This article outlines a protocol for the detection of one or more plasma and/or intracellular membrane proteins using Ground State Depletion (GSD) super-resolution microscopy in mammalian cells. Here, we discuss the benefits and considerations of using such approaches for the visualization and quantification of cellular proteins.
Advances in fluorescent microscopy and cell biology are intimately correlated, with the enhanced ability to visualize cellular events often leading to dramatic leaps in our understanding of how cells function. The development and availability of super-resolution microscopy has considerably extended the limits of optical resolution from ~250-20 nm. Biologists are no longer limited to describing molecular interactions in terms of colocalization within a diffraction limited area, rather it is now possible to visualize the dynamic interactions of individual molecules. Here, we outline a protocol for the visualization and quantification of cellular proteins by ground-state depletion microscopy for fixed cell imaging. We provide examples from two different membrane proteins, an element of the endoplasmic reticulum translocon, sec61β, and a plasma membrane-localized voltage-gated L-type Ca2+ channel (CaV1.2). Discussed are the specific microscope parameters, fixation methods, photo-switching buffer formulation, and pitfalls and challenges of image processing.
Cellular signaling reactions translate changing internal and external environments to initiate a cellular response. They regulate all aspects of human physiology, serving as the foundation for hormone and neurotransmitter release, the heartbeat, vision, fertilization, and cognitive function. Disruption of these signaling cascades can have severe consequences in the form of pathophysiological conditions including cancer, Parkinson's, and Alzheimer's disease. For decades, biological and medical investigators have successfully used fluorescent proteins, probes, and biosensors coupled with fluorescence microscopy as the primary tools to understand the precise spatial and temporal organization of these cellular signals.
The strengths of optical techniques such as epifluorescence, confocal, or total internal reflection fluorescence (TIRF) microscopy are their sensitivity, speed, and compatibility with live cell imaging, while the major limitation is their diffraction-limited resolution, meaning structures or protein complexes smaller than 200-250 nm cannot be resolved. With the theoretical and practical development of deterministic super-resolution (e.g., stimulated emission depletion microscopy (STED1), structured illumination microscopy (SIM2) or stochastic super-resolution (e.g., photoactivated localization microscopy (PALM3), or ground state depletion (GSD4,5)), lateral and axial resolution in fluorescence microscopy has been extended beyond the diffraction barrier, to the order of tens of nanometers. Thus, investigators now have the unparalleled ability to visualize and understand how protein dynamics and organization translates to function at the near-molecular level.
Ground state depletion microscopy followed by individual molecule return (GSDIM), or simply GSD as it is known, circumvents the diffraction limit by reducing the number of simultaneously emitting fluorophores4,5. High energy laser light is used to excite the fluorophore-labelled sample, bombarding electrons with photons and increasing the probability they will undergo a 'spin-flip' and enter the triplet or 'dark-state' from the excited state4. This effectively depletes the ground state, hence the name 'ground state depletion'. In the triplet state, fluorophores do not emit photons and the sample appears dimmer. However, these fluorophores stochastically return to the ground state and can go through several photon emitting excited-to-ground state transitions before returning to the triplet state. With less fluorophores emitting at any given time, photon bursts emitted from individual fluorophores become spatially and temporally distinct from neighboring fluorophores. The burst of photons can be fit with a gaussian function, the calculated centroid of which corresponds to the position of the fluorophore with a localization precision that is dependent on the numerical aperture (NA) of the lens, the wavelength of light used for excitation and crucially, the number of photons emitted per fluorophore. One limitation of GSD is that, since only a subset of fluorophores actively emits at any time, thousands of images must be collected over several minutes to build up a complete localization map. The long acquisition time combined with the high laser power requirement, means that GSD is better suited to fixed rather than live samples.
This article, describes the preparation of fixed samples for super-resolution microscopy imaging of membrane and endoplasmic reticulum (ER)-resident proteins (for a list of necessary consumables and reagents see the Table of Materials). Examples of how this protocol can be easily adapted to quantify the size and degree of clustering of L-type voltage-gated Ca2+ channels (Cav1.2) in the sarcolemma of cardiac myocytes, or used to visualize the cellular distribution of the ER, are demonstrated. Understanding the distribution and organization of these cellular components is critically important in understanding the initiation, translation, and ultimately the function of many Ca2+-dependent signaling cascades. For example, Cav1.2 channels are fundamentally important for excitation-contraction coupling, while receptor-mediated Ca2+ release from the ER is perhaps the most ubiquitous signaling cascade in mammalian cells.
1. Washing Glass Coverslips
2. Coating Glass Coverslips
NOTE: Steps in this section should be performed in a cell culture hood to prevent contamination.
3. Preparation of Cells
4. Plating Cells
5. Fixation of Cells
6. Blocking Non-specific Binding
7. Detection
8. Post-fixation (Optional Step)
9. Storage of Samples
10. GSD Super-resolution Imaging Photoswitching Buffer Preparation
11. Mounting Samples
12. Image Acquisition
13. Image Analysis
As documented in the introduction, there are many different super-resolution microscopy imaging modalities. This protocol, focuses on GSD super-resolution imaging. Representative images and localization maps are shown in Figure 2 and Figure 3.
Figure 2 shows a COS-7 cell transfected with the ER protein, mCherry-Sec61β, and processed using the method described above. Figure 2A-2B allow the comparison of images of the ER taken using a super-resolution microscope in TIRF mode (A) and using GSD acquisition mode (B). The images show an improvement in the axial resolution when acquired in GSD mode. This is further demonstrated in the accompanying plot profile, that can be generated using ImageJ, which shows the difference in the distribution of the normalized intensity curves at the areas of interest (yellow line). These curves represent the diameter of the ER tubules which appear to be much narrower when examined in GSD mode. GSD microscopy has a lateral localization precision of approximately 20 nm and thus represents approximately a ten-fold increase in resolution beyond the diffraction limit. This improvement in resolution results in a more accurate representation of the structure of the ER tubules.
The improvement in resolution offered by super-resolution GSD imaging is further demonstrated in Figure 3, showing a labeled cardiac myocyte with an anti-Cav1.2 antibody, processed as described above. The image in Figure 3A was taken using a GSD super-resolution microscope in TIRF setting. Clusters of CaV1.2 channels can be seen to organize along the t-tubule network. Figure 3B shows the same cell, however this image was acquired using GSD mode. The improvement in the axial resolution is more prominent in the panels which focus on single clusters of channels (Figure 3B), when a comparison is made between the same ROI imaged using TIRF and GSD, separate clusters of channels are easier to identify as a result of the improvement in resolution offered by GSD imaging. These panels also compare the difference in the detection threshold defined as the number of photons per pixel. This parameter must be chosen carefully and adapted to the requirements of the experiment. Panel 3D shows cluster outlines when the GSD image was processed using ImageJ software, from this the cluster area for each individual cluster in the image can be determined (see note 13.1.8 above and the Discussion section for important considerations when performing such a quantification). This information can then be used to generate the frequency histogram in panel 3C. This analysis can be used to examine changes in cluster size, or the number of clusters between samples under control versus test conditions (e.g., WT vs mutant channels or untreated vs drug-treated cells). Using the approaches outlined in this protocol alongside complementary step-wise photobleaching experiments, investigators have determined that CaV1.2 channels are distributed in clusters of, on average, 8 channels in cardiac myocytes15.
Figure 1: Schematic representation of the timeline of events for super-resolution imaging of membrane proteins. Day 0 refers to the first day of processing for antibody labeling. The protocol section refers to the section in the protocol where detailed information is found on each step. Please click here to view a larger version of this figure.
Figure 2: Super-resolution microscopy offers improved signal to noise and increased spatial resolution. (A) Left COS-7 cells expressing mCherry-Sec61β, fixed and labelled as described in the protocol and imaged using conventional TIRF microscopy. Right, upper panel: single ER tubule. Right, lower panel: plot profile taken across the width of the ER tubule (yellow line). (B) Same cell as (A) except the localization map was generated using GSD super-resolution. Please click here to view a larger version of this figure.
Figure 3: Super-resolution imaging of Cav1.2 channels in cardiac myocytes. (A) TIRF footprint from an isolated cardiac myocyte, fixed, and stained with anti-CaV1.2 antibody (FP1). (B) Left: Super-resolution TIRF footprint from the same cardiac myocyte as in (A). Right: enlarged portions of the localization map that have been subjected to different detection thresholds. Note that as one increases the detection threshold, the apparent size of CaV1.2 channel clusters decrease. (C) Frequency histogram of cluster sizes obtained from the localization map in (B). (D) Left: Outlines of the CaV1.2 clusters from (B). Right: enlarged portions of the image have been subjected to different detection thresholds. Note, similar to (B), as the detection threshold increases, the area occupied by the CaV1.2 channel clusters decreases. Please click here to view a larger version of this figure.
Fluorophore used | Alexa-647 |
Laser | 642 nm |
Emission filters | 623 HP-T |
Lens | 160X 1.43 NA |
Exposure time | 10 ms |
Detection threshold | 65 |
Incidence Angle (penetration depth) | 65.04° (150 nm) |
EM gain | 300 |
#frames acquired | 30,000–60,000 |
Laser intensity for pumping | 100% |
Laser intensity for acquisition | 50% |
Table 1: List of imaging parameters.
The recent explosion of technologies that allow imaging beyond the diffraction limit have offered new windows into the complexities of mammalian cell signaling in space and time. These technologies include STORM, STED, PALM, GSD, SIM, and their variants (e.g., dSTORM, FPALM). The ingenuity of the scientists behind these techniques has allowed us to circumvent the limitations imposed by laws of physics governing the diffraction of light. In spite of this huge accomplishment, each of these techniques makes some sort of compromise to achieve super-resolution and, as such, has its limitations. The ideal situation that cell biologists and biophysicists strive for is optimum sensitivity, high resolution, and fast acquisition, with no photobleaching. In addition, one should remain cognizant that by removing cells from animals, we inherently change them and potentially change molecular dynamics. Therefore, the quest to develop a super-resolution technology that permits dynamic, live cell, single molecule imaging in a living, breathing animal goes on. For now, we have tools at our disposal that can generate images with 10-fold improvements on diffraction limited resolution. In this article, we discuss sample preparation, image acquisition, and analysis for GSD that enable resolutions down to 20 and 50 nm in the lateral and axial dimensions, respectively.
Although the focus of this protocol is on Sec61β and CaV1.2 L-type Ca2+ channels, the protocol described above can serve as a template for researchers who wish to image different proteins in their own labs on a GSD microscope. The positive attributes of this type of protocol are that it is relatively straightforward, can be modified and applied to a number of different cell types, and can be easily adapted to multi-color imaging. The obvious limitations are that super-resolution systems, equipped with a sensitive EM-CCD camera capable of single-molecule detection, still tend to be prohibitively expensive for many laboratories.
As the number of laboratories that use super-resolution microscopy as their preferred imaging technique grow, more uniform understanding and agreement is needed about image acquisition, processing, and interpretation. How, for example, does one quantify the level of proximity of two proteins that appear colocalized in a diffraction-limited pixel but transpire to actually display little overlap at all in a super-resolution image/map? Indeed, this scenario has already arisen for several previously assumed colocalized proteins, such as the adhesion complex proteins paxillin and zyxin. As the resolution that microscopes achieve invariably increases, perhaps proteins will no longer be reported to 'colocalize' but rather to have non-randomly distributed patterns of preferred-localization around one another. After all, it is impossible for two proteins to physically occupy exactly the same 3-dimensional space. Some solutions to this analysis conundrum are starting to emerge in the literature, including stochastic optical reconstruction microscopy-based relative localization analysis (STORM-RLA), which can reportedly quantify the frequency and degree of overlap between two co-labeled proteins and also provide quantitative measurements of the distance between non-overlapping proteins13. When comparing one channel to another in super-resolution, it is of course critical to ensure you have correct registry between the two channels. This can be evaluated using multi-color microsphere fluorescent beads and imaging in each individual channel then overlaying the images. Additionally, since the SR-GSD 3D microscope can also function as a TIRF microscope and generate super-resolution localization maps in TIRF, it is important to match the penetration depth between two-color channels, as TIRF angles change depending on the wavelength of light used to excite the sample.
Further, as depicted in Figure 3B and Figure 3D, a localization map can appear vastly different depending on the degree of 'thresholding'. If the detection threshold is set too low, structures may appear to be larger than they truly are as the probability that one includes spurious non-specific labeling of 'noise' increases. Conversely, if the detection threshold is set too high, then structures may appear smaller than they truly are as 'real' events are excluded. Thus, thresholding should be applied with reason and caution. So how can a reasonable detection threshold for a given sample be determined? Controls are key. As with any immuno-labeling approach, positive and negative controls should be performed to demonstrate the specificity of antibodies. With appropriate controls, it is possible to derive a detection threshold above which only specific labeling should be observed. Appropriate controls include samples in which the primary antibody incubation has been omitted so a sample exposed only to secondary antibody can be observed. From such a control, the non-specific binding of a secondary antibody can be discerned. In a well-prepared sample, this should result in a much smaller event count per image, and a lower mean number of photons per pixel when compared to the primary and secondary incubated sample. The detection threshold of the image can then be set so that non-specific labeling can be eliminated. The same detection threshold should then be used when imaging the primary and secondary incubated sample to enhance confidence that 'noise' is eliminated. Further controls include those testing the specificity of the primary antibody. In transfected cells, if the labeled protein is not natively expressed in the cell line, then a simple test of the primary antibody specificity is to perform the labeling procedure on untransfected cells. To demonstrate primary antibody specificity in primary cells, a genetic knockout of the labeled protein is the gold standard. Detection thresholds may also be set using these primary antibody control experiments. In the absence of the target antigen, any fluorescence emission is non-specific. As with the secondary only controls, with a good primary antibody, less events per image should be observed and therefore, over the same number of frames, a lower mean number of photons per pixel should be evident. The detection threshold can thus be set to a level that will eliminate non-specific background staining due to off-target primary antibody binding. For complete transparency, it is suggested that authors should present images with their thresholding parameters clearly stated and perhaps with a link to the raw data in an online depository.
The investigator must also remain cognizant that multiple secondary antibodies can bind to a single primary antibody so a 1:1 primary:secondary binding ratio should not be assumed. Furthermore, commercial secondary antibodies are commonly conjugated to multiple fluorophores, for example, the secondary antibodies employed in this protocol are noted by the manufacturer to be conjugated to 2-8 fluorophores. As a work-around to this, a fluorophore can be directly conjugated to a primary antibody. Spectrophotometry can then be used to determine the average number of fluorophores per primary antibody. Several commercially available kits are available to perform this conjugation process. However, even if a 1:1 stoichiometry is achieved, the fluorophore molecule itself can create further overcounting problems due to the blinking and reversible switching of fluorophores. In practice, this means that a fluorophore may cycle several times between the ground and excited state emitting clusters of photons as it does so. These photons may be detected in several neighboring pixels and lead to overestimation of cluster size. Other investigators have addressed this issue by choosing to combine fluorescent emissions clustered over short periods of time and space16,17. This is a somewhat empirical approach that still does not eliminate the possibility that the same fluorophore could cycle back to the dark state for an extended period, then reverse to a cycling/emitting state once more and be judged as a second molecule. Therefore, the number of molecules in a cluster cannot be calculated based solely on the cluster size. At the moment, there is no perfect solution to these issues and, therefore, the use of super-resolution imaging in conjunction with another technique such as step-wise photobleaching can give an approximate estimate of the number of molecules per cluster. Relevant controls to increase confidence in cluster area measurements include comparing cluster sizes of known monomeric proteins (e.g., CD86) and known dimeric proteins (e.g., CTLA-4) to those of the protein of interest as suggested by Fricke et al.18.
In summary, in the present article, a straightforward immunolabeling protocol is set forth describing the preparation of fixed samples for super-resolution imaging. In addition, some common pitfalls in image acquisition and analysis are discussed. As super-resolution imaging becomes more commonplace, it may become necessary for journals to set forth a new set of guidelines to avert inappropriate manipulation of these complex images/localization maps. Super-resolution microscopy has added a powerful new tool to the toolbox of cell biologists and biophysicists and has already made an extraordinary impact on our understanding of cellular architecture and molecular organization.
The authors have nothing to disclose.
This work was supported by a grant from the AHA to R.E.D. (15SDG25560035). Authors would like to acknowledge Dr. Fernando Santana for use of his Leica SR GSD 3D microscope, and Dr. Johannes Hell for kindly providing the FP1 antibody.
KOH | Thermo Scientific | P250-500 | To clean coverglass |
#1.5 coverglass 18 x 18 mm | Marienfeld Superior | 0107032) | To grow/process/image cells |
10X PBS | Thermo Scientific | BP3994 | Dilute to 1X with de-ionized water |
Poly-L-Lysine | Sigma | P4832 | Aids with cell adhesion to cover glass |
laminin | Sigma | 114956-81-9 | Aids with cell adhesion to cover glass |
Medium 199 | Thermo Scientific | 11150-059 | Ventricular myocyte culture media |
DMEM 11995 | Gibco | 11995 | Cell culture media |
Fetal bovine Serum (FBS) | Thermo Scientific | 10437028 | Media supplement |
Penicillin/streptomycin | Sigma | P4333 | Media supplement |
0.05% trypsin-EDTA | Corning | 25-052-CL | Cell culture solution |
Lipofectamine 2000 | Invitrogen | 11668-019 | Transient transfection reagent |
Ca2+-free PBS | Gibco | 1419-144 | Cell culture |
100 % Methanol | Thermo Fisher Scientific | A414-4 | Cell Fixation |
Paraformaldehyde | Electron Microscopy Sciences | 15710 | Cell Fixation |
Glutaraldehyde | Sigma Aldrich | SLBR6504V | Cell Fixation |
SEAblock | Thermo Scientific | 37527 | BSA or other blocking solution alternatives exist |
Triton-X 100 | Sigma | T8787 | Detergent to permeabilize cells |
Rabbit anti-CaV1.2 (FP1) | Gift | N/A | Commercial anti-CaV1.2 antibodies exist such as Alomone Labs Rb anti-CaV1.2 (ACC-003) |
Mouse monoclonal anti-RFP | Rockland Inc. | 200-301-379 | Primary antibody |
Alexa Fluor 647 donket anti-rabbit IgG (H+L) | Invitrogen (Thermo Scientific) | A31573 | Secondary antibody |
Alexa Fluor 568 goat anti-mouse IgG (H+L) | Invitrogen (Thermo Scientific) | A11031 | Secondary antibody |
Sodium azide | Sigma | S2002 | Prevents microbial growth for long term storage of samples |
Catalase | Sigma | C40 | Photoswitching buffer ingredient |
Glucose oxidase | Sigma | G2133 | Photoswitching buffer ingredient |
Tris | Sigma | T6066 | Photoswitching buffer ingredient |
beta-Mercaptoethylamin hydrochloride | Fisher | BP2664100 | Photoswitching buffer ingredient |
β-mercaptoethanol | Sigma | 63689 | Photoswitching buffer ingredient |
NaCl | Fisher | S271-3 | Photoswitching buffer ingredient |
Dextrose | Fisher | D14-212 | Photoswitching buffer ingredient |
Glass Depression slides | Neolab | 1 – 6293 | To mount samples |
Twinsil | Picodent | 13001000 | To seal coverglass |
sec61β-mCherry plasmid | Addgene | 49155 | |
Leica SR GSD 3D microscope | Leica | ||
ImageJ | |||
Washing block solution | 20 % SEAblock in PBS | ||
Primary antibody incubation solution | 0.5 % Triton-X100, 20 % SEAblock, in PBS | ||
Secondary antibody incubation solution | 1:1000 in PBS |