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Translational Bioinformatics

Qingwang Chen1#, Xiaorou Guo1#, Haiyan Wang1#, Shanyue Sun2, He Jiang1, Peipei Zhang1, Erfei Shang1, Ruolan Zhang1, Zehui Cao1, Quanne Niu1, Chao Zhang1, Yaqing Liu1, Leming Shi1,3, Ying Yu1*, Wanwan Hou1*, and Yuanting Zheng1* 1State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai 200438, China 2Shandong Provincial Hospital, Shandong First Medical University, Jinan 250021, China 3The International Human Phenome Institutes, Shanghai 200438, China #These authors contributed equally: Qingwang Chen, Xiaorou Guo, and Haiyan Wang. *Corresponding authors: Ying Yu ( Wanwan Hou ( Yuanting Zheng (

Poster Not on Display

RNA sequencing (RNAseq) technology has become increasingly important in precision medicine and clinical diagnostics and emerged as a powerful tool for identifying protein-coding genes, performing differential gene analysis, and inferring immune cell composition. Human peripheral blood samples are widely used for RNAseq, providing valuable insights into individual biomolecular information. Blood samples can be classified as whole blood (WB), plasma, serum, and remaining sediment samples, including plasma-free blood (PFB) and serum-free blood (SFB) samples that are generally considered less useful byproducts during the processes of plasma and serum separation, respectively. However, the feasibility of using PFB and SFB samples for transcriptome analysis remains unclear. In this study, we aimed to assess the suitability of employing PFB or SFB samples as an alternative RNA source in transcriptomic analysis. We performed a comparative analysis of WB, PFB, and SFB samples for different applications. Our results revealed that PFB samples exhibit greater similarity to WB samples than SFB samples in terms of protein-coding gene expression patterns, detection of differentially expressed genes, and immunological characterizations, suggesting that PFB can serve as a viable alternative to WB for transcriptomic analysis. Our study contributes to the optimization of blood sample utilization and the advancement of precision medicine research.

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