Not What You Want: our new paper on a side effect of GFP nanobodies

We have a new preprint out – it is a cautionary tale about using GFP nanobodies in cells. This short post gives a bit of background to the work. Please read the paper if you are interested in using GFP nanobodies in cells, you can find it here.

Paper in a nutshell: Caution is needed when using GFP nanobodies because they can inhibit their target protein in cells.

People who did the work: Cansu Küey did most of the work for the paper. She discovered the inhibition side effect of the dongles. Gabrielle Larocque contributed a figure where she compared dongle-knocksideways with regular knocksideways. The project was initiated by Nick Clarke who made our first set of dongles and tested which fluorescent proteins the nanobody binds in cells. Lab people and their profiles can be found here.

Background: Many other labs have shown that nanobodies can be functionalised so that you can stick new protein domains onto GFP-tagged proteins to do new things. This is useful because it means you can “retrofit” an existing GFP knock-in cell line or organism to do new things like knocksideways without making new lines. In fact there was a recent preprint which described a suite of functionalised nanobodies that can confer all kinds of functions to GFP.

Like many other labs we were working on this method. We thought functionalised GFP nanobodies resembled “dongles” – those adaptors that Apple makes so much money from – that convert one port to another.

Dongles, dongles, dongles… (photo by Rex Hammock, licensed for reuse https://www.flickr.com/photos/rexblog/5575298582)

A while back we made several different dongles. We were most interested in a GFP nanobodies with an additional FKBP domain that would allow us to do knocksideways (or FerriTagging) in GFP knock-in cells. For those that don’t know, knocksideways is not a knockout or a knockdown, but a way of putting a protein somewhere else in the cell to inactivate it. The most common place to send a protein is to the mitochondria.

Knocksideways works by joining FKBP and FRB (on the mitochondria) using rapamycin. Normally FKBP is fused to the protein of interest (top). If we just have a GFP tag, we can’t do knocksideways (middle). If we add a dongle (bottom) we can attach FKBP domains to allow knocksideways to happen.

We found that dongle-knocksideways works really well and we were very excited about this method. Here we are removing GFP-clathrin from the mitotic spindle in seconds using dongle knocksideways.

GFP-clathrin, shown here in blue is sent to the mitochondria (yellow) using rapamycin. This effect is only possible because of the dongle which adds FKBP to GFP via a GFP nanobody.

Since there are no specific inhibitors of endocytosis, we thought dongle knocksideways would be cool to try in cells with dynamin-2 tagged with GFP at both alleles. There is a line from David Drubin’s lab which is widely used. This would mean we could put the dongle plasmids on Addgene and everyone could inhibit endocytosis on-demand!

Initial results were encouraging. We could put dynamin onto mitochondria alright.

Dynamin-2-GFP undergoing dongle-knocksideways. The Mitotrap is shown in red and dynamin is green.

But we hit a problem. It turns out that dongle binding to dynamin inhibits endocytosis. So we have unintended inhibition of the target protein. This is a big problem because the power of knocksideways comes from being able to observe normal function and then rapidly switch it off. So if there is inhibition before knocksideways, the method is useless.

Now, this problem could be specific to dynamin or it might be a general problem with all targets of dongles. Either way, we’ve switched from this method and wrote this manuscript to alert others to the side effects of dongles. We discuss possible ways forward for this method and also point out some applications of the nanobody technology that are unaffected by our observations.

The post title comes from “Not What You Want” by Sleater-Kinney from their wonderful Dig Me Out record.

All That Noise: The vesicle packing problem

This week Erick Martins Ratamero and I put up a preprint on vesicle packing. This post is a bit of backstory but please take a look at the paper, it’s very short and simple.

The paper started when I wanted to know how many receptors could fit in a clathrin-coated vesicle. Sounds like a simple problem – but it’s actually more complicated.

Of course, this problem is not as simple as calculating the surface area of the vesicle, the cross-sectional area of the receptor and dividing one by the other. The images above show the problem. The receptors would be the dimples on the golf ball… they can’t overlap… how many can you fit on the ball?

It turns out that a PhD student working in Groningen in 1930 posed a similar problem (known as the Tammes Problem) in his thesis. His concern was the even pattern of pores on a pollen grain, but the root of the problem is the Thomson Problem. This is the minimisation of energy that occurs when charged particles are on a spherical surface. The particles must distribute themselves as far away as possible from all other particles.

There are very few analytical solutions to the Tammes Problem (presently 3-14 and 24 are solved). Anyhow, our vesicle packing problem is the other way around. We want to know, for a vesicle of a certain size, and cargo of a certain size, how many can we fit in.

Fortunately stochastic Tammes solvers are available like this one, that we could adapt. It turns out that the numbers of receptors that could be packed is enormous: for a typical clathrin-coated vesicle almost 800 G Protein-Coupled Receptors could fit on the surface. Note, that this doesn’t take into account steric hinderance and assumes that the vesicle carries nothing else. Full details are in the paper.

Why does this matter? Many labs are developing ways to count molecules in cellular structures by light or electron microscopy. We wanted to have a way to check that our results were physically possible. For example, if we measure 1000 GPCRs in a clathrin-coated vesicle, we know something has gone wrong.

What else? This paper ticked a few things on my publishing bucket list: a paper that is solely theoretical, a coffee-break idea paper and one that is on a “fun” subject. Erick has previous form with theoretical/fun papers, previously publishing on modelling peloton dynamics in procycling.

We figured the paper was more substantial than a blog post yet too minimal to send to a journal. So unless a journal wants to publish it (and gets in touch with us), this will be my first preprint where bioRxiv is the final destination.

We got a sense that people might be interested in an answer to the vesicle packing problem because whenever we asked people for an estimate, we got hugely different answers! The paper has been well-received so far. We’ve had quite a few comments on Twitter and we’re glad that we wrote up the work.

The post title comes from the “All That Noise” LP by The Darkside. I picked this not because of the title, but because of the cover.

All That Noise cover shows a packing problem on a sphere

Small Talk: How big is your lab?

I really dislike being asked “how big is your lab?”. The question usually arises at scientific meetings when you are chatting to someone during a break. Small talk can lead to some banal questions being asked, and that’s fine, but when this question is asked seriously, the person asking really just wants to compare themselves to you in some way. This is one reason why I dislike being asked “how big is your lab?”.

The other reason I don’t like the question is that it can be difficult to answer. I don’t mean that I have so many people in my group that I can’t possibly count them. No, I mean that it can be difficult to give an accurate answer. There’s perhaps a student in the group who is currently writing up, or possibly they’ve handed in their thesis and they are awaiting a viva – do they count towards the tally? They are in your lab but they’re not in your lab. Perhaps you jointly supervise someone, or maybe there is someone who is away working in another lab somewhere. I’m guilty of overthinking this or at least fretting about giving an incorrect answer. Whatever the circumstance, I think that the size of most research groups is not very stable over time, so I dislike the question because it’s difficult answer accurately.

I looked at group size recently because the lab had surpassed the milestone of having 50 all-time members and I wanted to see how the group size had varied over time.

Timeline of people in the lab

The first timeline shows the arrival and departure of lab members over time. The role of each person is colour coded as indicated. Note that some people start in one role and get upgraded. PG to PhD, PhD to Post-doc (PDRA). So what it the group size over time?

Group size over time

It turns out that we peaked this year with a team size of 12. The smallest size (besides the period where I started out, when I was on my own!) was at the end of 2012 when I prepared to move the lab to a different university. What has the make up of the lab been during this time.

Constitution of the lab over time

In this last plot the fraction of the team that are PhD, post-doc etc. is shown over time. This plot is interesting because I can see that it was two years before a PhD student joined the group and also how the lab has become post-doc-heavy in the last 18 months.

So what is the answer to “how big is your lab?”. Well, take your pick. Right now it is 11 with someone just joined this week. Over the last year it has averaged at just over 10. Over the last five years it has been 8 to 9. It’s still not an easy question to answer even if you can see all the data.

Methods: I have been trying to use R for these type of posts, so that sharing the code is more useful, but I drew a blank with this one. I found several tools to plot the first timeline (timevis and vistime). To do the integration and breakdown plots, I struggled… I knew exactly how to make those plots in Igor, so that’s what I did. All that was required was a list of the people, their role, their start-end dates, and a few lines of code. I keep a record of this as previously mentioned.

The post title comes from “Small Talk” a track by American Culture on a Split 7″ with Boyracer on Emotional Response Records.

Super Automatic: computer-based tools for research

Since I have now written several posts on this. I thought I would summarise the computer-based tools that we are using in the lab to automate our work and organise ourselves.

Electronic lab notebook – there are previous posts from me on picking an ELN platform and how to set up a WordPress ELN as well as Dave Mason’s guide and CAMDU’s guide to help you to get this set up.

Slack – it took us ages to set up our lab slack. I wanted to try it out a few years ago but some people in the lab were reluctant (see below for some notes on getting people on board with new tech). I was also sceptical since we are a wet lab and I wondered whether slack would work as well for us as it does for bioinformaticians, for example. We just went for it one day and have not regretted it as it has improved lab communication in so many ways. There is a good guide available on the site to set up something that works for a lab.

Trello – organising projects and assigning to-do items (see this post). Since we switched our communication to Slack, we use Trello a lot less. It is still very good for checking progress on defined tasks, for brainstorming during a meeting, and to make sure stuff doesn’t get forgotten about. Besides our lab boards, I own other boards for work outside of my lab and these also work well, in some cases better than our lab boards.

Databases – infrastructure in the lab is centred on databases for key reagents and these can be cross-referenced in electronic lab notebooks. Keeping them updated is essential. We use FileMaker Pro to maintain them. Setup took some time, but very much worth it.

OMERO – The lab is lucky to have excellent computing support from CAMDU who set up our OMERO database and server. Images from microscopes are piped direct to the database on a per user basis. I’m a big fan.

Dropbox – an account is essential in academia. I have a shared folder with each person in the lab and a folder that all members share. File exchange is best done via our lab server, but Dropbox is still incredibly useful.

Overleaf – writing a paper collaboratively in LaTeX with Overleaf is a joy (see this post). There are Google-based tools for doing this but I am not keen on using them. Dropbox now has a collaborative writing function that my colleagues are using and enjoying.

Zotero – when writing collaboratively in Overleaf, a shared reference management system is essential. Zotero is a healthy alternative to Mendeley and it is now supported in Overleaf v2. I don’t store my PDFs in Zotero and I found no solution for an iTunes-for-PDF-files.

Calendars – while I’m not a fan of Google-based tools, we use the calendar functions for our lab. We inherited this from the Centre where we work, where these calendars are used for booking equipment. We have lab calendars for microscopes, equipment, workstations and for general lab stuff so we know when people are away. I set up a dummy account that belongs to all the lab calendars and this is linked to our lab Slack so that we get a digest of the days bookings every morning, and notifications if new events are added. The University has an outlook-based calendar system, the use of which is patchy amongst academics. However, the admin people use it and so I have blocked out times in here when I’m busy to reduce diary conflicts.

Filter, filter, filter – I set up many filters on my email, twitter… wherever I can… to keep out spam and irrelevant stuff.

Automating the little stuff – a previous post on being organised as a PI mentioned that I advocate writing scripts and macros to automate little things that you do often. Of course there is an xkcd cartoon for this. I have scripts set up to do things like assembling PDFs or converting or compressing file formats. We’ve also been automating the big stuff too. Figures are a good example. We write scripts to produce (and reproduce) figure panels. 

Sharing code – A while back I set up an update site to distribute our ImageJ macros among the lab, but also people from outside can subscribe and get the latest updates easily. Our Igor code is shared within the lab via a cloud-based updater which allows code to compiled on-demand. Lab code is maintained via git at GitHub and our centre forks repos from published projects to its own account.

Onboarding. None of these tools work unless everyone is on board. It is worth having a strategy to make this happen. Simple steps such as introducing on system at a time, providing initial training and some support for people who are slow to uptake. There’s a group effect to onboarding but getting to the tipping point can be hard.

The title for this post “Super Automatic” comes from the album of that name by Myracle Brah (the name of this band is not endorsed by quantixed).

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.

Pentagrammarspin: why twelve pentagons?

This post has been in my drafts folder for a while. With the World Cup here, it’s time to post it!

It’s a rule that a 3D assembly of hexagons must have at least twelve pentagons in order to be a closed polyhedral shape. This post takes a look at why this is true.

First, some examples from nature. The stinkhorn fungus Clathrus ruber, has a largely hexagonal layout, with pentagons inserted. The core of HIV has to contain twelve pentagons (shown in red, in this image from the Briggs group) amongst many hexagonal units. My personal favourite, the clathrin cage, can assemble into many buckminsterfullerene-like shapes, but all must contain at least twelve pentagons with a variable number of hexagons.

The case of clathrin is particularly interesting because clathrin triskelia can assemble into a flat hexagonal lattice on membranes. If clathrin is going to coat a vesicle, that means 12 pentagons need to be introduced. So there needs to be quite a bit of rearrangement in order to do this.

You can see the same rule in everyday objects. The best example is a football, or soccer ball, if you are reading in the USA.

The classic design of football has precisely twelve pentagons and twenty hexagonal panels. The roadsign for football stadia here in the UK shows a weirdly distorted hexagonal array that has no pentagons. 22,543 people signed a petition to pressurise the authorities to change it, but the Government responded that it was too costly to correct this geometrical error.

So why do all of these assemblies have 12 pentagons?

In the classic text “On Growth and Form” by D’Arcy Wentworth Thompson, polyhedral forms in nature are explored in some detail. In the wonderfully titled On Concretions, Specules etc. section, the author notes polyhedral forms in natural objects.

One example is Dorataspis, shown left. The layout is identical to the D6 hexagonal barrel assembly of a clathrin cage shown above. There is a belt of six hexagons, one at the top, one at the bottom (eight total) and twelve pentagons between the hexagons. In the book, there is an explanation of the maths behind why there must be twelve pentagons in such assemblies, but it’s obfuscated in bizarre footnotes in latin. I’ll attempt to explain it below.

To shed some light on this we need the help of Euler’s formulae. The surface of a polyhedron in 3D is composed of faces, edges and vertices. If we think back to the football the faces are the pentagons and hexgonal panels, the edges are the stitching where two panels meet and the vertices are where three edges come together. We can denote faces, edges and vertices as f, e and v, respectively. These are 2D, 1D and zero-dimensional objects, respectively. Euler’s formula which is true for all polyhedra is:

\(f – e + v = 2\)

If you think about a cube, it has six faces. It has 12 edges and 8 vertices. So, 6 – 12 + 8 = 2. We can also check out a the football above. This has 32 faces (twelve pentagons, twenty hexagons), 90 edges and sixty vertices. 32 – 90 + 60 = 2. Feel free to check it with other polyhedra!

Euler found a second formula which is true for polyhedra where three edges come together at a vertex.

\(\sum (6-n)f_{n} = 12\)

in this formula, \(f_{n}\) means number of n-gons.

So let’s say we have dodecahedron, which is a polyhedron made of 12 pentagons. So \(n\) = 5 and \(f_{n}\) = 12, and you can see that \((6-5)12 = 12\).

Let’s take a more complicated object, like the football. Now we have:

\(((6-6)20) + ((6-5)12) = 12\)

You can now see why the twelve pentagons are needed. Because 6-6 = 0, we can add as many hexagons as we like, this will add nothing to the left hand side. As long as the twelve pentagons are there, we will have a polyhedron. Without them we don’t. This is the answer to why there must be twelve pentagons in a closed polyhedral assembly.

So how did Euler get to the second equation? You might have spotted this yourself for the f, e, v values for the football. Did you notice that the ratio of edges to vertices is 3:2? This is because each edge has two vertices at either end (it is a 1D object) and remember we are dealing with polyhedra with three edges at each vertex. so \(v = \frac{2}{3}e\). Also, each edge is at the boundary of two polygons. So \( e = \frac{1}{2}\sum n f_{n}\). You can check that with the values for the cube or football above. We know that \(f = \sum f_{n}\), this just means that the number of faces is the sum of all the faces of all n-gons. This means that:

\(f – e + v = 2\)

Can be turned into

\(f – (1/3)e = \sum n f_{n} – \frac{1}{6}\sum n f_{n} = 2\)

Let’s multiply by 6 to get, oh yes

\(\sum (6-n)f_{n} = 12\)

There are some topics for further exploration here:

  • You can add 0, 2 or 10000 hexagons to 12 pentagons to make a polyhedron, but can you add just one?
  • What happens when you add a few heptagons into the array?

Image credits (free-to-use/wiki or):

Clathrus ruber – tineye search didn’t find source.

HIV cores – Briggs Group

Exploded football – Quora

The post title comes from “Pentagrammarspin” by Steve Hillage from the 2006 remaster of his LP Fish Rising

Do It Yourself: Lab Notebook Archiving Project

A while back, the lab moved to an electronic lab notebook (details here and here). One of the drivers for this move was the huge number of hard copy lab note books that had accumulated in the lab over >10 years. Switching to an ELN solved this problem for the future, but didn’t make the old lab note books disappear. So the next step was to archive them and free up some space.

We access the contents of these books fairly regularly so archiving had to mean digitising them as well as putting them into storage. I looked at a few options before settling on a very lo-fi solution.

Option 1: call in the professionals

I got a quote from our University’s preferred data archiving firm. The lab notebooks we use have 188 pages and I had 89 to archive. The quote was over £4000 + VAT for scanning only. This was too expensive and so I next looked at DIY options.

Option 2: scan the books

At the University we have good MPDs that will scan documents and store them on a server as a multipage PDF. There’s two resolutions at which you can scan, which are good-but-not-amazing quality. The scanners have a feeder which would automate the scan of a lab book, but it would mean destroying the books (which are hardbound) to scan them.

I tried scanning one book using this method. Disassembling a notebook with a razorblade was quite quick but the problem was that the scanner struggled with the little print outs that people stick in their lab books. Dealing with jams and misfired scans meant that this was not an option, and I didn’t want to destroy all of the books either.

Option 3: photography rigs

Next, I looked at book scanning projects to see how they were done. In these projects, the books are valuable and so can’t be destroyed, but it must be automated… I found that these projects use a cradle to sit the book in. A platen is pushed against the pages (to flatten the pages) and then two cameras take a picture of the two pages, triggered in sync using an external button or foot pedal. An example of one raspberry pi-powered rig is here. Building one of these appealed but would still require some expense (and time and effort). I asked around if anyone else wanted to help with the build, thinking that others may be wanting to archive their notebooks, but I got no takers.

Option 4: the zero-cost solution!

Inspiration came from a student who left my lab and wanted to photograph her lab books for future reference. She captured them on her camera phone by hand in a matter of minutes. Shooting two pages of a book from a single digital camera suspended above the notebook would be a good compromise. Luckily I had access to a digital camera and a few hundred Lego bricks. Total new spend = £0.

I know it looks terrible, but it was pretty effective!

I put the rig on a table (for ergonomic reasons), next to a window and photographed each book using natural light. It took around 10 min to photograph one lab book. I took the images over a few weeks amongst doing other stuff so that the job didn’t become too onerous. I shot the books at the highest resolution and stored the raw images on the server. I wrote a quick script to stack the images scale them down 25% and export to PDF to make an easy-to-consult PDF file for each lab book. Everyone in the lab can access these PDFs and if needed can pull down the high res versions. The lab books have now been stored in a sealed container. We can access the books if needed. However, having looked at the images, I think if something is not readable from the file, it won’t be readable in the hard copy.

Was it worth it?

I think so. It took a while to get everything digitised but I’m glad it’s done. The benefits are:

  1. Easy access to all lab books for every member of the lab.
  2. Clearing a load of clutter from my office.
  3. The rig can be rebuilt easily, but is not otherwise sitting around gathering dust.
  4. Some of the older lab books were deteriorating and so capturing them before they got worse was a good idea (see picture above for some sellotape degradation).

The post title is taken from the LP “Do It Yourself” by The Seahorses.

Dividing Line: not so simple division in ctenophores

This wonderful movie has repeatedly popped up into my twitter feed.

It was taken by Tessa Montague and is available here (tweet is here).

The movie is striking because of the way that cytokinesis starts at one side and moves to the other. Most model systems for cell division have symmetrical division.

Rob de Bruin commented that “it makes total sense to segregate this way”. Implying that if a cell just gets cut in half it deals with equal sharing of components. This got me thinking…

It does make sense to share n identical objects this way. For example, vesiculation of the Golgi generates many equally sized vesicles. Cutting the cell in half ensures that each cell gets approximately half of the Golgi (although there is another pathway that actively segregates vesicular material, reviewed here). However, for segregation of genetic material – where it is essential that each cell receives one (and exactly one) copy of the genome – a cutting-in-half mechanism simply doesn’t cut it (pardon the pun).

The error rate of such a mechanism would be approximately 50% which is far too high for something so important. Especially at this (first) division as shown in the movie.

I knew nothing about ctenophores (comb jellies) before seeing this movie and with a bit of searching I found this paper. In here they show that there is indeed a karyokinetic (mitotic) mechanism that segregates the genetic material and that this happens independently of the cytokinetic process which is actin-dependent. So not so different after all. The asymmetric division and the fact that these divisions are very rapid and synchronised is very interesting. It’s very different to the sorts of cells that we study in the lab. Thanks to Tessa Montague for the amazing video that got me thinking about this.

Footnote: the 50% error rate can be calculated as follows. Although segregation is in 3D, this is a 1D problem. If we assume that the cell divides down the centre of the long axis and that object 1 and object 2 can be randomly situated along the long axis. There is an equal probability of each object ending each cell. So object 1 can end in either cell 1 or cell 2, as can object 2. The probability that objects 1 and 2 end in the same cell is 50%. This is because there is a 25% chance of each outcome (object 1 in cell 1, object 2 in cell 2; object 1 in cell 2, object 2 in cell 1; object 1 and object 2 in cell 1; object 1 and object 2 in cell 2). It doesn’t matter how many objects we are talking about or the size of the cell. This is a highly simplified calculation but serves the purpose of showing that another solution is needed to segregate objects with identity during cell division.

The post title comes from “Dividing Line” from the Icons of Filth LP Onward Christian Soldiers.

Frankly, Mr. Shankly

I read about Antonio Sánchez Chinchón’s clever approach to use the Travelling Salesperson algorithm to generate some math-art in R. The follow up was even nicer in my opinion, Pencil Scribbles. The subject was Boris Karloff as the monster in Frankenstein. I was interested in running the code (available here and here), so I thought I’d run it on a famous scientist.

By happy chance one of the most famous scientists of the 20th Century, Rosalind Franklin, shares a nominative prefix with the original subject. There is also a famous portrait of her that I thought would work well.

I first needed needed to clear up the background because it was too dark.

Now to run the TSP code.

The pencil scribbles version is nicer I think.

The R scripts basically ran out-of-the-box. I was using a new computer that didn’t have X11quartz on it nor the packages required, but once that they were installed I just needed to edit the line to use a local file in my working directory. The code just ran. The outputs FrankyTSP and Franky_scribbles didn’t even need to be renamed, given my subject’s name.

Thanks to Antonio for making the code available and so easy to use.

The post title comes from “Frankly, Mr. Shankly” by The Smiths which appears on The Queen is Dead. If the choice of post title needs an explanation, it wasn’t a good choice…

Start Me Up: Endocytosis on demand

We have a new paper out. The title is New tools for ‘hot-wiring’ clathrin-mediated endocytosis with temporal and spatial precision. You can read it here.

Cells have a plasma membrane which is the barrier between the cell’s interior and the outside world. In order to import material from outside, cells have a special process called endocytosis. During endocytosis, cells form a tiny bubble of plasma membrane and pull it inside – taking with it a little pocket of the outside world. This process is very important to the cell. For example, it is one way that cells import nutrients to live. It also controls cell movement, growth, and how cells talk to one another. Because it is so important, cell biologists have studied how endocytosis works for decades.

Studying endocytosis is tricky. Like naughty children, cells simply do not do what they are told. There is no way to make a cell in the lab “do endocytosis”. It does it all the time, but we don’t know when or where on the cell surface a vesicle will be made. Not only that, but when a vesicle is made, we don’t really know what cargo it contains. It would be helpful to cell biologists if we could bring cells under control. This paper shows a way to do this. We demonstrate that clathrin-mediated endocytosis can be triggered, so that we can make it happen on-demand.

Endocytosis on-demand

Using a chemical which diffuses into the cell, we can trigger endocytosis to happen all over the cell. The movie on the right shows vesicles (bright white spots) forming after we add the chemical (at 0:00). The way that we designed the system means that the vesicles that form have one type of cargo in there. This is exciting because it means that we can now deliver things into cells using this cargo. So, we can trigger endocytosis on-demand and we can control the cargo, but we still cannot control where on the plasma membrane this happens.

We solved this problem by engineering a light-sensitive version of our system. With this new version we can use blue light to trigger endocytosis. Whereas the chemical diffused everywhere, the light can be focussed in a narrow region on the cell and endocytosis can be trigger only in that region. This means we control where, as well as when, a vesicle will form.

What does hot-wiring mean?

It is possible to start a car without a key by “hot-wiring” it. This happens in the movies, when the bad guy breaks into a car and just twists some wires together to start the car and make a getaway. To trigger endocytosis we used the cell’s own proteins, but we modified them. We chopped out all the unnecessary parts and just left the bare essentials. We call the process of triggering endocytosis “hot-wiring” because it is similar to just twisting the wires together rather than having a key.

It turns out that movies are not like real life, and hot-wiring a car is actually quite difficult and takes a while. So our systems are more like the Hollywood version than real life!

What is this useful for?

As mentioned above, the systems we have made are useful for cell biologists because they allow cells to be “tamed”. This means that we can accurately study the timing of endocytosis and which proteins are required in a very controlled way. It also potentially means that molecules can be delivered to cells that cannot normally enter. So we have a way to “force feed” cells with whatever we want. This would be most useful for drugs or nanoparticles that are not actively taken up by cells.

Who did the work?

Almost all of the work in the paper was by Laura Wood, a PhD student in the lab. She had help from fellow lab members Nick Clarke, who did the correlative light-electron microscopy, and Sourav Sarkar who did the binding experiments. Gabrielle Larocque, another PhD student did some fantastic work to revise the paper after Laura had departed for a post-doc position at another University. We put the paper up on bioRxiv in Summer 2016 and the paper has slowly made its way through peer review to be published in J Cell Biol today.

Wait? I’m a cell biologist! I want to know how this thing really works!

OK. The design is shown to the right. We made a plasma membrane “anchor” and a clathrin “hook” which is a fragment of protein which binds clathrin. The anchor and the hook have an FRB domain and an FKBP domain and these can be brought together by rapamycin. When the clathrin hook is at the membrane this is recognised by clathrin and vesicle formation can begin. The main hook we use is the appendage and hinge from the beta2 subunit of the AP2 complex.

Normally AP2, which has four subunits, needs to bind to PIP2 in the plasma membrane and undergo a conformational change to recognise a cargo molecule with a specific motif, only then can clathrin bind the beta2 appendage and hinge. By hot-wiring, we effectively remove all of those other proteins and all of those steps to just bring the clathrin binding bit to the membrane when we want. Being able to recreate endocytosis using such a minimalist system was a surprise. In vitro work from Dannhauser and Ungewickell had suggested this might be possible, but it really seems that the steps before clathrin engagement are not a precursor for endocytosis.

To make the light inducible version we used TULIPs (tunable light-controlled interacting proteins). So instead of FRB and FKBP we had a LOVpep and PDZ domain on the hook and anchor.

The post title comes from “Start Me Up” by The Rolling Stones. Originally on Tattoo You, but perhaps better known for its use by Microsoft in their Windows 95 advertising campaign. I’ve finally broken a rule that I wouldn’t use mainstream song titles for posts on this blog.