Parameter tuning differentiates granule cell subtypes enriching transmission properties at the cerebellum input stage
Authors: Umberto Laforenza
Francesco Moccia
Stefano Masoli
Egidio D&#x27;Angelo
Marialuisa Tognolina
Journal: Communications biology (2020)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: cerebellar granule cells; response patterns; adaptation; firing acceleration; TRPM4 currents
Affiliations: Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy., Department of Molecular Medicine, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy., Department of Biology and Biotechnology, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy., Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy. ., Brain Connectivity Center, IRCCS Mondino Foundation, Via Mondino 2, 27100, Pavia, Italy. .

Abstract:  The cerebellar granule cells (GrCs) are classically described as a homogeneous neuronal population discharging regularly without adaptation. We show that GrCs in fact generate diverse response patterns to current injection and synaptic activation, ranging from adaptation to acceleration of firing. Adaptation was predicted by parameter optimization in detailed computational models based on available knowledge on GrC ionic channels. The models also predicted that acceleration required additional mechanisms. We found that yet unrecognized TRPM4 currents specifically accounted for firing acceleration and that adapting GrCs outperformed accelerating GrCs in transmitting high-frequency mossy fiber (MF) bursts over a background discharge. This implied that GrC subtypes identified by their electroresponsiveness corresponded to specific neurotransmitter release probability values. Simulations showed that fine-tuning of pre- and post-synaptic parameters generated effective MF-GrC transmission channels, which could enrich the processing of input spike patterns and enhance spatio-temporal recoding at the cerebellar input stage. 

Retracted: No

PubPeer comments:
 Figure 1 and 2 show only three example cells each. Nowhere in the text are shown averages, standard deviation or number of experiments given. So, the reader have to guess what the general picture is outside the examples. The first population comparison is done around figure 3, but this is problematic one. K-means clustering is performed on 55 samples, that is inadequate. Also they use two input variables that are shown in figure 3a. Clearly, there are no groups in the data, and clustering is meaningless. Arguably, only the accelerating group looks different, but that group was hand-picked out before! Interestingly, this accelerating group is only different at 10pA, not different for other tested input current (i.e. 8 and 12-20pA). This difference is argued to be due to TRPM4 currents. The authors show some current input at long latency, which they argue is due to TRPM4. They show immunohistochemistry and use a blocker for TRPM4. Literature shows that TRPM4 is expressed in glial cells in the granular layer (Stokum et al., 2018), not in granule cells themselves, and immunohistochemistry in the paper certainly does not rule out this possibility. Figure 4 shows some physiology and in the text they reference Kim et al., 2013 to argue that current observed looks like TRPM4 channel. But, it doesnu2019t, it doesnu2019t look at all like a slow current (e.g. see also Lei et al., 2014). It looks like burst of inputs. It is interesting that no other blockers were present in the recording solution, or no other blockers were considered as control. So, why is it not input? Why is it shown that the cells are accelerating in panel 4a, but not that that u2018phenotypeu2019 was abolished by washing in TRPM4 blocker (for example in panel c)? And why do TRPM4 only activate at 10pA, and not at 8 or 12pA (e.g. figure 3b)? It doesnu2019t make any sense. Finally a few remarks on the references. They reference 16 papers from the Du2019angelo lab (if I count correctly), out of 70 references (23%), many of which only has very superficial relationship to the current work. At the same time they fail to reference relevant current work (e.g. Chabrol et al. 2015, Stokum et al., 2018; Lei et al., 2014). ,