Biology subjects

Royer, L.

Publications and source records attributed to Royer, L..

4 recordsLinked to original sources

bioRxiv2017

Content-Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy

Fluorescence microscopy is a key driver of discoveries in the life-sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how image restoration based on deep learning extends the range of biological phenomena observable by microscopy. On seven concrete examples we demonstrate how microscopy images can be restored even if 60-fold fewer photons are used during acquisition, how near isotropic resolution can be achieved with up to 10-fold under-sampling along the axial direction, and how tubular and granular structures smaller than the diffraction limit can be resolved at 20-times higher frame-rates compared to state-of-the-art methods. All developed image restoration methods are freely available as open source software in Python, FO_SCPLOWIJIC_SCPLOW, and KO_SCPLOWNIMEC_SCPLOW.

bioinformatics↗

bioRxiv2017

Rem2 regulates distinct homeostatic mechanisms in visual circuit plasticity

Activity-regulated genes sculpt neural circuits in response to sensory experience. These calcium-sensitive genes generally fall into two categories: transcription factors and proteins that function at synapses. Yet little is known about activity-regulated, cytosolic proteins that transduce signals between the neuronal membrane and the nucleus. Using the visual system as a model, we investigated the role of the activity-regulated, non-canonical Ras-like GTPase Rem2 in vivo. We demonstrate that Rem2-/- mice fail to exhibit normal ocular dominance plasticity during the critical period. At the circuit level, cortical layer 2/3 neurons in Rem2-/- mice show deficits in both postsynaptic scaling up of excitatory synapses and misregulation of intrinsic excitability. Further, we reveal that Rem2 plays a novel, cell-autonomous role in regulating neuronal intrinsic excitability. Thus, Rem2 is a critical regulator of neural circuit function and distinct homeostatic plasticity mechanisms in vivo.\n\nHIGHLIGHTSO_LIRem2 is required in excitatory cortical neurons for normal ocular dominance plasticity\nC_LIO_LIRem2 regulates postsynaptic homoeostatic synaptic scaling up\nC_LIO_LIRem2 alters the intrinsic excitability of neurons in a cell-autonomous manner\nC_LI

neuroscience↗

bioRxiv2017

The Ras-like GTPase Rem2 is a potent endogenous inhibitor of calcium/calmodulin-dependent kinase II activity

CaMKII is a well-characterized, abundant protein kinase that regulates a diverse set of functions in a tissue specific manner. For example, in heart muscle, CaMKII regulates Ca2+ homeostasis while in neurons CaMKII regulates activity-dependent dendritic remodeling and Long Term Potentiation (LTP), a biological correlate of learning and memory. Previously, we identified the noncanonical GTPase Rem2 as a critical regulator of dendrite branching and synapse formation in the vertebrate nervous system. Here, we report that Rem2 directly interacts with CaMKII and potently inhibits the activity of the intact holoenzyme, a previously undescribed function for the Rem2 protein. To date, only one other endogenous inhibitor of CaMKII has been described: CaMKIIN, which blocks CaMKII activity through binding to the catalytic domain. Our data suggest that Rem2 inhibits CaMKII through a novel mechanism, as inhibition requires the presence of the association domain of CaMKII. Our biochemical finding that Rem2 is a direct, endogenous inhibitor of CaMKII activity, coupled with known functions of Rem2 in neurons, provides a framework which will enable future experiments probing the physiological role of CaMKII inhibition in a cellular context.

biochemistry↗

bioRxiv2017

With Our Powers Combined: Integrating Behavioral And Genetic Data To Estimate Mating Success And Sexual Selection

The analysis of sexual selection classically relies on the regression of individual phenotypes against the marginal sums of a males x females matrix of pairwise reproductive success, assessed by genetic parentage analysis. When the matrix is binarized, the marginal sums give the individual mating success. Because such analysis treats male and female mating/reproductive success independently, it ignores that the success of a male x female sexual interaction can be attributable to the phenotype of both individuals. Also, because it is based on genetic data only, it is oblivious to costly yet unproductive matings, which may be documented by behavioral observations. To solve these problems, we propose a statistical model which combines matrices of offspring numbers and behavioral observations. It models reproduction on each mating occasion of a mating season as three stochastic and interdependent pairwise processes, each potentially affected by the phenotype of both individuals and by random individual effect: encounter (Bernoulli), concomitant gamete emission (Bernoulli), and offspring production (Poisson). Applied to data from a mating experiment on brown trout, the model yielded different results from the classical regression analysis, with only a limited effect of male body size on the probability of gamete release and a negative effect of female body size on the probability of encounter and gamete release. Because the general structure of the model can be adapted to other partitioning of the reproductive process, it can be used for a variety of biological systems where behavioral and genetic data are available.

evolutionary biology↗