Studying the dynamic shifts in gene expression during disease progression may provide important insights into the biological mechanisms that distinguish adaptive and maladaptive responses. Existing computational tools for the analysis of transcriptomic data are not designed to optimally identify distinct temporal patterns when analyzing dynamic differentially expressed genes (DDEGs). Moreover, there is also a lack of methods to assess and visualize the temporal progression of biological pathways or gene sets mapped from time course transcriptomic datasets. In this study, we developed the open-source R package TrendCatcher (https://github.com/jaleesr/TrendCatcher), which applies the smoothing spline ANOVA model and break point searching strategy to estimate dynamic signals. TrendCatcher identifies and visualizes distinct dynamic transcriptional gene signatures and biological processes from sequential datasets. We used TrendCatcher to perform a systematic temporal analysis of COVID-19 peripheral blood transcriptomic datasets, including both whole blood bulk RNA-seq and PBMC scRNA-seq time course data. TrendCatcher uncovered the early and persistent activation of neutrophils as well as impaired type I interferon (IFN-I) signaling in circulating cells as early hallmark of patients who progressed to develop severe COVID-19, whereas no such patterns were identified in individuals receiving SARS-CoV-2 vaccinations or patients with mild COVID-19. These results underscore the importance of systematic temporal analysis of gene expression during disease progression to identify biomarkers and possible therapeutic targets.
Systematic temporal analysis of transcriptomes using TrendCatcher identifies early and persistent neutrophil activation as an early hallmark of severe COVID-19