Pseudocell Tracer - A method for inferring dynamic trajectories using scRNAseq and its application to B cells undergoing immunoglobulin class switch recombination

Derek Reiman, Godhev Kumar Manakkat Vijay, Heping Xu, Andrew Sonin, Dianyu Chen, Nathan Salomonis, Harinder Singh, Aly A. Khan

Abstract: Author summary In the past decade advances in computing and single cell sequencing technologies have ushered in a new era of discovery in biology and medicine. However, the analysis of single cell data remains challenging, especially when analyzing heterogeneous cellular compartments with complex dynamics. This scenario is especially pronounced in dynamic immune responses of innate and adaptive immune cells. Existing computational tools typically analyze scRNAseq datasets without reference to any of the underlying biology of the system that generates the data. We reason that use of prior knowledge of the system can aid in the extraction of obscured information from scRNAseq datasets. We introduce a framework, Pseudocell Tracer, which takes advantage of validated biological knowledge to guide the inference of cellular trajectories. We apply and validate Pseudocell Tracer by scRNAseq analysis of antigen-specific B cells undergoing immunoglobulin class switch recombination during an antigen-induced humoral immune response. This framework is potentially applicable to single cell data from many other fields with complex dynamics.