RT Journal Article
SR Electronic
T1 Inferring cellular trajectories from scRNA-seq using Pseudocell Tracer
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.06.26.173179
DO 10.1101/2020.06.26.173179
A1 Derek Reiman
A1 Heping Xu
A1 Andrew Sonin
A1 Dianyu Chen
A1 Harinder Singh
A1 Aly A. Khan
YR 2020
UL http://biorxiv.org/content/early/2020/06/27/2020.06.26.173179.1.abstract
AB Single cell RNA sequencing (scRNA-seq) can be used to infer a temporal ordering of dynamic cellular states. Current methods for the inference of cellular trajectories rely on unbiased dimensionality reduction techniques. However, such biologically agnostic ordering can prove difficult for modeling complex developmental or differentiation processes. The cellular heterogeneity of dynamic biological compartments can result in sparse sampling of key intermediate cell states. This scenario is especially pronounced in dynamic immune responses of innate and adaptive immune cells. To overcome these limitations, we develop a supervised machine learning framework, called Pseudocell Tracer, which infers trajectories in pseudospace rather than in pseudotime. The method uses a supervised encoder, trained with adjacent biological information, to project scRNA-seq data into a low-dimensional cellular state space. Then a generative adversarial network (GAN) is used to simulate pesudocells at regular intervals along a virtual cell-state axis. We demonstrate the utility of Pseudocell Tracer by modeling B cells undergoing immunoglobulin class switch recombination (CSR) during a prototypic antigen-induced antibody response. Our results reveal an ordering of key transcription factors regulating CSR, including the concomitant induction of Nfkb1 and Stat6 prior to the upregulation of Bach2 expression. Furthermore, the expression dynamics of genes encoding cytokine receptors point to the existence of a regulatory mechanism that reinforces IL-4 signaling to direct CSR to the IgG1 isotype.Competing Interest StatementThe authors have declared no competing interest.