Tag Archives: reading

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.

Joining A Fanclub

When I started this blog, my plan was to write about interesting papers or at least blog about the ones from my lab. This post is a bit of both.

I was recently asked to write a “Journal Club” piece for Nature Reviews Molecular Cell Biology, which is now available online. It’s paywalled unfortunately. It’s also very short, due to the format. For these reasons, I thought I’d expand a bit on the papers I highlighted.

I picked two papers from Dick McIntosh’s group, published in J Cell Biol in the early 1990s as my subject. The two papers are McDonald et al. 1992 and Mastronarde et al. 1993.

Almost everything we know about the microanatomy of mitotic spindles comes from classical electron microscopy (EM) studies. How many microtubules are there in a kinetochore fibre? How do they contact the kinetochore? These questions have been addressed by EM. McIntosh’s group in Boulder, Colorado have published so many classic papers in this area, but there are many more coming from Conly Rieder, Alexey Khodjakov, Bruce McEwen and many others. Even with the advances in light microscopy which have improved spatial resolution (resulting in a Nobel Prize last year), EM is the only way to see individual microtubules within a complex subcellular structure like the mitotic spindle. The title of the piece, Super-duper resolution imaging of mitotic microtubules, is a bit of a dig at the fact that EM still exceeds the resolution available from super-resolution light microscopy. It’s not the first time that this gag has been used, but I thought it suited the piece quite well.

There are several reasons to highlight these papers over other electron microscopy studies of mitotic spindles.

It was the first time that 3D models of microtubules in mitotic spindles were built from electron micrographs of serial sections. This allowed spatial statistical methods to be applied to understand microtubule spacing and clustering. The software that was developed by David Mastronarde to do this was later packaged into IMOD. This is a great software suite that is actively maintained, free to download and is essential for doing electron microscopy. Taking on the same analysis today would be a lot faster, but still somewhat limited by cutting sections and imaging to get the resolution required to trace individual microtubules.

kfibreThe paper actually showed that some of the microtubules in kinetochore fibres travel all the way from the pole to the kinetochore, and that interpolar microtubules invade the bundle occasionally. This was an open question at the time and was really only definitively answered thanks to the ability to digitise and trace individual microtubules using computational methods.

The final thing I like about these papers is that it’s possible to reproduce the analysis. The methods sections are wonderfully detailed and of course the software is available to do similar work. This is in contrast to most papers nowadays, where it is difficult to understand how the work has been done in the first place, let alone to try and reproduce it in your own lab.

David Mastronarde and Dick McIntosh kindly commented on the piece that I wrote and also Faye Nixon in my lab made some helpful suggestions. There’s no acknowledgement section, so I’ll thank them all here.


McDonald, K. L., O’Toole, E. T., Mastronarde, D. N. & McIntosh, J. R. (1992) Kinetochore microtubules in PTK cells. J. Cell Biol. 118, 369—383

Mastronarde, D. N., McDonald, K. L., Ding, R. & McIntosh, J. R. (1993) Interpolar spindle microtubules in PTK cells. J. Cell Biol. 123, 1475—1489

Royle, S.J. (2015) Super-duper resolution imaging of mitotic microtubules. Nat. Rev. Mol. Cell. Biol. doi:10.1038/nrm3937 Published online 05 January 2015

The post title is taken from “Joining a Fanclub” by Jellyfish from their classic second and final LP “Spilt Milk”.

A Day In The Life II

I have been doing paper of the day (#potd) again in 2014. See my previous post about this.

My “rules” for paper of the day are:

  1. Read one paper each working day.
  2. If I am away, or reviewing a paper for a journal or colleague, then I get a pass.
  3. Read it sufficiently to be able to explain it to somebody else, i.e. don’t just scan the abstract and look at the figures. Really read it and understand it. Scan and skim as many other papers as you normally would!
  4. Only papers reporting primary research count. No reviews/opinion pieces etc.
  5. If it was really good or worth telling people about – tweet about it.
  6. Make a simple database in Excel – this helps you keep track, make notes about the paper (to see if you meet #3) and allows you to find the paper easily in the future (this last point turned out to be very useful).

This year has been difficult, especially sticking to #3. My stats for 2014 are:

  • 73% success rate. Down from 85% in 2013
  • Stats errors in 36% of papers I read!
  • 86% of papers were from 2014

Following last year, I wasn’t so surprised by the journals that the papers appeared in:

  1. eLife
  2. J Cell Biol
  3. Mol Biol Cell
  4. Dev Cell
  5. Nature Methods
  6. J Cell Sci
  7. J Neurosci
  8. Nature Cell Biol
  9. Traffic
  10. Curr Biol
  11. Nature
  12. Nature Comm
  13. Science

According to my database I only read one paper in Cell this year. I certainly have lots of them in “Saved for later” in Feedly (which is a black hole from which papers rarely emerge to be read). It’s possible that the reason Cell, Nature and Science are low on the list is that I might quickly glance at papers in those journals but not actually read them for #potd. Last year eLife was at number 9 and this year it is at number 1. This journal is definitely publishing a lot of exciting cell biology and also the lens format is very nice for reading.

I think I’ll try to continue this in 2015. The main thing it has made me realise is how few papers I read (I mean really read). I wonder if students and postdocs are actually the main consumers of the literature. If this is correct, do PIs rely on “subsistence reading”, i.e. when they write their own papers and check the immediate literature? Is their deep reading done only during peer reviewing other people’s work? Or do PIs rely on a constant infusion of the latest science at seminars and at meetings?

Sure To Fall

What does the life cycle of a scientific paper look like?

It stands to reason that after a paper is published, people download and read the paper and then if it generates sufficient interest, it will begin to be cited. At some point these citations will peak and the interest will die away as the work gets superseded or the field moves on. So each paper has a useful lifespan. When does the average paper start to accumulate citations, when do they peak and when do they die away?

Citation behaviours are known to be very field-specific. So to narrow things down, I focussed on cell biology and in one area “clathrin-mediated endocytosis” in particular. It’s an area that I’ve published in – of course this stuff is driven by self-interest. I downloaded data for 1000 papers from Web of Science that had accumulated the most citations. Reviews were excluded, as I assume their citation patterns are different from primary literature. The idea was just to take a large sample of papers on a topic. The data are pretty good, but there are some errors (see below).

Number-crunching (feel free to skip this bit): I imported the data into IgorPro making a 1D wave for each record (paper). I deleted the last point corresponding to cites in 2014 (the year is not complete). I aligned all records so that year of publication was 0. Next, the citations were normalised to the maximum number achieved in the peak year. This allows us to look at the lifecycle in a sensible way. Next I took out records to papers less than 6 years old as I reasoned these would have not have completed their lifecycle and could contaminate the analysis (it turned out to make little difference). The lifecycles were plotted and averaged. I also wrote a quick function to pull out the peak year for citations post hoc.

So what did it show?

Citations to a paper go up and go down, as expected (top left). When cumulative citations are plotted most of the articles have an initial burst and then level off. The exception are ~8 articles that continue to rise linearly (top right). On average a paper generates its peak citations three years after publication (box plot). The fall after this peak period is pretty linear and it’s apparently all over somewhere >15 years after publication (bottom left). To look at the decline in more detail I aligned the papers so that year 0 was the year of peak citations. The average now loses almost 40% of those peak citations in the following year and then declines steadily (bottom right).

Edit: The dreaded Impact Factor calculation takes the citations to articles published in the preceding 2 years and divides by the number of citable items in that period. This means that each paper only contributes to the Impact Factor in years 1 and 2. This is before the average paper reaches its peak citation period. Thanks to David Stephens (@david_s_bristol) for pointing this out. The alternative 5 year Impact Factor gets around this limitation.

Perhaps lifecycle is the wrong term: papers in this dataset don’t actually ‘die’, i.e. go to 0 citations. There is always a chance that a paper will pick up the odd citation. Papers published 15 years ago are still clocking 20% of their peak citations. Looking at papers cited at lower rates would be informative here.

Two other weaknesses that affect precision is that 1) a year is a long time and 2) publication is subject to long lag times. The analysis would be improved by categorising the records based on the month-year when the paper was published and the month-year when each citation comes in. Papers published in January in one year probably have a different peak than those published in December of the same year, but this is lost when looking at year alone. Secondly, due to publication lag, it is impossible to know when the peak period of influence for a paper truly is.
MisCytesProblems in the dataset. Some reviews remained despite being supposedly excluded, i.e. they are not properly tagged in the database. Also, some records have citations from years before the article was published! The numbers of citations are small enough to not worry for this analysis, but it makes you wonder about how accurate the whole dataset is. I’ve written before about how complete citation data may or may not be. These sorts of things are a concern for all of us who are judged by these things for hiring and promotion decisions.

The post title is taken from ‘Sure To Fall’ by The Beatles, recorded during The Decca Sessions.

All This And More

I was looking at the latest issue of Cell and marvelling at how many authors there are on each paper. It’s no secret that the raison d’être of Cell is to publish the “last word” on a topic (although whether it fulfils that objective is debatable). Definitive work needs to be comprehensive. So it follows that this means lots of techniques and ergo lots of authors. This means it is even more impressive when a dual author paper turns up in the table of contents for Cell. Anyway, I got to thinking: has it always been the case that Cell papers have lots of authors and if not, when did that change?

I downloaded the data for all articles published by Cell (and for comparison, J Cell Biol) from Scopus. The records required a bit of cleaning. For example, SnapShot papers needed to be removed and also the odd obituary etc. had been misclassified as an article. These could be quickly removed. I then went back through and filtered out ‘articles’ that were less than three pages as I think it is not possible for a paper to be two pages or fewer in length. The data could be loaded into IgorPro and boxplots generated per year to show how author number varied over time. Reviews that are misclassified as Articles will still be in the dataset, but I figured these would be minimal.

Authors1First off: Yes, there are more authors on average for a Cell paper versus a J Cell Biol paper. What is interesting is that both journals had similar numbers of authors when Cell was born (1974) and they crept up together until the early 2000s, when the number of Cell authors kept increasing, or JCell Biol flattened off, whichever way you look at it.

I think the overall trend to more authors is because understanding biology has increasingly required multiple approaches and the bar for evidence seems to be getting higher over time. The initial creep to more authors (1974-2000) might be due to a cultural change where people (technicians/students/women) began to get proper credit for their contributions. However, this doesn’t explain the divergence between J Cell Biol and Cell in recent years. One possibility is Cell takes more non-cell biology papers and that these papers necessarily have more authors. For example, the polar bear genome was published in Cell (29 authors), and this sort of paper would not appear in J Cell Biol. Another possibility is that J Cell Biol has a shorter and stricter revision procedure, which means that multiple rounds of revision, collecting new techniques and new authors is more limited than it is at Cell. Any other ideas?

AuthorI also quickly checked whether more authors means more citations, but found no evidence for such a relationship. For papers published in the years 2000-2004, the median citation number for papers with 1-10 authors was pretty constant for J Cell Biol. For Cell, these data mere more noisy. Three-author papers tended to be cited a bit more than those with two authors, but then four author papers were also lower.

The number of authors on papers from our lab ranges from 2-9 and median is 3.5. This would put an average paper from our lab in the bottom quartile for JCB and in the lower 10% for Cell in 2013. Ironically, our 9 author paper (an outlier) was published in J Cell Biol. Maybe we need to get more authors on our papers before we can start troubling Cell with our manuscripts…

The Post title is taken from ‘All This and More’ by The Wedding Present from their LP George Best.

Falling and Landing

A great quote from a classic paper by J.B.S. Haldane “On Being The Right Size” (1926).

You can drop a mouse down a thousand-yard mine shaft; and, on arriving at the bottom, it gets a slight shock and walks away, provided that the ground is fairly soft. A rat is killed, a man is broken, a horse splashes.

The paper is available here.

The post title is taken from ‘Falling and Landing’ by The Delgados from their LP ‘Domestiques’.

My Blank Pages

Books about the MRC Laboratory of Molecular Biology are plentiful. If you haven’t read any, the best place to start are the books written by some of the Nobelists themselves: “I Wish I’d Made You Angry Earlier” by Perutz, “My Life in Science” by Brenner. Also, “Sequences, Sequence, Sequences” by Sanger, “What Mad Pursuit” by Crick and even Watson’s “The Double Helix” cover ‘how it was done’ and ‘what the place is like’. After that are the biographies of the Nobelists and their associates. Then comes the next layer, the comprehensive but rather dry “Designs for Life: Molecular Biology after World War II” by de Chadarevian and hell, even “The Eighth Day of Creation” by Judson is substantially about the LMB, since so many major discoveries in Molecular Biology happened there.

If your appetite is not sated after wading through all of those, then there are the books for the insiders.

John Finch wrote a book “A Nobel Fellow on Every Floor” which was enjoyable, if rather selective on who and what was included. The latest book from the LMB Press is a collection of essays entitled “Memories and Consequences: Visiting Scientists at the MRC Laboratory of Molecular Biology, Cambridge”. It was edited by Hugh Huxley and was made available last summer (around the time of his death).
You can get it here



The premise of Memories and Consequences is that there were a large number of postdoctoral fellows, mainly from the USA, who spent time at the LMB (in the 60s, mainly) and then went away and had hugely successful scientific careers. At one point in the book, Tom Steitz writes that, of his friends during this period, 40% are now NAS members! The essays cover the time of these visitors in England and how it shaped their subsequent careers.

This is definitely a book to dip in and out of. The experiences are actually pretty repetitive: yes, we drive on the other side of the road; Cambridge is a very stuffy place and Max Perutz liked to be called Max. This repetition is amplified if the chapters are read one-after-the-other. Overall however, the essays are nice reminiscences of a booming time in Molecular Biology and many capture the magic of working at the LMB during this period. Brenner and Crick come to life and even Sir Lawrence Bragg looms large in many chapters filling the authors with awe.

When I first downloaded the book, I read the chapters by those whose work I am most familiar. I didn’t even know that Dick McIntosh had spent not one but two sabbaticals at the LMB. Tom Pollard, Harvey Lodish etc. followed. I then read the other chapters when I had more time.

The best chapters were those by Harry Noller and by Peter Moore who gave the right amount (for my taste) of personal insight to their stay at the LMB. I would recommend that the reader skips the chapter by William Dove and Alexandra Shevlovsky, who tried to be a bit clever and didn’t quite pull it off. Sid Altman’s chapter has previously been published and I actually witnessed him read this out (more-or-less) verbatim at the DNA50+1 celebrations – which was far more enjoyable than it sounds.

In short, I enjoyed the book and it’s worth reading some of the chapters if you have a leaning towards the history of science, but there are plenty of other books (listed above) where you should start if you want to find out what life is like inside the Nobel Prize Factory.

I’ll leave you with three quotes that I enjoyed immensely:

“I remember seeing copies of the journal Cell, where we all yearned to publish (though, I noticed, not the really great scientists, like John Sulston or Sydney Brenner). I would shudder and turn away; Cell was for other scientists, not for me.”
Cynthia Kenyon

“Like many others who worked at the LMB in that era, I still think of its modus operandi as exemplifying the blueprint that all scientific research establishments should aspire to emulate. Pack the very best scientists you can find into a building, so densely that they cannot avoid talking to each other, and encourage them to interact in every other way you can. A canteen or dining room might be a good idea. (The facility itself need not be luxurious, and indeed, it is probably better if it is not.) Give those scientists ample staff support, and all the money they need to get on with the job. Stir well, and then be patient because good science takes time. My subsequent career has taught me that this recipe is much harder to execute than it is to describe. I still wonder how the MRC managed to do it so well for so long.”
Peter Moore

“I learned that protein chemistry didn’t need me, that King’s College High Table was for tougher folk than I, and that Sydney talked but Francis conversed.”
Frank Stahl

A comprehensive guide to LMB books is available here

Don’t worry, book reviews will be a very infrequent feature as I hardly have any time to read books these days!

The post title is from My Blank Pages – Velvet Crush from their LP Teenage Symphonies to God. Presumably a play on the Dylan/Byrds song My Back Pages.

A Day In The Life

#paperoftheday #potd

A common complaint from other PIs is that they “don’t read enough any more”. I feel like this too and a solution was proposed by a friend of a friend*: try to read one paper per day.

This seemed like a good idea and I started to do this in 2013. The rules, obviously, can be set by you. Here’s my version:

  1. Read one paper each working day.
  2. If I am away, or reviewing a paper for a journal or colleague, then I get a pass.
  3. Read it sufficiently to be able to explain it to somebody else, i.e. don’t just scan the abstract and look at the figures. Really read it and understand it. Scan and skim as many other papers as you normally would!
  4. Only papers reporting primary research count towards #paperoftheday.
  5. If it was really good or worth telling people about – tweet about it.
  6. Make a simple database in Excel or Papers – this helps you keep track, make notes about the paper (to see if you meet #3) and allows you to find the paper easily in the future (this last point turned out to be very useful).

I started this in 2013 (for one full year) and am trying to continue in 2014. I feel that this is succeeding in making me read more than I would have otherwise done.

My stats for 2013 were:

  • 85% success rate. Filling that last 15% will be tough.
  • Stats errors in 48% of papers! Most common error was incorrect use of Student’s  t-test.
  • 68% of papers were from 2013 and 22% were from 2009-2012.

The big surprise was which journals I read most:

  1. J Cell Biol 13
  2. PLOS One 12
  3. Nat Cell Biol 10
  4. PNAS 10
  5. Curr Biol 9
  6. Mol Biol Cell 8
  7. Nature 8
  8. Dev Cell 7
  9. eLife 7
  10. Nature Methods 7
  11. Cell 6
  12. Neuron 6
  13. Traffic 6
  14. J Cell Sci 4
  15. Science 4

I thought that Cell would be much higher and PNAS would be much lower. Since where we publish is dictated by who is likely to see and read the paper, this list was thought-provoking.

*I think this was a colleague of @david_s_bristol who suggested it, sometime in 2012.

The post title is of course from A Day in The Life – The Beatles from the LP Sgt Pepper’s Lonely Hearts Club Band. For the first line…