Improved platelet separation performance from whole blood using an acoustic fluidics system.
Kazuko SakaiShuta OharaJunko TanakaKenichi SudaTakamichi MuramatsuChihiro UematsuYasuhiro TsutaniTetsuya MitsudomiKazuto NishioPublished in: Cancer science (2024)
This study investigated the effectiveness of acoustic separation for platelet analysis in patients with non-small-cell lung cancer (NSCLC), comparing it with traditional centrifugation methods. In total, 10 patients with NSCLC and 10 healthy volunteers provided peripheral blood samples, which were processed using either acoustic separation or centrifugation to isolate platelets. The study included whole transcriptome analysis of platelets, peripheral blood mononuclear cells, and tumor tissue samples, employing hierarchical clustering and Gene Ontology analysis to explore gene expression differences. Acoustic separation proved more efficient than centrifugation in terms of platelet yield, recovery rate, and RNA yield. Gene expression profiles of platelets from patients with NSCLC showed distinct patterns compared with healthy volunteers, indicating tumor-influenced alterations. Gene Ontology analysis revealed enrichment in pathways associated with platelet activation and the tumor microenvironment. This finding indicates the potential of acoustic isolation in platelet separation and its relevance in understanding the unique gene expression profile of platelets in patients with NSCLC. The findings of this study suggested that platelets from cancer patients separated by acoustic techniques exhibited tumor-specific alterations and provided new insights into the diagnosis of cancer in platelet analysis systems in clinical practice.
Keyphrases
- gene expression
- small cell lung cancer
- genome wide
- peripheral blood
- randomized controlled trial
- advanced non small cell lung cancer
- liquid chromatography
- dna methylation
- squamous cell carcinoma
- systematic review
- single cell
- genome wide identification
- risk assessment
- epidermal growth factor receptor
- high speed
- human health
- data analysis