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.
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).
. Alavi A, Ruffalo M, Parvangada A, Huang Z, Bar-Joseph Z. A web server for comparative analysis of single-cell RNA-seq data. Nat Commun 2018;9:4768.
. Barrett T, Edgar R. Gene expression omnibus: microarray data storage. Submission, Retrieval, Anal 2006;411:352–69.
. Beggs S, Trang T, Salter MW. P2X4R+ microglia drive neuropathic pain. Nat Neurosci 2012;15:1068–73.
. Crow M, Paul A, Ballouz S, Huang ZJ, Gillis J. Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nat Commun 2018;9:884.
. Fernandez-Zafra T, Gao T, Jurczak A, Sandor K, Hore Z, Agalave NM, Su J, Estelius J, Lampa J, Hokfelt T, Wiesenfeld-Hallin Z, Xu X, Denk F, Svensson CI. Exploring the transcriptome of resident spinal microglia after collagen antibody-induced arthritis. PAIN 2018;160:224.
. Hasegawa H, Abbott S, Han BX, Qi Y, Wang F. Analyzing somatosensory axon projections with the sensory neuron-specific Advillin gene. J Neurosci 2007;27:14404–14.
. Hicks SC, Townes FW, Teng M, Irizarry RA. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics 2018;19:562–78.
. Hockley JRF, Taylor TS, Callejo G, Wilbrey AL, Gutteridge A, Bach K, Winchester WJ, Bulmer DC, McMurray G, Smith ESJ. Single-cell RNAseq reveals seven classes of colonic sensory neuron. Gut 2019;68:633–44.
. Hunter DV, Smaila BD, Lopes DM, Takatoh J, Denk F, Ramer MS. Advillin is expressed in all adult neural crest-derived neurons. eNeuro 2018;5:e0077-18.2018.
. Lachmann A, Torre D, Keenan AB, Jagodnik KM, Lee HJ, Wang L, Silverstein MC, Ma'ayan A. Massive mining of publicly available RNA-seq data from human and mouse. Nat Commun 2018;9:1366.
. Marinov GK, Williams BA, McCue K, Schroth GP, Gertz J, Myers RM, Wold BJ. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res 2014;24:496–510.
. Megat S, Ray PR, Moy JK, Lou TF, Barragan-Iglesias P, Li Y, Pradhan G, Wanghzou A, Ahmad A, Burton MD, North RY, Dougherty PM, Khoutorsky A, Sonenberg N, Webster KR, Dussor G, Campbell ZT, Price TJ. Nociceptor translational profiling reveals the ragulator-rag GTPase complex as a critical generator of neuropathic pain. J Neurosci 2019;39:393–411.
. Ray P, Torck A, Quigley L, Wangzhou A, Neiman M, Rao C, Lam T, Kim JY, Kim TH, Zhang MQ, Dussor G, Price TJ. Comparative transcriptome profiling of the human and mouse dorsal root ganglia: an RNA-seq-based resource for pain and sensory neuroscience research. PAIN 2018;159:1325–45.
. Seqc/MaqcIII-Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat Biotechnol 2014;32:903–14.
. Shepherd AJ, Copits BA, Mickle AD, Karlsson P, Kadunganattil S, Haroutounian S, Tadinada SM, de Kloet AD, Valtcheva MV, McIlvried LA, Sheahan TD, Jain S, Ray PR, Usachev YM, Dussor G, Krause EG, Price TJ, Gereau RW, Mohapatra DP. Angiotensin II triggers peripheral macrophage-to-sensory neuron redox crosstalk to elicit pain. J Neurosci 2018;38:7032–57.
. Tay TL, Mai D, Dautzenberg J, Fernandez-Klett F, Lin G, Sagar, Datta M, Drougard A, Stempfl T, Ardura-Fabregat A, Staszewski O, Margineanu A, Sporbert A, Steinmetz LM, Pospisilik JA, Jung S, Priller J, Grun D, Ronneberger O, Prinz M. A new fate mapping system reveals context-dependent random or clonal expansion of microglia. Nat Neurosci 2017;20:793–803.
. Torre D, Lachmann A, Ma'ayan A. BioJupies: automated generation of interactive notebooks for RNA-seq data analysis in the cloud. Cell Syst 2018;7:556–61 e553.
. van den Brink SC, Sage F, Vertesy A, Spanjaard B, Peterson-Maduro J, Baron CS, Robin C, van Oudenaarden A. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat Methods 2017;14:935–6.
. Zeisel A, Hochgerner H, Lonnerberg P, Johnsson A, Memic F, van der Zwan J, Haring M, Braun E, Borm LE, La Manno G, Codeluppi S, Furlan A, Lee K, Skene N, Harris KD, Hjerling-Leffler J, Arenas E, Ernfors P, Marklund U, Linnarsson S. Molecular architecture of the mouse nervous system. Cell 2018;174:999–1014 e1022.
. Zoubarev A, Hamer KM, Keshav KD, McCarthy EL, Santos JR, Van Rossum T, McDonald C, Hall A, Wan X, Lim R, Gillis J, Pavlidis P. Gemma: a resource for the reuse, sharing and meta-analysis of expression profiling data. Bioinformatics 2012;28:2272–3.