Murugesan, G. | Search bioRxiv

bioRxiv2020

Regulatory T cell activation triggers specific changes in glycosylation associated with Siglec-1-dependent inflammatory responses

Siglec-1 is a macrophage lectin-like receptor that mediates sialic acid-dependent cellular interactions. It was shown previously to promote inflammation in autoimmune disease through suppressing the expansion of regulatory T cells (Tregs). We have investigated the molecular basis for Siglec-1 binding to these cells using in vitro-induced Tregs. Siglec-1 binding was strongly upregulated on activated cells, but lost under resting conditions. Glycosylation changes that affect Siglec-1 binding were studied by comparing activated and resting Tregs using RNA-Seq, glycomics, proteomics and binding of selected antibodies and lectins. A proximity labelling and proteomics strategy identified 49 glycoproteins expressed by activated Tregs that may function as Siglec-1 counter-receptors. These represent [~]5% of the total membrane protein pool and were mainly related to T cell activation and proliferation. We demonstrate that several of these counter-receptors are upregulated following activation of Tregs and provide initial evidence that their altered glycosylation may also be important for Siglec-1 binding.

immunology↗

bioRxiv2019

Classification of Brain Tumor IDH Status using MRI and Deep Learning

Isocitrate dehydrogenase (IDH) mutation status is an important marker in glioma diagnosis and therapy. We propose a novel automated pipeline for predicting IDH status noninvasively using deep learning and T2-weighted (T2w) MR images with minimal preprocessing (N4 bias correction and normalization to zero mean and unit variance). T2w MRI and genomic data were obtained from The Cancer Imaging Archive dataset (TCIA) for 260 subjects (120 High grade and 140 Low grade gliomas). A fully automated 2D densely connected model was trained to classify IDH mutation status on 208 subjects and tested on another held-out set of 52 subjects, using 5-fold cross validation. Data leakage was avoided by ensuring subject separation during the slice-wise randomization. Mean classification accuracy of 90.5% was achieved for each axial slice in predicting the three classes of no tumor, IDH mutated and IDH wild-type. Test accuracy of 83.8% was achieved in predicting IDH mutation status for individual subjects on the test dataset of 52 subjects. We demonstrate a deep learning method to predict IDH mutation status using T2w MRI alone. Radiologic imaging studies using deep learning methods must address data leakage (subject duplication) in the randomization process to avoid upward bias in the reported classification accuracy.

neuroscience↗