A Cell Biologist's Guide to High Resolution Imaging

CHAPTER TWO

A Cell Biologist’s Guide to High Resolution Imaging

Graeme Ball, Richard M. Parton, Russell S. Hamilton, and Ilan Davis

Contents 1. Introduction 30 2. Physical Limitations on the Resolution of Conventional Microscopy 31 2.1. The Abbe diffraction limit and optical resolution 31 2.2. Photon detection and signal-to-noise 32 2.3. Fluorophore properties 34 2.4. Temporal resolution limits 34 3. Preparations for High Resolution Fluorescence Imaging 35 3.1. Microscope setup 35 3.2. Sample preparation 37 4. High Resolution Image Data Acquisition and Processing 39 4.1. High resolution techniques 39 4.2. Improving the axial resolution 40 4.3. RESOLFT microscopy 40 4.4. Structured illumination microscopy (SIM) 41 4.5. Localization microscopy techniques 42 4.6. Three-dimensional super-resolution microscopy techniques 42 5. Processing 43 5.1. Denoising methods 44 5.2. Deblurring and deconvolution 46 6. Analysis of High Resolution Image Data 47 6.1. Intensity and molecular quantification 47 6.2. Accurate localization of fluorophores 47 6.3. Segmentation and particle tracking 47 6.4. Analysis of distribution and colocalization 48 6.5. Motility statistics and motion models 48 7. Conclusions 51 Acknowledgments 51 References 51

Department of Biochemistry, The University of Oxford, Oxford, United Kingdom

29 30 Graeme Ball et al.

Abstract Fluorescence microscopy is particularly well suited to the study of cell biology, due to its noninvasive nature, high sensitivity detection of specific molecules, and high spatial and temporal resolution. In recent years, there has been an important transition from imaging the static distributions of molecules as a snapshot in time in fixed material to live-cell imaging of the dynamics of molecules in cells: in essence visualizing biochemical processes in living cells. Furthermore, in the last 5 years, there have been important advances in so- called “super-resolution” imaging methods that have overcome the resolution limits imposed by the diffraction of light in optical systems. Live-cell imaging is now beginning to deliver in unprecedented detail, bridging the resolution gap between electron microscopy and light microscopy. We discuss the various factors that limit the spatial and temporal resolution of microscopy and how to overcome them, how to best prepare specimens for high resolution imaging, and the choice of fluorochromes. We also summarize the pros and cons of the different super-resolution techniques and introduce some of the key data analysis tasks that a cell biologist employing high resolution microscopy is typically interested in.

1. Introduction

Light microscopy in all its varied and sophisticated forms, has become one of the most important and ubiquitous techniques in cell biology. Fluorescence microscopy, in particular, has become of central importance for making quantitative measurements of molecular behavior and for testing mechanistic hypotheses. The discovery of green fluorescent protein (GFP; Chalfie et al., 1994) has been well recognized as a key milestone in the development of microscopy methods, resulting in the award of a Nobel prize (Nobelprize.org, 2008). The introduction of GFP and a plethora of related proteins and techniques have certainly revolutionized the field of cell biology, enabling the tagging of specific protein or mRNA components in living cells. The past 20 years have seen a stream of equally exciting developments. Perhaps, the simplest way to classify these milestones in fluorescence microscopy is into four areas: first, specimen preparation, which is highly dependent on the model system; second, the development of fluorochromes and methods of introducing them into cells; and third, the ever diversifying range of microscopes ranging from commercial to bespoke systems built for specific kinds of experiments. Finally, and equally important, is the post-acquisition analysis of the images, with the aim of extracting quantitative information about the dynamics and relationships of molecules inside cells. This chapter is structured around these distinct four parts of imaging technology and its major goal is to provide a practical guide to A Cell Biologist’s Guide to High Resolution Imaging 31 pushing the resolution limits of fluorescence microscopy and the benefits this affords. Fluorescence microscopy suffers from major limitations to both spatial and temporal resolution. Temporal resolution is limited by the speed and sensitivity of the instrument, but perhaps most importantly by the photon budget of the specimen, that is, the total number of photons that can be obtained before photobleaching. All impose limits on the questions a biologist is able to answer using fluorescence microscopy; the major issues being that poor spatial resolution prevents accurate identification and colocalization of features in a cell, and poor temporal resolution prevents accurate charac- terization of dynamic cellular processes. We describe current approaches used to address these problems: starting from sample preparation, through instru- mentation to the reconstruction and processing methods used to achieve maximum resolution.

2. Physical Limitations on the Resolution of Conventional Microscopy

2.1. The Abbe diffraction limit and optical resolution As discovered by Ernest Abbe in 1873, light from a point source with a wavelength l produces an observable image spot feature with a resolved lateral (X, Y) radius given by Eq. (2.1) below:

Resolutionx;y ¼ l=2NA; ð2:1Þ where NA is the numerical aperture of the lens. Abbe obtained a corresponding expression for axial (Z) resolution given by Eq. (2.2) below:

2 Resolutionz ¼ 2l=NA : ð2:2Þ

There are several different resolution criteria including the Rayleigh criterion, the Sparrow resolution limit, and the full width at half maximum (FWHM) criterion; which give similar resolution limits. Since the NA of a lens can reach 1.4 with current designs, this introduces a diffraction limit for the ability to resolve two point sources of light that is around half of the wavelength of the light. At the blue end of the visible spectrum, this implies a spatial resolution limit of 200 nm, increasing to 250 nm for green light, and 300 nm at the red end of the spectrum. The shapes of objects at, or below, the resolution limit are indistinguishable, resulting in an identical system-dependent point spread function (PSF) of the light. In the case of confocal laser scanning microscopyt (CLSM), the resolution is theoretically improved by a factor of 2 as an infinitely small pinhole is approached and 32 Graeme Ball et al. increasing amounts of out-of-focus light are excluded, but in practice this maximum gain in resolution is never achieved. As we describe below, there are several practical ways to evade this limit known as super-resolution techniques, but they all come at a cost in terms of speed and/or photodamage. The optical resolution is a fundamental limit of the optical components of an imaging system. However, the actual resolution achieved in the captured image data is also dependent upon how this data is sampled by the detector— so-called sampling theory. The criterion that must be considered in deter- mining an appropriate level of sampling is the Nyquist frequency. This is defined as slightly more than twice the highest frequency that must be sampled. In the context of microscope images, this refers to the spatial frequency of the minimum optically resolvable object given by Eq. (2.3).

Resolutionx;y ¼ 0:61l=NA: ð2:3Þ

In the case of a charge-coupled device (CCD) camera, this determines the magnification at which optimum resolution is achieved for a given NA and pixel size on the detector.

2.2. Photon detection and signal-to-noise An image is made up of detected photons. Ultimately, the resolution that can be usefully obtained from any imaging system is limited by the number of photons actually detected. This is commonly described as the “signal-to- noise” limit of imaging, or the signal in relation to the uncertainty or variation in that signal. The accuracy with which the number of photons (brightness) can be determined for a signal is limited by the “shot noise” or statistical variationt in that signal defined according to the Poisson statistics of photon counting ( n). Together with the optical limits, the signal-to-noise level imposes a practical restriction on achievable resolution. The more photons that can be detected, the closer the result is to the true optical limit. Recently, advances have been made in processing techniques which attempt to “recover” data limited by the number of photons detected, as discussed in Section 6. Modern imaging systems rely on a variety of photon detectors to collect the image data. The two most important families of detectors reflect the two different approaches to capturing microscope image data. CCD detectors and complementary metal-oxide semiconductor (CMOS) sensors consist of an array of photodetectors in an integrated circuit for simultaneous detection of photons from the whole image in a wide-field or multifocal scanning (e.g., spinning disc confocal) microscopy system. Photomultiplier tube (PMT) and avalanche photodiode (APD) detectors, on the other hand, are the devices most A Cell Biologist’s Guide to High Resolution Imaging 33 commonly used to detect the single stream of photons from a CLSM. The sensitivity of all types of photon detectors depends on two factors: the detection limit, and the quantum efficiency (electron output/photon input). The detection limit of a photodetector corresponds to the lowest photon signal that produces an electronic response distinguishable from system noise: this limit is termed the noise equivalent power (NEP), and the smaller it is, the better the detector. The most important sources of noise vary depending on the type of detector, but one that is unavoidable is the stochastic nature of photon arrivals, which results in a Poisson distribution of detected photons and therefore a Poisson or Shot noise equal to the square root of the number of detected photons. In the case of CCD cameras, readout noise is the second major contribution and increases in proportion to the square root of the readout speed (Goldman et al., 2009). Thermal noise makes a second, less important contribution, and can be reduced by cooling. Electron-multiplying CCD (EM-CCD) cameras use multiple multiplication steps prior to readout, making them better suited to low light levels, and reduces the importance of readout noise. However, EM-CCD devices are affected more by thermal, charge, and excess noise as a consequence of the extended multiplication process (Robbins and Hadwen, 2003). PMT devices have excellent noise characteristics, but suffer from poor quantum efficiency at higher wavelengths (Pawley, 1995). APD detectors, on the other hand, introduce a greater amount of noise during the signal multiplication process, but have generally superior quantum efficiency compared to PMTs. Finally, quantum efficiency is simply a measure of light collection efficiency and depends on the physical setup and properties of the detector. On this measure, CCD and CMOS detectors tend to give good results: 45–50% maximum efficiency (wavelength dependent) for front-illuminated detectors, and as much as 95% efficiency, or more typically 85%, for back-thinned detectors. PMT detectors typically have a quantum efficiency <25%, and APD detectors 70% (Pawley, 1995). The dynamic range of the detector is also extremely important and the linearity of its response to photons. CCD and CMOS detectors amplify the photon signal in a relatively linear fashion and are not particularly prone to saturation where increased signal does not register, with the exception of EM-CCD devices; whereas PMTs and APDs have a much less linear response and saturation must be carefully avoided by adjusting the gain that adjusts the degree to which the electrical output signal is amplified by the detector relative to the photon input (Pawley, 1995). However, some PMTs and APDs can be operated in a photon-counting (Geiger) mode which negates the nonlinearity of the amplification response provided that the photon flux is low enough that readout can be achieved between photon arrival events. 34 Graeme Ball et al.

2.3. Fluorophore properties Photobleaching and photodamage are the ultimate roadblocks for most strategies to overcome the diffraction and sensitivity limits that reduce the resolution of fluorescence microscopy. Photobleaching and photodamage are why sensitivity limitations cannot be simply bypassed by increasing excitation intensity, and why applying techniques such as Three-dimensional (3D)-SIM and STED to live-cell imaging remains a challenge. The fluorescence phenomenon involves excitation of the fluorophore by a photon at the excitation wavelength, which produces an excited electronic state in the molecule, followed by spontaneous relaxation back to the ground state with the emission of another photon at the emission wavelength. Fluorescence lifetimes for typical fluorophores are typically <20 ns, so this is not currently a limitation. However, there are various competing pathways for relaxation of the excited state, most notably con- version to a long-lived triplet state that is not fluorescent. These excited states are also chemically reactive, explaining the photobleaching and much of the photodamage caused by fluorescence imaging. A typical fluorophore will undergo thousands of excitation/emission cycles before bleaching occurs, however. In addition to the excitation and emission spectra and photostability, two of the most important properties of a fluorophore are the quantum yield which, in this context, is a measure of the number of photons emitted for the number absorbed; and the extinction coefficient, which is a measure of how readily the fluorophore absorbs excitation photons. There is a choice to be made between the various small organic molecule fluorophores such as the cyanine dyes, Alexa dyes, and Atto dyes, versus the recombinant fluorescent proteins derived from GFP and other natural fluorophores (Shaner et al., 2005). Small molecule dyes have two major drawbacks: the difficulty of labeling for live-cell imaging, and generally increased phototoxicity. However, some of the Alexa fluor and Atto dyes in particular have excellent brightness and photostability characteristics, and are the main fluorophores that have been successfully used in photon-intensive super-resolution techniques like STED (see Section 4.4). Finally, an important class of fluorophores for super-resolution imaging are the photoactivatable, photoconvertible, and photoswitchable molecules that are particularly applicable to the localization microscopy techniques described in Section 4.5 (Endesfelder et al., 2011). 2.4. Temporal resolution limits As mentioned above, the fluorescence lifetime is the ultimate limiting factor on temporal resolution, but modern fluorescence microscopy has a long way to go before reaching this limit. Both wide-field and laser scanning methods are currently limited to temporal resolutions of milliseconds (Pawley, 1995). In the case of laser scanning methods, PMT and APD A Cell Biologist’s Guide to High Resolution Imaging 35 detectors have very rapid response times of a few nanoseconds, but the necessity to raster scan the sample area results in much slower (milliseconds to seconds) temporal resolution. In the case of CCD and the faster CMOS cameras, readout times are of the order of milliseconds and the entire image field is imaged at once, which makes them the detectors of choice for fast 3D imaging. Other hardware limitations include shutter speeds and/or laser switching times.

3. Preparations for High Resolution Fluorescence Imaging

In any form of imaging, but especially for high resolution microscopy, the importance of correctly setting up and aligning the imaging system and of optimizing sample preparation for imaging cannot be overemphasized. As the spatial resolution is increased, so do the pitfalls and potential artifacts that can arise from optical aberrations and the inhomogeneity of the refractive index (RI) of live specimens.

3.1. Microscope setup A badly serviced and aligned microscope cannot achieve optimal performance. The best defense against this is regular calibration using appropriate test samples, usually fluorescent bead slides, which are easily prepared. We find three types of bead slide test samples particularly useful: very sparsely distributed 100 nm beads, either multicolor or single color; moderately distributed 200 nm beads, ideally with a wide excitation and emission range; very crowded 100 or 200 nm beads as a “bead lawn.” Section 3.1.1 describes the preparation of these different test slides.

3.1.1. Protocol 1: preparation of test slides 1. It is important to select good quality, clean slides, and coverslips to prepare your sample slides. Generally we use No. 1.5 coverslips with a nominal thickness of 170 mm (0.16–0.19 mm). 2. Beads should be mounted directly onto the coverslip, not the glass slide. Arrange your coverslips on sheets of filter paper to make it easier to work with them and use coarse forceps to aid handling. 3. It is helpful to poly-lysine treat your coverslip prior to mounting beads to localize and adhere them. Take neat poly-D-lysine solution and apply 100 ml to the center of a 22 22 mm coverslip. Leave for about 5 min and then remove with a pipette and wash off the residue with distilled water from a wash bottle. Tilt the coverslip and blot dry at the edge only with filter paper. Allow to dry fully. 36 Graeme Ball et al.

4. Make up your bead suspension. Often it helps to briefly sonicate the beads to help disperse clumps, you may also want to very briefly centrifuge the bead sample and take off the supernatant. You will need to dilute appropriately depending on the density of your stock solution, this is best determined by a dilution series. Beads can be diluted into water or ethanol, although the latter should be avoided for storage of beads. Depending upon the density of beads required, dilutions of between 1:1 and 1:10,000 may be used. 5. There are different ways to achieve a useful coating of beads: a. For very sparse dispersions we recommend using a poly-lysine-coated coverslip and applying about 20 ml of bead suspension. This should be allowed to settle for between 5 and 30 min. Ideally there should be some slight drying of the edge of the drop of beads. This should achieve a dense ring of beads where the suspension has been dried down, and a sparsely coated inner region. The dense ring facilitates location of beads when imaging. After settling, the excess can be very gently and briefly washed off with distilled water from a wash bottle and allowed to dry. b. For denser applications of beads or a “bead lawn,” bead suspensions in ethanol can be dried down onto the coverslip. As all the beads are concentrated on the glass surface, greater initial dilutions of the stock are usually required. To assist drying, the coverslip may be placed briefly on a 50 C heat block. When beads are dried down from ethanol, it is usually not necessary to assist adherence with poly-lysine. 6. The prepared bead-coated coverslips may then be mounted on glass slides with a drop of mountant. About 10–15 ml is usually appropriate for a22 22 mm coverslip. To avoid air bubbles, place the drop of mountant on the center of the glass slide and angle the coverslip down onto it using a needle to gently lower one side until it is resting flat. Allow the mountant time to spread and cover the whole area of the coverslip before sealing. 7. When using mountants such as Prolong Gold, which require a curing period to harden before sealing, it is best to leave overnight on a level surface in the dark. Note that the time of curing of these reagents will affect the eventual RI of the mountant. 8. Always remember to label slides with the characteristics of the beads, including the density and the type of mountant used. 9. Slides survive longest when they are double varnished and stored level in appropriate slide boxes at 4 C. Slides stored on their sides tend to leak the mountant, especially if excess mountant was used in the preparation. Sparsely prepared bead slides of 100 nm beads should be used to determine the PSF of your imaging system. The PSF is an image set of a single subresolution fluorescent bead. To obtain a PSF, a solitary bead should be A Cell Biologist’s Guide to High Resolution Imaging 37 selected and imaged through Z from about 5 mm above the focal plane to 5 mm below the focal plane. The resultant image set represents the optical characteristics of the imaging system for the specific lens used. The PSF provides information about the alignment of the system and aberrations present. The more symmetrical the PSF, the better the system is set up and the better the imaging will be. A moderate field of beads is useful for assessing spherical aberration (SA). In this case, the preparation may be modified such that beads are mounted directly on the glass slide as well as on the coverslip. By using different volumes of mountant applied to a 22 22 mm coverslip, it is possible to determine the effects of imaging at different defined depths into a specimen (calculated from the known volume and area of the coverslip). Mountants of different RI, for example, different dilutions of glycerol may be useful to match the conditions in live tissue. When imaging multiple channels of data, it is essential to be able to assess the correct registration of those channels. This is especially important when systems with multiple-independent detectors are used. Tetra spec or broad spectrum beads are imaged in multiple channels and the relative position of the beads are used to correct for XYZ shifts, rotation, and magnification. It is important to have a moderately even distribution of beads across the whole field of view. With modern algorithms, it is possible to automatically register channels to subpixel accuracy. This becomes even more important where registration of super-resolution images is being considered. More advanced alignment protocols can perform nonlinear transformations across the field of view. A dense bead lawn is very useful for assessing flatness of the field of view to determine if the excitation illumination is properly aligned. It can also be useful in assessing the success of super-resolution techniques. It should be possible to resolve the packing of subresolution 20–100 nm beads. For structured illumination techniques, a bead lawn is useful for assessing and optimizing the illumination pattern which is applied to the specimen, the quality of which determines the ability to increase resolution. When assessing the performance of an imaging system, it is important to bear in mind that individual objective lenses, even of the same type, can vary considerably in how well they perform. This will be manifested in the PSF. In purchasing objectives it is worth comparing several.

3.2. Sample preparation Sample preparation has always been an important aspect of microscopy from the inception of the method, but as the instruments used have increased in complexity and sophistication, so has the importance and diversity of the methods used to prepare samples for fixed and live imaging. This is of particular importance when considering super-resolution imaging. 38 Graeme Ball et al.

For example, additional resolution is only useful on fixed specimens if the fixation has preserved structural integrity to the appropriate degree. Further, as the spatial resolution is increased, so do the pitfalls and potential artifacts that can arise from optical aberrations and inhomogeneities in the RI of live specimens. The following paragraph discusses some of the key considera- tions for specimen preparation of both fixed and live tissue, when using super-resolution imaging modalities. SA poses a significant problem when imaging biological specimens. It is usually caused by mismatches in RI along the optical axis from the lens into the specimen. The consequence of this is reduced resolution in XY and particularly in Z, and decreased signal strength at the point of focus. The effects of SA are most easily seen as a distortion in the PSF when viewing subresolution beads manifesting as an elongation or “smearing” of the signal along the optical axis and an uneven distribution of signal above and below the plane of focus. The best way to avoid this problem is to match as far as possible the RI of the mounting medium and immersion medium for the lens with the RI of the glass coverslip. With fixed material, it is helpful to deposit beads (see Section 3.1.1)at the edge of the coverslip before mounting the specimen. By using either the “coverslip thickness correction” adjustment of the objective (where one exists) or by using immersion oils of systematically varying RI it is possible to correct for SA. In this way, the beads at the corner of the coverslip are imaged to monitor the extent of SA, and the conditions can be modified until it is fully corrected. SA increases with the depth of mismatched media through which the features of interest are imaged. The best imaging is, therefore, achieved within <15 mmofthecoverslip inner surface. When imaging deep into thick specimens cannot be avoided, SA can still be assessed and ameliorated by immersion media of appropriate RI, using thinner coverslips or, particularly with live imaging, using objectives designed to work with immersion media which more closely match that of the cell or tissues being imaged. Water, glycerol, and the new silicon immersion objectives (from Olympus) are all designed to reduce the affects of SA. The latter we have found particularly effective for high resolution imaging as it is available as a 1.3-NA 60 lens. A significant consideration when using immersion media and mounting media that differ significantly from that of the glass coverslip, is that if the latter is at an incline, this will introduce aberrations apparent as a distortion to the PSF. More recently, it has become possible to correct for imaging depth-dependent SA in a much more dynamic way. Adaptive optics using lens arrangements or deformable mirrors (Kner et al., 2010), which correct the phase of the light, can be calibrated to allow SA correction specific to the depth of imaging. So far commercial systems are limited such as the mSAC produced by Intelligent Imaging Innovations. A Cell Biologist’s Guide to High Resolution Imaging 39

Another significant limitation to achieving optimal resolution is that of limited signal strength (Section 2). Sample preparation can significantly help in this regard. In addition to SA correction, discussed in the preceding section, using the best labels and mountants which include antifade reagents can significantly improve the signal-to-noise and hence to achievable resolution. With fixed material, it is possible to introduce antibodies (Ab) coupled to stable high quantum efficiency dyes (Section 2). Even when GFP-tagged proteins are to be imaged, it is possible to boost this signal by using dye-conjugated Ab (Guizetti et al., 2011). With live material, it is often possible to enhance photostability by introducing antifades based upon vitamin E derivatives such as Trolox (Roche).

4. High Resolution Image Data Acquisition and Processing

The first step in acquiring useful high resolution image data is to carefully define the problem and then decide which of the various methods is compatible with the sample and will deliver the necessary resolution without compromising the biology. This means answering questions such as: What XY resolution is required? What Z resolution is required? What time resolution is required? What contrast-to-noise ratio (CNR) is required? How sensitive is the sample to photobleaching and photodamage? It is also essential to ensure that all necessary instrument calibrations have been properly carried out (e.g., checking registration of channels).

4.1. High resolution techniques When the guidelines above are followed, the maximum resolution of wide- field and confocal microscopy is given in Section 2.1, and temporal resolution has been discussed in Section 2.4. Some other important high resolution techniques that have not been mentioned until this section are light sheet fluorescence microscopy (LSFM), reviewed by Reynaud et al. (2008);two- photon microscopy (Denk et al., 1990); and total internal reflection microscopy (TIRFM; Axelrod, 1989). Light sheet microscopy (Reynaud et al., 2008; Santi, 2011; Verveer et al.,2007) uses a light sheet perpendicular to the objective for excitation illumination, affording good sectioning capability and reduced photobleaching and photodamage. The multiple views acquired to build up a 3D LSFM image can also be used in multiview deconvolution (Planchon et al., 2011; Verveer et al.,2007), resulting in improved, isotropic resolution. Two-photon microscopy involves high photon fluxes of low wavelength light which produce some two-photon absorption events that can excite a transition equal to the sum of the two photons’ energy, that is, at a 40 Graeme Ball et al. shorter wavelength. It also offers improved contrast when working with deep samples. TIRFM on the other hand is limited to imaging samples within around 100 nm of the coverslip, but this is also the advantage of the technique since only a thin slice is observed, and the Z position is known to a high level of precision. The 100-nm limitation stems from the rapid decay of the evanescent field with distance from the coverslip. There already exist a number of excellent reviews of the super-resolution techniques (Davis, 2009; Galbraith and Galbraith, 2011; Gustafsson, 1999; Huang et al., 2009; Ji et al., 2008; Schermelleh et al., 2010; Toomre and Bewersdorf, 2010; Toprak et al.,2010). Far-field super-resolution techniques fall into two broad categories: those that employ spatial patterning of the excitation illumination; and those that detect single molecules to enable high resolution spatial localization. The super-resolution microscopy, or nanoscopy field is in its infancy though (Evanko, 2009; Hell, 2009; Lippincott-Schwartz and Manley, 2009) and it is still important to corrobo- rate findings using other techniques such as conventional fluorescence microscopy and electron microscopy in a correlative light-EM scheme for example. 4.2. Improving the axial resolution As mentioned previously, the axial resolution of a typical microscopy is normally significantly poorer than the lateral resolution (Eqs. (2.1) and (2.2) above). The first successful far-field attempt to overcome this problem was the 4Pi microscope (Hell and Stelzer, 1992; Hell and Wichmann, 1994), which is a CLSM that uses a pair of objectives on either side of the sample to allow addition of the wavefronts observed from opposite sides to capture a greater proportion of the full spherical wavefront. The increase in axial resolution depends on the exact setup used (Hell and Wichmann, 1994), but is around fivefold, meaning that an axial resolution of about 100 nm is achievable. The I5M microscope (Gustafsson et al., 1999) is a wide-field setup that similarly uses two objectives, and can also achieve an axial resolution of around 100 nm. A comparison of 4Pi and I5M microscopes (Bewersdorf et al., 2006) shows that I5M is more sensitive, but suffers from more problematic artifacts due to pronounced and complicated side-lobe structures, which must be removed by processing. Both methods obviously suffer from the requirement to sandwich the sample between two objectives, meaning that they are tricky to set up and incompatible with many biological specimens, particularly those that are thick. 4.3. RESOLFT microscopy One way to overcome the diffraction limit is to use a pair of excitation lasers that interact with the fluorophore photophysics in a nonlinear fashion, effectively engineering a smaller PSF. The generic name for this approach A Cell Biologist’s Guide to High Resolution Imaging 41 is REversible Saturable or switchable OpticaL Fluorescence Transitions (RESOLFT; Hell, 2003). The first among this family of techniques to be reported was STtimulated Emission Depletion (STED; Hell and Wichmann, 1994; Klar et al., 2000, 2001). STED uses an excitation laser beam combined with a second “de-excitation” laser beam at a second wavelength with a ring- like de-excitation profile. Although the de-excitation beam is also diffraction limited, the dependence of the de-excitation process (stimulated emission) upon de-excitation intensity is nonlinear. This means that a small spot at the center of focus remains excited and fluorescence from this spot can be detected after the de-excitation beam. There are two major disadvantages of this approach: firstly, the high photon flux required results in rapid photobleaching and photodamage. Secondly, although the lateral (XY) resolution can be improved to 20 nm or less using STED, the axial resolution remains unaltered in a basic STED microscope, producing a needle-like PSF which is very elongated in the Z-axis. In spite of these caveats though, a commercial STED system is sold by Leica and live-cell STED imaging has been reported a number of times (Hein et al., 2008; Na¨gerl et al., 2008). Ground state depletion (GSD) microscopy (Bretschneider et al., 2007) also uses a nonlinear response to excitation photons. In this case, the same laser may be used for both excitation, and by a second excitation event, for GSD to a long-lived dark state. GSD can achieve a similar level of resolution to STED (Rittweger et al.,2009). The disadvantage of GSD vis-a-vis STED is that fluorophores in the triplet state are less photostable, and photobleaching is a major challenge.

4.4. Structured illumination microscopy (SIM) SIM is a wide-field method that makes use of illumination stripes of varying phase and angle to create low resolution Moire´ patterns that contain higher resolution information (Gustafsson, 2000), producing a potential doubling of the resolution to around 100 nm. The high resolution images must be computationally reconstructed from the raw data, unlike the RESOLFT methods described in the previous section, which is a disadvantage in that artifacts may be introduced and the reconstruction may fail if the signal-to-noise ratio is not adequate. However, SIM requires less excitation illumination in total than RESOLFT methods. SIM has also been successfully combined with TIRFM for live super-resolution imaging (Kner et al., 2009). Further improvement in the resolution of wide-field structured illumination methods requires using the same sort of nonlinear photophysical responses as employed by the RESOLFT methods. saturated structured illumination microscopy (SSIM; Gustafsson, 2005) and saturated patterned excitation microscopy (SPEM; Heintzmann et al., 2002) have been reported, but these techniques require very high photon fluxes. 42 Graeme Ball et al.

4.5. Localization microscopy techniques The localization microscopy techniques employ a completely different principle to the patterned excitation methods discussed above. Localization microscopy techniques rely on detection of photons from a single fluorophore in isolation, and fitting of the PSF pattern in order to localize the fluorophore to a high level of precision (Moerner, 2006; Toprak et al., 2010). The differences between the various methods are in the way in which the isolation of active fluorophores is achieved. The localization precision of the methods used to determine fluorophore positions is discussed in Section 6.2. Prior to reports of actual localization microscopy, fluorescence imaging to 1 nm accuracy (FIONA) was reported (Yildiz and Selvin, 2005). Actual super-resolution imaging was then reported in 2006 by a number of groups using different activation/switching mechanisms. Photoactivation light microscopy (PALM; Betzig et al., 2006) and fluorescence PALM (FPALM; Hess et al., 2006) use a brief pulse of activation wavelength light in the presence of a photoactivatable fluorophore to activate a small population that is subsequently imaged until bleached. Stochastic optical reconstruction microscopy (STORM; Rust et al., 2006) and direct STORM (dSTORM; Endesfelder et al., 2010) on the other hand, use photoswitchable fluorophores to achieve a limited population for sampling, and have also been shown to achieve an accuracy of 20 nm. Until recently, the localization microscopy techniques suffered from the disadvantage that acquisition was necessarily long in order to separate fluorophores tempo- rally, but rapid photoswitching (Endesfelder et al., 2010), and improved algorithms for reconstruction of images at higher fluorophore densities such as DAOSTORM (Holden et al., 2011) mean that live-cell imaging is becoming increasingly feasible using this super-resolution technique.

4.6. Three-dimensional super-resolution microscopy techniques Each of the classes of super-resolution microscopy introduced above has been successfully extended to a 3D version. isoSTED (Schmidt et al., 2008, 2009) uses two opposing lenses to achieve improved axial resolution. SIM has similarly been implemented in a system using two opposing lenses to improve the axial resolution, called I5S (Shao et al., 2008). A more practical 3D-SIM method using only one lens has also been created (Gustafsson et al., 2008; Schermelleh et al., 2008; Fig. 2.1). In the case of the localization microscopy techniques, 3D STORM has been reported (Huang et al., 2008) as well as two-photon PALM (York et al., 2011). Finally, a promising double-helical PSF (DH-PSF) technique (Pavani et al., 2009), to improve axial resolution, was recently reported in localization microscopy experiments studying nucleoid-associated proteins in bacteria (Lee et al., 2011). A Cell Biologist’s Guide to High Resolution Imaging 43

A Wide field Wide field - decon 3D-SIM

Jupiter-GFP (MT) red fluorescent beads B Wide field 3D-SIM

Moesin-GFP (actin)

Figure 2.1 Super-resolution 3D-SIM imaging of the cytoskeleton in living Drosophila macrophages captured with an OMX-Blaze microscope (manufactured and distributed by Applied Precision, a GE Healthcare Company). (A) Microtubules (Jupiter GFP) and endocytosed red fluorescent beads. A single time point is shown from a time lapse movie of 11 frames: Conventional wide-field, deconvolved, and SI-reconstructed images derived from the same data set are compared. Each 3D-SIM image requires 240 images/mmofZ depth (15 images/slice, 8 slices/mm) captured at a rate of about 1s/mm. (B) Actin cytoskeleton in a well-spread living macrophage (Moesin GFP). Wide-field and SI-reconstructed images derived from the same data set are compared as a projected Z series of 0.5 mm from a single time point. Resolution in 3D-SIM was about 120 nm, wide-field 275 nm, and deconvolved wide-field 250 nm.

5. Processing

Image processing has two basic aims: firstly, to remove any artifacts present in the images, and secondly, to present the data in a way that enables the human eye to make sense of the information therein. 44 Graeme Ball et al.

5.1. Denoising methods Denoising or noise filtering aims to remove the effects of noise contamination to recover a better estimate for the true features an image represents, that is, a classical inverse problem. A thorough treatment of image denoising can be found elsewhere (Buades et al., 2005). Instead, our aim here is to make a few important points and draw the reader’s attention to some of the most useful and versatile tools for dealing with the noise filtering problem. It is very important to remember that noise contamination is essentially irreversible from the point of view of the information content of the data. Sometimes there may be particular pieces of prior knowledge that can be used to filter out noise, such as knowledge about the imaging system or sample; but in the general case, the best one can hope for is to make the underlying information content of the data more apparent to the human eye. This is a reasonable goal, since human vision has a number of limitations including sensitivity, acuity, and the spatial and temporal intervals over which intensity information is integrated. Where true quantitative measurements based on the image data are the goal, however; it is generally better to fit the raw data taking account of noise contamination. The simplest denoising algorithms are spatial smoothing filters such as the mean filter, median filter, and Gaussian blur (convolution with a Gaussian kernel). These rely on the fact that in an oversampled microscope image, rapid intensity changes from one pixel to the next can only have their origin in noise or other artifacts. All result in some amount of smoothing and loss of resolution however. 3D and 4D versions of these filters for data sets with multiple Z planes and time points can actually be surprisingly effective considering their simplicity. An equivalent approach is to filter out the highest frequency information in Fourier space, and the Wiener filter (Gonzalez and Woods, 2002) is a moderately sophisticated version of this basic idea employing a statistical approach to identify the frequency spectrum of the noise. There exist a number of more intelligent spatial filters for denoising, which weigh the averaging process according to the estimated likelihood that a given pair of pixels are equivalent, reducing the degree of over- smoothing. The equivalence of pixels is usually determined on the basis of the similarity of patches of pixels surrounding the two pixels under consideration (Buades et al., 2005; Kervrann and Boulanger, 2008). Figure 2.2 shows the results of denoising using the patch-based algorithm of Kervrann and Boulanger (2008), which varies the neighborhood window adaptively. This algorithm has been used to good effect in 4D live-cell imaging sequences at extremely low levels of illumination (Carlton et al., 2010). An important point to note is that this class of denoising approach relies on redundancy in the image or image sequence. In the complex microscope images typically recorded by a cell biologist, truly repeating patterns within A Cell Biologist’s Guide to High Resolution Imaging 45

ACB

D EF

Figure 2.2 The effects of denoising and deblurring. Data are EB1:GFP in a stage 8 Drosophila oocyte, marking microtubule plus-ends; acquired using a Deltavision Core wide-field microscope (Applied Precision, a GE Healthcare Company) with EM-CCD camera. (A) Raw data at high excitation illumination intensity: Z-plane 2/3, time point 30/121. (B) A blowup of the region highlighted in (A); scale-bar is 5 mm. (C) The result of iterative constrained deconvolution for the image plane shown in (B) using soft- WoRx 4.5, “trailed” for 5 time points. (D) Raw data at low excitation illumination intensity for the same oocyte: Z-plane 2/3, time point 30/121, sequence taken immediately prior to the high excitation data shown in (A–C). (E) Result of denoising using the nD-SAFIR algorithm with a neighborhood size of 5 5 5 3(XYZT). (F) Result of deconvolution following denoising in (E), “trailed” for 5 time points. a single image are unlikely, and it is instead the immediately adjacent pixels and neighboring planes in Z and time that are likely to provide multiple samples of equivalent pixels. The Cramer-Rao lower bound (Kay, 1993) for a normally distributed variable states that N observations of a variable of unknownt mean lead to a reduction of the error in the estimate s by factor of N; and while we do not suggest that the noise in a typical microscope image is normally distributed, in our experience this gives a reasonable idea of the improvement that can be expected from effective denoising. For this reason, we find that denoising is a technique that lends itself to well-sampled 4D image sequences where the signal-to- noise ratio is often modest as a consequence of the need to keep total illumination to a minimum to reduce photobleaching. Where it is possible to make additional assumptions about the nature of the features in an image, signal-to-noise in feature-enhanced images can be further increased (Yang et al., 2010). 46 Graeme Ball et al.

There are many similarities between the denoising problem and the image compression problem: both seek to uncover redundancy in an image, the former to identify and remove noise, the latter to produce a more compact representation of the information. Some of the most effective noise filters have much in common with image compression techniques, including the bilateral filter (Tomasi and Manduchi, 1998), K-SVD (Elad and Aharon, 2006), and BM3D (Dabov et al.,2007). Other popular denoising tools such as nonlinear anisotropic diffusion (Perona and Malik, 1990)andtotal variation minimization (Rudin et al., 1992) make use of the gradients in an image to preserve feature boundaries while smoothing the noise.

5.2. Deblurring and deconvolution Blurring in raw microscope image data is a consequence of the PSF of the imaging system as well as inevitable aberrations due to imperfections of the optics. The most important and problematic aberrations are: SA, chromatic aberration, and astigmatism. A detailed discussion of the origins of the various types of aberration and their removal is beyond the scope of this chapter, but suffice it to say that one should check for their presence using a diagnostic standard sample such as that described in Section 3.1.1. The blurring that results from the PSF of the imaging system can be corrected by measuring this PSF using a standard sample and then performing deconvolution to recover an image in which the light has been reassigned to the true source. This is of much greater importance for wide-field microscope images than for confocal images in which a large part of the out-of-focus light has already been excluded by the pinhole, and spinning disk confocal images lie between the two extremes. One of the most popular, successful deconvolution algorithms is constrained iterative deconvolution (Agard et al., 1989), the results of which are shown in Fig. 2.2. If the PSF of the system is carefully measured, and there are no other aberrations or noise, then deconvolution problem is an easy one. In this case, an inverse filter can be used to arrive at a solution for the original object image. Unfortunately, however, microscope images are invariably contaminated with unpredictable noise. This renders inverse filtering ineffective. Instead, constrained iterative deconvolution utilizes a noise filter such as the Wiener filter to suppress noise, and makes successive estimates of the original image before reblurring these using knowledge of the PSF, and then subtracting these out-of-focus blur contributions to arrive at a better estimate for the original image. Note that only true 3D deconvolution preserves and indeed improves the distribution of intensity information in a microscope image, whereas 2D deblurring operations such as the unsharp mask and nearest neighbor deconvolution merely improve the appearance, and actually worsen the fidelity. For a more detailed practical guide to the different deconvolution methods, see Wallace et al. (2001). A Cell Biologist’s Guide to High Resolution Imaging 47

6. Analysis of High Resolution Image Data

6.1. Intensity and molecular quantification Rigorous interpretation of intensity information is not limited to high resolution imaging per se, but fluorescence intensity is of much greater utility when it can be assigned with confidence to a discrete structure in the cell. Hence, the better the spatial and temporal resolution one can achieve, the more useful it becomes to relate intensity to the number of fluorescently labeled molecules in a structure. A protocol for the quantification of fluorescently labeled molecules can be found in a book chapter written by members of the Singer lab (Goldman et al., 2009), who have pioneered many of the techniques to study mRNA molecules in vivo. In this protocol, they recommend wide-field fluorescence microscopy combined with a CCD camera due to the higher sensitivity compared to confocal methods. For a series of experiments, they point out that it is essential to use the same lens and camera settings, and that constrained iterative deconvolution should be used to reassign out-of-focus light to the correct position without introducing intensity artifacts. The basic procedure is to create a series of standard slides with known fluorophore concentration, and image these in order to build up a calibration curve of fluorescence intensity versus concentration that can be used to interpret the real data. It is also possible by careful calibration to determine the actual number of photon counts (Chen et al., 2003; Thompson et al., 2010).

6.2. Accurate localization of fluorophores Accurate localization of individual fluorophores is key to the localization microscopy techniques introduced in Section 4.5. This involves fitting the observed distribution of detected photons to determine a more accurate centroid position, producing a localization error that decreases with the square root of the number of measured photons (Thomann et al., 2002; Thompson et al., 2002). A resolution of 1 nm or better, as in the FIONA technique (Churchman et al., 2005; Yildiz and Selvin, 2005) was shown to be possible using this method, and more recently, subnanometer single molecule localization has been achieved (Pertsinidis et al., 2010). The limits of the resolution of localization microscopy and the fitting techniques used can be found in a recent series of chapters introduced here (Larson, 2010).

6.3. Segmentation and particle tracking Tracking moving objects in live cells is often a difficult undertaking, but the results can be extremely rewarding in terms of the information they provide about the underlying cellular processes. A sufficiently high temporal 48 Graeme Ball et al. resolution is crucially important for successful tracking, and this must be kept in mind when considering the trade-off with signal-to-noise and spatial resolution. In order to ensure that the tracking problem is tractable, one must achieve a temporal resolution such that the majority of particle displacements from one frame to the next are less than the average interpar- ticle distance. According to the Rose criterion (Rose, 1948), a CNR of 5 or more should be the aim to ensure reliable detection. High spatial resolution on the other hand, reduces the number of particle crossing events during which the individual particles cannot be resolved. There have been a number of reviews and comparisons of tracking algorithms (Cheezum et al., 2001; Meijering et al., 2006), and several powerful algorithms are freely available ( Jaqaman et al., 2008; Sbalzarini and Koumoutsakos, 2005). The segmentation and tracking problem is covered in detail elsewhere in this issue, and we refer the reader there.

6.4. Analysis of distribution and colocalization Fluorescence microscopy is the most important tool for the analysis of the distribution of macromolecules in live cells, and the higher the resolution achieved, the greater the utility and reliability of the technique. Two common questions posed are: Does the detected distribution of molecules indicate order of some kind, or is it entirely random? Where two different kinds of molecules are detected, is there any correlation between their distributions? Both problems belong to the domain of spatial statistics, particularly the treatment of spatial point patterns (Cressie, 1993), the analysis of which is most easily accomplished using the R statistical computing language (Bivand et al., 2008). In the case of the first problem, deciding whether a spatial distribution is random or not, researchers typically calculate statistics such as the nearest neighbor distance function G (Andrey et al., 2010) and Ripley’s K function (Lee et al., 2011). Colocalization is a concept that has received much interest from cell biologists from the inception of modern fluorescence microscopy, and important developments were the adoption of Pearson’s correlation coefficient (Manders et al., 1992) and the creation of Manders’ overlap coefficients M1 and M2. These overlap coefficients quantify the dependence of each channel on the other, since any dependence may not be mutual. For a review of the topic, see Bolte et al. (Bolte and Cordelie`res, 2006).

6.5. Motility statistics and motion models Motility statistics are an excellent way to interrogate dynamic cellular processes in order to understand the underlying mechanisms. Two of the most important applications include the study of molecular motor-driven A Cell Biologist’s Guide to High Resolution Imaging 49 transport processes and diffusion processes in membranes. Although mod- eling underlying processes with reference to the data is a rewarding approach for understanding a biological system, a much simpler method is to compare a parameter from two populations for which a different pheno- type is suspected, and ask the question: Do the two populations show a statistically significant difference? Table 2.1 provides a summary of common statistical tests and the situations in which they are appropriate. The most important motility statistics are speed, directionality, position, and for saltatory movements, run length and duration, as well as the duration of the pauses. Average values of these motility statistics can be valuable, but histograms of their distributions are even more informative, and are able to reveal whether there are multiple populations within the sample. Another useful graphical representation of motion that can aid analysis is a kymograph (meaning “wave drawing”), which displays the evolution of the one-dimensional position of a particle over time. In the case of directionality information about motility on the other hand, the most useful representation is the display of an overall “flow” or “wind map.”

Table 2.1 Choosing an appropriate statistical test

Statistical distributions Description/uses Normal (Gaussian distribution) a distribution of random variables distribution whose means cluster around a single value. Extreme value The generalized extreme value distribution is a family of distribution distributions describing normal distributions weighted in one tail. This occurs when selecting a particular feature from a population. For example, the heights of the tallest person in each city. Binomial A discrete probability distribution of the successes on a distribution series of events, for example, tossing a coin. Poisson A distribution of discrete random variables from events in distribution time, distance, area, or volume. t-Test Comparing distributions of speeds and run lengths. Regression Used to fit a line to a data set. The R statistic then gives an analysis indication of how well the line describes the data. Chi-squared test To test whether enumerated data are from a particular category. Rayleigh test Test for the significance of the mean direction in a circular distribution. von Mises A circular distribution, like a circular version of the distribution normal distribution. Watson’s test Tests goodness of fit to the von Mises circular distribution. 50 Graeme Ball et al.

We previously described an open source software package called ParticleStats that can produce these plots, as well as performing some of the relevant statistical tests. This is illustrated in Fig. 2.3 below. ParticleStats is described in Hamilton et al. (2010) and Oliveira et al. (2010), and can be accessed from www.particlestats.com. A common goal when tracking particle motility is to fit the observed movements to a specific motion model. What are the known motion models, and how does one go about deciding whether the data is fit? The process of diffusion normally results in a mean-squared displace- ment that increases linearly with time (Saxton, 1994). However, this simple case is unlikely in a living cell, where up to 40% of the total volume is taken up by various biomacromolecules. A plot of mean-squared displace- ment versus time will reveal the anomalous subdiffusion that results from

AB C

D E

6.34 6.03 1.83 1.73

Figure 2.3 ParticleStats: An open source software package for the analysis of intracellular particle motility. Panels A–C summarize EB1:GFP tracks that mark growing microtubule plus-end directionality in a stage 8 Drosophila oocyte. (A) ParticleStats: Directionality track plot, where colors represent directionality as for the wind map. (B) “Wind map” representing sum of track directions in each square, where colors corre- spond to the four sectors shown top left. (C) “Rose diagram” summarizing overall directionality, where each outer dot represents a track. (D) ParticleStats:Kymograph used to create a kymograph summarizing the movement of eight aligned kinetochore pairs during meiosis in a Drosophila embryo. (E) Kymograph for a mutant with poor kinetochore synchrony. A Cell Biologist’s Guide to High Resolution Imaging 51 the crowded intracellular environment, as well as the existence of corralled diffusion if this is the case. If, on the other hand, an active transport process is taking place, then super-diffusion will result in a mean-squared displace- ment that increases more rapidly than a linear rate with time.

7. Conclusions

This is an exciting time to be involved in high resolution imaging in cell biology. Each new improvement in the resolution of microscopy has delivered a wealth of scientific discoveries in diverse fields of research, leading to the ability to bypass old limits. New developments in super- resolution imaging, combined with advances in detector sensitivity and speed, efficient methods for image restoration, and new improved fluorophores mean that fluorescence microscopy is on the cusp of resolving intracellular interactions live, in real time, at the molecular level of detail. This promises to deliver profound benefits to research in cell biology, greatly expediting the dissection of complicated pathway interactions by visualizing them live in action.

ACKNOWLEDGMENTS

The authors would like to thank R. A. Oliveria for permission to include the Drosophila embryo kinetochore data shown in Fig. 2.3. We would also like to acknowledge Charles Kervrann and INRIA Rennes for providing the nD-SAFIR denoising algorithm, and John Sedat’s lab for the implementation in the Prism image processing and analysis suite. This work was supported by a Wellcome Trust Senior Fellowship to I.D. (Grant number 081858). G. B. is employed by the Micron Oxford Advanced Imaging Facility, which is supported by a Wellcome Trust Strategic Award.

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