Amacrine cells of the retina are conspicuously variable in their morphologies, their population demographics, and their ensuing functions. Vesicular glutamate transporter 3 (VGluT3) amacrine cells are a recently characterized type of amacrine cell exhibiting local dendritic autonomy. The present analysis has examined three features of this VGluT3 population, including their density, local distribution, and dendritic spread, to discern the extent to which these are interrelated, using male and female mice. We first demonstrate that Bax-mediated cell death transforms the mosaic of VGluT3 cells from a random distribution into a regular mosaic. We subsequently examine the relationship between cell density and mosaic regularity across recombinant inbred strains of mice, finding that, although both traits vary across the strains, they exhibit minimal covariation. Other genetic determinants must therefore contribute independently to final cell number and to mosaic order. Using a conditional KO approach, we further demonstrate that Bax acts via the bipolar cell population, rather than cell-intrinsically, to control VGluT3 cell number. Finally, we consider the relationship between the dendritic arbors of single VGluT3 cells and the distribution of their homotypic neighbors. Dendritic field area was found to be independent of Voronoi domain area, while dendritic coverage of single cells was not conserved, simply increasing with the size of the dendritic field. Bax-KO retinas exhibited a threefold increase in dendritic coverage. Each cell, however, contributed less dendrites at each depth within the plexus, intermingling their processes with those of neighboring cells to approximate a constant volumetric density, yielding a uniformity in process coverage across the population.SIGNIFICANCE STATEMENT Different types of retinal neuron spread their processes across the surface of the retina to achieve a degree of dendritic coverage that is characteristic of each type. Many of these types achieve a constant coverage by varying their dendritic field area inversely with the local density of like-type neighbors. Here we report a population of retinal amacrine cells that do not develop dendritic arbors in relation to the spatial positioning of such homotypic neighbors; rather, this cell type modulates the extent of its dendritic branching when faced with a variable number of overlapping dendritic fields to approximate a uniformity in dendritic density across the retina. 相似文献
Background: Aniridia is a rare developmental eye disorder characterized by complete or partial iris hypoplasia often accompanied with other ocular changes that affect the cornea, anterior chamber, lens, retina, and optic nerve. Most cases of aniridia are inherited with an autosomal dominant mode of inheritance caused by PAX6 mutations or deletions. To reveal the underlying genetic defect in a four-generation Iranian family with aniridia, we carried out a genetic screening of PAX6.
Methods: Complete ophthalmic examinations were performed for available affected family members. All PAX6 exons and their flanking regions were sequenced for affected individuals. Candidate variation was screened for segregation in the pedigree by Sanger sequencing. Bioinformatics prediction was done to evaluate the deleterious effects of the mutation on protein product. Real-time PCR was used to investigate the impact of the variant on PAX6 mRNA expression.
Results: All patients were diagnosed with isolated aniridia associated with variable phenotypic features including retinal detachment. A novel heterozygous deletion c.320_348delTGTCCGAGGGGGTCTGTACCAACGATAAC (p.Leu107HisfsX16) on PAX6 gene was detected. Decreased mRNA level of PAX6 in the affected individuals indicated that the mutation caused nonsense-mediated mRNA decay (NMD).
Conclusions: To the best of our knowledge, it is the first report on the genetics of aniridia in Iran. Segregation analysis, bioinformatics prediction and confirmation of NMD, all support the proposition that the novel observed PAX6 mutation is the cause of aniridia in the pedigree. Retinal detachment in some of the affected members, which is a rare reported phenotypic feature of aniridia patients, may be associated with this mutation. 相似文献
Particle size analysis in the pharmaceutical industry has long been a source of debate regarding how best to define measurement accuracy; the degree to which the result of a measurement or calculation conforms to the true value. Defining a “true” value for the size of a particle can be challenging as the output of its measurement will differ because of variations in measurement approaches, instrumental differences and calculation methods. Consequently, for “real” particles, a universal “true” value does not exist and accuracy is therefore not a definable characteristic. Accordingly, precision is then a measure of the ability to reproducibly achieve a measurement of unknown relevance.This article proposes, in place of accuracy, a means to define the “appropriateness” of a measurement in line with the critical quality attributes (CQA) of the material being characterized. The decision as to whether the measurement is correct should involve a link to the CQA; that is, correlation should be demonstrated, without which the measured particle size cannot be defined as a critical material attribute.Correspondingly, methods should also be able to provide sufficient precision to demonstrate discrimination relating to variation in the CQA. The benefits and challenges of this approach are discussed. 相似文献
IntroductionPredicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.Materials and methodsWe retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.ResultsThe WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).ConclusionOur study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction. 相似文献
Geneticists have, for years, understood the nature of genome‐wide association studies using common genomic variants. Recently, however, focus has shifted to the analysis of rare variants. This presents potential problems for researchers, as rare variants do not always behave in the same way common variants do, sometimes rendering decades of solid intuition moot. In this paper, we present examples of the differences between common and rare variants. We show why one must be significantly more careful about the origin of rare variants, and how failing to do so can lead to highly inflated type I error. We then explain how to best avoid such concerns with careful understanding and study design. Additionally, we demonstrate that a seemingly low error rate in next‐generation sequencing can dramatically impact the false‐positive rate for rare variants. This is due to the fact that rare variants are, by definition, seen infrequently, making it hard to distinguish between errors and real variants. Compounding this problem is the fact that the proportion of errors is likely to get worse, not better, with increasing sample size. One cannot simply scale their way up in order to solve this problem. Understanding these potential pitfalls is a key step in successfully identifying true associations between rare variants and diseases. 相似文献