Early 2016 immunology conferences

I'm very lucky to have attended two fantastic immunology conferences this year: the Keystone Systems Immunology conference, hosted at Big Sky in Montana, USA, and the Next Gen Immunology conference at the Weizmann Institute in Israel. I don't want to go into the details of either conference in any great depth (as frankly the standard of both was so high any thorough recounting would shortly turn into just a complete retelling of each event), but I thought it might be nice to recount a few of my impressions and recollections.

The Systems Immunology meeting was my first Keystone, fitting what I understand to be the regular format; talks in the morning and late afternoon, with a gap over lunch to allow people to sneak off to the slopes. This definitely seemed to be the strategy of a healthy proportion of the attendees - even a ski-novice like me managed to make it up there once or twice!

The Next Gen Immunology conference was a day shorter, but felt like a longer event by merit of absolutely jam-packing the talks and events in! I'm not sure that I've ever been to a busier conference, with talks running from before nine in the morning to past seven in the evening (albeit with a number of breaks to allow consumption of excessive amounts of coffee, even by general science standards). It also had the slickest branding of any conference I've ever been to, complete with it's own celebrity-filled introductory video (which was originally emailed round but has sadly now been made private!).

There were some interesting thematic differences between the conferences, despite having somewhat similar stated scopes. The Keystone talks were seemingly linked mostly in their approaches, displaying a fine array of highly-quantitative, systems level experiments. There was a definite emphasis on the single-cell technologies; it seemed that rare was the talk that wasn't employing at least either single-cell RNA-seq or 40-odd colour mass cytometry. The NGI talks on the other hand were much more diverse in terms of techniques employed, but converged more on the general area of research, which was the microbiome and the mucosal immune response.

Another observation that ticked me was the difference in plotting standards between the two conferences. At the Systems meeting, with (I think it's fair to say) a greater influence or prevalence of mathematical and computational backgrounds, there was a definite enrichment of LaTeX produced slides, and ggplot produced plots, whereas Prism and Excel were far more common in Israel. There was even a fairly large smattering of talks using comic sans, which came as a bit of surprise.

All together, I've kicked off the year with some fantastic science. I've ticked off a number of speakers that I've been wanting to hear for years – including Ron Germaine, Rick Flavell and David Baltimore – and added a few more to the list of stories I need to hear more from, like Aviv Regev's incredible bulk tumour scRNA-seq data, or the wonderful things that Ton Schumacher can do with T-cells.

Crucially, I also heard a little bit about the growing story of assembling T-cell receptor sequences out of scRNA-seq data (to be the topic of a future post I feel), which brings us to the shallows of one of the major goals of repertoire research; getting paired clonotype and phenotype in one fell swoop.

The key to finding TCR sequences in RNA-seq data

I had previously written a short blog post touching on how I'd tried to mine some PBMC RNA-seq data (from the ENCODE project) for rearranged T-cell receptor genes, to try and open up this huge resource for TCR repertoire analysis. However, I hadn't gotten very far, on account of finding very few TCR sequences per file.

That sets the background for an extremely pleasant surprise this morning, when I found that Scott Brown, Lisa Raeburn and Robert Holt from Vancouver (the latter of whom being notable for producing one of the very earliest high-throughput sequencing TCR repertoire papers) had published a very nice paper doing just that!

This is a lovely example of different groups seeing the same problem and coming up with different takes. I saw an extremely low rate of return when TCR-mining in RNA-seq data from heterogeneous cell types, and gave up on it as a search for needles in a haystack. The Holt group saw the same problem, and simply searched more haystacks!

This paper tidily exemplifies the re-purposing of biological datasets to allow us to ask new biological questions (something that I consider a practical and moral necessity, given the complexity of such data and the time and costs involved in their generation).

Moreover, they do some really nice tricks, like estimating TCR transcript proportions in other data sets based on constant region usage, investigate TCR diversity relative to CD3 expression, testing on simulated RNA-seq data sets as a control, looked for public or known-specificity receptors and inferred possible alpha-beta pairs by checking all each sample's possible combinations for their presence in at least one other sample (somewhat akin to Harlan Robins' pairSEQ approach).

All in all, a very nice paper indeed, and I hope we see more of this kind of data re-purposing in the field at large. Such approaches could certainly be adapted for immunoglobulin genes. I also wonder if, given whole-genome sequencing data from mixed blood cell populations, we might even be able to do a similar analysis on rearranged variable antigen receptors from gDNA.

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.

ProFlex Problems

Our lab is lucky enough to have just purchased a few new bits of kit, among which was an AB ProFlex thermal cycler, which replaces the increasingly erratic G-STORM unit we'd previously been working on.

As the nerdiest of the current lab crew, it fell to me to set it up, which involved surmounting a few small errors that popped up along the way. As it's a relatively new machine, I thought that I would relay my experiences here to help anyone similarly inconvenienced.

The first problem came when I tried to get it online using the Wi-Fi dongle that came with the order: it wouldn't accept that it was plugged in! Instead, I got the following error message:

Error

⚠

Please make sure the USB WiFi card is inserted and restart the instrument

No amount of restarting or re-seating the dongle made a difference: instead a bit of searching revealed that I needed to install a firmware update, available from the Thermo website (v 1.1.5). It's pretty simple to install: throw it on a USB stick (that contains no other *.update files), insert it into the PCR machine, go into Settings and update the system.

However, I now got another error message popping up on boot:

⚠

The instrument is due for servicing for the following test(s):

Temperature Non-Uniformity Test,

Temperature Verification Test,

Cycle Performance Test,

Heated Cover Test

This was a brand spanking new machine fresh out of the box, so I was pretty sure this couldn't be the case. However this time the googling came up empty, so I had to get help from tech support. It turns out that there was some issue with the default time stamp, so that when the machine got networked (and updated its clock) it thought it was much older than it actually was. They sent me another patch (a file named ProFlex-ServiceDateReset-1.0.0.update) which did the trick (although it's a bit annoying have to apply two patches within a week of opening a brand new cycler!). I can't find this online, but if you are suffering from a similar problem at least you'll know what to ask your rep for.

My final problem is with the app, which lets you interface remotely with the machine, letting you check availability and so on. Sounds like a fun idea, if a little gimmicky, but nice if you have a long protocol with many heat steps, or a busy lab (of which I have both) so I thought worth playing with.

The problem is that the app refuses to accept my Thermo Fisher account log in details, saying that the user name or password are incorrect (which they most certainly were not). Tech help to the rescue again, and it turns out that it's a known bug (potentially just with the Android version), which is due to be fixed in an update at the end of the month.

Apart from these minor inconveniences, I must say that it's actually quite a lovely machine! There's a couple of adjustments that I had to make - like using the plastic inserts to prevent tube-squashing, and turning off constant heated lids to speed up running my RNA-seq protocol which requires about 3 different lid temperatures - but otherwise it's all very intuitive!

Get single pages from a PDF with pdftk

Another little trick for file manipulation in Linux, which I find useful when making/reading papers, is how to convert a multi-page PDF document into multiple single-page files.

Here is how I do it in Ubuntu, using the lovely program pdftk:

 for i in {1..7}; do echo Working on page $i; pdftk A=INFILE.pdf cat A$i output OUTFILEpage$i.pdf; done