We have made a P-element derivative called Pc[ry], which carries the selectable marker gene rosy, but which acts like a nondefective, intact P element. It transposes autonomously into the germline chromosomes of an M-strain Drosophila embryo and it mobilizes in trans the defective P elements of the singed-weak allele. Frameshift mutations introduced into any of the four major open reading frames of the P sequence were each sufficient to eliminate the transposase activity, but none affected signals required in cis for transposition of the element. Complementation tests between pairs of mutant elements suggest that a single polypeptide comprises the transposase. We have examined transcripts of P elements both from natural P strains and from lines containing only nondefective Pc[ry] elements, and have identified two RNA species that appear to be specific for autonomous elements.

Phosphatidylinositol (PI) is an inositol-containing phospholipid synthesized in the endoplasmic reticulum (ER). PI is a precursor lipid for PI 4,5-bisphosphate (PI(4,5)P) in the plasma membrane (PM) important for Ca signaling in response to extracellular stimuli. Thus, ER-to-PM PI transfer becomes essential for cells to maintain PI(4,5)P homeostasis during receptor stimulation. In this chapter, we discuss two live-cell imaging protocols to analyze ER-to-PM PI transfer at ER-PM junctions, where the two membrane compartments make close appositions accommodating PI transfer. First, we describe how to monitor PI(4,5)P replenishment following receptor stimulation, as a readout of PI transfer, using a PI(4,5)P biosensor and total internal reflection fluorescence microscopy. The second protocol directly visualizes PI transfer proteins that accumulate at ER-PM junctions and mediate PI(4,5)P replenishment with PI in the ER in stimulated cells. These methods provide spatial and temporal analysis of ER-to-PM PI transfer during receptor stimulation and can be adapted to other research questions related to this topic.

The surface layers (S layers) of those bacteria and archaea that elaborate these crystalline structures have been studied for 40 years. However, most structural analysis has been based on electron microscopy of negatively stained S-layer fragments separated from cells, which can introduce staining artifacts and allow rearrangement of structures prone to self-assemble. We present a quantitative analysis of the structure and organization of the S layer on intact growing cells of the Gram-negative bacterium Caulobacter crescentus using cryo-electron tomography (CET) and statistical image processing. Instead of the expected long-range order, we observed different regions with hexagonally organized subunits exhibiting short-range order and a broad distribution of periodicities. Also, areas of stacked double layers were found, and these increased in extent when the S-layer protein (RsaA) expression level was elevated by addition of multiple rsaA copies. Finally, we combined high-resolution amino acid residue-specific Nanogold labeling and subtomogram averaging of CET volumes to improve our understanding of the correlation between the linear protein sequence and the structure at the 2-nm level of resolution that is presently available. The results support the view that the U-shaped RsaA monomer predicted from negative-stain tomography proceeds from the N terminus at one vertex, corresponding to the axis of 3-fold symmetry, to the C terminus at the opposite vertex, which forms the prominent 6-fold symmetry axis. Such information will help future efforts to analyze subunit interactions and guide selection of internal sites for display of heterologous protein segments.

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.

Automatic image segmentation is critical to scale up electron microscope (EM) connectome reconstruction. To this end, segmentation competitions, such as CREMI and SNEMI, exist to help researchers evaluate segmentation algorithms with the goal of improving them. Because generating ground truth is time-consuming, these competitions often fail to capture the challenges in segmenting larger datasets required in connectomics. More generally, the common metrics for EM image segmentation do not emphasize impact on downstream analysis and are often not very useful for isolating problem areas in the segmentation. For example, they do not capture connectivity information and often over-rate the quality of a segmentation as we demonstrate later. To address these issues, we introduce a novel strategy to enable evaluation of segmentation at large scales both in a supervised setting, where ground truth is available, or an unsupervised setting. To achieve this, we first introduce new metrics more closely aligned with the use of segmentation in downstream analysis and reconstruction. In particular, these include synapse connectivity and completeness metrics that provide both meaningful and intuitive interpretations of segmentation quality as it relates to the preservation of neuron connectivity. Also, we propose measures of segmentation correctness and completeness with respect to the percentage of "orphan" fragments and the concentrations of self-loops formed by segmentation failures, which are helpful in analysis and can be computed without ground truth. The introduction of new metrics intended to be used for practical applications involving large datasets necessitates a scalable software ecosystem, which is a critical contribution of this paper. To this end, we introduce a scalable, flexible software framework that enables integration of several different metrics and provides mechanisms to evaluate and debug differences between segmentations. We also introduce visualization software to help users to consume the various metrics collected. We evaluate our framework on two relatively large public groundtruth datasets providing novel insights on example segmentations.

Artificial activation of anatomically localized, genetically defined hypothalamic neuron populations is known to trigger distinct innate behaviors, suggesting a hypothalamic nucleus-centered organization of behavior control. To assess whether the encoding of behavior is similarly anatomically confined, we performed simultaneous neuron recordings across twenty hypothalamic regions in freely moving animals. Here we show that distinct but anatomically distributed neuron ensembles encode the social and fear behavior classes, primarily through mixed selectivity. While behavior class-encoding ensembles were spatially distributed, individual ensembles exhibited strong localization bias. Encoding models identified that behavior actions, but not motion-related variables, explained a large fraction of hypothalamic neuron activity variance. These results identify unexpected complexity in the hypothalamic encoding of instincts and provide a foundation for understanding the role of distributed neural representations in the expression of behaviors driven by hardwired circuits.

Vocal learning in songbirds requires an anatomically discrete and functionally dedicated circuit called the anterior forebrain pathway (AFP). The AFP is homologous to cortico-basal ganglia-thalamo-cortical loops in mammals. The basal ganglia portion of this pathway, Area X, shares many features characteristic of the mammalian striatum and pallidum, including cell types and connectivity. The AFP also deviates from mammalian basal ganglia circuits in fundamental ways. In addition, the microcircuitry, role of neuromodulators, and function of Area X are still unclear. Elucidating the mechanisms by which both mammalian-like and unique features of the AFP contribute to vocal learning may help lead to a broad understanding of the sensorimotor functions of basal ganglia circuits.

Intracellular recording allows precise measurement and manipulation of individual neurons, but it requires stable mechanical contact between the electrode and the cell membrane, and thus it has remained challenging to perform in behaving animals. Whole-cell recordings in freely moving animals can be obtained by rigidly fixing ('anchoring') the pipette electrode to the head; however, previous anchoring procedures were slow and often caused substantial pipette movement, resulting in loss of the recording or of recording quality. We describe a UV-transparent collar and UV-cured adhesive technique that rapidly (within 15 s) anchors pipettes in place with virtually no movement, thus substantially improving the reliability, yield and quality of freely moving whole-cell recordings. Recordings are first obtained from anesthetized or awake head-fixed rats. UV light cures the thin adhesive layers linking pipette to collar to head. Then, the animals are rapidly and smoothly released for recording during unrestrained behavior. The anesthetized-patched version can be completed in ∼4-7 h (excluding histology) and the awake-patched version requires ∼1-4 h per day for ∼2 weeks. These advances should greatly facilitate studies of neuronal integration and plasticity in identified cells during natural behaviors.

Many animals maintain an internal representation of their heading as they move through their surroundings. Such a compass representation was recently discovered in a neural population in the Drosophila melanogaster central complex, a brain region implicated in spatial navigation. Here, we use two-photon calcium imaging and electrophysiology in head-fixed walking flies to identify a different neural population that conjunctively encodes heading and angular velocity, and is excited selectively by turns in either the clockwise or counterclockwise direction. We show how these mirror-symmetric turn responses combine with the neurons' connectivity to the compass neurons to create an elegant mechanism for updating the fly's heading representation when the animal turns in darkness. This mechanism, which employs recurrent loops with an angular shift, bears a resemblance to those proposed in theoretical models for rodent head direction cells. Our results provide a striking example of structure matching function for a broadly relevant computation.