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• W2951906253 abstract "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." @default.
• W2951906253 created "2019-06-27" @default.
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• W2951906253 date "2019-07-01" @default.
• W2951906253 modified "2023-10-14" @default.
• W2951906253 title "RNA-seq data in pain research–an illustrated guide" @default.
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• W2951906253 doi "https://doi.org/10.1097/j.pain.0000000000001562" @default.
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