- Original article
- Open Access
A hydrophilic gel matrix for single-molecule super-resolution microscopy
- Patrick JM Zessin†1,
- Carmen L Krüger†1,
- Sebastian Malkusch†1,
- Ulrike Endesfelder1 and
- Mike Heilemann1Email author
© Zessin et al.; licensee Springer. 2013
Received: 13 March 2013
Accepted: 29 August 2013
Published: 10 September 2013
Novel microscopic techniques which bypass the resolution limit in light microscopy are becoming routinely established today. The higher spatial resolution of super-resolution microscopy techniques demands for precise correction of drift, spectral and spatial offset of images recorded at different axial planes.
We employ a hydrophilic gel matrix for super-resolution microscopy of cellular structures. The matrix allows distributing fiducial markers in 3D, and using these for drift correction and multi-channel registration. We demonstrate single-molecule super-resolution microscopy with photoswitchable fluorophores at different axial planes. We calculate a correction matrix for each spectral channel, correct for drift, spectral and spatial offset in 3D.
Results and discussion
We demonstrate single-molecule super-resolution microscopy with photoswitchable fluorophores in a hydrophilic gel matrix. We distribute multi-color fiducial markers in the gel matrix and correct for drift and register multiple imaging channels. We perform two-color super-resolution imaging of click-labeled DNA and histone H2B in different axial planes, and demonstrate the quality of drift correction and channel registration quantitatively. This approach delivers robust microscopic data which is a prerequisite for data interpretation.
Various techniques that bypass the resolution limit in light microscopy were established for cellular imaging (Galbraith and Galbraith 2011, Heilemann 2010, Hell 2009). These techniques, commonly summarized as super-resolution microscopy, resolve cellular structures up to the near-molecular level and still profit from the advantages of fluorescence microscopy, such as high contrast and live cell compatibility. However, at these small spatial scales, accurate correction for drift and spatial or spectral offsets is required. Single-molecule localization microscopy techniques (Betzig et al. 2006, Folling et al. 2008, Heilemann et al. 2008, Hess et al. 2006, Rust et al. 2006) are particularly prone to drift, as many thousands of images and thus long acquisition times are required.
Drift can be minimized by hardware, e.g. with temperature chambers that enclose the microscope (Adler and Pagakis 2003), devices which uncouple the sample from the sample holder (van de Linde et al. 2011) or by implementing feedback loops (Carter et al. 2007, Pertsinidis et al. 2010). Drift was reported to be minimized to 0.64 nm for an acquisition time of several hours (Pertsinidis et al. 2010). Software-based drift correction can be achieved by image correlation of bright-field (Mennella et al. 2012) or super-resolution images (Huang et al. 2008, Mlodzianoski et al. 2011). This approach works particularly well in the presence of structured or dense features. In a dual-objective configuration, anti-correlated changes of the point-spread function (PSF) were analyzed and used for drift correction (Xu et al. 2012). Alternatively, fiducial markers can be added to the sample (Betzig et al. 2006, Rust et al. 2006), and tracking their position over time can be used to correct for drift. This approach is independent of the structure or density of the sample. In addition, multi-spectral markers offer the additional advantage to correct for chromatic offset and register multi-color super-resolution images (Churchman et al. 2005, Malkusch et al. 2012). Fiducial markers are added to the sample and distributed at the surface of the cover slip. The level of lateral drift can be assessed by the standard deviation of the localization of the markers and were reported between 8 and 10 nm (Quan et al. 2010, Rust et al. 2006, Shtengel et al. 2009). Recently, drift correction through image correlation was demonstrated for 3D single-molecule localization microscopy (McGorthy et al. 2013).
Here, we introduce a simple experimental protocol to distribute fiducial markers in three dimensions using a hydrophilic extracellular matrix (ECM). Such hydrogels were developed for cell culture to support cell growth in 3D and serve as a scaffold (Lee et al. 2008), and comprise biocompatible compounds such as fibrin (Blomback 2004 #212), collagen (Bell et al. 1979) or alginate (Augst et al. 2006). Adding fiducial markers to the ECM gel and embedding the cells in the gel matrix leads to a distribution of the fiducial markers in 3D. We perform single-molecule localization microscopy of DNA and histone H2B in different axial planes, and use the fiducial markers to correct for both drift and spectral offset. A sufficiently dense distribution of fiducial markers further allows registering super-resolution images recorded in different axial planes.
Materials and methods
HeLa cells (300194, Cell Line Service, Eppelheim, Germany) were seeded into 8 chamber cell culture dishes with coverslip bottom (Sarstedt) and cultured at 37°C, 5% CO2 in RPMI 1640 (PAA Laboratories GmbH, Pasching, Austria) supplemented with 1% penicillin-streptomycin (PAA Laboratories GmbH, Germany), 2 mM L-glutamin (PAA Laboratories GmbH), 10% fetal bovine serum (FBS, Gibco/Invitrogen, Grand Island, NY, USA), 1% non-essential amino acids (PAA Laboratories GmbH) and 1 mM sodium pyruvate (PAA Laboratories GmbH).
Cloning and transfection
H2B was fused to mEos2 and cloned into a CMV promotor driven backbone (Clontech C2). 24 hours after seeding, HeLa cells were transiently transfected with this plasmid, using FugeneHD (Promega Corporation, Madison, WI, USA).
24 hours after seeding, 5-ethynyl-2′-deoxyuridine (EdU, Invitrogen, Eugene, Oregon, USA) was added to a final concentration of 10 μM. 15 minutes after incubation cells were fixed for 15 minutes with 4% formaldehyde (FA) solution in phosphate buffered saline (PBS, Sigma) pH 7, which was freshly prepared from 38% stock solution (Sigma). Subsequently, cells were permeabilized with 0.5% Triton X-100 (Sigma) and washed with PBS containing 3% (w/v) BSA (bovine serum albumin, Sigma). Click chemistry labeling of EdU with dye azide was performed in a reaction buffer based on protocol published by Qu et al. (2011) which was further optimized to preserve fluorescence emission of fluorescence proteins (100 mM HEPES pH 8.2, 1 mM CuSO 4, 50 mM aminoguanidine and 25 mM ascorbic acid). HeLa cells were incubated in the reaction buffer for 15 minutes, and washed with PBS. Labeling of EdU was performed with either Alexa Fluor 647 solely or Alexa Fluor 647 and Alexa Fluor 488 in equal amounts.
Extracellular matrix (ECM) gel from Engelbreth-Holm-Swarm murine sarcoma (Sigma, protein concentration 7–9 mg/mL, index of refraction 1.34) was thawed on ice. Fiducial markers (TetraSpecks, 100 nm, Invitrogen) were sonicated for 1 minute and diluted 1:5 in water. 29 μl of the liquid ECM gel and 1 μl of the fiducial marker solution were mixed, sonicated and carefully added to the fixed cells and incubated at 37°C until solidification (~1-2 h). The samples were post-fixated with 4% FA in PBS for 15 minutes to obtain a stable, temperature-independent solidification. The sample preparation is illustrated in Additional file 1: Figure S1a.
Single-molecule localization microscopy
Super-resolution imaging was performed either on a commercial system (N-STORM, Nikon) equipped with a cylindrical lens for 3D imaging and a feedback loop driven axial stabilizing system (Perfect Focus System, Nikon) or a custom-built microscope essentially described earlier (Endesfelder et al. 2013). Dual-color imaging of Alexa Fluor 647 and Alexa Fluor 488 was performed in PBS with 100 mM beta-mercaptoethylamine (MEA, Sigma) added and pH set to 7.5. Dual-color imaging of Alexa Fluor 647 and mEos2 was performed in 0.5 mg/ml (100 units/ml) glucose oxidase (Sigma), 40 mg/ml (2000 units/ml) catalase (Roche Applied Science), 10% w/v glucose and 10 mM MEA in PBS at pH 8 (Endesfelder et al. 2011). Imaging in different axial planes was realized with a highly inclined and laminated optical sheet (HILO) illumination mode (Tokunaga et al. 2008). The different spectral channels were recorded sequentially.
Drift correction and image registration
Single-molecule localization data was analyzed with rapid STORM (Wolter et al., 2010) or the N-STORM software (STORM plugin, NIS, Nikon). As the fiducial markers are found in a large number of frames, we obtain the coordinates of the markers over time, which we want to call trajectory. The trajectories of fiducial markers were extracted using either the “track emission” filter of rapid STORM or custom-written software.
Trajectories which did not span sufficient long time periods were rejected. All other trajectories were rendered with a Gaussian low pass filter. Drift correction was performed with affine matrices (see Additional file 1: Figure S1b and Additional file 2: Figure S2). The registration of different spectral channels was performed by identifying fiducial markers that were present in both spectral data sets. A non-linear translation matrix was calculated from all fiducial markers in both channels. All coordinate processing routines were written in Python, Scipy and Numpy (Peterson 2009) (Additional file 1: Figure S1b). Super-resolution images were generated with rapid STORM and overlaid in Fiji (Schindelin et al. 2012); 3D data was visualized using PyMol (Delano 2004).
Theory of drift correction error analysis
Theory of image registration error analysis
The 3D coordinate-based colocalization (CBC) algorithm (Malkusch et al. 2012) was used to quantify the error of the registration of different channels (offset correction). For this purpose, we calculated the distribution of single fluorophores around a center molecule using an all-distances function related to Ripley’s K-function (Ripley 1977). We calculated the distribution of (i) single molecules detected in channel one around the center molecule and of (ii) single molecules detected in channel two around the same center molecule. The two distributions were correlated (Spearman) and weighted, yielding a CBC value for the center molecule which can serve as a measure of colocalization with neighboring molecules. This procedure was repeated for every single molecule detected in the image. We applied this approach to fiducial markers to estimate the quality of the image registration (fiducial markers which were used for quality control were not used to calculate the registration matrix).
Results and discussion
Distribution of fiducial markers in 3D
For drift correction in super-resolution localization microscopy, the spatial position of fiducial markers had to be particularly stable within the gel matrix for the whole acquisition time. We verified this by tracking the trajectory of single fiducial markers with respect to the trajectories of the other markers. For that purpose, the distance of a specific fiducial marker to the center of mass of the remaining fiducial markers was calculated. This procedure was repeated for every fiducial marker, and allowed us identifying and removing those that showed an independent movement with respect to the other fiducial markers. In a representative experiment with 33 fiducial markers, we found two which matched these criteria and removed them from further analysis (Additional file 3: Figure S3).
Non-linear drift correction
In contrast to diffraction-limited microscopy, super-resolution images even suffer from drift in the nanometer range which reduces the spatial resolution (Mlodzianoski et al. 2011). Drift often comprises several non-linear components (Adler and Pagakis 2003). Linear drift correction is not suitable, and more sophisticated methods are needed, for example by using affine or non-linear matrices (see Materials and Methods, Additional file 2: Figure S2) (Betzig et al. 2006, Malkusch et al. 2012).
To first demonstrate drift correction we prepared a sample of fiducial markers immobilized on a glass surface, which was imaged under normal conditions and showed a high level of non-linear drift (an exemplary trajectory is shown in Additional file 2: Figure S2a). From the coordinates of the fiducial markers over time, trajectories were generated which contained information on the movement of the markers. These trajectories were split into spatial dimensions (Additional file 2: Figure S2b) and were convolved with a Gaussian low pass filter (Additional file 2: Figure S2c and S2d). From these trajectories, an affine transfer matrix was calculated for each frame and applied to the uncorrected coordinates of fiducial markers (Additional file 2: Figure S2e). In a second experiment, we embedded fiducial markers in the gel matrix in 3D. Lateral drift was described as above (Additional file 4: Figure S4), axial drift was controlled actively by hardware (see Methods).
Multi-channel image registration
Influence of illumination on image registration
A possible error source in multi-channel image registration is the use of TIRF or HILO excitation. Both methods are well suited for single-molecule localization microscopy due to the increase in signal-to-noise ratio by background reduction. Still, with both methods, experiments might suffer from non-uniform illumination and light scattering, which get more prominent when imaging with multiple channels deep inside the sample (Oheim and Schapper 2005, Rohrbach 2000; van ’t Hoff et al., 2008). Multi-channel imaging with super-resolution microscopy benefits from homogeneous TIRF and HILO illumination by scanning the focused laser spot in a circular orbit on the back focal plane (van ’t Hoff et al., 2008). Another approach is light sheet illumination, which is decoupling the illumination pathway from the detection pathway (Gao et al. 2012, Keller et al. 2008, Planchon et al. 2011).
3D imaging of histone H2B and chromosomal DNA
For the generation of a 3D super-resolution image from two 3D sections, we selected fiducial markers in the overlapping space between both planes (300 nm) (Additional file 6: Figure S6). This resulted in a 3D image with an axial range of 1.7 μm. The acquisition of more 3D sections will further increase the axial range, and whole cells can be visualized. The quality of drift correction and image registration is visualized by 3 fiducial markers at the upper right of the image (Figure 4a, d and Additional file 6: Figure S6). These fiducial markers were not part of the drift correction and registration matrix.
We introduce a hydrophilic gel matrix for single-molecule super-resolution microscopy. This matrix matches the index of refraction of aqueous buffers (1.34), and is compatible with photoswitching of synthetic fluorophores in appropriate switching buffers. We demonstrate that single-molecule super-resolution microscopy can be performed in the ECM gel, and that fiducial markers can be added to the ECM gel prior to sample embedding. These fiducial markers are distributed in 3D in the gel matrix, and can be used to correct for drift and image registration. The fiducial markers can be used for imaging away from the cover glass. In addition, this approach allows registration of images recorded in different axial planes, making use of fiducial markers that were recorded in multiple planes. In the future, this approach can be combined with a light sheet illumination scheme and a piezo-driven scanning mode, to allow for automated whole-cell imaging in 3D.
We thank Sven Proppert and Daniela Wengler for their support with initial experiments. The authors gratefully acknowledge financial support by the BMBF (FORSYS, research grant 0315262), the DFG (EXC 115) and the Goethe-University Frankfurt.
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