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Improved expansion microscopy method preserves signal from fluorescent proteins and antibodies using off-the-shelf reagents. Expansion microscopy (ExM) enables imaging of preserved specimens with nanoscale precision on diffraction-limited instead of specialized super-resolution microscopes. ExM works by physically separating fluorescent probes after anchoring them to a swellable gel. The first ExM method did not result in the retention of native proteins in the gel and relied on custom-made reagents that are not widely available. Here we describe protein retention ExM (proExM), a variant of ExM in which proteins are anchored to the swellable gel, allowing the use of conventional fluorescently labeled antibodies and streptavidin, and fluorescent proteins. We validated and demonstrated the utility of proExM for multicolor super-resolution (∼70 nm) imaging of cells and mammalian tissues on conventional microscopes.

In vitro studies conducted in Aplysia and chick sensory neurons indicate that in addition to microtubule assembly, long microtubules in the C-domain of the growth cone move forward as a coherent bundle during axonal elongation. Nonetheless, whether this mode of microtubule translocation contributes to growth cone motility in vivo is unknown. To address this question, we turned to the model system Drosophila. Using docked mitochondria as fiduciary markers for the translocation of long microtubules, we first examined motion along the axon to test if the pattern of axonal elongation is conserved between Drosophila and other species in vitro. When Drosophila neurons were cultured on Drosophila extracellular matrix proteins collected from the Drosophila Kc167 cell line, docked mitochondria moved in a pattern indicative of bulk microtubule translocation, similar to that observed in chick sensory neurons grown on laminin. To investigate whether the C-domain is stationary or advances in vivo, we tracked the movement of mitochondria during elongation of the aCC motor neuron in stage 16 Drosophila embryos. We found docked mitochondria moved forward along the axon shaft and in the growth cone C-domain. This work confirms that the physical mechanism of growth cone advance is similar between Drosophila and vertebrate neurons and suggests forward translocation of the microtubule meshwork in the axon underlies the advance of the growth cone C-domain in vivo. These results highlight the need for incorporating en masse microtubule translocation, in addition to assembly, into models of axonal elongation.

Identifying the cellular origins and mapping the dendritic and axonal arbors of neurons have been century old quests to understand the heterogeneity among these brain cells. Current Brainbow based transgenic animals take the advantage of multispectral labeling to differentiate neighboring cells or lineages, however, their applications are limited by the color capacity. To improve the analysis throughput, we designed Bitbow, a digital format of Brainbow which exponentially expands the color palette to provide tens of thousands of spectrally resolved unique labels. We generated transgenic Bitbow Drosophila lines, established statistical tools, and streamlined sample preparation, image processing, and data analysis pipelines to conveniently mapping neural lineages, studying neuronal morphology and revealing neural network patterns with unprecedented speed, scale, and resolution.

Dyneins are a small class of molecular motors that bind to microtubules and walk toward their minus ends. They are essential for the transport and distribution of organelles, signaling complexes and cytoskeletal elements. In addition dyneins generate forces on microtubule arrays that power the beating of cilia and flagella, cell division, migration and growth cone motility. Classical approaches to the study of dynein function in axons involve the depletion of dynein, expression of mutant/truncated forms of the motor, or interference with accessory subunits. By necessity, these approaches require prolonged time periods for the expression or manipulation of cellular dynein levels. With the discovery of the ciliobrevins, a class of cell permeable small molecule inhibitors of dynein, it is now possible to acutely disrupt dynein both globally and locally. In this review, we briefly summarize recent work using ciliobrevins to inhibit dynein and discuss the insights ciliobrevins have provided about dynein function in various cell types with a focus on neurons. We temper this with a discussion of the need for studies that will elucidate the mechanism of action of ciliobrevin and as well as the need for experiments to further analyze the specificity of ciliobreviens for dynein. Although much remains to be learned about ciliobrevins, these small molecules are proving themselves to be valuable novel tools to assess the cellular functions of dynein.

The study of neuron morphology requires robust and comprehensive methods to quantify the differences between neurons of different subtypes and animal species. Several software packages have been developed for the analysis of neuron tracing results stored in the standard SWC format. The packages, however, provide relatively simple quantifications and their non-extendable architecture prohibit their use for advanced data analysis and visualization. We developed nGauge , a Python toolkit to support the parsing and analysis of neuron morphology data. As an application programming interface (API), nGauge can be referenced by other popular open-source software to create custom informatics analysis pipelines and advanced visualizations. nGauge defines an extendable data structure that handles volumetric constructions (e.g. soma), in addition to the SWC linear reconstructions, while remaining lightweight. This greatly extends nGauge ’s data compatibility.

Neurons are complex cellular machines that utilize a dynamic cytoskeleton to elaborate long axonal processes. During embryonic development, these long processes eventually terminate and form a synapse with a prescribed target. Elongation is driven in part by a unique structure called the growth cone at the tip of the axon. A recently developed biophysical model for axonal elongation has proposed that forces cause the growth cone to translocate in bulk, while stretching the axon. This is followed by intercalated mass addition along the length of the axon to prevent thinning. As a result of axonal stretching, the cytoskeleton undergoes en masse translocation. While this has been observed in cultured neurons from a variety of different species, whether this occurs in vivo is unknown. In addition, the molecular force generating mechanisms in the axon that regulate axonal stretching and cytoskeleton translocation have not been characterized. Here, we use mitochondria docked to the cytoskeleton as fiduciary markers for bulk cytoskeletal movements. We use this technique in cultured Drosophila neurons to show that cytoskeleton translocation is conserved between vertebrates and invertebrates. Then we track the movement of docked mitochondria in the aCC motoneuron in stage 16 Drosophila embryos to show that the cytoskeleton translocates during axonal elongation. This suggests that axons grow by stretching in vivo.

Neuronal morphology reconstruction in fluorescence microscopy 3D images is essential for analyzing neuronal cell type and connectivity. Manual tracing of neurons in these images is time consuming and subjective. Automated tracing is highly desired yet is one of the foremost challenges in computational neuroscience. The multispectral labeling technique, Brainbow utilizes high dimensional spectral information to distinguish intermingled neuronal processes. It is particular interesting to develop new algorithms to include the spectral information into the tracing process. Recently, deep learning approaches achieved state-of-the-art in different computer vision and medical imaging applications. To benefit from the power of deep learning, in this paper, we propose an automated neural tracing approach in multispectral 3D Brainbow images based on recurrent neural net-work. We first adopt VBM4D approach to denoise multispectral 3D images. Then we generate cubes as training samples along the ground truth, manually traced paths. These cubes are the input to the recur-rent neural network. The proposed approach is simple and effective. The approach can be implemented with the deep learning toolbox \"Keras\" in 100 lines. Finally, to evaluate our approach, we computed the average and standard deviation of DIADEM metric from the ground truth results to our tracing results, and from our tracing results to the ground truth results. Extensive experimental results on the collected dataset demonstrate that the proposed approach performs well in Brainbow labeled mouse brain images.

Accurate and complete neuronal wiring diagrams are necessary for understanding brain function at many scales from long-range interregional projections to microcircuits. Traditionally, light microscopy-based anatomical reconstructions use monochromatic labeling and therefore necessitate sparse labeling to eliminate tracing ambiguity between intermingled neurons. Consequently, our knowledge of neuronal morphology has largely been based on averaged estimations across many samples. Recently developed second-generation Brainbow tools promise to circumvent this limitation by revealing fine anatomical details of many unambiguously identifiable neurons in densely labeled samples. Yet, a means to quantify and analyze the information is currently lacking. Therefore, we developed nTracer, an ImageJ plug-in capable of rapidly and accurately reconstructing whole-cell morphology of large neuronal populations in densely labeled brains. Footnotes * Title change and added author

The study of neuron morphology requires robust and comprehensive methods to quantify the differences between neurons of different subtypes and animal species. Several software packages have been developed for the analysis of neuron tracing results stored in the standard SWC format. However, providing relatively simple quantifications and their non-extendable architecture prohibit their use for advanced data analysis and visualization. We developed nGauge, a Python toolkit to support the parsing and analysis of neuron morphology data. As an application programming interface (API), nGauge can be referenced by other popular open-source software to create custom informatics analysis pipelines and advanced visualizations. nGauge defines an extendable data structure that handles volumetric constructions (e.g. soma), in addition to the SWC linear reconstructions, while remaining light-weight. This greatly extends nGauge's data compatibility. Competing Interest Statement The authors have declared no competing interest.

Over the last decade, the advances in Brainbow labeling allowed labeling hundreds of neurons with distinct colors in the same field of view of a brain [1, 2]. Reconstruction (or \"tracing\") of the 3D structures of these images has been enabled by a growing set of software tools for automatic and manual annotation. It is common, however, to have errors introduced by heuristics used by tracing software, namely that they assume the \"best\" path is the highest intensity one, a more pertinent issue when dealing with multicolor microscope images. Here, we report nCorrect, an algorithm for correcting this error by reanalyzing previously created neuron traces to produce more physiologically-relevant ones. Specifically, we use a four dimensional minimization algorithm to identify a more-optimal reconstruction of the image, allowing us to better take advantage of existing manual tracing results. We define a new metric (hyperspectral cosine similarity) for describing the similarity of different neuron colors to each other. Our code is available in an open source license and forms the basis for future improved neuron tracing software. Competing Interest Statement The authors have declared no competing interest.