Weber, T. S. | Search bioRxiv

bioRxiv2018

scRNA-seq mixology: towards better benchmarking of single cell RNA-seq protocols and analysis methods

Single cell RNA sequencing (scRNA-seq) technology has undergone rapid development in recent years, bringing with it new challenges in data processing and analysis. This has led to an explosion of tailored analysis methods for scRNA-seq to address various biological questions. However, the current lack of gold-standard benchmarking datasets makes it difficult for researchers to evaluate the performance of the many methods. Here, we designed and carried out a realistic benchmark experiment that included mixtures of single cells or pseudo-cells created by sampling admixtures of cells or RNA from 3 distinct cancer cell lines. Altogether we generated 10 datasets using a combination of droplet and plate-based scRNA-seq protocols, with varying data quality, population heterogeneity and noise levels. Using these benchmark datasets, we compared different protocols, evaluated the spike-in standard and multiple data analysis methods for tasks ranging from normalization and imputation, to clustering, trajectory analysis and data integration. Evaluation of methods across multiple datasets revealed some that performed well in general and others that suited specific situations. Our dataset and analysis provide a comprehensive comparison framework for benchmarking most popular scRNA-seq analysis tasks.

bioinformatics↗

bioRxiv2017

Cell cycle modulates CXCR4 expression in germinal center B cells

Adaptation of antibody-mediated immunity occurs in germinal centers (GC). It is where affinity maturation, class switching, memory and plasma cell differentiation synergize to generate specific high affinity antibodies that help both to clear and protect against reinfection of invading pathogens. Within GCs, light and dark zone are two compartments instrumental in regulating this process, by segregating T cell dependent selection and differentiation from generation of GC B cells bearing hypermutated antigen receptors. Spatial segregation of GC B cells into the two zones relies on the chemokine receptor CXCR4, with textbook models attributing high and low expression levels to a dark and light zone phenotype. This bipolarity is however not reflected in the CXCR4 expression profile of GC B cells, which is unimodal and markedly heterogeneous, indicating a continuum of intermediate CXCR4 levels rather than a binary dark or light zone phenotype. Here analysis of published BrdU pulse-chase data reveals that throughout cell cycle, average CXCR4 expression in GC B cells steadily increases by up to 75%, scaling with cell surface area. CXCR4 expression in recently divided GC B cells in G0/G1 phase shows intermediate levels compared to cells in G2 and M phase, consistent with their smaller size. The least number of CXCR4 receptors are displayed by GC B cells in G0/G1 that have not been in cell cycle for several hours. The latter, upon entering S phase however, ramp up relative CXCR4 expression twice as much as recently divided cells. Twelve hours after the BrdU pulse, labelled GC B cells, while initially in S phase, are fully desynchronized in terms of cell cycle and match the CXCR4 expression of unlabeled cells. A model is discussed in which CXCR4 expression in GC B cell increases with cell cycle and cell surface area, with highest levels in G2 and M phase, coinciding with GC B cell receptor signaling in G2 and immediately preceding activation-induced cytidine deaminase (AID) activity in early G1. In the model, GC B cells compete for immobilized or expressed CXCL12 on the basis of their CXCR4 expression levels, gaining a relative advantage as they progress in cell cycle, but loosing the advantage at the moment they divide.

immunology↗