LAG-3 Inhibitory Receptor Expression Identifies Immunosuppressive Natural Regulatory Plasma Cells.

B lymphocytes can suppress immunity through interleukin (IL)-10 production in infectious, autoimmune, and malignant diseases. Here, we have identified a natural plasma cell subset that distinctively expresses the inhibitory receptor LAG-3 and mediates this function in vivo. These plasma cells also express the inhibitory receptors CD200, PD-L1, and PD-L2. They develop from various B cell subsets in a B cell receptor (BCR)-dependent manner independently of microbiota in naive mice. After challenge they upregulate IL-10 expression via a Toll-like receptor-driven mechanism within hours and without proliferating. This function is associated with a unique transcriptome and epigenome, including the lowest amount of DNA methylation at the Il10 locus compared to other B cell subsets. Their augmented accumulation in naive mutant mice with increased BCR signaling correlates with the inhibition of memory T cell formation and vaccine efficacy after challenge. These natural regulatory plasma cells may be of broad relevance for disease intervention.

DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification.

BACKGROUND: Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data. RESULTS: We present DECONbench, a standardized unbiased benchmarking resource, applied to the evaluation of computational methods quantifying cell-type heterogeneity in cancer. DECONbench includes gold standard simulated benchmark datasets, consisting of transcriptome and methylome profiles mimicking pancreatic adenocarcinoma molecular heterogeneity, and a set of baseline deconvolution methods (reference-free algorithms inferring cell-type proportions). DECONbench performs a systematic performance evaluation of each new methodological contribution and provides the possibility to publicly share source code and scoring. CONCLUSION: DECONbench allows continuous submission of new methods in a user-friendly fashion, each novel contribution being automatically compared to the reference baseline methods, which enables crowdsourced benchmarking. DECONbench is designed to serve as a reference platform for the benchmarking of deconvolution methods in the evaluation of cancer heterogeneity. We believe it will contribute to leverage the benchmarking practices in the biomedical and life science communities. DECONbench is hosted on the open source Codalab competition platform. It is freely available at: https://competitions.codalab.org/competitions/27453 .