Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19.

Xinge Wang,Mark A Sanborn,Yang Dai,Jalees Rehman

doi:10.1172/jci.insight.157255

Abstract

Studying temporal gene expression shifts during disease progression provides important insights into the biological mechanisms that distinguish adaptive and maladaptive responses. Existing tools for the analysis of time course transcriptomic data are not designed to optimally identify distinct temporal patterns when analyzing dynamic differentially expressed genes (DDEGs). Moreover, there are not enough methods to assess and visualize the temporal progression of biological pathways mapped from time course transcriptomic data sets. In this study, we developed an open-source R package TrendCatcher (https://github.com/jaleesr/TrendCatcher), which applies the smoothing spline ANOVA model and break point searching strategy, to identify and visualize distinct dynamic transcriptional gene signatures and biological processes from longitudinal data sets. We used TrendCatcher to perform a systematic temporal analysis of COVID-19 peripheral blood transcriptomes, including bulk and single-cell RNA-Seq time course data. TrendCatcher uncovered the early and persistent activation of neutrophils and coagulation pathways, as well as impaired type I IFN (IFN-I) signaling in circulating cells as a hallmark of patients who progressed to 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 to identify early biomarkers and possible pathogenic therapeutic targets.

Highlights

Time course transcriptomic profiling has been widely used to study and model dynamic biological processes in cells [1]
TrendCatcher accurately identifies dynamic differentially expressed genes (DDEGs) in simulated datasets First, we tested the prediction performance of the TrendCatcher platform (Figure 1A) using a set of simulated time course RNA sequencing (RNA-seq) datasets, because simulated data provides defined standards to assess the accuracy of novel analytical platforms
Compared to DESeq2, DESeq2Spline and ImpulseDE2 using receiver operator characteristic (ROC), TrendCatcher had the largest area under the curve (AUC) in a mixed simulated dataset for time course data with 7 time points (Figure 1B)

Summary

Introduction

Time course transcriptomic profiling has been widely used to study and model dynamic biological processes in cells [1]. A complementary strategy is to treat time as a continuous variable and fit the time expression data into a spline-like model These methods include DESeq2Spline (DESeq adopted with spline model for temporal RNA-seq datasets) fitting, ImpulseDE2 [6] and maSigPro [7]. The former strategies focus on the magnitude of change instead of the time order of gene expression, and may suffer from a relative loss of statistical testing power, especially if many time points are assessed [6]. There is a lack of tools that leverage existing knowledge of functional pathway databases to infer and visualize pathway trajectories instead of individual gene trajectories only

Methods

Results

Conclusion