RNA-seq for Pain Researchers
Introduction to RNA-seq and Transcriptomics for Pain Researchers
Background Information about RNA-seq
- What is RNA-seq?
- How does it work?
- What is it good for?
- Single Cell vs. Bulk
Stark, R., Grzelak, M., Hadfield, J. (2019). RNA sequencing: the teenage years Nature Reviews Genetics https://dx.doi.org/10.1038/s41576-019-0150-2
Wang, Z., Gerstein, M., Snyder, M. (2009).RNA-Seq: a revolutionary tool for transcriptomics Nature Reviews Genetics 10(1)https://dx.doi.org/10.1038/nrg2484
Haque, A., Engel, J., Teichmann, S., Lönnberg, T. (2017). A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications Genome Medicine 9(1), 75. https://dx.doi.org/10.1186/s13073-017-0467-4
Chen, X., Teichmann, S., Meyer, K. (2018). From Tissues to Cell Types and Back: Single-Cell Gene Expression Analysis of Tissue Architecture Annual Review of Biomedical Data Science 1(1), 29-51. https://dx.doi.org/10.1146/annurev-biodatasci-080917-013452
At the Bench
So you want to actually learn to analyze RNA-seq data yourself. If you’ve never done it before, this is where you should begin. These are the most useful resources I’ve found in my own learning.
- Bioinformatics Training at the Harvard Chan Bioinformatics Core: I think these are some of the most user-friendly, organized and up-to-date tutorials around. They are also very practical. You can often use the code examples directly for your analyses with a little tweaking. You’d do well to work through these step by step, starting with the Basic Data Skills (if you’re new to any kind of computational analysis) and working your way through the subsequent workshops.
- Bioinformatics Data Skills - Vince Buffalo: I also recommend this excellent book as one of the first introductory books because it teaches a mix of highly practical and timeless skills that one uses throughout computational analysis. Check if your institution offers the eBook for free.
- Practical Computing for Biologists: This is the first book I picked up back in the early days of my PhD (ca. early 2013) when I wanted to learn about using computers to help me do science. As the title indicates, it is very practical and focuses on teaching skills that are immediately useful. I just wish there were an online version. If you want this one, you’ll need to pay, but it’s definitely worthwhile.
Single Cell Analyses
- Software Carpentry Workshops: A suite of high quality introductory to intermediate-level courses (free) to learn R, Python and the command line. However, they won’t necessarily teach you how to completely analyze RNA-seq data. The Harvard Chan Bioinformatics Tutorials are more specific and can get you through an entire RNA-seq analysis, but these SC tutorials are a good complement for extending your skills.
- Biostars: The most popular Q&A forum for bioinformatics. In the course of your own analyses, no doubt you will hit road blocks or have questions. Go here first. Chances are someone before has asked and answered your question. Part of getting good at computational work is knowing how to hunt down the answers to the myriad obstacles and challenges you’ll encounter. So get familiar with this site.
- Stack Overflow: Same as above. SO was the first and most prominent Q&A site for programming-related questions. Many answers also here.
- Gitub: Github is a site where people host code under verison control with Git (something you’ll learn about in the tutorials above). The reason you should know about this now is because many of the software packages you use will be on Github, and going to the “Issues” tab of a repository is where you often find answers to package-specific questions. You can also ask other users of the software for help if you get an error or there is some bug in the software. Another good way to use Github is to explore other people’s code and to learn from it. I do this all the time. Find quality code and take in the lessons by imitation.
Transcriptomics Studies in Pain Research
Ray, P., Khan, J., Wangzhou, A., Tavares-Ferreira, D., Akopian, A., Dussor, G., Price, T. (2019). Transcriptome Analysis of the Human Tibial Nerve Identifies Sexually Dimorphic Expression of Genes Involved in Pain, Inflammation, and Neuro-Immunity. - PubMed - NCBI Frontiers in Molecular Neuroscience 12(), 861. https://dx.doi.org/10.3389/fnmol.2019.00037
Ray, P., Torck, A., Quigley, L., Wangzhou, A., Neiman, M., Rao, C., Lam, T., Kim, J., Kim, T., Zhang, M., Dussor, G., Price, T. (2018). Comparative transcriptome profiling of the human and mouse dorsal root ganglia PAIN 159(7), 1325 - 1345. https://dx.doi.org/10.1097/j.pain.0000000000001217
Megat, S., Ray, P., Moy, J., Lou, T., Barragán-Iglesias, P., Li, Y., Pradhan, G., Wanghzou, A., Ahmad, A., Burton, M., North, R., Dougherty, P., Khoutorsky, A., Sonenberg, N., Webster, K., Dussor, G., Campbell, Z., Price, T. (2019). Nociceptor Translational Profiling Reveals the Ragulator-Rag GTPase Complex as a Critical Generator of Neuropathic Pain The Journal of Neuroscience 39(3), 393 - 411. https://dx.doi.org/10.1523/jneurosci.2661-18.2018
Guan, Z., Kuhn, J., Wang, X., Colquitt, B., Solorzano, C., Vaman, S., Guan, A., Evans-Reinsch, Z., Braz, J., Devor, M., Abboud-Werner, S., Lanier, L., Lomvardas, S., Basbaum, A. (2015). Injured sensory neuron-derived CSF1 induces microglial proliferation and DAP12-dependent pain Nature Neuroscience 19(1)https://dx.doi.org/10.1038/nn.4189
Liang, Z., Hore, Z., Harley, P., Stanley, F., Michrowska, A., Dahiya, M., Russa, F., Jager, S., Villa-Hernandez, S., Denk, F. (2019). A transcriptional toolbox for exploring peripheral neuro-immune interactions bioRxiv https://dx.doi.org/10.1101/813980
Denk, F., Crow, M., Didangelos, A., Lopes, D., McMahon, S. (2016). Persistent Alterations in Microglial Enhancers in a Model of Chronic Pain. Cell Reports 15(8), 1771 - 1781. https://dx.doi.org/10.1016/j.celrep.2016.04.063
Chamessian, A., Young, M., Qadri, Y., Berta, T., Ji, R., Ven, T. (2018). Transcriptional Profiling of Somatostatin Interneurons in the Spinal Dorsal Horn Scientific Reports 8(1), 6809. https://dx.doi.org/10.1038/s41598-018-25110-7
Sathyamurthy, A., Johnson, K., Matson, K., Dobrott, C., Li, L., Ryba, A., Bergman, T., Kelly, M., Kelley, M., Levine, A. (2018). Massively Parallel Single Nucleus Transcriptional Profiling Defines Spinal Cord Neurons and Their Activity during Behavior. Cell Reports 22(8), 2216 - 2225. https://dx.doi.org/10.1016/j.celrep.2018.02.003
Usoskin, D., Furlan, A., Islam, S., Abdo, H., Lönnerberg, P., Lou, D., Hjerling-Leffler, J., Haeggström, J., Kharchenko, O., Kharchenko, P., Linnarsson, S., Ernfors, P. (2014). Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nature Neuroscience 18(1), nn.3881. https://dx.doi.org/10.1038/nn.3881
Sharma, N., Flaherty, K., Lezgiyeva, K., Wagner, D., Klein, A., Ginty, D. (2020). The emergence of transcriptional identity in somatosensory neurons Nature https://dx.doi.org/10.1038/s41586-019-1900-1
Renthal, W., Tochitsky, I., Yang, L., Cheng, Y., Li, E., Kawaguchi, R., Geschwind, D., Woolf, C. (2019). Transcriptional reprogramming of distinct peripheral sensory neuron subtypes after axonal injury bioRxiv https://dx.doi.org/10.1101/838854
Nguyen, M., Pichon, C., Ryba, N. (2019). Stereotyped transcriptomic transformation of somatosensory neurons in response to injury eLife 8(), e49679. https://dx.doi.org/10.7554/elife.49679
Li, C., Li, K., Wu, D., Chen, Y., Luo, H., Zhao, J., Wang, S., Sun, M., Lu, Y., Zhong, Y., Hu, X., Hou, R., Zhou, B., Bao, L., Xiao, H., Zhang, X. (2016). Somatosensory neuron types identified by high-coverage single-cell RNA-sequencing and functional heterogeneity. Cell Research 26(1), 83 - 102. https://dx.doi.org/10.1038/cr.2015.149
Cobos, E., Nickerson, C., Gao, F., Chandran, V., Bravo-Caparrós, I., González-Cano, R., Riva, P., Andrews, N., Latremoliere, A., Seehus, C., Perazzoli, G., Nieto, F., Joller, N., Painter, M., Ma, C., Omura, T., Chesler, E., Geschwind, D., Coppola, G., Rangachari, M., Woolf, C., Costigan, M. (2018). Mechanistic Differences in Neuropathic Pain Modalities Revealed by Correlating Behavior with Global Expression Profiling. Cell Reports 22(5), 1301 - 1312. https://dx.doi.org/10.1016/j.celrep.2018.01.006
Costigan, M., Moss, A., Latremoliere, A., Johnston, C., Verma-Gandhu, M., Herbert, T., Barrett, L., Brenner, G., Vardeh, D., Woolf, C., Fitzgerald, M. (2009). T-cell infiltration and signaling in the adult dorsal spinal cord is a major contributor to neuropathic pain-like hypersensitivity. Journal of Neuroscience 29(46), 14415 - 14422. https://dx.doi.org/10.1523/jneurosci.4569-09.2009
Bangash, M., Alles, S., Santana-Varela, S., Millet, Q., Sikandar, S., Clauser, L., Heegde, F., Habib, A., Pereira, V., Sexton, J., Emery, E., Li, S., Luiz, A., Erdos, J., Gossage, S., Zhao, J., Cox, J., Wood, J. (2018). Distinct transcriptional responses of mouse sensory neurons in models of human chronic pain conditions Wellcome Open Research 3(), 78. https://dx.doi.org/10.12688/wellcomeopenres.14641.1
Costigan, M., Befort, K., Karchewski, L., Griffin, R., D’Urso, D., Allchorne, A., Sitarski, J., Mannion, J., Pratt, R., Woolf, C. (2002). Replicate high-density rat genome oligonucleotide microarrays reveal hundreds of regulated genes in the dorsal root ganglion after peripheral nerve injury. BMC Neuroscience 3(1), 16. https://dx.doi.org/10.1186/1471-2202-3-16
LaCroix-Fralish, M., Austin, J., Zheng, F., Levitin, D., Mogil, J. (2011). Patterns of pain: Meta-analysis of microarray studies of pain Pain 152(8), 1888-1898. https://dx.doi.org/10.1016/j.pain.2011.04.014
Berta, T., Perrin, F., Pertin, M., Tonello, R., Liu, Y., Chamessian, A., Kato, A., Ji, R., Decosterd, I. (2017). Gene Expression Profiling of Cutaneous Injured and Non-Injured Nociceptors in SNI Animal Model of Neuropathic Pain. Scientific Reports 7(1), 9367. https://dx.doi.org/10.1038/s41598-017-08865-3