Abstract
Transcriptomics is the high-throughput characterization of RNA. It has played an important role in defining the pathogenic characteristics of Kawasaki disease. It has aided in clarifying Kawasaki disease etiology and identifying its key mediators, which will further help to compensate for the limitations of existing intravenous immunoglobulin treatments. In addition, transcriptomics is being used for immune monitoring, diagnostic and prognostic biomarker identification. These features can be applied in stratifying patients, monitoring molecular changes related to disease severity, defining personalized treatment strategies, as well as providing clinical evidence. This chapter discusses the progress of transcriptomics in determining Kawasaki disease etiology and pathogenesis and developing diagnostic and predictive biomarkers. We also explore some analytical methods for extracting valuable information from high-dimensional datasets to improve our biological knowledge. Lastly, we discuss the emerging technology of transcriptomics in the study of the diversity of expression quantitative trait loci, B-cell and T-cell receptor repertoires, and assessment of Kawasaki disease heterogeneity using high-throughput single-cell sequencing.
Original language | English |
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Title of host publication | Kawasaki Disease |
Publisher | Springer Nature |
Pages | 123-130 |
Number of pages | 8 |
ISBN (Electronic) | 9789811929441 |
ISBN (Print) | 9789811929434 |
DOIs | |
State | Published - 1 Jan 2022 |
Keywords
- CD177
- Immune receptor repertoire
- Intravenous immunoglobulin
- Kawasaki disease
- Neutrophil
- Next-generation sequencing
- RNA
- Single-cell analysis
- Transcriptomics