Type I collagen is the main component of bone matrix and other connective tissues. Rerouting of its procollagen precursor to a degradative pathway is crucial for osteoblast survival in pathologies involving excessive intracellular buildup of procollagen that is improperly folded and/or trafficked. What cellular mechanisms underlie this rerouting remains unclear. To study these mechanisms, we employed live-cell imaging and correlative light and electron microscopy (CLEM) to examine procollagen trafficking both in wild-type mouse osteoblasts and osteoblasts expressing a bone pathology-causing mutant procollagen. We found that although most procollagen molecules successfully trafficked through the secretory pathway in these cells, a subpopulation did not. The latter molecules appeared in numerous dispersed puncta colocalizing with COPII subunits, autophagy markers and ubiquitin machinery, with more puncta seen in mutant procollagen-expressing cells. Blocking endoplasmic reticulum exit site (ERES) formation suppressed the number of these puncta, suggesting they formed after procollagen entry into ERESs. The punctate structures containing procollagen, COPII, and autophagic markers did not move toward the Golgi but instead were relatively immobile. They appeared to be quickly engulfed by nearby lysosomes through a bafilomycin-insensitive pathway. CLEM and fluorescence recovery after photobleaching experiments suggested engulfment occurred through a noncanonical form of autophagy resembling microautophagy of ERESs. Overall, our findings reveal that a subset of procollagen molecules is directed toward lysosomal degradation through an autophagic pathway originating at ERESs, providing a mechanism to remove excess procollagen from cells.

True physiological imaging of subcellular dynamics requires studying cells within their parent organisms, where all the environmental cues that drive gene expression, and hence the phenotypes that we actually observe, are present. A complete understanding also requires volumetric imaging of the cell and its surroundings at high spatiotemporal resolution, without inducing undue stress on either. We combined lattice light-sheet microscopy with adaptive optics to achieve, across large multicellular volumes, noninvasive aberration-free imaging of subcellular processes, including endocytosis, organelle remodeling during mitosis, and the migration of axons, immune cells, and metastatic cancer cells in vivo. The technology reveals the phenotypic diversity within cells across different organisms and developmental stages and may offer insights into how cells harness their intrinsic variability to adapt to different physiological environments.

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 brain is a network of neurons and its biological output is behaviour. This is an exciting age, with a growing acknowledgement that the comprehensive compilation of synaptic circuits densely reconstructed in the brains of model species is now both technologically feasible and a scientifically enabling possibility in neurobiology, much as 30 years ago genomics was in molecular biology and genetics. Implemented by huge advances in electron microscope technology, especially focused ion beam-scanning electron microscope (FIB-SEM) milling (see Glossary), image capture and alignment, and computer-aided reconstruction of neuron morphologies, enormous progress has been made in the last decade in the detailed knowledge of the actual synaptic circuits formed by real neurons, in various brain regions of the fly It is useful to distinguish synaptic pathways that are major, with 100 or more presynaptic contacts, from those that are minor, with fewer than about 10; most neurites are both presynaptic and postsynaptic, and all synaptic sites have multiple postsynaptic dendrites. Work on has spearheaded these advances because cell numbers are manageable, and neuron classes are morphologically discrete and genetically identifiable, many confirmed by reporters. Recent advances are destined within the next few years to reveal the complete connectome in an adult fly, paralleling advances in the larval brain that offer the same prospect possibly within an even shorter time frame. The final amendment and validation of segmented bodies by human proof-readers remains the most time-consuming step, however. The value of a complete connectome in is that, by targeting to specific neurons transgenes that either silence or activate morphologically identified circuits, and then identifying the resulting behavioural outcome, we can determine the causal mechanism for behaviour from its loss or gain. More importantly, the connectome reveals hitherto unsuspected pathways, leading us to seek novel behaviours for these. Circuit information will eventually be required to understand how differences between brains underlie differences in behaviour, and especially to herald yet more advanced connectomic strategies for the vertebrate brain, with an eventual prospect of understanding cognitive disorders having a connectomic basis. Connectomes also help us to identify common synaptic circuits in different species and thus to reveal an evolutionary progression in candidate pathways.

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.

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.

The use of genetically encoded 'self-labeling tags' with chemical fluorophore ligands enables rapid labeling of specific cells in neural tissue. To improve the chemical tagging of neurons, we synthesized and evaluated new fluorophore ligands based on Cy, Janelia Fluor, Alexa Fluor, and ATTO dyes and tested these with recently improved Drosophila melanogaster transgenes. We found that tissue clearing and mounting in DPX substantially improves signal quality when combined with specific non-cyanine fluorophores. We compared and combined this labeling technique with standard immunohistochemistry in the Drosophila brain.

In most animals, the brain makes behavioral decisions that are transmitted by descending neurons to the nerve cord circuitry that produces behaviors. In insects, only a few descending neurons have been associated with specific behaviors. To explore how descending neurons control an insect's movements, we developed a novel method to systematically assay the behavioral effects of activating individual neurons on freely behaving terrestrial D. melanogaster. We calculated a two-dimensional representation of the entire behavior space explored by these flies and we associated descending neurons with specific behaviors by identifying regions of this space that were visited with increased frequency during optogenetic activation. Applying this approach across a large collection of descending neurons, we found that (1) activation of most of the descending neurons drove stereotyped behaviors, (2) in many cases multiple descending neurons activated similar behaviors, and (3) optogenetically-activated behaviors were often dependent on the behavioral state prior to activation.

Recurrent connections are thought to be a common feature of the neural circuits that encode memories, but how memories are laid down in such circuits is not fully understood. Here we present evidence that courtship memory in Drosophila relies on the recurrent circuit between mushroom body gamma (MBg), M6 output, and aSP13 dopaminergic neurons. We demonstrate persistent neuronal activity of aSP13 neurons and show that it transiently potentiates synaptic transmission from MBγ>M6 neurons. M6 neurons in turn provide input to aSP13 neurons, prolonging potentiation of MBγ>M6 synapses over time periods that match short-term memory. These data support a model in which persistent aSP13 activity within a recurrent circuit lays the foundation for a short-term memory.