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Review
. 2018 Jul 1;17(4):233-239.
doi: 10.1093/bfgp/elx035.

Experimental design for single-cell RNA sequencing

Jeanette Baran-Gale  1 , Tamir Chandra  1 , Kristina Kirschner  2
Affiliations
Review

Experimental design for single-cell RNA sequencing

Jeanette Baran-Gale et al. Brief Funct Genomics. .

Abstract

Single-cell RNA sequencing (scRNA-seq) has opened new avenues for the characterization of heterogeneity in a large variety of cellular systems. As this is a relatively new technique, the field is fast evolving. Here, we discuss general considerations in experimental design and the two most popular approaches, plate-based Smart-Seq2 and microdroplet-based scRNA-seq at the example of 10x Chromium. We discuss advantages and disadvantages of both methods and point out major factors to consider in designing successful experiments.

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Figures

Figure 1.
Figure 1.
Comparison of 10x and SMART-seq2. (A) The protocols differ in the fraction of the gene covered by reads. While full-length protocols (such as Smart-Seq2) have reads covering the entire gene body, 3′-tag methods (such as 10x) concentrate reads upstream of the polyA tail or internally upstream of A-rich regions of the transcript. (B) Percentages (%) of reads aligning to ERCCs can be used in Smart-Seq2 data sets to identify high-quality cells. In comparison, non-ERCC data sets rely on library size/total UMI counts and the number of features detected. (C–E) Characteristics of a representative 10x data set of 1384 growing IMR90 cells. (F–H) Characteristics of a representative Smart-Seq2 data set of 75 growing IMR90 cells. (E and H) The cumulative number of genes detected at > 0 reads (black) or > 1 UMI or 10 reads (red) across cells in each data set.
Figure 2.
Figure 2.
Experimental design examples. In the confounded design, cells are isolated from each sample onto separate plates, processed at potentially different times and the two groups (indicated by different colors) are sequenced on separate lanes of the sequencer. In the balanced design on the right, all samples are evenly distributed across all stages of the experiment, thus reducing the sources of technical variation in the experiment.
Figure 3.
Figure 3.
Estimate of cells required for experiments with various parameters. (A) The plot shows the log10(#Cells) required to capture at least 50 cell types based on the parameters on the X- and Y-axes. (B) The plot shows the log10(#Cells) required to capture the number of cells on the Y-axis if the population consists of 20 cell types.
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