Novel methods for temporally varying gene identification in longitudinal studies reveal bleeding and clotting pathway activation caused by blood draws

Recent advances in sequencing technology enable the capture of gene expression dynamics through longitudinal study designs. However, the field lacks robust analytical tools for these high-dimensional datasets. To address this, we developed bioinformatic methods to determine baseline gene expression variability and identify temporally varying genes (TVGs). Our dynamic cut-off metric enhances detection of differentially expressed genes (DEGs), reducing false positives and negatives, while our TVG scoring system identifies genes with fluctuating expression over time. In a 21-day longitudinal RNA-seq dataset from rats, we identified 502 DEGs and 300 high-confidence TVGs, revealing that repeated blood sampling activates pathways related to bleeding, coagulation, and inflammatory responses. These findings were recapitulated in a 9-day human longitudinal RNA-seq study, which revealed similar pathway enrichments and subject-specific expression dynamics. Additional controls confirmed that these gene expression changes were not induced by handling artifacts, and score threshold analysis enabled a tunable balance between sensitivity and specificity. Our study introduces a robust analytical framework and the largest high-frequency longitudinal RNA-seq dataset of its kind, now publicly available. These resources provide valuable insights into dynamic gene regulation and offer new approaches in systems biology, pharmacogenomics, and translational medicine.