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RNA-seq data in pain research–an illustrated guide

Crow, Megana; Denk, Franziskab,*

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doi: 10.1097/j.pain.0000000000001562
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Cell type–specific RNA-seq studies have become increasingly common, whether they examine an entire population in bulk,5,12 or study its heterogeneity using single-cell analysis.8,19 Fortunately, bioinformaticians have worked to provide easy access to these data2,10,12 as well as developing re-analysis and meta-analysis tools for nonexpert users.1,17,20 Today, therefore, everyone can benefit from a wealth of independently replicated transcriptional information to enrich existing literature and quickly generate new hypotheses.

Using some of these new resources, it takes just a few minutes to see that, in contrast to previous suggestions, the angiotensin type II receptor (AGTR2) is absent from human dorsal root ganglia samples (,15; another few minutes to find evidence that advillin (Avil), a gene which was believed to be sensory neuron-specific,6 is also transcribed in mouse sympathetic neurons (; and an hour of browsing various data sets,5,16 to discover that RNA-seq results currently do not support the hypothesis that Bdnf is expressed in microglia.3

Of course, RNA-seq data need to be considered in the context of their technical shortcomings. They are influenced by sequencing depth in a nonlinear way: typical guidelines for obtaining replicable results suggest removing the bottom third of all transcripts by expression level.14 Biases will be introduced by sorting methods (eg, cellular stress as a result of dissociation18) and choices in library preparation (eg, poly-A amplification misses many noncoding RNAs). Single-cell sequencing data suffer from a reverse transcription bottleneck where only a small fraction of all transcripts in a particular cell tend to be amplified.11 For instance, in a given cell, only 20 of 100 actin molecules might seem to be expressed, while lowly expressed genes may appear completely absent; however, all genes are usually visible when single-cell data are summed up to simulate a bulk profile.11 Unless cells are molecularly targeted, single-cell analysis also requires an additional cell type inference step, usually through clustering, that may be inaccurate.4 Finally, both bulk and single-cell RNA-seq experiments are very susceptible to batch effects7,14 and need to be carefully designed to avoid them.

Beyond technical issues, it is best practice to validate RNA-seq with other methodologies, such as in situ hybridization and protein analyses. A good example of the latter are recent data on Agtr2 using a reporter mouse15 and Avil using a knockout validated antibody.9

Bearing this information in mind, the accompanying picture is designed as a cheat sheet–helping you understand the basics of RNA-seq and how to access and interpret the data. Navigating these resources will take some practice, but once you have mastered the art of browsing, RNA-seq data can be a great source of information and even joy–like sudden access to a secret library full of riveting books.

Conflict of interest statement

The authors have no conflict of interest to declare.


F. Denk is funded by an MRC New Investigator Research Grant (MR/P010814/1). M. Crow is supported by the National Institute of Mental Health (K99MH120050) and a NARSAD Young Investigator grant from the Brain & Behavior Research Foundation (26140).


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