Volumetric imaging tools that are simple to adopt, flexible, and robust are in high demand in the field of neuroscience, where the ability to image neurons and their networks with high spatiotemporal resolution is essential. Using an axially elongated focus approximating a Bessel beam, in combination with two-photon fluorescence microscopy, has proven successful at such an endeavor. Here, we demonstrate three-photon fluorescence imaging with an axially extended Bessel focus. We use an axicon-based module that allowed for the generation of Bessel foci of varying numerical apertures and axial lengths, and apply this volumetric imaging tool to image mouse brain slices and for in vivo imaging of the mouse brain.

Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies. We have developed a computational ecosystem that enables storage, visualization, and analysis of these data in the cloud, thusfar spanning 20+ publications and 100+ terabytes including nanoscale ultrastructure, microscale synaptogenetic diversity, and mesoscale whole brain connectivity, making NeuroData the largest and most diverse open repository of brain data.

Alternative gene splicing gives rise to N-methyl-D-aspartate (NMDA) receptor ion channels with defined functional properties and unique contributions to calcium signaling in a given chemical environment in the mammalian brain. Splice variants possessing the exon-5-encoded motif at the amino-terminal domain (ATD) of the GluN1 subunit are known to display robustly altered deactivation rates and pH sensitivity, but the underlying mechanism for this functional modification is largely unknown. Here, we show through cryoelectron microscopy (cryo-EM) that the presence of the exon 5 motif in GluN1 alters the local architecture of heterotetrameric GluN1-GluN2 NMDA receptors and creates contacts with the ligand-binding domains (LBDs) of the GluN1 and GluN2 subunits, which are absent in NMDA receptors lacking the exon 5 motif. The unique interactions established by the exon 5 motif are essential to the stability of the ATD/LBD and LBD/LBD interfaces that are critically involved in controlling proton sensitivity and deactivation.

Living in a social environment requires the ability to respond to specific social stimuli and to incorporate information obtained from prior interactions into future ones. One of the mechanisms that facilitates social interaction is pheromone-based communication. In Drosophila melanogaster, the male-specific pheromone cis-vaccenyl acetate (cVA) elicits different responses in male and female flies, and functions to modulate behavior in a context and experience-dependent manner. Although it is the most studied pheromone in flies, the mechanisms that determine the complexity of the response, its intensity and final output with respect to social context, sex and prior interaction, are still not well understood. Here we explored the functional link between social interaction and pheromone-based communication and discovered an odorant binding protein that links social interaction to sex specific changes in cVA related responses. Odorant binding protein 69a (Obp69a) is expressed in auxiliary cells and secreted into the olfactory sensilla. Its expression is inversely regulated in male and female flies by social interactions: cVA exposure reduces its levels in male flies and increases its levels in female flies. Increasing or decreasing Obp69a levels by genetic means establishes a functional link between Obp69a levels and the extent of male aggression and female receptivity. We show that activation of cVA-sensing neurons is sufficeint to regulate Obp69a levels in the absence of cVA, and requires active neurotransmission between the sensory neuron to the second order olfactory neuron. The cross-talk between sensory neurons and non-neuronal auxiliary cells at the olfactory sensilla, represents an additional component in the machinery that promotes behavioral plasticity to the same sensory stimuli in male and female flies.

The development of the adult optic lobe (OL) of is directed by a wave of ingrowth of the photoreceptors over a two day period at the outset of metamorphosis which is accompanied by the appearance of the pupal-specific transcription factor Broad-Z3 (Br-Z3) and expression of early drivers in OL neurons. During this time, there are pulses of ecdysteroids that time the metamorphic events. At the outset, the transient appearance of juvenile hormone (JH) prevents precocious development of the OL caused by the ecdysteroid peak that initiates pupariation, but the artificial maintenance of JH after this time misdirects subsequent development. Axon ingrowth, Br-Z3 appearance and the expression of early drivers were unaffected, but aspects of later development such as the dendritic expansion of the lamina monopolar neurons and the expression of late drivers were suppressed. This effect of the exogenous JH mimic (JHM) pyriproxifen is lost by 24 hr after pupariation. Part of this effect of JHM is due to its suppression of the appearance of ecdysone receptor EcR-B1 that occurs after pupation and during early adult development.

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.

Transcription factors (TFs) control gene expression by binding to genomic DNA in a sequence-specific manner. Mutations in TF binding sites are increasingly found to be associated with human disease, yet we currently lack robust methods to predict these sites. Here, we developed a versatile maximum likelihood framework named No Read Left Behind (NRLB) that infers a biophysical model of protein-DNA recognition across the full affinity range from a library of in vitro selected DNA binding sites. NRLB predicts human Max homodimer binding in near-perfect agreement with existing low-throughput measurements. It can capture the specificity of the p53 tetramer and distinguish multiple binding modes within a single sample. Additionally, we confirm that newly identified low-affinity enhancer binding sites are functional in vivo, and that their contribution to gene expression matches their predicted affinity. Our results establish a powerful paradigm for identifying protein binding sites and interpreting gene regulatory sequences in eukaryotic genomes.

A recent study reports a novel form of lateral inhibition between photoreceptors supporting colour vision in the vinegar fly, Drosophila melanogaster.

Understanding how neural circuits control behavior requires monitoring a large population of neurons with high spatial resolution and volume rate. Here we report an axicon-based Bessel beam module with continuously adjustable depth of focus (CADoF), that turns frame rate into volume rate by extending the excitation focus in the axial direction while maintaining high lateral resolutions. Cost-effective and compact, this CADoF Bessel module can be easily integrated into existing two-photon fluorescence microscopes. Simply translating one of the relay lenses along its optical axis enabled continuous adjustment of the axial length of the Bessel focus. We used this module to simultaneously monitor activity of spinal projection neurons extending over 60 µm depth in larval zebrafish at 50 Hz volume rate with adjustable axial extent of the imaged volume.