Close to Optimal Cell Sensing Ensures the Robustness of Tissue Differentiation Process: The Avian Photoreceptor Mosaic Case

The way that progenitor cell fate decisions and the associated environmental sensing are regulated to ensure the robustness of the spatial and temporal order in which cells are generated towards a fully differentiating tissue still remains elusive. Here, we investigate how cells regulate their sensing intensity and radius to guarantee the required thermodynamic robustness of a differentiated tissue. In particular, we are interested in finding the conditions where dedifferentiation at cell level is possible (microscopic reversibility), but tissue maintains its spatial order and differentiation integrity (macroscopic irreversibility). In order to tackle this, we exploit the recently postulated Least microEnvironmental Uncertainty Principle (LEUP) to develop a theory of stochastic thermodynamics for cell differentiation. To assess the predictive and explanatory power of our theory, we challenge it against the avian photoreceptor mosaic data. By calibrating a single parameter, the LEUP can predict the cone color spatial distribution in the avian retina and, at the same time, suggest that such a spatial pattern is associated with quasi-optimal cell sensing. By means of the stochastic thermodynamics formalism, we find out that thermodynamic robustness of differentiated tissues depends on cell metabolism and cell sensing properties. In turn, we calculate the limits of the cell sensing radius that ensure the robustness of differentiated tissue spatial order. Finally, we further constrain our model predictions to the avian photoreceptor mosaic.

Close to optimal cell sensing ensures the robustness of tissue differentiation process: the avian photoreceptor mosaic case

The way that progenitor cell fate decisions and the associated environmental sensing are regulated to ensure the robustness of the spatial and temporal order in which cells are generated towards a fully differentiating tissue still remains elusive. Here, we investigate how cells regulate their sensing intensity and radius to guarantee the required thermodynamic robustness of a differentiated tissue. In particular, we are interested in finding the conditions where dedifferentiation at cell level is possible (microscopic reversibility) but tissue maintains its spatial order and differentiation integrity (macroscopic irreversibility). In order to tackle this, we exploit the recently postulated Least microEnvironmental Uncertainty Principle (LEUP) to develop a theory of stochastic thermodynamics for cell differentiation. To assess the predictive and explanatory power of our theory, we challenge it against the avian photoreceptor mosaic data. By calibrating a single parameter, the LEUP can predict the cone color spatial distribution in the avian retina and, at the same time, suggest that such a spatial pattern is associated with quasi-optimal cell sensing. By means of the stochastic thermodynamics formalism, we find out that thermodynamic robustness of differentiated tissues depends on cell metabolism and cell sensing properties. In turn, we calculate the limits of the cell sensing radius that ensure the robustness of differentiated tissue spatial order. Finally, we further constrain our model predictions to the avian photoreceptor mosaic.

Assessing photoreceptor structure in patients with traumatic head injury

ObjectivePrevious work using adaptive optics scanning light ophthalmoscopy (AOSLO) imaging has shown photoreceptor disruption to be a common finding in head and ocular trauma patients. Here an expanded trauma population was examined using a novel imaging technique, split-detector AOSLO, to assess remnant cone structure in areas with significant disruption on confocal AOSLO imaging and to follow photoreceptor changes longitudinally.Methods and AnalysisEight eyes from seven subjects with head and/or ocular trauma underwent imaging with spectral domain optical coherence tomography, confocal AOSLO and split-detector AOSLO to assess foveal and parafoveal photoreceptor structure.ResultsConfocal AOSLO imaging revealed hyporeflective foveal regions in two of eight eyes. Split-detector imaging within the hyporeflective confocal areas showed both remnant and absent inner-segment structure. Both of these eyes were imaged longitudinally and showed variation of the photoreceptor mosaic over time. Four other eyes demonstrated subclinical regions of abnormal waveguiding photoreceptors on multimodal AOSLO imagery but were otherwise normal. Two eyes demonstrated normal foveal cone packing without disruption.ConclusionMultimodal imaging can detect subtle photoreceptor abnormalities not necessarily detected by conventional clinical imaging. The addition of split-detector AOSLO revealed the variable condition of inner segments within confocal photoreceptor disruption, confirming the usefulness of dual-modality AOSLO imaging in assessing photoreceptor structure and integrity. Longitudinal imaging demonstrated the dynamic nature of the photoreceptor mosaic after trauma. Multimodal imaging with dual-modality AOSLO improves understanding of visual symptoms and photoreceptor structure changes in patients with head and ocular trauma.

Computational-Observer Analysis of Illumination Discrimination

The spectral properties of the ambient illumination provide useful information about time of day and weather. We study the perceptual representation of illumination by analyzing measurements of how well people discriminate between illuminations across scene configurations. More specifically, we compare human performance to a computational-observer analysis that evaluates the information available in the isomerizations of the cones in a model human photoreceptor mosaic. Some patterns of human performance are predicted by the computational observer, other aspects are not. The analysis clarifies which aspects of performance require additional explanation in terms of the action of visual mechanisms beyond the isomerization of light by the cones.