Validation of methods for Low-volume RNA-seq


Recently, a number of protocols extending RNA-sequencing to the single-cell regime have been published. However, we were concerned that the additional steps to deal with such minute quantities of input sample would introduce serious biases that would make analysis of the data using existing approaches invalid. In this study, we performed a critical evaluation of several of these low-volume RNA-seq protocols, and found that they performed slightly less well in metrics of interest to us than a more standard protocol, but with at least two orders of magnitude less sample required. We also explored a simple modification to one of these protocols that, for many samples, reduced the cost of library preparation to approximately $20/sample.


Second-generation sequencing of RNA (RNA-seq) has proven to be a sensitive and increasingly inexpensive approach for a number of different experiments, including annotating genes in genomes, quantifying gene expression levels in a broad range of sample types, and determining differential expression between samples. As technology improves, transcriptome profiling has been able to be applied to smaller and smaller samples, allowing for more powerful assays to determine transcriptional output. For instance, our lab has used RNA-seq on single Drosophila embryos to measure zygotic gene activation (Lott 2011) and medium-resolution spatial patterning (Combs 2013). Further improvements will allow an even broader array of potential experiments on samples that were previously too small.

For instance, over the past few years, a number of groups have published descriptions of protocols to perform RNA-seq on single cells (typically mammalian cells) (Tang 2009, Ramsköld 2012, Sasagawa 2013, Hashimshony 2012, Islam 2011). A number of studies, both from the original authors of the single-cell RNA-seq protocols and from others, have assessed various aspects of these protocols, both individually and competitively (Bhargava 2014, Wu 2014, Marinov 2013). One particularly powerful use of these approaches is to sequence individual cells in bulk tissues, revealing different states and cellular identies (Buganim 2012, Treutlein 2014).

However, we felt that published descriptions of single-cell and other low-volume protocols did not adequately address whether a change in concentration of a given RNA between two samples would result in a proportional change in the FPKM (or any other measure of tr