Periodic Reporting for period 1 - T-GRAND-SLAM (Translating the Global Refined Analysis of Newly transcribed RNA and Decay rates by SLAM-seq)
Okres sprawozdawczy: 2019-03-01 do 2020-08-31
The goal of the ERC PoC grant T-GRAND-SLAM (“Translating GRAND-SLAM”) was to further develop our computational tool GRAND-SLAM for commercial exploitation by large-scale sequencing companies and demonstrate its scientific potential for novel single cell approaches. The scSLAM-seq protocol of our initial study was based on sorting of individual cells into multi-well plates. It was thus restricted to the analysis of dozens to hundreds of cells at high costs (~40€/cell). In the frame of T-GRAND-SLAM, we now managed to make scSLAM-seq compatible with droplet-based high-throughput single cell RNA-seq approaches. This facilitates the analysis of thousands of cells at <1€/cell. Next, we had planned to apply scSLAM-seq to a model of the highest clinical relevance, namely, the functional role of the major oncogene c-myc at single cell level. Unfortunately, we observed unexpected toxicity of metabolic 4sU labeling in our c-myc tet-off model. This was independent of the expression of c-myc. We think this was due to a propensity of the respective tumor cells for initiating apoptosis. Subsequent control experiments covering a range of standard laboratory cell lines as well as primary cells showed no overt toxicity at standard 4sU working concentrations. In the light of the COVID-19 pandemic, we are currently establishing the experimental setup to perform scSLAM-seq on SARS-CoV-2 infection of human lung epithelial cells. Furthermore, work is ongoing to develop GRAND-SLAM for handling scSLAM-seq data of thousands of cells and genes.
Regarding the commercialization of GRAND-SLAM, we analyzed pilot data for a potential customer company and are currently in negotiations with a number of companies regarding licensing of our patent.