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RNA-seq data in pain research–an illustrated guide

Crow, Megana; Denk, Franziskab,*

doi: 10.1097/j.pain.0000000000001562
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aCold Spring Harbor Laboratory, NY, United States

bKing's College London, Wolfson Centre for Age-Related Diseases, London, SE1 1UL, United Kingdom

Corresponding author. Address: King's College London, Wolfson Centre for Age-Related Diseases, Guy's Campus, London, SE1 1UL. Tel.: 0044 207 848 8054. E-mail address: franziska.denk@kcl.ac.uk (F. Denk).

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

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 (https://www.utdallas.edu/bbs/painneurosciencelab/)13,15; 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 (mousebrain.org)19; 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.

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Conflict of interest statement

The authors have no conflict of interest to declare.

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Acknowledgements

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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