L-Arginine Ameliorates Defective Autophagy in GM2 Gangliosidoses by mTOR Modulation.

AIMS: Tay-Sachs and Sandhoff diseases (GM2 gangliosidosis) are autosomal recessive disorders of lysosomal function that cause progressive neurodegeneration in infants and young children. Impaired hydrolysis catalysed by β-hexosaminidase A (HexA) leads to the accumulation of GM2 ganglioside in neuronal lysosomes. Despite the storage phenotype, the role of autophagy and its regulation by mTOR has yet to be explored in the neuropathogenesis. Accordingly, we investigated the effects on autophagy and lysosomal integrity using skin fibroblasts obtained from patients with Tay-Sachs and Sandhoff diseases. RESULTS: Pathological autophagosomes with impaired autophagic flux, an abnormality confirmed by electron microscopy and biochemical studies revealing the accelerated release of mature cathepsins and HexA into the cytosol, indicating increased lysosomal permeability. GM2 fibroblasts showed diminished mTOR signalling with reduced basal mTOR activity. Accordingly, provision of a positive nutrient signal by L-arginine supplementation partially restored mTOR activity and ameliorated the cytopathological abnormalities. INNOVATION: Our data provide a novel molecular mechanism underlying GM2 gangliosidosis. Impaired autophagy caused by insufficient lysosomal function might represent a new therapeutic target for these diseases. CONCLUSIONS: We contend that the expression of autophagy/lysosome/mTOR-associated molecules may prove useful peripheral biomarkers for facile monitoring of treatment of GM2 gangliosidosis and neurodegenerative disorders that affect the lysosomal function and disrupt autophagy

Label-free detection of neuronal differentiation in cell populations using high-throughput live-cell imaging of PC12 cells.

<p>Detection of neuronal cell differentiation is essential to study cell fate decisions under various stimuli and/or environmental conditions. Many tools exist that quantify differentiation by neurite length measurements of single cells. However, quantification of differentiation in whole cell populations remains elusive so far. Because such populations can consist of both proliferating and differentiating cells, the task to assess the overall differentiation status is not trivial and requires a high-throughput, fully automated approach to analyze sufficient data for a statistically significant discrimination to determine cell differentiation. We address the problem of detecting differentiation in a mixed population of proliferating and differentiating cells over time by supervised classification. Using nerve growth factor induced differentiation of PC12 cells, we monitor the changes in cell morphology over 6 days by phase-contrast live-cell imaging. For general applicability, the classification procedure starts out with many features to identify those that maximize discrimination of differentiated and undifferentiated cells and to eliminate features sensitive to systematic measurement artifacts. The resulting image analysis determines the optimal post treatment day for training and achieves a near perfect classification of differentiation, which we confirmed in technically and biologically independent as well as differently designed experiments. Our approach allows to monitor neuronal cell populations repeatedly over days without any interference. It requires only an initial calibration and training step and is thereafter capable to discriminate further experiments. In conclusion, this enables long-term, large-scale studies of cell populations with minimized costs and efforts for detecting effects of external manipulation of neuronal cell differentiation.</p

Proliferation kinetics.

<p>Slopes of the linear regression lines for proliferation, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056690#pone-0056690-g002" target="_blank">Figure 2</a>.</p

Phase contrast images of live-cells under the monitored conditions for days and after treatment.

<p>The images have been selected using the FDA classifier that has its median FDA score on training day . For clearer examples of the differentiated state, we selected for the NGF without mitomycin treatment images which correspond to the % quantile instead of the % median. Small inserts show the resulting ROI for each image. The white arrows indicate outgrown neurites on day . The proliferative effect of both EGF and NGF treatment without mitomycin is evident from the increased ROI on day . Mitomycin treatment inhibits proliferation and enhances the visibility of neurites. Bar, m.</p

Sensitivity and specificity of the classifier, when considering NGF stimulated cells versus non stimulated CTL cells and EGF stimulated cells as negative reference.

<p>Sensitivity (A) and Specificity (B) for stimulation without mitomycin; (C) and (D) for stimulation under mitomycin treatment, respectively. The solid lines in panel (A) and (B) correspond to the technical replicate in the training data set. The dashed lines denote the biologically independent data set and include two technical replicates per condition. The dotted green line corresponds to the experimentally distinct data set at a lower cell density. Panels (C) and (D) show the results for the mitomycin treated cells from two technical replicates stemming from two cell culture wells. The symbols represent the median of the five independent FDA classifiers and error bars mark the % c.i. While the overall performance for day and degrades in the case of no mitomycin for the experiments with higher cell density, the experimentally distinct data set conducted at of the respective cell density does not show a degradation in detection performance as cell clumps are not growing and hence do not limit the detection of the differentiated phenotype.</p

Comparison of Gini indices for the hold-out data, the technically independent data set, the biologically independent, and the experimentally distinct data set.

<p>(A) stimulation without mitomycin (B) stimulation under mitomycin treatment. The hold-out data and technically independent data sets are marked by the solid lines with blue triangles or red circles, respectively. Additionally, the dashed/dotted lines in panel (A) depict the biologically independent/experimentally distinct test sample, wherein the triangles and circles mark the respective technically independent replicates within this setup. Each point represents the median Gini index of the five independent FDA classifiers and the error bars mark the % c.i. A Gini index of corresponds to perfect separation of differentiated and undifferentiated images. Since in the training data set and in the biologically independent data set the final cell density was high, the detection performance degrades such that the Gini index declines at later days. This is due to increasing build up of cell clumps rendering the differentiated cell morphology much harder to detect, even to the human eye. The experimentally distinct data set started from a lower cell density such that fewer cell clumps occurred, making the detection of differentiation feasible until day .</p

Median responses of the five independent FDA classifiers using the hold-out data set.

<p>The error bars depict the % c.i. Panels (A) and (B) show the FDA classifier response () to cells without or with mitomycin treatment, respectively. The lines are shown to guide the eye and depict the measurement medians for the different treatment conditions. CTL: red circles and solid lines, NGF: green squares and dotted lines, EGF: blue triangles and dashed lines. The horizontal line through the origin marks the decision threshold. FDA scores larger than correspond to the differentiated cell status while scores below correspond to undifferentiated cells. Only on day EGF treated without mitomycin in panel (A) are slightly above , such that approximately % of the cells under this condition are falsely classified as differentiated. However, NGF treated cells are always far away from the decision threshold within the first four days. Hence, a more conservative threshold would remedy the false decision for EGF on day while still correctly classifying NGF treated cells.</p

Feature Definition.

<p>Initial feature set considered for image analysis. The per segment calculated image features are summarized for each image by their mean and standard deviation. All image features that measure an area are recorded in percent of the total number of pixels. Pixel intensity related image features are given in percent of the maximal pixel intensity. For applied transformations see Material and Methods.</p

Parameters.

<p>Parameters used for image feature extraction.</p