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Quantitative Analysis of Speckle Microscopy

00:00:11.15 You get these millions and millions of speckles
00:00:13.12 in one speckle movie, and you know, if you ask a graduate
00:00:18.03 student to track them, they won't be around very long.
00:00:20.26 So, what you need are simple and also complex quantitative
00:00:26.13 analysis techniques. The simplest quantitative analysis
00:00:29.08 technique that just gives you a general feel for what's going on
00:00:32.18 is a kymograph. So you take a time lapse speckle movie,
00:00:35.20 in this case, it's frames from a speckle time lapse movie of the actin
00:00:41.17 cytoskeleton. And you just take a thin slice of each movie in the
00:00:45.26 time series, and slam them all together into this kymograph.
00:00:50.22 And what you see are streaks on this kymograph that correspond
00:00:55.04 to speckles that over time would be moving down this bar.
00:00:59.11 Okay, so a streak -- a diagonal streak in this kymograph tells
00:01:04.09 you, the slope of that streak tells you about the velocity of
00:01:09.14 speckled motion down this bar. So this is a very simple method.
00:01:14.03 More complex analysis has developed in collaboration with
00:01:19.10 Gaudenz Danuser's lab. He runs a computer vision software
00:01:23.28 lab at Harvard Medical School, and has written a series of
00:01:27.23 algorithms that allow us to either detect single speckles and
00:01:33.26 follow the intensity fluctuations and motions of single speckles.
00:01:37.08 Or basically do correlation based tracking, where you have a little
00:01:42.22 region of speckled texture, and you track that region of texture
00:01:48.15 over time, as opposed to tracking individual speckles.
00:01:52.01 So, each one of these parts of the flowchart represents an
00:01:57.14 algorithm that he's written that allows both low resolution
00:02:00.19 and high resolution speed maps, low resolution and high resolution
00:02:03.18 assembly/disassembly maps. And you know, by thinking about the
00:02:10.04 problem, for example, that actin only moves in retrograde flow
00:02:14.03 in one direction at the leading edge, you can have some problem specific
00:02:18.17 post-processing. And then you do molecular perturbations and
00:02:21.04 you compare two different states of a cell, and you get mechanistic
00:02:26.05 information about how signal transduction cascades and molecules
00:02:31.03 control the motion and dynamics of, in this case, the actin
00:02:35.20 cytoskeleton. And from this analysis, we were able to define
00:02:40.14 sort of distinct machines in the actin cytoskeleton. One at the
00:02:45.19 leading edge, where this is the leading edge of a migrating cell,
00:02:48.10 where you see this speckle image here, the myosin motor proteins
00:02:51.10 here. This is not the colocalization of actin and myosin in red
00:02:56.05 and green, this is actually a map of assembly and disassembly
00:02:59.24 rates, where bright red is rapid assembly and bright green is
00:03:04.02 rapid disassembly. And this was generated by these algorithms,
00:03:07.10 by analyzing an actin speckled time lapse movie. And this is a
00:03:13.09 speed map, where you see the direction encoded by these little vectors.
00:03:17.16 And the magnitude of the speed encoded by the color, and what
00:03:22.08 we found was that there's these distinct regions of lamellipodium
00:03:26.04 along the leading edge with active actin retrograde flow, and
00:03:29.25 coupled assembly and disassembly treadmilling that lacks myosin II
00:03:35.03 motors. And then further back in the cell, this region that's packed with
00:03:39.10 actomyosin motors and undergoes this convergent flow.
00:03:43.07 And this is typical of migrating cells of various types from
00:03:48.16 various tissues. So it's sort of the fundamental machines of
00:03:52.03 migration that the actin cytoskeleton builds. In addition,
00:03:54.24 we developed what we call "correlational speckle microscopy."
00:04:00.03 Where we correlate the motion of speckles in two different
00:04:05.18 channels, so it's sort of a cheap version of fluorescence correlation
00:04:08.13 spectroscopy for molecules that are moving much more slowly
00:04:13.07 than diffusion. So what we did was we correlated the motion
00:04:18.08 dynamics of focal adhesion proteins with the movement of the
00:04:22.09 actin cytoskeleton, to try to understand how actin is coupled to
00:04:27.12 the adhesions to generate force on the extracellular matrix and
00:04:31.06 drive migration. So we expressed a focal adhesion protein at
00:04:34.27 low level in green, and we have a low level of actin in the cell in red.
00:04:39.25 We do dual wavelength total internal reflection fluorescence
00:04:43.29 speckle microscopy of the adhesion proteins. We track the
00:04:47.17 flow of the actin and adhesion speckles in the two different
00:04:50.22 channels. And then we segment out the focal adhesions, looking
00:04:54.15 just at the motion of molecules in these focal adhesions. We
00:04:58.04 interpolate the vectors in the two channels onto a common
00:05:00.12 grid, and correlate the direction and velocity within each vector pair.
00:05:04.27 So now we've got a vector pair where we've got an actin speed
00:05:07.29 and direction and a focal adhesion speed and direction, and we say
00:05:11.07 how much is that focal adhesion molecule moving at the same
00:05:14.16 speed as the actin? And that's basically a velocity coupling
00:05:18.27 score, which is pretty much just a dot product of the
00:05:21.29 vectors. And how much are they going in the same direction? So
00:05:25.19 is the focal adhesion protein moving at an angle relative to the actin?
00:05:29.29 And that's just the cosine of the angle between these two vectors
00:05:34.05 in the pair. And this tells us how coupled are these structures
00:05:38.20 in a migrating cell. And so this is another quantitative analysis
00:05:45.13 approach that gives you information on how protein macromolecular
00:05:49.26 ensembles interact with each other in a living cell.

This Talk
Speaker: Clare Waterman
Audience:
  • Researcher
Recorded: July 2012
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Talk Overview

Fluorescent speckle microscopy is a technique that allows monitoring of dynamics in polymeric structures by doping in a very low level of fluorescently labeled monomer. The small number of fluorescent molecules make fluorescent speckles that show up as diffraction-limited bright spots in the image. Here, Clare Waterman, developer of this technique, describes computational tools (developed by Gaudenz Danuser) for automatic quantitative analysis of speckle microscopy data.

Speaker Bio

Clare Waterman

Clare Waterman

Dr. Waterman is chief of the Laboratory of Cell and Tissue Morphodynamics at the National Heart Lung and Blood Institute, National Institutes of Health and she has been co-director of the Physiology Course at the Marine Biological Laboratory for the past 4 years. Waterman’s lab studies the interactions between actin and focal adhesions, taking advantage… Continue Reading

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