Arakawa et al. discovered that the autoimmune response in psoriasis is directed against melanocytes. They show that the main psoriasis risk allele HLA-C*06:02 mediates melanocyte-specific autoimmunity and identify ADAMTSL5 as a melanocyte autoantigen, which stimulates IL-17 and IFN-γ production in CD8+ T cells.

Background: Nickel is the most frequent cause of T cell-mediated allergic contact dermatitis worldwide. In vitro, CD4+ T cells from all donors respond to nickel but the involved αβ T cell receptor (TCR) repertoire has not been comprehensively analyzed. Methods: We introduce CD154 (CD40L) upregulation as a fast, unbiased, and quantitative method to detect nickel-specific CD4+ T cells ex vivo in blood of clinically characterized allergic and non allergic donors. Naïve (CCR7+ CD45RA+) and memory (not naïve) CD154+ CD4+ T cells were analyzed by flow cytometry after 5 hours of stimulation with 200 µmol/L NiSO 4 ., TCR α-and β-chains of sorted nickel-specific and control cells were studied by high-throughput sequencing. Results: Stimulation of PBMCs with NiSO 4 induced CD154 expression on ~0.1% (mean) of naïve and memory CD4+ T cells. In allergic donors with recent positive patch test, memory frequencies further increased ~13-fold and were associated with markers of in vivo activation. CD154 expression was TCR-mediated since single clones could be This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

In an in vitro nanotoxicity system, cell−nanoparticle (NP) interaction leads to the surface adsorption, uptake, and changes into nuclei/cell phenotype and chemistry, as an indicator of oxidative stress, genotoxicity, and carcinogenicity. Different types of nanomaterials and their chemical composition or "corona" have been widely studied in context with nanotoxicology. However, rare reports are available, which delineate the details of the cell shape index (CSI) and nuclear area factors (NAFs) as a descriptor of the type of nanomaterials. In this paper, we propose a machine-learning-based graph modeling and correlation-establishing approach using tight junction protein ZO-1-mediated alteration in the cell/nuclei phenotype to quantify and propose it as indices of cell−NP interactions. We believe that the phenotypic variation (CSI and NAF) in the epithelial cell is governed by the physicochemical descriptors (e.g., shape, size, zeta potential, concentration, diffusion coefficients, polydispersity, and so on) of the different classes of nanomaterials, which critically determines the intracellular uptake or cell membrane interactions when exposed to the epithelial cells at sub-lethal concentrations. The intrinsic and extrinsic physicochemical properties of the representative nanomaterials (NMs) were measured using optical (dynamic light scattering, NP tracking analysis) methods to create a set of nanodescriptors contributing to cell−NM interactions via phenotype adjustments. We used correlation function as a machine-learning algorithm to successfully predict cell and nuclei shapes and polarity functions as phenotypic markers for five different classes of nanomaterials studied herein this report. The CSI and NAF as nanodescriptors can be used as intuitive cell phenotypic parameters to define the safety of nanomaterials extensively used in consumer products and nanomedicine.

Cytotoxic CD8(+) T cells recognize the antigenic peptides presented by class I major histocompatibility complex (MHC) molecules. These T cells have key roles in infectious diseases, autoimmunity and tumor immunology, but there is currently no unbiased method for the reliable identification of their target antigens. This is because of the low affinities of antigen-specific T cell receptors (TCR) to their target MHC-peptide complexes, the polyspecificity of these TCRs and the requirement that these TCRs recognize protein antigens that have been processed by antigen-presenting cells (APCs). Here we describe a technology for the unbiased identification of the antigenic peptides presented by MHC class I molecules. The technology uses plasmid-encoded combinatorial peptide libraries and a single-cell detection system. We validated this approach using a well-characterized influenza-virus–specific TCR, MHC and peptide combination. Single APCs carrying antigenic peptides can be detected among several million APCs that carry irrelevant peptides. The identified peptide sequences showed a converging pattern of mimotopes that revealed the parent influenza antigen. This technique should be generally applicable to the identification of disease-relevant T cell antigens.