Cancer is one of the major causes of death worldwide. Early detection leads to treatment initiation in earlier cancer stages and therefore increases the chance of survival. However, appropriate diagnosis can be challenging because tissue acquisition of tumor samples for molecular testing often requires invasive procedures. To overcome these challenges researchers have focused on the real-time monitoring of molecules and cells in easily obtainable peripheral blood to gain diagnostic information. While many studies concentrated on the analysis of circulating tumor cells or tumor DNA, a recent study proposed the utilization of ‘tumor-educated’ platelets as a diagnostic biomarker in these ‘liquid biopsies’. However, the molecular mechanisms underlying this tumor education are unclear. How do these diverse tumors, originating from different parts of the body, affect the RNA content of platelets? The propose project focused on deciphering the origin of the tumor education signals by analyzing gene expression of single platelets from cancer patients and healthy control using single cell RNA sequencing (scRNAseq) technology. Despite promising results in other contexts, we were unable to obtain high quality scRNAseq data from platelets. Therefore, the researcher developed computational methods designed to improve analysis of scRNAseq data. The computational methods developed in this project and the application thereof to lung scRNAseq data were published in peer-reviewed journals. These findings will greatly benefit researchers using scRNAseq technology.
