Premotor circuits in the lumbar spinal cord
Dr Marco Beato
|Tel: +44 (0) 20 7679 3767|
Dr Marco Beato graduated in 1995 with a degree in theoretical physics from the University “La Sapienza” in Rome. He obtained a PhD in Biophysics from the International School for Advanced Studies (SISSA/ISAS, Trieste-Italy) in 1999. He started his post-doctoral work at the School of Pharmacy in the lab of Dr Lucia Sivilotti and moved to UCL in 2001 with a MRC Training Research Fellowship. In 2005 he was awarded a Wellcome Trust Career Development Award and a Royal Society University Research Fellowship for continuing his studies on glycinergic transmission in the spinal cord.
My current research is focussed on the characterization of premotor interneurons in the ventral horn of the spinal cord. Premotor networks are involved in all motor circuits and are responsible for the coordination of locomotor activity and for the execution and tuning of all motor tasks.
My main aim is to describe the properties of the network of premotor interneurons in relation to their function and to characterize their individual synaptic connections.
In the past I have studied the kinetic of glycine receptors using single channel recordings and fast concentration jumps techniques, combined with mathematical modeling and kinetic analysis. Now I am using electrophysiological recordings to record from multiple cells and from synaptically connected interneurone-motoneurone pairs in slices or in the en bloc spinal cord.
In collaboration with Professors Andrew Todd and David Maxwell (University of Glasgow) we have characterized glycinergic premotor interneurones and measured the strength of Renshaw cells synapses onto motoneurones. We will now use a rabies based trans-synaptic tracer to label networks of flexor and extensor related premotor interneurons and to drive the expression of channelrhodopsin. Our aim is to determine a map of functional connectivity and to relate it to the electrophysiological and neurochemical profiles of premotor interneurons.
Quantal analysis methods and kinetic modeling are used to characterize the properties of synapses.
My work is funded by a Royal Society University Research Fellowship, by the Leverhulme Trust and by a BBSRC project grant (joint with the University of Glasgow)
Figure 1 legend: Schematic diagram of the Renshaw cell (RC) motoneuron (Mn) recurrent inhibitory circuit with representative paired recordings of the inhibitory (green) and excitatory (black) connections.
Figure 2: Example of the anatomical reconstruction of a motoneuron (red) and a Renshaw cell (yellow soma and green axon) with the identification of individual synaptic contacts (insets). Paired recordings (right) revealed that Renshaw cells form high efficacy synapses onto motoneurons.
Figure 3: Rabies infected interneurons and motoneurons are viable for electrophysiological recordings up to ten days after infection. Hyperpolarizing and depolarizing current steps in an infected interneuron (left) and motoneuron (right). No differences in spike size and kinetics were observed between infected and non infected cells (coloured vs. grey bars)
Bayesian Quantal Analysis Python code download
We are currently tidying up the Python code for Bayesian Quantal Analysis (BQA) and compiling a list of instructions for its use.
The code is described in: Bhumbra, G. S., & Beato, M. Reliable evaluation of the quantal determinants of synaptic efficacy using Bayesian analysis.
Journal of Neurophysiology (innovative methodology section, (2013) 109 (2) 603 - 620. doi:10.1152/jn.00528.2012 )