As newer single-cell protocols generate increasingly more cells at reduced sequencing depths, the value of a higher read depth may be overlooked. Using data from three different single-cell RNA-seq protocols that lend themselves to having either higher read depth (Smart-seq) or many cells (MARS-seq and 10X), University of Florida and University of Wisconsin Madison researchers evaluate their ability to recapitulate biological signals in the context of spatial reconstruction. Overall, they found gene expression profiles after spatial reconstruction analysis are highly reproducible between datasets despite being generated by different protocols and using different computational algorithms. While UMI-based protocols such as 10X and MARS-seq allow for capturing more cells, Smart-seq’s higher sensitivity and read-depth allow for analysis of lower expressed genes and isoforms. Additionally, the researchers evaluate trade-offs for each protocol by performing subsampling analyses and find that optimizing the balance between sequencing depth and number of cells within a protocol is necessary for efficient use of resources. This analysis emphasizes the importance of selecting a protocol based on the biological questions and features of interest.
Genes and isoforms found in the full-length dataset and not in the UMI datasets
A) Six genes found to be significantly zonated in the Smart-seq dataset that were not detected in either the MARS-seq or 10X datasets. The log2 of expression values are represented on the y-axis and the spatially ordered cells are found on the x-axis. B) Examples of genes with two transcript variants expressed differently across reordered cells from the Smart-seq dataset.