A Sponsored Supplement to Science

Microscopy Now Getting the Most fromYour Imaging

Sponsored by Produced by the

Science/AAAS Custom Publishing Office

TABLE OF CONTENTS

1

See beyond the limit of diffraction and start changing tomorrow todayDeltaVision OMX™ super resolution microscope incorporates the power of SIM technology into an optimized, stable platform.

Visit us at www.gelifesciences.com/deltavision, to find out how to get physiologically relevant data from images that cannot be obtained with standard or confocal microscopes.

See more today, know more tomorrow.

Amersham I ÄKTA I Cytell I Biacore I Whatman I Xuri*

www.gelifesciences.com/deltavision

GE and GE monogram are trademarks of General Electric Company. DeltaVision OMX, *Amersham, ÄKTA, Cytell, Biacore, Whatman, and Xuri are trademarks of General Electric Company or one of its subsidiaries. © 2015 General Electric Company—All rights reserved. First published Mar. 2015GE Healthcare UK Ltd, Amersham Place, Little Chalfont, Buckinghamshire, HP7 9NA, UK

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Microscopy Noww Getting the Most fromYour Imaging

Introductions2 Are your samples small, dim, or live? The cell analysis team at GE Healthcare

3 Spoiled for choice? Sean Sanders, Ph.D. Science/AAAS

White papers4 Finding the right tool for the job: Confocal vs. widefieldvs.deconvolution 7 Making your microscope work for you: Tips and tweaks for optimal performance

Webinar: questions & answers10 Microscopy in focus: The art and science of image quality

12 Live cell imaging: The future for discoveries

Imaging papers14 Light microscopy techniques for live cell imaging David J. Stephens and Victoria J. Allan

19 FAM123A binds to microtubules and inhibits the guanine nucleotide exchange factor ARHGEF2 to decrease actomyosin contractility Priscila F. Siesser, Marta Motolese, Matthew P. Walker et al.

33 Apical abscission alters cell polarity and dismantles the primary cilium during neurogenesis Raman M. Das and Kate G. Storey

38 Technical Notes

40 Science Webinar Series LinksBILL MORAN, GLOBAL DIRECTOR Custom Publishingbmoran@aaas.org+1-202-326-6438

ROGER GONCALVES, SALES MANAGERCustom Publishing Europe and Middle East+41 43 243-1358rgoncalves@science-int.co.uk

© 2015 by The American Association for the Advancement of Science.All rights reserved. 7 May 2015

Editor: Sean Sanders, Ph.D.Proofreader/Copyeditor: Yuse LajiminmuhipDesigner: Amy Hardcastle

About the cover: Toxoplasma gondii parasite in a human foreskin fibroblast cell stained for the apicoplast organelle (green), ISP1 protein (red), and DNA (blue).Credit: Muthugapatti K. Kandasamy, University of Georgia, U.S.

This booklet was produced by the Science/AAAS Custom Publishing Office and supported by GE Healthcare.

It was not too long ago that, if your experiments called for micros-copy, your biggest decision was what magnification to use on your light microscope, or whether to splurge on an oil objective. These days, things are very different. Apart from the latest light

microscopes, complete with state-of-the-art LED illumination technol-ogy, there are a dizzying array of fluorescent microscopes with an alphabet soup of acronyms, including SPIM, STED, TIRF, and PALM, to name a few. There are systems optimized for live cell imaging and others for looking deep into tissues. Some are automated and can count specific cell types, or can image an insect larva as it develops in real time.

For the average biologist wishing to include imaging in their protocols—whether basic fixed section imaging or advanced super-resolution with live cells—it is challenging to know which system or technology will serve them best. In particular, should they use wide-field or confocal microscopy, and what role might deconvolution play in allowing them to improve their image quality? Furthermore, once they have a system in place, what steps can they take to get the best images and data from their microscope? And what are the pitfalls they should be looking out for?

In this ebooklet we present a range of articles and information that we hope will help you, the researcher, choose a system that best suits your needs and apply it to generate the best data possible. We start with two white papers that promote a deeper understanding of the tools and techniques used in microscopy. The first discusses the differences, similarities, and pros and cons of widefield vs. confocal vs. deconvolution microscopy. The second provides tips, tweaks, and best practices for optimizing the setup for your microscope.

Next, we have included a selection of questions and answers from two of our past webinars, both dealing with imaging, in which our panels of key opinion leaders provided responses to questions sub-mitted by the online audience.

Finally, we have chosen three papers from Science and Science Signaling that exemplify what can be achieved using both widefield and confocal microscopy. The 2003 paper from Stephens and Allan provides a fascinating historical perspective on the state of imaging at the turn of the millennium and outlines some of the early work on live cell imaging, also making mention of some of the first superreso-lution modalities. Next, a 2012 paper published in Science Signaling describes the characterization of a FAM123 protein, FAM123A, using a number of microscopy techniques, including confocal with decon-volution, regular confocal, and total internal reflection fluorescence (TIRF). Finally, in a Science paper from 2014 the authors make use of high-resolution live cell imaging to demonstrate with amazing clarity the intricacies of neural development in a chick embryo. This work was made possible by recent advances in imaging technology.

We trust that you will find this ebooklet helpful and can use it as a reference guide for your forays into microscopy now and in the future.

Sean Sanders, Ph.D.Editor, Custom PublishingScience/AAAS

INTRODUCTIONSMICROSCOP Y NOW: GET TING THE MOST FROM YOUR IMAGING

Are your samples small, dim, or live?

Spoiled for choice?

L ight microscopy has been used for studying cells for many years and has advanced our understanding of key cellular processes. However, fixation involves non-physiological procedures and only provides a snapshot view of cells at a

single point in time. To truly understand cellular function, we need to extend our imaging capabilities in ways that enable us to fol-low sequential events in real time, monitor the kinetics of dynamic processes, and record sensitive or transient events. With the advent of live cell imaging and the development of high- and superresolu-tion technologies, it is now possible to acquire data on viable cells in a biologically relevant context providing us with a greater insight of cellular function than has previously been possible.

GE has long been involved in the imaging of live “samples.” The discovery of X-rays in 1895 set in motion a period in which the imag-ing of the human body became associated with GE’s brand of innova-tion. In the 1970s when the power of X-rays fully came into its own, GE became the first X-ray equipment leader to make a strong move in computed tomography. In 1983, GE Medical started investing heavily in magnetic resonance imaging technology. Today, GE Healthcare is recognized as one of the worlds’ most innovative makers of medical-imaging machines, magnetic resonance imaging, and cardiac tomog-raphy scanners for faster, clearer imaging of the human body.

Our aim is that this ebooklet will help you get the most from your cellular imaging particularly when your samples are small, dim, or live. And why have we focused on small, dim, and live? Because by enabling you to image at the limits of detection our goal is to help you generate data that is most likely to advance our knowledge of living processes.

We hope this collection of white papers, peer-reviewed papers, webinars, and Q&As will make at least a small difference to your research.

The cell analysis team at GE Healthcare

3SCIENCE sciencemag.org2 sciencemag.org SCIENCE

GE Healthcarewww.gelifesciences.com

GE and GE monogram are trademarks of General Electric Company.© 2015 General Electric Company—All rights reserved. First published Apr. 2015GE Healthcare UK Ltd, Amersham Place, Little Chalfont, Buckinghamshire, HP7 9NA, UK

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MICROSCOP Y NOW: GET TING THE MOST FROM YOUR IMAGING

A sk a typical cell biologist to name the best overall microscope for fluorescent microscopy applications, and chances are they’ll say a

confocal microscope. Certainly, confocal microscopy has been a boon

for cell biologists. The technology promises far sharper images of certain biological samples than routine widefield microscopy can deliver, as well as the ability to create and explore three-dimensional (3-D) renderings of those samples. For many relatively thick, relatively bright, fixed samples—in other words, samples with significant light scattering—confocal may be the ideal microscopy solution.

But in the world of imaging, there is no one-size-fits all solution, no option that is the best in all situations. “In microscopy, as in life, everything is a series of tradeoffs,” says Jason Swedlow, professor of quantitative cell biology at the University of Dundee. For instance, there are plenty of scenarios in which widefield microscopy delivers better results. It certainly delivers them faster and for less money. And despite what you may have heard, confocal does not have a monopoly on 3-D imagery, provided the microscopy system features automated focusing and the appropriate software.

Here, we compare the mechanisms, strengths and weaknesses, and applications for confocal, widefield, and deconvolution microscopy.

Confocal vs. widefield Confocal microscopy solves a very specific

problem in cell biology. In widefield fluorescence microscopy, the entire field of view is evenly bathed in excitation light. As a result, every fluorophore in the field of view fluoresces, sending its emission light towards the detector—typically an area detector of the types found in a digital camera, such as a charge-coupled device (CCD), cooled electron-multiplying

CCD, or complementary metal oxide silicon (CMOS) array.

The problem is, as the user focuses up and down through the sample (i.e., in the z direction, or along the optical axis) emission light from the focal plane competes with fluorescence originating from outside the plane, creating a confused, low-contrast image, like trying to see the stars in a city overcome by light pollution. This is especially true when imaging thicker samples (those thicker than about 15 to 30 µm) and those with a substantial fluorescence signal both within the cells and in the extracellular matrix.

Confocal microscopy addresses these problems by inserting small apertures, usually pinholes, in front of the light source and detector that are in the same conjugate plane as—that is, “confocal to”—a thin focal plane in the sample. Only a diffraction-limited volume of the sample is illuminated in this way, and only light from that focal plane reaches the detector, because the pinhole blocks most out-of-focus light. The result is a sharp, high-contrast image of a distinct optical plane. But only under certain circumstances is the confocal image better resolved than a comparable widefield one—confocal microscopes are still constrained by the same diffraction limit of light as any other conventional (i.e., non-superresolution) microscope.

By stepping the image plane up and down in the axial direction, a set of virtual optical sections (a “z-stack”) can be acquired and stored. These sections can be perused like a cellular “flipbook” to identify the most informative scans, or computationally reassembled into a 3-D representation, providing researchers the unparalleled opportunity to explore their sample’s internal structure.

To achieve that performance, confocal microscopes use a fundamentally different excitation and detection scheme than widefield systems. Rather than uniformly

illuminating the sample, a pinhole-sized point of excitation energy is projected into the sample. In the popular point-scanning confocal microscope configuration, fluorescence from only this single point reaches the detector, which in this case is usually a photomultiplier tube. Once those intensity data are collected, the point is translated to a new position and the next data point is captured, rasterizing like the electron gun in an old tube television. As a result, a confocal image cannot be viewed in real time through the microscope eyepieces; it is captured point-by-point and computationally reconstructed after the fact.

Confocal microscopes thus trade efficiency for clarity. The pinholes block most of the emission sig-nal, for instance, so relatively little reaches the detec-tor. Also, says Swedlow, confocal microscopes often have less time to dwell on any given point, in order to capture an entire frame in a reasonable time. To compensate, confocals typically use high-intensity laser excitation. But this can damage live cells and photobleach fluorescent molecules, precluding long-term imaging, especially of live cells. And, because the image is collected one point at a time, the confocal frame rate is typically lower than with widefield microscopy, complicating the study and analysis of fast biological processes.

Alternative confocal configurations can miti-gate this frame-rate problem to some extent. In a spinning-disk or multipoint-scanning confocal microscope, excitation and emission light passes through multiple holes in a spinning disk (called a “Nipkow disk”), allowing for the detection of multiple points simultaneously on a CCD camera or similar detector. A line-scanning confocal microscope im-ages through a slit aperture rather than a pinhole to achieve the same end.

DeconvolutionThere are other approaches besides confocal mi-

croscopy for cleaning up out-of-focus light. Total in-ternal reflection fluorescence (TIRF) microscopy, for instance, reduces background by confining fluores-cent excitation to a narrow sliver of tissue (about 100 nm thick) at the slide/sample interface. Thus, it typi-cally is used to image membrane-localized events. Multiphoton instruments eliminate out of focus light

by confining fluorescence to the optical sections, and light-sheet microscopes illuminate sample planes one at a time with relatively low-intensity light, imag-ing in an orthogonal direction.

But these microscopy configurations, including confocal, tend to be expensive, and often are found only in well-furnished core facilities. A less expen-sive, effective, and more readily available option for many users is widefield microscopy, especially when paired with the computational process called decon-volution.

As in every microscope, the light in a fluorescent widefield microscope passes from the excitation source, through the sample, filters, and lenses, to the detector. If every element in the light path were optically “perfect,” the projected image would match what the sample actually looks like—a spherical bead, for instance, would appear as a perfect sphere. But should there be any imperfections or light degrada-tion in the optical path, the image will be distorted. The sphere would appear, say, slightly flattened.

In practice, no microscope is perfect, and some distortion is inevitable. But it is possible to create a mathematical model of the microscope’s opti-cal characteristics using the instrument’s so-called “point spread function” (PSF), which describes the behavior of an infinitely small fluorescent point. Deconvolution uses this model to back-calculate the original appearance of the imaged field of view. Effectively, the process treats an image as an array of point spread functions and attempts to reassign every point of light back into its correct focal plane, mathematically erasing the out-of-focus noise to cre-ate a clearer image.

Deconvolution thus cleans up a preexisting image by digitally reconstructing it. By contrast, confo-cal microscopes filter that noise from their datasets at the source by using a physical barrier. Yet the net result is often the same or at least similar, and indeed, some researchers apply deconvolution to confocal sections, as well. But that also means that widefield microscopes, like confocal microscopes, are capable of generating clear 3-D datasets, at least for relatively thin samples, such as a layer of cells on a microscope coverslip. All that is required is that the microscope support sufficiently fine automated focus control to collect the necessary z-stack.

Finding the right tool for the jobConfocal vs. widefield vs. deconvolutionBy Jeffrey M. Perkel

continued>

Confocal microscopy solves a very specific problem in cell biology.

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Live cell imagingThe issue of which microscopy modality to use

is particularly significant when it comes to research involving live cell imaging. For one thing, live cells, by definition, are not fixed. Thus, their internal com-

ponents—the very objects users may wish to capture—are often moving as they are being imaged. Cells and their subcellular components also are often acutely sensitive to light, and overexposure can lead to cellu-

lar damage, a phenomenon called photodamage or phototoxicity. Photodamaged cells may stop divid-ing or otherwise alter their biological behavior, nega-tively impacting experimental analysis and interpre-tation. Researchers try to minimize such damage as much as possible.

Live cell imaging therefore presents particular challenges for microscopists. Since the cells are not fixed, fluorescent staining options are limited by the ability of the fluors to get through the cell membrane in order to label internal structures. The advent of genetically encoded fluorophores, such as fluores-cent proteins has greatly enhanced the options for live cell imaging. An additional challenge is that the fluorophores must be imaged quickly, and with as little excitation energy as possible, especially if the goal is to record the cells’ behavior over extended periods.

These challenges do not preclude confocal microscopy, of course—it all depends on what the user is trying to observe. For relatively bright objects and relatively slow cellular processes (such as mitosis), confocal microscopy will work just fine, especially multipoint or line-scanning confocal microscopy. But for dimmer objects and faster processes like vesicle trafficking, widefield still may be preferred. “Anything that reduces the amount of light in live cell imaging, is something you want to do,” says Swedlow.

Indeed, widefield, or widefield plus deconvolution, offers several advantages over confocal microscopy for live cell imaging. The overall input energy is lower and distributed over the entire field of view, mean-ing the per-cell energy input may be lower. And the frame rate is faster in widefield microscopy, meaning users are more likely to capture rapid cellular pro-cesses.

Widefield microscopy also offers the advantage that it captures all of the sample’s light output for later deconvolution, whereas confocal microscopes reject any light that is out of focus, meaning there are fewer photons with which to work in the first place. Widefield also captures those images using CCD or similar detectors, which typically have higher quantum efficiencies than the point detectors used in confocal instruments. As a result, especially for weakly stained or sparse fluorescent objects, confo-cal microscopy can be akin to taking a picture in a darkened room with a cell phone camera—grainy and littered with electronic noise.

Confocal microscope users typically circumvent this problem with fixed samples by sampling each point multiple times (effectively lengthening the exposure), which boosts signal-to-noise ratios, but is also slower, more phototoxic to live samples, and more likely induce photobleaching. For live cell imag-ing, confocal users often opt to open the pinhole ap-erture to allow in more light, defeating the purpose of confocal microscopy in the first place. Instead, they are left with effectively a widefield image collected at a slower, more phototoxic pace.

The bottom line is that, as with most techniques in the lab, confocal and widefield technologies are com-plementary rather than competing and form part of a complete imaging toolbox. For many projects and applications, particularly when the sample is thick or bright, users may be well served by a powerful confo-cal instrument. Just remember that there are other options. Particularly in circumstances when you have live cells or dim samples, you may be better off with a properly outfitted widefield microscope. And there’s a good chance you already have one available.

Jeffrey M. Perkel is a freelance science writer based in Pocatello, Idaho.

R esearch-grade microscopes represent hefty investments. These are precision instruments with tightly calibrated components. Yet they

are only as good as the researchers who use them, the procedures they adhere to, and the quality of the sam-ples they study. Just as a sports car can only achieve its potential if properly maintained, a microscope will produce substandard data if it isn’t treated with care.

Given a top-notch sample, even a mediocre micro-scope can produce quality data. But place a poorly prepared sample on the stage, and the best micro-scope in the world will be of little help. As the saying goes, “garbage in, garbage out.”

But a successful microscopy experiment is about more than just sample prep. Will you image live cells or fixed cells? How will you stain them, and what will you grow them on? What objectives will you use to image your cells, and what tradeoffs does that deci-sion represent?

Here we review a few of the many variables researchers need to consider when planning their microscopy work in order to get the most from their imaging.

Coverslip choiceLike your objectives, filters, and light source, the

coverslip is part of the optical path that produces your image. As such, its properties—and in particular, its thickness—can have a tremendous influence on image quality.

The coverslip most typically used in biological microscopy is the #1.5 coverslip, which is around 170 μm thick. Coverslip type #1 are a tad thinner at about 150 μm, while #2 coverslips are thicker (220 μm).

That thickness matters because as light transitions from one medium to another—from air, through the coverslip, through the sample, then back into the air and into the objective—it diffracts. Modern

microscopes typically are designed to account for a certain thickness of glass between the objective and the sample, and can correct for this diffraction.

Typically, the expected amount of glass is 170 μm—that is, the objective “expects” the sample to be mounted using a #1.5 coverslip. If a #1 coverslip is used instead, image quality could suffer, especially if the sample’s refractive index (RI) does not match that of the glass itself (RI ~1.52), or when a live, thick sample is imaged in culture medium (RI ~1.33). If a sample is fixed and embedded in plastic or resin, which has approximately the same RI as glass, this is less of an issue. In that case, the sample is effectively an extension of the glass itself.

Though the nominal thickness of a #1.5 coverslip is 170 μm, coverslips actually have a range of thicknesses, anywhere from 160 to 190 μm. In order to obtain sharper images and more reliable, comparable data, users can purchase coverslips with tighter tolerances (that is, closer to 170 μm). Or, they can use an objective with an “adjustment collar,” which allows the image quality to be tweaked by correcting for coverslip thickness.

Choose your immersion mediaAs light passes from the sample towards the

objective, the media it travels through dictate how much of it can be captured and how much will be lost. For instance, light passing from a glass coverslip into air diffracts away from the optical axis, effectively distorting the image and reducing the amount of light reaching the detectors. Microscope manufacturers have developed immersion media and immersion objectives intended to mitigate these effects.

Five types of objectives are commonly used in bio-logical research: dry, water-immersion, oil-immersion, glycerin-immersion, and water-dipping objectives. Dry objectives are used in air. Immersion lenses

Making your microscope work for you Tips and tweaks for optimal performanceBy Jeffrey M. Perkel

Confocal and widefield technologies are complementary rather than competing and form part of a complete imaging toolbox.

Total internal reflection

fluorescence (TIRF)

microscopy reduces

background by confining

fluorescent excitation to

a narrow sliver of tissue.

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are intended to have a droplet of liquid between the sample and the front lens of the objective. Water-dip-ping objectives are designed to allow the objective to be dipped into culture media or buffer without being damaged by the salts in the media.

Immersion media effectively reduce diffraction by erasing RI differences as light travels from the sample into the objective. Oil, for instance, has an RI (~1.52) very similar to glass. Thus, an oil immersion lens ef-fectively couples the glass coverslip and glass objec-tive in a block of uniform material. Glycerin has an RI of ~1.47, making it compatible with some common sample mounting media, while water’s RI is ~1.33.

Matching the immersion media to the sample is critical for microscopy. For instance, an oil-immersion objective imaging into an aqueous media (e.g., cul-ture media) will create spherical aberrations due to a mismatch in RIs. Spherical aberration is an optical distortion that causes objects to appear stretched or compressed, and which also results in an apparent loss of photons.

According to a microscopy primer published in 2006 in the Journal of Cell Biology, “Spherical aber-ration describes the phenomenon whereby light rays passing through the lens at different distances from its center are focused to different positions in the z axis. It is the major cause of the loss in signal intensity and resolution with increasing focus depth through thick specimens” (1).

Among other advice for reducing spherical aber-ration, the primer suggests mounting the specimen “on or as close to the coverslip as possible,” as well as using the correct coverslip and avoiding air bubbles in the immersion medium.

Microscopy tools vendors offer multiple immer-sion media to satisfy a range of experimental condi-tions. To help identify the best medium for a given microscopy experiment, GE Healthcare Life Sciences has developed an Immersion Oil Calculator web and smartphone application (2). Simply supply the objec-tive working distance, coverslip thickness, specimen RI, and distance from coverslip to specimen, as well as the working temperature and wavelength used, and the calculator will suggest the optimal RI of the im-mersion oil that will yield the best performance.

For instance, using a 60x/1.42 NA Oil PlanApoN objective lens with a 0.15 mm working distance, a #1.5

coverslip, a specimen in glycerol, a sample directly on top of the coverslip, and imaging at 37°C with 550 nm excitation light, the calculator suggests an immersion medium RI of 1.521. (NA, or numerical aperture, describes an objective’s “light-gathering ability,” and is also crucial in minimizing spherical aberration.)

Once you’ve selected the proper medium and ob-jective, be sure to protect your hardware investment. Beginning microscopists tend to overdo it on the oil or glycerin, which can gum up the objective’s delicate parts. For instance, modern lenses are spring-loaded to prevent the lens from punching through a sample. Excess oil can collect in that mechanism, potentially ruining the objective. Similarly, water-immersion ob-jectives, if not properly cared for, can be fouled by dried salt crystals from culture media. Media can also contaminate the body of the microscope itself if there are open slots in the objective turret, an even more expensive repair.

One quick and simple method for keeping your microscope safe is to wrap the objective barrel with an inexpensive cloth hair “scrunchie,” which can catch overflowing oil or medium. It’s far easier to replace that, than the objective itself.

Go liveLive cell imaging is perhaps one of the most re-

warding and challenging of microscopy applications. Rather than viewing a frozen moment in cellular time and trying to work out the events that preceded and followed that moment, live cell imaging provides re-searchers with a real time, video record. Yet collecting reliable data requires some careful attention to detail.

First is the question of how you will visualize the action. Intracellular staining options are limited in live cell work, as few dyes can cross the cellular mem-brane. One option is to image live cells without stain-ing them, for instance in transmitted-light differential interference contrast (DIC) mode. Or, to focus on spe-cific molecules, researchers can genetically tag them with a fluorescent protein. (Often, researchers image in both fluorescence and DIC modes, and overlay the two images.)

Genetic tagging requires molecular engineering to make the cells express the protein, and possibly some tinkering, too, as it is necessary to strike a balance: Too little protein, and you may not see

what you’re looking for; too much, and you may end up with a noisy image and possibly poison the cell, or divert it too far from its normal tasks, potentially skewing your data.

Live cell imaging also requires balancing imaging with cell behavior. You need to use sufficient excita-tion energy to efficiently stimulate fluorescence, for instance, but not so much as to induce phototoxicity or photobleaching. And you need to ensure a suf-ficiently fast frame rate to capture the behavior you hope to document.

If you hope to image the cells for an extended period of time, you have another concern: Keeping the cells alive and healthy. Eukaryotic cells are usu-ally grown in an incubator at 37°C and fixed carbon dioxide concentration. If you plan to image them for, say, 30 minutes, it’s probably enough to maintain their temperature on the microscope stage; otherwise, the cells could experience a temperature shock that alters their behavior.

For longer imaging experiments, other conditions come into play, such as humidity, CO2 concentration, and gas exchange—all of which can usually be con-trolled using an environmental control chamber. This adds additional cost and complication to your experi-mental setup, but will be worthwhile for maintaining the optimal environment for normal cell behavior and for obtaining accurate and meaningful results.

Correct your colorsWhether performing live- or fixed-cell microscopy,

researchers increasingly are imaging multiple fluorescent colors. But as anyone who has used a prism knows, light of different colors bends to different degrees in glass. As a result, green light and red light will focus to different locations, making it difficult to determine, for instance, whether two proteins are coincident, or lie on opposite sides of a membrane. This is especially true in the z (optical) axis, causing two coincident points to appear in separate optical sections.

Apochromatic lenses are color-corrected to account for these differences. Of course, researchers imaging just a single color (for instance, green fluorescent protein) can utilize uncorrected objectives, as they don’t need to worry about registering different images. But as most researchers use at least two

colors, often overlaying, say, GFP expression with DAPI nuclear staining, apochromatic objectives are typically key components of the microscopy toolkit.

It is important to note that apochromatic lenses tend to be less efficient light collectors than their uncorrected alternatives. Another key consideration is that color-corrected lenses tend only to be corrected for specific wavelengths. If, say, your objective is optimized for red light at 620 nm, but your fluorescent dye produces its strongest emission at 650 nm, then for that particular experiment the correction is only partial.

Another tool for ensuring color alignment is multi-colored microspheres. These beads contain a shallow layer of fluorescent dye that, when optically sectioned using confocal microscopy, produces a thin ring of fluorescence. In a non-color-corrected microscope, these beads appear as a set of non-overlapping col-ored rings when imaged at different wavelengths. But by adjusting the microscope alignment, researchers can correct for that issue, thereby ensuring their data will be in proper register.

Color dispersion and spherical aberrations can both influence microscopy operation, and both can be influenced by immersion media. Generally speak-ing, researchers can optimize one or the other, but not both. If absolute color alignment is key—for instance, for colocalization studies—pick an oil that works best for that purpose. On the other hand, if every photon counts, for instance under low-light, live cell imaging conditions, an oil optimized to correct spherical aber-ration may be a better choice.

Bottom line: Ask your microscope manufacturer for the precise specifications of your hardware and re-agents so that you can get as close as possible to the optimal conditions. And be prepared for some optimi-zation. It might take time to get everything operating just right. But it will all be worth it, once you see the beautiful images that emerge.

1. A. J. North, “Seeing is believing? A beginners’ guide to practical pitfalls in image acquisition,” J. Cell Biol. 172, 9–18 (2006).2. www.gelifesciences.com/oilcalculator

Jeffrey M. Perkel is a freelance science writer based in Pocatello, Idaho.

Live cell imaging is perhaps one of the most rewarding and challenging of microscopy applications.

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JS: The only thing I would add is the standard provison, which is that just because two things are colocalized at some resolution limit does not mean that they are actually molecularly interacting. So the question needs to be decorated with: what are you trying to achieve in that colocalization experiment? Are you just trying to say both of these signals are in the same organelle? Or that these two molecules are interacting? Those are two very different questions.

Q: In terms of confocality, what is the best distance between each acquisition of a series for fixed samples? Is getting more images always a better choice? PG: No, there’s photodamage and photobleaching being caused, even in a fixed sample, if you oversample in the z axis, and there’s a consequence in the data that you acquire. I take a look at the numerical aperture of the lens I’m using. From that I make an estimate of my expected depth of field or z resolution, and try to sample that distance about twice. So in a high NA, 1.4 NA objective, the z resolution is about 600 nm, so I would try to sample about every 300 nm.

JM: I would agree with that. I would say that as Paul said there’s a very clear theoretical answer to that question. The problem is that in biological applications, the amount of photobleaching prevents you from getting a useful set of images if you actually follow the theoretical limits.

Q: What do you think will be the next big revolution in microscopy? Or what you would like to see that would help your research? JS: I’m very excited about a lot of the new live cell imaging technologies. We’ve briefly mentioned superresolution technologies applied to live cell imaging. Seeing not only better spatial resolution, but also better temporal resolution, I think that’s very important.

to-noise ratio, and the rejection of background as necessary.

PG: If I’m looking at single molecules on a coverslip, I think widefield alone, or maybe TIRF, would be sufficient. But as I get more and more complex samples, I have to use the more complicated tools in my tool chest in order to get to an answer.

Q: Are confocal microscopes less affected by spherical aberrations then widefield systems? PG: I think John actually mentioned this in his presentation. There’s actual-ly a greater sensitivity to aberration in a confocal microscope and the same is also true in multiphoton. You need to make sure you don’t have spherical aberration, because that robs so much of the light from both the detection and the illumination of the specimen. Those sorts of aberrations actually have a big effect on all the imaging that we do and ideally need to be corrected at the beginning of the ex-periment.

Q: Have you tried to obtain deconvoluted or 3D-SIM images in real time?JS: First examples of that were published by Mats Gustafsson and colleagues. You have to speed up generation of the pattern and need very, very fast detectors. Commercial realizations of that system are now available and we’re very excited about it too.

Q: What is the best parameter to represent colocalization of two fluorophores in confocal imaging? JM: The best parameter, is overlap of intensities; and one must do the appropriate controls. Make sure that you’re not pushing your interpretation of the localization beyond the scale at which one is likely to get reasonable results, i.e., check the chromatic aberration and make sure that, on a specimen that you know is labeled with two fluorophores, those two images do superimpose on one another.

Jason Swedlow, Ph.D.University of DundeeDundee, Scotland

John Murray, M.D., Ph.D.Indiana UniversityBloomington, IN

widefield microscope than it would be to image a piece of glass. But typically we’re looking at groups of cells grown on a coverslip under the microscope. We can easily get through 20 to 30 microns. Beyond that, in more complex samples where there’ll be a lot of fluorescence light, we may be more limited to only a few microns.

Dr. John Murray: It is certainly speci-men-dependent and one needs to try all the available methods, but I think Paul’s limits are a good guideline.

Q: What happens if the sample thickness of the specimen you’re looking at varies across the sample? How do you deal with that?JM: That means we will probably get a better image in the thin spots than in the thick spots. But that can’t be helped, that’s biology, it’s going to vary.

Q: Which microscopic techniques are best for live cell imaging and which is the best choice: confocal versus widefield versus deconvolution?JS: The bottom line is that imaging, especially fluorescent imaging of biological samples, is quite difficult. The cells themselves are intrinsically sensitive to light. When you add fluorophores to them, they become even more so. So the best method is whichever method uses the least amount of light to achieve the signal-to-noise ratio you need in order to see the sample.

Different imaging methods use different illumination strategies. A widefield microscope usually uses a lamp or an LED light source, which usually use less illumination than laser illumination in confocal or multi-photon. However, it makes absolutely no sense to try to use a widefield microscope to image thick samples with a lot of out-of-focus fluorescence as John showed in his presentation. It’s a series of compromises and tradeoffs to reduce the amount of light you’re using, achieve the signal-

Microscopy in focus: The

art and science of image

quality

Science/GE webinar March 7 2013Text edited for brevity and clarity

Q: How can a researcher be sure that a deconvolution algorithm is quantitative and not just creating artifacts or misleading data? Dr. Jason Swedlow: This is a very, very important question. I showed you an example of the validation that we did, which was a lot of work. We wanted to ensure that the methods we were using could be used for making relative intensity measure-ments. I think the general answer is that you have to test the different

methods on the kinds of samples that you use with the kind of imaging that you do.

We published a comparison back in 2002 [J. R. Swedlow et al., Proc. Natl. Acad. Sci. U. S. A. 99, 2014 (2002)].

Mr. Paul Goodwin: What Jason says is absolutely correct in that you want to make measurements with your samples versus different techniques and I think that’s true for any tech-nique in science. You need to validate, whether you’re quantitating DNA in a gel or whether you’re imaging with a confocal or with deconvolution. You want to make sure that you can make the statements that you hope to make in your paper by running care-ful controls and doing the calibration methods yourself‡.

Q: What is the depth or the thickness limits for widefield imaging plus deconvolution versus confocal imaging? PG: As John Murray showed, it’s not so much the depth as the complexity of the sample. You can imagine it would be a much more difficult to image a brick with a

Paul C. Goodwin, M.ScGE HealthcareIssaquah, WA

Speakers

cont.>

questions & answersWebinar

To view this webinar, go to:bitly.com/artandsciencewebinar

‡At GE Healthcare we use a deconvolution algorithm originally created by David Agard in his work with John Sedat at the University of California, San Francisco. In this algorithm we carefully measure the optical performance of the lens and use that empirical measurement of the lens performance.

MICROSCOP Y NOW: GET TING THE MOST FROM YOUR IMAGING SECTION TWO | Q AND A

12 sciencemag.org SCIENCE 13SCIENCE sciencemag.org

What’s coming out of all of that work, however, is this increasing complexity and difficulty understanding what we’re seeing. We have absolutely stunning movies now of tissues forming, for instance, and now we need the tools to turn those very complex images into some sort of understanding. PG: I think the next big revolution is in various software applications. We’re particularly interested in a number of methods that would allow us to work with noisier images, in other words to start off using raw data with lower signal-to-noise ratio and still be able to achieve the same quality of image in the end. Everything that we can do to be able to reduce that amount of light is going to be critical to reduce photodamage. There are new opportunities coming out of signal theory that I think will result in huge advances over the next couple of years in being able to reduce the light load on a sample.

JM: Having worked through the intro-duction of confocal microscopy, and having seen the totally unpredictable techniques that arose as a result, I predict that the introduction of su-perresolution microscopy into bio-logical imaging will have equally un-predictable effects. And I have no idea what will come! But I’m looking forward to it with great excitement.

instrument possible, meaning you don’t have to have long exposure times. In our GE IN Cell 6000 system, you can dial in confocality when you need it, but you don’t have to have it running in all different channels. That means you’re not blasting all of your cells in all of the channels with more light than they need to get you the data.

LT: The point is less is more. If you can get away with fewer time points, less z slices, it’s better. But you can’t avoid photobleaching completely. You just have to make sure that you have some measurement of how healthy your cells are and stop be-fore they’re no longer healthy.

Q: What are the pros and cons of fluorescent chemical probes versus fluorescent proteins?NT: Some of those GFP experiments I showed were done 10 years ago. Those techniques work, so, “if ain’t broke, don’t fix it”! Nowadays there are more advanced fluorescent proteins giving a wider choice of colors and more stability. Leaving the phototoxicity issue aside for now, there are now tagging systems that allow you to put a tag on any protein and deliver a fluor into a cell. I like GFP—there’s a certain essence about it and you have all of the literature as precedent. And with the increase in sensitivity of instrumentation, you can actually cut down on the amount of protein you need to express in a cell.

systems or not. We keep everything hot so we never turn off the live cell environmental chambers and we keep the microscope, the objectives, and the whole box at 37℃ or 38℃ so that we don’t have metal expanding and contracting. We keep the oil and spare objectives inside the box to keep them warm. Also, focal drift during experiments maybe due to evaporation, so we keep the environmental chambers full of beakers of water and soggy tissues to keep the evaporation to a minimum.

Q: How can photobleaching be avoided and is phototoxicity observable at all wavelengths? Is it possible to use different wavelengths to reduce the amount of phototoxicity? EC: For photo bleaching, a big advancement has been EMCCD [electron multiplying charge couple device] cameras, which really work. They have a function called In Chip gain, which allows you to collect all of the imaginable data using tricks of physics. Another factor is the plat-form you’re using. We talked about the difference between confocals and widefield deconvolution micro-scope. There’s an article by John Murray, a true microscopy expert, who has shown that the photon cap-ture efficiency of a deconvolution microscope is better than many other systems. Essentially, deconvolution “restores” light to its origin before scattering. So you can get by with less exposure and this is the key to reducing photobleaching.

To get to the viewer’s question, cells are very much like us in that the light that affects them the most is UV. There are filters to prevent the UV light from getting to your sample, but they will sometimes preclude the use of CFP. But if I had to keep my cells alive, I would use GFP and Cherry or YFP and Cherry because they’re further away from that UV spectrum of light.

NT: From an HCA perspective it’s about using the most sensitive

Live cell imaging: The

future for discoveries

Science/GE webinar May 22 2013Text edited for brevity and clarity

Q: One of the key issues for live cell imaging of any duration seem to be maintaining focus in z. Do you have any tips and tricks you can share? Dr. Edward Campbell: One new technology is a laser guided system that detects the distance between your lens and your specimen. They are invaluable especially in long-duration imaging experiments.

Dr. Nick Thomas: The issues with HCA are somewhat different. Instru-mentation for HCA is set up to do imaging automatically. Generally, you have hardware autofocus, so you need to ensure that’s working prop-erly and use the right plates. A lot of people don’t take enough care about selecting their plates. If you’re do-ing live cell imaging, it’s even more important to have the right size of plates, make sure that they’re clean and that you don’t get dust in them as that might throw out the focus.

Dr. Lynne Turnbull: There are a lot of things you can do just to help yourself whether you have automatic

EC: I think it’s relevant to think that it’s not an either/or. So we’ve not really found a far red fluorescent protein that we enjoy, but a lot of the conjugatable dyes work very well in the Cy5 range.

Q: Could you talk about some of the applications of this work in terms of health care, public health, and disease research?NT: We spend a lot of time looking at how we can use these new technolo-gies to determine both the safety and the efficacy of drugs. In the past couple of years, a game changer has been people recognizing the poten-tial of stem cell-derived models for looking at drug toxicity and efficacy. Companies making those available have allowed researchers to conceive of experiments that they couldn’t do before. For cardiotoxicity, we use both fixed and live cell assays, and we integrate that data from both with other analytical platforms. So you get what I call a holistic surveillance of what that drug is doing to a cell. An advantage of live cell imaging is that often you can distinguish between the mode of action of two drugs that look the same. There’s really no such thing as a completely safe drug, but what we don’t want to do is fail drugs because we don’t have a clear under-standing of the mode of action. Be-cause then the general population is not getting the best drugs that they could have. So that’s what drives me in what I do.

LT: With live cell imaging, you’re observing processes as they hap-pen, which gives you a better under-standing of the stop points where you might be able to intervene, for instance in the infection process. Additionally, live cell imaging allows one to examine how a new drug works and which part of the process it impacts, which you don’t always get from fixed imaging. This can be very informative.

Webinar questions & answers continued

Edward M. Campbell, Ph.D.Loyola UniversityChicago, IL

Nick Thomas, Ph.D.GE HealthcareCardiff, Wales

Speakers

“Live cell imaging allows one

to examine how a new

drug works and which part

of the process it impacts,

which you don’t always get

from fixed imaging.”

To view this webinar, go to:bitly.com/livecellwebinar

Lynne Turnbull, Ph.D. University of TechnologySydney, Australia

MICROSCOP Y NOW: GET TING THE MOST FROM YOUR IMAGING SECTION THREE | ARTICLES:REVIEW

We have an incredibly detailed view of how proteins and lipids interact in-side cells to govern the generation, maintenance, and function of cellular organization, as determined from bio-

chemical and genetic experiments spanning diverse approaches from in vitro reconstitu-tion of cellular processes to atomic resolu-tion structure determination. However, these techniques only provide a static, snapshot view of cells. Being able to observe processes as they happen within the cell by light mi-croscopy adds a vital extra dimension to our understanding of cell function. Perhaps the commonest approach for studying dynamic cellular events is live cell fluorescence micros-copy, and we will discuss this in some detail. However, transmitted light techniques also have an important part to play (1) and not just as an adjunct to fluorescence imaging.

Environmental considerations. Regard-less of the imaging technique to be used, it is crucial to consider the cells’ health on the microscope stage. Cells are sensitive to photo-damage, particularly in the presence of fluo-rophores (which generate free radicals upon photobleaching), and there are many ways of trying to limit light-induced damage. It is also vital to keep the cellular environment constant. There are a number of solutions to this problem, including the control of tem-perature, humidity, and CO

2. Environmental

control ranges from simple heating jackets to Perspex boxes that fully encase a system. The relative importance of each parameter will vary between samples, but the overriding concern for all three is stability. In time-lapse experiments, once a sample is being imaged, the focal plane must remain stable. Autofocus routines are available, which can compensate for focus drift to some extent, but they require additional illumination of the sample. One important, but often overlooked, cause of fo-cus drift is air conditioning units, which can cause cyclic changes in focus as they turn on and off.

Fluorescence imaging

Whilst it is sometimes possible to image en-dogenous cellular molecules such as NAD(P)H (2) by their inherent fluorescence, it is far more common to introduce exogenous fluorescent molecules. The advent of green fluorescent protein (GFP) technology has revolutionized live cell imaging because an autofluorescent molecule can be genetically encoded as a fusion with the cDNA of inter-est (3). The spectral variants of GFP and the unrelated red fluorescent protein (4) make it feasible to perform multicolor imaging of liv-ing cells. The simultaneous study of multiple fluorophores or ratiometric analysis of a sin-gle probe requires spectral separation of both the excitation and emission light. Commercial systems are now available for “spectral unmix-ing” of data, and this allows the use of closely related fluorophores, but, where possible, it is better to use probes with distinct excitation and emission spectra that are separable at the point of image acquisition.

GFP-based biosensors are opening many fields to optical techniques, notably the spa-tio-temporal analysis of signaling events fol-lowing the development of probes for diverse processes including heterotrimeric G protein activity (5) and phosphoinositide signaling using GFP-tagged pleckstrin homology (PH) domain constructs (6). The field of calcium imaging also makes use of GFP-based probes (7), allowing organelle-specific analysis of cal-cium dynamics. GFP-tagging is also being ap-plied to high-throughput analyses to provide further functional annotation of genome se-quences (8).

FlAsH (fluorescent arsenical helix binder) labeling provides another means for fluores-cent labeling of genetically encoded probes (9). It is mediated by engineering a tetracysteine motif into the target protein and then incu-bating cells with a nonfluorescent biarsenical compound that becomes strongly fluorescent upon binding to this tetracysteine motif. A recent development enables multicolor label-ing and photoconversion of diaminobenzidine for correlative electron microscopy (10). Un-fortunately FlAsH compounds can also label endogenous proteins containing similar tet-racysteine motifs (11). In addition, the cyste-ine residues must be in the reduced state for

labeling to occur, and antidotes must be added simultaneously with labeling to avoid toxicity problems.

There are, of course, many other potential probes that can be introduced into cells. Spe-cific fluorescent lipid molecules and organ-elle-specific dyes are often cell permeable and can simply be added to the culture medium (12). Fluorescently labeled proteins can be introduced by microinjection, and the useful-ness of such probes is also continually driv-ing the technology for studying intracellular dynamics. A prime example of this is the de-velopment of fluorescence speckle microscopy (13). Here, introduction of a limited amount of fluorescent protein to a polymeric struc-ture, such as a microtubule or actin filament, results in a “speckled” appearance. These speckles can then be imaged over time and tracked within the cell to provide accurate quantitative analysis of polymer dynamics. Finally, a number of probes can be activated by light, allowing specific detection only after a pulse of illumination (14–16).

Live cell imaging

When selecting which system to use for im-aging living cells, one should consider three things: sensitivity of detection, speed of ac-quisition, and the viability of the specimen. Light microscopy of living versus fixed sam-ples is essentially a trade-off between acquir-ing images with a high signal-to-noise ratio and damaging the sample under observation; this is a particularly critical issue in live cell imaging. Other important questions center on the sample you want to image. Is it thick or thin? Is the process to be observed fast or slow? Do you need to image for seconds, minutes, hours, or days, and at how many dif-ferent wavelengths does the image need to be sampled? How bright is your signal? You also need to consider several further questions: Will you want to use a specialized technique such as photobleaching? Are transmitted light images required, and if so, of what quality? In many cases, no single microscope system will be best, and compromises will have to be made. A good understanding of the pros and cons of different microscopes is needed, and it is also helpful to understand the resolution of the light microscope (17). The basic features of three types of fluorescence microscope sys-tems are illustrated in Fig. 1.

Limiting cell damage. Because illumina-tion of fluorophores causes photobleaching and therefore cell damage, everything pos-sible should be done to limit the duration and intensity of illumination. A minimum requirement is the ability to shut off illumi-nation light when it is not needed; this is in-herent in confocal systems and can easily be achieved for widefield systems that use elec-tronic shutters controlled by computer (which will usually control the image acquisition as well). Care should also be taken to remove

Since the earliest examination of cellular structures, biologists have been fascinated by observing cells using light microscopy. The advent of fluorescent labeling technologies plus the plethora of sophisticated light microscope techniques now available make study-ing dynamic processes in living cells almost commonplace. For anyone new to this area, however, it can be daunting to decide which techniques or equipment to try. Here, we aim to give a brief overview of the main approaches to live cell imaging, with some mention of their pros and cons.

David J. Stephens1 and Victoria J. Allan2*

Light microscopy techniques for live cell imaging

1Department of Biochemistry, University of Bristol, School of Medical Sciences, University Walk, Bristol, BS8 1TD, UK. 2School of Biological Sciences, Oxford Road, University of Manchester, Manchester, M13 9PT, UK.*Corresponding author. Email: viki.allan@man.ac.uk

unwanted wavelengths of light and not to rely simply on the excitation filters. Reducing the level of oxygen can help reduce photobleach-ing and free radical production. Oxygen can be removed from the medium as long as the cells are in a sealed chamber (13), providing the cells tolerate oxygen withdrawal. Finally,

omitting phenol red and serum from the me-dium (again, if your cells will stand it) will help reduce background fluorescence.

The system must also make best possible use of the light, so high numerical aperture objectives should be used, and there should be as few optical elements in the light path

as possible. The sensitivity of the camera (or photomultiplier tube, if using a confocal) will be vital (18), because the more sensitive the detector, the lower the illumination intensity needed. Using an intensified camera is one way of increasing sensitivity, at the expense of increasing noise in the image. Alternatively, sensitive back-illuminated charge-coupled device (CCD) cameras with thinned chips are available. A new type of camera that ampli-fies the CCD readout signal on the chip (19) offers further possible advantages. Another simple way of increasing camera sensitivity is to combine signals from multiple pixels (called binning), although this process re-duces image resolution.

Speed of acquisition. A key consideration is speed of data acquisition, particularly when multiple fluorophores are imaged simultane-ously or when a single probe is analyzed ra-tiometrically. Switching between laser lines, filters, or output from a monochromator will slow data acquisition (Fig. 1). Monochroma-tor-based systems have the advantage of rapid switching between excitation wavelengths (typically <3 ms) but suffer from reduced il-lumination intensity, principally due to fiber optic coupling to the microscope. Filter wheel configurations usually have higher light throughput but are often slower in switching. Data acquisition rates of conventional scan-ning confocal microscopes are fast enough for rapid imaging if only small regions are sampled. To image very fast processes such as neuronal network activity, one may obtain faster scanning by using resonant galvanome-ters (which are optional on many commercial systems). Another important consideration is that scanning systems acquire data pixel by pixel, whereas CCD cameras acquire a whole field of view at once.

Scanning speed in confocal microscopy can also be improved with the use of multifocal imaging (20). Here, the excitation light beam is split into multiple foci from which data are collected simultaneously with a CCD (Fig. 1). Nipkow disk confocal microscopy is available commercially through a number of suppli-ers and can achieve speeds of 360 frames per second. Sensitivity and data acquisition rates of Nipkow disk systems, like widefield micro-scopes, depend on the quality and the readout time of the detector CCD. No single camera will perform optimally for all tasks, and cor-rect matching of optics and electronics is es-sential for best performance.

Three- and four-dimensional (3- and 4D) imaging. Researchers are often attracted to confocal systems because high-resolution 3D images (Fig. 2) can be acquired simply. How-ever, many experiments, particularly those using live cells, may be better performed us-ing widefield (conventional) systems with subsequent deconvolution of the data series. Widefield microscopes do not exclude light from any plane of focus; they collect it all. The contribution of light from an infinitely

1514 Originally published 4 April 2003 in SCIENCE SCIENCE sciencemag.orgsciencemag.org SCIENCE

Fig. 1. Comparison of widefield, scanning confocal, and spinning disk confocal systems, with schematics of each. All systems are capable of being equipped for 3D and 4D data acquisition. Excitation beams are shown in green; emission beams, in blue. The differences between these systems mean that no single system is suited for every experiment. Typical system configurations are shown, and user modification and options allow great flexibility.

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MICROSCOP Y NOW: GET TING THE MOST FROM YOUR IMAGING SECTION THREE | ARTICLES:REVIEW

including photobleaching. However, a key ad-vantage of FCS is that it is also applicable to single molecule studies.

Photobleaching and photoactivation ap-proaches. Because scanning confocal mi-croscopy has control over the region of illumination, it is ideal for photobleaching techniques such as fluorescence recov-ery after photobleaching (FRAP) and fluores-cence loss in photobleaching (FLIP) (43, 44). Both are now widely used to measure dif-fusional mobility of GFP-tagged proteins in cells. Increasingly, they are combined with kinetic modeling of cellular processes (45) to study topics as diverse as membrane traffic and nuclear architecture and function (46). Probes that can be light-activated such as photoactivatable-GFP and Kaede (14), both of which show greatly enhanced fluorescence emission following activation at ∼400 nm, al-low selective labeling of subdomains of cells and organelles.

Fluorescence resonance energy transfer (FRET). Intermolecular interactions form the basis of all processes in live cells and can

small point source to a plane of focus some distance away from that point source is de-scribed by the point spread function (PSF) of the objective. Determination of the PSF of a system enables mathematical reassignment of the out-of-focus light back to its point source by deconvolution (21). This approach has been used with great success in both cell and devel-opmental biology, and it can be particularly advantageous in imaging very weakly fluo-rescent structures such as microtubules (Fig. 2) (22). Deconvolution must be applied with great care and accuracy, however, to avoid the generation of artefacts (21). Deconvolution of large 4D data series can now be achieved in minutes to hours with the use of dual proces-sor personal computers.

Most cellular processes occur in three dimensions over time, so to get a complete picture we need to image cells in four dimen-sions. Most confocal systems and epi-illumi-nation microscopes are either provided with or can be simply adapted to include a means of acquiring data series in four dimensions. Perhaps the most important consideration is the speed, accuracy, and reproducibility of thez position change. Piezoelectric ob-jective drives have the edge here, enabling high-speed acquisition of stacks at multiple wavelengths over time. Multidimensional live cell imaging also requires tools for data

analysis (23). Tools are continually being developed for particle track-ing of objects moving inside cells such as transport vesicles (24). Most reconstruction approaches assume a uniform refractive index through the sample, which is not encoun-tered often in reality (25); further developments are needed to address this issue.

Multiphoton approaches to in vivo imaging. Multiphoton confocal sys-tems are now available from several companies. The two-photon effect ex-cites a chromophore not by a single photon but from two photons being absorbed within a femtosecond time scale (26). This enables the use of longer wavelength excitation, which penetrates deeper into samples and reduces photobleaching. Notably, the analysis of intact organisms or tis-sues greatly benefits from this tech-nique, allowing imaging in situ (27). Such systems have been used for im-aging both tumor development (28) and the pathophysiology of Alzheim-er’s (29) by replacing a small part of the skull with a coverslip. Alterna-tively, imaging of neuronal processes through thinned skulls is also pos-sible (27). However, other techniques, using single photon excitation, have also been applied with great success to the imaging of protein interactions (30) and the analysis of gene expres-

sion (31) in living animals. These approaches are likely to be developed toward medically applicable systems for diagnosis and treat-ment of patients, extending the capabilities of existing magnetic resonance imaging and positron emission tomography technologies. Optical imaging is likely to be of great benefit to the application of gene therapy in combina-tion with nontoxic fluorescent reporters and of monitoring cancer progression and treat-ment. Similarly, confocal imaging has recently been coupled with endoscopy (32) with diag-nostic potential.

Other imaging modes

Bright-field imaging. Imaging living cells with transmitted light is often used along with fluorescence microscopy in order to provide information on cell shape, position, and motil-ity. This is absolutely vital when studying pro-cesses such as apoptosis and mitosis, where cells undergo drastic shape changes. Phase contrast and differential interference contrast (DIC, also called Normarski) microscopy are the most commonly used. To switch between transmitted and fluorescence imaging under computer control requires a shutter on the transmitted light path in addition to a fluo-rescence shutter. A complication with DIC is that it needs a polarizing filter (the analyzer)

between the objective and the camera, and if this is left in place during fluorescence im-age capture it will reduce the intensity of the image reaching the camera. There are some systems available to avoid this, which have a motorized analyzer that can be moved out of the light path, or an analyzer that works only at certain wavelengths of light.

Whilst a simple image captured by the mi-croscope camera system will be enough for many experiments, there is huge potential for obtaining detailed insight into cell function by pushing transmitted systems to the limits

by using the best possible optics and specialized image-processing equipment (33). This is clearly il-lustrated in Fig. 3 and associated movies S1 and S2 [see also (1)]. In addition, there are other special-ized light microscopy techniques, such as reflection contrast mi-croscopy (34) and DRIMAPS [Digitally Recorded Interfer-ence Microscopy with Automatic Phase Shifting, (35)], that provide different types of information about cell structure and function. However, all of these advanced transmitted light techniques do require specialist equipment and knowledge, which probably explains their rather limited use at present. In addition, research-ers will often only be interested in a single organelle or structure, which will make fluorescence the method of choice.

Total internal reflection. Many cellular processes occur in specif-ically restricted areas of the cell, such as the plasma membrane. Total internal reflection fluores-cence microscopy (TIRFM) (Fig. 4) provides a means of direct imaging of processes within very close proximity to the coverslip (36). Excitation at a critical angle generates an evanescent field of excitation light that decays rapidly with distance from the coverslip, limiting the depth of excitation to a distance of ∼100 nm. TIRFM of live cells has given insight into the role of actin and dynamin in endocytosis (37) and can also be combined with other techniques such as photobleaching (38) or widefield im-aging. One of the most exciting recent devel-opments has been the ability to image single molecules in living cells (39), and examples of this include growth factor receptor signaling (40) and viral infection (41). Although techni-cally demanding and requiring state-of-the-art equipment, the coupling of these technologies with GFP-based expression strategies is sure to lead to further developments in the near future. Systems for TIRFM are now commer-cially available but, as with most of the recent developments in microscopy, require skilled use and careful interpretation of data.

Fluorescence correlation spectroscopy. Cel-lular processes can also be imaged using a very small area of illumination by fluores-cence correlation spectroscopy (FCS), which is used to quantitatively measure local con-centration and diffusion of particles through a very small volume and can now be applied to live cells (42). Due to its high sensitivity, the technique is prone to imaging artefacts such as intramolecular changes in fluorescence,

be monitored by measuring the proximity of one component to another. In the context of light microscopy, this can be achieved with the use of FRET. FRET oc-curs when two spectrally overlap-ping fluorophores are very close together and in an orientation such that dipole-dipole coupling results in a transfer of energy from one probe to another (47). Because the efficiency of FRET depends on the inverse sixth power of the distance between the donor and acceptor (47), this allows measurement of protein-protein interactions in live cells. Limitations of this approach are that FRET is extremely in-efficient, and many hypotheti-cal FRET pairs do not produce FRET in live cells. The most reli-able and reproducible examples of FRET occur when the donor and acceptor fluorophores are covalently linked to one another (48). Excellent examples of this include the elimination of FRET after caspase cleavage of a linker between donor and acceptor mol-ecules (49) and the application of FRET to biosensors measuring intracellular processes such as calcium flux (7). Further devel-opments of FRET pairs and im-provements in imaging methods (50) will doubtless enhance the applicability of FRET to live cell studies.

Fluorescence lifetime imaging (FLIM). The detection of fluores-cent probes is typically achieved

by counting the number of photons emitted by the excited state of a fluorophore. An al-ternative approach is to measure the lifetime of this excited state with the use of FLIM (51). This provides a means for detecting multiple fluorophores in live cells including spec-trally related molecules such as GFP vari-ants (52), which have different fluorescence lifetimes despite substantially overlapping spectra. FLIM provides an excellent means for measuring FRET because the lifetime of the excited state decreases greatly when FRET is occurring (essentially there is an additional means for decay from the excited state). FLIM measurement of FRET has re-cently been applied to the imaging of kinase activation (53). However, there are a number of limitations of the approach: resolution is limited, and FLIM is extremely difficult to perform on live cells. Despite the advent of commercial add-on packages for confocal microscopes, a key limitation remains that FLIM is technically very demanding and also requires complicated mathematical analysis of results.

17SCIENCE sciencemag.org

Fig. 2. Examples of 3D images obtained by confocal and widefield deconvolution microscopy. (A) A mitotic spindle in a DLD1 cell imaged by single photon confocal microscopy (61). (B and C) A mitotic spindle in a Xenopus XLK2 cell imaged by 3D widefield microscopy [adapted from (21), with permission from Eaton Publishing]. A single plane of a z series without additional processing [(B), original data] and the same data set after restoration by constrained iterative deconvolution [(C), restored] are shown. Scale bar, 2 μm.

Fig. 3. Video-enhanced transmitted light microscopy. Imaging living cells by video-enhanced differential interference contrast (A and B) and phase contrast (C and D) microscopy reveals a wealth of information on organelles including mitochondria (arrows) and the endoplasmic reticulum (ER) (arrowheads). The nucleus (N) and centrosomal area (C) are marked in (A). The ER is more obvious in the associated movie clips of a Vero cell [(A) and (B), from movie S1] and a Xenopus tissue culture (XTC) cell [(C) and (D), from movie S2], imaged as described (61). An immunofluorescence image of the ER in an XTC cell (61) is shown for comparison (E and F). (B), (D), and (F) are enlargements of the boxed areas in (A), (C), and (E). Scale bars in (A), (C), and (E), 2 μm ; in (B), (D), and (F), 1 μm.

Fig. 4. An evanescent field occurs when incident light passes from a medium of high refractive index (glass) to one of low refractive index (water or a cell). Total internal reflection occurs when the angle of inci-dent light exceeds a critical angle α. This field decays rapidly and, there-fore, only illuminates ∼100 nm of the sample closest to the coverslip. This enables specific visualization of only those fluorophores in direct proxim-ity to the plasma membrane (shown in red), not those lying further away (green). This illumination mode can be coupled with conventional widefield microscopy to allow combined imaging of events close to and away from the coverslip.

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Priscila F. Siesser,1* Marta Motolese,2*† Matthew P. Walker,1 Dennis Goldfarb,1, 3

Kelly Gewain,1 Feng Yan,1 Rima M. Kulikauskas,4 Andy J. Chien,4 Linda Wordeman,5 Michael B. Major1‡

INTRODUCTION

The FAM123 gene family comprises three members: FAM123A, which is also known as AMER2; FAM123B, which is also known as WTX,AMER1, and OSCS; and FAM123C. The founding member,

FAM123B (hereinafter referred to as WTX), plays fundamental roles in normal devel-opment and human disease. Mutations in WTX contribute to various diseases, such as Wilms tumor, a pediatric kidney cancer (1, 2), and osteopathia striata congenita with cranial sclerosis (OSCS), an X-linked devel-opmental disorder that causes bone-related defects in females (3) and is lethal in males, often at embryonic or neonatal developmen-

FAM123A binds to microtubules and inhibits the guanine nucleotide exchange factor ARHGEF2to decrease actomyosin contractility

1Department of Cell and Developmental Biology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill School of Medicine, Box 7295, Chapel Hill, NC 27599, USA. 2Howard Hughes Medical Institute, Department of Pharmacology, and Institute for Stem Cell and Regenerative Medicine, University of Washington School of Medicine, Box 357370, Seattle, WA 98195, USA. 3Department of Computer Science, University of North Carolina at Chapel Hill, Box 3175, Chapel Hill, NC 27599, USA. 4Division of Dermatology, Department of Medicine, University of Washington School of Medicine, Box 358056, 815 Mercer Street, Seattle, WA 98109, USA. 5Department of Physiology and Biophysics, University of Washington School of Medicine, Box 357370, Seattle, WA 98195, USA.*These authors contributed equally to this work.†Present address: Istituto NeurologicoMediterraneo Neuromed, Pozzilli 86077, Italy.*Corresponding author. E-mail: benmajor@med.unc.edu

tal stages. Mice lacking WTX display various developmental abnormalities in tissues of mesenchymal origin, such as increased bone mass and decreased adipose tissue (4). Cel-lular and molecular analyses of these tissues indicate a critical role for WTX in regulating cell differentiation programs in mesenchymal progenitors.

Mass spectrometry (MS)–based proteomic dissection of WTX protein complexes has revealed several core components of the β-catenin–dependent WNT signal transduc-tion pathway, including β-catenin (encoded by CTNNB1), βTrCP2 (encoded by FBXW11), and adenomatous polyposis coli (APC) (5, 6). Sub-sequent functional studies in various cells and organisms demonstrated that WTX inhibits WNT pathway activity (4, 5, 7). In vitro stud-ies and cell-based assays suggest that WTX promotes β-catenin ubiquitination and sub-sequent proteasomal degradation, perhaps by serving as a membrane-bound scaffold for the β-catenin phosphorylation complex (5–8).

Of the FAM123 family members, WTX and FAM123A share greatest homology, par-ticularly in their N termini (6, 9, 10). Two conserved functional domains in WTX and FAM123A have been identified and charac-terized (6, 11). First, both proteins share an N-terminal phosphatidylinositol (4, 5)-bispho-sphate–binding domain that localizes these

proteins to the plasma membrane and is re-quired for WTX- and FAM123A-mediated in-hibition of WNT signal transduction. Second, WTX and FAM123A directly bind to APC and regulate its subcellular distribution, recruit-ing it from the microtubule tip complex to the plasma membrane. Although the functional consequences of this redistribution are not completely understood, the role of APC in microtubule stabilization and maintenance of cell-cell junctions suggests that WTX and FAM123A may influence directional cell mi-gration and polarity (6). Whether the more distantly related family member FAM123C also regulates WNT signaling, localizes to the plasma membrane, or binds APC remains unknown.

In contrast to WTX, the cellular, devel-opmental, and disease contributions of FAM123A and FAM123C remain less under-stood. Thus, we defined and compared the protein-protein interaction networks for each member in the FAM123 family. Functional annotation of the resulting protein interac-tion network and comparative protein dy-namic studies supports both conserved and divergent functions for the FAM123 family members. Here, we report a “family-unique” function for FAM123A in controlling com-munication between the microtubule and actomyosin cytoskeletal networks. We found that FAM123A binds the microtubule plus-end tracking proteins end-binding protein 1 (EB1) and EB3; moves on microtubules; and controls microtubule dynamics, actomyosin organization, and cell migration. We present a model wherein FAM123A binds to and in-hibits guanine nucleotide exchange factor H1 (GEF-H1; encoded by ARHGEF2) to decrease actomyosin contractility.

RESULTS

Comparative proteomics of the WTX family reveals shared and unique interactions

To provide insight into the cellular functions of FAM123A and FAM123C, we defined their protein interaction networks by shotgun liq-uid chromatography–tandem MS of affinity-purified protein complexes. Integration of the resulting protein interaction networks with a previously defined WTX protein interaction network revealed shared and unique protein-protein interactions (Fig. 1A and table S1). Consistent with the homology relationships within the family, FAM123A and WTX shared several common interacting proteins; in con-trast, the FAM123C protein interaction net-work was distinct (Fig. 1B and table S1).

Because of their homology, overlapping protein interaction networks, and shared function as inhibitors of WNT signaling, protein interactions from the WTX and FAM123A networks were validated. FAM123A or WTX protein complexes were isolated by streptavidin-affinity purification from

The FAM123 gene family comprises three members: FAM123A, the tumor suppressor WTX (also known as FAM123B), and FAM123C. WTX is required for normal development and causally contributes to human disease, in part through its regulation of β-catenin–depen-dent WNT signaling. The roles of FAM123A and FAM123C in signaling, cell behavior, and human disease remain less understood. We defined and compared the protein-protein interaction networks for each member of the FAM123 family by affinity purification and mass spectrometry. Protein localization and functional studies suggest that the FAM123 family members have conserved and divergent cellular roles. In contrast to WTX and FAM123C, we found that microtubule-associated proteins were enriched in the FAM123A protein interaction network. FAM123A interacted with and tracked with the plus end of dynamic microtubules. Domain interaction experiments revealed a “SKIP” amino acid motif in FAM123A that mediated interaction with the microtubule tip tracking proteins end-binding protein 1 (EB1) and EB3—and therefore with microtubules. Cells depleted of FAM123A showed compartment-specific effects on microtubule dynamics, increased actomyosin contractility, larger focal adhesions, and decreased cell migration. These effects required binding of FAM123A to and inhibition of the guanine nucleotide exchange factor ARHGEF2, a microtubule-associated activator of RhoA. Together, these data sug-gest that the SKIP motif enables FAM123A, but not the other FAM123 family members, to bind to EB proteins, localize to microtubules, and coordinate microtubule dynamics and actomyosin contractility.

Conclusions and perspectives

The rapid development of live cell micros-copy has required input from biologists, who have provided new fluorescent probes that can be easily adapted to study myriad different proteins, and physicists, who have driven the improvement in microscope sys-tems and software. These contributions have led to unprecedented access to sophisticated imaging technology. For example, many re-searchers who have no previous microscopy experience may now use a departmental con-focal microscope. It is clear that research in specialist laboratories will continue to drive developments in light microscopy. The reso-lution attainable by light microscopy is being enhanced by recent developments in imaging that break the diffraction limit (54), and such approaches can be applied to live cells. Re-cent work using stimulated emission deple-tion (STED) to quench excited fluorophores at the rim of the focal illumination spot has enabled a substantial increase in resolution to below the diffraction limit, giving a spot size of 100 nm (55). 4Pi confocal microscopy, in which two opposing objective lenses are used to sharpen the point spread function of illumination (56), has been used for live cell imaging, and incorporation of STED with 4Pi microscopy has reduced the spot size to 33 nm (57). Computational adaptive optics, widely used by astronomers, can be used to correct for changes in refractive in-dex within thick specimens (58). Alternative approaches to increase attainable resolution include scanning near-field optical micros-copy (SNOM, also known as NSOM). SNOM is similar to atomic force microscopy in that a sharp probe physically scans the surface of the sample; it can also be coupled to fluores-cence imaging (59), where excitation light is guided through this probe, and its applica-tion to living cells is under development.

The field of live cell imaging is also of great interest to pharmaceutical and bio-technology companies (60). Many are now developing high-throughput and high-content screening platforms for automated analysis of intracellula r localization and dynamics. This is paralleled with the increasing devel-opment of fluorescent biosensor assays that provide an optical readout of a physiologi-cal effect, often based on GFP technology or bioluminescence. Clearly, future develop-ments in this field will be of great interest and benefit to both biotechnology and curiosity-driven research.

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SUPPORTING ONLINE MATERIALwww.sciencemag.org/cgi/contentfull/300/ 5616/82/ DC1Materials and MethodsMovies S1 and S2

Originally published 4 September 2012 in SCIENCE SIGNALING

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human embryonic kidney (HEK) 293T cells, and associated endogenous proteins were detected by Western blot. Whereas both WTX and FAM123A proteins associated with APC and βTrCP1 (encoded by BTRC) and βTrCP2 (encoded by FBXW11), WTX specifically bound β-catenin, confirming the proteomic data (Fig. 1C). These results strengthen models in which WTX and FAM123A functionally regulate β-catenin–dependent WNT signaling at the level of the destruction complex, most likely through interactions with APC, βTrCP1, or βTrCP2.

We chose to further investigate the enrich-ment of microtubule-associated proteins in the FAM123A protein interaction network

(Fig. 1A, green nodes), such as EB1 (encoded by MAPRE1), an obligate component of the microtubule plus-end tip tracking protein complex (12–14). Western blot analysis of affinity-purified protein complexes revealed an interaction between FAM123A and en-dogenous EB1 but not between WTX and EB1 (Fig. 1C). Additionally, several other proteins in the FAM123A network bind to the micro-tubule cytoskeleton: the microtubule affin-ity–regulating kinases MARK2 and MARK3, GEF-H1, EB3 (encoded by MAPRE3), ACF7 (actin-crosslinking factor 7; encoded by MACF1), CLASP2 (cytoplasmic linker pro-tein–associated protein 2), and APC (15–20) (table S1). By affinity purification and mass

spectrometry (AP/MS), these proteins are the frequently observed FAM123A interact-ing proteins, and are largely absent from the WTX protein interaction network (Fig. 1A).

FAM123A moves on growing microtubules

WTX and FAM123A, but not FAM123C, localize predominantly to the cytoplasmic face of the plasma membrane through interactions with phospholipids (fig. S1) (6). Given the relative enrichment of microtubule-associated pro-teins in the FAM123A protein interaction net-work, we hypothesized that a pool of FAM123A might localize to and move on microtubules.

Fig. 1. FAM123A associates with a microtubule-enriched protein interaction network and moves on microtubules. (A) WTX and FAM123A protein interaction networks as defined by AP/MS. Proteins shown were represented by at least two unique peptides in at least two (of four) WTX experiments or at least two (of three) in FAM123A experiments. DB, database. (B) The FAM123C protein interaction network, as defined by proteins that were identified by at least two independent peptides in at least two of three experiments. In (A) and (B), node size and coloring reflect spectral counts and gene ontology, respectively. (C) Streptavidin affinity–purified protein complexes from HEK293T cells stably expressing SBPHA-GFP, SBPHA-WTX, or SBPHA-FAM123A were analyzed by Western blot for the indicated endogenous proteins (SBP, streptavidin binding peptide; HA, hemagglutinin). Data represent two biological replicates. (D) Images from movies of HT1080 cells that were transiently transfected with Venus-tagged FAM123A (movie S1) or WTX (movie S2) constructs. Data are representative of two independent biological replicates. Scale bar, 20 μm. (E) HeLa cells were transfected with EGFP-FAM123A and mCherry-tagged α-tubulin, treated with dimethyl sulfoxide (DMSO) or nocodazole, fixed, and imaged by deconvolution microscopy. The FAM123A image is representative of five independent experiments imaged on five different days. Of 65 imaged cells, 39 exhibited long filamentous structures in cells expressing Venus-FAM123A that colocalized with microtubules. Scale bar, 5 μm.

Fig. 2. Interaction of FAM123A with EB1. (A) EB1 or anti-mouse immunoprecipitates from HEK293T cells stably expressing SBPHA-FAM123A were immunoblotted for the indicated proteins. Data represent four biological replicates. (B) Top: FAM123A mRNA quantitation by quantitative PCR of HEK293T cells transfected with the indicated siRNAs. The mRNA copy number of FAM123A was normalized to 18S ribosomal RNA. Error bars represent SD in the PCR reactions. Bottom: Western blot analysis of FAM123A abundance in HEK293T cells stably expressing SBPHA-FAM123A that were transfected with the indicated siRNAs. (C) Western blot analysis of endogenous FAM123A in HEK293T cells transfected with control or FAM123A-specific siRNA#2. (D) Endogenous EB1 protein complexes from HEK293T cells were immunoblotted for FAM123A. (E) Endogenous EB1 immunoprecipitates from HeLa cells were immunoblotted for FAM123A. (F) HeLa cells transfected with EGFP-FAM123A were fixed, stained for endogenous EB1, and imaged with confocal microscopy. A Z-projection for all captured 0.2-μm slices and a single 0.2-μm slice at the bottom of the cell is shown. The colocalization of EGFP-FAM123A with EB1 was analyzed in two independent experiments imaged on two different days. For 20 EGFP-FAM123A cells analyzed, all 20 showed colocalization with EB1. Scale bar, 10 μm.

Fig. 3. FAM123A binds APC and EB1 through distinct domains. (A) Cells stably expressing SBPHA-FAM123A were transiently transfected with the indicated siRNAs, lysed, subjected to streptavidin-affinity purification (AP), and Western-blotted. (B and C) HEK293T cells transiently transfected with EB1-EGFP (B) or APC (amino acids 1 to 1060) (C) and the indicated SBPHA-FAM123A fragment were lysed, subjected to streptavidin-affinity purification, and Western-blotted. Data represent three biological replicates. (D) Protein domain interaction mapping shown in (B) and (C) and fig. S2.

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Imaging of Venus-FAM123A or Venus-WTX in live HT1080 human sarcoma cells (Fig. 1D) indicated that both proteins exhibited mainly membranous distribution, seemed to be en-riched at cell ruffles, and induced cell death when overexpressed. In cells expressing low to moderate amounts, FAM123A (movie S1), but not WTX (movie S2), localized to filamen-tous structures resembling microtubules. This localization pattern was often polarized with respect to the direction of cell movement; of 142 cells with low to moderate expression of FAM123A, 94 cells showed FAM123A decorat-ing filamentous structures polarized in the direction of cell movement. Colocalization of FAM123A and α-tubulin confirmed the mi-crotubule localization suggested by live-cell imaging (Fig. 1E), which was abolished by the addition of the microtubule destabilizing drug nocodazole. By high-resolution live-cell imaging, EGFP (enhanced green fluorescent protein)–FAM123A was observed to predomi-nantly move on segments of microtubules and was also observed as dots that moved on lin-ear trajectories from the cell body toward the cell periphery (movie S3). Thus, both compar-ative proteomics and live-cell imaging demon-strate that FAM123A, but not WTX, associates with and moves on microtubules.

FAM123A interacts with APC and EB1 through distinct domains

MS and Western blot analysis revealed that FAM123A associated with the plus-end tracking protein EB1 (Fig. 1, A and C). Sta-bly expressed hemagglutinin (HA)–tagged FAM123A was detected in protein complexes isolated by immunopurification of endog-enous EB1 (Fig. 2A). To demonstrate the in-teraction between the endogenous proteins, we generated an antibody against FAM123A and confirmed its specificity with two non-overlapping FAM123A-specific small inter-fering RNAs (siRNAs) (Fig. 2, B and C). In both HEK293T and HeLa cells, endogenous FAM123A was detected within endogenous EB1 immunopurified protein complexes (Fig. 2, D and E). Confocal Z-series images of HeLa cells expressing EGFP-FAM123A and immu-nofluorescently labeled for endogenous EB1 demonstrated basal colocalization of these proteins with increased intensity at the mi-crotubule distal ends toward the leading edge (Fig. 2F).

APC directly interacts with EB1 and FAM123A. To determine whether FAM123A indirectly binds EB1 through APC (21), we purified FAM123A from cells transfected with siRNAs targeting either APC or EB1, and found that FAM123A interacts with EB1 in the absence of APC, and similarly inter-acts with APC after silencing of EB1 (Fig. 3A). These results suggest that FAM123A indepen-dently binds APC and EB1. Although WTX as-sociates with APC, it did not affinity-purify with EB1 (Fig. 1C). To map the domains of

FAM123A that interacted with APC and EB1, we generated a series of FAM123A deletion mutants on the basis of homology within the WTX family and of secondary structure pre-diction for FAM123A (http://robetta.baker-lab.org/). Affinity purification of full-length FAM123A protein or the truncated fragments from cells expressing EB1 or APC indicated that FAM123A interacted with EB1 through its extreme C terminus, specifically amino ac-ids 457 to 552 (Fig. 3B) and with APC through an N-terminal domain comprising amino acids 261 to 349 (Fig. 3C). Additionally, FAM123A interacted with βTrCP2 through a C-terminal region encompassing residues 261 to 470, which overlaps with the APC binding domain (Fig. 3D and fig. S2). These results demonstrate that FAM123A independently interacts with EB1 and APC through nonover-lapping domains.

FAM123A interacts with EB proteins through the EB binding motif “Ser-x-Ile-Pro”

Many plus-end tracking proteins contain a characteristic EB-binding motif, which is de-fined by a Ser-x-Ile-Pro (SxIP) consensus (Fig. 4A) (22), and which enables direct binding to EB1 and EB3 and, consequently, localiza-tion to the growing microtubule plus-end. We found a SKIP487–490 and a TKIP518–521 motif within FAM123A, both of which are in the EB1 binding region identified by domain mapping (Fig. 3B). To determine whether the SKIP487–490 motif is required for binding to EB1 and EB3, we created a mutant in which Ile489 and Pro490 residues were changed to alanine (FAM123A-IPAA) (Fig. 4A). Affinity purification and Western blot analysis revealed that in contrast to wild-type FAM123A, the IPAA mutant did not pull down EB1 or EB3 (Fig. 4, B and C). These results demonstrate that the SKIP487–490 motif in FAM123A is necessary for association with EB1 and EB3; whether the TKIP518–521 mo-tif contributes to binding in the presence of the SKIP487–490 motif remains to be tested.

Many plus-end tracking proteins contain-ing the “SxIP motif” bind a coiled-coil do-main within the C terminus of EBs, referred to as the EBH domain (17, 22). To determine whether FAM123A also binds to the EBH do-main of EB1, we generated two EB1 deletion constructs that encoded the N-terminal micro-tubule-binding domain (amino acids 1 to 135) or the C-terminal EBH domain (amino acids 136 to 268). Affinity purification of FAM123A from cells coexpressing these EB1 truncations revealed an association between FAM123A and the EBH domain of EB1 (Fig. 4D). Thus, FAM123A interacts with the C-terminal region of EB1 through the consensus EB1 binding motif SxIP.

The SxIP motif targets functionally and structurally unrelated plus-end tracking proteins to growing microtubule ends in an EB-dependent manner (22). To determine

whether FAM123A microtubule localization and plus-end tracking require EB associa-tion, we compared the protein distribution and dynamics of FAM123A and FAM123A-IPAA by live-cell imaging (Fig. 4E and movies S4 and S5). Wild-type FAM123A coated the distal ends of the microtubule and cotracked with EB3 in the cell body (movie S4). In con-trast, FAM123A-IPAA exhibited decreased EB3-associated plus-end tip tracking and mi-crotubule decoration, coating very few micro-tubule stretches at the cell periphery (movie S5). A C-terminal fragment of FAM123A that contains the SKIP487–490 EB-interaction motif did not decorate the microtubule lattice but rather behaved as a classic plus-end micro-tubule-binding protein showing robust EB3-associated tip tracking (fig. S3 and movie S6). Mutation of the SKIP487–490 motif com-pletely abolished microtubule colocalization and EB3-associated tip tracking (fig. S3 and movie S7).

Despite the autonomous microtubule-binding capability of EB proteins in vitro, FAM123A could influence EB1 loading or distribution on microtubules ex vivo. How-ever, depletion of FAM123A in HeLa cells did not affect the subcellular distribution of en-dogenous EB1 (Fig. 4F). To complement the loss-of-function approach, we localized EB1 in cells stably overexpressing FAM123A (fig. S4). Forced expression of FAM123A, but not of FAM123A-IPAA, relocalized EB1 to the microtubule lattice at the bottom of the cell and to the plasma membrane more apically. Although consistent with its ability to bind EB1, it remains to be seen whether EB1 re-distribution after FAM123A overexpression occurs normally. Together, these data demon-strate that (i) FAM123A is a microtubule-as-sociated protein with EB-dependent plus-end tip tracking capabilities; (ii) FAM123A binds EB proteins through the SKIP487–490 motif; (iii) FAM123A predominantly decorates the microtubule lattice at the cell periphery in a largely EB-dependent fashion; and (iv) FAM123A silencing does not affect EB1 sub-cellular localization.

EB-protein association is not required for FAM123A regulation of Wnt signaling

Of the three FAM123 family members, only FAM123A contains an SxIP motif, which is consistent with the lack of association be-tween WTX and EB1 (Fig. 1, A and C). Given this unique protein interaction within the FAM123 family, we predicted that the EB1 as-sociation would be dispensable for the com-mon functions of WTX and FAM123A, such as regulation of β-catenin–dependent WNT sig-naling (5, 7, 11). Indeed, siRNA-based knock-down of FAM123A or WTX increased the activity of a β-catenin reporter gene (fig. S5A). In a gain-of-function approach, overexpres-sion of FAM123A or FAM123A-IPAA reduced WNT3A-dependent reporter activation in a

concentration-dependent manner (fig. S5, B and C). These data suggest that the ability of FAM123A to modulate the Wnt–β-catenin pathway is independent of its interaction with EB1 and microtubules.

FAM123A controls microtubule organization and growth rates

Because FAM123A bound EB proteins and moved on microtubules, we tested whether its loss affected microtubule organization and dynamics. siRNA-mediated silencing of FAM123A in HeLa cells induced disorganiza-tion of the microtubule network with exces-sively curved microtubules and increased

microtubule density (Fig. 5, A and B). In contrast, siRNA-mediated silencing of WTX yielded a distinct microtubule organization (Fig. 5A). We used total internal reflective flu-orescence (TIRF) microscopy to image GFP-tagged EB3 in siRNA-transfected HeLa cells, and found that FAM123A promotes microtu-bule growth within the cell body because cells lacking FAM123A had significantly slower microtubule polymerization (Fig. 5, C and D). In contrast, in the absence of FAM123A, mi-crotubules demonstrated less dynamic move-ment near F-actin bundles, particularly near adhesion complexes (Fig. 5D). Thus, FAM123A regulates microtubule dynamics and the over-all organization of the microtubule network.

FAM123A inhibits actin contractility by suppressing the GEF-H1–RhoA–ROCK– MLC pathway

In addition to the altered microtubule orga-nization, we noticed that FAM123A-depleted cells had phase dark cortical membranes (Fig. 6A). These observations and the increase in cortical F-actin detected by TIRF microscopy (Fig. 5D) suggested involvement of the signal-ing pathway that mediates cross talk between the microtubule and actomyosin cytoskeletal networks (15, 23). Specifically, microtubule depolymerization induces actin stress fiber formation and cell contractility through activation of the Rho-specific exchange fac-tor GEF-H1, which subsequently activates

Fig. 4. EB1 association is required for FAM123A microtubule localization. (A) Protein sequence alignment of FAM123A and the SxIP domains of various plus-end tracking proteins. (B and C) Western blot analysis of streptavidin-affinity pulldowns from HEK293T transfected with EB1-EGFP (B) or EB3-EGFP (C) and the indicated SBPHA-FAM123A constructs. Data represent three biological replicates. (D) Western blot analysis of streptavidin-affinity pulldown assays followed by Western blot analysis of SBPHA-FAM123A cells transfected with EGFP, EB1-EGFP, EGFP-EB1-N, or EGFP-EB1-C. Data represent two biological replicates. (E) Images from live-cell imaging of HeLa cells expressing EGFP-FAM123A (movie S4) or EGFP-FAM123A-IPAA (movie S5). The colocalization of EGFP-FAM123A was compared to EGFP-FAM123A-IPAA in three replicate experiments imaged on three separate days. For EGFP-FAM123A, a total of 37 cells were imaged live in conjunction with mRFP-EB3. All cells exhibited greater than or equal to 75% localization of EGFP-FAM123A with mRFP-EB3 in thresholded images. Of 15 EGFP-FAM123A-IPAA cells, no cell exhibited more than 5% colocalization of EGFP-FAM123A-IPAA with mRFP-EB3. Scale bar, 5 μm. (F) HeLa cells transfected with the indicated siRNAs were costained for EB1 and 4′,6-diamidino-2-phenylindole (DAPI). EB1 staining is representative of two biological replicates in which 46 FAM123A-depleted cells and 33 control siRNA–transfected cells were imaged. Scale bar, 20 μm.

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(Fig. 6K). Together, these data demonstrate that FAM123A decreases actin contractility by inhibiting the GEF-H1 pathway and that,

epistatically, FAM123A functions upstream of GEF-H1.

Because cellular morphology and cytoskel-

etal organization are particularly sensitive to transfection and siRNA-based off-target ef-fects, we further validated the specificity of

RhoA, Rho kinase (ROCK), and myosin light chain (MLC) (Fig. 6B) (24, 25). In FAM123A-silenced HeLa cells, thick and short actin stress fibers were disposed in a nonparallel arrangement, and microtubule depolymer-ization induced by nocodazole treatment resulted in several morphological changes, including thick bundles of contracted actin stress fibers frequently localized at the cell center, kidney-shaped nuclei shifted to the cell periphery, and cytoplasmic pools of free tubulin confined to a small area close to the nucleus or near the cell center (or both) (Fig. 6D). Thus, FAM123A-depleted cells display an altered microtubule network and increased actomyosin contractility that is exacerbated with nocodazole treatment.

We used multiple experimental approaches to determine whether FAM123A-induced

actomyosin contractility requires the GEF-H1–RhoA–ROCK–MLC2 pathway. First, treat-ment of cells with the ROCK inhibitor Y-27632 (26) abolished actin stress fiber formation and cell contractility induced by FAM123A knockdown (Fig. 6C). Second, siRNAs tar-geting GEF-H1 reversed the actomyosin con-tractility induced by FAM123A depletion but did not rescue the disordered microtubule phenotype induced by FAM123A silencing (Fig. 6D). By blinded quantitation, 78% of FAM123A-depleted cells were scored as dis-playing increased actomyosin contractility; in the absence of GEF-H1, this was reduced to 38% of cells (Fig. 6E). As was previously reported, silencing of GEF-H1 resulted in de-creased actin bundling (fig. S6) (24). Third, activation of RhoA was higher in cells de-pleted of FAM123A than in control siRNA–

transfected cells, an effect that required GEF-H1 (Fig. 6F). Fourth, phosphorylation of myosin was increased in FAM123A-silenced cells but not in cells in which both FAM123A and GEF-H1 were depleted (Fig. 6, G to I). By immunofluorescence, phosphorylated MLC localized primarily to stress fibers near the cell periphery after FAM123A loss (Fig. 6I), compared with the more uniform distri-bution seen in control siRNA–transfected cells. Finally, we used a serum response fac-tor (SRF) transcriptional activity assay as an indirect readout for RhoA activity and actomyosin contractility (27). FAM123A silencing induced SRF reporter activity in a GEF-H1–dependent fashion (Fig. 6J). FAM123A overexpression repressed SRF-mediated transcription induced by both nocodazole and GEF-H1 overexpression

Fig. 5. FAM123A depletion results in altered microtubule organization and increased actomyosin contractility. (A) HeLa cells were transfected with the indicated siRNAs and stained with an anti–α-tubulin antibody. Images are representative of three independent biological replicates. (B) Plots of fluorescence intensity of α-tubulin staining in cells from (A). The integrated intensity was measured within a 5-μm2 box at 5-μm distance from the cell periphery, in three different regions per cell for 23 control and FAM123A siRNA–transfected cells (*P < 0.0001, Welch-corrected t test). (C) Microtubule polymerization rates in siRNA-transfected HeLa cells. Ten microtubules were measured in each cell. Nine cells were analyzed for each siRNA (*P = 0.0129, Welch-corrected t test). (D) TIRF movies of siRNA-transfected HeLa cells expressing EB3-GFP (left) and RFP-Utr (middle). Left: RGB-colorized 5-s Z-projections of EB3-GFP–labeled growing microtubule ends. Growing filaments are labeled either red, green, or blue and overlapping stationary microtubule ends are white. Regions in cells lacking FAM123 where the microtubule ends are stationary (not assembling) are indicated (white arrows). Middle: Expressed RFP-Utr shows the filamentous actin near the cell substratum. Right: A 15-frame (75 s) Z-stack of EB3-GFP (green) and RFP-Utr (red) over time. Stationary microtubule ends (white arrows) are in close apposition to actin bundles at the cell periphery (red open arrows).

Fig. 6. FAM123A regulates actomyosin contractility through GEF-H1. (A) HeLa cells transfected with siRNAs were imaged by phase-contrast microscopy. Images represent more than three biological replicates. Cell morphologies were confirmed with three different FAM123A siRNAs and two different WTX siRNAs. Scale bar, 50 μm. (B) Major signaling proteins connecting microtubule destabilization to actin contractility. (C) HeLa cells were transfected with control or FAM123A siRNA. Where indicated, cells were treated with the indicated drug before costaining with an anti–α-tubulin antibody, Alexa Fluor 647–conjugated phalloidin, and DAPI. Images are representative of three independent biological replicate experiments. Scale bar, 20 μm. (D) siRNA-transfected HeLa cells were treated with nocodazole and costained with an anti–α-tubulin antibody, Alexa Fluor 647–conjugated phalloidin, and DAPI. Images are representative of three biological replicates. (E) Quantification of cell phenotypes in (D). Samples were scored in a blinded fashion by three independent investigators. n, number of cells scored. (F) siRNA-transfected HeLa cells were serum-starved, treated with nocodazole, and analyzed for RhoA activity (P = 0.002, Student’s t test of biological triplicate experiments). (G) siRNA-transfected HeLa cells were immunoblotted for the indicated proteins. Data are representative of five independent biological experiments. (H) Total MLC2 and phosphorylation of MLC2 at Ser19 from biological triplicate experiments were quantitated by LI-COR and plotted (*P = 0.0159, Student’s t test). (I) siRNA-transfected HeLa cells were stained with phospho-MLC2 (Ser19) antibody, Alexa Fluor 647–conjugated phalloidin, and DAPI. Images are representative of three biological replicates. (J) Quantitation of luciferase activity in HeLa cells expressing an SRF firefly luciferase reporter and a cytomegalovirus (CMV)–driven Renilla luciferase reporter. Cells were treated with DMSO or nocodazole before luciferase quantitation. (K) Quantitation of luciferase activity in HeLa cells that were transiently transfected with the indicated expression construct(s), SRF firefly luciferase reporter, and a CMV-driven Renilla luciferase reporter (*P < 0.05, Welch-corrected t test of biological triplicates).

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the FAM123A phenotype. HeLa cells express-ing one of two siRNAs that targeted the 3′ untranslated region of FAM123A (fig. S7A) showed an exacerbated actomyosin contrac-tility phenotype after nocodazole treatment, similar to the open reading frame–directed FAM123A#1 and #2 siRNAs. This pheno-type was rescued by forced expression of a FAM123A-GFP fusion protein (fig. S7B). To-gether, these data confirm that FAM123A de-creases actomyosin contractility.

FAM123A binds to and inhibits GEF-H1 activity

Proteomic analysis of the FAM123A protein complex identified GEF-H1 as a potential interacting protein, suggesting a molecu-lar mechanism by which FAM123A controls actomyosin contractility (Fig. 1A). First, we confirmed the GEF-H1–FAM123A interaction by Western blot analysis of affinity-purified FAM123A protein complexes or immunopu-rified GEF-H1 protein complexes (Fig. 7, A and B). Endogenous GEF-H1 was detected in affinity-purified FAM123A protein com-plexes (Fig. 7A), and FAM123A immunopu-rified with endogenous GEF-H1 (Fig. 7B). The FAM123A-IPAA mutant, which lacks the ability to bind EB proteins, also associated with GEF-H1 (Fig. 7, A and B). Additionally, we detected the endogenous forms of the microtubule-associated proteins CLASP2 and MARK2 in affinity-purified FAM123A protein complexes, as was suggested by the FAM123A proteomic analyses (Fig. 7A).

As a first test of the functional relation-ship between FAM123A and GEF-H1, we assessed the activation status of GEF-H1 in FAM123A-depleted cells by purifying ac-tive GEF-H1 with a nucleotide-free mutant of RhoA, RhoA-G17A coupled to glutathi-one S-transferase (GST) (28). Western blot analysis of RhoA-G17A pulldowns revealed a significant increase in active GEF-H1 after FAM123A depletion (Fig. 7C). We attempted to examine the subcellular localization of GEF-H1 after FAM123A overexpression or knockdown; however, we were unable to vi-sualize microtubule localization of GEF-H1 with various commercially available anti-bodies and fixation techniques. We reasoned that an effect of FAM123A on GEF-H1 activity could be indirectly assessed by global inter-rogation of the GEF-H1 protein interaction network. For this approach, we used SILAC (stable isotope labeling with amino acids in cell culture)–based quantitative proteomics of immunopurified endogenous GEF-H1 pro-tein complexes from parental cells or cells overexpressing FAM123A. Averaging the SI-LAC ratios from biological replicates identi-fied GEF-H1 protein interactions that were increased by FAM123A expression, such as the association between GEF-H1 and DSTN, an F-actin–depolymerizing protein (29, 30), and those that were decreased by FAM123A

expression, such as the association between GEF-H1 and SLK, a kinase involved in actin and microtubule organization (31–33). To-gether, these data demonstrate that FAM123A controls GEF-H1 activity and GEF-H1 protein-protein interactions.

FAM123A regulates cell adhesion and cell migration

Actin stress fibers and focal adhesions are physically and functionally tethered; increases in cellular contractility or application of exter-nal mechanical force induces simultaneous growth of focal adhesions and the attached ac-tin stress fibers (34, 35). To determine whether actomyosin contractility induced by FAM123A loss affected focal adhesions, we visualized vinculin as a marker of focal adhesions (Fig. 8A). Consistent with higher contractility, FAM123A-depleted cells had larger adhesions than control cells (Fig. 8A). To assess cell ad-hesion and spreading assay, we performed real-time quantitation of electrical impedance during cell spreading, which revealed no effect of FAM123A silencing during the early stages of cell attachment and spreading (Fig. 8B). However, FAM123A-depleted cells had higher impedance values than control cells after the first hour of cell plating, indicating increased adhesion (Fig. 8C), a phenotype that required GEF-H1 (Fig. 8, B and C). We also examined the formation of adhesion complexes by im-munostaining during cell spreading (Fig. 8D and fig. S8). Consistent with the results from the cell spreading assay, FAM123A-depleted and control cells showed phenotypically simi-lar formation of adhesion and actin stress fibers in the early stages of cell spreading. However, after the first hour, the adhesions in FAM123A-depleted cells were larger and had thicker actin stress fibers, effects that were GEF-H1–dependent because cells depleted of both FAM123A and GEF-H1 or GEF-H1 alone resembled the control siRNA–transfected cells. Given these cytoskeletal and adhesion pheno-types, we tested whether FAM123A silencing affected cell migration. HeLa cells transfected with either control or FAM123A siRNAs were plated to confluency, scratched, and imaged by live-cell microscopy. Compared with control siRNA–transfected cells, FAM123A depletion resulted in a 20% decrease in cell migration, which may have resulted from increased adhe-sion (Fig. 8E).

DISCUSSIONWe performed unbiased protein-protein in-teraction screens to discover new functions for members of the FAM123 protein family. Here, we have characterized a family-unique physical and functional relationship between FAM123A and the microtubule and acto-myosin cytoskeletal networks. We found that FAM123A binds EB proteins and interacts with dynamic microtubules through its SKIP487–490 motif. FAM123A knockdown resulted in com-

partment-specific effects on microtubule dy-namics, a globally disorganized microtubule network, increased GEF-H1 activity, increased actomyosin contractility, increased cell adhe-sion, and decreased cell migration.

Members of the FAM123 gene family control β-catenin–dependent WNT signaling

FAM123A inhibits β-catenin–dependent WNT signaling (11). Our data both confirm this findings and provide additional mechanistic insight. First, comparative protein-protein interaction studies of WTX and FAM123A re-vealed a robust association between β-catenin and WTX but not between β-catenin and FAM123A. Although a low affinity or transient interaction between FAM123A and β-catenin may occur, we interpret our data to suggest that association with β-catenin is not required for regulation of WNT signaling by WTX (or FAM123A). Consistent with this notion, the REA repeats in WTX that are responsible for mediating direct interaction with β-catenin are found only in mammalian orthologs of WTX, although WTX inhibits WNT signal-ing in zebrafish and Xenopus (7). On the ba-sis of our findings, it is likely that WTX and FAM123A inhibit WNT signaling through interactions with APC or βTrCP1/2 (or both), which associate with both WTX and FAM123A.

Our data demonstrate that the FAM123A-EB interaction is dispensable for WNT regula-tion, at least with respect to nonpolarized cells grown in two dimensions. In contrast to WTX, which is uniformly distributed across human tissues, FAM123A is largely restricted to neu-ronal tissues (9). These disparities in distribu-tion may provide an explanation as to why the ability to control cytoskeletal dynamics is specific to FAM123A, which may have evolved to remodel the cytoskeleton during neuronal migration (11). Because the FAM123C protein interaction network did not share common protein associations with FAM123A or WTX, our data suggest that FAM123C lacks regula-tory functions over WNT signaling, although this remains to be formally demonstrated.

FAM123A tracks growing microtubules

Plus-end tracking proteins comprise a struc-turally and functionally diverse group of microtubule-associated proteins that accu-mulate at the ends of growing microtubules (15, 17, 19, 36). Many plus-end tracking pro-teins have a conserved SxIP motif that di-rectly associates with EB proteins and thus mediates localization to the microtubule plus-end (22). In at least two ways, however, our protein dynamic studies also differenti-ate FAM123A from other plus-end tracking proteins.

First, unlike EB1 and many plus-end track-ing proteins that localize to microtubule tips throughout the cell body, FAM123A pre-

dominantly decorates the distal ends of mi-crotubules oriented in the direction of cell movement, a polarized distribution also seen for CLASPs, APC, and CDK5RAP2 (37–40). Through asymmetric distribution to the leading edge, these plus-end tracking pro-teins modulate microtubule dynamics and consequently promote cell polarization and directional migration (41, 42). We found that FAM123A loss differentially affects microtu-bule dynamics in different subcellular com-partments (Fig. 5). Therefore, it is possible that FAM123A functions to regulate spatially confined microtubule stability, which presum-ably contributes to the establishment, main-tenance, or modulation of asymmetric cell behavior.

Second, although they bind EB proteins for plus-end tracking, many plus-end track-ing proteins can also directly associate with microtubules. By contrast, FAM123A micro-tubule tracking and localization was largely attenuated in the absence of EB association; one possibility is that the remaining microtu-bule localization is due to bridging proteins

that tether the N terminus of FAM123A to the microtubule, such as APC. Moreover, the sub-cellular distribution of full-length FAM123A and the C-terminal fragment are substantially different, although both bind EB proteins and track with assembling microtubule ends. Whereas the C terminus colocalizes with EB comets, full-length FAM123A exhibits slower microtubule tracking near the cortex, fre-quently coating the microtubule lattice. These data suggest that FAM123A may be involved in coupling assembling microtubules to mem-brane signaling pathways.

FAM123A regulates microtubule dynamics and organization

Like many other EB-dependent plus-end tracking proteins, we found that FAM123A de-pletion results in a disorganized microtubule network. Whereas FAM123A asymmetrically localized to microtubules in the direction of cell movement, its depletion affected micro-tubule architecture throughout the entire cell body. FAM123A depletion resulted in de-

creased microtubule polymeriza-tion rates within the cell body and less dynamic and stabilized microtubules in apposition to ac-tin adhesion complexes. Although untested, it is possible that the decreased microtubule polymer-ization rates occur secondarily to the increased cortical stability because of reprogramming over-all tubulin homeostasis. For ex-ample, decreases in free tubulin dimers as a result of increased stability in some microtubules may result in global effects on microtubule dynamics (43). It also remains possible that acto-myosin contractility induced by FAM123A silencing functions in a feed-forward loop to globally influence microtubule dynamics (44). Although our data do not provide an exact mechanism, we propose that FAM123A may con-trol microtubule dynamics by reg-ulating cortical capture through the tethering of microtubules to the plasma membrane. That is, FAM123A complexes with several plus-end tracking proteins that are thought to stabilize microtu-bules at the leading edge, such as EB1, EB3, CLASPs, APC, and ACF7 (15). It is possible that in associa-tion with these plus-end tracking proteins, FAM123A asymmetri-cally tracks microtubules in the direction of cell movement and promotes both cortical microtu-bule stabilization and polarized cargo delivery to the cortical membrane. Because WTX and

FAM123A bind phospholipids and localize predominantly to the cytoplasmic membrane, an elaboration on this model might have FAM123A “jumping” off microtubules onto a phospholipid landing pad, perhaps deliver-ing its associated proteins, such as GEF-H1 and APC, to the leading edge. In agreement with this model, FAM123A recruits APC to the plasma membrane and drives EB1 to the mem-brane when overexpressed (fig. S4) (6, 11).

FAM123A inhibits actomyosin contractility, thereby regulating adhesion and cell migration

Our data suggest that FAM123A binds to and inhibits GEF-H1, a guanine nucleotide ex-change factor that localizes to microtubules, is activated by microtubule depolymerization, and activates RhoA-dependent actomyosin contraction (24, 25). We show that FAM123A depletion results in GEF-H1–dependent in-creased actomyosin contractility, enlarged fo-cal adhesions, increased cellular adhesion, and decreased cell migration (Fig. 8). These obser-

Fig. 7. FAM123A binds to GEF-H1 and regulates its activity. (A) Streptavidin-affinity pulldowns of HEK293T cells stably expressing SBPHA-tagged CCDC94, FAM123A, or FAM123A-IPAA were immunoblotted for endogenous associated proteins. (B) With the HEK293T stable cell lines described in (A), endogenous GEF-H1 was immunoprecipitated and protein complexes were analyzed by Western blot. (C) Active GEF-H1 and total cellular GEF-H1 were determined by densitometry (*P < 0.05, paired Student’s t test of six biological replicate experiments; error bars represent SE). (D) The GEF-H1 protein interaction network, illustrating the FAM123A-sensitive interactions as determined by quantitative IP/MS. The average SILAC ratios from biological duplicate experiments are shown below the network.

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vations demonstrate that FAM123A is simi-lar to p21-activated protein kinase 1 (PAK1), PAK4, calpain-6, MARK2, and extracellular signal–regulated kinase (ERK), all of which regulate GEF-H1 to control spatiotemporal regulation of the actin cytoskeleton (45–54). That said, several important questions remain regarding the precise molecular mechanism by which FAM123A regulates GEF-H1.

First, our data demonstrate that FAM123A is epistatically upstream of GEF-H1 with respect to RhoA, ROCK, MLC2, actomyosin contractil-ity, and cell adhesion but not to microtubule organization (Fig. 6). Therefore, actomyosin contractility likely underlies the FAM123A-dependent cell adhesion phenotype, rather than the altered microtubule dynamics. Given

the limitations of interpreting cause and effect from siRNA-based experiments, additional studies are needed to more precisely define the role of FAM123A in microtubule dynam-ics and to understand how that effect is com-municated through GEF-H1 to the actomyosin network. Second, as mentioned, the precise mechanism by which FAM123A inhibits GEF-H1 remains to be established. Although we show that ectopic FAM123A binds endogenous GEF-H1 (and vice versa), it remains to be de-termined whether the endogenous proteins associate. Moreover, we were unable to dem-onstrate GEF-H1 subcellular colocalization with FAM123A. Third, we found that FAM123A inhibits GEF-H1 in the presence and absence of microtubules. Specifically, the IPAA mutant

form of FAM123A, which does not bind mi-crotubules, associates with GEF-H1 (Fig. 7), and FAM123A knockdown exacerbates actin contraction in the absence of microtubules (Fig. 6). These data suggest that FAM123A functionally affects GEF-H1 independently of microtubule polymerization status, similar to tumor necrosis factor–α–mediated GEF-H1 activation in tubular epithelia (48, 49). Finally, we found that FAM123A altered the GEF-H1 protein interaction network, which we inter-pret as further corroborative evidence that FAM123A controls GEF-H1 activity or subcel-lular localization. The biological implications of these GEF-H1 interactions and their control by FAM123A await further study.

In summary, we found, using comparative

proteomic analyses of the FAM123 family, that FAM123A associates with numerous micro-tubule-binding proteins. Subsequent protein dynamic studies and functional interrogation revealed that FAM123A controls microtubule polymerization rates, actomyosin contractil-ity, and, consequently, cell adhesion and cell migration.

MATERIALS AND METHODS

Constructs

FAM123A isoform 2 complementary DNA (cDNA) was obtained by polymerase chain reaction (PCR) amplification from clone BCO32653 (Open Biosystems). The mutant FAM123A-IPAA (Ile489-Pro490 mutagenized to Ala489-Ala490) was created by standard PCR-based mutagenesis. The WTX constructs were previously described (5). The EB3-pEG-FPN1 and EB3-RFP (red fluorescent protein) constructs were provided by A. Akhmanova (Erasmus Medical Center, Rotterdam, Neth-erlands). The EB1-pEGFPN1 construct was provided by L. Cassimeris (Lehigh University, Bethlehem, PA). EB1-pEGFPN2 and the dele-tions were obtained by amplifying EB1 by PCR from pEGFPN1-EB1 construct. The mCherry-tubulin plasmid was made by replacing GFP at the Bsr GI and Bam HI sites in pEGFP-Tub with mCherry. The β-catenin–activated firefly reporter (pBAR) and pcDNA3.1-Flag-APC (amino acids 1 to 1060) were previ-ously described (5). The SRF-RE reporter pGL4.34[luc2P/SRF-RE/Hygro] was obtained from Promega. GFP-GEF-H1 was kindly pro-vided by R. Garcia-Mata (UNC-Chapel Hill).

Reagents

Wnt3A and control conditioned media were produced with mouse fibroblasts (L cells) ac-cording to the American Type Culture Collec-tion protocol. Nocodazole and Y-27632 were purchased from Sigma-Aldrich Corporation (catalog nos. M1404 and Y0503).

Tissue culture, transfections, and reporter assays

All cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicil-lin/streptomycin, in a 37°C humidified incu-bator with 5% CO

2. Transient transfections of

siRNAs were performed with Lipofectamine RNAiMAX, as directed by the manufacturer (Invitrogen). All siRNAs were used at a final concentration of 20 nM and for 72 hours un-less otherwise stated. siRNAs targeting human forms of WTX (siRNA#1 and #2), APC, axin1/2, and β-catenin have been previously published (5). The following siRNAs were used: Stealth M2 (Invitrogen) as control; EB1, AAGUGAAAU-UCCAAGCUAAGC (42);FAM123A siRNA#1, GCCGGCUCUGUCUAAAAAG[dT ][dT ]; FAM123A siRNA#2, GAGAUAUUAUUG-

CAGACCAAGAGG; FAM123A siRNA#3, CCUACGUUCAGUUGUUAGAUAUGCA; FAM123A siRNA#4, CCUCUCAAGAU-AAGUCCCUGAGAAU; GEF-H1 siRNA#1, AGCAGGCCACGGAACUGGCAUUACU; GEF-H1 siRNA#2, UCCAUACACGCUUC-CUCAGCCAGCU; WTX siRNA#3, AAAGGCA-GUCAUCUCCAGGUGGAGA. All siRNAs were synthesized by Invitrogen as Stealths, except for EB1 and FAM123A#1. Expression constructs were transiently transfected into HEK293T cells with Lipofectamine 2000 (In-vitrogen) or into HeLa cells with FuGENE 6-HD (Roche), as directed by the manufac-turer. For gain-of-function reporter assays, cells were seeded in 48-well plates before transfection with a firefly luciferase reporter, Renilla luciferase control reporter, and effec-tor plasmids. Cells stably engineered with these reporters were used for the loss-of-function experiments. Luciferase activity was quantitated with the Dual-Glo Luciferase As-say System (Promega).

Affinity purification and MS

Tandem affinity purification of FAM123A (isoform 2) was performed as previously de-scribed (55, 56), with minor modifications. Protein complexes were eluted from the streptavidin beads with 5 mM biotin in the absence of TEV protease. PPS Silent Surfac-tant (0.1%; Protein Discovery) was included in the final elution from the calmodulin beads. Before MS, PPS was acid-cleaved at 37°C for 30 min. Flag-based AP/MS of FAM123C was performed in triplicate as pre-viously described (57). Quantitative immuno-precipitation and mass spectrometry (IP/MS) of endogenous GEF-H1 was performed in du-plicate via SILAC (K6/R10) labeling. Briefly, protein lysates (~150 mg) from the following HEK293T-derived cell lines were compared at low confluency: parental cells (light) and Flag-FAM123A (heavy) or GFP (light) and HA-FAM123A (heavy). Cells were lysed in 50 mM Hepes-NaOH (pH 8.0), 150 mM NaCl, 10% glycerol, 0.1% NP-40, 2 mM EDTA, 2 mM dithiothreitol, and protease and phosphatase inhibitor cocktails (Roche) and subjected to immunoprecipitation with 10 μg of GEF-H1 antibody (A301-929A, Bethyl Labs). Follow-ing an on-beads digestion with FASP Protein Digestion Kit (Protein Discovery), tryptic peptides were cleaned up with C18 spin col-umn (Thermo Scientific), then separated by reversed-phase nano–high-performance liquid chromatography with a nanoAquity UPLC system (Waters Corp.). Peptides were first trapped in a 2-cm trapping column [75-μm inside diameter (ID), Michrom Magic C18 beads of 5.0-μm particle size, 200-Å pore size] and then separated on a self-packed 25-cm column (75-μm ID, Michrom Magic C18 beads of 3.0-μm particle size, 100-Å pore size) at room temperature. The flow rate was 200 nl/min over a gradient of 1% buffer B (0.1%

formic acid in acetonitrile) to 30% buffer B in 180 min. Then, a following wash raised buffer B to 70%. The identity of the eluted peptides was determined with an in-line LTQ-Orbitrap Velos mass spectrometer (Thermo Scientific). The ion source was operated at 2.0 to 2.4 kV with the ion transfer tube temperature set at 275°C. Full MS scan [300 to 2000 mass/charge ratio (m/z)] was acquired in Orbitrap at 60,000 resolution setting; data-dependent MS2 spectra were acquired in LTQ by colli-sion-induced dissociation with the 20 most intense ions. Precursor ions were selected on the basis of charge states (1, 2, or 3) and intensity thresholds (above 2000) from the full scan; dynamic exclusion (one repeat dur-ing 30 s, with a 60-s exclusion time window) was also taken into account. The polysiloxane lock mass of 445.120030 was used throughout spectral acquisition.

Protein identification and quantification

All raw data were converted to mzXML for-mat before a search of the resultant spectra with Sorcerer-SEQUEST (build 4.0.4, Sage-N Research) and the Trans-Proteomic Pipeline (TPP v4.3.1). Data were searched against the human UniProtKB/Swiss-Prot sequence database (Release 2011_08) supplemented with common contaminants, such as porcine (Swiss-Prot P00761) and bovine (P00760) trypsin, and further concatenated with its reversed copy as a decoy (40,494 total se-quences). Search parameters used were a pre-cursor mass between 400 and 4500 atomic mass units (amu), up to 2 missed cleavages, a precursor-ion tolerance of 3 amu, accurate mass binning within PeptideProphet (58), semi-tryptic digestion, a static carbamido-methyl cysteine modification, variable me-thionine oxidation, and additional variable modifications of R10 and K6 for SILAC ex-periments. SILAC ratios were calculated with TPP’s XPRESS (59), and protein abundance ratios were first normalized by the bait’s ratio, then combined from replicate experiments by taking a weighted average using the number of quantified spectra for the protein in each replicate. False discovery rates (FDRs) were determined by ProteinProphet (58), and mini-mum protein probability cutoffs resulting in a 1% FDR were selected individually for each experiment. Further filtering of identified proteins was accomplished using the follow-ing criteria: at least two unique peptides were identified for the protein in each of at least two (of four) WTX experiments, at least two (of three) for proteins in FAM123C experi-ments, two (of two) for ARHGEF2, and two (of three) for FAM123A. Common contami-nants were removed at the authors’ discretion on the basis of previous experiments, such as keratins, ribosomal, and DEAD box proteins. Unfiltered data are provided in table S1 and may be downloaded from ProteomeCom-

Fig. 8. FAM123A regulates cell adhesion and migration. (A) siRNA-transfected HeLa cells were fixed and stained with an anti-vinculin antibody, Alexa Fluor 594–phalloidin, and DAPI. Data represent four biological replicates. (B) Adhesion quantification of siRNA-transfected HeLa cells that were subjected to cell attachment with real-time acquisition of electrode impedance. (C) Histogram of relative impedance index 180 min after cell plating. Error bars represent SE across the biological replicates. (*P = 0.005 and **P = 0.005, paired Student’s t test; n, biological replicates). (D) siRNA-transfected HeLa cells were allowed to attach to fibronectin-coated coverslips for the indicated times before fixation and immunostaining with an anti-vinculin antibody. FN, fibronectin. (E) HeLa cells transfected with control or FAM123A siRNA were scratch-wounded and imaged over 24 hours. (*P = 0.03 and **P = 0.018 by Student’s t test). n = 3 biological replicates.

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Bioinformatics

PeptideProphet/ProteinProphet results for each AP/MS experiment were stored in a lo-cal ProHits database (60). ProHits performed the mapping of UniProtKB accession identi-fiers to Entrez Gene IDs. These results were then imported into Cytoscape v2.8.2 (61) for network visualization. Gene Ontology annota-tions were imported from the National Center for Biotechnology Information Entrez Gene through Cytoscape. Known protein-protein interactions were extracted from the follow-ing databases: BIND, BioGRID, DIP, HPRD, IntAct, MINT, and Reactome—downloaded on 15 August 2010.

Affinity pulldowns, immunoprecipitation, and Western blotting

In all biochemical experiments, cells were lysed in a buffer containing 50 mM tris-HCl (pH 8.0), 150 mM NaCl, 10% glycerol, 1% Tri-ton X-100, 2 mM EDTA, and protease and phosphatase inhibitor cocktails (Roche). For streptavidin-affinity purification, cleared ly-sates were incubated for 1 hour with strepta-vidin resin (GE Healthcare) and subsequently washed and eluted. Immunoprecipitation of endogenous GEF-H1 was performed with anti–GEF-H1 antibodies (A301-929A, Bethyl Labs). Detection of proteins by Western blot was performed with the following antibod-ies: anti–Flag M2 monoclonal (Sigma-Aldrich Corporation), anti-HA polyclonal (1867423, Roche), anti-GFP polyclonal (ab290, Abcam), anti–β-catenin polyclonal (9562, Cell Signal-ing Technology), anti-βTrCP1 monoclonal (37-3400, Invitrogen), anti–β-tubulin mono-clonal (T7816, Sigma-Aldrich Corporation), anti–GEF-H1 (A301-928A, Bethyl Labs), anti-MARK2 (Roche), anti-CLASP2 (A302-155A, Bethyl Labs), anti-APC polyclonal (A300-180A, A300-981A, Bethyl Labs), anti-EB1 monoclo-nal (610534, BD Transduction Laboratories), anti-MLC2 (3672, Cell Signaling Technology), and anti–phospho-MLC2 (Ser19) (no. 3671, Cell Signaling Technology). For detection of endogenous FAM123A, a custom monoclonal antibody was produced (Abmart).

For phospho-MLC detection, HeLa cells were transfected with siRNA at 10 nM for 48 hours. Cells were lysed in the well with UPX sample buffer (Protein Discovery) supple-mented with protease and phosphatase in-hibitors. Samples were boiled for 3 min before the addition of 4× sample buffer and sonica-tion. Proteins were detected by Western blot and quantified with Odyssey Imager and Soft-ware by LI-COR Biosciences.

Immunofluorescence

For immunofluorescence, HeLa sarcoma cells were plated on fibronectin (10 μg/ml)–coated coverslips in DMEM supplemented with 10% FBS and allowed to attach and spread. Cells were fixed in 4% paraformaldehyde (PFA; Electron Microscopy Sciences) in cytoskeletal buffer [5 mM Pipes (pH 6), 137 mM NaCl, 5 mM KCl, 1.1 mM sodium phosphate buffer, 0.4 mM KH

2PO

4, 0.4 mM MgCl

2, 0.4 mM

NaHCO3, 2 mM EGTA, 50 mM glucose] for 15

min and permeabilized with 0.1% Triton in phosphate-buffered saline (PBS) for 5 min. After being blocked in 1% bovine serum al-bumin/PBS for 1 hour, cells were incubated with primary antibodies at 4°C overnight, followed by incubation with appropriate sec-ondary antibodies, RRX-conjugated donkey anti-mouse immunoglobulin G (IgG) and fluorescein isothiocyanate–conjugated don-key anti-mouse IgG (Jackson ImmunoRe-search Laboratories), at room temperature for 1 hour. For staining of endogenous EB1, cells were fixed in methanol at −20°C for 5 min. Staining of proteins was performed with the following antibodies: monoclonal anti–α-tubulin (clone DM1A, T9026, Sigma-Aldrich Corporation), monoclonal anti-vinculin (clone nVIN-1, V9131, Sigma-Aldrich Corporation), and anti-EB1 monoclonal (610534, BD Trans-duction Laboratories). Actin was stained with Alexa Fluor 647–phalloidin or Alexa Fluor 594–phalloidin (Invitrogen). Coverslips were mounted to slides with the ProLong Gold Antifade reagent (Invitrogen). Staining was analyzed with an Olympus IX 81-ZDC inverted fluorescence microscope (Olympus Corporation of the Americas) equipped with a 60×/1.42 Oil PlanApo objective lens and a Hamamatsu C10600-10B camera (OrcaR2, Hamamatsu Photonics Ltd.).

For subcellular localization of FAM123A, HeLa cells were grown on 18-mm glass cov-erslips coated with poly-L-lysine (Sigma-Aldrich Corporation) and cotransfected with EGFP-FAM123A and mCherry-tubulin. Cells were in fixed in 1% PFA in pure −20°C methanol for 2 min and mounted with Pro-Long Gold Antifade with DAPI (Invitrogen). The imaging was performed on a DeltaVision deconvolution microscope (Applied Preci-sion). For costaining of FAM123A-GFP and EB1, cells were fixed similarly and EB1 was stained with anti-EB1 antibody (BD Trans-duction Laboratories), followed by incubation with donkey anti-mouse secondary antibody (Jackson ImmunoResearch Laboratories). Fixed cells were imaged with a complete Z-series in 0.2-μm sections, deconvolved, and projected with Adobe Photoshop CS and Im-ageJ software.

For comparing the distribution of FAM123A family members, HEK293T transiently ex-pressing fluorescent-tagged proteins were plated on fibronectin-coated coverslips, fixed, and mounted as described above and imaged

with a Zeiss LSM5 Pascal confocal laser scan-ning microscope equipped with a 63×/1.42 Oil PlanApo objective lens.

Live-cell imaging

For low-resolution imaging analysis, HT1080 sarcoma cells were plated onto fibronectin (5 μg/ml)–coated MatTek dishes (MatTek Corporation) in DMEM supplemented with 10% FBS and allowed to attach and spread. Cells were transfected with Venus-FAM123A or Venus-WTX with FuGENE HD (Promega) according to the manufacturer’s instructions. Cells were imaged the next day in contrast phase and GFP fluorescence on a Nikon Bio-station (every 5 min for 10 hours), with a 20× objective, at 37°C and 5% CO

2.

For dynamic analysis of fluorescence-tagged proteins at higher magnification, HT1080 were plated onto fibronectin (5 μg/ml)–coated Delta T dishes (Bioptechs Inc.) in DMEM supplemented with 10% FBS and transfected with EGFP-FAM123A with Fu-GENE HD (Promega). The next day, cells were imaged at 37°C and 5% CO

2 with an Olym-

pus IX 81-ZDC inverted fluorescence micro-scope (Olympus Corporation of the Americas) equipped with a Delta T Open Dish System (Bioptechs Inc.) with a heated lid. Time-lapse images were captured every 10 s with a heated 60×/1.42 Oil PlanApo objective lens and a Hamamatsu C10600-10B camera (OrcaR2, Hamamatsu Photonics Ltd.). Data acquisition was carried out with Velocity (version 5.5.1, PerkinElmer), and image processing was per-formed with ImageJ and Adobe Photoshop CS software.

HeLa cells were transfected with either EGFP-FAM123A, EGFP–C-terminal FAM123A, or EGFP–C-terminal FAM123A-IPAA and in some cases cotransfected with EB3-RFP with Nucleofector II (Amaxa Biosystems) and plated onto poly-D-lysine–coated MatTek dishes. After 24 hours of expression, the du-ally transfected cells were imaged at 37°C at a rate of 1 frame/5 s on a DeltaVision RT micro-scope (Applied Precision). Cells transfected with only one construct (movies S6 and S7) were imaged at a rate of 1 frame/2 s.

For microtubule dynamic studies, HeLa cells were transfected with EB3-GFP and RFP-Utr expressing DNA constructs and plated onto fibronectin-coated MatTek film-ing dishes; RFP-Utr encodes an RFP-fused calponin homology domain of the utrophin protein (62). The cells were transfected for 48 hours with either control siRNA or siRNA directed against FAM123A. The cells were then imaged with a Personal DeltaVision mi-croscope custom-outfitted with TIRF light paths and a 60× Olympus TIRF objective. Images were collected at 5-s intervals. Z-pro-jections were made using successive 5-frame intervals for a total of 15 frames. Each 5-frame projection was colored red, green, or blue to visualize the assembling microtubule

ends. With this regimen, stationary microtu-bule ends consisting of RGB overlap are de-picted as white.

For quantitation of cell migration, HeLa cells were transfected with 10 nM siRNA and plated onto a 12-well plate (4 × 105 cells per well) in complete growth medium 48 hours after transfections. After 12 hours, the mono-layer of cells was wounded by manual scratch-ing with a pipette tip, washed with PBS, and placed into complete growth medium. Time-lapse phase-contrast images were acquired ev-ery 7 min for 18 hours with an Olympus IX70 inverted fluorescence microscope, enclosed within an environmental chamber controlled for temperature, relative humidity, and CO

2,

and equipped with a 4× 0.13 Uplan FL PhL lens and a Hamamatsu Orca C4742-95 charge-coupled device camera. Data were acquired with Velocity (version 5.5.1, PerkinElmer). For calculation of relative migration, the scratch area was determined from movie-derived im-ages at time 0 and at 6 hours. The difference in area between t= 0 and t = 6 at three loca-tions along the scratch for each of the biologi-cal triplicate experiments was used to plot the relative rate of wound closure.

RhoA and GEF-H1 activity assays

Purified GST-RhoA(G17A) was provided by C. Guilluy and K. Burridge (UNC-Chapel Hill). Affinity precipitation of exchange factors with the nucleotide-free RhoA mutant (G17A) was performed as previously described (28). For quantitation, Western blot data from six bio-logical replicate experiments were analyzed by densitometry. Data were first normalized to a loading control and then to 1 before aver-aging and plotting. Paired Student’s t test was calculated using raw data. For RhoA activity quantitation, the G-LISA RhoA Activation As-say Biochem Kit (Cytoskeleton Inc.) was used. Specifically, 4 × 105 HeLa cells were plated in 60-mm plates. Cells were transfected over-night with siRNA and allowed to recover in 10% FBS/DMEM for 8 hours. Cells were then washed with PBS and starved in DMEM for 48 hours with one medium change at 24 hours. After 1-hour treatment with nocodazole, cells were lysed in 140 μl of cell lysis buffer. Plates were read on a Synergy HT microplate reader from BioTek.

Cell adhesion and spreading assay

Cell adhesion and spreading were measured as changes in impedance with the RT-CES sys-tem (ACEA Biosciences Inc.). The 16-well E-plates were coated with fibronectin (10 μg/ml) for 1 hour at 37°C. HeLa cells were transfected with 10 nM siRNAs and subjected to cell spreading assay about 65 hours after transfec-tion. For measurements, the same number of cells (4 × 103) was added to each well. The E-plates containing HeLa cells were incubated at room temperature for 10 min before being

placed on the device station in the incubator for continuous recording of impedance-based cell index (every 30 s, for 3 hours). Addition-ally, siRNA-transfected cells were plated on fibronectin-coated coverslips, allowed to at-tach and spread for various periods, fixed, and stained for vinculin according to the methods described earlier.

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ACKNOWLEDGMENTSWe thank R. T. Moon and the members of the Moon laboratory and Major laboratory for their invalu-able assistance. We also thank R. T. Moon and the Howard Hughes Medical Institute for providing space and funding for M.M. during the early stages of this work. We thank F. Nicoletti for financial sup-port to M.M. We thank A. Akhmanova, C. Guilluy, and N. Mitin for helpful advice. Funding: M.B.M. is supported by the NIH through the NIH Director’s New Innovator Award, 1-DP2-OD007149-01, and a Scholar Award from the Sidney Kimmel Cancer Foundation. L.W. is supported by GM69429/GM/NIGMS from the NIH. Author contributions: M.B.M., P.F.S., and M.M. designed the experiments and analyzed the data; these and all other authors performed theexperiments. M.B.M., F.Y., and D.G. performed the MS analysis. P.F.S., M.M., and M.B.M.wrote the manuscript. Competing interests: The authors declare that they have no compet-ing interests. Data and materials availability: The data associated with this manuscript may be downloaded from ProteomeCommons.

org Tranche using the following hash: SG+Lb+ X7PAOmf9pdlyQREvVmA7sOZX/ iwKCV7zQBv-mofGaBNxvXg8D9dZkUs5CgbiDMIBp6JlNW1f-h0EgeWb8SOaaxMAAAAAAAADcA==.

SUPPLEMENTARY MATERIALSwww.sciencesignaling.org/cgi/content/full/5/240/ra64/DC1Fig. S1. FAM123C does not localize to the cytoplasmic membrane.Fig. S2. FAM123A binds FBXW11.Fig. S3. Mutation of the SKIP motif in FAM123A abrogates its microtubule localization and tracking.Fig. S4. FAM123A overexpression alters the subcellular localization of EB1.Fig. S5. EB1 and microtubule association is dispensable for the FAM123A control of β-catenin–dependent WNT signaling.Fig. S6. GEF-H1 silencing alters microtubule organization and actomyosin contractility.Fig. S7. The increased actin contractility observed in FAM123A siRNA–transfected cells is due to the specific silencing of FAM123A.Fig. S8. FAM123A controls maturation of focal adhesions during cell spreading.Table S1. Affinity purification and MS data.Movie S1. Venus-fused FAM123A localizes to the cell membrane and to filamentous structures in the cell lamellipodium.Movie S2. Venus-fused WTX localizes predominantly to the cell membrane.Movie S3. EGFP-fused FAM123A moves on presumed microtubules toward the cell cortex.Movie S4. EGFP-fused FAM123A tracks the tips and distal ends of EB3-associated microtubules.Movie S5. FAM123A-IPAA shows decreased EB3-associated plus-end tip tracking and decoration of distal ends of microtubules.Movie S6. The C-terminal fragment of FAM123A, which contains the SKIP EB-interaction motif, behaves as a classic +TIP showing robust microtubule tip tracking, but does not decorate the microtubule lattice.Movie S7. The C-terminal fragment of FAM123A with a mutated SKIP motif does not show colocalization or tip tracking of microtubules.

Raman M. Das,  Kate G. Storey*

Newborn neurons detach an apical cell-process from the ventricular surface and then migrate to the lateral neural tube or to form cortical layers within the brain (1, 2). This step is required for the

generation of neuronal and tissue architec-ture (2, 3), and its failure leads to human peri-ventricular heterotopia (4). Down-regulation of N-cadherin is associated with this event (3,  5), as is loss of apical complex proteins (6, 7). The latter may be mediated by down-regulation; protein modification/degradation or relocalization; or loss of apical membrane.

To investigate cell behavior underlying neu-ron birth, we labeled membranes of individu-al cells by mosaic transfection of green fluo-rescent protein– glycosylphosphatidylinositol (pCAGGS-GFP-GPI) into the chick embryonic spinal cord (8). We then monitored neurogen-esis in ex vivo embryo slice cultures (1) using wide-field time-lapse microscopy (8). New-born neurons have a basally located cell body and extend a long, thin cell-process to the api-cal/ventricular surface. Movies of such cells revealed that shedding of the apical-most cell membrane preceded withdrawal of this cell-process (Fig. 1A). This event, which we name apical abscission, takes ~1 hour (56 min, SD = 18 min, n = 21 cells). It begins with formation of a bulb-like “bouton,” followed by subapical constriction, membrane thinning, and even-tual abscission, after which the apical cell-process withdraws (42 abscising cells in 34 embryos; all stages observed in 21 cells) (Fig. 1A, fig. S1, and movies S1 to S3). Abscised par-ticles tracked so far remain at the ventricle.

Using structured illumination microscopy (8) to generate super-resolution images of abscising cells transfected with membrane-lo-

Apical abscission alters cell polarity and dismantles the primary cilium during neurogenesis

Neural Development Group, Division of Cell and DevelopmentalBiology, College of Life Sciences, University of Dundee,Dundee DD1 5EH, UK*Corresponding author. E-mail: k.g.storey@dundee.ac.uk

calized Tag–red fluorescent protein–Farnesyl (TagRFP-Farn) revealed a thin membranous connection between apical cell-process and the abscising particle. This confirmed the ex-istence of abscission events in fixed tissue not subject to culture and imaging regimes (n = 5 cells in 3 embryos) (fig. S2 and movie S4). We also observed apical abscission in completely unmanipulated embryos fixed and labeled to reveal the early neuronal marker Tuj1 (class III beta-tubulin). Some Tuj1+ cells with a basally localized nucleus and a ventricle-contacting apical cell-process were found to have a dis-tinct constriction, coincident with subapical actin (n  = 31 of 78 cells in 5 embryos) (Fig. 1B and movie S5). To characterize the abscised membrane, we assessed localization of endog-enous apical Par-complex protein, atypical protein kinase C (aPKC) (9) in such Tuj1+ cells; aPKC was confined to the abscising particle (n = 31 of 31 cells in 5 embryos) (Fig. 1B and movie S5). This indicates that differentiating neurons experience rapid loss of apical polar-ity. It is also consistent with the absence of Par-complex proteins from withdrawing cell-processes (6,  7), which, now liberated from apical-junctional complexes, extend transient membrane protrusions (18 cells in 9 embryos) (e.g., see movies S1 and S2). Similar apical constrictions were visible in Tuj1+  ventricle-contacting cells in mouse spinal cord (22 of 40 cells in 4 embryos) (Fig. 1C and movie S6), with aPKC confined to the abscising particle (22 of 22 cells). This demonstrates that api-cal abscission is conserved across species. In chick, we further characterized cells poised to abscise as indicated by a basally located nucleus and ventricle-contacting apical cell-process revealed by TagRFP-Farn labeling and found a similar localization of actin and aPKC (21 of 21 cells in 6 embryos) (Fig. 1D and mov-ie S7). Many such TagRFP-Farn–labeled cells with this morphology also express low levels of the early neuronal marker, NeuroM (26 of

29 cells in 5 embryos) (Fig. 1, E to G). These NeuroM-positive cells were further found to express the interneuron marker Lim1/2 (23 of 23 cells) but not the later neuronal marker HuC/D (0 of 12 cells) nor the postmitotic cell marker Cdk-inhibitor p27/Kip1 (0 of 11 cells) [(10) and see (7)], identifying these cells as immature neurons that have yet to commit to cell cycle exit (Fig. 1, E to G).

Neuroepithelial cells contain a subapical actin cable that mediates normal cell con-striction at the ventricular surface. To inves-tigate whether apical abscission involves ac-tin dynamics, we cotransfected GFP-GPI and Actin-TagRFP vectors into chick neural tube and monitored protein localization. Subapical actin was visible in cells poised to abscise and coincided with the region of constriction be-fore abscission (Fig. 2A and movie S8). As ab-scission began, Actin-TagRFP signal intensity increased (8), reaching a maximum shortly before abscission completion (Fig. 2B); actin was then depleted from the withdrawing cell-process tip (n = 24 abscising cells in 18 em-bryos) (Fig. 2A, fig. S3, and movies S8 to S10). This local actin increase raised the possibility that actin-myosin contraction mediates apical abscission

We therefore next surveyed myosin local-ization using a myosin regulatory light chain 2 GFP construct (MRLC2-GFP); this revealed strong subapical localization and diffuse cy-tosolic distribution in all cells (fig. S4). Be-cause myosin phosphorylation is essential for actin-mediated apical constriction, we next discriminated sites of myosin activity by monitoring MRLC2T18DS19D-GFP, a constitu-tively active form of MRLC2. To increase the incidence of neuronal differentiation, we co-transfected cells with a plasmid encoding the proneural gene  Neurog2  [pCAGGS-Neurog2-IRES-nucGFP (pCIG-Neurog2)], which pro-motes neuronal differentiation (10). In such cells, also coexpressing TagRFP-Farn to label cell membranes, active MRLC2 localized sub-apically until shortly after abscission (8) (n = 12 cells in 9 embryos) (Fig. 2, C and D, and movies S11 to S13). Thus, actin is localized and myosin is active in the subapical region of the abscising neuron.

To investigate the requirement for myosin activity, cells were transfected with GFP-GPI and pCIG-Neurog2, and slices were cultured in medium containing blebbistatin (inhibitor of myosin motor function), ML-7 (inhibitor of myosin light chain kinase MLCK, which phos-phorylates Myosin II) (see fig. S5), or dimethyl sulfoxide (DMSO) control. Although few cells in control slices failed to abscise and retract their cell-processes within an 8-hour period (n = 4 of 33 cells in 5 embryos) (Fig. 2E and movies S14 to S16), the majority of cells ex-posed to blebbistatin (n = 33 of 36 cells in 6 embryos) (Fig. 2F and movies S17 to S19; api-cal surface definition, fig. S6) or ML7 (n = 68 of 83 cells in 15 embryos; Fig. 2G and movies S20 to S22; apical surface definition, fig. S6)

Withdrawal of differentiating cells from proliferative tissue is critical for embryonic development and adult tissue homeostasis; however, the mechanisms that control this cell behavior are poorly understood. Using high-resolution live-cell imaging in chick neural tube, we uncover a form of cell subdivision that abscises apical cell membrane and mediates neuron detachment from the ventricle. This mechanism operates in chick and mouse, is dependent on actin-myosin contraction, and results in loss of apical cell polarity. Apical abscission also dismantles the primary cilium, known to transduce sonic-hedgehog signals, and is required for expression of cell-cycle-exit gene p27/Kip1. We further show that N-cadherin levels, regulated by neuronal-differentiation factor Neurog2, determine cilium disassembly and final abscission. This cell-biological mechanism may mediate such cell transitions in other epithelia in normal and cancerous conditions.

Originally published 10 January 2014 in SCIENCE

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remained attached at the ventricle. Further-more, coexpression of active MRLC2 in the presence of ML-7 rescued its effects, with most cells now abscising within 8 hours (n = 14 of 18 cells in 7 embryos) (Fig. 2H  and movies S23 to S25). Misexpression of active MRLC2 alone, however, did not increase neuron num-bers, so the potential to increase actin-myosin contraction is by itself insufficient to promote apical abscission (fig. S7). These data indicate that myosin activity is required, but not suf-ficient, for apical abscission.

Neuroepithelial cells lining the ventricle possess a primary cilium. This projects from the basal body/centrosome located at the apical pole. While this cilium plays a key role in transducing sonic hedgehog (Shh) (and possibly other) signals that maintain neuroepithelial cells in a proliferative state (11), the centrosome is further implicated in positioning axon outgrowth (12). To observe the effect of apical abscission on the primary cilium, we transfected GFP-GPI and pCIG-Neurog2 into the neural tube together

with a construct containing a pericentrin-AKAP450 centrosomal targeting (PACT) domain sequence that confers centrosomal localization fused to TagRFP (PACT-TagRFP). As abscission began, the centrosome localized to the withdrawing cell-process (n = 45 cells in 15 embryos) (Fig. 3Aand movies S26 to S28). Conversely, the primary cilium, identified with ciliary membrane–associated Arl13b-TagRFP, remained attached to the abscised apical membrane (n = 21 cells in 7 embryos) (Fig. 3B  and movies S29 to S31). During

Fig. 1. Apical abscis-sion during neuronal differentiation. (A) Time-lapse sequence of a cell undergoing distinct stages of api-cal abscission [movie S1; these are addi-tional frames of a cell shown in supplemen-tary movie 2 in (6)]. (B to D) Maximum intensity projections of constricting abscis-sion site (white ar-rowheads) visible in Tuj1+ventricle-contact-ing cells in chick (B) and mouse (C) embry-os and TagRFP-Farn–labeled cell in chick (D); the abscising par-ticle is distal to actin and contains the apical Par-complex, marked by aPKC [three-dimen-sional reconstructions of (B), (C), and (D) in movies S4, S5, and S6, respectively]. (E to G) Cells poised to abscise express NeuroM and Lim1/2 (E) but not HuC/D (F) nor p27 (G) (magenta arrows). Ab-scission site (white ar-rowheads), withdraw-ing apical cell-process (white arrows), ab-scised particle (yellow arrows), and apical surface (white dashed line) here and in all figures. Scale bars, 10 μm; enlarged regions, 2 μm.

Fig. 2. Apical abscission depends on actin-myosin activity. (A and B) Time-lapse sequence showing actin localization (A) (movie S8) and (B) quantification of Actin-TagRFP intensity during apical abscission (average normalized values for four cells; error bars, mean ± SEM). (C and D) Active myosin (MRLC2T18DS19D-GFP) (C, green at cell-process tip; movie S11) localiz-es to abscission site (D) MRLC2T18DS19D-GFP intensity during apical abscission

(average normalized values for five cells error bars, mean ± SEM). (E to H) Cells exposed to control DMSO undergo abscission (E) (movie S14), but not in the presence of blebbistatin (F) (movie S17) or ML-7 (G) (movie S20). ML-7 abscission inhibition is rescued by expression of MRLC2T18DS19D-GFP (H) (movie S23). For definition of apical surfaces, N-cadherin, and aPKC localization, see fig. S6. [(B) and (D)] Membrane thinning, black arrow; abscission complete, black arrowhead. Scale bars, 10 μm; enlarged regions, 2 μm.

Fig. 3. Apical abscission dismantles the primary cilium. (A and B) Time-lapse sequences showing centrosome release into the apical cell-process (A) (movie S26), while Arl13b-labeled cilium is retained at the apical membrane (B) (movie S29). (C) Widefield and (C′) structured illumination imaging (white dotted outline) (movie S32) of TagRFP-Farn labeled apical cell-process and abscised particle containing Arl13b-GFP–labeled cilium. (D and E) Tuj1+ cells with ventricle-contacting apical cell-processes exhibit Smo (D) (movie S33) and Gli2 accumulation (E) (movie S34) (empty arrowheads) in their primary cilium [identified with Arl13b-GFP or Ift88 (intraflagellar-transport-protein 88), respectively]. Scale bars, 10 μm; enlarged regions and C and C′, 2 μm.

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apical abscission, the Arl13b-labeled cilium also shortened [two-fold reduction in cilium length; n = 5 abscising cells (8)]. We further used structured illumination microscopy to confirm the presence of Arl13b-GFP in particles abscised from TagRFP-Farn–labeled cells in tissue not subject to culture and live imaging (n  = 5 cells) (Fig. 3C  and movie S32).

Active Shh signaling is indicated by accu-mulation of the Shh transducer Smoothened (Smo) and its key pathway effector Gli2 in the primary cilium (13, 14). Shh signaling is high-est in the ventral half of the neural tube, and we therefore assessed localization of endog-enous Smo and Gli2 in Tuj1+ cells with ventri-cle-contacting cell-processes in this region. This revealed many cells with ciliary accu-mulation of Smo (n = 38 of 43 cells in 4 chick embryos) (Fig. 3D and movie S33) or Gli2 (n = 33 of 35 cells in 3 mouse embryos) (Fig. 3E and movie S34). This localization of pro-teins suggests that cells poised to abscise are responding to Shh signals and predicts that disjunction of the centrosome and Arl13b-la-beled cilium during apical abscission curtails Shh signaling.

Onset of neuronal differentiation is char-acterized by down-regulation of N-cadherin (3, 5), which forms subapical adherens junc-tions between neuroepithelial cells (15), and abnormal persistence of N-cadherin inhibits apical cell-process withdrawal (3). Cadherins are connected intracellularly to the contrac-tile actin cable, and this serves to maintain tension at apical junctions and cell-cell adhe-sion (15). Declining N-cadherin levels within the prospective neuron may therefore trigger apical abscission by loosening cell-cell junc-tions and connection with the intracellular actin-myosin cable. Because the centrosome is localized in the withdrawing cell-process, it must be released from the cilium before final abscission. To determine how persis-tent N-cadherin affects apical abscission, we misexpressed N-cadherin-YFP (yellow fluo-rescent protein) together with GFP-GPI andPACT-TagRFP. Increased N-cadherin blocked cell-process withdrawal, and the centrosome remained at the apical pole (16 cells in 10 embryos) (Fig. 4A  and movies S35 to S37). This indicates that N-cadherin down-regulation is required for centrosome release from the apical surface, as well as for final

abscission of apical membrane.One consequence of failure to undergo

N-cadherin down-regulation and apical ab-scission might therefore be maintenance of Shh signaling and therefore inhibition of cell cycle exit. To assess the relationship between cell cycle regulation and apical abscission, we next determined the effect of persistent N-cadherin on expression of p27/Kip1, which normally begins after apical cell-process withdrawal [Figs. 1G  and  4B′ and see (7)]. N-cadherin misexpressing cells lacked p27/Kip1 after 24 hours [N-cad-YFP+TagRFP-Farn misexpressing cells 3% p27/Kip1 posi-tive (25 of 689 cells in 4 embryos) (Fig. 4B); control TagRFP-Farn only expressing cells 13% p27/Kip1 positive (76 of 649 cells in 4 embryos) (Fig. 4B′]. These findings therefore place N-cadherin loss and apical abscission, including cilium disassembly, upstream of cell cycle exit as defined by p27/Kip1 expres-sion. Furthermore, driving premature cell cycle exit by p27/Kip1 misexpression did not promote apical abscission or neuronal differ-entiation (fig. S8), consistent with proneural genes promoting expression of Cdk inhibi-tors, which then act in concert with other proneural targets to orchestrate neuronal differentiation (16).

N-cadherin down-regulation in prospec-tive neurons is mediated by the transcrip-tion factor FoxP2/4, expression of which is promoted by the proneural gene Neurog2 (3,  10). To determine whether Neurog2 mis-expression is sufficient to overcome excess N-cadherin, we cotransfected constructs en-coding these two genes into the neural tube. Despite excess N-cadherin, cells with excess Neurog2 dismantled their cilium and under-went abscission and cell-process withdrawal (16 cells in 7 embryos) (Fig. 4C  and movies S38 to S40). In this context, N-cadherin-TagRFP was localized to the abscission site and then lost before abscission (n = 19 cells in 8 embryos) (Fig. 4D and movies S41 to S43). This indicates that localization and regula-tion of N-cadherin protein [(as well as tran-scriptional down-regulation of endogenous N-cadherin (3)] is directed by factors down-stream of Neurog2.

These findings uncover a cell biological mechanism, apical abscission, that takes place downstream of N-cadherin loss and in-volves actin-myosin–dependent cell constric-tion and dismantling of the primary cilium. This abscission event detaches newborn neu-rons from the ventricular surface and results in loss of apical-complex–containing cell membrane and therefore apical polarity. By separating centrosome from Arl13b-labeled cilium, apical abscission may curtail active Shh signaling, as indicated by ciliary accu-mulation of Smo and Gli2 in cells poised to abscise. Consistent with a loss of mitogenic Shh, abscission is also required for expres-sion of cell-cycle exit gene p27/Kip1 (fig. S9). Apical abscission is thus a decisive event in

Fig. 4. N-cadherin misexpression blocks apical abscission. (A) Cells misexpressing N-cad-YFP constrict (white arrowheads) but do not abscise and the centrosome (PACT-TagRFP, magenta arrows) remains at the apical cell pole (movie S35). (B) N-cad-YFP misexpressing cells do not express p27 (B′), which is normally detected after apical cell-process detachment (cell nuclei; empty arrowheads). (C) Neurog2 misexpression rescues centrosome release and abscission in N-cad-YFP–expressing cells (movie S38). (D) Neurog2 misexpression decreases subapical N-cad-TagRFP levels (magenta arrows), followed by abscission and cell-process withdrawal. A second underlying cell has yet to withdraw (movie S41). Scale bars, 10 μm; enlarged regions, 2 μm.

the neuronal differentiation program, which triggers reorganization of the newborn neu-ron and its withdrawal from the ventricular environment. Abscising the apical mem-brane and leaving this, at least initially, at the apical surface may also help to maintain tissue integrity. During mitosis, cilia are re-sorbed or partially internalized (17) rather than shed, and regulated cilium shedding has only been reported in the alga  Chlam-ydomonas  (18). Loss of apical complex pro-teins also characterizes cells undergoing an epithelial to mesenchymal transition, includ-ing tumor cell metastasis (19), and some cancers exhibit cilia loss (20). Investigation of apical abscission in normal and also onco-genic epithelia may therefore provide insight into mechanisms that direct critical cell state transitions.

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Kriegstein, Nat. Neurosci. 7, 136–144 (2004). 3. D. L. Rousso et al., Neuron 74, 314–330

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Leung, W. A. Harris, J. Neurosci. 32, 223–228 (2012).

6. R. M. Das, K. G. Storey, EMBO Rep. 13, 448–454 (2012).

7. S. Ghosh et al., Proc. Natl. Acad. Sci. U.S.A. 105, 335–340 (2008).

8. Materials and methods are available as supplementary material on Science Online.

9. S. Ohno, Curr. Opin. Cell Biol. 13, 641–648 (2001).

10. N. Bertrand, D. S. Castro, F. Guillemot, Nat. Rev. Neurosci. 3, 517–530 (2002).

11. A. Louvi, E. A. Grove, Neuron 69, 1046–1060 (2011).12. M. Kuijpers, C. C. Hoogenraad, Mol. Cell.

Neurosci. 48, 349–358 (2011).13. K. C. Corbit et al., Nature 437, 1018–1021

(2005).14. J. Kim, M. Kato, P. A. Beachy, Proc. Natl. Acad.

Sci. U.S.A. 106, 21666–21671 (2009).15. A. Ratheesh, A. S. Yap, Nat. Rev. Mol. Cell Biol.

13, 673–679 (2012).16. H. Gui, S. Li, M. P. Matise, Dev. Biol. 301, 14–26 (2007).17. J. T. M. L. Paridaen, M. Wilsch-Bräuninger, W.

B. Huttner, Cell 155, 333–344 (2013).18. L. M. Quarmby, in Int. Rev. Cytol. (Academic

Press, 2004), vol. 233, pp. 47–91.19. B. Xue, K. Krishnamurthy, D. C. Allred, S. K.

Muthuswamy, Nat. Cell Biol. 15, 189–200

(2013).20. N. B. Hassounah, T. A. Bunch, K. M.

McDermott, Clin. Cancer Res. 18, 2429–2435 (2012).

ACKNOWLEDGMENTSWe thank C. Weijer, A. Muller, and J. Januschke for comments; J. Swedlow for imaging advice; and S. Swift and C. Thomson in the College of Life Sciences Light Microscopy Facility (LMF) for tech-nical support. Structuredillumination microscopy was carried out with assistance of M. Posch (LMF) and L. Ferrand (GE Healthcare) and supported by the Medical Research Council Next Generation Optical Microscopy Award (MR/K015869/1). R.M.D. and K.G.S. are funded by Wellcome Trust program grant 083611/Z/07/Z.

SUPPLEMENTARY MATERIALSwww.sciencemag.org/content/343/6167/200 suppl/DC1Materials and MethodsFigs. S1 to S9Movies S1 to S43References (21–30)22 October 2013; accepted 2 December 201310.1126/science.1247521

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Microscopy in focusThe art and science of image quality

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Jason Swedlow, Ph.D.University of DundeeDundee, Scotland

John Murray, M.D., Ph.D.Indiana UniversityBloomington, IN

Paul C. Goodwin, M.ScGE HealthcareIssaquah, WA

Speakers

Live cell imagingThe future for discoveries

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Edward M. Campbell, Ph.D.Loyola UniversityChicago, IL

Lynne Turnbull, Ph.D. University of TechnologySydney, Australia

Nick Thomas, Ph.D.GE HealthcareCardiff, Wales

Speakers

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