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Biology subjects

Bonini, P.

Publications and source records attributed to Bonini, P..

2 recordsLinked to original sources

Shedding light on human olfaction: electrophysiological recordings from sensory neurons in acute slices of olfactory epithelium

The COVID-19 pandemic brought attention to our limited understanding of human olfactory physiology. While the cellular composition of the human olfactory epithelium is similar to that of other vertebrates, its functional properties are largely unknown. We prepared acute slices of human olfactory epithelium from nasal biopsies and used the whole-cell patch-clamp technique to record electrical properties of cells. We measured voltage-gated currents in human olfactory sensory neurons and supporting cells, and action potentials in neurons. Additionally, inward currents and action potentials responses of neurons to a phosphodiesterase inhibitor indicated that the transduction cascade involves cAMP as a second messenger. Furthermore, responses to odorant mixtures demonstrated that the transduction cascade was intact in this preparation. This study provides the first electrophysiological characterization of olfactory sensory neurons in acute slices of the human olfactory epithelium, paving the way for future research to expand our knowledge of human olfactory physiology.

neuroscience↗

MS-DIAL 4: accelerating lipidomics using an MS/MS, CCS, and retention time atlas

To the EditorWe formulated mass spectral fragmentations of lipids across 117 lipid subclasses and included ion mobility tandem mass spectrometry (MS/MS) to provide a comprehensive lipidome atlas with retention time, collision cross section, and MS/MS information. The all-in-one solution from import of raw MS data to export of a common output format (mztab-M) was packaged in MS-DIAL 4 (http://prime.psc.riken.jp/) providing an enhanced standardized untargeted lipidomics procedure following lipidomics standards initiative (LSI) semi-quantitative definitions and shorthand notation system of lipid structures with a 1-2% estimated false discovery rate, which will contribute to harmonizing lipidomics data across laboratories to accelerate lipids research.

bioinformatics↗