For clinicians and researchers betting on blood-based RNA signatures to diagnose or monitor disease, a fundamental question has gone underexplored: how much does a healthy person's own gene expression fluctuate over time, independent of any disease? The answer, it turns out, is quite a lot — and the implications reshape how transcriptomic biomarkers should be designed, validated, and interpreted.
This large-scale longitudinal study mapped RNA variability in peripheral blood across 333 healthy individuals sampled three times over six months, then validated findings in a twin cohort of 314 individuals and a cross-sectional cohort of 3,480 people. A striking 85% of genes and 99% of transcripts showed greater variation within a single individual over time than between different individuals — meaning a person's own biology changes more than it differs from others. This intra-individual instability was primarily driven by fluctuating housekeeping pathway activity rather than disease-related signals. Critically, immune transcripts tied to T and B cell function proved notably stable, while splicing variation — not expression levels — was the dominant driver of within-person fluctuation. Seasonal signatures and sex-specific expression patterns were also identified and replicated, adding further layers of biological noise to account for. In the twin cohort, genes with high between-person variability showed elevated heritability, linking steady-state expression differences to genetic architecture.
This work constitutes one of the most rigorous reference maps of normal transcriptomic dynamics in blood assembled to date, and its conclusions are potentially paradigm-shifting for the biomarker field. Many published blood RNA biomarkers were derived from cross-sectional cohorts that could not distinguish true disease signals from within-person temporal noise — a limitation this dataset quantifies precisely. The finding that splicing variation dominates intra-individual change also suggests that most existing expression-level biomarkers may be measuring relatively stable signal, while splice-site variation remains underutilized and undercontrolled. For longevity researchers relying on transcriptomic aging clocks or immune profiling, accounting for temporal sampling windows and seasonal timing may now be non-negotiable methodological requirements.