Does Müller cell differentiation occur prior to the emergence of synapses in embryonic turtle retina?

Müller cells are the main glial cells in the retina, and are related to plexiform layer activity. Recent studies have demonstrated that Müller cells are involved in the synaptic conservation, plasticity, development and metabolism of glutamate. During turtle retinal development, layers, cells and synapses appear at different times. The aim of this research is to study the emergence of Müller cells during embryonic development and their relationship with the synaptogenesis. The authors used retinas from Trachemys scripta elegans embryos at stages S14, 18, 20, 23, and 26. Some retinas were processed with immunocytochemistry in order to detect the presence of glutamine synthetase in Müller cells, which was used as a marker of these cells. Other retinas from the same stages were processed for ultrastructural studies. Samples were observed in confocal and transmission electron microscopes, respectively. The present results show that glutamine synthetase expression in Müller cells occurs at S18, before the emergence of the retinal layers and the early synapses.

Characterization of a new murine retinal cell line (MU-PH1) with glial, progenitor and photoreceptor characteristics

Unlike fish and amphibians, mammals do not regenerate retinal neurons throughout life. However, neurogenic potential may be conserved in adult mammal retina and it is necessary to identify the factors that regulate retinal progenitor cells (RPC) proliferative capacity to scope their therapeutic potential. Müller cells can be progenitors for retinal neuronal cells and can play an essential role in the restoration of visual function after retinal injury. Some members of the Toll-like receptor (TLR) family, TLR2, TLR3 and TLR4, are related to progenitor cells proliferation. Müller cells are important in retinal regeneration and stable cell lines are useful for the study of retinal stem cell biology. Our purpose was to obtain a Müller-derived cell line with progenitor characteristics and potential interest in regeneration processes. We obtained and characterized a murine Müller-derived cell line (MU-PH1), which proliferates indefinitely in vitro. Our results show that (i) MU-PH1 cells expresses the Müller cell markers Vimentin, S-100, glutamine synthetase and the progenitor and stem cell markers Nestin, Abcg2, Ascl1, α-tubulin and β-III-tubulin, whereas lacks the expression of CRALBP, GFAP, Chx10, Pax6 and Notch1 markers; (ii) MU-PH1 cell line stably express the photoreceptor markers recoverin, transducin, rhodopsin, blue and red/green opsins and also melanopsin; (iii) the presence of opsins was confirmed by the recording of intracellular free calcium levels during light stimulation; (iv) MU-PH1 cell line also expresses the melatonin MT1 and MT2 receptors; (v) MU-PH1 cells express TLR1, 2, 4 and 6 mRNA; (vi) MU-PH1 express TLR2 at cell surface level; (vii) Candida albicans increases TLR2 and TLR6 mRNA expression; (viii) C. albicans or TLR selective agonists (Pam(3)CysSK(4), LPS) did not elicit morphological changes nor TNF-α secretion; (ix) C. albicans and Pam(3)CysSK(4) augmented MU-PH1 neurospheres formation in a statistically significant manner. Our results indicate that MU-PH1 cell line could be of great interest both as a photoreceptor model and in retinal regeneration approaches and that TLR2 may also play a role in retinal cell proliferation.

μ-MAR: Multiplane 3D Marker based Registration for depth-sensing cameras

Many applications including object reconstruction, robot guidance, and. scene mapping require the registration of multiple views from a scene to generate a complete geometric and appearance model of it. In real situations, transformations between views are unknown and it is necessary to apply expert inference to estimate them. In the last few years, the emergence of low-cost depth-sensing cameras has strengthened the research on this topic, motivating a plethora of new applications. Although they have enough resolution and accuracy for many applications, some situations may not be solved with general state-of-the-art registration methods due to the signal-to-noise ratio (SNR) and the resolution of the data provided. The problem of working with low SNR data, in general terms, may appear in any 3D system, then it is necessary to propose novel solutions in this aspect. In this paper, we propose a method, μ-MAR, able to both coarse and fine register sets of 3D points provided by low-cost depth-sensing cameras, despite it is not restricted to these sensors, into a common coordinate system. The method is able to overcome the noisy data problem by means of using a model-based solution of multiplane registration. Specifically, it iteratively registers 3D markers composed by multiple planes extracted from points of multiple views of the scene. As the markers and the object of interest are static in the scenario, the transformations obtained for the markers are applied to the object in order to reconstruct it. Experiments have been performed using synthetic and real data. The synthetic data allows a qualitative and quantitative evaluation by means of visual inspection and Hausdorff distance respectively. The real data experiments show the performance of the proposal using data acquired by a Primesense Carmine RGB-D sensor. The method has been compared to several state-of-the-art methods. The results show the good performance of the μ-MAR to register objects with high accuracy in presence of noisy data outperforming the existing methods.