Systematic cross-study assessment of RNA-Seq experimental workflows for plasma cell-free transcriptome profiling

Plasma cell-free RNA (cfRNA) is a promising source of non-invasive biomarkers, but its clinical translation is hindered by technical challenges and a lack of protocol standardization, which compromises reproducibility and comparability across studies. There is a need for a systematic evaluation of existing cfRNA-Seq workflows to understand the drivers of technical variability. Here, we address this gap by performing a comprehensive cross-study analysis of 2,356 sequencing samples from 17 published studies and an in-house generated dataset, applying a uniform bioinformatics pipeline to enable a controlled comparison of experimental workflows. Our analysis reveals that the vast majority of transcriptomic variation is explained not by biology, but by inter-laboratory batch effects. The main determinants of these effects are technical, principally genomic DNA contamination levels and library diversity. This technical noise is so profound that variation within plasma cfRNA samples exceeds that found across a wide range of human tissues – a biologically implausible result. Furthermore, we demonstrate that critical pre-analytical factors are often confounded with patient phenotypes, jeopardizing the validity of biomarker discovery efforts. Our work serves as a comprehensive benchmark of current cfRNA-Seq methodologies and provides evidence-based guidelines to improve experimental design. By highlighting the dominance of controllable technical factors, we offer a path towards more robust and reproducible cfRNA research.