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Vignali, D.

Publications and source records attributed to Vignali, D..

3 recordsLinked to original sources

De novo identification of CD4+ T cell epitopes

CD4+ T cells recognize peptide antigens presented on class II Major Histocompatibility Complex (MHC-II) molecules to carry out their function. The remarkable diversity of T cell receptor (TCR) sequences and lack of antigen discovery approaches for MHC-II make profiling the specificities of CD4+ T cells challenging. We have expanded our platform of Signaling and Antigen-presenting Bifunctional Receptors to encode MHC-II molecules presenting covalently linked peptides (SABR-IIs) for CD4+ cell antigen discovery. SABR-IIs can present epitopes to CD4+ T cells and induce signaling upon their recognition, allowing a readable output. Here, we demonstrate that SABR-IIs libraries presenting endogenous and post-translationally modified epitopes can be used for antigen discovery. Using SABR-II libraries in conjunction with single cell RNA sequencing, we de-convoluted multiple highly expanded TCRs from pancreatic islets of Non-Obese Diabetic (NOD) mice. We compounded antigen discovery by incorporating computational TCR similarity prediction metrics followed by experimental validation. Finally, we showed SABR-IIs presenting epitopes in class II HLA alleles can be used for antigen discovery for human CD4+ T cells. Taken together, we have developed a rapid, flexible, scalable, and versatile approach for the de novo identification of CD4+ T cell ligands from single cell RNA sequencing data using experimental and computational approaches.

immunology↗

Aplp1 and the Aplp1-Lag3 Complex facilitates transmission of pathologic alpha-synuclein

Pathologic -synuclein (-syn) spreads from cell-to-cell, in part, through binding to the lymphocyte-activation gene 3 (Lag3). Here we report that amyloid {beta} precursor-like protein 1 (Aplp1) forms a complex with Lag3 that facilitates the binding, internalization, transmission, and toxicity of pathologic -syn. Deletion of both Aplp1 and Lag3 eliminates the loss of dopaminergic neurons and the accompanying behavioral deficits induced by -syn preformed fibrils (PFF). Anti-Lag3 prevents the internalization of -syn PFF by disrupting the interaction of Aplp1 and Lag3, and blocks the neurodegeneration induced by -syn PFF in vivo. The identification of Aplp1 and the interplay with Lag3 for -syn PFF induced pathology advances our understanding of the molecular mechanism of cell-to-cell transmission of pathologic -syn and provides additional targets for therapeutic strategies aimed at preventing neurodegeneration in Parkinsons disease and related -synucleinopathies. One Sentence SummaryAplp1 forms a complex with Lag3 that facilitates the binding, internalization, transmission, and toxicity of pathologic -synuclein. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/442157v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1d746bforg.highwire.dtl.DTLVardef@65acb2org.highwire.dtl.DTLVardef@12cf2f0org.highwire.dtl.DTLVardef@19d6e_HPS_FORMAT_FIGEXP M_FIG Aplp1 and the Aplp1-Lag3 complex facilitates transmission of pathologic -synuclein. Aplp1 is a receptor that drives pathologic -syn transmission, and genetic depletion of Aplp1 can significantly reduce the -synuclein pathogenesis. Aplp1 and Lag3 forms an Aplp1-Lag3 complex that accounts for substantial binding of pathologic -syn to cortical neurons. Together Aplp1 and Lag3 play a major role in pathologic -syn internalization, transmission and toxicity. Double knockout of Aplp1 and Lag3 and or a Lag3 antibody that disrupts the Aplp1 and Lag3 complex almost completely blocks -syn PFF-induced neurodegeneration. C_FIG

neuroscience↗

BAMM-SC: A Bayesian mixture model for clustering droplet-based single cell transcriptomic data from population studies

The recently developed droplet-based single cell transcriptome sequencing (scRNA-seq) technology makes it feasible to perform a population-scale scRNA-seq study, in which the transcriptome is measured for tens of thousands of single cells from multiple individuals. Despite the advances of many clustering methods, there are few tailored methods for population-scale scRNA-seq studies. Here, we have developed a BAyesiany Mixture Model for Single Cell sequencing (BAMM-SC) method to cluster scRNA-seq data from multiple individuals simultaneously. Specifically, BAMM-SC takes raw data as input and can account for data heterogeneity and batch effect among multiple individuals in a unified Bayesian hierarchical model framework. Results from extensive simulations and application of BAMM-SC to in-house scRNA-seq datasets using blood, lung and skin cells from humans or mice demonstrated that BAMM-SC outperformed existing clustering methods with improved clustering accuracy and reduced impact from batch effects. BAMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/~Cwec47/singlecell.html.

bioinformatics↗