Cross-Talk-Free Multi-Color STORM Imaging Using a Single Fluorophore

Multi-color stochastic optical reconstruction microscopy (STORM) is routinely performed; however, the various approaches for achieving multiple colors have important caveats. Color cross-talk, limited availability of spectrally distinct fluorophores with optimal brightness and duty cycle, incompatibility of imaging buffers for different fluorophores, and chromatic aberrations impact the spatial resolution and ultimately the number of colors that can be achieved. We overcome these complexities and develop a simple approach for multi-color STORM imaging using a single fluorophore and sequential labelling. In addition, we present a simple and versatile method to locate the same region of interest on different days and even on different microscopes. In combination, these approaches enable cross-talk-free multi-color imaging of sub-cellular structures.Peer ReviewedPostprint (published version

Single molecule evaluation of fluorescent protein photoactivation efficiency using an in vivo nanotemplate

Photoswitchable fluorescent probes are central to localization-based super-resolution microscopy. Among these probes, fluorescent proteins are appealing because they are genetically encoded. Moreover, the ability to achieve a 1:1 labeling ratio between the fluorescent protein and the protein of interest makes these probes attractive for quantitative single-molecule counting. The percentage of fluorescent protein that is photoactivated into a fluorescently detectable form (i.e., the photoactivation efficiency) plays a crucial part in properly interpreting the quantitative information. It is important to characterize the photoactivation efficiency at the single-molecule level under the conditions used in super-resolution imaging. Here, we used the human glycine receptor expressed in Xenopus oocytes and stepwise photobleaching or single-molecule counting photoactivated localization microcopy (PALM) to determine the photoactivation efficiency of fluorescent proteins mEos2, mEos3.1, mEos3.2, Dendra2, mClavGR2, mMaple, PA-GFP and PA-mCherry. This analysis provides important information that must be considered when using these fluorescent proteins in quantitative super-resolution microscopy.Peer ReviewedPostprint (author's final draft

Single molecule evaluation of fluorescent protein photoactivation efficiency using an in vivo nanotemplate

Photoswitchable fluorescent probes are central to localization-based super-resolution microscopy. Among these probes, fluorescent proteins are appealing because they are genetically encoded. Moreover, the ability to achieve a 1:1 labeling ratio between the fluorescent protein and the protein of interest makes these probes attractive for quantitative single-molecule counting. The percentage of fluorescent protein that is photoactivated into a fluorescently detectable form (i.e., the photoactivation efficiency) plays a crucial part in properly interpreting the quantitative information. It is important to characterize the photoactivation efficiency at the single-molecule level under the conditions used in super-resolution imaging. Here, we used the human glycine receptor expressed in Xenopus oocytes and stepwise photobleaching or single-molecule counting photoactivated localization microcopy (PALM) to determine the photoactivation efficiency of fluorescent proteins mEos2, mEos3.1, mEos3.2, Dendra2, mClavGR2, mMaple, PA-GFP and PA-mCherry. This analysis provides important information that must be considered when using these fluorescent proteins in quantitative super-resolution microscopy.Peer Reviewe

Multi-color STORM imaging using overlapping antibody species.

<p>(A) An image of ATP-synthase (localized to mitochondria) and LAMP2 (localized to lysosomes) both labelled using a mouse monoclonal primary and anti-mouse secondary antibody and imaged at the same time. (B) An image of Tom20, a mitochondrial outer membrane protein. Since Tom20 and ATP-synthase colocalize on mitochondria (arrows), the colocalization can be used to separate the initial image into separate colors. (C) ATP-synthase is identified as those molecules which colocalize with Tom20. Lysosomes are identified as those molecules which do not colocalize with Tom20. (D) A zoom-out of the three color Tom20 (magenta), ATP-Synthase (green), lysosome (orange) STORM image. (E) A five-color STORM image of mitochondrial outer membrane protein Tom20 (orange), mitochondrial inner membrane protein ATP-synthase (cyan), lysosomal protein Lamp2 (red), total tubulin (green) and acetylated tubulin (magenta). The five-color image is split between the two panels to more clearly display the different structures. The acetylated tubulin, ATP-synthase, and Lamp2 are all imaged using mouse primary antibodies. The acetylated tubulin colocalizes with total tubulin and ATP-synthase colocalizes with Tom20; Lamp2 does not colocalize with either total tubulin nor Tom20. Scale bars, 500 nm (C), 2 µm (D) and 5 µm (E).</p

A Microfluidic Platform for Correlative Live-Cell and Super-Resolution Microscopy

<div><p>Recently, super-resolution microscopy methods such as stochastic optical reconstruction microscopy (STORM) have enabled visualization of subcellular structures below the optical resolution limit. Due to the poor temporal resolution, however, these methods have mostly been used to image fixed cells or dynamic processes that evolve on slow time-scales. In particular, fast dynamic processes and their relationship to the underlying ultrastructure or nanoscale protein organization cannot be discerned. To overcome this limitation, we have recently developed a correlative and sequential imaging method that combines live-cell and super-resolution microscopy. This approach adds dynamic background to ultrastructural images providing a new dimension to the interpretation of super-resolution data. However, currently, it suffers from the need to carry out tedious steps of sample preparation manually. To alleviate this problem, we implemented a simple and versatile microfluidic platform that streamlines the sample preparation steps in between live-cell and super-resolution imaging. The platform is based on a microfluidic chip with parallel, miniaturized imaging chambers and an automated fluid-injection device, which delivers a precise amount of a specified reagent to the selected imaging chamber at a specific time within the experiment. We demonstrate that this system can be used for live-cell imaging, automated fixation, and immunostaining of adherent mammalian cells <i>in situ</i> followed by STORM imaging. We further demonstrate an application by correlating mitochondrial dynamics, morphology, and nanoscale mitochondrial protein distribution in live and super-resolution images.</p></div

Distribution of mitochondrial dynamics.

<p>Distribution of mitochondrial dynamics.</p

Examples of mitochondria events observed using correlative live-cell and super-resolution microscopy in a microfluidic device with a fluid delivery system for computer-controlled fixation and immunostaining.

<p>Each row represents one dataset, showing a region of interest within the cell at three different time points selected from a sequence of live cell images acquired at 50 ms per frame during live-cell imaging followed by the STORM image (from left to right). In each dataset, mitochondria were systematically identified and categorized based on their dynamic category using both the STORM image and the live-cell video (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115512#pone.0115512.s002" target="_blank">S1 Movie</a>). Examples of dynamic categories are labeled using color-coded arrows (red  =  static, green  =  dynamic-slow, yellow  =  dynamic-fast; sharp arrowhead  =  interacting, flat arrowhead  =  isolated). Scale bar, 1 µm.</p

Mitochondrial dynamics in relation to size and protein distribution (S, Stationary, D-S, Dynamic-Slow, D-F, Dynamic-Fast; Int, Interacting, Iso, Isolated).

<p>The average is indicated by a horizontal line. Tom20 density is given as the number of Tom20 localizations per unit area normalized to the overall median density. All dynamic categories were significantly different from each other in area (***, p<0.001), but were not significantly different in Tom20 density.</p