The complex synaptic pathways onto a looming-detector neuron revealed using serial block-face scanning electron microscopy.

The ability of locusts to detect looming stimuli and avoid collisions or predators depends on a neuronal circuit in the locust's optic lobe. Although comprehensively studied for over three decades, there are still major questions about the computational steps of this circuit. We used fourth instar larvae of Locusta migratoria to describe the connection between the lobula giant movement detector 1 (LGMD1) neuron in the lobula complex and the upstream neuropil, the medulla. Serial block-face scanning electron microscopy (SBEM) was used to characterize the morphology of the connecting neurons termed trans-medullary afferent (TmA) neurons and their synaptic connectivity. This enabled us to trace neurons over several hundred micrometers between the medulla and the lobula complex while identifying their synapses. We traced two different TmA neurons, each from a different individual, from their synapses with the LGMD in the lobula complex up into the medulla and describe their synaptic relationships. There is not a simple downstream transmission of the signal from a lamina neuron onto these TmA neurons; there is also a feedback loop in place with TmA neurons making outputs as well as receiving inputs. More than one type of neuron shapes the signal of the TmA neurons in the medulla. We found both columnar and trans-columnar neurons connected with the traced TmA neurons in the medulla. These findings indicate that there are computational steps in the medulla that have not been included in models of the neuronal pathway for looming detection.

Optimization of ultrastructural preservation of human brain for transmission electron microscopy after long post-mortem intervals.

Electron microscopy (EM) provides the necessary resolution to visualize the finer structures of nervous tissue morphology, which is important to understand healthy and pathological conditions in the brain. However, for the interpretation of the micrographs the tissue preservation is crucial. The quality of the tissue structure is mostly influenced by the post mortem interval (PMI), the time of death until the preservation of the tissue. Therefore, the aim of this study was to optimize the preparation-procedure for the human frontal lobe to preserve the ultrastructure as well as possible despite the long PMIs. Combining chemical pre- and post-fixation with cryo-fixation and cryo-substitution ("hybrid freezing"), it was possible to improve the preservation of the neuronal profiles of human brain samples compared to the "standard" epoxy resin embedding method. In conclusion short PMIs are generally desirable but up to a PMI of 16 h the ultrastructure can be preserved on an acceptable level with a high contrast using the "hybrid freezing" protocol described here.

An Unbiased Approach of Sampling TEM Sections in Neuroscience.

Investigations of the ultrastructural features of neurons and their synapses are only possible with electron microscopy. Especially for comparative studies of the changes in densities and distributions of such features, an unbiased sampling protocol is vital for reliable results. Here, we present a workflow for the image acquisition of brain samples. The workflow allows systematic uniform random sampling within a defined brain region, and the images can be analyzed using a disector. This technique is much faster than extensive examination of serial sections but still presents a feasible approach to estimate the densities and distributions of ultrastructure features. Before embedding, stained vibratome sections were used as a reference to identify the brain region under investigation, which helped speed up the overall specimen preparation process. This approach was used for comparative studies investigating the effect of an enriched-housing environment on several ultrastructural parameters in the mouse brain. Based on the successful use of the workflow, we adapted it for the purpose of elemental analysis of brain samples. We optimized the protocol in terms of the time of user-interaction. Automating all the time-consuming steps by compiling a script for the open source software SerialEM helps the user to focus on the main work of acquiring the elemental maps. As in the original workflow, we paid attention to the unbiased sampling approach to guarantee reliable results.

Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy.

BACKGROUND: Elucidating the anatomy of neuronal circuits and localizing the synaptic connections between neurons, can give us important insights in how the neuronal circuits work. We are using serial block-face scanning electron microscopy (SBEM) to investigate the anatomy of a collision detection circuit including the Lobula Giant Movement Detector (LGMD) neuron in the locust, Locusta migratoria. For this, thousands of serial electron micrographs are produced that allow us to trace the neuronal branching pattern. NEW METHOD: The reconstruction of neurons was previously done manually by drawing cell outlines of each cell in each image separately. This approach was very time consuming and troublesome. To make the process more efficient a new interactive software was developed. It uses the contrast between the neuron under investigation and its surrounding for semi-automatic segmentation. RESULTS: For segmentation the user sets starting regions manually and the algorithm automatically selects a volume within the neuron until the edges corresponding to the neuronal outline are reached. Internally the algorithm optimizes a 3D active contour segmentation model formulated as a cost function taking the SEM image edges into account. This reduced the reconstruction time, while staying close to the manual reference segmentation result. COMPARISON WITH EXISTING METHODS: Our algorithm is easy to use for a fast segmentation process, unlike previous methods it does not require image training nor an extended computing capacity. CONCLUSION: Our semi-automatic segmentation algorithm led to a dramatic reduction in processing time for the 3D-reconstruction of identified neurons.