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Fazeli, E.

Publications and source records attributed to Fazeli, E..

2 recordsLinked to original sources

A familial missense variant in the AD gene SORL1 impairs its maturation and endosomal sorting.

The SORL1 gene has recently emerged as a strong Alzheimers Disease (AD) risk gene. Over 500 different variants have been identified in the gene and the contribution of individual variants to AD development and progression is still largely unknown. Here, we describe a family consisting of 2 parents and 5 offspring. Both parents were affected with dementia and one had confirmed AD pathology with an age of onset >75 years. All offspring were affected with AD with ages at onset ranging from 53yrs-74yrs. DNA was available from the parent with confirmed AD and 5 offspring. We identified a coding variant, p.(Arg953Cys), in SORL1 in 5 of 6 individuals affected by AD. Notably, variant carriers had severe AD pathology, and the SORL1 variant segregated with TDP-43 pathology (LATE-NC). We further characterized this variant and show that this Arginine substitution occurs at a critical position in the YWTD-domain of the SORL1 translation product, SORL1. Functional studies further show that the p.R953C variant leads to retention of the SORL1 protein in the endoplasmic reticulum which leads to decreased maturation and shedding of the receptor and prevents its normal endosomal trafficking. Together, our analysis suggests that p.R953C is a pathogenic variant of SORL1 and sheds light on mechanisms of how missense SORL1 variants may lead to AD.

neuroscience↗

Automated cell tracking using StarDist and TrackMate

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipelines usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.

cell biology↗