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Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe
Xinge Wang1,2,3, Mark A Sanborn1,2,3, Yang Dai1
1Department of Biomedical Engineering, University of Illinois Colleges of Engineering and Medicine, Chicago, Illinois, USA.
JCI Insight
|February 17, 2022
Summary
TrendCatcher identifies distinct gene expression patterns over time in diseases. This R package revealed neutrophil activation and impaired interferon signaling in severe COVID-19 patients, aiding biomarker discovery.
Area of Science:
- Genomics
- Computational Biology
- Immunology
Background:
- Temporal gene expression analysis is crucial for understanding disease mechanisms.
- Current tools struggle to identify distinct temporal patterns in dynamic differentially expressed genes (DDEGs).
- Limited methods exist for assessing and visualizing temporal progression of biological pathways from time-course transcriptomic data.
Purpose of the Study:
- To develop an open-source R package, TrendCatcher, for identifying and visualizing distinct temporal gene expression patterns.
- To apply TrendCatcher to analyze longitudinal transcriptomic data, specifically in the context of COVID-19 progression.
Main Methods:
- Developed TrendCatcher, an R package utilizing smoothing spline ANOVA and breakpoint searching.
- Applied TrendCatcher to bulk and single-cell RNA-Seq time-course data from COVID-19 patients and vaccinated individuals.
- Systematic temporal analysis of peripheral blood transcriptomes.
Main Results:
- TrendCatcher identified early and persistent neutrophil activation and coagulation pathway activation in severe COVID-19.
- Impaired type I interferon (IFN-I) signaling was a hallmark of severe COVID-19 progression.
- These distinct temporal patterns were absent in mild COVID-19 cases and SARS-CoV-2 vaccinated individuals.
Conclusions:
- TrendCatcher effectively identifies dynamic transcriptional signatures and biological processes from longitudinal data.
- Systematic temporal analysis is vital for discovering early biomarkers and therapeutic targets in diseases like COVID-19.
- The findings highlight specific immune dysregulations associated with severe COVID-19 progression.
