Biology subjects

Telenczuk, M.

Publications and source records attributed to Telenczuk, M..

4 recordsLinked to original sources

bioRxiv2022

PyDESeq2: a python package for bulk RNA-seq differential expression analysis

SummaryWe present PyDESeq2, a python implementation of the DESeq2 workflow for differential expression analysis on bulk RNA-seq data. This implementation achieves better precision, allows speed improvements on large datasets, as shown in experiments on TCGA data, and can be more easily interfaced with modern python-based data science tools. Availability and ImplementationPyDESeq2 is released as an open-source software under the MIT license. The source code is available on GitHub at https://github.com/owkin/PyDESeq2. Contactboris.muzellec@owkin.com

bioinformatics↗

bioRxiv2020

A kernel-based method to calculate local field potentials from networks of spiking neurons

BackgroundThe local field potential (LFP) is usually calculated from current sources arising from transmembrane currents, in particular in asymmetric cellular morphologies such as pyramidal neurons. New methodHere, we adopt a different point of view and relate the spiking of neurons to the LFP through efferent synaptic connections and provide a method to calculate LFPs. ResultsWe show that the so-called unitary LFPs (uLFP) provide the key to such a calculation. We show experimental measurements and simulations of uLFPs in neocortex and hippocampus, for both excitatory and inhibitory neurons. We fit a "kernel" function to measurements of uLFPs, and we estimate its spatial and temporal spread by using simulations of morphologically detailed reconstructions of hippocampal pyramidal neurons. Assuming that LFPs are the sum of uLFPs generated by every neuron in the network, the LFP generated by excitatory and inhibitory neurons can be calculated by convolving the trains of action potentials with the kernels estimated from uLFPs. This provides a method to calculate the LFP from networks of spiking neurons, even for point neurons for which the LFP is not easily defined. We show examples of LFPs calculated from networks of point neurons and compare to the LFP calculated from synaptic currents. ConclusionsThe kernel-based method provides a practical way to calculate LFPs from networks of point neurons. HighlightsO_LIWe provide a method to estimate the LFP from spiking neurons C_LIO_LIThis method is based on kernels, estimated from experimental data C_LIO_LIWe show applications of this method to calculate the LFP from networks of spiking neurons C_LIO_LIWe show that the kernel-based method is a low-pass filtered version of the LFP calculated from synaptic currents C_LI

neuroscience↗

bioRxiv2019

Modeling unitary fields and the single neuron contribution to local field potentials in the hippocampus

Synaptic currents represent a major contribution to the local field potential (LFP) in brain tissue, but the respective contribution of excitatory and inhibitory synapses is not known. Here, we provide estimates of this contribution by using computational models of hippocampal pyramidal neurons, constrained by in vitro recordings. We focus on the unitary LFP (uLFP) generated by single neurons in the CA3 region of the hippocampus. We first reproduce experimental results for hippocampal basket cells, and in particular how inhibitory uLFP are distributed within hippocampal layers. Next, we calculate the uLFP generated by pyramidal neurons, using morphologically-reconstructed CA3 pyramidal cells. The model shows that the excitatory uLFP is of small amplitude, smaller than inhibitory uLFPs. Indeed, when the two are simulated together, inhibitory uLFPs mask excitatory uLFPs, which might create the illusion that the inhibitory field is generated by pyramidal cells. These results provide an explanation for the observation that excitatory and inhibitory uLFPs are of the same polarity, in vivo and in vitro. These results also show that somatic inhibitory currents are large contributors of the LFP, which is important information to interpret this signal. Finally, the results of our model might form the basis of a simple method to compute the LFP, which could be applied to point neurons for each cell type, thus providing a simple biologically-grounded method to calculate LFPs from neural networks.

neuroscience↗

bioRxiv2018

Contribution of the axon initial segment to action potentials recorded extracellularly

Action potentials (APs) are electric phenomena that are recorded both intracellularly and extracellularly. APs are usually initiated in the short segment of the axon called the axon initial segment (AIS). It was recently proposed that at onset of an AP the soma and the AIS form a dipole. We study the extracellular signature (the extracellular action potential, EAP) generated by such a dipole. First, we demonstrate the formation of the dipole and its extracellular signature in detailed morphological models of a reconstructed pyramidal neuron. Then, we study the EAP waveform and its spatial dependence in models with axonal AP initiation and contrast it with the EAP obtained in models with somatic AP initiation. We show that in the models with axonal AP initiation the dipole forms between somatodendritic compartments and the AIS, and not between soma and dendrites as in the classical models. Soma-dendrites dipole is present only in models with somatic AP initiation. Our study has consequences for interpreting extracellular recordings of single-neuron activity and determining electrophysiological neuron types, but also for better understanding the origins of the high-frequency macroscopic electric fields recorded in the brain.\n\nNew & NoteworthyWe studied the consequences of the action potential (AP) initiation site on the extracellular signatures of APs. We show that: (1) at the time of AP initiation the action initial segment (AIS) forms a dipole with the soma, (2) the width but not (3) amplitude of the extracellular AP generated by this dipole increases with the soma-AIS distance. This may help to monitor dynamic changes in the AIS position in experimental in vivo recordings.

neuroscience↗