nCounter® Pro Analysis System

NanoString’s nCounter Pro technology is based on direct detection of target molecules using color-coded molecular barcodes, providing a digital count of the number of target molecules. More accurate and highly multiplexed than qPCR and simpler than NGS, the nCounter Pro allows for digital examination of multiple pathways in a single tube.

• Broad sample compatibility – total RNA, cell lysates, FFPE-derived RNA
• 800+ plex capability
• Extensive selection of off-the-shelf panels nCounter Assays & Panels | Nanostring
• Standard panels are customisable with 55 genes using panel plus
• Full custom panel design
• Sample requirements: 100 ng to 300 ng (FFPE) RNA
• Data export compatible with standard analysis programs, basic statistical outputs, and publication ready figures can be achieved quickly and easily with no extra cost or need for bioinformatics support (nSolver software)
• Further information

High content phenotypic screening identifies serotonin receptor modulators with selective activity upon breast cancer cell cycle and cytokine signaling pathways (2020). Scott J. Warchal, John C. Dawson, Emelie Shepherd, Alison F. Munro, Rebecca E. Hughes, Ashraff Makda, Neil O. Carragher.

High content phenotypic screening identifies serotonin receptor modulators with selective activity upon breast cancer cell cycle and cytokine signaling pathways - PubMed

Using a cell painting assay as an unbiased phenotypic profiling method, Warchal et al sought to identify compounds that induced distinct phenotypic responses between cell lines. Twelve phenotypically active compounds were identified from a compound library of 1280 FDA-approved compounds, 4 of which showed previously reported serotonin modulator activity. The serotonin-induced signalling pathways that promote tumour progression are however complex and only partly understood. Warchal et al chose 2 compounds to test further (protriptyline and triflupromazine) in HCC1954 and T47D, 2 breast cancer cell lines with differing sensitivity. Analysis using the pan cancer panel from NanoString revealed a large number of significantly changed genes following triflupromazine treatment in T47D cells but not HCC1954 cells (Figure A). Differential analysis of drug-induced gene expression changes relative to DMSO in T47D cells revealed a number of significantly up and down regulated genes following treatment with triflupromazine (Figure B and C). Network analysis revealed two large connected networks of genes; a large network of down regulated cell cycle associated genes, and upregulation of the TNFR1 signalling pathway suggesting induction of apoptosis via TNF signalling.