Internal signals from the body and external signals from the environment are processed by brain-wide circuits to guide behavior. However, the complete brain-wide circuit activity underlying interoception—the perception of bodily signals—and its interactions with sensorimotor circuits remain unclear due to technical barriers to accessing whole-brain activity at the cellular level during organ physiology perturbations. We developed an all-optical system for whole-brain neuronal imaging in behaving larval zebrafish during optical uncaging of gut-targeted nutrients and visuo-motor stimulation. Widespread neural activity throughout the brain encoded nutrient delivery, unfolding on multiple timescales across many specific peripheral and central regions. Evoked activity depended on delivery location and occurred with amino acids and D-glucose, but not L-glucose. Many gut-sensitive neurons also responded to swimming and visual stimuli, with brainstem areas primarily integrating gut and motor signals and midbrain regions integrating gut and visual signals. This platform links body-brain communication studies to brain-wide neural computation in awake, behaving vertebrates.

Cells contain hundreds of organelles and macromolecular assemblies. Obtaining a complete understanding of their intricate organization requires the nanometre-level, three-dimensional reconstruction of whole cells, which is only feasible with robust and scalable automatic methods. Here, to support the development of such methods, we annotated up to 35 different cellular organelle classes-ranging from endoplasmic reticulum to microtubules to ribosomes-in diverse sample volumes from multiple cell types imaged at a near-isotropic resolution of 4 nm per voxel with focused ion beam scanning electron microscopy (FIB-SEM). We trained deep learning architectures to segment these structures in 4 nm and 8 nm per voxel FIB-SEM volumes, validated their performance and showed that automatic reconstructions can be used to directly quantify previously inaccessible metrics including spatial interactions between cellular components. We also show that such reconstructions can be used to automatically register light and electron microscopy images for correlative studies. We have created an open data and open-source web repository, 'OpenOrganelle', to share the data, computer code and trained models, which will enable scientists everywhere to query and further improve automatic reconstruction of these datasets.

The patch-clamp technique and the whole-cell measurements derived from it have greatly advanced our understanding of the coding properties of individual neurons by allowing for a detailed analysis of their excitatory/inhibitory synaptic inputs, intrinsic electrical properties, and morphology. Because such measurements require a high level of mechanical stability they have for a long time been limited to in vitro and anesthetized preparations. Recently, however, a considerable amount of effort has been devoted to extending these techniques to awake restrained/head-fixed preparations allowing for the study of the input-output functions of neurons during behavior. In this chapter we describe a technique extending patch-clamp recordings to awake animals free to explore their environments.

Intracellular recording is an essential technique for investigating cellular mechanisms underlying complex brain functions. Despite the high sensitivity of the technique to mechanical disturbances, intracellular recording has been applied to awake, behaving, and even freely moving, animals. Here we summarize recent advances in these methods and their application to the measurement and manipulation of membrane potential dynamics for understanding neuronal computations in behaving animals.

Intracellular recording, which allows direct measurement of the membrane potential and currents of individual neurons, requires a very mechanically stable preparation and has thus been limited to in vitro and head-immobilized in vivo experiments. This restriction constitutes a major obstacle for linking cellular and synaptic physiology with animal behavior. To overcome this limitation we have developed a method for performing whole-cell recordings in freely moving rats. We constructed a miniature head-mountable recording device, with mechanical stabilization achieved by anchoring the recording pipette rigidly in place after the whole-cell configuration is established. We obtain long-duration recordings (mean of approximately 20 min, maximum 60 min) in freely moving animals that are remarkably insensitive to mechanical disturbances, then reconstruct the anatomy of the recorded cells. This head-anchored whole-cell recording technique will enable a wide range of new studies involving detailed measurement and manipulation of the physiological properties of identified cells during natural behaviors.

We demonstrate STED microscopy of whole bacterial and eukaryotic cells using fluorogenic labels that reversibly bind to their target structure. A constant exchange of labels guarantees the removal of photobleached fluorophores and their replacement by intact fluorophores, thereby circumventing bleaching-related limitations of STED super-resolution imaging. We achieve a constant labeling density and demonstrate a fluorescence signal for long and theoretically unlimited acquisition times. Using this concept, we demonstrate whole-cell, 3D, multi-color and live cell STED microscopy.

Single molecule-based superresolution imaging has become an essential tool in modern cell biology. Because of the limited depth of field of optical imaging systems, one of the major challenges in superresolution imaging resides in capturing the 3D nanoscale morphology of the whole cell. Despite many previous attempts to extend the application of photo-activated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) techniques into three dimensions, effective localization depths do not typically exceed 1.2 µm. Thus, 3D imaging of whole cells (or even large organelles) still demands sequential acquisition at different axial positions and, therefore, suffers from the combined effects of out-of-focus molecule activation (increased background) and bleaching (loss of detections). Here, we present the use of multifocus microscopy for volumetric multicolor superresolution imaging. By simultaneously imaging nine different focal planes, the multifocus microscope instantaneously captures the distribution of single molecules (either fluorescent proteins or synthetic dyes) throughout an ∼4-µm-deep volume, with lateral and axial localization precisions of ∼20 and 50 nm, respectively. The capabilities of multifocus microscopy to rapidly image the 3D organization of intracellular structures are illustrated by superresolution imaging of the mammalian mitochondrial network and yeast microtubules during cell division.

Understanding how the brain works in tight concert with the rest of the central nervous system (CNS) hinges upon knowledge of coordinated activity patterns across the whole CNS. We present a method for measuring activity in an entire, non-transparent CNS with high spatiotemporal resolution. We combine a light-sheet microscope capable of simultaneous multi-view imaging at volumetric speeds 25-fold faster than the state-of-the-art, a whole-CNS imaging assay for the isolated Drosophila larval CNS and a computational framework for analysing multi-view, whole-CNS calcium imaging data. We image both brain and ventral nerve cord, covering the entire CNS at 2 or 5 Hz with two- or one-photon excitation, respectively. By mapping network activity during fictive behaviours and quantitatively comparing high-resolution whole-CNS activity maps across individuals, we predict functional connections between CNS regions and reveal neurons in the brain that identify type and temporal state of motor programs executed in the ventral nerve cord.

Approximately four in five neurons are excitatory. This is true across functional regions and species. Why do we have so many excitatory neurons? Little is known. Here we provide a normative answer to this question. We designed a task-agnostic, learning-independent and experiment-testable measurement of functional complexity, which quantifies the network’s ability to solve complex problems. Using the larval Drosophila whole-brain electron microscopy connectome, we discovered the optimal Excitatory-Inhibitory (E-I) ratio that maximizes the functional complexity: 75-81% percentage of neurons are excitatory. This number is consistent with the true distribution observed via scRNA-seq. We found that the abundance of excitatory neurons confers an advantage in functional complexity, but only when inhibitory neurons are highly connected. In contrast, when the E-I identities are sampled uniformly (not dependent on connectivity), the optimal E-I ratio falls around equal population size, and its overall achieved functional complexity is sub-optimal. Our functional complexity measurement offers a normative explanation for the over-abundance of excitatory neurons in the brain. We anticipate that this approach will further uncover the functional significance of various neural network structures.

An important strategy for efficient neural coding is to match the range of cellular responses to the distribution of relevant input signals. However, the structure and relevance of sensory signals depend on behavioral state. Here, we show that behavior modifies neural activity at the earliest stages of fly vision. We describe a class of wide-field neurons that provide feedback to the most peripheral layer of the Drosophila visual system, the lamina. Using in vivo patch-clamp electrophysiology, we found that lamina wide-field neurons respond to low-frequency luminance fluctuations. Recordings in flying flies revealed that the gain and frequency tuning of wide-field neurons change during flight, and that these effects are mimicked by the neuromodulator octopamine. Genetically silencing wide-field neurons increased behavioral responses to slow-motion stimuli. Together, these findings identify a cell type that is gated by behavior to enhance neural coding by subtracting low-frequency signals from the inputs to motion detection circuits.