Targeted manipulation of activity in specific populations of neurons is important for investigating the neural circuit basis of behavior. Optogenetic approaches using light-sensitive microbial rhodopsins have permitted manipulations to reach a level of temporal precision that is enabling functional circuit dissection. As demand for more precise perturbations to serve specific experimental goals increases, a palette of opsins with diverse selectivity, kinetics, and spectral properties will be needed. Here, we introduce a novel approach of "topological engineering"-inversion of opsins in the plasma membrane-and demonstrate that it can produce variants with unique functional properties of interest for circuit neuroscience. In one striking example, inversion of a Channelrhodopsin variant converted it from a potent activator into a fast-acting inhibitor that operates as a cation pump. Our findings argue that membrane topology provides a useful orthogonal dimension of protein engineering that immediately permits as much as a doubling of the available toolkit.

New reconstruction techniques are generating connectomes of unprecedented size. These must be analyzed to generate human comprehensible results. The analyses being used fall into three general categories. The first is interactive tools used during reconstruction, to help guide the effort, look for possible errors, identify potential cell classes, and answer other preliminary questions. The second type of analysis is support for formal documents such as papers and theses. Scientific norms here require that the data be archived and accessible, and the analysis reproducible. In contrast to some other "omic" fields such as genomics, where a few specific analyses dominate usage, connectomics is rapidly evolving and the analyses used are often specific to the connectome being analyzed. These analyses are typically performed in a variety of conventional programming language, such as Matlab, R, Python, or C++, and read the connectomic data either from a file or through database queries, neither of which are standardized. In the short term we see no alternative to the use of specific analyses, so the best that can be done is to publish the analysis code, and the interface by which it reads connectomic data. A similar situation exists for archiving connectome data. Each group independently makes their data available, but there is no standardized format and long-term accessibility is neither enforced nor funded. In the long term, as connectomics becomes more common, a natural evolution would be a central facility for storing and querying connectomic data, playing a role similar to the National Center for Biotechnology Information for genomes. The final form of analysis is the import of connectome data into downstream tools such as neural simulation or machine learning. In this process, there are two main problems that need to be addressed. First, the reconstructed circuits contain huge amounts of detail, which must be intelligently reduced to a form the downstream tools can use. Second, much of the data needed for these downstream operations must be obtained by other methods (such as genetic or optical) and must be merged with the extracted connectome.

Animals adapt their behavior in response to informative sensory cues using multiple brain circuits. The activity of midbrain dopaminergic neurons is thought to convey a critical teaching signal: reward-prediction error. Although reward-prediction error signals are thought to be essential to learning, little is known about the dynamic changes in the activity of midbrain dopaminergic neurons as animals learn about novel sensory cues and appetitive rewards. Here we describe a large dataset of cell-attached recordings of identified dopaminergic neurons as naive mice learned a novel cue-reward association. During learning midbrain dopaminergic neuron activity results from the summation of sensory cue-related and movement initiation-related response components. These components are both a function of reward expectation yet they are dissociable. Learning produces an increasingly precise coordination of action initiation following sensory cues that results in apparent reward-prediction error correlates. Our data thus provide new insights into the circuit mechanisms that underlie a critical computation in a highly conserved learning circuit.

Epigenetic mechanisms play fundamental roles in brain function and behavior and stressors such as social isolation can alter animal behavior via epigenetic mechanisms. However, due to cellular heterogeneity, identifying cell-type-specific epigenetic changes in the brain is challenging. Here we report first use of a modified INTACT method in behavioral epigenetics of Drosophila: a method we call mini-INTACT. Using ChIP-seq on mini-INTACT purified dopaminergic nuclei, we identified epigenetic signatures in socially-isolated and socially-enriched Drosophila males. Social experience altered the epigenetic landscape in clusters of genes involved in transcription and neural function. Some of these alterations were predicted by expression changes of four transcription factors and the prevalence of their binding sites in several clusters. These transcription factors were previously identified as activity-regulated genes and their knockdown in dopaminergic neurons reduced the effects of social experience on sleep. Our work enables the use of Drosophila as a model for cell-type-specific behavioral epigenetics.

Abstract ingle molecule localisation microscopy (SMLM), experimentally achieved over a decade ago, has become a routinely used analytical tool across the life sciences. Synergistic advances in probe chemistry, optical physics and data analysis has propelled SMLM into the quantitative realm, enabling unprecedented access to the cellular machinery at the nanoscale. In its early years, SMLM primarily served as a platform for impressive rendered images of sub diffraction scale structures, however more recently a shift towards interrogating SMLM point pattern data in a robust mathematical framework has occurred. A prevalent theme in the SMLM field is the need for quantitative analytical methods, to better understand the underlying processes on which SMLM reports and to extract statistically valid biological insights. Whilst some forms of post processing analytics, for example cluster analysis, have been widely studied, others such as fibre analysis remain in their infancy. Here, we review the current state of the art of cluster analysis and fibre analysis and present new methods for their implementation in both 3D SMLM data sets and multi-colour data.

Calcium imaging is commonly used to measure the neural activity of large groups of neurons in mice. Genetically encoded calcium indicators (GECIs) can be delivered for this purpose using non-invasive genetic methods. Compared to viral gene transfer, transgenic targeting of GECIs provides stable long-term expression and obviates the need for invasive viral injections. Transgenic mice expressing the green GECI GCaMP6 are already widely used. Here we present the generation and characterization of transgenic mice expressing the sensitive red GECI jRGECO1a, driven by the Thy1 promoter. Four transgenic lines with different expression patterns showed sufficiently high expression for cellular in vivo imaging. We used two-photon microscopy to characterize visual responses of individual neurons in the visual cortex in vivo. The signal-to-noise ratio in transgenic mice was comparable to, or better than, mice transduced with adeno-associated virus. In addition, we show that Thy1-jRGECO1a transgenic mice are useful for transcranial population imaging and functional mapping using widefield fluorescence microscopy. We also demonstrate imaging of visual responses in retinal ganglion cells in vitro. Thy1-jRGECO1a transgenic mice are therefore a useful addition to the toolbox for imaging activity in intact neural networks.

Diverse traits often covary between species [1-3]. The possibility that a single mutation could contribute to the evolution of several characters between species [3] is rarely investigated as relatively few cases are dissected at the nucleotide level. Drosophila santomea has evolved additional sex comb sensory teeth on its legs and has lost two sensory bristles on its genitalia. We present evidence that a single nucleotide substitution in an enhancer of the scute gene contributes to both changes. The mutation alters a binding site for the Hox protein Abdominal-B in the developing genitalia, leading to bristle loss, and for another factor in the developing leg, leading to bristle gain. Our study suggests that morphological evolution between species can occur through a single nucleotide change affecting several sexually dimorphic traits. VIDEO ABSTRACT.

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.

Maintenance of the bacterial homeostasis initially emanates from interactions between proteins and the bacterial nucleoid. Investigating their spatial correlation requires high spatial resolution, especially in tiny, highly confined and crowded bacterial cells. Here, we present super-resolution microscopy using a palette of fluorescent labels that bind transiently to either the membrane or the nucleoid of fixed E. coli cells. The presented labels are easily applicable, versatile and allow long-term single-molecule super-resolution imaging independent of photobleaching. The different spectral properties allow for multiplexed imaging in combination with other localisation-based super-resolution imaging techniques. As examples for applications, we demonstrate correlated super-resolution imaging of the bacterial nucleoid with the position of genetic loci, of nascent DNA in correlation to the entire nucleoid, and of the nucleoid of metabolically arrested cells. We furthermore show that DNA- and membrane-targeting labels can be combined with photoactivatable fluorescent proteins and visualise the nano-scale distribution of RNA polymerase relative to the nucleoid in drug-treated E. coli cells.