Background: SorLA binds APP and decreases the production of A␤; however, the molecular mechanisms controlling these processes are poorly understood. Results: We identified CR(5-8) as an APP-binding site in SorLA. Conclusion: The CR-cluster is essential for the SorLA-dependent decrease in APP proteolysis. Significance: Details regarding the function of SorLA in APP metabolism might lead to an understanding of the genetic association of SorLA with Alzheimer disease.

High-resolution flow cytometers (hFCM) are used for the detection of extracellular vesicles (EV) in various biological fluids. Due to the increased sensitivity of hFCM, new artifacts with the potential of interfering with data interpretation are introduced, such as detection of antibody aggregates. The aim of this study was to investigate the extent of aggregates in labels commonly used for the characterization of EVs by hFCM. Furthermore, we aimed to compare the efficacy of centrifugation and filtering treatments to remove aggregates, as well as to quantify the effect of the treatments in reducing aggregates. For this purpose, we labeled phosphate buffered saline (PBS) with fluorescently conjugated protein labels and antibodies after submitting them to 5, 10, or 30 min centrifugation, filtering or washed filtering. We investigated samples by hFCM and quantified the amount of aggregates found in PBS labeled with untreated and pre-treated labels. We found a varying amount of aggregates in all labels investigated, and further that filtering is most efficient in removing all but the smallest aggregates. Filtering protein labels can reduce the extent of aggregates; however, how much remains depends on the specific labels and their combination. Therefore, it is still necessary to include appropriate controls in a hFCM study of EVs.

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INTRODUCTION: Recent evidence suggests that glioblastoma is driven by a subset of tumor initiating (TI) cells characterized by their capacity to form tumors in xenograft models and self-renew in vitro. These TI cells share many properties of neural stem/progenitor cells, including the expression of certain cell surface markers. With serial passage, many cells lose their capacity to TI. The transition between TI-proficient and -deficient states remains poorly understood. METHODS AND RESULTS: There are two theoretic models for the maintenance of TI states. In the "elite" model, TI activity is restricted to a predetermined subpopulation of cells. The alternative "stochastic" model suggests that any tumor cell has a finite chance of acquiring TI capacity through random fluctuations in cell physiology. To address this issue, we examined the TI capacity of distinct subclones isolated from a distinct glioblastoma line as well as the TI capacity of single cells derived from each distinct clone. We found that only a subset of subclones from a single glioblastoma line displayed capacity for TI, suggestive of the elite model. However, single cells derived from any single subclone exhibited a wide range of TI capacity, suggesting a stochastic component to this process. Transcriptome profiling of the subclones of differing TI revealed a gene signature associated with TI capacity. Analysis of this signature showed enrichment for genes regulated by c-Myc. Indeed, clones with increased TI capacity tend to harbor increased c-Myc expression. Additionally, over-expression of c-myc increased the TI capacity of glioblastoma cells in xenograft models and led to the formation of intracranial tumor in an Ink4a/ARF null transgenic murine model. CONCLUSION: Our results are most consistent with a threshold model in which TI states in glioblastomas are driven by expression levels of critical factors such as c-Myc.