PlateletSeq: A novel method for discovery of blood-based biomarkers

Platelets are small circulating fragments of cells that play important roles in thrombosis, haemostasis, immune response, inflammation and cancer growth. Although anucleate, they contain a rich RNA repertoire which offers an opportunity to characterise changes in platelet gene expression in health and disease. Whilst this can be achieved with conventional RNA sequencing, a large input of high-quality RNA, and hence blood volume, is required (unless a pre-amplification step is added), along with specialist bioinformatic skills for data analysis and interpretation. We have developed a transcriptomics next-generation sequencing-based approach that overcomes these limitations. Termed PlateletSeq, this method requires very low levels of RNA input and does not require specialist bioinformatic analytical skills. Here we describe the methodology, from sample collection to processing and data analysis. Specifically, blood samples can be stored for up to 8 days at 4 degrees C prior to analysis. Platelets are isolated using multi-step centrifugation and a purity of <= 1 leucocyte per 0.26x106 platelets is optimal for gene expression analysis. We have applied PlateletSeq to normal adult blood samples and show there are no ageassociated variations and only minor gender-associated differences. In contrast, platelets from patients with myeloproliferative neoplasms show differences in platelet transcript profiles from normal and between disease subtypes. This illustrates the potential applicability of PlateletSeq for biomarker discovery and studying platelet biology in patient samples. It also opens avenues for assessing platelet quality in other fields such as transfusion research.

Automated summary

Platelets, small circulating fragments of cells, play important roles in various physiological and pathological processes. We have developed a method called PlateletSeq, which allows for the analysis of platelet gene expression using low levels of RNA input without the need for specialist bioinformatic skills. This method has the potential to be applied in biomarker discovery and studying platelet biology in patient samples.