Sheridan, R. M. | bioRxiv Science

bioRxiv2020

Molecular tracking devices quantify antigen distribution and archiving in the lymph node

Live, attenuated vaccines generate humoral and cellular immune memory, increasing the duration of protective immune memory. We previously found that antigens derived from vaccination or viral infection persist within lymphatic endothelial cells (LECs) beyond the clearance of the infection, a process we termed "antigen archiving". Technical limitations of fluorescent labeling have precluded a complete picture of antigen archiving across cell types in the lymph node. We developed a "molecular tracking device" to follow the distribution, acquisition, and retention of antigen in the lymph node. We immunized mice with an antigen conjugated to a nuclease-resistant DNA tag and used single-cell mRNA sequencing to quantify its abundance in lymph node hematopoietic and non-hematopoietic cell types. At early and late time points after vaccination we found antigen acquisition by dendritic cell populations (DCs), associated expression of genes involved in DC activation and antigen processing, and antigen acquisition and archiving by LECs as well as unexpected stromal cell types. Variable antigen levels in LECs enabled the identification of caveolar endocytosis as a mechanism of antigen acquisition or retention. Molecular tracking devices enable new approaches to study dynamic tissue dissemination of antigens and identify new mechanisms of antigen acquisition and retention at cellular resolution in vivo.

immunology↗

bioRxiv2019

clustifyr: An R package for automated single-cell RNA sequencing cluster classification

BackgroundIn single-cell RNA sequencing (scRNA-seq) analysis, assignment of likely cell types remains a time-consuming, error-prone, and biased process. Current packages for identity assignment use limited types of reference data, and often have rigid data structure requirements. As such, a more flexible tool, capable of handling multiple types of reference data and data structures, would be beneficial. FindingsTo address difficulties in cluster identity assignment, we developed the clustifyr R package. The package leverages external datasets, including gene expression profiles from scRNA-seq, bulk RNA-seq, microarray expression data, and/or signature gene lists, to assign likely cell types. We benchmark various parameters of a correlation-based approach, and also implement a variety of gene list enrichment methods. By providing tools for exploratory data analysis, we demonstrate the feasibility of a simple and effective data-driven approach for cell type assignment in scRNA-seq cell clusters. Conclusionsclustifyr is a lightweight and effective cell type assignment tool developed for compatibility with various scRNA-seq analysis workflows. clustifyr is publicly available at https://github.com/rnabioco/clustifyr

bioinformatics↗