White label: the growth of bioRxiv

bioRxiv, the preprint server for biology, recently turned 2 years old. This seems a good point to take a look at how bioRxiv has developed over this time and to discuss any concerns sceptical people may have about using the service.

Firstly, thanks to Richard Sever (@cshperspectives) for posting the data below. The first plot shows the number of new preprints deposited and the number that were revised, per month since bioRxiv opened in Nov 2013. There are now about 200 preprints being deposited per month and this number will continue to increase. The cumulative article count (of new preprints) shows that, as of the end of last month, there are >2500 preprints deposited at bioRxiv. overall2

subject2

What is take up like across biology? To look at this, the number of articles in different subject categories can be totted up. Evolutionary Biology, Bioinformatics and Genomics/Genetics are the front-running disciplines. Obviously counting articles should be corrected for the size of these fields, but it’s clear that some large disciplines have not adopted preprinting in the same way. Cell biology, my own field, has some catching up to do. It’s likely that this reflects cultures within different fields. For example, genomics has a rich history of data deposition, sharing and openness. Other fields, less so…

So what are we waiting for?

I’d recommend that people wondering about preprinting go and read Stephen Curry’s post “just do it“. Any people who remain sceptical should keep reading…

Do I really want to deposit my best work on bioRxiv?

I’ve picked six preprints that were deposited in 2015. This selection demonstrates how important work is appearing first at bioRxiv and is being downloaded thousands of times before the papers appear in the pages of scientific journals.

  1. Accelerating scientific publishing in biology. A preprint about preprinting from Ron Vale, subsequently published in PNAS.
  2. Analysis of protein-coding genetic variation in 60,706 humans. A preprint summarising a huge effort from ExAC Exome Aggregation Consortium. 12,366 views, 4,534 downloads.
  3. TP53 copy number expansion correlates with the evolution of increased body size and an enhanced DNA damage response in elephants. This preprint was all over the news, e.g. Science.
  4. Sampling the conformational space of the catalytic subunit of human γ-secretase. CryoEM is the hottest technique in biology right now. Sjors Scheres’ group have been at the forefront of this revolution. This paper is now out in eLife.
  5. The genome of the tardigrade Hypsibius dujardini. The recent controversy over horizontal gene transfer in Tardigrades was rapidfire thanks to preprinting.
  6. CRISPR with independent transgenes is a safe and robust alternative to autonomous gene drives in basic research. This preprint concerning biosafety of CRISPR/Cas technology could be accessed immediately thanks to preprinting.

But many journals consider preprints to be previous publications!

Wrong. It is true that some journals have yet to change their policy, but the majority – including Nature, Cell and Science – are happy to consider manuscripts that have been preprinted. There are many examples of biology preprints that went on to be published in Nature (ancient genomes) and Science (hotspots in birds). If you are worried about whether the journal you want to submit your work to will allow preprinting, check this page first or the SHERPA/RoMEO resource. The journal “information to authors” page should have a statement about this, but you can always ask the Editor.

I’m going to get scooped

Preprints establish priority. It isn’t possible to be scooped if you deposit a preprint that is time-stamped showing that you were the first. The alternative is to send it to a journal where no record will exist that you submitted it if the paper is rejected, or sometimes even if they end up publishing it (see discussion here). Personally, I feel that the fear of scooping in science is overblown. In fields that are so hot that papers are coming out really fast the fear of scooping is high, everyone sees the work if its on bioRxiv or elsewhere – who was first is clear to all. Think of it this way: depositing a preprint at bioRxiv is just the same as giving a talk at a meeting. Preprints mean that there is a verifiable record available to everyone.

Preprints look ugly, I don’t want people to see my paper like that.

The depositor can format their preprint however they like! Check out Christophe Leterrier’s beautifully formatted preprint, or this one from Dennis Eckmeier. Both authors made their templates available so you can follow their example (1 and 2).

Yes but does -insert name of famous scientist- deposit preprints?

Lots of high profile scientists have already used bioRxiv. David Bartel, Ewan Birney, George Church, Ray Deshaies, Jennifer Doudna, Steve Henikoff, Rudy Jaenisch, Sophien Kamoun, Eric Karsenti, Maria Leptin, Rong Li, Andrew Murray, Pam Silver, Bruce Stillman, Leslie Vosshall and many more. Some sceptical people may find this argument compelling.

I know how publishing works now and I don’t want to disrupt the status quo

It’s paradoxical how science is all about pushing the frontiers, yet when it comes to publishing, scientists are incredibly conservative. Physics and Mathematics have been using preprinting as part of the standard route to publication for decades and so adoption by biology is nothing unusual and actually, we will simply be catching up. One vision for the future of scientific publishing is that we will deposit preprints and then journals will search out the best work from the server to highlight in their pages. The journals that will do this are called “overlay journals”. Sounds crazy? It’s already happening in Mathematics. Terry Tao, a Fields medal-winning mathematician recently deposited a solution to the Erdos discrepency problem on arXiv (he actually put them on his blog first). This was then “published” in Discrete Analysis, an overlay journal. Read about this here.

Disclaimer: other preprint services are available. F1000 Research, PeerJ Preprints and of course arXiv itself has quantitative biology section. My lab have deposited work at bioRxiv (1, 2 and 3) and I am an affiliate for the service, which means I check preprints before they go online.

Edit 14/12/15 07:13 put the scientists in alphabetical order. Added a part about scooping.

The post title comes from the term “white label” which is used for promotional vinyl copies of records ahead of their official release.

Parallel Lines: Spatial statistics of microtubules in 3D

Our recent paper on “the mesh” in kinetochore fibres (K-fibres) of the mitotic spindle was our first adventure in 3D electron microscopy. This post is about some of the new data analysis challenges that were thrown up by this study. I promised a more technical post about this paper and here it is, better late than never.

Figure 6In the paper we describe how over-expression of TACC3 causes the microtubules (MTs) in K-fibres to become “more wonky”. This was one of those observations that we could see by eye in the tomograms, but we needed a way to quantify it. And this meant coming up with a new spatial statistic.

After a few false starts*, we generated a method that I’ll describe here in the hope that the extra detail will be useful for other people interested in similar problems in cell biology.

The difficulty in this analysis comes from the fact that the fibres are randomly oriented, because of the way that the experiment is done. We section orthogonally to the spindle axis, but the fibre is rarely pointing exactly orthogonal to the tomogram. So the challenge is to reorient all the fibres to be able to pool numbers from across different fibres to derive any measurements. The IgorPro code to do this was made available with the paper. I have recently updated this code for a more streamlined workflow (available here).

We had two 3D point sets, one representing the position of each microtubule in the fibre at bottom of our tomogram and the other set is the position at the top. After creating individual MT waves from these point sets to work with, these waves could be plotted in 3D to have a look at them.

TempMovieThis is done in IgorPro by using a Gizmo. Shown here is a set of MTs from one K-fibre, rotated to show how the waves look in 3D, note that the scaling in z is exaggerated compared with x and y.

We need to normalise the fibres by getting them to all point in the same direction. We found that trying to pull out the average trajectory for the fibre didn’t work so well if there were lots of wonky MTs. So we came up with the following method:

  • Calculate the total cartesian distance of all MT waves in an xy view, i.e. the sum of all projections of vectors on an xy plane.
  • Rotate the fibre.
  • Recalculate the total distance.
  • Repeat.

So we start off with this set of waves (Original). We rotate through 3D space and plot the total distance at each rotation to find the minimum, i.e. when most MTs are pointing straight at the viewer. This plot (Finding Minimum) is coloured so that hot colours are the smallest distance, it shows this calculation for a range of rotations in phi and theta. Once this minimum is found, the MT waves can be rotated by this value and the set is then normalised (you need to click on the pictures to see them properly).

Now we have all of the fibres that we imaged oriented in the same way, pointing to the zenith. This means we can look at angles relative to the z axis and derive statistics.

The next challenge was to make a measure of “wonkiness”. In other words, test how parallel the MTs are.

Violin plots of theta don’t really get across the wonkiness of the TACC3 overexpressed K-fibres (see figure above). To visualise this more clearly, each MT was turned into a vector starting at the origin and the point where the vector intersected with an xy plane set at an arbitrary distance in z (100 nm) was calculated. The scatter of these intersections demonstrates nicely how parallel the MTs are. If all MTs were perfectly parallel, they would all intersect at 0,0. In the control this is more-or-less true, with a bit of noise. In contrast, the TACC3-overexpressed group have much more scatter. What was nice is that the radial scatter was homogeneous, which showed that there was no bias in the acquisition of tomograms. The final touch was to generate a bivariate histogram which shows the scatter around 0,0 but it is normalised for the total number of points. Note that none of this possible without the first normalisation step.

Parallelism

The only thing that we didn’t have was a good term to describe what we were studying. “Wonkiness” didn’t sound very scientific and “parallelness” was also a bit strange. Parallelism is a word used in the humanities to describe analogies in art, film etc. However, it seemed the best term to describe the study of how parallel the MTs in a fibre are.

With a little help from my friends

The development of this method was borne out of discussions with Tom Honnor and Julia Brettschneider in the Statistics department in Warwick. The idea for the intersecting scatter plot came from Anne Straube in the office next door to me. They are all acknowledged in our paper for their input. A.G. at WaveMetrics helped me speed up my code by using MatrixOP and Euler’s rotation. His other suggestion of using PCA to do this would undoubtedly be faster, but I haven’t implemented this – yet. The bivariate histograms were made using JointHistogram() found here. JointHistogram now ships with Igor 7.

* as we describe in the paper

Several other strategies were explored to analyze deviations in trajectory versus the fiber axis. These were: examining the variance in trajectory angles, pairwise comparison of all MTs in the bundle, comparison to a reference MT that represented the fiber axis, using spherical rotation and rotating by an average value. These produced similar results, however, the one described here was the most robust and represents our best method for this kind of spatial statistical analysis.

The post title is taken from the Blondie LP “Parallel Lines”.

At a Crawl: Analysis of Cell Migration in IgorPro

In the lab we have been doing quite a bit of analysis of cell migration in 2D. Typically RPE1 cells migrating on fibronectin-coated glass. There are quite a few tools out there to track cell movements and to analyse their migration. Naturally, none of these did quite what we wanted and none fitted nicely into our analysis workflow. This meant writing something from scratch in IgorPro. You can access the code from my GitHub pages.

We’ve previously published a paper doing these kinds of analysis, but this code is all-new, faster and more efficient

The CellMigration repo contains an ipf that has three functions which will import tracks into Igor and analyse them. We use manual tracking in ImageJ/FIJI to obtain the co-ordinates for analysis, this is the only non-Igor part. I figured it was not worth porting this part too. Instructions are given in the repo and are hopefully self-explanatory. Here’s a screenshot of a typical experiment.

CMScreenShot

Acknowledgements: Colour palette is taken from the SRON stylesheet. The excellent igorutils (by yamad) gave me the idea for a structure and jtigor helped me with referencing StrConstants.

The post title is taken from the track “At a Crawl” by The Melvins from their Ozma LP

Creep Diets: Fewer papers published at JCB

JCBdietA couple of years ago, a colleague sent me this picture* to say “who put J Cell Biol on a diet?”. I joked that maybe they publish too many autophagy papers and didn’t think much more of it.

Recently, Ron Vale put up this very interesting piece on bioRxiv discussing what it takes to publish a paper in the field of cell biology these days. In the main, he questions whether this is now out of reach of many trainees in our labs. It raises some great points and I recommend reading it.

One (of many) interesting stats in the article is that J Cell Biol now publishes fewer papers than it used to. Which made me think back to the photo and wonder why there has been a decline. Elsewhere, Vale notes that a cell biology paper now contains >2 the amount of data than papers of yesteryear. I’ve also written before about the creeping increase in the number of authors per paper at J Cell Biol and (more so) at Cell. Publication in Science is something of an arms race and his point is really that the amount of data, the time taken, the effort/people involved has got to an untenable level.

The data in the preprint is a bit limited as he only looks at two snapshots in time – because he looks at two cohorts of students at UCSF. So I thought I’d look at the decrease in JCB papers over time – did it really fall off? by how much? when did it start?.

JCBNCBHist

Getting the data is straightforward. In fact, PubMed will give you a csv of frequency of papers for a given search term (it even shows you a snapshot in the main search window). I wanted a bit more control, so I exported the records for JCB and NCB. I filtered out interviews and commentary as best as I could and plotted out the records as two histograms using a bin width of 6 months. It’s pretty clear that J Cell Biol is indeed publishing fewer papers now than it used to. It looks like the trend started around 2002, possibly accelerating in the last 5 years (the photo agrees with this). The six month output at JCB in 2015 is similar to what it was in 1975!

In the comments section of the preprint, there is a bit of discussion of why this may be. Overall, there are more and more papers being published every year. There’s no reason to think that the number of cell biology papers has remained static or fallen. So if J Cell Biol have not taken a decision to limit the number of papers, why is there a decline? One commenter suggests  Nature Cell Biology has “taken” some of these papers. So I plotted those numbers out too. The number of papers at NCB is capped and has been constant since the launch of the journal. It does look like NCB could be responsible, but it’s a complex question. Personally, I think it’s unlikely. When NCB was launched this marked a period of expansion in the number of scientific journals and it’s likely that the increase in number of venues that a paper can go to (rather than the creation of NCB per se) has affected publication at JCB. One simple cause could be financial, i.e. the page number being limited by RUP. If this is true, why not move the journal online? There’s so many datasets and movies in papers these days that it barely makes sense to print JCB any more.

I love reading papers in JCB. They are sufficiently detailed so that you know what’s going on. They’re definitely on Cell Biology, not some tangential area of molecular biology. The Editors are active cell biologists and it has had a long history of publishing some truly landmark discoveries in our field. For these reasons, I’m sad that there are fewer JCB papers these days. If it’s an editorial decision to try to make the journal more exclusive, this is even more regrettable. I wonder if the Editors feel that they just don’t get enough high quality papers. If this is the case, then maybe the expectations for what a paper “should be” need to be brought back in line with reality. Which is one of the points that Ron Vale is making in his article.

* I cropped the picture to remove some identifying things on the bookshelf.

Update @ 07:07 17/7/15: Rebecca Alvinia from JCB had left a comment on Ron Vale’s piece on bioRxiv to say that JCB are not purposely limiting the number of papers. Fillip Port then asked why JCB does not take preprints. Rebecca has now replied saying that following a change of policy, J Cell Biol and the other RUP journals will take preprinted papers. This is great news!

Creep Diets is the title track from the second album by the oddly named Fudge Tunnel, released on Earache Records in 1993

Pull Together: our new paper on “The Mesh”

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

Title of the paper: The mesh is a network of microtubule connectors that stabilizes individual kinetochore fibers of the mitotic spindle

bundle1What’s it about? When a cell divides, the two new cells need to get the right number of chromosomes. If this process goes wrong, it is a disaster which may lead to disease e.g. cancer. The cell shares the chromosomes using a “mitotic spindle”. This is a tiny machine made of microtubules and other proteins. We have found that the microtubules are held together by something called “the mesh”. This is a weblike structure which connects the microtubules and gives them structural support.

Does this have anything to do with cancer? Some human cancer cells have high levels of  proteins called TACC3 and Aurora A kinase. We know that TACC3 is changed by Aurora A kinase. This changed form of TACC3 is part of the mesh. In our paper we mimic the cancer condition by increasing TACC3 levels. The mesh changes and the microtubules become wonky. This causes problems for dividing cells. It might be possible to target TACC3 using drugs to treat certain types of cancer, but this is a long way in the future.

Who did the work? Faye Nixon, a PhD student in the lab did most of the work. She used a method to look at mitotic spindles in 3D to study the mesh. My lab actually discovered the mesh by accident. A previous student, Dan Booth – back in 2011 – was looking at mitotic spindles to try and get 3D electron microscopy (tomography) working in the lab. Tomography works just like a CAT scan in a hospital, but on a much smaller scale. The mesh is found in the gaps between microtubules that are 25 nanometre wide (1 nanometre is 1 billionth of a metre), this is about 3,000 times smaller than a human hair, so it is very small! It was Dan who found the mesh and gave it the name. Other people in the lab did some really nice work which helped us to understand how the mesh works in dividing cells. Cristina Gutiérrez-Caballero did some experiments using a different type of microscope and Fiona Hood contributed some test tube experiments. Ian Prior at University of Liverpool, co-supervises Faye and helped with electron microscopy.

Have you discovered a new structure in cells? Yes and No. All cell biologists dream of finding a new structure in cells. It’s so unlikely though. Scientists have been looking at cells since the 17th Century and so the chances of seeing something that no-one has seen before are very small. In the 1970s, “inter-microtubule bridges” in the mitotic spindle were described using 2D electron microscopy. What we have done is to look at these structures in 3D for the first time and find that they are a network rather than individual connectors.

The work was funded by Cancer Research UK and North West Cancer Research Fund.

References

Nixon, F.M., Gutiérrez-Caballero, C., Hood, F.E., Booth, D.G., Prior, I.A. & Royle, S.J. (2015) The mesh is a network of microtubule connectors that stabilizes individual kinetochore fibers of the mitotic spindle eLife, doi: 10.7554/eLife.07635

This post is written in plain English to try to describe what is in the paper. I’m planning on writing a more technical post on some of the spatial statistics we developed as part of this paper.

The post title is from “Pull Together” a track from Shack’s H.M.S. Fable album.

Green is the Colour: mNeonGreen spectra

I was searching for the excitation and emission spectra for mNeonGreen. I was able to find an image, but no values for the spectra. Here is an approximation of the spectra (xlsx format, still haven’t figured out csv for wordpress).

I got these values using IgorThief.ipf a very handy tool that allows the extraction of XY coordinates from a bitmapped plot (below).

mNeonGreen is available from Allele Biotechnologies.

Here is a great site for comparing fluorescent protein properties.

Edit @ 06:54 4/7/14: A web-based data thief was suggested by @dozenoaks

The post title is taken from “Green is the Colour” from the Pink Floyd LP “More”

Zero Tolerance

We were asked to write a Preview piece for Developmental Cell. Two interesting papers which deal with the insertion of amphipathic helices in membranes to influence membrane curvature during endocytosis were scheduled for publication and the journal wanted some “front matter” to promote them.

Our Preview is paywalled – sorry about that – but I can briefly tell you why these two papers are worth a read.

The first paper – a collaboration between EMBL scientists led by Marko Kaksonen – deals with the yeast proteins Ent1 and Sla2. Ent1 has an ENTH domain and Sla2 has an ANTH domain. ENTH stands for Epsin N-terminal homology whereas ANTH means AP180 N-terminal homology. These two domains are known to bind membrane and in the case of ENTH to tubulate and vesiculate giant unilamellar vesicles (GUVs). Ent1 does this via an amphipathic helix “Helix 0” that inserts into the outer leaflet to bend the membrane. The new paper shows that Ent1 and Sla2 can bind together (regulated by PIP2) and that ANTH regulates ENTH so that it doesn’t make lots of vesicles, instead the two team up to make regular membrane tubules. The tubules are decorated with a regular “coat” of these adaptor proteins. This coat could prepattern the clathrin lattice. Also, because Sla2 links to actin, then actin can presumably pull on this lattice to help drive the formation of a new vesicle. The regular spacing might distribute the forces evenly over large expanses of membrane.

The second paper – from David Owen’s lab at CIMR in Cambridge – shows that CALM (a protein with an ANTH domain) actually has a secret Helix 0! They show that this forms on contact with lipid. CALM influences the size of clathrin-coated pits and vesicles, by influencing curvature. They propose a model where cargo size needs to be matched to vesicle size, simply due to the energetics of pit formation. The idea is that cells do this by regulating the ratio of AP2 to CALM.

You can read our preview and the papers by Skruzny et al and Miller et al in the latest issue of Dev Cell.

The post title and the title of our Preview is taken from “Zero Tolerance” by Death from their Symbolic LP. I didn’t want to be outdone by these Swedish scientists who have been using Bob Dylan song titles and lyrics in their papers for years.

Waiting to Happen: Publication lag times in Cell Biology Journals

My interest in publication lag times continues. Previous posts have looked at how long it takes my lab to publish our work, how often trainees publish and I also looked at very long lag times at Oncogene. I recently read a blog post on automated calculation of publication lag times for Bioinformatics journals. I thought it would be great to do this for Cell Biology journals too. Hopefully people will find it useful and can use this list when thinking about where to send their paper.

What is publication lag time?

If you are reading this, you probably know how science publication works. Feel free to skip. Otherwise, it goes something like this. After writing up your work for publication, you submit it to a journal. Assuming that this journal will eventually publish the paper (there is usually a period of submitting, getting rejected, resubmitting to a different journal etc.), they receive the paper on a certain date. They send it out to review, they collate the reviews and send back a decision, you (almost always) revise your paper further and then send it back. This can happen several times. At some point it gets accepted on a certain date. The journal then prepares the paper for publication in a scheduled issue on a specific date (they can also immediately post papers online without formatting). All of these steps add significant delays. It typically takes 9 months to publish a paper in the biomedical sciences. In 2015 this sounds very silly, when world-wide dissemination of information is as simple as a few clicks on a trackpad. The bigger problem is that we rely on papers as a currency to get jobs or funding and so these delays can be more than just a frustration, they can affect your ability to actually do more science.

The good news is that it is very straightforward to parse the received, accepted and published dates from PubMed. So we can easily calculate the publication lags for cell biology journals. If you don’t work in cell biology, just follow the instructions below to make your own list.

The bad news is that the deposition of the date information in PubMed depends on the journal. The extra bad news is that three of the major cell biology journals do not deposit their data: J Cell Biol, Mol Biol Cell and J Cell Sci. My original plan was to compare these three journals with Traffic, Nat Cell Biol and Dev Cell. Instead, I extended the list to include other journals which take non-cell biology papers (and deposit their data).

LagTimes1

A summary of the last ten years

Three sets of box plots here show the publication lags for eight journals that take cell biology papers. The journals are Cell, Cell Stem Cell, Current Biology, Developmental Cell, EMBO Journal, Nature Cell Biology, Nature Methods and Traffic (see note at the end about eLife). They are shown in alphabetical order. The box plots show the median and the IQR, whiskers show the 10th and 90th percentiles. The three plots show the time from Received-to-Published (Rec-Pub), and then a breakdown of this time into Received-to-Accepted (Rec-Acc) and Accepted-to-Published (Rec-Pub). The colours are just to make it easier to tell the journals apart and don’t have any significance.

You can see from these plots that the journals differ widely in the time it takes to publish a paper there. Current Biology is very fast, whereas Cell Stem Cell is relatively slow. The time it takes the journals to move them from acceptance to publication is pretty constant. Apart from Traffic where it takes an average of ~3 months to get something in to print. Remember that the paper is often online for this period so this is not necessarily a bad thing. I was not surprised that Current Biology was the fastest. At this journal, a presubmission inquiry is required and the referees are often lined up in advance. The staff are keen to publish rapidly, hence the name, Current Biology. I was amazed at Nature Cell Biology having such a short time from Received-to-Acceptance. The delay in Review-to-Acceptance comes from multiple rounds of revision and from doing extra experimental work. Anecdotally, it seems that the review at Nature Cell Biol should be just as lengthy as at Dev Cell or EMBO J. I wonder if the received date is accurate… it is possible to massage this date by first rejecting the paper, but allowing a resubmission. Then using the resubmission date as the received date [Edit: see below]. One way to legitimately limit this delay is to only allow a certain time for revisions and only allow one round of corrections. This is what happens at J Cell Biol, unfortunately we don’t have this data to see how effective this is.

lagtimes2

How has the lag time changed over the last ten years?

Have the slow journals always been slow? When did they become slow?  Again three plots are shown (side-by-side) depicting the Rec-Pub and then the Rec-Acc and Acc-Pub time. Now the intensity of red or blue shows the data for each year (2014 is the most intense colour). Again you can see that the dataset is not complete with missing date information for Traffic for many years, for example.

Interestingly, the publication lag has been pretty constant for some journals but not others. Cell Stem Cell and Dev Cell (but not the mothership – Cell) have seen increases as have Nature Cell Biology and Nature Methods. On the whole Acc-Pub times are stable, except for Nature Methods which is the only journal in the list to see an increase over the time period. This just leaves us with the task of drawing up a ranked list of the fastest to the slowest journal. Then we can see which of these journals is likely to delay dissemination of our work the most.

The Median times (in days) for 2013 are below. The journals are ranked in order of fastest to slowest for Received-to-Publication. I had to use 2013 because EMBO J is missing data for 2014.

Journal Rec-Pub Rec-Acc Acc-Pub
Curr Biol 159 99.5 56
Nat Methods 192 125 68
Cell 195 169 35
EMBO J 203 142 61
Nature Cell Biol 237 180 59
Traffic 244 161 86
Dev Cell 247 204 43
Cell Stem Cell 284 205 66

You’ll see that only Cell Stem Cell is over the threshold where it would be faster to conceive and give birth to a human being than to publish a paper there (on average). If the additional time wasted in submitting your manuscript to other journals is factored in, it is likely that most papers are at least on a par with the median gestation time.

If you are wondering why eLife is missing… as a new journal it didn’t have ten years worth of data to analyse. It did have a reasonably complete set for 2013 (but Rec-Acc only). The median time was 89 days, beating Current Biology by 10.5 days.

Methods

Please check out Neil Saunders’ post on how to do this. I did a PubMed search for (journal1[ta] OR journal2[ta] OR ...) AND journal article[pt] to make sure I didn’t get any reviews or letters etc. I limited the search from 2003 onwards to make sure I had 10 years of data for the journals that deposited it. I downloaded the file as xml and I used Ruby/Nokogiri to parse the file to csv. Installing Nokogiri is reasonably straightforward, but the documentation is pretty impenetrable. The ruby script I used was from Neil’s post (step 3) with a few lines added:


#!/usr/bin/ruby

require 'nokogiri'

f = File.open(ARGV.first)
doc = Nokogiri::XML(f)
f.close

doc.xpath("//PubmedArticle").each do |a|
r = ["", "", "", "", "", "", "", "", "", "", ""]
r[0] = a.xpath("MedlineCitation/Article/Journal/ISOAbbreviation").text
r[1] = a.xpath("MedlineCitation/PMID").text
r[2] = a.xpath("PubmedData/History/PubMedPubDate[@PubStatus='received']/Year").text
r[3] = a.xpath("PubmedData/History/PubMedPubDate[@PubStatus='received']/Month").text
r[4] = a.xpath("PubmedData/History/PubMedPubDate[@PubStatus='received']/Day").text
r[5] = a.xpath("PubmedData/History/PubMedPubDate[@PubStatus='accepted']/Year").text
r[6] = a.xpath("PubmedData/History/PubMedPubDate[@PubStatus='accepted']/Month").text
r[7] = a.xpath("PubmedData/History/PubMedPubDate[@PubStatus='accepted']/Day").text
r[8] = a.xpath("MedlineCitation/Article/Journal/JournalIssue/Pubdate/Year").text
r[9] = a.xpath("MedlineCitation/Article/Journal/JournalIssue/Pubdate/Month").text
r[10] = a.xpath("MedlineCitation/Article/Journal/JournalIssue/Pubdate/Day").text
puts r.join(",")
end

and then executed as described. The csv could then be imported into IgorPro and processed. Neil’s post describes a workflow for R, or you could use Excel or whatever at this point. As he notes, quite a few records are missing the date information and some of it is wrong, i.e. published before it was accepted. These need to be cleaned up. The other problem is that the month is sometimes an integer and sometimes a three-letter code. He uses lubridate in R to get around this, a loop-replace in Igor is easy to construct and even Excel can handle this with an IF statement, e.g. IF(LEN(G2)=3,MONTH(1&LEFT(G2,3)),G2) if the month is in G2. Good luck!

Edit 9/3/15 @ 17:17 several people (including Deborah Sweet and Bernd Pulverer from Cell Press/Cell Stem Cell and EMBO, respectively) have confirmed via Twitter that some journals use the date of resubmission as the submitted date. Cell Stem Cell and EMBO journals use the real dates. There is no way to tell whether a journal does this or not (from the deposited data). Stuart Cantrill from Nature Chemistry pointed out that his journal do declare that they sometimes reset the clock. I’m not sure about other journals. My own feeling is that – for full transparency – journals should 1) record the actual dates of submission, acceptance and publication, 2) deposit them in PubMed and add them to the paper. As pointed out by Jim Woodgett, scientists want the actual dates on their paper, partly because they are the real dates, but also to claim priority in certain cases. There is a conflict here, because journals might appear inefficient if they have long publication lag times. I think this should be an incentive for Editors to simplify revisions by giving clear guidance and limiting successive revision cycles. (This Edit was corrected 10/3/15 @ 11:04).

The post title is taken from “Waiting to Happen” by Super Furry Animals from the “Something 4 The Weekend” single.

Division Day: using PCA in cell biology

In this post I’ll describe a computational method for splitting two sides of a cell biological structure. It’s a simple method that relies on principal component analysis, otherwise known as PCA. Like all things mathematical there are some great resources on the web, if you want to understand this operation in more detail (for example, this great post by Lior Pachter). PCA can applied to many biological problems, you’ve probably seen it used to find patterns in large data sets, e.g. from proteomic studies. It can also be useful for analysing microscopy data. Since our analysis using this method is unlikely to make it into print any time soon, I thought I’d put it up on Quantixed.

Mitotic spindle in 3D. Kinetochores are green. Microtubules are red.
Mitotic spindle in 3D. Kinetochores are green. Microtubules are red.

During mitosis, a cell forms a mitotic spindle to share copied chromosomes equally to the two new cells. Our lab is working on how this process works and how it goes wrong in cancer. The chromosomes attach to the spindle via kinetochores and during prometaphase they are moved to the middle of the cell. Here, the chromosomes are organised into a disc-like structure called the metaphase plate. The disc is thin in the direction of the spindle axis, but much larger in width and height. To examine the spatial distribution of kinetochores on the plate we wanted a way to approximately separate kinetochores on one side if the plate from the other.

Kinetochores can be easily detected in 3D confocal images of mitotic cells by particle analysis. Kinetochores are easily stained and appear as bright spots that a computer can pick out (we use Imaris for this). The cartesian coordinates of each detected kinetochore were saved as csv and fed into IgorPro. A procedure could then be run which works in three steps. The code is shown at the bottom, it is wrapped in further code that deals with multiple datasets from many cells/experiments etc. The three steps are:

  1. PCA
  2. Point-to-plane
  3. Analysis on each subset

I’ll describe each step and how it works.

1. Principal component analysis

This is used to find the 3rd eigenvector, which can be used to define a plane passing through the centre of the plate. This plane is used for division.

PCAtestGIFNow, because the metaphase plate is a disc it has three dimensions, the third of which – “thickness” – is the smallest. PCA will find the principal component, i.e. the direction in which there is most variance. Orthogonal to that is the second biggest variance and orthogonal to that direction is the smallest. These directions are called eigenvectors and their magnitude is the eigenvalue. As there are three dimensions to the data we can get all three eigenvectors out and the 3rd eigenvector corresponds to thickness of the metaphase plate. Metaphase plates in cells grown on coverslips are orientated similarly, but the cells themselves are at random orientations. PCA takes no notice of this and can simply reveal the direction of the smallest dimension of a 3D structure. The movie shows this in action for a simulated data set. The black spots are arranged in a disk shape about the origin. They are rotated about x by 45° (the blue spots). We then run PCA and show the eigenvectors as unit vectors (red lines). The 3rd eigenvector is normal to the plane of division, i.e. the 1st and 2nd eigenvectors lie on the plane of division.

Also, the centroid needs to be defined. This is simply the cartesian coordinates for the average of each dimension. It is sometimes referred to as the mean vector. In the example this was the origin, in reality this will depend on the position and the overall height of the cell.

A much longer method to get the eigenvectors is to define the variance-covariance matrix (sometimes called the dispersion matrix) for each dimension, for all kinetochores and then do an eigenvector decomposition on the matrix. PCA is one command, whereas the matrix calculation would be an extra loop followed by an additional command.

2. Point-to-plane

The distance of each kinetochore to the plane that we defined is calculated. If it is a positive value then the kinetochore lies on the same side as the normal vector (defined above). If it is negative then it is on the other side. The maths behind how to do this are in section 10.3.1 of Geometric Tools for Computer Graphics by Schneider & Eberly (starting on p. 374). Google it, there is a PDF version on the web. I’ll save you some time, you just need one equation that defines a plane,

\(ax+by+cz+d=0\)

Where the unit normal vector is [a b c] and a point on the plane is [x y z]. We’ll use the coordinates of the centroid as a point on the plane to find d. Now that we know this, we can use a similar equation to find the distance of any point to the plane,

\(ax_{i}+by_{i}+cz_{i}+d\)

Results for each kinetochore are used to sort each side of the plane into separate waves for further calculation. In the movie below, the red dots and blue dots show the positions of the kinetochores on either side of the division plane. It’s a bit of an optical illusion, but the cube is turning in a right hand fashion.

KinetMovie

3. Analysis on each subset

Now that the data have been sorted, separate calculations can be carried out on each. In the example, we were interested in how the kinetochores were organised spatially and so we looked at the distance to nearest neighbour. This is done by finding the Euclidean distance from each kinetochore to every other kinetochore and putting the lowest value for each kinetochore into a new wave. However, this calculation can be anything you want. If there are further waves that specify other properties of the kinetochores, e.g. brightness, then these can be similarly processed here.

Other notes

The code in its present form (not very streamlined) was fast and could be run on every cell from a number of experiments, reading out positional data for 10,000 kinetochores in ~2 s. For QC it is possible to display the two separated coordinated sets to check that the division worked fine (see above). The power of this method is that it doesn’t rely on imaging spindle poles or anything else to work out the orientation of the metaphase plate. It works well for metaphase cells, but cells with any misaligned chromosomes ruin the calculation. It is possible to remove these and still fit the plane, but for our analysis we focused on cells at metaphase with a defined plate.

What else can it be used for?

Other structures in the cell can be segregated in a similar way. For example, the Golgi apparatus has a trans and a cis side, which could be similarly divided (although using the 2nd eigenvector as normal to the plane, rather than the 3rd).

Acknowledgements: I’d like to thank A.G. at WaveMetrics Inc. for encouraging me to try PCA rather than my dispersion matrix approach.

KinetNNcode

 

If you want to use it, the code is available here (it seems I can only upload PDF at wordpress.com). I used pygments for annotation.

The post title comes from “Division Day” a great single by Elliott Smith.

Sticky End

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

The work was mainly done by Cristina Gutiérrez Caballero, a post-doc in the lab. We had some help from Selena Burgess and Richard Bayliss at the University of Leicester, with whom we have an ongoing collaboration.

The paper in a nutshell

We found that TACC3 binds the plus-ends of microtubules via an interaction with ch-TOG. So TACC3 is a +TIP.

What is a +TIP?

EBTACCMitotic
EB3 (red) and TACC3 (green) at the tips of microtubules in mitotic spindle

This is a term used to describe proteins that bind to the plus-ends of microtubules. Microtubules are a major component of the cell’s cytoskeleton. They are polymers of alpha/beta-tubulin that grow and shrink, a feature known as dynamic instability. A microtubule has polarity, the fast growing end is known as the plus-end, and the slower growing end is referred to as the minus-end. There are many proteins that bind to the plus-end and these are termed +TIPs.

OK, so what are TACC3 and ch-TOG?

They are two proteins found on the mitotic spindle. TACC3 is an acronym for transforming acidic coiled-coil protein 3, and ch-TOG stands for colonic hepatic tumour overexpressed gene. As you can tell from the names they were discovered due to their altered expression in certain human cancers. TACC3 is a well-known substrate for Aurora A kinase, which is an enzyme that is often amplified in cancer. The ch-TOG protein is thought to be a microtubule polymerase, i.e. an enzyme that helps microtubules grow. In the paper, we describe how TACC3 and ch-TOG stick together at the microtubule end. TACC3 and ch-TOG are at the very end of the microtubule, they move ahead of other +TIPs like “end-binding proteins”, e.g. EB3.

What is the function of TACC3 as a +TIP?

We think that TACC3 is piggybacking on ch-TOG while it is acting as a polymerase, but any biological function or consequence of this piggybacking was difficult to detect. We couldn’t see any clear effect on microtubule dynamics when we removed or overexpressed TACC3. We did find that loss of TACC3 affects how cells migrate, but this is not likely to be due to a change in microtubule dynamics.

I thought TACC3 and ch-TOG were centrosomal proteins…

In the paper we look again at this and find that there are different pools of TACC3, ch-TOG and clathrin (alone and in combination) and describe how they reside in different places in the cell. Although ch-TOG is clearly at centrosomes, we don’t find TACC3 at centrosomes, although it is on microtubules that cluster near the centrosomes at the spindle pole. TACC3 is often described as a centrosomal protein in lots of other papers, but this is quite misleading.

What else?

NeonCellWe were on the cover – whatever that means in the digital age! We imaged a cell expressing tagged EB3 proteins, EB3 is another +TIP. We coloured consecutive frames different colours and the result looked pretty striking. Biology Open picked it as their cover, which we were really pleased about. Our paper is AOP at the moment and so hopefully they won’t change their mind by the time it appears in the next issue.

Preprinting

This is the second paper that we have deposited as a preprint at bioRxiv (not counting a third paper that we preprinted after it was accepted). I was keen to preprint this particular paper because we became aware that two other groups had similar results following a meeting last summer. Strangely, a week or so after preprinting and submitting to a journal, a paper from a completely different group appeared with a very similar finding! We’d been “scooped”. They had found that the Xenopus homologue of TACC3 was a +TIP in retinal neuronal cultures. The other group had clearly beaten us to it, having submitted their paper some time before our preprint. The reviewers of our paper complained that our data was no longer novel and our paper was rejected. This was annoying because there were lots of novel findings in our paper that weren’t in theirs (and vice versa). The reviewers did make some other constructive suggestions that we incorporated into the manuscript. We updated our preprint and then submitted to Biology Open. One advantage of the preprinting process is that the changes we made can be seen by all. Biology Open were great and took a decision based on our comments from the other journal and the changes we had made in response to them. Their decision to provisionally accept the paper was made in four days. Like our last experience publishing in Biology Open, it was very positive.

References

Gutiérrez-Caballero, C., Burgess, S.G., Bayliss, R. & Royle, S.J. (2015) TACC3-ch-TOG track the growing tips of microtubules independently of clathrin and Aurora-A phosphorylation. Biol. Open doi:10.1242/​bio.201410843.

Nwagbara, B. U., Faris, A. E., Bearce, E. A., Erdogan, B., Ebbert, P. T., Evans, M. F., Rutherford, E. L., Enzenbacher, T. B. and Lowery, L. A. (2014) TACC3 is a microtubule plus end-tracking protein that promotes axon elongation and also regulates microtubule plus end dynamics in multiple embryonic cell types. Mol. Biol. Cell 25, 3350-3362.

The post title is taken from the last track on The Orb’s U.F.Orb album.