35 3 An Introduction to Live-Cell Super-Resolution Imaging Siân Culley, Pedro Matos Pereira, Romain F. Laine, and Ricardo Henriques Fluorescence microscopy has been a crucial tool in the advancement of modern cell biology because of its nonin- vasive nature, compatibility with imaging live samples, and molecule-specific labeling tools. However, the resolving power of conventional fluorescence microscopy is limited to ~250–300 nm. To resolve cellular structures on a smaller size scale than this, researchers have typically relied on electron microscopy, which can provide insight into structures on a nanometer scale. While electron microscopy continues to be a valuable tool for investigating fine intracellular structures, incompatibility with live samples and its limited labeling capabilities remain obstacles to studying dynamic phenom- ena with high confidence in molecular identities. This has led to the development of super-resolution microscopy methods, which were developed in the early 2000s. Super-resolution microscopy bridges the resolution gap between conventional fluorescence microscopy and electron microscopy while retaining the advantages associated with light microscopy. This chapter provides a brief overview of commonly used super-resolution microscopy techniques, their applications to live-cell imaging, and future directions for this family of techniques. CONTENTS 3.1 The Diffraction Limit .......................................................................................................................................................... 36 3.2 Super-Resolution Microscopy Techniques ........................................................................................................................... 36 3.2.1 Structured Illumination Microscopy ....................................................................................................................... 36 3.2.1.1 Axial Resolution ...................................................................................................................................... 38 3.2.1.2 Temporal Resolution ................................................................................................................................ 38 3.2.1.3 Hardware and Sample Preparation .......................................................................................................... 38 3.2.1.4 Examples of Live-Cell SIM ..................................................................................................................... 39 3.2.1.5 Limitations of SIM................................................................................................................................... 39 3.2.1.6 Other Microscopy Implementations Based on the SIM Principle ........................................................... 39 3.2.2 Stimulated Emission Depletion Microscopy ........................................................................................................... 39 3.2.2.1 Axial Resolution ...................................................................................................................................... 41 3.2.2.2 Temporal Resolution ................................................................................................................................ 41 3.2.2.3 Hardware and Sample Preparation .......................................................................................................... 41 3.2.2.4 Case Studies of Live-Cell STED Microscopy ......................................................................................... 41 3.2.2.5 Limitations of STED Microscopy ........................................................................................................... 41 3.2.2.6 Other Microscopy Techniques Based on the Principles of STED ........................................................... 42 3.2.3 Single Molecule Localization Microscopy ............................................................................................................. 42 3.2.3.1 Axial resolution ........................................................................................................................................ 43 3.2.3.2 Temporal Resolution ................................................................................................................................ 43 3.2.3.3 Hardware and Sample Preparation .......................................................................................................... 43 3.2.3.4 Case Studies of Live-Cell SMLM ...........................................................................................................44 3.2.3.5 Limitations of SMLM ..............................................................................................................................44 3.2.3.6 Promising Approaches for SMLM ..........................................................................................................44 3.3 Emerging Techniques for Live-Cell Super-Resolution Microscopy ....................................................................................44 3.3.1 Hardware Developments .........................................................................................................................................44 3.3.2 Analytical developments ......................................................................................................................................... 45 3.3.3 Labeling Developments ...........................................................................................................................................46 3.4 Super-Resolution Data Evaluation .......................................................................................................................................46 3.5 Conclusion ............................................................................................................................................................................46 References ...................................................................................................................................................................................... 49 TNF_03_K32983_C003_docbook_new_indd.indd 35 8/15/2020 09:11:21 ----!@#$NewPage!@#$---- 36 Imaging from Cells to Animals In Vivo 3.1 The Diffraction Limit The resolution of light microscopy has a fundamental limit. This is commonly referred to as the diffraction limit, as it arises from the diffraction of light that occurs as it passes through an aperture. In the case of a microscope, the aperture is the microscope objective whose physical size is related to the numerical aperture (NA) of the microscope by Equation 3.1 (Figure 3.1a): NA n nD f = sinq » 2 (3.1) where n is the refractive index of the medium between the lens and the imaged object, D is the effective diameter of the lens aperture, and f is its focal length (distance between the lens and an in-focus object). After a point source of light undergoes diffraction through a microscope objective, it reaches the observer, not as an infinitesimally small point, but, rather, a blurred diffraction pattern called the point spread function (PSF). The three-dimensional (3D) shape of the PSF depends on both the physical properties of the microscope optics and the wavelength of the light (Figure 3.1b). The size of the PSF compared to the size of subcellular structures being imaged imposes a limit on the structures that can be accurately resolved using fluorescence microscopy. This is referred to as the diffraction limit and was first formal- ized by Ernst Abbe as: D » d NA x y, l 2 (3.2) where Δdx,y is the smallest resolvable distance between two light-emitting objects in the lateral dimension. Figure 3.1c shows how closely separated points become indistinguishable because of the diffraction limit. The axial resolution of a microscope is considerably poorer than its lateral resolution and is given by Equation 3.3 (Inoue, 2006). D d = n z 2 2 l NA (3.3) Thus, the axial resolution is worse by a factor of 4n/NA, and resolutions are typically on the order of >1,000 nm. Equations 3.2 and 3.3 describe the best possible resolution achievable in an ideal imaging system; in reality, optical aberrations within the microscope optics and scattering of light induced by the sample degrade resolution further. 3.2 Super-Resolution Microscopy Techniques Super-resolution microscopy refers to any technique based on optical microscopy that is capable of resolving object separa- tions smaller than allowed by Abbe’s diffraction limit. There are three main techniques within the field: structured illumina- tion microscopy (SIM), stimulated emission depletion (STED) microscopy, and single molecule localization microscopy (SMLM). Table 3.1 summarizes some key features of these methods. The features described in Table 3.1 are achievable only if the user pays due attention to sample preparation and imaging conditions. These are outlined in Table 3.2. 3.2.1 Structured Illumination Microscopy SIM is a widefield super-resolution technique that uses the mathematical analysis of a sequence of images capturing a sample fluorescently excited by a patterned illumination to retrieve subdiffraction limit features. It was first described by Mats Gustafsson 2000). FIGURE 3.1 The diffraction limit. The resolution of fluorescence microscopy is dependent on diffraction through the optics of the microscope and varies with wavelength and properties of the objective. (a) Simplified diagram of the geometry of imaging a point source of light through a microscope objective (not to scale). (b) Simulated point spread functions (PSFs) for three different microscope objectives for imaging at λ = 500nm in the lateral (top row) and axial (bottom row) dimensions. The images in the top row correspond to the focus of the beam, highlighted with a dashed red box on the axial images. Scale bars = 500nm. Simulations were performed using PSF Generator (Kirshner et al., 2013). (c) Simulated microscope images of two point sources of light (red points) separated by different separations for a system where ∆dx,y = 210nm. Yellow lines indicate line profiles across center of image. Scale bars = 250nm. TNF_03_K32983_C003_docbook_new_indd.indd 36 8/15/2020 09:11:22 ----!@#$NewPage!@#$---- 37 An Introduction to Live-Cell Super-Resolution Imaging When two patterns are overlaid, interference between the two results in a new emergent pattern known as moiré fringes (Figure 3.2). Relative shifts between the two patterns leads to the emergence of new moiré fringes (Figure 3.2b); as a result these fringes contain information about both underlying pat- terns. In SIM, the illumination field is patterned, usually in a striped or sinusoidal distribution. When the fluorescently labeled structure in the sample, which can also be thought of as a pattern, is excited with patterned illumination, the result- ing collected fluorescence will thus contain moiré fringes. As one pattern is known (the illumination), computational analysis can disentangle the data from the moiré fringes in the acquired image to provide higher resolution information of the unknown pattern (the structure being imaged). Here, it is useful to discuss the principle behind SIM in Fourier space. Typical microscopy images are a representa- tion of the spatial information contained within the data; the Fourier transform displays the same data but in the frequency domain. The Fourier transform of a widefield fluorescence microscopy image is shown in Figure 3.3a, and it can be seen that the frequency information is contained within a central circular distribution. The radius of this circle is k d 0 =1 / D , where Δd is the lateral resolution limit as described in Equation 3.2. Within this representation, smaller structures (i.e. higher frequencies) lie further toward the edge of the circle, whereas larger structures (i.e. lower frequencies) are toward the center. In SIM, the striped illumination pattern replicates the fre- quency distribution of the imaged structure into two addi- tional circles within Fourier space, which represent the moiré fringes (Figure 3.3b). The positions of these new circles from the center depend on the spatial frequency of the illumination pattern (k1) and the angle at which this pattern is incident on the sample. If the fluorophore pattern within the sample has a spatial frequency k, then moiré fringes will be created at the difference frequency (k – k1). This difference frequency relocates higher frequency information (i.e. subdiffraction limit structures) into the range of frequencies observable by the microscope (i.e. the k0 circle). To isolate the contribution of the fluorophore pattern from the illumination pattern, the illumination is shifted at different orientations and phases; this series of images is then processed to reconstruct the super-resolution image (Figure 3.3c). This super-resolution image has a maximum frequency of (k0 + k1). However, the spatial frequency of the illumination field (k1) is also governed by the diffraction limit, so the highest frequency within the illumination pattern is limited to k0. This limits the highest observable image frequency in SIM to ~2k0, i.e. a dou- bling in the resolution. TABLE 3.1 Features of Routine Applications of Commonly Used Super-Resolution Microscopy Techniques SIM STED SMLM Type of microscope Widefield Confocal Widefield Lateral resolution ~150 nm ~50 nm ~30 nm Axial resolution ~300 nm ~100 nm ~50 nm Temporal resolution ~1 s ~100 ms >10 s Optical complexity High High Low Computational requirements High Low High Multicolor compatibility ≤ 4 colors ≤ 2 colors ≤ 2 colors Sensitivity to sample preparation Low Medium High Values in this table represent a typical “best-case” for each method, although these can be improved using custom hardware, software, and reagents/fluorophores. TABLE 3.2 Key Parameters to Optimize or Calibrate for Each Super-Resolution Technique SIM STED SMLM Acquisition Brightness of the sample and photobleaching rate during a single SIM acquisition. Correction collar (if available) of the objective to minimize spherical aberrations. Refractive index matching of immersion medium. Choice of good STED dye(s), dense labeling, sample embedding. Depletion power, number of line and frame accumulations, gating position (for gated-STED only). Choice of reducing agent and oxygen scavenging buffer composition (for dSTORM). Choice of dyes with appropriate photoswitching properties. Illumination and reactivation laser intensities. Mechanical stability and fiducial markers for drift correction. High labeling density required. Reconstruction Deconvolution parameters None, but deconvolution is frequently applied for image restoration. Method of localization and visualization (and their respective parameters). Pixel size. Applying appropriate thresholding of molecule localizations to reject poor fitting. Drift correction method. TNF_03_K32983_C003_docbook_new_indd.indd 37 8/15/2020 09:11:22 ----!@#$NewPage!@#$---- 38 Imaging from Cells to Animals In Vivo 3.2.1.1 Axial Resolution The implementation of SIM described above increases only the lateral resolution; however, it is also possible to improve the resolution in the axial dimension by varying the illumination pattern such that rather than a striped field, a 3D checkerboard- like pattern is produced with periodic variations in the axial direction as well as the lateral. This implementation of SIM is known as three-beam SIM (for this reason, two-dimensional (2D) SIM is often referred to as two-beam SIM) (Gustafsson et  al., 2008). Again, the increase in axial resolution is lim- ited to a two-fold improvement over the diffraction limit (as described in Equation 3.3). 3.2.1.2 Temporal Resolution The factors affecting the temporal resolution of a SIM image are the camera exposure time, the number of different grating rotations (usually either three or five), and the number of phase shifts per rotation (usually five). Depending on the optics used to generate the illumination pattern, there may also be a time delay introduced as the illumination pattern is changed. For example, for 30ms exposure, the minimum acquisition time for a raw SIM data set comprising three rotations and five phases would theo- retically be 450ms. However, when testing on a commercial SIM microscope (Zeiss Elyra PS.1), the acquisition time was in fact 1.41s. It is generally accepted that the best temporal resolution of SIM is of the order of 1 second for a single color image on a commercial system. It is important to note that while the raw data acquisition can be performed at this rate the reconstructed super-resolution image is typically not immediately available for visualization, as the reconstruction is computationally intensive. 3.2.1.3 Hardware and Sample Preparation SIM typically requires a widefield microscope body and a laser illumination source with the appropriate optics for beam FIGURE 3.2 Moiré fringes are generated when two patterns (a) are overlaid (b). These fringes change according to the relative displacement and orientation of the two patterns. FIGURE 3.3 Structured illumination microscopy. SIM involves taking images of a labeled structure with patterned illumination and then recon- structing these into a single super-resolution image. (a) A widefield image of microtubules labeled with Alexa Fluor 488 (top) can be represented in the frequency domain by plotting its Fourier transform (bottom). The frequency information in the Fourier transform is contained within a circle whose radius is the reciprocal of the diffraction limit. (b) The same field of view is imaged using a striped illumination pattern at different rotation angles and phases. Top: Moiré patterns generated by imaging the sample at five different illumination rotations as illustrated in the top right corners. Middle: A single SIM acquisition requires that each rotation is also shifted to several different phases (typically five). Here the moiré fringes from three of the five phases are overlaid for each angle in red, green, and blue. Bottom: The Fourier transforms of the moiré fringe images now contain frequency information beyond that available in (a). (c) Computationally reconstructed SIM image (top) and its Fourier transform (bottom). The Fourier transform contains twice the frequency information of its widefield equivalent. Scale bars = 5µm. TNF_03_K32983_C003_docbook_new_indd.indd 38 8/15/2020 09:11:25 ----!@#$NewPage!@#$---- 39 An Introduction to Live-Cell Super-Resolution Imaging shaping and filtering to produce the illumination pattern. The illumination pattern is usually generated with a spatial light modulator such as a liquid crystal device. One advantage of SIM is that it is compatible with any wavelength of illumina- tion; however, it should be noted that, as the best resolution achievable with SIM is Δd/2, a longer wavelength of illumi- nation results in poorer resolution than with shorter wave- lengths. Scientific complementary metal oxide semiconductor (sCMOS) cameras are commonly used as detectors for SIM, as they have a large chip capable of imaging a large field of view and can also image at a high frame rate. Commercial systems for SIM imaging are available from Cytiva (DeltaVision OMX), Nikon (N-SIM), and Zeiss (Elyra). These platforms also include their own proprietary software for reconstruction of the super-resolution data sets. However, it is also fairly straightforward to build a SIM path for an exist- ing microscope. There are useful step-by-step guides available for this (Young, Ströhl, and Kaminski, 2016; Lu-Walther et al., 2015). An ImageJ/Fiji (Schneider, Rasband, and Eliceiri, 2012; Schindelin et al., 2012) plugin is also available for open-source reconstruction of SIM data acquired using both home-built and commercial equipment (Müller et al., 2016). The main considerations in fluorophore choice for SIM are the same as for any laser-based widefield microscopy: good signal-to-noise ratio (SNR) and resilience to photobleaching. Fluorophores that do not meet these two criteria will lead to the generation of poor-quality images containing artifacts. Aside from fluorophore choice, SIM does not have any specific imaging buffer or sample mounting requirements, apart from needing attention to minimize refractive index mismatch in the optical path (see Table 3.2). 3.2.1.4 Examples of Live-Cell SIM SIM has been used extensively for live-cell super-resolution microscopy for a variety of cell biology applications, which is testament to its ease of use and low phototoxicity. Fiolka et al. (2012) provided an early example showcasing multicolor live-cell 3D SIM imaging for a range of biological structures, such as mitochondria, cytoskeleton, and clathrin-coated pits. SIM has also been used for quantification of actin and mem- brane reorganisation at the immunological synapse (Ashdown et al., 2014, 2017), visualization of vesicular trafficking in the neuronal growth cone (Nozumi et al., 2017), and uncovering dynamic interactions between lysosomes and mitochondria (Han et al., 2017). 3.2.1.5 Limitations of SIM The major limitation of SIM is that it fundamentally cannot attain the same resolutions as STED and SMLM techniques. Insufficient SNR in the raw images can also yield artificial structures, such as its common honeycomb-like patterning. Because SIM relies on the generation and the measurement of subtle optical fringes within the sample, any optical aberra- tions or distortions of the signal lead to artifactual reconstruc- tions and poor image quality. This limits the applicability of SIM approaches in thick samples, e.g. in tissue or in animal models, and, realistically, most 2D SIM applications are most successful when combined with total internal reflection fluo- rescence (TIRF). 3.2.1.6 Other Microscopy Implementations Based on the SIM Principle A number of techniques based on similar principles to SIM have been developed. A subset of these specifically exploits the concept of photon reassignment in point-scanning confocal microscopy in so-called image scanning microscopy (ISM) methods (Sheppard, 1988; Sheppard, Mehta, and Heintzmann, 2013; Müller and Enderlein, 2010). The ISM principle has also been parallelized for increased imaging speed (York et  al., 2012). These techniques are reviewed in further detail in Ströhl and Kaminski (2016). Another form of photon reassignment capable of achieving a 1.7-fold resolution increase in both the lateral and axial dimensions uses a novel “Airyscan” detector acting as a collection of small-diameter pinholes (Huff, 2015); this is commercially available from Zeiss. Another variant, known as “instant SIM” (iSIM), uses advanced optical paths to generate images with 2 -fold improved resolution without the need for any computational reconstruction (York et  al., 2013; Guo et al., 2018). A commercial implementation of iSIM is available from VisiTech International. Other related SIM variants, such as multifocal SIM (York et al., 2012) and rescan confocal microscopy (De Luca et al., 2013) are also available commercially (including from confocal.nl and Olympus). Nonlinear methods also exist for increasing the resolution of SIM beyond its 2-fold limit. In theory, the resolution of SIM can be increased by increasing the illumination intensity such that fluorophore excitation becomes saturated, leading to the generation of additional harmonics (i.e. higher multiples of k0) (Gustafsson, 2005). However, the laser illuminations required for this are so large that they are highly incompatible with live cell imaging. Alternatively, photoswitchable fluorescent pro- teins can be used in conjunction with patterned illumination to achieve a >2-fold improvement in resolution (Li et al., 2015). 3.2.2 Stimulated Emission Depletion Microscopy STED microscopy is a laser-scanning confocal technique that uses an additional laser beam to deplete fluorescence from the periphery of the excitation volume and confine the emit- ted fluorescence to a subdiffraction limit region. The theory of STED microscopy was first described by Stefan Hell and Jan Wichmann (1994), and the first practical demonstration was performed in 2000 on yeast and E. coli (Klar et al., 2000). In confocal microscopy a diffraction-limited spot of laser light is scanned across a region of interest in a labeled sam- ple, and the resulting fluorescence is collected simultaneously at each point to build up an image pixel-by-pixel. This is in contrast to widefield techniques in which the whole sample is illuminated simultaneously. In STED microscopy, in addi- tion to this “excitation” beam, which promotes the fluorophore from the ground state into the excited state, a second so-called “depletion” beam also scans the sample (Figure 3.4a). The wavelength of the depletion beam is chosen to coincide with the emission spectrum of the fluorophore but not the excitation spectrum (Figure 3.4b). As a result, the depletion beam causes TNF_03_K32983_C003_docbook_new_indd.indd 39 8/15/2020 09:11:25 ----!@#$NewPage!@#$---- 40 Imaging from Cells to Animals In Vivo stimulated emission. This is a resonant process that returns fluorophores to the ground state via a transition matching the energy of the depletion beam photons. If the flux of depletion photons incident upon an excited fluorophore is large enough (i.e. the depletion beam has a sufficiently high intensity), then the fluorophore will return to the ground state preferentially via stimulated emission rather than by fluorescence. During stimulated emission, as the fluorophores are driven to de- excite through the transition dictated by the depletion beam, this excess energy will be released in the form of a photon of the same wavelength as the depletion beam. STED microscopy exploits stimulated emission by shap- ing the depletion beam into an annular, or “doughnut” shape (Figure 3.4c). The doughnut beam is overlapped with the exci- tation beam, and both beams scan the sample simultaneously. Beneath the intense regions of the doughnut beam, stimulated emission prevents fluorescence from occurring, whereas in the dark hole at the center of the doughnut, fluorescence can occur without inhibition. In this way, STED microscopy reduces the size of the fluorescence PSF to the size of the subdiffraction limit hole in the center of the depletion beam (Figure 3.4c). Resolution in STED microscopy scales with the size of the hole in the center of the doughnut; a smaller hole will generate a smaller fluorescent spot and therefore a higher resolution. The size of this hole is proportional to the efficiency of stimu- lated emission and therefore is dependent upon the intensity of the depletion beam (Figure 3.4d). This is described by a modi- fication to Abbe’s equation, as shown in Equation 3.4. D = + d I I x y, STED sat NA l 2 1 (3.4) Here I is the peak intensity of the doughnut beam and Isat is the “saturation intensity” defined as the laser intensity required to reduce the fluorescence emission of a fluorophore to 50% of its undepleted intensity (Harke et al., 2008). It should be noted that Isat depends on not just the identity of the fluorophore, but also the depletion wavelength, fluorophore microenvironment, and polarisation effects among other factors; therefore, it is not a straightforward value to ascertain reliably. Resolutions in STED microscopy acquired with high-intensity lasers can reach ~50–60 nm. The spatial resolution of STED microscopy can also be improved via temporal “gating” of the detected fluorescence (gated-STED) (Vicidomini et al., 2011). If an excitation beam that is pulsed in time at high frequency (e.g. 80 MHz) is used in conjunction with a doughnut shaped depletion beam, the prob- ability that a detected photon originated from the central hole (as opposed to unwanted bleed-through from the intense part of the doughnut beam) increases with time from the excitation pulse on a nanosecond timescale. Therefore, resolution can FIGURE 3.4 STED microscopy. STED microscopy uses the photophysics of stimulated emission and beam shaping to achieve super-resolution images. (a) Energy level diagrams showing fluorescence (left) and stimulated emission (right) processes. Each wavy line represents a single photon. (b) The excitation and emission spectra of two commonly used STED dyes, Oregon Green 488 (OG488) and Alexa Fluor 660 (AF660), with appropriate choices of excitation (exc.) and depletion (dep.) laser lines. (c) Schematic showing the 2D shapes of the excitation and emission beams and the resultant light produced from this beam pair. The green-dashed line in the “detected fluorescence” panel indicates the perimeter of the PSF if the excitation beam alone was used. (d) Resolution in STED microscopy can be improved by increasing the power of the depletion beam and, in the case of gated- STED, increasing the time gate delay. Images are simulations based on the PSF sizes and count rates from Vicidomini et al. (2011) in which a single nitrogen vacancy was imaged with STED and gated-STED to obtain the super-resolution PSF. Power for the time gate panel is constant at 156 mW; all images are on same intensity scale. Scale bars = 200nm. TNF_03_K32983_C003_docbook_new_indd.indd 40 8/15/2020 09:11:26 ----!@#$NewPage!@#$---- 41 An Introduction to Live-Cell Super-Resolution Imaging be increased by rejecting any “early” fluorescence – typically the first few nanoseconds after the fluorophore being excited – and creating the image from only later-arriving photons (Figure 3.4d). Again, the optimum time gate will depend on the properties of the specific fluorophore being imaged, namely its fluorescence lifetime. 3.2.2.1 Axial Resolution As STED microscopy is based on confocal microscopy, images are optically sectioned. However, the depletion beam as described above increases only the resolution in the lateral dimension and provides no other increase in axial resolution. To gain an increase in the axial resolution, an additional depletion beam is required to suppress fluorescence from above and below the focus, again via stimulated emission. As a result, dedicated 3D-STED microscopes are capable of achieving axial resolu- tions <100 nm but only when the additional optics for the axial depletion beam are present (Hein, Willig, and Hell, 2008). 3.2.2.2 Temporal Resolution As the physical process of stimulated emission occurs on the same timescale as fluorescence, i.e. nanoseconds, this is much faster than the typical pixel dwell time in laser scan- ning microscopy; therefore, the underlying physics does not limit the acquisition speed (unlike with SMLM, see next sec- tion). Aside from the increase in the time taken to image a region of interest because of the smaller pixel sizes, in theory, the temporal resolution of STED microscopy is the same as that of confocal microscopy (Galbraith and Galbraith, 2011). However in practice, as far fewer photons are collected in STED microscopy, pixel dwell times are often increased, or multiple frames are averaged to increase the SNR of the resul- tant images (Wegel et al., 2016). 3.2.2.3 Hardware and Sample Preparation STED microscopy requires a microscope with scanning mir- rors for fast laser scanning and at least one highly sensitive point detector, such as a photomultiplier tube, avalanche pho- todiode, or hybrid photodetector. The major modifications to convert a conventional confocal microscope into a STED microscope are the depletion laser, beam-shaping optics, and additional filter sets. For gated-STED, the excitation laser must be pulsed, and photon counting electronics are required (Vicidomini et al., 2011). Researchers in optical physics and spectroscopy labs often opt to build custom STED microscopes for greater accessibil- ity to optical components for a more flexible system. However, building a STED microscope is not a trivial matter and requires sufficient expertise to align and maintain the microscope. An excellent guide to building a STED microscope has been pub- lished by Wu et al. (Wu et al., 2015). Leica Microsystems and Abberior Instruments also manufacture commercial STED microscopes with 3D capability and multiple depletion laser wavelengths. In theory, STED microscopy does not require special fluo- rophores or sample preparation, as all fluorescent molecules are physically capable of undergoing stimulated emission. However, there are some features of fluorophores that will impact upon the quality and resolution of the super-resolution image. The probability of stimulated emission occurring fol- lows approximately the same distribution as the emission spec- trum of the fluorophore (Vicidomini et al., 2012), so there must be an overlap between the emission spectrum of the dye and the wavelength of the depletion laser. On the other hand, over- lap between the excitation spectrum of the fluorophore and the depletion wavelength must be minimal. Additionally, deple- tion lasers need to provide sufficient power for high resolution (see Equation 3.4); therefore, the depletion laser(s) of a STED microscope will often limit the choice of fluorophores that can be used, and it is highly desirable to use fluorophores that have well-separated excitation and emission spectra (Sednev, Belov, and Hell, 2015). Other desirable photophysical proper- ties of fluorophores for STED microscopy are good photosta- bility, resistance to bleaching, and long fluorescence lifetimes. Care must be taken during sample preparation to minimize the presence of any autofluorescent species within the mounting media, as the intense depletion beam may cause unexpected signal from these species that is not seen when using the exci- tation beam alone. The mounting medium Vectashield, for example, should be avoided, as it exhibits autofluorescence throughout most of the visible spectrum (Olivier et al., 2013); Mowiol is a popular alternative for STED microscopy (Lau et al., 2012). 3.2.2.4 Case Studies of Live-Cell STED Microscopy STED microscopy for live-cell two-color imaging has been demonstrated for a range of intracellular structures (Bottanelli et al., 2016). Following this work, a further live- cell study revealed the role that the GTPase ARF1 plays in tubule formation in the Golgi apparatus (Bottanelli et  al., 2017). A field that has particularly benefited from live-cell STED imaging is neuroscience; for example, in studying axon morphology and synaptic architecture (Tønnesen et al., 2014; Chéreau et al., 2017; Wegner et al., 2018). Indeed, the first live-cell STED microscopy study was investigating syn- aptotagmin clustering in living neurons (Willig et al., 2006), and STED microscopy is the only technique that has been used to see periodic cytoskeleton patterning in living neu- rons (D’Este et al., 2015). 3.2.2.5 Limitations of STED Microscopy In practice, STED only works for a limited range of dyes with high photostability, and achieving a good SNR often requires the use of immersion media to improve the stability of the fluo- rescence (Blom and Widengren, 2017). These aspects limit its use in live-cell microscopy. The performance of commercial STED microscopes for live-cell imaging also appears to be limited at this time, as all of the example studies listed above used custom-built microscopes. This could be because day- to-day maintenance is required to preserve the precise beam alignment necessary to ensure sufficient power throughput of the depletion beam and accurate overlap of the excitation/ depletion beam pair. TNF_03_K32983_C003_docbook_new_indd.indd 41 8/15/2020 09:11:26 ----!@#$NewPage!@#$---- 42 Imaging from Cells to Animals In Vivo 3.2.2.6 Other Microscopy Techniques Based on the Principles of STED The idea of depleting fluorescence emission around a dif- fraction-limited spot in order to shrink the emission point- spread function and improve resolution can be exploited by using reversibly switchable fluorescent proteins (rsFPs) as fluorescent markers. Here, instead of using a strong depletion laser, a doughnut-shaped “off”-switching laser can be used to drive rsFPs into an “off” state. This method, called reversible saturable optical linear fluorescence transitions (RESOLFT), requires much lower laser power and, therefore, is more com- patible with live imaging than standard STED (Hofmann et al., 2005). Both STED and RESOLFT techniques can be parallel- ized in order to speed up the acquisition times (Chmyrov et al., 2013; Yang et al. 2013). 3.2.3 Single Molecule Localization Microscopy SMLM refers to a family of widefield super-resolution tech- niques in which photoswitchable/photoactivatable fluoro- phores are used to isolate fluorescence from individual single molecules such that they can be mapped with nanometer precision. SMLM was originally described independently as stochastic optical reconstruction microscopy (STORM) (Rust, Bates, and Zhuang, 2006), photoactivated localization microscopy (PALM) (Betzig et  al., 2006), and fluorescence photoactivation localization microscopy (FPALM) (Hess, Girirajan, and Mason, 2006). The central principle of SMLM is that, rather than acquir- ing one single image containing the simultaneous emission from all molecules (as is the case in conventional widefield imaging), a large number of images are acquired, each con- taining a small random subset of emitting “on” molecules. It follows that, in each acquired image, it is necessary that the vast majority of fluorophores reside in a temporary “off” state. Each image will contain a different subset of emitting fluoro- phores; if enough images are acquired, all of the fluorophores labeling the structure of interest eventually will be sampled. If the emitting molecules are separated by distances >>∆dx,y (i.e. the image is “sparse”) the center of each molecule can be calculated with very high accuracy and thus used to generate the final super-resolution image (Figure 3.5a). In order to acquire sparse images, the fluorophore, laser properties, and imaging microenvironment must be carefully selected. The original STORM implementation exploited acti- vator-reporter pairs of dye molecules (e.g. Cy3–Cy5) acting as molecular switches. The activator, in the presence of appropri- ate wavelength illumination, allows for the reporter to tran- sition between off and on states. Sparsely activated reporter molecules in the on state are then imaged. However, a more recent method, called direct STORM (dSTORM), bypasses the requirement for imaged fluorophores to have an activator molecule (Heilemann et  al., 2008). In dSTORM, switching FIGURE 3.5 Single molecule localization microscopy. (a) Schematic of an SMLM acquisition workflow for a simulated structure. Inset: localizing the center (black circle) of a single molecule by Gaussian fitting. Scale bars = 500nm. (b) Different methods for encoding the axial position of a fluo- rophore in SMLM techniques. AS = astigmatism, DH = double helix, BP = biplane. Data is from the z-stack calibration sets available for the SMLM software benchmarking 2016 challenge (“Http: //Big www.E pfl.C h/Sml m/Cha lleng e2016 /Inde x.Htm l?P=d atase ts,” n.d.). (c) Fluorophore choice can strongly impact data quality in SMLM, as demonstrated for dSTORM data of dual-labeled fixed samples imaged with a Zeiss Elyra PS.1 in blinking buffer. Left: Actin labeled with Alexa Fluor 488 displays sparse blinking but at very poor signal-to-noise ratio when laser intensity is increased to its maximum. Right: Vimentin labeled with Alexa Fluor 647 displays sparse blinking with good signal-to-noise ratio as laser intensity is increased, lead- ing to a good reconstructed super-resolution (SR) image. Scale bars = 1µm. TNF_03_K32983_C003_docbook_new_indd.indd 42 8/15/2020 09:11:27 ----!@#$NewPage!@#$---- 43 An Introduction to Live-Cell Super-Resolution Imaging behavior is induced in conventional dyes using high-intensity illumination and redox reactions to cycle in and out of the off state. The on state, in this case, is the singlet state of the mol- ecule as would be imaged in conventional microscopy. PALM techniques use photoswitchable fluorescent pro- teins rather than dye molecules. There are two main classes of fluorescent protein suitable for PALM imaging: proteins, such as rsGFP, which has an emitting on state and dark off state, and proteins such as Kaede (Ando et al., 2002) or mEos (Wiedenmann et al., 2004) for which the on and off states are both emissive but emit at different wavelengths (e.g. a green- emitting on state and red-emitting off state). In both classes, transitions between on and off states are induced using activat- ing laser illumination at a wavelength distinct to that used for imaging (Shcherbakova et al., 2014). Generation of the final super-resolution image requires com- putational processing to localize the centers of the individual emitting molecules. This analysis typically involves initial detection of individual molecules followed by high-accuracy fitting of PSF-like functions to determine the center of the PSF and, therefore, the localization of the underlying fluorescent molecule. The precision of molecule localization (and hence resolution) is largely determined by the number of photons emitted from the molecule, N, as described in Equation 3.5 (Thompson, Larson, and Webb, 2002). D » d N x y, SMLM NA l 2 (3.5) However, many other factors impact the final reconstructed image resolution. These include the sparsity of imaged mol- ecules within each frame, the total number of molecules imaged, and the underlying labeling density on the sample. There is a wealth of algorithms available for performing this analysis, as reviewed in Sage et al. (2015) of varying levels of complexity and usability. Two recommended software pack- ages for performing this analysis are QuickPALM (Henriques et al., 2010) and ThunderSTORM (Ovesný et al., 2014), both of which are available as free plugins for ImageJ/Fiji. 3.2.3.1 Axial resolution In the SMLM approach, in order to encode information about the axial position of a fluorophore, additional hardware is required. The three most common methods for achieving this are astigmatism (Huang et al., 2008), biplane imaging (Juette et al., 2008), and double-helix PSF engineering (Pavani et al., 2009) (Figure 3.5b). The astigmatism method involves insert- ing a cylindrical lens into the detection path of a SMLM microscope. This introduces elongation of the PSF along the y-axis for fluorophores below the focal plane, no change in the PSF shape for in-focus molecules, and elongation of the PSF along the x-axis for fluorophores above the focal plane. The degree of elongation is proportional to the distance from the focal plane, and this can be measured, calibrated, and used to infer the axial position of a fluorophore with 50–60 nm resolu- tion within a depth of ~1 µm. Biplane imaging requires detec- tion of two different focal planes simultaneously, either using two halves of the same camera chip or using two separate cameras. The two resulting image stacks of the same lateral field of view are then analyzed, with the differences between the fitted widths of the PSFs for the individual molecules used to infer the z-position. The axial resolution of biplane imaging is <100 nm but works across a wider depth of ~2 µm. Double- helix 3D SMLM uses beam-shaping optics in the detection path to transform the PSF into a double-helical shape. As a result, the fluorescence from an individual molecule appears on the camera not as a 2D Gaussian distribution, but as two lobes. The relative orientation of these two lobes encodes the axial location of the molecule, and the lateral location of the molecule is the central minimum between the two lobes. As a result, double-helix SMLM can yield axial resolutions of 20–60 nm (Carr et al., 2017) across a few microns. However, it should be noted that reconstruction approached for 3D SMLM needs to be adapted to take into account the changes in PSF shape induced to encode the axial position of the molecule. 3.2.3.2 Temporal Resolution The temporal resolution of SMLM is severely limited by the required acquisition time for building up a large enough num- ber of localizations and generating a complete image of the underlying structure. In standard SMLM experiments, typi- cally 10,000–100,000 frames are required to generate a single super-resolution image. Coupled with the 10–50 ms exposure time of cameras used for SMLM acquisitions, this yields a temporal resolution of 2–90 minutes. This poor temporal reso- lution is one of the features of SMLM addressed by next-gen- eration analytics (discussed below). 3.2.3.3 Hardware and Sample Preparation The hardware requirements for SMLM are simple compared to SIM and STED, but SMLM requires much closer attention to sample preparation. The only hardware requirements for SMLM imaging are a widefield microscope with a high-NA objective, a sensitive camera (such as an electron multiplying charge coupled device [EMCCD]), and sufficiently powerful lasers to induce photoswitching behavior in the fluorophores and obtain a high SNR. As a result, home-built SMLM sys- tems are straightforward, popular, and relatively cheap (Ma et al., 2017; Kanchanawong et al., 2010; Kwakwa et al., 2016). There are also commercial systems available, some of which offer additional features, such as optics for achieving 3D super-resolution, including the Nikon N-STORM, Zeiss Elyra, and the Oxford Nanoimaging Nanoimager. The most important sample preparation requirement for SMLM is a fluorophore that can undergo photoswitching. For the approaches described above, important features of fluoro- phores include: the fraction of time that the fluorophore spends in its on and off states (its “blinking statistics”), the quantum yield of the fluorophore and the wavelength(s) required for acti- vation and/or excitation of the fluorophore. Figure 3.5c shows the behavior of two different fluorophores for dSTORM at dif- ferent illumination intensities. In-depth studies of the behavior and properties of photoswitchable fluorophores have been pub- lished for organic dye molecules (Lehmann et al., 2015; Wang et al., 2014) and fluorescent proteins (Siyuan Wang et al., 2014; TNF_03_K32983_C003_docbook_new_indd.indd 43 8/15/2020 09:11:27 ----!@#$NewPage!@#$---- 44 Imaging from Cells to Animals In Vivo Pennacchietti, Gould, and Hess, 2017). Buffering is also a key component in maintaining the photophysical properties of the fluorophores, especially for dSTORM. Again, different fluoro- phores have different buffering requirements (Dempsey et al., 2011; Olivier et al., 2013). Because of the high illumination intensities applied dur- ing SMLM imaging and the single-molecule sensitivity of the system, the presence of unwanted fluorescent species in the sample must be minimized. These species include the usual suspects, such as phenol red present in many cell culture media, but also dust and dirt on the coverslip, which frequently display blinking behavior when subjected to high laser intensi- ties. The latter can be avoided by careful cleaning of the cover- slips and slides used for sample preparation (Pereira, Almada, and Henriques, 2015). 3.2.3.4 Case Studies of Live-Cell SMLM The major modality for live-cell SMLM has been PALM, as the use of fluorescent proteins allows for less invasive labeling of intracellular structures compared to methods required for labeling with dyes. Photoswitching mechanisms in PALM also typically require lower intensity irradiation to induce transi- tions to the off state compared to methods such as dSTORM. Examples of live-cell PALM include studying assembly of the FtsZ division ring in E. coli (Fu et al., 2010), the role of actin in hemagglutinin membrane clustering (Gudheti et al., 2013), the distribution of different phosphoinositides within the plasma membrane (Ji et al., 2015) and dynamic evolution of focal adhesion complexes (Shroff et al., 2008). Live-cell STORM imaging has been used to image clathrin-coated pits and the transferrin receptor (Jones et al., 2011) and various intracellular organelles through inducing photoswitching in commonly used probes such as Mitotracker (Shim et al., 2012). Photoactivatable fluorescent proteins were originally used for single-particle tracking studies (SPT) (Yu, 2016), and, as such, this dynamic information can be combined with the spatial accuracy of PALM in the sptPALM technique (Manley et al. 2008). 3.2.3.5 Limitations of SMLM As SMLM approaches require a large number of camera frames with sparse emitting fluorescent molecules, long acquisition times are typically necessary (2–90 min). As many intracellular processes occur on shorter timescales than this, movement of the imaged structure will manifest as motion blur within the reconstructed image, and fine details will be lost. The laser requirements for SMLM imaging are also limit- ing for live-cell microscopy. This is because the photophys- ical mechanisms underlying transitions between on and off states frequently rely upon high photon fluxes (Figure 3.5c) or ultraviolet (UV) wavelengths, especially for synthetic fluorophores. Both of these imaging conditions are highly phototoxic to cells (Wäldchen et al., 2015) and, when com- bined with the long acquisition times discussed above, can no longer be considered as noninvasive. Furthermore, the buffers used for dSTORM imaging are typically cytotoxic, as they include reducing agents and oxygen-scavenging systems. Care must also be exercised within the computational post- processing of SMLM data sets in order to avoid manifestation of artifacts in the images, such as interstructure merging and molecule mislocalization (Burgert et al., 2015). 3.2.3.6 Promising Approaches for SMLM To circumvent the requirement for dependence on high- intensity/UV-wavelength illumination for achieving sparsely distributed emitters, a technique called PAINT (point- accumulation for imaging in nanoscale topography) has been developed (Sharonov and Hochstrasser, 2006). On/Off transitions in PAINT are achieved by transient binding and unbinding of free-floating fluorophores in solution to the tar- get structure of interest. Here, the on state corresponds to when the fluorophore is bound to the structure and the off state is the diffusing fluorophore in the media. A popular implementation is DNA-PAINT, whereby fluorophores are conjugated to short ssDNA strands that transiently bind and unbind to a complementary strand conjugated to the target structure of interest (Jungmann et  al., 2014; Schnitzbauer et al., 2017). 3.3 Emerging Techniques for Live-Cell Super-Resolution Microscopy In the last part of this chapter, we will focus on strategies that have been developed specifically to enable live-cell super-res- olution microscopy. Of the techniques described above, only SIM is routinely capable of imaging living samples with mini- mal photodamage, yet this has the limitations of a complex optical setup and only modest improvements in resolution. There are three main fronts of development for enabling bet- ter live imaging at near-molecular resolution: new microscope architectures, advanced analytics, and the design of novel fluorophores. 3.3.1 Hardware Developments A common limiting factor to resolution in both conventional fluorescence microscopy and super-resolution microscopy is the numerical aperture of the objective (Equations 3.2–3.5). Zeiss makes a 1.57 NA objective, and Olympus has recently produced a 1.7 NA objective (commonly used “high” numer- ical objectives are normally 1.4–1.45 NA). Thus, increased resolution can be instantly obtained by using this objec- tive in a super-resolution microscope. For example, the 1.7 NA objective has been combined with fast multicolor SIM imaging to achieve sub-100 nm resolution for live-cell imag- ing of actomyosin dynamics and focal adhesions (Li et al., 2015). However, it should be noted that this is not neces- sarily a cost-effective method for improving resolution, as these objectives require specialized immersion media and coverslips. TNF_03_K32983_C003_docbook_new_indd.indd 44 8/15/2020 09:11:27 ----!@#$NewPage!@#$---- 45 An Introduction to Live-Cell Super-Resolution Imaging There are also hardware developments aimed at increasing the throughput of live-cell SMLM. For example, these include using sCMOS cameras for larger fields of view and rapid frame rates (Almada, Culley, and Henriques, 2015; Huang et  al., 2013). Another method for increasing throughput is using automated microfluidics for online fixation at the microscope, allowing for transitions between live cells at low laser intensi- ties, and fixed-cell super-resolution imaging at high intensities (Almada et al., 2019). Volumetric fluorescence imaging techniques such as light sheet microscopy are becoming increasingly widespread because of their capabilities for 3D imaging and, importantly, lower illumination doses to the sample. As a result, light sheet techniques are an attractive area for further developing live- cell super-resolution imaging. For example, 150nm lateral and 280nm axial resolutions have been achieved using lattice light sheet microscopy, albeit in a very specialized optical setup (Chen et al., 2014). Further developments in light sheet microscopy for super-resolution are reviewed in Girkin and Carvalho (2018). Other hardware improvements for live-cell super-resolu- tion microscopy center on adaptive illumination to decrease the light dose to the sample. The basic premise of adaptive illumination is that only regions containing the structure of interest are illuminated at high intensity, with the laser inten- sity attenuated in nonfluorophore-containing regions of the field of view. This approach has been demonstrated in both SIM (Chakrova et al., 2016) and STED (Staudt et al., 2011; Heine et  al., 2017). While neither technique has yet been demonstrated in living samples, previous diffraction-limited studies using adaptive illumination have shown a marked increase in cell viability (Hoebe et al., 2007). Adaptive illumi- nation STED has now also been commercially implemented by Abberior Instruments. Another strategy for decreasing harmful light dose is to use LED illumination rather than laser illumination, which has been recently implemented for SIM (Pospíšil et al., 2018). 3.3.2 Analytical developments A large number of analytical developments have centered on algorithms for SMLM techniques. While the most obvi- ous avenue for decreasing phototoxicity in SMLM imag- ing is to decrease the illumination laser intensity, this has the result of increasing the number of on-state molecules within a single frame, and, as such, the emission of these molecules overlaps. Such so-called “high density” data sets cause problems for the molecule localization algo- rithms typically used for SMLM data, leading to artifac- tual images. In order to enable accurate reconstructions of high-density data sets, a number of novel analytical approaches have been developed. In contrast to algorithms relying on sparse data sets, which identify single-emitting molecules and localize their centers using fitting, high- density algorithms examine features of the image such as the temporal statistics of the fluorescence or apply spatial transforms to the image. Examples of some of these tech- niques are summarized in Table 3.3. TABLE 3.3 Examples of Algorithms for High-Density SMLM Data Sets Algorithm Resolution Raw data requirements Usability SRRF: Super-resolution radial fluctuations (N. Gustafsson et al. 2016; Culley, Tosheva, et al. 2018) Lateral: 60–150 nm Axial: No improvement vs. diffraction-limited Temporal: ~1 second ≥100 frames per super-resolution image. Exposure times ~10–50 ms per frame for best temporal resolution. No specific fluorophore requirements. Demonstrated to work with most conventional fluorescence microscopes. Available as a plugin for ImageJ/Fiji. Analysis is GPU-enabled for speed. Moderate number of parameters to adjust. SOFI: Super-resolution optical fluctuation imaging (Dertinger et al. 2009; Geissbuehler et al. 2012, 2014) Lateral: 110 nm Axial: 500 nm (with additional optics for multiplane imaging) Temporal: 0.6–5 seconds ≥200 frames per super-resolution image. Exposure times ~3–20 ms per frame for best temporal resolution. Fluorophores must display and switch between discrete emission states e.g. on- and off-states Available as a standalone Matlab toolbox (“Https ://Do cumen ts.Ep fl.Ch /User s/l/L e/ Leu teneg /Www/ Balan cedSO FI/In dex.H tml,” n.d.) or in the Localizer software for IgorPro/Matlab (Dedecker et al. 2012). Analysis is fast. Small number of parameters to adjust. 3B: Bayesian analysis of bleaching and blinking (Cox et al. 2011; Rosten, Jones, and Cox 2013) Lateral: 50 nm Axial: No improvement vs. diffraction-limited Temporal: 4 seconds ≥200 frames per super-resolution image. Fluorophore should exhibit on-/ off- switching and bleaching characteristics. Demonstrated with both laser illumination and xenon arc lamp illumination. Available as a plugin for Fiji/ImageJ. Algorithm requires ~6 hours to produce an image for a small (60×60 pixels × 300 frames) data set. A cluster implementation is available (Hu et al. 2013) but is no longer maintained. Other algorithms include deconSTORM (Mukamel, Babcock, and Zhuang, 2012), csSTORM (Zhu et al., 2012), FALCON (Min et al., 2015) and MUSICAL (Agarwal and Macháň, 2016), but use of these is less widespread. Resolutions are as reported for live cells in the publications. TNF_03_K32983_C003_docbook_new_indd.indd 45 8/15/2020 09:11:27 ----!@#$NewPage!@#$---- 46 Imaging from Cells to Animals In Vivo Another area of development is the burgeoning applica- tion of machine learning and deep learning to fluorescence microscopy data. This involves training a model with pairs of raw input and corresponding high-accuracy output images. Once fully trained, the model can then be applied to input data alone and thus generate high-quality output images. There are several examples of this approach in super-res- olution microscopy. For example, deep learning has been applied to accept sparse SMLM raw data sets and output super-resolution renderings, hence bypassing conventional localization algorithms (Nehme et al., 2018). Deep-learning based techniques can reconstruct super-resolution images with fewer frames than typically used in SMLM (Ouyang et  al., 2018). A broader application of machine learning to fluorescence imaging is content-aware image restora- tion (CARE) (Weigert et al., 2018), in which, in addition to improving SNRs of images and restoring isotropic 3D resolu- tion from under-sampled data sets, extraction of super-reso- lution images from widefield images has also been achieved. Deep-learning techniques must however come with a word of caution: The models are typically only capable of construct- ing image features that they have been trained on and can be prone to biases and are not necessarily suitable for structural discoveries. 3.3.3 Labeling Developments In fluorescence microscopy, the protein or structure of inter- est is usually nonfluorescent. Therefore, a fluorescent mol- ecule (fluorescent protein or synthetic fluorophore, either on its own or linked to a probe, such as an antibody for exam- ple) is attached to the target of interest and used as a proxy for its localization. Making sure this assumption is true is fundamental for all insights provided by fluorescent micros- copy, although this is often a challenge. In super-resolution microscopy for example, the veracity of this assumption may be disputable. The nanometer scale resolutions achievable often highlight defects of the labeling approach chosen that otherwise would be hidden within the ~250–300 nm dif- fraction limit. This is a consequence of resolutions smaller than the distance between fluorophore and target of interest (Ries et al., 2012; Laine et al., 2015), incomplete labeling of target structures (Durisic et al., 2014; Burgert et al., 2015; Lau et  al., 2012), and function and/or localization defects resulting from the labeling strategy used (Lelek et al., 2012; Hammond et  al., 2010). Additionally, one common aspect to all super-resolution approaches is the need for high SNR. In SIM, insufficient SNRs can lead to artifactual recon- structions; in STED most of the fluorescence is suppressed, and undepleted molecules must emit sufficient numbers of photons; in SMLM, resolution scales with the number of photons emitted per single molecule event (Equation 3.5). Obviously, the choice of the right probe and fluorophore to use is of critical importance and, consequently, a research field developing at a considerable pace. Hence, rather than providing an in-depth description of the multiple options available for labeling, we have grouped the larger families of labeling approaches and provide pros and cons for each one in Table 3.4. We aim for the reader to have a general view of the options available and a comprehensive list of further reading material to explore any useful aspect. To avoid an overly simplistic view of this field, it is worth pointing out that all the labeling strategies described in Table 3.4 are complementary, each having pros and cons for any given application. This complementary nature allows researchers to explore the spatial and temporal dynamics of biological systems in multiple ways, obtaining multiple degrees of information. Hence, the labeling strategies and their validity are, and will continue to be, enticing challenges for researchers developing new labeling methods and explor- ing the most stimulating biological questions. These label- ing strategies will evolve in parallel fueled by advances in molecular genetics, biochemistry, and chemistry as well as hardware development breakthroughs and progress in the analytical methods available to analyze live-cell super-reso- lution data. 3.4 Super-Resolution Data Evaluation While super-resolution microscopy techniques are becoming increasingly commonplace, it is important to note that they are prone to artifacts if sample preparation (Pereira et al. 2018), labeling, imaging, and reconstruction protocols are not appro- priately followed. Furthermore, the optics and computation required for generating super-resolution reconstructions often yield nonlinear images (when compared to their diffraction- limited equivalents), which makes assessment of image quality more challenging. There are now several different methods for assessing the resolution and quality of super-resolution data, summarized in Table 3.5. It is easy to assume that very high resolution is an indicator of “good” super-resolution imaging; however, it has been shown that image resolution does not necessarily correlate with image quality and fidelity (Culley et al., 2018). 3.5 Conclusion Super-resolution microscopy techniques provide a valu- able bridge between the ultrahigh resolution of electron microscopy and the noninvasive methodologies of conven- tional fluorescence microscopy. Although these techniques have become well-established and their potential impact for cell biology research recognized through the awarding of the 2014 Nobel Prize in Chemistry to three pioneers in the field, applications to live-cell imaging are still in their infancy. The development of novel hardware, fluorescent labels, and analytical methods will help translate super- resolution imaging into live-cell applications that hold the promise of uncovering cellular dynamics with unprec- edented detail. TNF_03_K32983_C003_docbook_new_indd.indd 46 8/15/2020 09:11:27 ----!@#$NewPage!@#$---- 47 An Introduction to Live-Cell Super-Resolution Imaging TABLE 3.4 Overview of Available Labeling Approaches for Live-Cell Super-Resolution Microscopy Labeling approach Advantages Disadvantages Exciting prospects Refs High-affinity probes Antibodies (Ab) • ~150 kDa • ~10 nm Fab fragments • ~50 kDa • ~5 nm Single-domain Ab (sdAb, nanobodies); Affimers (Adhirons); Affibodies; DARpins; Aptamers (DNA/RNA based) • ~15 kDa • ~2 nm No need for genetic encoding (if fusion functionality is an issue). Wide variety of epitopes and affinities. Can be combined with multiple synthetic fluorophores. Multiple size ranges. Probe libraries for easy selection (e.g. phage display). Not membrane permeable unless combined with transitory membrane disruption (e.g. cell squeezing, toxins). Smaller probes are still not available for all Ab targets. Linker error, may mask epitopes or induce clustering (Ab). Mostly for external epitopes, difficult for intracellular live-cell imaging. Indirect target detection. High number of small probes available (to match classical Ab). Small probes against encoded targets (e.g. BC2tag/BC2 sdAb, GFP/RFP nanobody). Combination with extremely robust approaches (DNA-PAINT, FRET-PAINT). Jungmann et al, 2014; Ries et al, 2012; Laine et al, 2015; Traenkle & Rothbauer, 2017; Schlichthaerle et al, 2018; Ståhl et al, 2017; Lavis 2017, Jungmann et al, 2010; Iinuma et al, 2014; Auer et al, 118; Schueder, Lara-Gutiérrez et al, 2017; Schueder, Strauss et al, 2017; Kollmannsperger et al, 2016; Canton et al, 2013; Lambert et al, 1990; Teng et al, 2016; Braun et al, 2016; Bird et al, 1988; Pleiner et al, 2017; Opazo et al, 2012; Bedford et al, 2017; Pleiner et al, 2015; Mikhaylova et al, 2015; Platonova et al, 2015 Fluorescent Proteins (FPs) Classical (e.g. mScarlet), Photoactivatable (e.g. PA-mCH), Photoconvertible (e.g. mEos3.2), Photoswitchable (e.g. rsKame) • ~30 kDa • ~3 nm Genetic encoding. Easy live-cell imaging. Tight control of fusion position (C-, N- or internal). Flexibility for multiple techniques (e.g. SR, FRET, FRAP). Small linker error and no artificial clustering (if monomer). Very reproducible (e.g. stable cell lines). Direct target detection. Laborious selection protocol for specific applications. Low(er) photon-budget/ brightness. Protein fusion functionality issues. Needs to be genetically encoded. Limited variety of wavelengths (with identical high quality). Combining with CRISPR for endogenous expression levels. Primed conversion for live-cell SMLM. Brighter and more photostable new FPs. FPs with properties adapted for specific techniques. Biosensors in combination with SR. Gustafsson, 2000; Hein, Willig & Hell 2008; Hoffmann et al, 2005; Shcherbakova et al, 2014; Gustafsson et al, 2016; Khan et al, 2017; Ratz et al, 2015; Shaner, Steinbach & Tsien, 2005; Chudakov, Lukyanov & Lukyanov 2005; Wiedenmann, Oswald & Nienhaus 2009; Kremers et al, 2011; Rego et al, 2012; Nägerl et al, 2008; Tønnesen et al, 2011; Zhang et al, 2016; Hense et al, 2015; Grotjohann et al, 2012; Shaner et al, 2013; Tiwari et al, 2015; Brakemann et al, 2011; Zhang et al, 2015; Uno et al, 2015; Dempsey et al, 2015; Turkowyd et al, 2017; Mo et al, 2017; Wang et al, 2017; Richardson et al, 2017; Kaberniuk et al, 2017; Mishina et al, 2015; Sanford & Palmer, 2017; Ni, Mehta & Zhang, 2017; Bajar et al, 2016; Nienhaus & Nienhaus, 2014; Zhang et al, 2012; Hostettler et al, 2017; Rosenbloom et al, 2014; Bindels et al, 2016 (Continued) TNF_03_K32983_C003_docbook_new_indd.indd 47 8/15/2020 09:11:27 ----!@#$NewPage!@#$---- 48 Imaging from Cells to Animals In Vivo TABLE 3.4 (CONTINUED) Overview of Available Labeling Approaches for Live-Cell Super-Resolution Microscopy Labeling approach Advantages Disadvantages Exciting prospects Refs Synthetic Fluorophores Classical conjugation (e.g. NHS ester). Site specific labeling: Click chemistry (e.g. Tetrazine). Enzymatic (e.g. Sortase/LPXTG motif). Self-labeling systems: CLIP-tag, SNAP-tag • ~20 kDa • ~3 nm Halo-tag • ~30 kDa • ~5 nm TMP-eDHFR-tag ~18 kDa ~3 nm Small tags (e.g. HIS-tag/TrisNTA, FlAsH/ReAsH, Versatile Interacting Peptide, CoilY). Off-the-shelf dyes (e.g. Mitotracker, SiRActin). Wide variety of fluorophore properties (e.g. wavelength). Can be combined with high affinity probes. Genetic encoding is possible (for cell-permeable dyes). Genetic-encoded options allow for the same target to be localized with different fluorophores. Off-the-shelf options to target specific cellular structures. Fluorogenic options. High(er) photon-budget/ brightness. Easier targeted design for specific applications Selected option may not be membrane permeable or available for selected tagging system. Great variety of options may be daunting - difficult to access the best option for a specific problem. Frequently have to be combined with a tag or high-affinity probe. Click Chemistry combined with genetic-code expansion and CRISPR. Fluorogenic dyes. Abundance of technique specific options. Janelia Fluor® dyes. Shcherbakova et al, 2014; Shim et al, 2012; Wäldchen et al, 2015; Durisic et al, 2014; Lelek et al, 2012; Lavis, 2017a; Kollmannsperger et al, 2016; Khan et al, 2017; Ratz et al, 2015; Nienhaus & Nienhaus, 2014; Sednev, Belov & Hell, 2015; Dempsey et al, 2011; Grimm et al, 2017; Mateos-Gill et al, 2016; Li, Tebo & Gautier, 2017; Uttamapinant et al, 2015; Thompson et al, 2017; Lukinavičius et al, 2014; Lavis, 2017b; Gautier et al, 2008; Griffin, Adams & Tsien, 1998; Keppler et al, 2003; Los et al, 2008; Wombacher et al, 2010; Butkevich et al, 2018; Song et al, 2017; Wang, Song & Xiao, 2017; Leng et al, 2017; Lukinavičius et al, 2016; Liu et al, 2014; Prifti et al, 2014; Butkevich et al, 2017; Lukinavičius et al, 2013; Clark et al, 2016; Vreja et al, 2015; Uttamapinant et al, 2010; Liu et al, 2014; Cohen, Thompson & Ting, 2011; Howarth et al, 2005; Zane et al, 2017; Nikić & Lemke, 2015; Nikić et al, 2016; Sakin et al, 2017; Schvartz et al, 2017; Lukinavičius et al, 2015; Uno et al, 2014; Uno et al, 2017; Takakura et al, 2017; Wang et al, 2017; Thompson et al, 2017 TNF_03_K32983_C003_docbook_new_indd.indd 48 8/15/2020 09:11:27 ----!@#$NewPage!@#$---- 49 An Introduction to Live-Cell Super-Resolution Imaging REFERENCES Agarwal, Krishna, and Radek Macháň. 2016. “Multiple Signal Classification Algorithm for Super-Resolution Fluorescence Microscopy.” Nature Communications 7 (December): 13752. doi: 10.1038/ncomms13752 Almada, Pedro, Siân Culley, and Ricardo Henriques. 2015. “PALM and STORM: Into Large Fields and High- Throughput Microscopy with SCMOS Detectors.” Methods 88 (October): 109–21. doi: 10.1016/J. YMETH.2015.06.004 Almada, Pedro, Pedro Pereira, Siân Culley, Ghislaine Caillol, Fanny Boroni-Rueda, Christina L. Dix, Romain F. Laine, et  al. 2019. “Automating Multimodal Microscopy with NanoJ-Fluidics.” Nature Communications 10 (1223): 1–9. doi: 10.1038/s41467-019-09231-9 Ando, Ryoko, Hiroshi Hama, Miki Yamamoto-Hino, Hideaki Mizuno, and Atsushi Miyawaki. 2002. “An Optical Marker Based on the UV-Induced Green-to-Red Photoconversion of a Fluorescent Protein.” Proceedings of the National Academy of Sciences of the United States of America 99 (20): 12651–56. doi: 10.1073/pnas.202320599 Ashdown, George W., Andrew Cope, Paul W. Wiseman, and Dylan M. Owen. 2014. “Molecular Flow Quantified beyond the Diffraction Limit by Spatiotemporal Image Correlation of Structured Illumination Microscopy Data.” Biophysical Journal 107 (9): L21–23. doi: 10.1016/j. bpj.2014.09.018 Ashdown, George W., Garth L. Burn, David J. Williamson, Elvis Pandžić, Ruby Peters, Michael Holden, Helge Ewers, et  al. 2017. “Live-Cell Super-Resolution Reveals F-Actin and Plasma Membrane Dynamics at the T Cell Synapse.” Biophysical Journal 112 (8): 1703–13. doi: 10.1016/j.bpj.2017.01.038 Auer, Alexander, Maximilian T. Strauss, Thomas Schlichthaerle, and Ralf Jungmann. 2017. “Fast, Background-Free DNA- PAINT Imaging Using FRET-Based Probes.” Nano Letters 17 (10): 6428–34. doi: 10.1021/acs.nanolett.7b03425 Bajar, Bryce T., Emily S. Wang, Shu Zhang, Michael Z. Lin, and Jun Chu. 2016. “A Guide to Fluorescent Protein FRET Pairs.” Sensors (Basel, Switzerland) 16 (9): 1–24. doi: 10.3390/s16091488 Ball, Graeme, Justin Demmerle, Rainer Kaufmann, Ilan Davis, Ian M. Dobbie, and Lothar Schermelleh. 2015. “SIMcheck: A Toolbox for Successful Super-Resolution Structured Illumination Microscopy.” Scientific Reports 5 (1): 15915. doi: 10.1038/srep15915 Bedford, R., C. Tiede, R. Hughes, A. Curd, M. J. McPherson, Michelle Peckham, and Darren C. Tomlinson. 2017. “Alternative Reagents to Antibodies in Imaging Applications.” Biophysical Reviews 9: 299–308. doi: 10.1007/s12551-017-0278-2 Betzig, Eric, George H. Patterson, Rachid Sougrat, O. Wolf Lindwasser, Scott Olenych, Juan S. Bonifacino, Michael W. Davidson, et al. 2006. “Imaging Intracellular Fluorescent Proteins at Nanometer Resolution.” Science (New York, N.Y.) 313 (5793): 1642–45. doi: 10.1126/science.1127344 TABLE 3.5 Different Methods for Assessment of Super-Resolution Images Method Assessed feature Applicable modalities Summary Measuring width/separation of structures Resolution SIM, STED, SMLM Image resolution is determined by measuring the width of a line profile drawn through a structure of interest or the distance between peaks in a line profile drawn across closely separated structures. This is simple but crude. The choice of structure to measure is subjective and is not robust against techniques that may cause artificial sharpening (namely SMLM). Fourier Ring Correlation (FRC) Resolution Most robust with SMLM (Nieuwenhuizen et al. 2013), also reported for STED (Tortarolo et al. 2018) The super-resolution image is divided into two images of the same structure, each containing half of the image information (e.g. in SMLM by creating one super-resolution image from odd-number acquired frames and another from even-number acquired frames). These images are then correlated in Fourier space to give a global estimate of image resolution. This method is unbiased from the user’s perspective, but some image features, such as punctate structures, can skew results. SIMCheck (Ball et al. 2015) Image quality, resolution SIM A plugin for ImageJ/Fiji for assessing the quality of raw and reconstructed SIM images. The manuscript provides useful guidance on how quality metrics can be used to improve SIM imaging. Localization reclassification (Fox-Roberts et al. 2017) Image quality SMLM A Matlab toolbox that presents the user with localizations from an inputted SMLM data set. The user classifies these as accurate or not and the algorithm then uses this information to determine the local quality within the reconstructed image. SQUIRREL (Culley, Albrecht, et al. 2018) Image quality, resolution SIM, STED, SMLM A plugin for ImageJ/Fiji that examines a super-resolution image and the diffraction-limited equivalent of the same field-of-view to map image artifacts and provide quality metrics. The plugin also features a method for local mapping of resolution based on FRC. TNF_03_K32983_C003_docbook_new_indd.indd 49 8/15/2020 09:11:27 ----!@#$NewPage!@#$---- 50 Imaging from Cells to Animals In Vivo Bindels, Daphne S., Lindsay Haarbosch, Laura Van Weeren, Marten Postma, Katrin E. Wiese, Marieke Mastop, Sylvain Aumonier, et al. 2016. “MScarlet: A Bright Monomeric Red Fluorescent Protein for Cellular Imaging.” Nature Methods 14 (1): 53–6. doi: 10.1038/nmeth.4074 Bird, Robert E., Karl D. Hardman, James W. Jacobson, Syd Johnson, Bennett M. Kaufman, Shwu M. Lee, Timothy Lee, et al. 1988. “Single-Chain Antigen-Binding Proteins.” Science (New York, N.Y.) 242 (4877): 423–26. doi: 10.1126/science.3140379 Blom, Hans, and Jerker Widengren. 2017. “Stimulated Emission Depletion Microscopy.” Chemical Reviews 117 (11): 7377– 427. doi: 10.1021/acs.chemrev.6b00653 Bottanelli, Francesca, Nicole Kilian, Andreas M. Ernst, Felix Rivera-Molina, Lena K. Schroeder, Emil B. Kromann, et al. 2017. “A Novel Physiological Role for ARF1 in the Formation of Bidirectional Tubules from the Golgi.” Molecular Biology of the Cell 28 (12): 1676–87. doi: 10.1091/mbc.E16-12-0863 Bottanelli, Francesca, Emil B. Kromann, Edward S. Allgeyer, Roman S. Erdmann, Stephanie Wood Baguley, George Sirinakis, Alanna Schepartz, et al. 2016. “Two-Colour Live- Cell Nanoscale Imaging of Intracellular Targets.” Nature Communications 7 (March): 10778. doi: 10.1038/ncomms10778 Brakemann, Tanja, Andre C. Stiel, Gert Weber, Martin Andresen, Ilaria Testa, Tim Grotjohann, Marcel Leutenegger, et  al. 2011. “A Reversibly Photoswitchable GFP-like Protein with Fluorescence Excitation Decoupled from Switching.” Nature Biotechnology 29 (10): 942–50. doi: 10.1038/nbt.1952 Braun, Michael B., Bjoern Traenkle, Philipp A. Koch, Felix Emele, Frederik Weiss, Oliver Poetz, Thilo Stehle, et  al. 2016. “Peptides in Headlock–A Novel High-Affinity and Versatile Peptide-Binding Nanobody for Proteomics and Microscopy.” Scientific Reports 6 (October 2015): 19211. doi: 10.1038/srep19211 Burgert, Anne, Sebastian Letschert, Sören Doose, and Markus Sauer. 2015. “Artifacts in Single-Molecule Localization Microscopy.” Histochemistry and Cell Biology 144 (2): 123–31. doi: 10.1007/s00418-015-1340-4 Butkevich, Alexey N., Vladimir N. Belov, Kirill Kolmakov, Viktor V. Sokolov, Heydar Shojaei, Sven C. Sidenstein, Dirk Kamin, et  al. 2017. “Hydroxylated Fluorescent Dyes for Live-Cell Labeling: Synthesis, Spectra and Super-Resolution STED.” Chemistry (Weinheim an Der Bergstrasse, Germany) 23 (50): 12114–9. doi: 10.1002/ chem.201701216 Butkevich, Alexey N., Haisen T. A., Michael Ratz, Stefan Stold, Stefan Jakobs, Vladimir N. Belov, and Stefan W. Hell. 2018. “Two-Color 810 Nm STED Nanoscopy of Living Cells with Endogenous SNAP-Tagged Fusion Proteins.” ACS Chemical Biology. doi: 10.1021/acschembio.7b00616 Canton, Irene, Marzia Massignani, Nisa Patikarnmonthon, Luca Chierico, James Robertson, Stephen A. Renshaw, Nicholas J. Warren, et al. 2013. “Fully Synthetic Polymer Vesicles for Intracellular Delivery of Antibodies in Live Cells.” FASEB Journal 27 (1): 98–108. doi: 10.1096/fj.12-212183 Carr, Alexander R., Aleks Ponjavic, Srinjan Basu, James McColl, Ana Mafalda Santos, Simon Davis, Ernest D. Laue, David Klenerman, et  al. 2017. “Three-Dimensional Super- Resolution in Eukaryotic Cells Using the Double-Helix Point Spread Function.” Biophysical Journal 112 (7): 1444– 54. doi: 10.1016/J.BPJ.2017.02.023 Chakrova, Nadya, Alicia Soler Canton, Christophe Danelon, Sjoerd Stallinga, and Bernd Rieger. 2016. “Adaptive Illumination Reduces Photobleaching in Structured Illumination Microscopy.” Biomedical Optics Express 7 (10): 4263–74. doi: 10.1364/BOE.7.004263 Chen, B.-C., Wesley R. Legant, K. Wang, Lin Shao, Daniel E. Milkie, Michael W. Davidson, Chris Janetopoulos, et  al. 2014. “Lattice Light-Sheet Microscopy: Imaging Molecules to Embryos at High Spatiotemporal Resolution.” Science 346 (6208): 1257998. doi: 10.1126/ science.1257998 Chéreau, Ronan, G. Ezequiel Saraceno, Julie Angibaud, Daniel Cattaert, and U. Valentin Nägerl. 2017. “Superresolution Imaging Reveals Activity-Dependent Plasticity of Axon Morphology Linked to Changes in Action Potential Conduction Velocity.” Proceedings of the National Academy of Sciences of the United States of America 114 (6): 1401–6. doi: 10.1073/pnas.1607541114 Chmyrov, Andriy, Jan Keller, Tim Grotjohann, Michael Ratz, Elisa d’Este, Stefan Jakobs, Christian Eggeling, et al. 2013. “Nanoscopy with More than 100,000 ‘Doughnuts.’” Nature Methods 10 (8): 737–40. doi: 10.1038/nmeth.2556 Chudakov, Dmitriy M., Sergey Lukyanov, and Konstantin A. Lukyanov. 2005. “Fluorescent Proteins as a Toolkit for in Vivo Imaging.” Trends in Biotechnology 23 (12): 605–13. doi: 10.1016/j.tibtech.2005.10.005 Clark, Spencer A., Vijay Singh, Daniel Vega Mendoza, William Margolin, and Eric T. Kool. 2016. “Light-Up ‘Channel Dyes’ for Haloalkane-Based Protein Labeling in Vitro and in Bacterial Cells.” Bioconjugate Chemistry 27 (12): 2839– 43. doi: 10.1021/acs.bioconjchem.6b00613 Cohen, Justin D., Samuel Thompson, and Alice Y. Ting. 2011. “Structure-Guided Engineering of a Pacific Blue Fluorophore Ligase for Specific Protein Imaging in Living Cells.” Biochemistry 50 (38): 8221–25. doi: 10.1021/ bi201037r Cox, Susan, Edward Rosten, James Monypenny, Tijana Jovanovic- Talisman, Dylan T. Burnette, Jennifer, Lippincott- Schwartz, et al. 2011. “Bayesian Localization Microscopy Reveals Nanoscale Podosome Dynamics.” Nature Methods 9 (2): 195–200. doi: 10.1038/nmeth.1812 Culley, Siân, David Albrecht, Caron Jacobs, Pedro Matos Pereira, Christophe Leterrier, Jason Mercer, and Ricardo Henriques. 2018. “Quantitative Mapping and Minimization of Super- Resolution Optical Imaging Artifacts.” Nature Methods 15 (4): 263–66. doi: 10.1038/nmeth.4605 Culley, Siân, Kalina L. Tosheva, Pedro Matos Pereira, and Ricardo Henriques. 2018. “SRRF: Universal Live-Cell Super-Resolution Microscopy.” The International Journal of Biochemistry & Cell Biology 101 (August): 74–9. doi: 10.1016/J.BIOCEL.2018.05.014 D’Este, Elisa, Dirk Kamin, Fabian Göttfert, Ahmed El-Hady, and Stefan W. Hell. 2015. “STED Nanoscopy Reveals the Ubiquity of Subcortical Cytoskeleton Periodicity in Living Neurons.” Cell Reports 10 (8): 1246–51. doi: 10.1016/J. CELREP.2015.02.007 Dedecker, Peter, Sam Duwé, Robert K. Neely, and Jin Zhang. 2012. “Localizer: Fast, Accurate, Open-Source, and Modular Software Package for Superresolution Microscopy.” Journal of Biomedical Optics 17 (12): 126008. doi: 10.1117/1.JBO. 17.12.126008 TNF_03_K32983_C003_docbook_new_indd.indd 50 8/15/2020 09:11:28 ----!@#$NewPage!@#$---- 51 An Introduction to Live-Cell Super-Resolution Imaging Dempsey, Graham T., Joshua C. Vaughan, Kok Hao Chen, Mark Bates, and Xiaowei Zhuang. 2011. “Evaluation of Fluorophores for Optimal Performance in Localization- Based Super-Resolution Imaging.” Nature Methods 8 (12): 1027–36. doi: 10.1038/nmeth.1768 Dempsey, William P., Lada Georgieva, Patrick M. Helbling, Ali Y. Sonay, Thai V. Truong, Michel Haffner, and Periklis Pantazis. 2015. “In Vivo Single-Cell Labeling by Confined Primed Conversion.” Nature Methods 12 (7): 645–48. doi: 10.1038/nmeth.3405 Dertinger, T., R. Colyer, G. Iyer, S. Weiss, and J. Enderlein. 2009. “Fast, Background-Free, 3D Super-Resolution Optical Fluctuation Imaging (SOFI).” Proceedings of the National Academy of Sciences 106 (52): 22287–92. doi: 10.1073/ pnas.0907866106 Durisic, Nela, Lara Laparra-Cuervo, Angel Sandoval-Álvarez, Joseph Steven Borbely, and Melike Lakadamyali. 2014. “Single-Molecule Evaluation of Fluorescent Protein Photoactivation Efficiency Using an in Vivo Nanotemplate.” Nature Methods 11 (2): 156–62. doi: 10.1038/nmeth.2784 Fiolka, Reto, Lin Shao, E. Hesper Rego, Michael W. Davidson, and Mats G. L. Gustafsson. 2012. “Time-Lapse Two-Color 3D Imaging of Live Cells with Doubled Resolution Using Structured Illumination.” Proceedings of the National Academy of Sciences of the United States of America 109 (14): 5311–15. doi: 10.1073/pnas.1119262109 Fox-Roberts, Patrick, Richard Marsh, Karin Pfisterer, Asier Jayo, Maddy Parsons, and Susan Cox. 2017. “Local Dimensionality Determines Imaging Speed in Localization Microscopy.” Nature Communications 8 (January): 13558. doi: 10.1038/ncomms13558 Fu, Guo, Tao Huang, Jackson Buss, Carla Coltharp, Zach Hensel, and Jie Xiao. 2010. “In Vivo Structure of the E. Coli FtsZ- Ring Revealed by Photoactivated Localization Microscopy (PALM).” Edited by Michael Polymenis. PLoS ONE 5 (9): e12680. doi: 10.1371/journal.pone.0012680 Galbraith, Catherine G., and James A. Galbraith. 2011. “Super- Resolution Microscopy at a Glance.” Journal of Cell Science 124 (Pt 10): 1607–11. doi: 10.1242/jcs.080085 Gautier, Arnaud, Alexandre Juillerat, Christian Heinis, Ivan Reis Corrêa, Maik Kindermann, Florent Beaufils, and Kai Johnsson. 2008. “An Engineered Protein Tag for Multiprotein Labeling in Living Cells.” Chemistry and Biology 15 (2): 128–36. doi: 10.1016/j.chembiol.2008.01.007 Geissbuehler, Stefan, Noelia L. Bocchio, Claudio Dellagiacoma, Corinne Berclaz, Marcel Leutenegger, and Theo Lasser. 2012. “Mapping Molecular Statistics with Balanced Super- Resolution Optical Fluctuation Imaging (BSOFI).” Optical Nanoscopy 1 (1): 4. doi: 10.1186/2192-2853-1-4 Geissbuehler, Stefan, Azat Sharipov, Aurélien Godinat, Noelia L. Bocchio, Patrick A. Sandoz, Anja Huss, Nickels A. Jensen, et  al. 2014. “Live-Cell Multiplane Three-Dimensional Super-Resolution Optical Fluctuation Imaging.” Nature Communications 5: 1–7. doi: 10.1038/ncomms6830 Girkin, J. M., and M. T. Carvalho. 2018. “The Light-Sheet Microscopy Revolution.” Journal of Optics 20 (5): 053002. doi: 10.1088/2040-8986/aab58a Griffin, B. Albert, Stephen R. Adams, and Roger Y. Tsien. 1998. “Specific Covalent Labeling of Recombinant Protein Molecules inside Live Cells.” Science (New York, N.Y.) 281 (5374): 269–72. doi: 10.1126/science.281.5374.269 Grimm, Jonathan B., Anand K. Muthusamy, Yajie Liang, Timothy A. Brown, William C. Lemon, Ronak Patel, Rongwen Lu, et al. 2017. “A General Method to Fine-Tune Fluorophores for Live-Cell and in Vivo Imaging.” Nature Methods 14 (10): 987–94. doi: 10.1038/nmeth.4403 Grotjohann, Tim, Ilaria Testa, Matthias Reuss, Tanja Brakemann, Christian Eggeling, Stefan W. Hell, and Stefan Jakobs. 2012. “RsEGFP2 Enables Fast RESOLFT Nanoscopy of Living Cells.” ELife 2012 (1): 1–14. doi: 10.7554/eLife.00248 Gudheti, Manasa V., Nikki M. Curthoys, Travis J. Gould, Dahan Kim, Mudalige S. Gunewardene, Kristin A. Gabor, Julie A. Gosse, Carol H. Kim, Joshua Zimmerberg, and Samuel T. Hess. 2013. “Actin Mediates the Nanoscale Membrane Organization of the Clustered Membrane Protein Influenza Hemagglutinin.” Biophysical Journal 104 (10): 2182–92. doi: 10.1016/J.BPJ.2013.03.054 Guo, Min, Panagiotis Chandris, John Paul Giannini, Adam J. Trexler, Robert Fischer, Jiji Chen, Harshad D. Vishwasrao, et  al. 2018. “Single-Shot Super-Resolution Total Internal Reflection Fluorescence Microscopy.” Nature Methods 15 (6): 425–28. doi: 10.1038/s41592-018-0004-4 Gustafsson, M. G. 2000. “Surpassing the Lateral Resolution Limit by a Factor of Two Using Structured Illumination Microscopy.” Journal of Microscopy 198 (Pt 2): 82–7. doi: 10.1046/j.1365-2818.2000.00710.x Gustafsson, Mats G. L., Lin Shao, Peter M. Carlton, C. J. Rachel Wang, Inna N. Golubovskaya, W. Zacheus Cande, David A. Agard, and John W. Sedat. 2008. “Three-Dimensional Resolution Doubling in Wide-Field Fluorescence Microscopy by Structured Illumination.” Biophysical Journal 94 (12): 4957–70. doi: 10.1529/biophysj.107.120345 Gustafsson, Mats G. L. 2005. “Nonlinear Structured-Illumination Microscopy: Wide-Field Fluorescence Imaging with Theoretically Unlimited Resolution.” Proceedings of the National Academy of Sciences of the United States of America 102 (37): 13081–86. doi: 10.1073/pnas.0406877102 Gustafsson, Nils, Siân Culley, George Ashdown, Dylan M. Owen, Pedro Matos Pereira, and Ricardo Henriques. 2016. “Fast Live-Cell Conventional Fluorophore Nanoscopy with ImageJ through Super-Resolution Radial Fluctuations.” Nature Communications 7 (12471): 1–9. doi: 10.1038/ ncomms12471 Hammond, Jennetta W., T. Lynne Blasius, Virupakshi Soppina, Dawen Cai, and Kristen J. Verhey. 2010. “Autoinhibition of the Kinesin-2 Motor KIF17 via Dual Intramolecular Mechanisms.” Journal of Cell Biology 189 (6): 1013–25. doi: 10.1083/jcb.201001057 Han, Yubing, Meihua Li, Fengwu Qiu, Meng Zhang, and Yu-Hui Zhang. 2017. “Cell-Permeable Organic Fluorescent Probes for Live-Cell Long-Term Super-Resolution Imaging Reveal Lysosome-Mitochondrion Interactions.” Nature Communications 8 (1): 1307. doi: 10.1038/s41467-017- 01503-6 Harke, Benjamin, Jan Keller, Chaitanya K. Ullal, Volker Westphal, Andreas Schönle, and Stefan W. Hell. 2008. “Resolution Scaling in STED Microscopy.” Optics Express 16 (6): 4154. doi: 10.1364/OE.16.004154 Heilemann, Mike, Sebastian van de Linde, Mark Schüttpelz, Robert Kasper, Britta Seefeldt, Anindita Mukherjee, Philip Tinnefeld, and Markus Sauer. 2008. “Subdiffraction- Resolution Fluorescence Imaging with Conventional TNF_03_K32983_C003_docbook_new_indd.indd 51 8/15/2020 09:11:28 ----!@#$NewPage!@#$---- 52 Imaging from Cells to Animals In Vivo Fluorescent Probes.” Angewandte Chemie (International Ed. in English) 47 (33): 6172–76. doi: 10.1002/ anie.200802376 Hein, Birka, Katrin I. Willig, and Stefan W. Hell. 2008. “Stimulated Emission Depletion (STED) Nanoscopy of a Fluorescent Protein-Labeled Organelle inside a Living Cell.” Proceedings of the National Academy of Sciences 105 (38): 14271–76. doi: 10.1073/pnas.0807705105 Heine, Jörn, Matthias Reuss, Benjamin Harke, Elisa D’Este, Steffen J. Sahl, and Stefan W. Hell. 2017. “Adaptive- Illumination STED Nanoscopy.” Proceedings of the National Academy of Sciences of the United States of America 114 (37): 9797–802. doi: 10.1073/pnas.1708304114 Henriques, Ricardo, Mickael Lelek, Eugenio F. Fornasiero, Flavia Valtorta, Christophe Zimmer, and Musa M. Mhlanga. 2010. “QuickPALM: 3D Real-Time Photoactivation Nanoscopy Image Processing in ImageJ.” Nature Methods 7 (5): 339– 40. doi: 10.1038/nmeth0510-339 Hense, Anika, Benedikt Prunsche, Peng Gao, Yuji Ishitsuka, Karin Nienhaus, and G. Ulrich Nienhaus. 2015. “Monomeric Garnet, a Far-Red Fluorescent Protein for Live-Cell STED Imaging.” Scientific Reports 5: 1–10. doi: 10.1038/srep18006 Hess, Samuel T., Thanu P. K. Girirajan, and Michael D. Mason. 2006. “Ultra-High Resolution Imaging by Fluorescence Photoactivation Localization Microscopy.” Biophysical Journal 91 (11): 4258–72. doi: 10.1529/ BIOPHYSJ.106.091116 Hoebe, R. A., C. H. Van Oven, T. W. J. Gadella, P. B. Dhonukshe, C. J. F. Van Noorden, and E. M. M. Manders. 2007. “Controlled Light-Exposure Microscopy Reduces Photobleaching and Phototoxicity in Fluorescence Live-Cell Imaging.” Nature Biotechnology 25 (2): 249–53. doi: 10.1038/nbt1278. Hofmann, Michael, Christian Eggeling, Stefan Jakobs, and Stefan W. Hell. 2005. “Breaking the Diffraction Barrier in Fluorescence Microscopy at Low Light Intensities by Using Reversibly Photoswitchable Proteins.” Proceedings of the National Academy of Sciences 102 (49): 17565–69. doi: 10.1073/pnas.0506010102 Hostettler, Lola, Laura Grundy, Stéphanie Käser-Pébernard, Chantal Wicky, William R. Schafer, and Dominique A. Glauser. 2017. “The Bright Fluorescent Protein MNeonGreen Facilitates Protein Expression Analysis In Vivo.” G3: Genes|Genomes|Genetics 7 (2): 607–15. doi: 10.1534/g3.116.038133. Howarth, Mark, Keizo Takao, Yasunori Hayashi, and Alice Y. Ting. 2005. “Targeting Quantum Dots to Surface Proteins in Living Cells with Biotin Ligase.” Proceedings of the National Academy of Sciences 102 (21): 7583–88. doi: 10.1073/pnas.0503125102 “Http ://Bi gwww. Epfl. Ch/Sm lm/Ch allen ge201 6/Ind ex.Ht ml?P= datas ets.” n.d. “Https ://Do cumen ts.Ep fl.Ch /User s/l/L e/Leu teneg /Www/ Balan cedSO FI/In dex.H tml.” n.d. Hu, Ying S., Xiaolin Nan, Prabuddha Sengupta, Jennifer Lippincott-Schwartz, and Hu Cang. 2013. “Accelerating 3B Single-Molecule Super-Resolution Microscopy with Cloud Computing.” Nature Methods 10 (2): 96–7. doi: 10.1038/ nmeth.2335 Huang, Bo, Wenqin Wang, Mark Bates, and Xiaowei Zhuang. 2008. “Three-Dimensional Super-Resolution Imaging by Stochastic Optical Reconstruction Microscopy.” Science (New York, N.Y.) 319 (5864): 810–13. doi: 10.1126/science.1153529 Huang, Fang, Tobias M. P. Hartwich, Felix E. Rivera-Molina, Yu Lin, Whitney C. Duim, Jane J. Long, Pradeep D. Uchil, et al. 2013. “Video-Rate Nanoscopy Using SCMOS Camera- Specific Single-Molecule Localization Algorithms.” Nature Methods 10 (7): 653–58. doi: 10.1038/nmeth.2488 Huff, Joseph. 2015. “The Airyscan Detector from ZEISS: Confocal Imaging with Improved Signal-to-Noise Ratio and Super-Resolution.” Nature Methods 12 (12): i–ii. doi: 10.1038/nmeth.f.388 Iinuma, Ryosuke, Yonggang Ke, Ralf Jungmann, Thomas Schlichthaerle, Johannes B. Woehrstein, and Peng Yin. 2014. “Polyhedra Self-Assembled from DNA Tripods and Characterized with 3D DNA-PAINT.” Science 344 (6179): 65–9. doi: 10.1126/science.1250944 Inoue, Shinya. 2006. “Foundations of Confocal Scanned Imaging in Light Microscopy.” In Handbook of Biological Confocal Microscopy. Edited by James B. Pawley (3rd edition). New York, US: Springer Science. Ji, Chen, Yongdeng Zhang, Pingyong Xu, Tao Xu, and Xuelin Lou. 2015. “Nanoscale Landscape of Phosphoinositides Revealed by Specific Pleckstrin Homology (PH) Domains Using Single-Molecule Superresolution Imaging in the Plasma Membrane.” The Journal of Biological Chemistry 290 (45): 26978–93. doi: 10.1074/jbc.M115.663013 Jones, Sara A., Sang-Hee Shim, Jiang He, and Xiaowei Zhuang. 2011. “Fast, Three-Dimensional Super-Resolution Imaging of Live Cells.” Nature Methods 8 (6): 499–505. doi: 10.1038/ nmeth.1605 Juette, Manuel F., Travis J. Gould, Mark D. Lessard, Michael J. Mlodzianoski, Bhupendra S. Nagpure, Brian T. Bennett, Samuel T. Hess, and Joerg Bewersdorf. 2008. “Three- Dimensional Sub–100 Nm Resolution Fluorescence Microscopy of Thick Samples.” Nature Methods 5 (6): 527– 29. doi: 10.1038/nmeth.1211 Jungmann, Ralf, Maier S. Avendaño, Johannes B. Woehrstein, Mingjie Dai, William M. Shih, and Peng Yin. 2014. “Multiplexed 3D Cellular Super-Resolution Imaging with DNA-PAINT and Exchange-PAINT.” Nature Methods 11 (3): 313–18. doi: 10.1038/nmeth.2835 Jungmann, Ralf, Christian Steinhauer, Max Scheible, Anton Kuzyk, Philip Tinnefeld, and Friedrich C. Simmel. 2010. “Single-Molecule Kinetics and Super-Resolution Microscopy by Fluorescence Imaging of Transient Binding on DNA Origami.” Nano Letters 10 (11): 4756–61. doi: 10.1021/nl103427w Kaberniuk, Andrii A., Nicholas C. Morano, Vladislav V. Verkhusha, and Erik Lee Snapp. 2017. “MoxDendra2: An Inert Photoswitchable Protein for Oxidizing Environments.” Chemical Communications 53 (13): 2106–9. doi: 10.1039/ C6CC09997A. Kanchanawong, Pakorn, Gleb Shtengel, Ana M. Pasapera, Ericka B. Ramko, Michael W. Davidson, Harald F. Hess, and Clare M. Waterman. 2010. “Nanoscale Architecture of Integrin- Based Cell Adhesions.” Nature 468 (7323): 580–4. doi: 10.1038/nature09621 TNF_03_K32983_C003_docbook_new_indd.indd 52 8/15/2020 09:11:28 ----!@#$NewPage!@#$---- 53 An Introduction to Live-Cell Super-Resolution Imaging Keppler, Antje, Susanne Gendreizig, Thomas Gronemeyer, Horst Pick, Horst Vogel, and Kai Johnsson. 2003. “A General Method for the Covalent Labeling of Fusion Proteins with Small Molecules in Vivo.” Nature Biotechnology 21 (1): 86–9. doi: 10.1038/nbt765 Khan, Abdullah O., Victoria A. Simms, Jeremy A. Pike, Steven G. Thomas, and Neil V. Morgan. 2017. “CRISPR-Cas9 Mediated Labelling Allows for Single Molecule Imaging and Resolution.” Scientific Reports 7 (1): 8450. doi: 10.1038/ s41598-017-08493-x Kirshner, Hagai, Franois Aguet, Daniel Sage, and Michael Unser. 2013. “3-D PSF Fitting for Fluorescence Microscopy: Implementation and Localization Application.” Journal of Microscopy 249 (1): 13–25. doi: 10.1111/j.1365-2818. 2012.03675.x. Klar, Thomas A., Stefan Jakobs, Marcus Dyba, Alexander Egner, and Stefan W. Hell. 2000. “Fluorescence Microscopy with Diffraction Resolution Barrier Broken by Stimulated Emission.” Proceedings of the National Academy of Sciences of the United States of America 97 (15): 8206–10. doi: 10.1073/PNAS.97.15.8206 Kollmannsperger, Alina, Armon Sharei, Anika Raulf, Mike Heilemann, Robert Langer, Klavs F. Jensen, Ralph Wieneke, and Robert Tampé. 2016. “Live-Cell Protein Labelling with Nanometre Precision by Cell Squeezing.” Nature Communications 7: 10372. doi: 10.1038/ncomms 10372 Kremers, Gert-Jan, Sarah G. Gilbert, Paula J. Cranfill, Michael W. Davidson, and David W. Piston. 2011. “Fluorescent Proteins at a Glance.” Journal of Cell Science 124 (15): 2676. doi: 10.1242/jcs.095059 Kwakwa, Kwasi, Alexander Savell, Timothy Davies, Ian Munro, Simona Parrinello, Marco A. Purbhoo, Chris Dunsby, Mark A. A. Neil, and Paul M. W. French. 2016. “EasySTORM: A Robust, Lower-Cost Approach to Localisation and TIRF Microscopy.” Journal of Biophotonics 9 (9): 948–57. doi: 10.1002/jbio.201500324. Laine, Romain F., Anna Albecka, Sebastian Van De Linde, Eric J. Rees, Colin M. Crump, and Clemens F. Kaminski. 2015. “Structural Analysis of Herpes Simplex Virus by Optical Super-Resolution Imaging.” Nature Communications 6: 1–10. doi: 10.1038/ncomms6980. Lambert, Helene, Roumen Pankov, Johanne Gauthier, and Ronald Hancock. 1990. “Electroporation-Mediated Uptake of Proteins into Mammalian Cells.” Biochemistry and Cell Biology = Biochimie et Biologie Cellulaire 68 (4): 729–34. Lau, Lana, Yin Loon Loon Lee, Steffen J. J. Sahl, Tim Stearns, and W. E. E. Moerner. 2012. “STED Microscopy with Optimized Labeling Density Reveals 9-Fold Arrangement of a Centriole Protein.” Biophysical Journal 102 (12): 2926–35. doi: 10.1016/j.bpj.2012.05.015 Lavis, Luke D. 2017a. “Chemistry Is Dead. Long Live Chemistry!” Biochemistry 56 (39): 5165–70. doi: 10.1021/ acs.biochem.7b00529 Lavis, Luke D. 2017b. “Teaching Old Dyes New Tricks: Biological Probes Built from Fluoresceins and Rhodamines.” Annual Review of Biochemistry 86 (1): 825–43. doi: 10.1146/ annurev-biochem-061516-044839 Lehmann, Martin, Gregor Lichtner, Haider Klenz, and Jan Schmoranzer. 2016. “Novel Organic Dyes for Multicolor Localization-Based Super-Resolution Microscopy.” Journal of Biophotonics 9 (1–2): 161–70. doi: 10.1002/ jbio.201500119 Lelek, Mickaël, Francesca Di Nunzio, Ricardo Henriques, Pierre Charneau, Nathalie Arhel, and Christophe Zimmer. 2012. “Superresolution Imaging of HIV in Infected Cells with FlAsH-PALM.” Proceedings of the National Academy of Sciences of the United States of America 109 (22): 8564–69. doi: 10.1073/pnas.1013267109 Leng, Shuang, Qinglong Qiao, Lu Miao, Wuguo Deng, Jingnan Cui, and Zhaochao Xu. 2017. “A Wash-Free SNAP- Tag Fluorogenic Probe Based on the Additive Effects of Quencher Release and Environmental Sensitivity.” Chemical Communications (Cambridge, England) 53 (48): 6448–51. doi: 10.1039/c7cc01483j Li, Chenge, Alison G. Tebo, and Arnaud Gautier. 2017. “Fluorogenic Labeling Strategies for Biological Imaging.” International Journal of Molecular Sciences 18 (7): 1473. doi: 10.3390/ijms18071473 Li, D., L. Shao, B.-C. Chen, X. Zhang, M. Zhang, B. Moses, D. E. Milkie, et  al. 2015. “Extended-Resolution Structured Illumination Imaging of Endocytic and Cytoskeletal Dynamics.” Science 349 (6251): aab3500. doi: 10.1126/sci- ence.aab3500. Liu, Daniel S., Lucas G. Nivón, Florian Richter, Peter J. Goldman, Thomas J. Deerinck, Jennifer Z. Yao, Douglas Richardson, et al. 2014. “Computational Design of a Red Fluorophore Ligase for Site-Specific Protein Labeling in Living Cells.” Proceedings of the National Academy of Sciences of the United States of America 111 (43): E4551–9. doi: 10.1073/ pnas.1404736111 Liu, Tao Kai, Pei Ying Hsieh, Yu De Zhuang, Chi Yang Hsia, Chi Ling Huang, Hsiu Ping Lai, Hung Sheung Lin, I. Chia Chen, Hsin Yun Hsu, and Kui Thong Tan. 2014. “A Rapid SNAP-Tag Fluorogenic Probe Based on an Environment- Sensitive Fluorophore for No-Wash Live Cell Imaging.” ACS Chemical Biology 9 (10): 2359–65. doi: 10.1021/cb500502n Liu, Yajun, and Jian Qiu Wu. 2016. “Cytokinesis: Going Super- Resolution in Live Cells.” Current Biology 26 (21): R1150– 52. doi: 10.1016/j.cub.2016.09.026 Los, Georgyi V., Lance P. Encell, Mark G. McDougall, Danette D. Hartzell, Natasha Karassina, Chad Zimprich, Monika G. Wood, et al. 2008. “HaloTag: A Novel Protein Labeling Technology for Cell Imaging and Protein Analysis.” ACS Chemical Biology 3 (6): 373–82. doi: 10.1021/cb800025k Lu-Walther, Hui-Wen, Martin Kielhorn, Ronny Förster, Aurélie Jost, Kai Wicker, and Rainer Heintzmann. 2015. “FastSIM: A Practical Implementation of Fast Structured Illumination Microscopy.” Methods and Applications in Fluorescence 3 (1): 014001. doi: 10.1088/2050-6120/3/1/014001 Luca, Giulia M. R. De, Ronald M. P. Breedijk, Rick A. J. Brandt, Christiaan H. C. Zeelenberg, Babette E. de Jong, Wendy Timmermans, Leila Nahidi Azar, Ron A. Hoebe, Sjoerd Stallinga, and Erik M. M. Manders. 2013. “Re-Scan Confocal Microscopy: Scanning Twice for Better Resolution.” Biomedical Optics Express 4 (11): 2644–56. doi: 10.1364/BOE.4.002644 TNF_03_K32983_C003_docbook_new_indd.indd 53 8/15/2020 09:11:28 ----!@#$NewPage!@#$---- 54 Imaging from Cells to Animals In Vivo Lukinavičius, Gražvydas, Claudia Blaukopf, Elias Pershagen, Alberto Schena, Luc Reymond, Emmanuel Derivery, Marcos Gonzalez-Gaitan, et  al. 2015. “SiR-Hoechst Is a Far-Red DNA Stain for Live-Cell Nanoscopy.” Nature Communications 6: 1–7. doi: 10.1038/ncomms9497 Lukinavičius, Gražvydas, Luc Reymond, Elisa D’Este, Anastasiya Masharina, Fabian Göttfert, Haisen Ta, Angelika Güther, et al. 2014. “Fluorogenic Probes for Live-Cell Imaging of the Cytoskeleton.” Nature Methods 11 (7): 731–33. doi: 10.1038/nmeth.2972 Lukinavičius, Gražvydas, Luc Reymond, Keitaro Umezawa, Olivier Sallin, Elisa D’Este, Fabian Göttfert, Haisen Ta, Stefan W. Hell, Yasuteru Urano, and Kai Johnsson. 2016. “Fluorogenic Probes for Multicolor Imaging in Living Cells.” Journal of the American Chemical Society 138 (30): 9365–68. doi: 10.1021/jacs.6b04782 Lukinavičius, Gražvydas, Keitaro Umezawa, Nicolas Olivier, Alf Honigmann, Guoying Yang, Tilman Plass, Veronika Mueller, et al. 2013. “A Near-Infrared Fluorophore for Live- Cell Super-Resolution Microscopy of Cellular Proteins.” Nature Chemistry 5 (2): 132–39. doi: 10.1038/nchem.1546 Ma, Hongqiang, Rao Fu, Jianquan Xu, and Yang Liu. 2017. “A Simple and Cost-Effective Setup for Super-Resolution Localization Microscopy.” Scientific Reports 7 (1): 1542. doi: 10.1038/s41598-017-01606-6 Manley, Suliana, Jennifer M. Gillette, George H. Patterson, Hari Shroff, Harald F. Hess, Eric Betzig, and Jennifer Lippincott- Schwartz. 2008. “High-Density Mapping of Single-Molecule Trajectories with Photoactivated Localization Microscopy.” Nature Methods 5 (2): 155–57. doi: 10.1038/nmeth.1176 Mateos-Gil, Pablo, Sebastian Letschert, Sören Doose, and Markus Sauer. 2016. “Super-Resolution Imaging of Plasma Membrane Proteins with Click Chemistry.” Frontiers in Cell and Developmental Biology 4 (September): 98. doi: 10.3389/fcell.2016.00098 Mikhaylova, Marina, Bas M. C. Cloin, Kieran Finan, Robert van den Berg, Jalmar Teeuw, Marta M. Kijanka, Mikolaj Sokolowski, et al. 2015. “Resolving Bundled Microtubules Using Anti-Tubulin Nanobodies.” Nature Communications 6 (May): 7933. doi: 10.1038/ncomms8933 Min, Junhong, Cédric Vonesch, Hagai Kirshner, Lina Carlini, Nicolas Olivier, Seamus Holden, Suliana Manley, Jong Chul Ye, and Michael Unser. 2015. “FALCON: Fast and Unbiased Reconstruction of High-Density Super- Resolution Microscopy Data.” Scientific Reports 4 (1): 4577. doi: 10.1038/srep04577 Mishina, Natalia M., Alexander S. Mishin, Yury Belyaev, Ekaterina A. Bogdanova, Sergey Lukyanov, Carsten Schultz, and Vsevolod V. Belousov. 2015. “Live-Cell STED Microscopy with Genetically Encoded Biosensor.” Nano Letters 15 (5): 2928–32. doi: 10.1021/nl504710z Mo, Gary C. H., Brian Ross, Fabian Hertel, Premashis Manna, Xinxing Yang, Eric Greenwald, Chris Booth, et al. 2017. “Genetically Encoded Biosensors for Visualizing Live- Cell Biochemical Activity at Super-Resolution.” Nature Methods 14 (4): 427–34. doi: 10.1038/nmeth.4221 Mukamel, Eran A., Hazen Babcock, and Xiaowei Zhuang. 2012. “Statistical Deconvolution for Superresolution Fluorescence Microscopy.” Biophysical Journal 102 (10): 2391–400. doi: 10.1016/j.bpj.2012.03.070 Müller, Claus B., and Jörg Enderlein. 2010. “Image Scanning Microscopy.” Physical Review Letters 104 (19): 198101. doi: 10.1103/PhysRevLett.104.198101 Müller, Marcel, Viola Mönkemöller, Simon Hennig, Wolfgang Hübner, and Thomas Huser. 2016. “Open-Source Image Reconstruction of Super-Resolution Structured Illumination Microscopy Data in ImageJ.” Nature Communications 7: 10980. doi: 10.1038/ncomms10980 Nägerl, U. Valentin, Katrin I. Willig, Birka Hein, Stefan W. Hell, and Tobias Bonhoeffer. 2008. “Live-Cell Imaging of Dendritic Spines by STED Microscopy.” Proceedings of the National Academy of Sciences of the United States of America 105 (48): 18982–87. doi: 10.1073/pnas.0810028105 Nehme, Elias, Lucien E. Weiss, Tomer Mchaeli, and Yoav Shechtman. 2018. “Deep-STORM: Super-Resolution Single-Molecule Microscopy by Deep Learning.” Optica 5 (4): 458–64. Ni, Qiang, Sohum Mehta, and Jin Zhang. 2017. “Live-Cell Imaging of Cell Signaling Using Genetically Encoded Fluorescent Reporters.” The FEBS Journal 285: 203-219 June, 1–17. doi: 10.1111/febs.14134 Nienhaus, Karin, and G. Ulrich Nienhaus. 2014. “Fluorescent Proteins for Live-Cell Imaging with Super-Resolution.” Chemical Society Reviews 43 (4): 1088–106. doi: 10.1039/ C3CS60171D Nieuwenhuizen, Robert P. J., Keith a Lidke, Mark Bates, Daniela Leyton Puig, David Grünwald, Sjoerd Stallinga, and Bernd Rieger. 2013. “Measuring Image Resolution in Optical Nanoscopy.” Nature Methods 10 (6): 557–62. doi: 10.1038/ nmeth.2448 Nikić, Ivana, Gemma Estrada Girona, Jun Hee Kang, Giulia Paci, Sofya Mikhaleva, Christine Koehler, Nataliia V. Shymanska, et  al. 2016. “Debugging Eukaryotic Genetic Code Expansion for Site-Specific Click-PAINT Super-Resolution Microscopy.” Angewandte Chemie (International Ed. in English) 55 (52): 16172–76. doi: 10.1002/anie.201608284 Nikić, Ivana, and Edward A. Lemke. 2015. “Genetic Code Expansion Enabled Site-Specific Dual-Color Protein Labeling: Superresolution Microscopy and Beyond.” Current Opinion in Chemical Biology 28: 164–73. doi: 10.1016/j.cbpa.2015.07.021 Nozumi, Motohiro, Fubito Nakatsu, Kaoru Katoh, and Michihiro Igarashi. 2017. “Coordinated Movement of Vesicles and Actin Bundles during Nerve Growth Revealed by Superresolution Microscopy.” Cell Reports 18 (9): 2203–16. doi: 10.1016/J.CELREP.2017.02.008 Olivier, Nicolas, Debora Keller, Pierre Gönczy, and Suliana Manley. 2013. “Resolution Doubling in 3D-STORM Imaging through Improved Buffers.” Edited by Markus Sauer. PLoS ONE 8 (7): e69004. doi: 10.1371/journal. pone.0069004 Olivier, Nicolas, Debora Keller, Vinoth Sundar Rajan, Pierre Gönczy, and Suliana Manley. 2013. “Simple Buffers for 3D STORM Microscopy.” Biomedical Optics Express 4 (6): 885–99. doi: 10.1364/BOE.4.000885 Opazo, Felipe, Matthew Levy, Michelle Byrom, Christina Schäfer, Claudia Geisler, Teja W. Groemer, Andrew D. Ellington, and Silvio O. Rizzoli. 2012. “Aptamers as Potential Tools for Super-Resolution Microscopy.” Nature Methods 9 (10): 938–39. doi: 10.1038/nmeth.2179 TNF_03_K32983_C003_docbook_new_indd.indd 54 8/15/2020 09:11:28 ----!@#$NewPage!@#$---- 55 An Introduction to Live-Cell Super-Resolution Imaging Ouyang, Wei, Andrey Aristov, Mickaël Lelek, Xian Hao, and Christophe Zimmer. 2018. “Deep Learning Massively Accelerates Super-Resolution Localization Microscopy.” Nature Biotechnology 36 (February): 460–68. doi: 10.1038/ nbt.4106 Ovesný, Martin, Pavel Křížek, Josef Borkovec, Zdeněk Švindrych, and Guy M. Hagen. 2014. “ThunderSTORM: A Comprehensive ImageJ Plug-in for PALM and STORM Data Analysis and Super-Resolution Imaging.” Bioinformatics 30 (16): 2389–90. doi: 10.1093/bioinformatics/btu202 Pavani, Sri Rama Prasanna, Michael A. Thompson, Julie S. Biteen, Samuel J. Lord, Na Liu, Robert J. Twieg, Rafael Piestun, and W. E. Moerner. 2009. “Three-Dimensional, Single-Molecule Fluorescence Imaging beyond the Diffraction Limit by Using a Double-Helix Point Spread Function.” Proceedings of the National Academy of Sciences of the United States of America 106 (9): 2995–99. doi: 10.1073/pnas.0900245106 Pennacchietti, Francesca, Travis J. Gould, and Samuel T. Hess. 2017. “The Role of Probe Photophysics in Localization- Based Superresolution Microscopy.” Biophysical Journal 113 (9): 2037–54. doi: 10.1016/j.bpj.2017.08.054 Pereira, Pedro M., David Albrecht, Caron Jacobs, Mark Marsh, Jason Mercer, and Ricardo Henriques. 2018. “Fix Your Membrane Receptor Imaging: Actin Cytoskeleton and CD4 Membrane Organization Disruption by Chemical Fixation.” BioRxiv, October, 450635. doi: 10.1101/450635 Pereira, Pedro M., Pedro Almada, and Ricardo Henriques. 2015. “High-Content 3D Multicolor Super-Resolution Localization Microscopy.” Methods in Cell Biology 125: 95–117. doi: 10.1016/bs.mcb.2014.10.004 Platonova, Evgenia, Christian M. Winterflood, and Helge Ewers. 2015. “A Simple Method for GFP- and RFP-Based Dual Color Single-Molecule Localization Microscopy.” ACS Chemical Biology 10 (6): 1411–16. doi: 10.1021/ acschembio.5b00046 Pleiner, Tino, Mark Bates, and Dirk Görlich. 2017. “A Toolbox of Anti-Mouse and Anti-Rabbit IgG Secondary Nanobodies.” The Journal of Cell Biology 217 (3): 1143–1154. doi: 10.1083/jcb.201709115 Pleiner, Tino, Mark Bates, Sergei Trakhanov, Chung Tien Lee, Jan Erik Schliep, Hema Chug, Marc Böhning, et al. 2015. “Nanobodies: Site-Specific Labeling for Super-Resolution Imaging, Rapid Epitope- Mapping and Native Protein Complex Isolation.” ELife 4 (December 2015): e11349. doi: 10.7554/eLife.11349 Pospíšil, Jakub, Tomáš Lukeš, Justin Bendesky, Karel Fliegel, Kathrin Spendier, and Guy M. Hagen. 2018. “Imaging Tissues and Cells beyond the Diffraction Limit with Structured Illumination Microscopy and Bayesian Image Reconstruction.” GigaScience 8 (1): 1-12 doi: 10.1093/gigascience/giy126 Prifti Efthymia, Luc Reymond, Miwa Umebayashi, Ruud Hovius, Howard Riezman, and Kai Johnsson. 2014. “A Fluorogenic Probe for Snap-Tagged Plasma Membrane Proteins Based on the Solvatochromic Molecule Nile Red.” ACS Chemical Biology 9 (3): 606–12. doi: 10.1021/cb400819c Ratz, Michael, Ilaria Testa, Stefan W. Hell, and Stefan Jakobs. 2015. “CRISPR/Cas9-Mediated Endogenous Protein Tagging for RESOLFT Super-Resolution Microscopy of Living Human Cells.” Scientific Reports 5: 1–6. doi: 10.1038/ srep09592 Rego, E. H., L. Shao, J. J. Macklin, L. Winoto, G. A. Johansson, N. Kamps-Hughes, M. W. Davidson, and M. G. L. Gustafsson. 2012. “Nonlinear Structured-Illumination Microscopy with a Photoswitchable Protein Reveals Cellular Structures at 50-Nm Resolution.” Proceedings of the National Academy of Sciences 109 (3): E135–43. doi: 10.1073/pnas.1107547108 Richardson, Douglas S., Carola Gregor, Franziska R. Winter, Nicolai T. Urban, Steffen J. Sahl, Katrin I. Willig, and Stefan W. Hell. 2017. “SRpHi Ratiometric PH Biosensors for Super-Resolution Microscopy.” Nature Communications 8 (1). doi: 10.1038/s41467-017-00606-4 Ries, Jonas, Charlotte Kaplan, Evgenia Platonova, Hadi Eghlidi, and Helge Ewers. 2012. “A Simple, Versatile Method for GFP-Based Super-Resolution Microscopy via Nanobodies.” Nature Methods 9 (6): 582–84. doi: 10.1038/ nmeth.1991 Rosenbloom, Alyssa B., Sang-Hyuk Lee, Milton To, Antony Lee, Jae Yen Shin, and Carlos Bustamante. 2014. “Optimized Two-Color Super Resolution Imaging of Drp1 during Mitochondrial Fission with a Slow-Switching Dronpa Variant.” Proceedings of the National Academy of Sciences 111 (36): 13093–98. doi: 10.1073/pnas.1320044111 Rosten, Edward, Gareth E. Jones, and Susan Cox. 2013. “ImageJ Plug-in for Bayesian Analysis of Blinking and Bleaching.” Nature Methods 10 (2): 97–98. doi: 10.1038/nmeth.2342 Rust, Michael J., Mark Bates, and Xiaowei Zhuang. 2006. “Sub-Diffraction-Limit Imaging by Stochastic Optical Reconstruction Microscopy (STORM).” Nature Methods 3 (10): 793–95. doi: 10.1038/nmeth929 Sage, Daniel, Hagai Kirshner, Thomas Pengo, Nico Stuurman, Junhong Min, Suliana Manley, and Michael Unser. 2015. “Quantitative Evaluation of Software Packages for Single- Molecule Localization Microscopy.” Nature Methods 12 (8): 717–24. doi: 10.1038/nmeth.3442 Sakin, Volkan, Janina Hanne, Jessica Dunder, Maria Anders- Össwein, Vibor Laketa, Ivana Nikić, Hans Georg Kräusslich, et  al. 2017. “A Versatile Tool for Live-Cell Imaging and Super-Resolution Nanoscopy Studies of HIV-1 Env Distribution and Mobility.” Cell Chemical Biology 24 (5): 635–45.e5. doi: 10.1016/j.chembiol.2017.04.007 Sanford, Lynn, and Amy Palmer. 2017. “Recent Advances in Development of Genetically Encoded Fluorescent Sensors.” Methods in Enzymology 589: 1–49. doi: 10.1016/ bs.mie.2017.01.019 Schindelin, Johannes, Ignacio Arganda-Carreras, Erwin Frise, Verena Kaynig, Mark Longair, Tobias Pietzsch, Stephan Preibisch, et al. 2012. “Fiji: An Open-Source Platform for Biological-Image Analysis.” Nature Methods 9 (7): 676–82. doi: 10.1038/nmeth.2019 Schlichthaerle, Thomas, Alexandra Eklund, Florian Schueder, Maximilian Strauss, Christian Tiede, Alistair Curd, Jonas Ries, Michelle Peckham, Darren Tomlinson, and Ralf Jungmann. 2018. “Site-Specific Labeling of Affimers for DNA-PAINT Microscopy.” Angewandte Chemie (International Ed. in English) 57: 11060–3. doi: 10.1002/ anie.201804020 Schneider, Caroline A., Wayne S. Rasband, and Kevin W. Eliceiri. 2012. “NIH Image to ImageJ: 25 Years of Image Analysis.” Nature Methods 9 (7): 671–75. doi: 10.1038/nmeth.2089. TNF_03_K32983_C003_docbook_new_indd.indd 55 8/15/2020 09:11:28 ----!@#$NewPage!@#$---- 56 Imaging from Cells to Animals In Vivo Schnitzbauer, Joerg, Maximilian T. Strauss, Thomas Schlichthaerle, Florian Schueder, and Ralf Jungmann. 2017. “Super-Resolution Microscopy with DNA-PAINT.” Nature Protocols 12 (6): 1198–228. doi: 10.1038/nprot.2017.024 Schueder, Florian, Juanita Lara-Gutiérrez, Brian J. Beliveau, Sinem K. Saka, Hiroshi M. Sasaki, Johannes B. Woehrstein, Maximilian T. Strauss, et  al. 2017. “Multiplexed 3D Super-Resolution Imaging of Whole Cells Using Spinning Disk Confocal Microscopy and DNA- PAINT.” Nature Communications 8 (1): 2090. doi: 10.1038/ s41467-017-02028-8. Schueder, Florian, Maximilian T. Strauss, David Hoerl, Joerg Schnitzbauer, Thomas Schlichthaerle, Sebastian Strauss, Peng Yin, et  al. 2017. “Universal Super-Resolution Multiplexing by DNA Exchange.” Angewandte Chemie - International Edition 56 (14): 4052–55. doi: 10.1002/ anie.201611729 Schvartz, Tomer, Noa Aloush, Inna Goliand, Inbar Segal, Dikla Nachmias, Eyal Arbely, and Natalie Elia. 2017. “Direct Fluorescent-Dye Labeling of α-Tubulin in Mammalian Cells for Live Cell and Superresolution Imaging.” Molecular Biology of the Cell 28 (21): 2747–56. doi: 10.1091/mbc.E17-03-0161 Sednev, Maksim V., Vladimir N. Belov, and Stefan W. Hell. 2015. “Fluorescent Dyes with Large Stokes Shifts for Super- Resolution Optical Microscopy of Biological Objects: A Review.” Methods and Applications in Fluorescence 3 (4): 042004. doi: 10.1088/2050-6120/3/4/042004 Shaner, Nathan C., Gerard G. Lambert, Andrew Chammas, Yuhui Ni, Paula J. Cranfill, Michelle A. Baird, Brittney R. Sell, et al. 2013. “A Bright Monomeric Green Fluorescent Protein Derived from Branchiostoma Lanceolatum.” Nature Methods 10 (5): 407–9. doi: 10.1038/nmeth.2413 Shaner, Nathan C., Paul A. Steinbach, and Roger Y. Tsien. 2005. “A Guide to Choosing Fluorescent Proteins.” Nature Methods 2 (12): 905–9. doi: 10.1038/nmeth819 Sharonov, Alexey, and Robin M. Hochstrasser. 2006. “Wide- Field Subdiffraction Imaging by Accumulated Binding of Diffusing Probes.” Proceedings of the National Academy of Sciences of the United States of America 103 (50): 18911– 16. doi: 10.1073/pnas.0609643104 Shcherbakova, Daria M., Prabuddha Sengupta, Jennifer Lippincott-Schwartz, and Vladislav V. Verkhusha. 2014. “Photocontrollable Fluorescent Proteins for Superresolution Imaging.” Annual Review of Biophysics 43 (1): 303–29. doi: 10.1146/annurev-biophys-051013-022836 Sheppard, C. J. R. 1988. “Super-Resolution in Confocal Imaging.” Optik (Stuttgart) 80: 53–4. Sheppard, Colin J. R., Shalin B. Mehta, and Rainer Heintzmann. 2013. “Superresolution by Image Scanning Microscopy Using Pixel Reassignment.” Optics Letters 38 (15): 2889. doi: 10.1364/OL.38.002889 Shim, Sang-Hee, Chenglong Xia, Guisheng Zhong, Hazen P. Babcock, Joshua C. Vaughan, Bo Huang, Xun Wang, et  al. 2012. “Super-Resolution Fluorescence Imaging of Organelles in Live Cells with Photoswitchable Membrane Probes.” Proceedings of the National Academy of Sciences 109 (35): 13978–83. doi: 10.1073/pnas.1201882109 Shroff, Hari, Catherine G. Galbraith, James A. Galbraith, and Eric Betzig. 2008. “Live-Cell Photoactivated Localization Microscopy of Nanoscale Adhesion Dynamics.” Nature Methods 5 (5): 417–23. doi: 10.1038/nmeth.1202 Song, Xinbo, Hui Bian, Chao Wang, Mingyu Hu, Ning Li, and Yi Xiao. 2017. “Development and Applications of a Near- Infrared Dye-Benzylguanine Conjugate to Specifically Label SNAP-Tagged Proteins.” Organic & Biomolecular Chemistry 15 (38): 8091–101. doi: 10.1039/c7ob01698k Ståhl, Stefan, Torbjörn Gräslund, Amelie Eriksson Karlström, Fredrik Y. Frejd, Per Åke Nygren, and John Löfblom. 2017. “Affibody Molecules in Biotechnological and Medical Applications.” Trends in Biotechnology 35 (8): 691–712. doi: 10.1016/j.tibtech.2017.04.007. Staudt, Thorsten, Andreas Engler, Eva Rittweger, Benjamin Harke, Johann Engelhardt, and Stefan W. Hell. 2011. “Far-Field Optical Nanoscopy with Reduced Number of State Transition Cycles.” Optics Express 19 (6): 5644. doi: 10.1364/OE.19.005644 Ströhl, Florian, and Clemens F. Kaminski. 2016. “Frontiers in Structured Illumination Microscopy.” Optica 3 (6): 667. doi: 10.1364/OPTICA.3.000667 Takakura, Hideo, Yongdeng Zhang, Roman S. Erdmann, Alexander D. Thompson, Yu Lin, Brian McNellis, Felix Rivera-Molina, et al. 2017. “Long Time-Lapse Nanoscopy with Spontaneously Blinking Membrane Probes.” Nature Biotechnology 35 (8): 773–80. doi: 10.1038/nbt.3876 Teng, Kai Wen, Yuji Ishitsuka, Pin Ren, Yeoan Youn, Xiang Deng, Pinghua Ge, Andrew S. Belmont, et  al. 2016. “Labeling Proteins inside Living Cells Using External Fluorophores for Microscopy.” ELife 5 (December 2016): 1–13. doi: 10.7554/eLife.20378. Thompson, Alexander D., Joerg Bewersdorf, Derek Toomre, and Alanna Schepartz. 2017. “HIDE Probes: A New Toolkit for Visualizing Organelle Dynamics, Longer and at Super- Resolution.” Biochemistry 56 (39): 5194–201. doi: 10.1021/ acs.biochem.7b00545 Thompson, Alexander D., Mitchell H. Omar, Felix Rivera- Molina, Zhiqun Xi, Anthony J. Koleske, Derek K. Toomre, et  al. 2017. “Long-Term Live-Cell STED Nanoscopy of Primary and Cultured Cells with the Plasma Membrane HIDE Probe DiI-SiR.” Angewandte Chemie - International Edition 56 (35): 10408–12. doi: 10.1002/anie.201704783 Thompson, Russell E., Daniel R. Larson, and Watt W. Webb. 2002. “Precise Nanometer Localization Analysis for Individual Fluorescent Probes.” Biophysical Journal 82 (5): 2775–83. doi: 10.1016/S0006-3495(02)75618-X Tiwari, Dhermendra K., Yoshiyuki Arai, Masahito Yamanaka, Tomoki Matsuda, Masakazu Agetsuma, Masahiro Nakano, et al. 2015. “A Fast- and Positively Photoswitchable Fluorescent Protein for Ultralow-Laser-Power RESOLFT Nanoscopy.” Nature Methods 12 (6): 515–18. doi: 10.1038/nmeth.3362 Tønnesen, Jan, Gergely Katona, Balázs Rózsa, and U. Valentin Nägerl. 2014. “Spine Neck Plasticity Regulates Compartmentalization of Synapses.” Nature Neuroscience 17 (5): 678–85. doi: 10.1038/nn.3682 Tønnesen, Jan, Fabien Nadrigny, Katrin I. Willig, Roland Wedlich-Söldner, and U. Valentin Nägerl. 2011. “Two- Color STED Microscopy of Living Synapses Using a Single Laser-Beam Pair.” Biophysical Journal 101 (10): 2545–52. doi: 10.1016/j.bpj.2011.10.011 Tortarolo, Giorgio, Marco Castello, Alberto Diaspro, Sami Koho, and Giuseppe Vicidomini. 2018. “Evaluating Image Resolution in Stimulated Emission Depletion Microscopy.” Optica 5 (1): 32. doi: 10.1364/OPTICA.5.000032 TNF_03_K32983_C003_docbook_new_indd.indd 56 8/15/2020 09:11:28 ----!@#$NewPage!@#$---- 57 An Introduction to Live-Cell Super-Resolution Imaging Traenkle, Bjoern, and Ulrich Rothbauer. 2017. “Under the Microscope: Single-Domain Antibodies for Live-Cell Imaging and Super-Resolution Microscopy.” Frontiers in Immunology 8 (August): 1–8. doi: 10.3389/fimmu.2017. 01030 Turkowyd, Bartosz, Alexander Balinovic, David Virant, Haruko G. Gölz Carnero, Fabienne Caldana, Marc Endesfelder, Dominique Bourgeois, et al. 2017. “A General Mechanism of Photoconversion of Green-to-Red Fluorescent Proteins Based on Blue and Infrared Light Reduces Phototoxicity in Live-Cell Single-Molecule Imaging.” Angewandte Chemie - International Edition 56 (38): 11634–39. doi: 10.1002/ anie.201702870 Uno, Shin-nosuke, Mako Kamiya, Akihiko Morozumi, and Yasuteru Urano. 2017. “A Green-Light-Emitting, Spontaneously Blinking Fluorophore Based on Intramolecular Spirocyclization for Dual-Colour Super- Resolution Imaging.” Chemical Communications (Cambridge, England) 54 (1): 102–5. doi: 10.1039/c7cc0 7783a Uno, Shin-nosuke, Mako Kamiya, Toshitada Yoshihara, Ko Sugawara, Kohki Okabe, Mehmet C. Tarhan, Hiroyuki Fujita, et al. 2014. “A Spontaneously Blinking Fluorophore Based on Intramolecular Spirocyclization for Live-Cell Super-Resolution Imaging.” Nature Chemistry 6 (8): 681– 89. doi: 10.1038/nchem.2002 Uno, Shin Nosuke, Dhermendra K. Tiwari, Mako Kamiya, Yoshiyuki Arai, Takeharu Nagai, and Yasuteru Urano. 2015. “A Guide to Use Photocontrollable Fluorescent Proteins and Synthetic Smart Fluorophores for Nanoscopy.” Microscopy 64 (4): 263–77. doi: 10.1093/jmicro/dfv037 Uttamapinant, Chayasith, Jonathan D. Howe, Kathrin Lang, Václav Beránek, Lloyd Davis, Mohan Mahesh, Nicholas P. Barry, et al. 2015. “Genetic Code Expansion Enables Live-Cell and Super-Resolution Imaging of Site- Specifically Labeled Cellular Proteins.” Journal of the American Chemical Society 137 (14): 4602–5. doi: 10.1021/ja512838z Uttamapinant, Chayasith, Katharine A. White, Hemanta Baruah, Samuel Thompson, Marta Fernández-Suárez, Sujiet Puthenveetil, and Alice Y. Ting. 2010. “A Fluorophore Ligase for Site-Specific Protein Labeling inside Living Cells.” Proceedings of the National Academy of Sciences of the United States of America 107 (24): 10914–19. doi: 10.1073/pnas.0914067107 Vicidomini, Giuseppe, Gael Moneron, Christian Eggeling, Eva Rittweger, and Stefan W. Hell. 2012. “STED with Wavelengths Closer to the Emission Maximum.” Optics Express 20 (5): 5225. doi: 10.1364/OE.20.005225 Vicidomini, Giuseppe, Gael Moneron, Kyu Y. Han, Volker Westphal, Haisen Ta, Matthias Reuss, Johann Engelhardt, Christian Eggeling, et al. 2011. “Sharper Low-Power STED Nanoscopy by Time Gating.” Nature Methods 8 (7): 571–3. doi: 10.1038/nmeth.1624 Vreja, Ingrid C., Ivana Nikić, Fabian Göttfert, Mark Bates, Katharina Kröhnert, Tiago F. Outeiro, Stefan W. Hell, Edward A. Lemke, and Silvio O. Rizzoli. 2015. “Super- Resolution Microscopy of Clickable Amino Acids Reveals the Effects of Fluorescent Protein Tagging on Protein Assemblies.” ACS Nano 9 (11): 11034–41. doi: 10.1021/ acsnano.5b04434 Wäldchen, Sina, Julian Lehmann, Teresa Klein, Sebastian van de Linde, and Markus Sauer. 2015. “Light-Induced Cell Damage in Live-Cell Super-Resolution Microscopy.” Scientific Reports 5: 15348. doi: 10.1038/srep15348 Wang, Chao, Xinbo Song, and Yi Xiao. 2017. “SNAP-Tag-Based Subcellular Protein Labeling and Fluorescent Imaging with Naphthalimides.” Chembiochem : A European Journal of Chemical Biology 18 (17): 1762–9. doi: 10.1002/ cbic.201700161 Wang, Chenguang, Masayasu Taki, Yoshikatsu Sato, Aiko Fukazawa, Tetsuya Higashiyama, and Shigehiro Yamaguchi. 2017. “Super-Photostable Phosphole- Based Dye for Multiple-Acquisition Stimulated Emission Depletion Imaging.” Journal of the American Chemical Society 139 (30): 10374–81. doi: 10.1021/jacs. 7b04418 Wang, Sheng, Miao Ding, Xuanze Chen, Lei Chang, and Yujie Sun. 2017. “Development of Bimolecular Fluorescence Complementation Using RsEGFP2 for Detection and Super-Resolution Imaging of Protein-Protein Interactions in Live Cells.” Biomedical Optics Express 8 (6): 3119–31. doi: 10.1364/BOE.8.003119 Wang, Siyuan, Jeffrey R. Moffitt, Graham T. Dempsey, X. Sunney Xie, and Xiaowei Zhuang. 2014. “Characterization and Development of Photoactivatable Fluorescent Proteins for Single-Molecule-Based Superresolution Imaging.” Proceedings of the National Academy of Sciences of the United States of America 111 (23): 8452–7. doi: 10.1073/ pnas.1406593111 Wegel, Eva, Antonia Göhler, B. Christoffer Lagerholm, Alan Wainman, Stephan Uphoff, Rainer Kaufmann, and Ian M. Dobbie. 2016. “Imaging Cellular Structures in Super- Resolution with SIM, STED and Localisation Microscopy: A Practical Comparison.” Scientific Reports 6 (1): 27290. doi: 10.1038/srep27290 Wegner, Waja, Alexander C. Mott, Seth G. N. Grant, Heinz Steffens, and Katrin I. Willig. 2018. “In Vivo STED Microscopy Visualizes PSD95 Sub-Structures and Morphological Changes over Several Hours in the Mouse Visual Cortex.” Scientific Reports 8 (1): 219. doi: 10.1038/ s41598-017-18640-z Weigert, Martin, Uwe Schmidt, Tobias Boothe, Andreas Müller, Alexandr Dibrov, Akanksha Jain, Benjamin Wilhelm, et al. 2018. “Content-Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy.” Nature Methods 15 (12): 1090–7. doi: 10.1038/s41592-018-0216-7 Wichmann, Jan, and Stefan W. Hell. 1994. “Breaking the Diffraction Resolution Limit by Stimulated Emission: Stimulated-Emission-Depletion Fluorescence Microscopy.” Optics Letters 19 (11): 780–2. doi: 10.1364/OL.19.000780 Wiedenmann, Jörg, Sergey Ivanchenko, Franz Oswald, Florian Schmitt, Carlheinz Röcker, Anya Salih, Klaus-Dieter Spindler, et  al. 2004. “EosFP, a Fluorescent Marker Protein with UV-Inducible Green-to-Red Fluorescence Conversion.” Proceedings of the National Academy of Sciences of the United States of America 101 (45): 15905– 10. doi: 10.1073/pnas.0403668101 Wiedenmann, Jörg, Franz Oswald, and Gerd Ulrich Nienhaus. 2009. “Fluorescent Proteins for Live Cell Imaging: Opportunities, Limitations, and Challenges.” IUBMB Life 61 (11): 1029–42. doi: 10.1002/iub.256 TNF_03_K32983_C003_docbook_new_indd.indd 57 8/15/2020 09:11:28 ----!@#$NewPage!@#$---- 58 Imaging from Cells to Animals In Vivo Willig, Katrin I., Silvio O. Rizzoli, Volker Westphal, Reinhard Jahn, and Stefan W. Hell. 2006. “STED Microscopy Reveals That Synaptotagmin Remains Clustered after Synaptic Vesicle Exocytosis.” Nature 440 (7086): 935–9. doi: 10.1038/nature04592 Wombacher, Richard, Meike Heidbreder, Sebastian van de Linde, Michael P. Sheetz, Mike Heilemann, Virginia W. Cornish, and Markus Sauer. 2010. “Live-Cell Super-Resolution Imaging with Trimethoprim Conjugates.” Nature Methods 7 (9): 717–9. doi: 10.1038/nmeth.1489 Wu Yong, Wu Xundong, Toro Ligia, and Stefani Enrico. 2015. “Resonant-scanning dual-color STED microscopy with ultrafast photon counting: A concise guide,” https ://dx .doi. org/1 0.101 6%2Fj .ymet h.201 5.06. 01 Yang, Bin, Frédéric Przybilla, Michael Mestre, Jean-Baptiste Trebbia, and Brahim Lounis. 2013. “Massive Parallelization of STED Nanoscopy Using Optical Lattices.” Optics Express 22 (5): 5581. doi: 10.1364/OE.22.005581 York, Andrew G., Panagiotis Chandris, Damian Dalle Nogare, Jeffrey Head, Peter Wawrzusin, Robert S. Fischer, Ajay Chitnis, and Hari Shroff. 2013. “Instant Super-Resolution Imaging in Live Cells and Embryos via Analog Image Processing.” Nature Methods 10 (11): 1122–6. doi: 10.1038/nmeth.2687 York, Andrew G., Sapun H. Parekh, Damian Dalle Nogare, Robert S. Fischer, Kelsey Temprine, Marina Mione, Ajay B. Chitnis, et  al. 2012. “Resolution Doubling in Live, Multicellular Organisms via Multifocal Structured Illumination Microscopy.” Nature Methods 9 (7): 749–54. doi: 10.1038/nmeth.2025 Young, Laurence J., Florian Ströhl, and Clemens F. Kaminski. 2016. “A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors.” Journal of Visualized Experiments, no. 111 (May): e53988–e53988. doi: 10.3791/53988 Yu, Ji. 2016. “Single-Molecule Studies in Live Cells.” Annual Review of Physical Chemistry 67 (1): 565–85. doi: 10.1146/ annurev-physchem-040215-112451. Zane, Hannah K., Julia K. Doh, Caroline A. Enns, and Kimberly E. Beatty. 2017. “Versatile Interacting Peptide (VIP) Tags for Labeling Proteins with Bright Chemical Reporters.” ChemBioChem 18 (5): 470–4. doi: 10.1002/ cbic.201600627 Zhang, Mingshu, Hao Chang, Yongdeng Zhang, Junwei Yu, Lijie Wu, Wei Ji, Juanjuan Chen, et al. 2012. “Rational Design of True Monomeric and Bright Photoactivatable Fluorescent Proteins.” Nature Methods 9 (7): 727–9. doi: 10.1038/ nmeth.2021 Zhang, Xi, Xuanze Chen, Zhiping Zeng, Mingshu Zhang, Yujie Sun, Peng Xi, Jianxin Peng, et al. 2015. “Development of a Reversibly Switchable Fluorescent Protein for Super- Resolution Optical Fluctuation Imaging (SOFI).” ACS Nano 9 (3): 2659–67. doi: 10.1021/nn5064387. Zhang, Xi, Mingshu Zhang, Dong Li, Wenting He, Jianxin Peng, Eric Betzig, and Pingyong Xu. 2016. “Highly Photostable, Reversibly Photoswitchable Fluorescent Protein with High Contrast Ratio for Live-Cell Superresolution Microscopy.” Proceedings of the National Academy of Sciences 113 (37): 10364–9. doi: 10.1073/pnas.1611038113. Zhu, Lei, Wei Zhang, Daniel Elnatan, and Bo Huang. 2012. “Faster STORM Using Compressed Sensing.” Nature Methods 9 (7): 721–3. doi: 10.1038/nmeth.1978. Zhu, Xuekai, Lei Wang, Rongzhi Liu, Barry Flutter, Shenghua Li, Jie Ding, Hua Tao, et  al. 2010. “COMBODY: One- Domain Antibody Multimer with Improved Avidity.” Immunology and Cell Biology 88 (6): 667–75. doi: 10.1038/ icb.2010.21. TNF_03_K32983_C003_docbook_new_indd.indd 58 8/15/2020 09:11:28