FluidFM. I would have called it “Squirty AFM.” They put a little hole through the tip of an AFM cantilever, so they can squirt fluid through the tip.
Or add dye to just one cell.
The cover story of February’s issue of Physics Today, the publication of the American Institute of Physics, is the physics of sailing. I like sailing: grew up on the coast of Maine sailing Lasers—and eventually and Ensign—every summer. Obviously, I can’t afford to sail out here in San Francisco, but I get to go home every summer and sail in Casco Bay.
I thought the cover of Physics Today was a little cruel: “Hey you, in that dark laser lab, check out what these smart people are doing: Sailing!” But the article was actually pretty cool. The basic stuff was in there, and it even had equations (like Reynolds number). But really, I just looked at the figures.
This is a helpful one: you can go faster on a beam reach than when running downwind. If you don’t realize that a (modern) sailboat is wings on the water, going directly downwind is what you would intuitively want to do. And going upwind is mind-boggling.
But my favorite figure was this image of sailboats racing in the fog:
The beauty of vortices trailing off in the fog for thousands of feet was stunning. I also like that you can see that the third boat from the top is tacking and temporarily interrupting the trail. Cool.
Well, here’s a PDF of the article if you’re interested.
(Or: How I Learned to Stop Worrying and Love the Awards.)
So I give you the first EverydayScientist’ Extraordinary Laud (EDSEL) award for the Coolest Paper of Early January 2008:
Huang, B., Wang, W., Bates, M., Zhuang, X. Three-dimensional super-resolution imaging by stochastic optical reconstruction microscopy. Science 2008 (published online Jan 3).1
Stochastic optical reconstruction microscopy (STORM) is Xiowei’s cool super-resolution technique (Eric Betzig has a similar version called “PALM”). And I’ve already blogged about Bo’s talk at ACS Boston.
There’s not anything revolutionary in this paper: they’ve used their STORM technique, and simply added an extra lens to distort the PSF of single molecules, causing those above or below the focal plane to distort in a consistent manner. That way, they “imprinted” axial information on the image, and can generate 3D representations from the fitting data.2 But, while the technique isn’t a breakthrough, this paper is in Science because the images produced are really amazing:
You can see that the microtubules in the cell move down! Cool. And the supplemental material included this beautiful movie of some microtubules crossing over each other (the scale bar is only 200 nm, below the diffraction limit):
[local /wp-content/uploads/3d-storm_movie.mov View Movie]
And I also really loved this comparison of 2D STORM (top) versus a 100-nm thick x-y cross-section in the 3D image (bottom) of some clathrin-coated pits. You can really see that they are hollow!
Now, all these images are of fixed (read: “dead”) cells. Because STORM imaging requires cycling acquisition, each frame generally takes a long time. This makes living-cell imaging and measuring dynamics difficult.3 And this is really a proof-of-principle study: the results don’t answer any biophysical questions. Nevertheless, the images are really beautiful!
I fully expect this technique—and the other super-resolution approaches—to become another tool in the biophysical toolbox (along with TIRF, FRET, FLIM, FRAP, and other acronyms). Just you wait…
1 I tried to be good and requested permission from AAAS to reprint these images and movies. But they haven’t gotten back to me. So I’ll just post them anyway. Don’t sue me, Bo. [UPDATE: Reprinted with permission from AAAS. I finally received permission to use these images. If you wish to reuse these images you can obtain permission from AAAS by following the guidelines here.]
2It is generally known that the dipole-emission pattern of single emitters contain information about axial depth. It is also straightforward to introduce an astigmatic distortion to the optical system to imbed depth information.
3 Not impossible: someday it will be done. Stefan Hell already is quite fast with his PALMIRA imaging.
We were discussing some grammar at Chemical Physics Journal Club this week: which is the (more) correct sentence?
1. It is important to deconvolute the fluorescence lifetime from the instrument-response function.
2. It is important to deconvolve the fluorescence lifetime from the instrument-response function.
I think sentence 2 is better. To me, “convolve” is to (usu. mathematically) roll together multiple things, while “convolute” means to make complex: you can convolve two mathematical functions or signals, and you can convolute a sentence. (Unfortunately, the noun form of each is “convolution.”)
I don’t think the official Webster or Wikipedia definitions agree with me, or clear up the mess (it’s so convoluted!), but my argument makes so much sense in my head that I can’t give it up. I get annoyed when scientific papers state they “deconvoluted” something, unless they mean that they made something less complicated.
I suppose convolving is a type of convoluting.
Other thoughts, here?
In Chemical Physics Journal Club this week, someone presented a pretty good simulation paper about glass transitions in polymer thin films. But this paper had some pretty bad figures. For instance, is this figure really worth it?
I didn’t really find this too revealing. But, later in the paper, there’s an even better figure:
It’s that great? The best part is that they actually offset the curves; really all the distributions are exactly the same. That’s certainly worth conveying in plot form. Congrats Jain and de Pablo, your figures are entered into the worst-figure contest. (Cool paper, though.)
I presented a paper at my Chemical Physics Journal Club a while back. Here’s a citation: Hirsch, J. E. An index to quantify an individual’s scientific research output. Proc. Nat. Acad. Sci. 2005, 102(46), 16569–16572.
The basic idea is that h measures your (career) scientific output, the higher the value, the greater the output (convolved with impact). h is the number of papers that you have authored, each with h or more citations. In other words, if you have 50 papers, but only 30 of them have been cited 30 or more times, your h = 30. This method tries to avoid giving a lot of credit to people who write oft-cited review articles or people who write millions of papers that no one ever reads.
It’s mostly sorta bullshit, but I enjoyed calculating the “h value” (or “h index” or “h number” or “Hirsch number” or…) of several of the profs at Stanford:
But there are some things that are unfair: Pande is a younger scientist, so he hasn’t had as much time to publish or have his works cited as, say, Zare or Fayer. So we can normalize to the number of years since earning PhD (where m = h/#years):
Comparing m values is probably a little more fair. But now there are some more problems. Pande is in his steep period, which will presumably level off like this:
So you get your PhD, then you die. The value of m also doesn’t take into account that Andersen is a theorist or that W.E. spent until the mid-90s in industry, where people normally don’t publish as much. Oh well, it’s still pretty fun.
By the way, hsam = 1
UPDATE: Check out my new measure, the Lord h-bar index.