Prostate cancer (PCa), the second most common cancer diagnosed in men, represents a quarter of all newly diagnosed cancers in Australia. Despite advances in treatments, patients often relapse and > 90% of patients will die with bone metastatic disease from their lethal PCa. It has been reported that bone metastases can arise years after primary tumour treatment because of the ability of disseminated tumour cells (DTCs) to enter dormancy in the bone marrow and evade therapies. Residual DTCs have been demonstrated to be genetically heterogeneous, highlighting the importance of studying distinct PCa sub-populations at the single cell level. Here, we describe a single-cell RNA sequencing workflow we implemented to investigate the transcriptional heterogeneity within PCa cells, particularly looking at the differences between the dormant and proliferative sub-populations.
Dormant PCa represent a rare population of cells in murine experimental models (<10 cells per bone); it limits the use of any automated system to capture and sequence individual cells. Consequently, we have developed a single cell RNA-Seq pipeline from rare samples. We first optimised our workflow using the PC3 cell line in vitro to determine the technical variability of our methodology using single PC3 cells and 10 pg of RNA from bulk populations. Pairwise comparison between replicates and ERCC Spike-In controls supported a high sensitivity and reproducibility in our workflow. We then examined PC3 single and bulk cells (10 cells), cultured in vitro with or without serum, to identify dormant and proliferative signatures. We were able to compare differences in gene expression and identify various regulators of proliferation and gene signature pathways in the PC3 model. To compare our signatures identified in vitro, we applied our methodology on single dormant and proliferative PC3 cells isolated in vivo from murine femur or tibia. We have successfully sequenced 10 high quality single dormant and 40 proliferative cells. However, the sequencing of more dormant cells from this rare population is required for statistical analysis.
In conclusion, the establishment of our single cell workflow and analysis pipeline would allow us to define the molecular signatures involved in dormant to proliferative cell transition, which has shown to be critical point in PCa metastasis.