Large-scale analysis of temporal gene expression variation in peripheral blood
- Neha Mishra
- Franziska Kimmig
- Doris Vandeputte
- Valentina Talevi
- Lindsey De Commer
- Chloe Verspecht
- Arnau Vich Vila
- Julia S. El-Sayed Moustafa
- Lukasz Kreft
- Alexander Botzki
- Youssef El Darzi
- Sebastian Proost
- Lindsay Devolder
- Dongmeng Wang
- Joana P. Bernardes
- N. Ahmad Aziz
- Konrad Aden
- Vibeke Andersen
- Aggelos Banos
- George Bertsias
- Marc Beyer
- Johanna I. Blase
- Dimitrios Boumpas
- Paraskevi Christofidou
- Axel Finckh
- Gilles Gasparoni
- Michel Georges
- Wei Gu
- Robert Häsler
- Stephan Huthmacher
- Mohamad Jawhara
- Amy Kenyon
- Christina Kratsch
- Roland Krause
- Gordan Lauc
- Paul A. Lyons
- Massimo Mangino
- Eoin F. McKinney
- Gioacchino Natoli
- Karl Nordström
- Marek Ostaszewski
- Silja H. Overgaard
- Marija Pezer
- Souad Rahmouni
- Benedikt Reiz
- Elisa Rosati
- Despina Sanoudou
- Venkata Satagopam
- Reinhard Schneider
- Jonas Schulte-Schrepping
2026-05-29
Transcriptomic profiling of peripheral blood offers a promising, non-invasive approach for disease diagnosis and monitoring. However, its clinical translation is hindered by limited knowledge of the natural temporal variation. Here, we present a comprehensive reference map of longitudinal transcriptomic variability, based on RNA-sequencing of 333 healthy individuals sampled at three time points over six months. We find that 85% of genes and 99% of transcripts exhibit greater intra-individual than inter-individual variation, primarily driven by dynamic regulation of housekeeping pathways. In contrast, immune-related transcripts –particularly those linked to T and B cell activity– are strikingly stable over time. Gene expression levels drive inter-individual differences, while splicing variation contributes more to intra-individual fluctuation. In an independent twin cohort (148 monozygotic, 166 dizygotic), genes with high inter-individual variability show greater heritability, suggesting genetic control of steady-state expression. By integrating extensive clinical and environmental data, we trace temporal expression changes to genetic, compositional, and external factors, and identify robust seasonal and sex-specific signatures. These findings were validated in a third, cross-sectional cohort of 3,480 individuals. The observed temporal variation patterns have important implications for cohort-based transcriptomic analyses, as they may limit discovery and reproducibility of expression quantitative trait loci and increase the risk of spurious associations in cross-sectional studies. This resource provides a critical baseline for distinguishing disease-associated transcriptomic changes from normal physiological variation, advancing the reliability of blood-based biomarkers in clinical practice.