Single cell RNA-Seq (scRNA Seq) is a tool that enables simple and robust access to the transcriptomes of thousands of single cells – giving unprecedented insight into tissues at single cell resolution. This offers vital information about heterogeneity and cell-specific dynamics, which is key to understanding many diseases, immunity, development and more.
This is in contrast to traditional techniques, that either allowed analysis of a few genes in thousands of individual cells (e.g., in situ hybridization), or the expression profile of thousands of genes, but only on a tissue homogenate.
Understanding tumour heterogeneity and clonal evolution – including studying lineage analysis, cancer stem cells, and drug-resistant and metastatic clones.
Understanding complex tissues with diverse cell compositions (e.g. brain, tumour, plant leaf).
High resolution identification of cell types and marker genes, and understanding differentiation pathways in developmental and systems biology.
ScRNA-Seq on Nadia is a simple and robust, high throughput, single cell transcriptomics droplet-based protocol based on a single cell RNA-Seq method published in 2015 (Macosko E., et al. “Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.” Cell 161:1202). Streamlined improvements to the original DropSeq protocol allow thousands of cells to be captured individually in droplets, together with lysis reagents and a barcoded bead, all within a few minutes.
Dolomite Bio’s Nadia Instrument allows users to generate high-quality single cell data with a high gene capture rate, at a low cost per cell thanks to the Nadia platform’s reagent flexibility.
The fully automated Nadia Instrument guides users through all the relevant scRNA-Seq steps to encapsulate up to 8 samples in parallel in under 20 minutes. Over 50,000 single cells can be captured per cartridge in a single run. Once sequenced Nadia data can be seamlessly analyzed by easy to use and affordable bioinformatic pipelines such as dropSeqPipe or Partek Flow.