Category: publishing

For What It’s Worth: Influence of our papers on our papers

This post is about a citation analysis that didn’t quite work out.

I liked this blackboard project by Manuel Théry looking at the influence of each paper authored by David Pellman’s lab on the future directions of the Pellman lab.

It reminds me that papers can have impact in the field while others might be influential to the group itself. I wondered which of the papers on which I’m an author have been most influential to my other papers and whether this correlates with a measure of their impact on the field.

There’s no code in this post. I retrieved the relevant records from Scopus and used the difference in “with” and “without” self-citation to pull together the numbers.

Influence: I used the number of citations to a paper from any of our papers as the number for self-citation. This was divided by the total number of future papers. This means if I have 50 papers, and the 23rd paper that was published has collected 27 self-citations, this has a score of 1 (the 23rd paper nor any of the preceding 22 papers, can cite the 23rd paper, but the 27 that follow, could).  This is our metric for influence.

Impact: As a measure of general impact I took the total number of citations for each paper and divided this by the number of years since publication to get average cites per year for each paper.

Plot of influence against impact

Reviews and methods papers are shown in blue, while research papers are in red. I was surprised that some papers have been cited by as much as half of the papers that followed.

Generally, the articles that were most influential to us were also the papers with the biggest impact. Although the correlation is not very strong. There is an obvious outlier paper that gets 30 cites per year (over a 12 year period, I should say) but this paper has not influenced our work as much as other papers have. This is partly because the paper is a citation magnet and partly because we’ve stopped working on this topic in the last few years.

Obviously, the most recent papers were the least informative. There are no future papers to test if they were influential and there are few citations so far to understand their impact.

It’s difficult to say what the correlation between impact and influence on our own work really means, if anything. Does it mean that we have tended to pursue projects because of their impact (I would hope not)? Perhaps these papers are generally useful to the field and to us.

In summary, I don’t think this analysis was successful. I had wanted to construct some citation networks – similar to the Pellman tree idea above – to look at influence in more detail, but I lost confidence in the method. Many of our self-citations are for methodological reasons and so I’m not sure if we’re measuring influence or utility here. Either way, the dataset is not big enough (yet) to do more meaningful number crunching. Having said this, the approach I’ve described here will work for any scholar and could be done at scale.

—

There are several song titles in the database called ‘For What It’s Worth’. This one is Chapterhouse on Rownderbout.


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Ferrous: new paper on FerriTagging proteins in cells

We have a new paper out. It’s not exactly news, because the paper has been up on bioRxiv since December 2016 and hasn’t changed too much. All of the work was done by Nick Clarke when he was a PhD student in the lab. This post is to explain our new paper to a general audience.

The paper in a nutshell

We have invented a new way to tag proteins in living cells so that you can see them by light microscopy and by electron microscopy.

Why would you want to do that?

Proteins do almost all of the jobs in cells that scientists want to study. We can learn a lot about how proteins work by simply watching them down the microscope. We want to know their precise location. Light microscopy means that the cells are alive and we can watch the proteins move around. It’s a great method but it has low resolution, so seeing a protein’s precise location is not possible. We can overcome this limitation by using electron microscopy. This gives us higher resolution, but the proteins are stuck in one location. When we correlate images from one microscope to the other, we can watch proteins move and then look at them with high resolution. All we need is a way to see the proteins so that they can be seen in both types of microscope. We do this with tagging.

Tagging proteins so that we can see them by light microscopy is easy. A widely used method is to use a fluorescent protein such as GFP. We can’t see GFP in the electron microscope (EM) so we need another method. Again, there are several tags available but they all have drawbacks. They are not precise enough, or they don’t work on single proteins. So we came up with a new one and fused it with a fluorescent protein.

What is your EM tag?

We call it FerriTag. It is based on Ferritin which is a large protein shell that cells use to store iron. Because iron scatters electrons, this protein shell can be seen by EM as a particle. There was a problem though. If Ferritin is fused to a protein, we end up with a mush. So, we changed Ferritin so that it could be attached to the protein of interest by using a drug. This meant that we could put the FerriTag onto the protein we want to image in a few seconds. In the picture on the right you can see how this works to FerriTag clathrin, a component of vesicles in cells.

We can watch the tagging process happening in cells before looking by EM. The movie on the right shows green spots (clathrin-coated pits in a living cell) turning orange/yellow when we do FerriTagging. The cool thing about FerriTag is that it is genetically encoded. That means that we get the cell to make the tag itself and we don’t have to put it in from outside which would damage the cell.

What can you use FerriTag for?

Well, it can be used to tag many proteins in cells. We wanted to precisely localise a protein called HIP1R which links clathrin-coated pits to the cytoskeleton. We FerriTagged HIP1R and carried out what we call “contextual nanoscale mapping”. This is just a fancy way of saying that we could find the FerriTagged HIP1R and map where it is relative to the clathrin-coated pit. This allowed us to see that HIP1R is found at the pit and surrounding membrane. We could even see small changes in the shape of HIP1R in the different locations.

We’re using FerriTag for lots of projects. Our motivation to make FerriTag was so that we could look at proteins that are important for cell division and this is what we are doing now.

Is the work freely available?

Yes! The paper is available here under CC-BY licence. All of the code we wrote to analyse the data and run computer simulations is available here. All of the plasmids needed to do FerriTagging are available from Addgene (a non-profit company, there is a small fee) so that anyone can use them in the lab to FerriTag their favourite protein.

How long did it take to do this project?

Nick worked for four years on this project. Our first attempt at using ribosomes to tag proteins failed, but Nick then managed to get Ferritin working as a tag. This paper has broken our lab record for longest publication delay from first submission to final publication. The diagram below tells the whole saga.

 

The publication process was frustratingly slow. It took a few months to write the paper and then we submitted to the first journal after Christmas 2016. We got a rapid desk rejection and sent the paper to another journal and it went out for review. We had two positive referees and one negative one, but we felt we could address the comments and checked with the journal who said that they would consider a revised paper as an appeal. We did some work and resubmitted the paper. Almost six months after first submission the paper was rejected, but with the offer of a rapid (ha!) publication at Nature Communications using the peer review file from the other journal.

Hindsight is a wonderful thing but I now regret agreeing to transfer the paper to Nature Communications. It was far from rapid. They drafted in a new reviewer who came with a list of new questions, as well as being slow to respond. Sure, a huge chunk of the delay was caused by us doing revision experiments (the revisions took longer than they should because Nick defended his PhD, was working on other projects and also became a parent). However, the journal was really slow. The Editor assigned to our paper left the journal which didn’t help and the reviewer they drafted in was slow to respond each time (6 and 7 weeks, respectively). Particularly at the end, after the paper was ‘accepted in principle’ it took them three weeks to actually accept the paper (seemingly a week to figure out what a bib file is and another to ask us something about chi-squared tests). Then a further three weeks to send us the proofs, and then another three weeks until publication. You can see from the graphic that we sent back the paper in the third week of February and only incurred a 9-day delay ourselves, yet the paper was not published until July.

Did the paper improve as a result of this process? Yes and no. We actually added some things in the first revision cycle (for Journal #2) that got removed in subsequent peer review cycles! And the message in the final paper is exactly the same as the version on bioRxiv, posted 18 months previously. So in that sense, no it didn’t. It wasn’t all a total waste of time though, the extra reviewer convinced us to add some new analysis which made the paper more convincing in the end. Was this worth an 18-month delay? You can download our paper and the preprint and judge for yourself.

Were we unlucky with this slow experience? Maybe, but I know other authors who’ve had similar (and worse) experiences at this journal. As described in a previous post, the publication lag times are getting longer at Nature Communications. This suggests that our lengthy wait is not unique.

There’s lots to like about this journal:

  • It is open access.
  • It has the Nature branding (which, like it or not, impresses many people).
  • Peer review file is available
  • The papers look great (in print and online).

But there are downsides too.

  • The APC for each paper is £3300 ($5200). Obviously open access must cost something, but there a cheaper OA journals available (albeit without the Nature branding).
  • Ironically, paying a premium for this reputation is complicated since the journal covers a wide range of science and its kudos varies depending on subfield.
  • It’s also slow, and especially so when you consider that papers have often transferred here from somewhere else.
  • It’s essentially a mega journal, so your paper doesn’t get the same exposure as it would in a community-focused journal.
  • There’s the whole ReadCube/SpringerNature thing…

Overall it was a negative publication experience with this paper. Transferring a paper along with the peer review file to another journal has worked out well for us recently and has been rapid, but not this time. Please leave a comment particularly if you’ve had a positive experience and redress the balance.

—

The post title comes from “Ferrous” by Circle from their album Meronia.

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Rollercoaster III: yet more on Google Scholar

In a previous post I made a little R script to crunch Google Scholar data for a given scientist. The graphics were done in base R and looked a bit ropey. I thought I’d give the code a spring clean – it’s available here. The script is called ggScholar.R (rather than gScholar.R). Feel free to run it and raise an issue or leave a comment if you have some ideas.

I’m still learning how to get things looking how I want them using ggplot2, but this is an improvement on the base R version.

—

As described earlier I have many Rollercoaster songs in my library. This time it’s the song and album by slowcore/dream pop outfit Red House Painters.

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Ten Years vs The Spread: Calculating publication lag times in R

There have been several posts on this site about publication lag times. You can read them here. Lag times are the delays in the dissemination of scientific data introduced by the process of publishing the paper in a journal. Nowadays, your paper can be online in a few hours using a preprint server. However, this work is not peer reviewed. Journals organise a formal peer review and provide some sort of certification of the work. They typeset the work and all of this adds delays the dissemination of work in a journal.

To look at publication delays, you can use PubMed data, which is incomplete but can give insight into how long these delays can be. Previous posts have involved the use of a ruby script to make a csv file from PubMed XML output and then use this in Igor to calculate the publication lag times. There is another method detailed in this excellent post by Daniel Himmelstein.

I recently posted a figure for Nature Communications lag times on Twitter and was asked to generate others. I figured that I should write an R script and people can make their own!

The PubMedLagR code is available here with instructions for use.

A query for Nature Communications data at PubMed, such as:

nat commun[ta] AND 2000 : 2018[pdat] AND journal article[pt]

Retrieves all paper for this journal. The range from 2010 to 2018 is for illustration, this journal has only been in operation for these years. Filtering for journal articles rather and attempting to get rid of reviews and front matter is wise, but doesn’t always work. Again this journal doesn’t carry this material so this is for illustration. Getting your query right is very important.

Save the results in XML format and then run the R script as directed. This should give a csv of the data and a png of the lag times.

This is data from Nature Communications. Colleagues had two separate papers accepted at this journal and experienced long delays. I was interested to see if papers were generally taking longer to publish here. Of course we do not know why. Delays are partly the fault of the authors, the reviewers and the journal and it is not possible to say why publication lag times are increasing for this journal year-on-year. The journal has grown in terms of number of papers published, has this introduced inefficiencies? Are reviewers being slow to review? Are they being more demanding? Are Editors not marshalling the referee reports and providing clear guidance to authors? Allowing too much time and too many rounds of revision? Are authors being too slow to do further experimental work? The answer will be yes to some of these questions for some of the papers.

This is not to focus on Nature Communications, it’s one of a few journals that many colleagues complain is too slow to publish their work. With this code you can have a look at the journal you are interested in submitting to and consider whether there is a more rapid venue for your work.

Update:

I changed the code slightly and prettified the plots just a little. Below are some plots for Nature Cell Biology, Nature Neuroscience. I also did a search for clathrin or CRISPR papers over the same time period. These keyword searches are fairly flat, whereas the journal-specific increase in publication lag time can be seen.

The lag times at Nature Neuroscience look artificially low and then seem to have jumped up in 2016 to be something similar to Nature Cell Biology or Nature Communications.

Edit

I neglected to point out that the code truncates the y-axis in the bottom right plot to 1000 days or the maximum lag time, whichever is smaller. This is because it gets difficult to see the data points if there is an outlier, which might be due to an error in PubMed data.

A reader commented on Twitter that some poor paper had a lag time almost 1000 days. Well, due to the y-axis truncation we don’t see that 9 papers in Nature Communications since 2010 have lag times (RecAcc) of > 1000 days. The record holder has a lag time of 1561 days! I checked that this was not a PubMed error by looking at the dates on the paper.

Notes

Date information is not available in PubMed for every paper unfortunately. This is especially true of older papers.

The date information is supplied to PubMed from the journal. These dates are not necessarily accurate: 1) you can see occasional errors in the data, 2) journals sometimes “reset the clock” on papers and treat resubmissions as new submissions.

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The post title is taken from “10 Years vs The Spread” by Wing-Tipped Sloat from the LP Chewyfoot. Obviously the song has nothing to do with smoothed kernel density estimates of journal publication lag times, but the title was incredibly apt.

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Rollercoaster II: more on Google Scholar citations

I’ve previously written about Google Scholar. Its usefulness and its instability. I just read a post by Jon Tennant on how to harvest Google Scholar data in R and I thought I would use his code as the basis to generate some nice plots based on Google Scholar data.

A script for R is below and can be found here. Graphics are base R but do the job.

First of all I took it for a spin on my own data. The outputs are shown here:

These were the most interesting plots that sprang to mind. First is a ranked citation plot which also shows y=x to find the Hirsch number. Second, was to look at total citations per year to all papers over time. Google Scholar shows the last few years of this plot in the profile page. Third, older papers accrue more citations, but how does this look for all papers? Finally, a prediction of what my H-index will do over time (no prizes for guessing that it will go up!). As Jon noted, the calculation comes from this paper.

While that’s interesting, we need to get  the data of a scholar with a huge number of papers and citations. Here is George Church.

At the time of writing he has 763 papers with over 90,000 citations in total and a H-index of 147. Interestingly ~10% of his total citations come from a monster paper in PNAS with Wally Gilbert in the mid 80s on genome sequencing.

Feel free to grab/fork this code and have a play yourself. If you have other ideas for plots or calculations, add a comment here or an issue at GitHub.

if(!require(scholar)){
     install.packages("scholar")
}
library(scholar)
# Add Google Scholar ID of interest here
ID <- ""
# If you didn't add one to the script prompt user to add one
if(ID == ""){
     ID <- readline(prompt="Enter Scholar ID: ")
}
# Get the citation history
citeByYear<-get_citation_history(ID)
# Get profile information
profile <- get_profile(ID)
# Get publications and save as a csv
pubs <- get_publications(ID)
write.csv(pubs, file = "citations.csv")
# Predict h-index
hIndex <- predict_h_index(ID)
# Now make some plots
# Plot of total citations by year
png(file = "citationsByYear.png")
plot(citeByYear$year,citeByYear$cites,
     type="h", xlab="Year", ylab = "Total Cites")
dev.off()
# Plot of ranked paper by citation with h
png(file = "citationsAndH.png")
plot(pubs$cites, type="l",
     xlab="Paper rank", ylab = "Citations per paper")
abline(0,1)
text(nrow(pubs),max(pubs$cites, na.rm = TRUE),
     profile$h_index)
dev.off()
# Plot of cites to paper by year
png(file = "citesByYear.png")
plot(pubs$year, pubs$cites,
     xlab="Year", ylab = "Citations per paper")
dev.off()
# Plot of h-index prediction
thisYear <- as.integer(format(Sys.Date(), "%Y"))
png(file = "hPred.png")
     plot(hIndex$years_ahead+thisYear,hIndex$h_index,
     ylim = c(0, max(hIndex$h_index, na.rm = TRUE)),
     type = "h",
     xlab="Year", ylab = "H-index prediction") 
dev.off()

Note that my previous code used a python script to grab Google Scholar data. While that script worked well, the scholar package for R seems a lot more reliable.

—

I have a surprising number of tracks in my library with Rollercoaster in the title. This time I will go with the Jesus & Mary Chain track from Honey’s Dead.

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Scoop: some practical advice

So quantixed occasionally gets correspondence from other researchers asking for advice. A recent email came from someone who had been “scooped”. What should they do?

Before we get into this topic we have to define what we mean by being scooped.

In the most straightforward sense being scooped means that an article appeared online before you managed to get your article online.

You were working on something that someone else was also working on – maybe you knew about this or not and vice versa – but they got their work out before you did. They are the scooper and you are the scoopee.

There is another use of the term, primarily used in highly competitive fields, which define the act of scooping as the scooper have gained some unfair advantage to make the scoop. In the worst case, this can be done by receiving your article to review confidentially and then delaying your work while using your information to accelerate their own work (Ginsparg, 2016).

However it happens, the scoop can classified as an overscoop or an underscoop. An overscoop is where the scooper has much more data and a far more complete story. Maybe the scooper’s paper appears in high profile journal while the scoopee was planning on submitting to a less-selective journal.  Perhaps the scooper has the cell data, an animal model, the biochemical data and a crystal structure; while the scoopee had some nice data in cells and a bit of biochemistry. An underscoop is where a key observation that the scoopee was building into a full paper is partially revealed. The scoopee could have more data or better quality results and maybe the full mechanism, but the scooper’s paper gives away a key detail (Mole, 2004).

All of these definitions are different from the journalistic definition which simply means “the scoop” is the big story. What the science and journalistic term share is the belief that being second with a story is worthless. In science, being second and getting the details right is valuable and more weight should be given that it currently is. I think follow-up work is valued by the community, but it is fair to say that it is unlikely to receive the same billing and attention as the scooper’s paper.

How often does scooping actually happen?

To qualify as being scooped, you need to have a paper that you are preparing for publication when the other paper appears. If you are not at that point, someone else was just working on something similar and they’ve published a paper. They haven’t scooped you. This is easiest to take when you have just had an idea or have maybe done a few experiments and then you see a paper on the same thing. It must’ve been a good idea! The other paper has saved you some time! Great. Move on. The problem comes when you have invested a lot of time doing a whole bunch of work and then the other paper appears. This is very annoying, but to reiterate, you haven’t really been scooped if you weren’t actually at the point of preparing your work for publication.

As you might have gathered, I am not even sure scooping is a real thing. For sure the fear of being scooped is real. And there are instances of scooping happening. But most of the time the scoopee has not actually been scooped. And even then, the scoopee does not just abandon their work.

So what is the advice to someone who has discovered that they have been scooped?

Firstly, don’t panic! The scoopers paper is not going to go away and you have to deal with the fact you now have the follow up paper. It can be hard to change your mindset, but you must rewrite your paper to take their work into account. Going into denial mode and trying to publish your work as though the other paper doesn’t exist is a huge mistake.

Second, read their work carefully. I doubt that the scooper has left you with no room for manoeuvre. Even in the case of the overscoop, you probably still have something that the other paper doesn’t have that you can still salvage. There’s bound to be some details on which your work does not agree and this can feature in your paper. If it’s an underscoop, you have even less to worry about. There will be a way forward – you just need to identify it and move on.

The main message is that “being scooped” is not the end. You just need to figure out your way forward.

How do I stop it from happening to me?

Be original! It’s a truism that if you are working on something interesting, it’s likely that someone else is too. And if you work in a highly competitive area, there might be many groups working on the same thing and it is more likely that you will be scooped. Some questions are obvious next steps and it might be worth thinking twice about pursuing them. This is especially true if you come up with an idea based on a paper you’ve read. Work takes so long to appear that the lab who published that paper is likely far ahead of you.

Having your own niche gives the best protection. If you have carved out your own question you probably have the lead and will be associated with work in this area anyway. Other labs will back off. If you have a highly specialised method, again you can contribute in ways that others can’t and so your chances of being scooped decrease.

Have a backup plan. Do you have a side project which you can switch to if too much novelty is taken away from your main project? You can insulate yourself from scoop damage by not working on projects that are all-or-nothing. Horror stories about scooping in structural biology (which is all about “the big reveal”) are commonplace. Investing energy in alternative approaches or new assays as well as getting a structure might help here.

If you find out about competition, maybe from a poster or a talk at a meeting, you need to evaluate whether it is worth carrying on. If you can, talk to the other lab. Most labs do not want to compete and would prefer to collaborate or at least co-ordinate submission of manuscripts.

Use preprints! If you deposit your work on a preprint server, you get a DOI and a date stamp. You can prove that your work existed on that date and in what form. This is ultimate protection against being scooped. If someone else’s work appears online before you do this, then as I said above, you haven’t really been scooped. If work appears and you already have a DOI, well, then you haven’t been scooped either. Some journals see things this way. For example, EMBO J have a scoop protection policy that states that the preprint deposition timestamp is the date at which priority is assessed.

—

The post title is taken from “Scoop” by The Auctioneers. I have this track on an extended C86 3-Disc set.

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In a Word: LaTeX to Word and vice versa

Here’s a quick tech tip. We’ve been writing papers in TeX recently, using Overleaf as a way to write collaboratively. This works great but sometimes, a Word file is required by the publisher. So how do you convert from one to the other quickly and with the least hassle?

If you Google this question (as I did), you will find a number of suggestions which vary in the amount of effort required. Methods include latex2rtf or pandoc. Here’s what worked for me:

  • Exporting the TeX file as PDF from Overleaf
  • Opening it in Microsoft Word
  • That was it!

OK, that wasn’t quite it. It did not work at all on a Mac. I had to use a Windows machine running Word. The formatting was maintained and the pictures imported OK. Note that this was a short article with three figures and hardly any special notation (it’s possible this doesn’t work as well on more complex documents). A couple of corrections were needed: hyphenation at the end of the line was deleted during the import which borked actual hyphenated words which happened to span two lines; and the units generated by siunitx were missing a space between the number and unit. Otherwise it was pretty straightforward. So straightforward that I thought I’d write a quick post in case it helps other people.

What about going the other way?

Again, on Windows I used Apache OpenOffice to open my Word document and save it as an otd file. I then used the writer2latex filter to make a .tex file with all the embedded images saved in a folder. These could then be uploaded to Overleaf. With a bit of formatting work, I was up-and-running.

I had heard that many publishers, even those that say that they accept manuscripts as TeX files actually require a Word document for typesetting. This is because, I guess, they have workflows set up to make the publisher version which must start with a Word document and nothing else. What’s more worrying is that in these cases, if you don’t supply one, they will convert it for you before putting into the workflow. It’s probably better to do this yourself and check the conversion to reduce errors at the proof stage.

—

The post title is taken from “In A Word” the compilation album by Nottingham noise-rockers Fudge Tunnel.

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Some Things Last A Long Time II

Back in 2014, I posted an analysis of the time my lab takes to publish our work. This post is very popular. Probably because it looks at the total time it takes us to publish our work. It was time for an update. Here is the latest version.

The colours have changed a bit but again the graphic shows that the journey to publication in four “eras”:

  1. Pre-time (before 0 on the x-axis): this is the time from first submission to the first journal. A dark time which involves rejection.
  2. Submission at the final journal (starting at time 0). Again, the lime-coloured periods are when the manuscript is with the journal and the green ones, when it is with us (being revised).
  3. Acceptance! This is where the lime bar stops. The manuscript is then readied for publication (blank area).
  4. Published online. A red period that ends with final publication in print.

Since 2013 we have been preprinting our work, which means that the manuscript is available while it is under review. This procedure means that the journey to publication only delays the work appearing in the journal and not its use by other scientists. If you want to find out more about preprints in biology check out ASAPbio.org or my posts here and here.

The mean time from first submission to the paper appearing online in the journal is 226 days (median 210). Which is shorter than the last time I did this analysis (250 days). Sadly though we managed to set a new record for longest time to publication with 450 days! This is sad for the first author concerned who worked hard (259 days in total) revising the paper when she could have been doing other stuff. It is not all bad though. That paper was put up on bioRxiv the day we first submitted it so the pain is offset somewhat.

What is not shown in the graphic is the other papers that are still making their way through the process. These manuscripts will change the stats again likely pushing up the times. As I said in the last post, I think the delays we experience are pretty typical for our field and if anything, my group are quite quick to publish.

If you’d like to read more about publication lag times see here.

Thanks to Jessica Polka for nudging me to update this post.

—

The post title comes again from Daniel Johnston’s track “Some Things Last A Long Time” from his “1990” LP.

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Fusion confusion: new paper on FGFR3-TACC3 fusions in cancer

We have a new paper out! This post is to explain what it’s about.

Cancer cells often have gene fusions. This happens because the DNA in cancer cells is really messed up. Sometimes, chromosomes can break and get reattached to a different one in a strange way. This means you get a fusion between one gene and another which makes a new gene, called a gene fusion. There are famous fusions that are known to cause cancer, such as the Philadelphia chromosome in chronic myelogenous leukaemia. This rearrangement of chromosomes 9 and 22 result in a fusion called BCR-ABL. There are lots of different gene fusions and a few years ago, a new fusion was discovered in bladder and brain cancers, called FGFR3-TACC3.

Genes encode proteins and proteins do jobs in cells. So the question is: how are the proteins from gene fusions different to their normal versions, and how do they cause cancer? Many of the gene fusions that scientists have found result in a protein that continues to send a signal to the cell when it shouldn’t. It’s thought that this transforms the cell to divide uncontrollably. FGFR3-TACC3 is no different. FGFR3 can send signals and the TACC3 part probably makes it do this uncontrollably. But, what about the TACC3 part? Does that do anything, or is this all about FGFR3 going wrong?

What is TACC3?

Chromosomes getting shared to the two daughter cells

TACC3, or transforming acidic coiled-coil protein 3 to give it its full name, is a protein important for cell division. It helps to share the chromosomes to the two daughter cells when a cell divides. Chromosomes are shared out by a machine built inside the cell called the mitotic spindle. This is made up of tiny threads called microtubules. TACC3 stabilises these microtubules and adds strength to this machine.

We wondered if cancer cells with FGFR3-TACC3 had problems in cell division. If they did, this might be because the TACC3 part of FGFR3-TACC3 is changed.

We weren’t the first people to have this idea. The scientists that found the gene fusion suggested that FGFR3-TACC3 might bind to the mitotic spindle but not be able to work properly. We decided to take a closer look…

What did you find?

First of all FGFR3-TACC3 is not actually bound to the mitotic spindle. It is at the cells membrane and in small vesicles in the cell. So if it is not part of the mitotic spindle, how can it affect cell division? One unusual thing about TACC3 is that it is a dimer, meaning two TACC3s are stuck together. Stranger than that, these dimers can stick to more dimers and multimerise into a much bigger protein. When we looked at the normal TACC3 in the cell we noticed that the amount bound to the spindle had decreased. We wondered whether the FGFR3-TACC3 was hoovering the normal TACC3 off the spindle, preventing normal cell division.

We made the cancer cells express a bit more normal TACC3 and this rescued the faulty division. We also got rid of the FGFR3-TACC3 fusion, and that also put things back to normal. Finally, we made a fake FGFR3-TACC3 which had a dummy part in place of FGFR3 and this was just as good at hoovering up normal TACC3 and causing cell division problems. So our idea seemed to be right!

What does this mean for cancer?

This project was to look at what is going on inside cancer cells and it is a long way from any cancer treatments. Drug companies can develop chemicals which stop cell signalling from fusions, these could work as anti-cancer agents. In the case of FGFR3-TACC3, what we are saying is: even if you stop the signalling there will still be cell division problems in the cancer cells. So an ideal treatment might be to block TACC3 interactions as well as stopping signalling. This is very difficult to do and is far in the future. Doing work like this is important to understand all the possible ways to tackle a specific cancer and to find any problems with potential treatments.

The people

Sourav Sarkar did virtually all the work for this paper and he is first author. Sourav left the lab before we managed to submit this paper and so the revision experiments requested by the peer reviewers were done by Ellis Ryan.

Why didn’t we post this paper as a preprint?

My group have generally been posting our new manuscripts as preprints while they undergo peer review, but we didn’t post this one. I was reluctant because many cancer journals at the time of submission did not allow preprints. This has changed a bit in the last few months, but back in February several key cancer journals did not accept papers that had appeared first as preprints.

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The title of the post comes from “Fusion Confusion” 4th track on the Hazy EP by Dr Phibes & The House of Wax Equations.

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Parallel lines: new paper on modelling mitotic microtubules in 3D

We have a new paper out! You can access it here.

The people

This paper really was a team effort. Faye Nixon and Tom Honnor are joint-first authors. Faye did most of the experimental work in the final months of her PhD and Tom came up with the idea for the mathematical modelling and helped to rewrite our analysis method in R. Other people helped in lots of ways. George did extra segmentation, rendering and movie making. Nick helped during the revisions of the paper. Ali helped to image samples… the list is quite long.

The paper in a nutshell

We used a 3D imaging technique called SBF-SEM to see microtubules in dividing cells, then used computers to describe their organisation.

What’s SBF-SEM?

Serial block face scanning electron microscopy. This method allows us to take an image of a cell and then remove a tiny slice, take another image and so on. We then have a pile of images which covers the entire cell. Next we need to put them back together and make some sense of them.

How do you do that?

We use a computer to track where all the microtubules are in the cell. In dividing cells – in mitosis – the microtubules are in the form of a mitotic spindle. This is a machine that the cell builds to share the chromosomes to the two new cells. It’s very important that this process goes right. If it fails, mistakes can lead to diseases such as cancer. Before we started, it wasn’t known whether SBF-SEM had the power to see microtubules, but we show in this paper that it is possible.

We can see lots of other cool things inside the cell too like chromosomes, kinetochores, mitochondria, membranes. We made many interesting observations in the paper, although the focus was on the microtubules.

So you can see all the microtubules, what’s interesting about that?

The interesting thing is that our resolution is really good, and is at a large scale. This means we can determine the direction of all the microtubules in the spindle and use this for understanding how well the microtubules are organised. Previous work had suggested that proteins whose expression is altered in cancer cause changes in the organisation of spindle microtubules. Our computational methods allowed us to test these ideas for the first time.

Resolution at a large scale, what does that mean?

The spindle is made of thousands of microtubules. With a normal light microscope, we can see the spindle but we can’t tell individual microtubules apart. There are improvements in light microscopy (called super-resolution) but even with those improvements, right in the body of the spindle it is still not possible to resolve individual microtubules. SBF-SEM can do this. It doesn’t have the best resolution available though. A method called Electron Tomography has much higher resolution. However, to image microtubules at this large scale (meaning for one whole spindle), it would take months or years of effort! SBF-SEM takes a few hours. Our resolution is better than light microscopy, worse than electron tomography, but because we can see the whole spindle and image more samples, it has huge benefits.

What mathematical modelling did you do?

Cells are beautiful things but they are far from perfect. The microtubules in a mitotic spindle follow a pattern, but don’t do so exactly. So what we did was to create a “virtual spindle” where each microtubule had been made perfect. It was a bit like “photoshopping” the cell. Instead of straightening the noses of actresses, we corrected the path of every microtubule. How much photoshopping was needed told us how imperfect the microtubule’s direction was. This measure – which was a readout of microtubule “wonkiness” – could be done on thousands of microtubules and tell us whether cancer-associated proteins really cause the microtubules to lose organisation.

The publication process

The paper is published in Journal of Cell Science and it was a great experience. Last November, we put up a preprint on this work and left it up for a few weeks. We got some great feedback and modified the paper a bit before submitting it to a journal. One reviewer gave us a long list of useful comments that we needed to address. However, the other two reviewers didn’t think our paper was a big enough breakthrough for that journal. Our paper was rejected*. This can happen sometimes and it is frustrating as an author because it is difficult for anybody to judge which papers will go on to make an impact and which ones won’t. One of the two reviewers thought that because the resolution of SBF-SEM is lower than electron tomography, our paper was not good enough. The other one thought that because SBF-SEM will not surpass light microscopy as an imaging method (really!**) and because EM cannot be done live (the cells have to be fixed), it was not enough of a breakthrough. As I explained above, the power is that SBF-SEM is between these two methods. Somehow, the referees weren’t convinced. We did some more work, revised the paper, and sent it to J Cell Sci.

J Cell Sci is a great journal which is published by Company of Biologists, a not-for-profit organisation who put a lot of money back into cell biology in the UK. They are preprint friendly, they allow the submission of papers in any format, and most importantly, they have a fast-track*** option. This allowed me to send on the reviews we had and including our response to them. They sent the paper back to the reviewer who had a list of useful comments and they were happy with the changes we made. It was accepted just 18 days after we sent it in and it was online 8 days later. I’m really pleased with the whole publishing experience with J Cell Sci.

 

* I’m writing about this because we all have papers rejected. There’s no shame in that at all. Moreover, it’s obvious from the dates on the preprint and on the JCS paper that our manuscript was rejected from another journal first.

** Anyone who knows something about microscopy will find this amusing and/or ridiculous.

*** Fast-track is offered by lots of journals nowadays. It allows authors to send in a paper that has been reviewed elsewhere with the peer review file. How the paper has been revised in light of those comments is assessed by at the Editor and one peer reviewer.

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Parallel lines is of course the title of the seminal Blondie LP. I have used this title before for a blog post, but it matches the topic so well.

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