Tuesday, 17 November 2015

Heterogeneity in the polymerase chain reaction

I've touched briefly on some of the insights I made writing my thesis in a previous blog post. The other thing I've been doing a lot of over the last year or so is writing and contributing to papers. I've been thinking that it might be nice to write a few little blog posts on these, to give a little background information on the papers themselves, and maybe (in the theme of this blog) share a little insight into the processes that went into making them.
The paper I'll cover in this piece was published in Scientific Reports in October. I won't go into great detail on this one, not least because I'm only a (actually, the) middle author on it: this was primarily the excellent work of my friends and colleagues Katharine Best and Theres Oakes, who performed the bulk of the analysis and wet-lab work respectively (although I also did a little of both). Also, our supervisor Benny Chain summarised the findings of the article itself on his own blog, which covers the principles very succinctly.
Instead, I thought I'd write this blog to share that piece of information that I always wonder about when I read a paper: what made them look at this, what put them on this path? This is where I think I made my major contribution to this paper, as (based on my recollections) it began with observations made during my PhD.
My PhD primarily dealt with the development and application of deep-sequencing protocols for measuring T-cell receptor (TCR) repertoires (which, when I started, there were not many published protocols for). As a part of optimising our library preparation strategies, I thought that we might use random nucleotide sequences in our PCR products – which were originally added to increase diversity, overcoming a limitation in the Illumina sequencing technology – to act as unique molecular barcodes. Basically, adding random sequences to our target DNA before amplification uniquely labels each molecule. Then, in the final data we can infer that any matching sequences that share the same barcode are probably just PCR duplicates, if we have enough random barcodes*, meaning that sequence was less prevalent in the original sample than one might think based on raw read counts. Not only does this provide better quantitative data, but by looking to see whether different sequences share a barcode we can find likely erroneous sequences produced during PCR or sequencing, improving the qualitative aspects of the data as well. Therefore we thought (and still do!) that we were on to a good thing.
(Please note that we are not saying that we invented this, just that we have done it: it has of course been done before, both in RNA-seq (e.g. Fu et al, 2011 and Shiroguchi et al, 2012) at large and in variable antigen receptor sequencing (Weinstein et al, 2009), but it certainly wasn't widespread at the time; indeed there's really only one other lab I know of even now that's doing it (Shugay et al, 2014).)
However, in writing the scripts to 'collapse' the data (i.e. remove artificial sequence inflation due to PCR amplification, and throw out erroneous sequences) I noticed that the degree to which different TCR sequences were amplified followed an interesting distribution:

Here I've plotted the raw, uncollapsed frequency of a given TCR sequence (i.e. the number of reads containing that TCR, here slightly inaccurately labelled 'clonal frequency') against that value divided by the number of random barcodes it associated with, giving a 'duplication rate' (not great axis labels I agree, but this is pulling the plots straight out of a lab meeting I gave three years ago). The two plots show the same data, with a shortened X axis on the right to show the bulk of the spread better.
We can see that above a given frequency – in this case about 500, although it varies – we observe a 'duplication rate' around 70. This means that above a certain size, sequences are generally amplified at a rate proportional to their prevalence (give or take the odd outlier), or that for every input molecule of cDNA it gets amplified and observed seventy times. This is the scenario we'd generally imagine for PCR. However, below that variable threshold there is a very different, very noisy picture, where the amount to which a sequence is found to be amplified and observed is not related to the collapsed prevalence. This was the bait on the hook that lead our lab down this path.
Now, everyone knows PCR doesn't behave like it does in the diagrams, like it should. That's what everyone always says (usually as they stick another gel picture containing mysteriously sized bands into their labbooks). However, people have rarely looked at what's actually going on. There's a bit of special PCR magic that goes on, and a million different target and reaction variables that might affect things: you just optimise your reaction until your output looks like what you'd expect. It's only with the relatively recent advances in DNA sequencing technology that we can actually look at exactly what molecules are being made in the reaction that we can start to get actual data showing how just un-like the schematics the reaction can in fact behave.
This is exactly what Katharine's paper chases up, applying the same unique molecular barcoding strategy to TCR sequences from both polyclonal and monoclonal** T-cells. I won't go into the details, because hey, you can just read the paper (which says it much better), but the important thing is that this variability is not due to the standard things you might expect like CG content, DNA motifs or amplicon length, because it happens even for identical sequences. It doesn't matter how well you control your reactions, the noise in the system breeds variability. This makes unique molecular barcoding hugely important, at least if you want accurate relative quantitation of DNA species across the entire dynamic range of your data.
* Theoretically about 16.7 million in our case, or 412, as we use twelve random nucleotides in our barcodes.

** Although it's worth saying that while the line used, KT-2, is monoclonal, that doesn't mean the TCR repertoire is exactly as clean as you'd expect. T-cell receptor expression in T-cell lines is another thing that isn't simple as the textbook diagrams pretend.

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