an-unbiased-approach-of-sampling-tem-sections-in-neuroscience

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 ...

Medical University of Graz

The small size of some insects, and the crystalline regularity of their eyes, have made them ideal for large-scale reconstructions of visual circuits. In phylogenetically recent muscomorph flies, like Drosophila, precisely coordinated output to different motion-processing pathways is delivered by photoreceptors (R cells), targeting four different postsynaptic cells at each synapse (tetrad). Tetrads were linked to the evolution of aerial agility. To reconstruct circuits for vision in the larger brain of a locust, a phylogenetically old, flying insect, we adapted serial block-face scanning electron microscopy (SBEM). Locust lamina monopolar cells, L1 and L2, were the main targets of the R cell pathway, L1 and L2 each fed a different circuit, only L1 providing feedback onto R cells. Unexpectedly, 40% of all locust R cell synapses onto both L1 and L2 were tetrads, revealing the emergence of tetrads in an arthropod group present 200 million years before muscomorph flies appeared, coinciding with the early evolution of flight.

Synaptic connections of first-stage visual neurons in the locust Schistocerca gregaria extend evolution of tetrad synapses back 200 million years.

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.

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

In this work the development of an electrochemical sensor for the determination of polyunsaturated fatty acids (PUFAs), in particular linoleic acid, in commercially available safflower oil as complex matrix is described. The sensor consists of a carbon paste electrode with cobalt(II) phthalocyanine, Co(II)Pc, as mediator and multiwalled carbon-nanotubes (MWCNT) as nanomaterial. As carrier medium a sodium borate buffer (0.1 M, pH 9) was used. PUFAs were detected at a working voltage of 0.35-0.45 V (vs. Ag/AgCl). The sensor development was carried out in a batch system with differential pulse voltammetry (DPV) and cyclic voltammetry (CV). Sensor specification was tested by using various fatty acids (stearic acid, oleic acid, linoleic acid, linolenic acid and arachidonic acid) as well as esterified fatty acids, resulting in a specific applicability towards PUFAs, especially linoleic acid (LAH). The optimized sensor was applied in a flow injection analysis system (FIA) for the analysis of PUFAs in complex matrix. Linoleic acid was used as standard substrate to determine the analytical parameters. The linearity ranges between 7.5 and 200 μg mL LAH, while the limit of detection was determined to be 2.5 μg mL and the limit of quantification is approximately 7.5 μg mL LAH. The LAH content was successfully detected in commercially available safflower oil via standard addition method and the results could be confirmed by a reference method. The PUFA content was calculated as LAH-equivalent.

Article	Year
Synaptic connections of first-stage visual neurons in the locust Schistocerca gregaria extend evolution of tetrad synapses back 200 million years. The Journal of comparative neurology\| PubMed ID: 25255709	2015
Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy. Journal of neuroscience methods\| PubMed ID: 26928258	2016
Development of a cobalt(II) phthalocyanine- MWCNT modified carbon paste electrode for the detection of polyunsaturated fatty acids. Analytica chimica acta\| PubMed ID: 30278907	2018

Stefan Wernitznig

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