To smooth the academic-to-industry transition, one institution is experimenting with offering biomedical researchers pre-commercial open access to new optical imaging systems still under development. The approach, the authors of this case study suggest, can be a win on both sides.

For goal-directed behaviour it is critical that we can both select the appropriate action and learn to modify the underlying movements (for example, the pitch of a note or velocity of a reach) to improve outcomes. The basal ganglia are a critical nexus where circuits necessary for the production of behaviour, such as the neocortex and thalamus, are integrated with reward signalling to reinforce successful, purposive actions. The dorsal striatum, a major input structure of basal ganglia, is composed of two opponent pathways, direct and indirect, thought to select actions that elicit positive outcomes and suppress actions that do not, respectively. Activity-dependent plasticity modulated by reward is thought to be sufficient for selecting actions in the striatum. Although perturbations of basal ganglia function produce profound changes in movement, it remains unknown whether activity-dependent plasticity is sufficient to produce learned changes in movement kinematics, such as velocity. Here we use cell-type-specific stimulation in mice delivered in closed loop during movement to demonstrate that activity in either the direct or indirect pathway is sufficient to produce specific and sustained increases or decreases in velocity, without affecting action selection or motivation. These behavioural changes were a form of learning that accumulated over trials, persisted after the cessation of stimulation, and were abolished in the presence of dopamine antagonists. Our results reveal that the direct and indirect pathways can each bidirectionally control movement velocity, demonstrating unprecedented specificity and flexibility in the control of volition by the basal ganglia.

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

Major resources are now available to develop tools and technologies aimed at dissecting the circuitry and computations underlying behavior, unraveling the underpinnings of brain disorders, and understanding the neural substrates of cognition. Scientists from around the world shared their views around new tools and technologies to drive advances in neuroscience.

Neural stem cells show age-dependent developmental potentials, as evidenced by their production of distinct neuron types at different developmental times. Drosophila neuroblasts produce long, stereotyped lineages of neurons. We searched for factors that could regulate neural temporal fate by RNA-sequencing lineage-specific neuroblasts at various developmental times. We found that two RNA-binding proteins, IGF-II mRNA-binding protein (Imp) and Syncrip (Syp), display opposing high-to-low and low-to-high temporal gradients with lineage-specific temporal dynamics. Imp and Syp promote early and late fates, respectively, in both a slowly progressing and a rapidly changing lineage. Imp and Syp control neuronal fates in the mushroom body lineages by regulating the temporal transcription factor Chinmo translation. Together, the opposing Imp/Syp gradients encode stem cell age, specifying multiple cell fates within a lineage.

Two-photon excitation fluorescence microscopy has revolutionized our understanding of brain structure and function through the high resolution and large penetration depth it offers. Investigating neural structures in vivo requires gaining optical access to the brain, which is typically achieved by replacing a part of the skull with one or several layers of cover glass windows. To compensate for the spherical aberrations caused by the presence of these layers of glass, collar-correction objectives are typically used. However, the efficiency of this correction has been shown to depend significantly on the tilt angle between the glass window surface and the optical axis of the imaging system. Here, we first expand these observations and characterize the effect of the tilt angle on the collected fluorescence signal with thicker windows (double cover slide) and compare these results with an objective devoid of collar-correction. Second, we present a simple optical alignment device designed to rapidly minimize the tilt angle in vivo and align the optical axis of the microscope perpendicularly to the glass window to an angle below 0.25°, thereby significantly improving the imaging quality. Finally, we describe a tilt-correction procedure for users in an in vivo setting, enabling the accurate alignment with a resolution of <0.2° in only few iterations.

Receptor tyrosine kinases (RTK) are important cell surface receptors that transduce extracellular signals across the plasma membrane. The traditional view of how these receptors function is that ligand binding to the extracellular domains acts as a master-switch that enables receptor monomers to dimerize and subsequently trans-phosphorylate each other on their intracellular domains. However, a growing body of evidence suggests that receptor oligomerization is not merely a consequence of ligand binding, but is instead part of a complex process responsible for regulation of receptor activation. Importantly, the oligomerization dynamics and subsequent activation of these receptors are affected by other cellular components, such as cytoskeletal machineries and cell membrane lipid characteristics. Thus receptor activation is not an isolated molecular event mediated by the ligand-receptor interaction, but instead involves orchestrated interactions between the receptors and other cellular components. Measuring receptor oligomerization dynamics on live cells can yield important insights into the characteristics of these interactions. Therefore, it is imperative to develop techniques that can probe receptor movements on the plasma membrane with optimal temporal and spatial resolutions. Various microscopic techniques have been used for this purpose. Optical techniques including single molecule tracking (SMT) and fluorescence correlation spectroscopy (FCS) measure receptor diffusion on live cells. Receptor-receptor interactions can also be assessed by detecting Förster resonance energy transfer (FRET) between fluorescently-labeled receptors situated in close proximity or by counting the number of receptors within a diffraction limited fluorescence spot (stepwise bleaching). This review will describe recent developments of optical techniques that have been used to study receptor oligomerization on living cells. This article is part of a Special Issue entitled: Interactions between membrane receptors in cellular membranes edited by Kalina Hristova.

Sighted animals use visual signals to discern directional motion in their environment. Motion is not directly detected by visual neurons, and it must instead be computed from light signals that vary over space and time. This makes visual motion estimation a near universal neural computation, and decades of research have revealed much about the algorithms and mechanisms that generate directional signals. The idea that sensory systems are optimized for performance in natural environments has deeply impacted this research. In this article, we review the many ways that optimization has been used to quantitatively model visual motion estimation and reveal its underlying principles. We emphasize that no single optimization theory has dominated the literature. Instead, researchers have adeptly incorporated different computational demands and biological constraints that are pertinent to the specific brain system and animal model under study. The successes and failures of the resulting optimization models have thereby provided insights into how computational demands and biological constraints together shape neural computation.

Genetically encoded calcium indicators (GECIs) are powerful tools for systems neuroscience. Recent efforts in protein engineering have significantly increased the performance of GECIs. The state-of-the art single-wavelength GECI, GCaMP3, has been deployed in a number of model organisms and can reliably detect three or more action potentials in short bursts in several systems in vivo . Through protein structure determination, targeted mutagenesis, high-throughput screening, and a battery of in vitro assays, we have increased the dynamic range of GCaMP3 by severalfold, creating a family of “GCaMP5” sensors. We tested GCaMP5s in several systems: cultured neurons and astrocytes, mouse retina, and in vivo in Caenorhabditis chemosensory neurons, Drosophila larval neuromuscular junction and adult antennal lobe, zebrafish retina and tectum, and mouse visual cortex. Signal-to-noise ratio was improved by at least 2- to 3-fold. In the visual cortex, two GCaMP5 variants detected twice as many visual stimulus-responsive cells as GCaMP3. By combining in vivo imaging with electrophysiology we show that GCaMP5 fluorescence provides a more reliable measure of neuronal activity than its predecessor GCaMP3.GCaMP5allows more sensitive detection of neural activity in vivo andmayfind widespread applications for cellular imaging in general.

Correlative light and electron microscopy (CLEM) combines the power of electron microscopy, with its excellent resolution and contrast, with that of fluorescence imaging, which allows the staining of specific molecules, organelles, and cell populations. Fluorescence imaging is also readily compatible with live cells and behaving animals, facilitating real-time visualization of cellular processes, potentially followed by electron microscopic reconstruction. Super-resolution single-molecule localization microscopy is a relatively new modality that harnesses the ability of some fluorophores to photoconvert, through which localization precision better than Abbe’s diffraction limit is achieved through iterative high-resolution localization of single-molecule emitters. Here we describe our lab’s recent progress in the development of reagents and techniques for super-resolution single-molecule localization CLEM and their applications to biological problems.