Droplet-based single-cell RNA sequencing (scRNA-Seq) has moved rapidly from a technology with great potential to a method applied to ever-broader questions. The detailed information that scRNA-Seq offers has proven incredibly powerful in resolving cell responses to developmental and environmental cues. However, to maximize the potential of this technology, a panoply of upstream, practical points require consideration. Principal among these are the optimization of cell-isolation procedures, accommodating biotic/abiotic stress responses, and discerning the number of cells and sequencing reads needed. To complement reviews outlining applications and data analysis tools for scRNA-Seq, University of Tübingen researchers discuss these considerations and provide practical tips to tailor experimental design and ensure the best possible outcome.
Considerations and tips for high-throughput
single-cell RNA sequencing library preparation and sequencing
A flowchart indicating major experimental steps with pertinent practical considerations for library preparation and sequencing. t-Distributed stochastic neighbor embedding (t-SNE) images depict the problem of batch effects. Left. A merged dataset of two good replicates (dark blue and light blue), showing that cells from both replicates are evenly distributed across clusters. Right. Data afflicted by batch effects; cells from the two datasets are not evenly dispersed.