Validation of RNAseq Experiments by qPCR?

This post is in response to a couple of twitter discussions regarding whether its "useful" to do qPCR validation of RNAseq hits:

As preliminary data for a talk can I graph transcript counts of particular genes (transcriptome data) instead of qPCR (haven't done it yet)?
— Sciencegurl (@sciencegurlz0) October 27, 2014

In response to the that I posted a response I made in response to a similar query from a reviewer for a recent manuscript (original available here). Based on the fact that this came up a couple times in two weeks, I thought that I'd try to be more clear on my thoughts (and put them somewhere easier for others to find). This is how we address this issue experimentally, and in response to reviewer requests. Feel free to use any of these arguments yourself but your mileage with your supervisor/manuscript or grant reviewers may vary. Most importantly, if you have some suggestions/data/papers that we should include please comment below and I'll try to keep this post up to date.

The Answer We Gave:

We had considered performing qPCR studies to ‘re-validate’ some of our gene-expression findings but there is little evidence that qPCR analyses from the same samples will add any extra utility to our data so we decided to eschew those experiments. Previous studies have shown extremely close correlations between qPCR and RNAseq data [1-4]. Ideally, we would re-validate our findings (potentially by qPCR) in a separate cohort of samples, but due to the difficulty in accessing these samples, those experiments are not possible at this time.

What Do We Mean By Validation of RNAseq Results?

RNAseq, like microarrays before them generate a lot of data. Ideally you can determine the levels of every gene/transcript/exon in the genome, and given proper experimental design determine a large number of significantly differentially expressed genes. To follow up these findings, we often want to test how valid those observations are. In my interpretation, this can mean a few different things:

1. Are these transcripts really differentially expressed in these samples (technical reproducibility)?
2. Are these transcripts really generally differentially expressed in other samples (biological reproducibility)?
3. Do these transcriptional changes represent phenotypic differences (significance)?

For the third question, I'd say you'd want to perform some non-transcriptional response such as a western blot, enzymatic or cell based assay to show that the transcriptional change has some biological phenotype. Ideally, you may even go farther and manipulate the expression of a gene of interest back to the control condition, to test the hypothesis that a change in that gene is causative of some particular phenotype. 

Why was qPCR the traditional validation experiment from microarray studies?

Normally though, the question of validation stems from one of the first two questions. This is probably based on prior work with microarrays. Microarrays were/are great tools for transcriptomic analysis (though I am hard pressed to think of a reason to do them in lieuw of RNAseq). One major problem with microarrays was probe bias. What this means is that there was a limited number of hybridization probes in a microarray experiment, and its possible that this probe may not be representative of the transcript as a whole. Furthermore, not all transcripts of interest may be present on the microarray of choice. As a result, the standard in the field was to examine transcript abundance by qPCR to technically validate microarray results.

How similar are RNAseq and qPCR results?

Probe bias, poor sensitivity and reduced linear range are not as problematic in RNAseq experiments, since the entire transcript is assessed in a more or less unbiased manner [5]. Several studies have compared RNAseq results to qPCR data, and have found excellent correlation between these methods [1-4]. In cases where there are discrepancies exist, I would argue that it is most likely due to bias in the qPCR experiment (which has its own probe-bias based on what region of the cDNA is amplified). Therefore, it is unlikely to yield new information, and when it does, the information is probably worse than the quality of RNAseq data.

Under What Conditions Would qPCR Be a Good Validation Method?

This isn't to say qPCR isn't useful, we use it all the time in my lab. Its a great tool for looking at a small number of genes in samples for example. But when would it be good to use in the context of validating a RNAseq experiment? I would argue that it is most useful in the case where you have independent samples from those that you did your RNAseq studies on. For example, maybe for economical reasons, you examined 5 control samples and 5 drug-treated samples but you have another 20 samples available. qPCR would be a great way to test whether the differences observed are also true in separate samples, thus answering question #2. I think this is especially important as in my experience a lot of RNAseq studies are underpowered to answer the questions asked (if you want a quick and easy way to check the power of your experimental design I like Scotty).

References

Thanks to Matthew MacManes (@peroMHC) and Alejandro Montenegro (@aemonten) for spurring this discussion, and sending me towards a nice paper by Timothy Hughes [6], which is a great summary of similar, more general issues.

1. Griffith M, Griffith OL, Mwenifumbo J, Goya R, Morrissy a S, et al. (2010) Alternative expression analysis by RNA sequencing. Nat Methods 7: 843–847. doi:10.1038/nmeth.1503.
2. Asmann YW, Klee EW, Thompson EA, Perez E a, Middha S, et al. (2009) 3’ tag digital gene expression profiling of human brain and universal reference RNA using Illumina Genome Analyzer. BMC Genomics 10: 531. doi:10.1186/1471-2164-10-531.
3. Wu AR, Neff NF, Kalisky T, Dalerba P, Treutlein B, et al. (2014) Quantitative assessment of single-cell RNA-sequencing methods. Nat Methods 11: 41–46. doi:10.1038/nmeth.2694.
4. Shi Y, He M (2014) Differential gene expression identified by RNA-Seq and qPCR in two sizes of pearl oyster (Pinctada fucata). Gene 538: 313–322. doi:10.1016/j.gene.2014.01.031.
5. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10: 57–63. doi: 10.1038/nrg2484.
6. Hughes TR (2009) “Validation” in genome-scale research. J Biol 8: 3. doi:10.1186/jbiol104.

Preprints: Trying Something New in Publishing

As a trainee, having my papers reviewed by experts in the field has been both a frustrating and positive experience.  It has been positive, in that in nearly every case my publications have been improved by the process.  The enhancements from little embarrassing typos to new ways of conceptualizing our data, and certainly these papers are better for it.

On the other hand, some times it takes forever.  One paper went through 18 rounds of submission/resubmission, lasting over 3 years.  Another took almost 3 years and 10 submissions.  Some of these delays were certainly self-inflicted but in general it takes a really long time for papers to work through their pipeline.  Steven Royle recently looked at this more rigorously for the papers his group has published here.  In his experience, the average has been about 9 months

This can be bad for the careers of those involved, and for those for whom the data might help.  To get around this, we are trying something new with our next paper.  We submitted it to bioRxiv as a preprint.  The paper can be found here, so go ahead and take a look, I'll wait.  The posted version is identical to the submitted version, which was sent to a normal peer-reviewed journal.

What do I hope to gain?

This has been covered really well over the internet including in science, at Haldane's Sieve, and in PLOS Biology.  I hope that this will give people in my field a chance to read our work earlier.  I also hope that the people who may be interested in reading it will provide some feedback.  This paper, like all of our papers gets informally reviewed by colleagues and lab members before it goes out.  By putting it out online, I would like a broader audience to be empowered to take a look and give us their thoughts, before the 'final' version is done.

What are the downsides?

There are a couple, we could be scooped, or it could affect our ability to publish it in another journal.  For the latter, we used SHERPA/ROMEO to pick a journal that has an established policy that pre-prints are acceptable.  As far as getting scooped, I am even less concerned about that.  The data is freely available for this paper on GitHub for anyone to use and I think the risk of scooping is dramatically overstated in science.

So take a look, and let us know here or at the paper what you think.

My Favorite Papers of 2012

These are the papers that I thought were the most interesting in my world this year. It was a big year for hormonal messengers involved in obesity and diabetes.  These include Fgf21 (Kim et. al) and VEGF (Hagberg et. al).  From a biochemistry perspective there was a lot of great work on the role of SREBP1 (Moon et. al) and its regulation both from a dietary (Haas et al.) perspective and from a mechanistic perspective (Owen et al.).  Finally both Kim et al. and Kusminski et al. highlighted the importance of mitochondrial function in the systemic response to obesity.



Haas, J., Miao, J., Chanda, D., Wang, Y., Zhao, E., Haas, M., Hirschey, M., Vaitheesvaran, B., Farese, R., Kurland, I., Graham, M., Crooke, R., Foufelle, F., & Biddinger, S. (2012). Hepatic Insulin Signaling Is Required for Obesity-Dependent Expression of SREBP-1c mRNA but Not for Feeding-Dependent Expression Cell Metabolism, 15 (6), 873-884 DOI: 10.1016/j.cmet.2012.05.002

Hagberg, C., Mehlem, A., Falkevall, A., Muhl, L., Fam, B., Ortsäter, H., Scotney, P., Nyqvist, D., Samén, E., Lu, L., Stone-Elander, S., Proietto, J., Andrikopoulos, S., Sjöholm, A., Nash, A., & Eriksson, U. (2012). Targeting VEGF-B as a novel treatment for insulin resistance and type 2 diabetes Nature, 490 (7420), 426-430 DOI: 10.1038/nature11464

Kusminski, C., Holland, W., Sun, K., Park, J., Spurgin, S., Lin, Y., Askew, G., Simcox, J., McClain, D., Li, C., & Scherer, P. (2012). MitoNEET-driven alterations in adipocyte mitochondrial activity reveal a crucial adaptive process that preserves insulin sensitivity in obesity Nature Medicine, 18 (10), 1539-1549 DOI: 10.1038/nm.2899

Moon, Y., Liang, G., Xie, X., Frank-Kamenetsky, M., Fitzgerald, K., Koteliansky, V., Brown, M., Goldstein, J., & Horton, J. (2012). The Scap/SREBP Pathway Is Essential for Developing Diabetic Fatty Liver and Carbohydrate-Induced Hypertriglyceridemia in Animals Cell Metabolism, 15 (2), 240-246 DOI: 10.1016/j.cmet.2011.12.017

Kim, K., Jeong, Y., Oh, H., Kim, S., Cho, J., Kim, Y., Kim, S., Kim, D., Hur, K., Kim, H., Ko, T., Han, J., Kim, H., Kim, J., Back, S., Komatsu, M., Chen, H., Chan, D., Konishi, M., Itoh, N., Choi, C., & Lee, M. (2012). Autophagy deficiency leads to protection from obesity and insulin resistance by inducing Fgf21 as a mitokine Nature Medicine DOI: 10.1038/nm.3014

Owen, J., Zhang, Y., Bae, S., Farooqi, M., Liang, G., Hammer, R., Goldstein, J., & Brown, M. (2012). From the Cover: Insulin stimulation of SREBP-1c processing in transgenic rat hepatocytes requires p70 S6-kinase Proceedings of the National Academy of Sciences, 109 (40), 16184-16189 DOI: 10.1073/pnas.1213343109

My Favorite Papers of 2012 by Dave Bridges is licensed under a Creative Commons Attribution 3.0 Unported License.