This breakthrough will facilitate extended hydrophilic and hydrogel-based clearing and expansion methods for achieving long-term high-resolution 3D imaging of cleared biological tissues by effectively safeguarding fluorescent proteins within the tissue.

Three dimensional and four dimensional live imaging to study mechanisms of progressive neurodegeneration

Neurodegenerative diseases are complex and progressive, posing challenges to their study and understanding. Recent advances in microscopy imaging technologies have enabled the exploration of neurons in three spatial dimensions (3D) over time (4D). When applied to 3D cultures, tissues, or animals, these technologies can provide valuable insights into the dynamic and spatial nature of neurodegenerative diseases. This review focuses on the use of imaging techniques and neurodegenerative disease models to study neurodegeneration in 4D. Imaging techniques such as confocal microscopy, two-photon microscopy, miniscope imaging, light sheet microscopy, and robotic microscopy offer powerful tools to visualize and analyze neuronal changes over time in 3D tissue. Application of these technologies to in vitro models of neurodegeneration such as mouse organotypic culture systems and human organoid models provide versatile platforms to study neurodegeneration in a physiologically relevant context. Additionally, use of 4D imaging in vivo, including in mouse and zebrafish models of neurodegenerative diseases, allows for the investigation of early dysfunction and behavioral changes associated with neurodegeneration. We propose that these studies have the power to overcome the limitations of two-dimensional monolayer neuronal cultures and pave the way for improved understanding of the dynamics of neurodegenerative diseases and the development of effective therapeutic strategies.

Current and future applications of light-sheet imaging for identifying molecular and developmental processes in autism spectrum disorders

Autism spectrum disorder (ASD) is identified by a set of neurodevelopmental divergences that typically affect the social communication domain. ASD is also characterized by heterogeneous cognitive impairments and is associated with cooccurring physical and medical conditions. As behaviors emerge as the brain matures, it is particularly essential to identify any gaps in neurodevelopmental trajectories during early perinatal life. Here, we introduce the potential of light-sheet imaging for studying developmental biology and cross-scale interactions among genetic, cellular, molecular and macroscale levels of circuitry and connectivity. We first report the core principles of light-sheet imaging and the recent progress in studying brain development in preclinical animal models and human organoids. We also present studies using light-sheet imaging to understand the development and function of other organs, such as the skin and gastrointestinal tract. We also provide information on the potential of light-sheet imaging in preclinical drug development. Finally, we speculate on the translational benefits of light-sheet imaging for studying individual brain-body interactions in advancing ASD research and creating personalized interventions.

Unifying community-wide whole-brain imaging datasets enables robust automated neuron identification and reveals determinants of neuron positioning in C. elegans

We develop a data harmonization approach for C. elegans volumetric microscopy data, still or video, consisting of a standardized format, data pre-processing techniques, and a set of human-in-the-loop machine learning based analysis software tools. We unify a diverse collection of 118 whole-brain neural activity imaging datasets from 5 labs, storing these and accompanying tools in an online repository called WormID (wormid.org). We use this repository to train three existing automated cell identification algorithms to, for the first time, enable accuracy in neural identification that generalizes across labs, approaching human performance in some cases. We mine this repository to identify factors that influence the developmental positioning of neurons. To facilitate communal use of this repository, we created open-source software, code, web-based tools, and tutorials to explore and curate datasets for contribution to the scientific community. This repository provides a growing resource for experimentalists, theorists, and toolmakers to (a) study neuroanatomical organization and neural activity across diverse experimental paradigms, (b) develop and benchmark algorithms for automated neuron detection, segmentation, cell identification, tracking, and activity extraction, and (c) inform models of neurobiological development and function.

Hyperactive mTORC1 disrupts habenula function and light preference in zebrafish model of Tuberous sclerosis complex

Mechanistic target of rapamycin complex 1 (mTORC1) is an integration hub for extracellular and intracellular signals necessary for brain development. Hyperactive mTORC1 is found in autism spectrum disorder (ASD) characterized by atypical reactivity to sensory stimuli, among other symptoms. In Tuberous sclerosis complex (TSC) inactivating mutations in the TSC1 or TSC2 genes result in hyperactivation of the mTORC1 pathway and ASD. Here, we show that lack of light preference of the TSC zebrafish model, tsc2 vu242/vu242 is caused by aberrant processing of light stimuli in the left dorsal habenula and tsc2 vu242/vu242 fish have impaired function of the left dorsal habenula, in which neurons exhibited higher activity and lacked habituation to the light stimuli. These characteristics were rescued by rapamycin. We thus discovered that hyperactive mTorC1 caused aberrant habenula function resulting in lack of light preference. Our results suggest that mTORC1 hyperactivity contributes to atypical reactivity to sensory stimuli in ASD.

Pathological cell assembly dynamics in a striatal MSN network model

Under normal conditions the principal cells of the striatum, medium spiny neurons (MSNs), show structured cell assembly activity patterns which alternate sequentially over exceedingly long timescales of many minutes. It is important to understand this activity since it is characteristically disrupted in multiple pathologies, such as Parkinson's disease and dyskinesia, and thought to be caused by alterations in the MSN to MSN lateral inhibitory connections and in the strength and distribution of cortical excitation to MSNs. To understand how these long timescales arise we extended a previous network model of MSN cells to include synapses with short-term plasticity, with parameters taken from a recent detailed striatal connectome study. We first confirmed the presence of sequentially switching cell clusters using the non-linear dimensionality reduction technique, Uniform Manifold Approximation and Projection (UMAP). We found that the network could generate non-stationary activity patterns varying extremely slowly on the order of minutes under biologically realistic conditions. Next we used Simulation Based Inference (SBI) to train a deep net to map features of the MSN network generated cell assembly activity to MSN network parameters. We used the trained SBI model to estimate MSN network parameters from ex-vivo brain slice calcium imaging data. We found that best fit network parameters were very close to their physiologically observed values. On the other hand network parameters estimated from Parkinsonian, decorticated and dyskinetic ex-vivo slice preparations were different. Our work may provide a pipeline for diagnosis of basal ganglia pathology from spiking data as well as for the design pharmacological treatments.

Brain implantation of tissue-level-soft bioelectronics via embryonic development

The design of bioelectronics capable of stably tracking brain-wide, single-cell, and millisecond-resolved neural activities in the developing brain is critical to the study of neuroscience and neurodevelopmental disorders. During development, the three-dimensional (3D) structure of the vertebrate brain arises from a 2D neural plate 1,2 . These large morphological changes previously posed a challenge for implantable bioelectronics to track neural activity throughout brain development 3-9 . Here, we present a tissue-level-soft, sub-micrometer-thick, stretchable mesh microelectrode array capable of integrating into the embryonic neural plate of vertebrates by leveraging the 2D-to-3D reconfiguration process of the tissue itself. Driven by the expansion and folding processes of organogenesis, the stretchable mesh electrode array deforms, stretches, and distributes throughout the entire brain, fully integrating into the 3D tissue structure. Immunostaining, gene expression analysis, and behavioral testing show no discernible impact on brain development or function. The embedded electrode array enables long-term, stable, brain-wide, single-unit-single-spike-resolved electrical mapping throughout brain development, illustrating how neural electrical activities and population dynamics emerge and evolve during brain development.

Speckle-enabled in vivo demixing of neural activity in the mouse brain

Functional imaging of neuronal activity in awake animals, using a combination of fluorescent reporters of neuronal activity and various types of microscopy modalities, has become an indispensable tool in neuroscience. While various imaging modalities based on one-photon (1P) excitation and parallel (camera-based) acquisition have been successfully used for imaging more transparent samples, when imaging mammalian brain tissue, due to their scattering properties, two-photon (2P) microscopy systems are necessary. In 2P microscopy, the longer excitation wavelengths reduce the amount of scattering while the diffraction-limited 3D localization of excitation largely eliminates out-of-focus fluorescence. However, this comes at the cost of time-consuming serial scanning of the excitation spot and more complex and expensive instrumentation. Thus, functional 1P imaging modalities that can be used beyond the most transparent specimen are highly desirable. Here, we transform light scattering from an obstacle into a tool. We use speckles with their unique patterns and contrast, formed when fluorescence from individual neurons propagates through rodent cortical tissue, to encode neuronal activity. Spatiotemporal demixing of these patterns then enables functional recording of neuronal activity from a group of discriminable sources. For the first time, we provide an experimental, in vivo characterization of speckle generation, speckle imaging and speckle-assisted demixing of neuronal activity signals in the scattering mammalian brain tissue. We found that despite an initial fast speckle decorrelation, substantial correlation was maintained over minute-long timescales that contributed to our ability to demix temporal activity traces in the mouse brain in vivo. Informed by in vivo quantifications of speckle patterns from single and multiple neurons excited using 2P scanning excitation, we recorded and demixed activity from several sources excited using 1P oblique illumination. In our proof-of-principle experiments, we demonstrate in vivo speckle-assisted demixing of functional signals from groups of sources in a depth range of 220-320 µm in mouse cortex, limited by available speckle contrast. Our results serve as a basis for designing an in vivo functional speckle imaging modality and for maximizing the key resource in any such modality, the speckle contrast. We anticipate that our results will provide critical quantitative guidance to the community for designing techniques that overcome light scattering as a fundamental limitation in bioimaging.

Spatio-temporal brain invasion pattern of Streptococcus pneumoniae and dynamic changes in the cellular environment in bacteremia-derived meningitis

Streptococcus pneumoniae (the pneumococcus) is the major cause of bacterial meningitis globally, and pneumococcal meningitis is associated with increased risk of long-term neurological sequelae. These include several sensorimotor functions that are controlled by specific brain regions which, during bacterial meningitis, are damaged by a neuroinflammatory response and the deleterious action of bacterial toxins in the brain. However, little is known about the invasion pattern of the pneumococcus into the brain. Using a bacteremia-derived meningitis mouse model, we combined 3D whole brain imaging with brain microdissection to show that all brain regions were equally affected during disease progression, with the presence of pneumococci closely associated to the microvasculature. In the hippocampus, the invasion provoked microglial activation, while the neurogenic niche showed increased proliferation and migration of neuroblasts. Our results indicate that, even before the outbreak of symptoms, the bacterial load throughout the brain is high and causes neuroinflammation and cell death, a pathological scenario which ultimately leads to a failing regeneration of new neurons.

BIFROST: a method for registering diverse imaging datasets of the Drosophila brain

The heterogeneity of brain imaging methods in neuroscience provides rich data that cannot be captured by a single technique, and our interpretations benefit from approaches that enable easy comparison both within and across different data types. For example, comparing brain-wide neural dynamics across experiments and aligning such data to anatomical resources, such as gene expression patterns or connectomes, requires precise alignment to a common set of anatomical coordinates. However, this is challenging because registering in vivo functional imaging data to ex vivo reference atlases requires accommodating differences in imaging modality, microscope specification, and sample preparation. We overcome these challenges in Drosophila by building an in vivo reference atlas from multiphoton-imaged brains, called the Functional Drosophila Atlas (FDA). We then develop a two-step pipeline, BrIdge For Registering Over Statistical Templates (BIFROST), for transforming neural imaging data into this common space and for importing ex vivo resources such as connectomes. Using genetically labeled cell types as ground truth, we demonstrate registration with a precision of less than 10 microns. Overall, BIFROST provides a pipeline for registering functional imaging datasets in the fly, both within and across experiments.

Significance

Large-scale functional imaging experiments in Drosophila have given us new insights into neural activity in various sensory and behavioral contexts. However, precisely registering volumetric images from different studies has proven challenging, limiting quantitative comparisons of data across experiments. Here, we address this limitation by developing BIFROST, a registration pipeline robust to differences across experimental setups and datasets. We benchmark this pipeline by genetically labeling cell types in the fly brain and demonstrate sub-10 micron registration precision, both across specimens and across laboratories. We further demonstrate accurate registration between in-vivo brain volumes and ultrastructural connectomes, enabling direct structure-function comparisons in future experiments.

Simultaneous, cortex-wide dynamics of up to 1 million neurons reveal unbounded scaling of dimensionality with neuron number

The brain's remarkable properties arise from the collective activity of millions of neurons. Widespread application of dimensionality reduction to multi-neuron recordings implies that neural dynamics can be approximated by low-dimensional "latent" signals reflecting neural computations. However, can such low-dimensional representations truly explain the vast range of brain activity, and if not, what is the appropriate resolution and scale of recording to capture them? Imaging neural activity at cellular resolution and near-simultaneously across the mouse cortex, we demonstrate an unbounded scaling of dimensionality with neuron number in populations up to 1 million neurons. Although half of the neural variance is contained within sixteen dimensions correlated with behavior, our discovered scaling of dimensionality corresponds to an ever-increasing number of neuronal ensembles without immediate behavioral or sensory correlates. The activity patterns underlying these higher dimensions are fine grained and cortex wide, highlighting that large-scale, cellular-resolution recording is required to uncover the full substrates of neuronal computations.

Real-time analysis of large-scale neuronal imaging enables closed-loop investigation of neural dynamics

Large-scale imaging of neuronal activities is crucial for understanding brain functions. However, it is challenging to analyze large-scale imaging data in real time, preventing closed-loop investigation of neural circuitry. Here we develop a real-time analysis system with a field programmable gate array-graphics processing unit design for an up to 500-megabyte-per-second image stream. Adapted to whole-brain imaging of awake larval zebrafish, the system timely extracts activity from up to 100,000 neurons and enables closed-loop perturbations of neural dynamics.

Development of wafer-scale multifunctional nanophotonic neural probes for brain activity mapping

Optical techniques, such as optogenetic stimulation and functional fluorescence imaging, have been revolutionary for neuroscience by enabling neural circuit analysis with cell-type specificity. To probe deep brain regions, implantable light sources are crucial. Silicon photonics, commonly used for data communications, shows great promise in creating implantable devices with complex optical systems in a compact form factor compatible with high volume manufacturing practices. This article reviews recent developments of wafer-scale multifunctional nanophotonic neural probes. The probes can be realized on 200 or 300 mm wafers in commercial foundries and integrate light emitters for photostimulation, microelectrodes for electrophysiological recording, and microfluidic channels for chemical delivery and sampling. By integrating active optical devices to the probes, denser emitter arrays, enhanced on-chip biosensing, and increased ease of use may be realized. Silicon photonics technology makes possible highly versatile implantable neural probes that can transform neuroscience experiments.