Biology subjects

Pircher, B.

Publications and source records attributed to Pircher, B..

2 recordsLinked to original sources

bioRxiv2021

Ionotropic glutamate receptors in the retina - a bioinformatical meta-analysis

Glutamate is an essential neurotransmitter for signal processing in the vertical pathway of the mammalian retina, where it is involved in the distribution of visual information into several parallel channels. The excitatory effects of glutamate are mediated by AMPA-, kainate-, and NMDA-type ionotropic glutamate receptors (iGluRs). The expression patterns of these receptors in the vertebrate retina have been investigated so far with mainly immunocytochemical, in-situ hybridization, and electrophysiological/pharmacological techniques. Here, we have used scRNA sequencing data from chicken, mouse, macaque, and human retina to describe and compare the profile of iGluR expression in major retinal cell types across species. Our results suggest that major retinal cell types each express a unique set of iGluRs with substantial differences between non-mammalian and mammalian retinae. Expression of iGluRs has been investigated in more detail for amacrine and bipolar cell types of the human retina, each showing minor variations of a common pattern. The differential expression of iGluRs is likely to convey unique signal processing properties to individual elements of the retinal circuitry.

neuroscience↗

bioRxiv2021

A novel machine learning-based approach for the detection and analysis of spontaneous synaptic currents

Spontaneous synaptic activity is a hallmark of neural networks. A thorough description of these synaptic signals is essential for understanding neurotransmitter release and the generation of a postsynaptic response. However, the complexity of synaptic current trajectories has either precluded an in-depth analysis or it has forced human observers to resort to manual or semi-automated approaches based on subjective amplitude and area threshold settings. Both procedures are time-consuming, error-prone and likely affected by human bias. Here, we present three complimentary methods for a fully automated analysis of spontaneous excitatory postsynaptic currents measured in major cell types of the mouse retina and in a primary culture of mouse auditory cortex. Two approaches rely on classical threshold methods, while the third represents a novel machine learning-based algorithm. Comparison with frequently used existing methods demonstrates the suitability of our algorithms for an unbiased and efficient analysis of synaptic signals in the central nervous system.

neuroscience↗