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Super-Resolution Imaging of Plasma Membrane Proteins with Click Chemistry

View Article: PubMed Central - PubMed

ABSTRACT

Besides its function as a passive cell wall, the plasma membrane (PM) serves as a platform for different physiological processes such as signal transduction and cell adhesion, determining the ability of cells to communicate with the exterior, and form tissues. Therefore, the spatial distribution of PM components, and the molecular mechanisms underlying it, have important implications in various biological fields including cell development, neurobiology, and immunology. The existence of confined compartments in the plasma membrane that vary on many length scales from protein multimers to micrometer-size domains with different protein and lipid composition is today beyond all questions. As much as the physiology of cells is controlled by the spatial organization of PM components, the study of distribution, size, and composition remains challenging. Visualization of the molecular distribution of PM components has been impeded mainly due to two problems: the specific labeling of lipids and proteins without perturbing their native distribution and the diffraction-limit of fluorescence microscopy restricting the resolution to about half the wavelength of light. Here, we present a bioorthogonal chemical reporter strategy based on click chemistry and metabolic labeling for efficient and specific visualization of PM proteins and glycans with organic fluorophores in combination with super-resolution fluorescence imaging by direct stochastic optical reconstruction microscopy (dSTORM) with single-molecule sensitivity.

No MeSH data available.


Spatial distribution analysis by Ripley's h function. The data show Ripley's h functions computed from experimental data (blue lines) of PM proteins stained via (A) L-AHA (CuAAC), (B) L-AHA (SPAAC), (C) Ac4GalNAz (CuAAC), and (D) Ac4GalNAz (SPAAC). Plotted curves represent mean values (thick lines) together with 95% confidence intervals (thin) over 5 regions in total (5 × 5 μm2 size) from independent cells, which appeared rather homogeneous by visual inspection. For comparison Ripley's function was computed from two simulated random point process, i.e., Neyman-Scott process (red lines) and Poisson point process (black lines). Simulation parameters, such as process intensity, average of offspring events, and spatial distribution around their parent event, where chosen to mimic localization density, photoswitching cycles, and localization precision obtained experimentally. The peak observed on short length scales for Neyman-Scott process and experimental data indicates artificial clustering due to repeated localizations from identical fluorophores within a Gauss distributions equal to localization precision, i.e., standard deviation ~8 nm. For all four staining schemes presented, Ripley's h functions show further clustering on longer length scales but more pronounced for L-AHA samples.
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Figure 6: Spatial distribution analysis by Ripley's h function. The data show Ripley's h functions computed from experimental data (blue lines) of PM proteins stained via (A) L-AHA (CuAAC), (B) L-AHA (SPAAC), (C) Ac4GalNAz (CuAAC), and (D) Ac4GalNAz (SPAAC). Plotted curves represent mean values (thick lines) together with 95% confidence intervals (thin) over 5 regions in total (5 × 5 μm2 size) from independent cells, which appeared rather homogeneous by visual inspection. For comparison Ripley's function was computed from two simulated random point process, i.e., Neyman-Scott process (red lines) and Poisson point process (black lines). Simulation parameters, such as process intensity, average of offspring events, and spatial distribution around their parent event, where chosen to mimic localization density, photoswitching cycles, and localization precision obtained experimentally. The peak observed on short length scales for Neyman-Scott process and experimental data indicates artificial clustering due to repeated localizations from identical fluorophores within a Gauss distributions equal to localization precision, i.e., standard deviation ~8 nm. For all four staining schemes presented, Ripley's h functions show further clustering on longer length scales but more pronounced for L-AHA samples.

Mentions: Ripley's k function reveals possible combinations of homogeneous distributions on large scales and clustering on small scales (e.g., due to the repeated blinking of individual labels). Figure 6 shows direct comparison between experimental (blue line) and simulated data for a Poisson and Neyman-Scott process (black and red line respectively). For all the labeling schemes inspected, our data showed maximum clustering on a length scale similar to the estimated localization precision (i.e., d ~20–30 nm). Therefore, clustering might reflect single fluorophore photoswitching. Since the maximum value of Ripley's h function for a simulated Neyman-Scott process is close to that of experimental data, we conclude that single fluorophore blinking is the only significant clustering process on this length scale. In addition, all the data indicate a small but significant deviation from complete spatial randomness on length scales from 30 to 800 nm. It is important to note that there is no characteristic length scale above 30 nm for any clusters of a well-defined size that can be identified. The indicated deviations from complete spatial randomness can have their origin in the various PM deformations e.g., due to the onset of vesicle formation or membrane ruffling. Whereas, it is possible to find small areas with a distribution that perfectly resemble a Neyman-Scott process (with clusters originating only from single emitter blinking), Ripley's h function for data in areas of 5 × 5 μm2 in well-labeled cells under the nucleus (excluding double membrane contributions) typically appear as presented.


Super-Resolution Imaging of Plasma Membrane Proteins with Click Chemistry
Spatial distribution analysis by Ripley's h function. The data show Ripley's h functions computed from experimental data (blue lines) of PM proteins stained via (A) L-AHA (CuAAC), (B) L-AHA (SPAAC), (C) Ac4GalNAz (CuAAC), and (D) Ac4GalNAz (SPAAC). Plotted curves represent mean values (thick lines) together with 95% confidence intervals (thin) over 5 regions in total (5 × 5 μm2 size) from independent cells, which appeared rather homogeneous by visual inspection. For comparison Ripley's function was computed from two simulated random point process, i.e., Neyman-Scott process (red lines) and Poisson point process (black lines). Simulation parameters, such as process intensity, average of offspring events, and spatial distribution around their parent event, where chosen to mimic localization density, photoswitching cycles, and localization precision obtained experimentally. The peak observed on short length scales for Neyman-Scott process and experimental data indicates artificial clustering due to repeated localizations from identical fluorophores within a Gauss distributions equal to localization precision, i.e., standard deviation ~8 nm. For all four staining schemes presented, Ripley's h functions show further clustering on longer length scales but more pronounced for L-AHA samples.
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Figure 6: Spatial distribution analysis by Ripley's h function. The data show Ripley's h functions computed from experimental data (blue lines) of PM proteins stained via (A) L-AHA (CuAAC), (B) L-AHA (SPAAC), (C) Ac4GalNAz (CuAAC), and (D) Ac4GalNAz (SPAAC). Plotted curves represent mean values (thick lines) together with 95% confidence intervals (thin) over 5 regions in total (5 × 5 μm2 size) from independent cells, which appeared rather homogeneous by visual inspection. For comparison Ripley's function was computed from two simulated random point process, i.e., Neyman-Scott process (red lines) and Poisson point process (black lines). Simulation parameters, such as process intensity, average of offspring events, and spatial distribution around their parent event, where chosen to mimic localization density, photoswitching cycles, and localization precision obtained experimentally. The peak observed on short length scales for Neyman-Scott process and experimental data indicates artificial clustering due to repeated localizations from identical fluorophores within a Gauss distributions equal to localization precision, i.e., standard deviation ~8 nm. For all four staining schemes presented, Ripley's h functions show further clustering on longer length scales but more pronounced for L-AHA samples.
Mentions: Ripley's k function reveals possible combinations of homogeneous distributions on large scales and clustering on small scales (e.g., due to the repeated blinking of individual labels). Figure 6 shows direct comparison between experimental (blue line) and simulated data for a Poisson and Neyman-Scott process (black and red line respectively). For all the labeling schemes inspected, our data showed maximum clustering on a length scale similar to the estimated localization precision (i.e., d ~20–30 nm). Therefore, clustering might reflect single fluorophore photoswitching. Since the maximum value of Ripley's h function for a simulated Neyman-Scott process is close to that of experimental data, we conclude that single fluorophore blinking is the only significant clustering process on this length scale. In addition, all the data indicate a small but significant deviation from complete spatial randomness on length scales from 30 to 800 nm. It is important to note that there is no characteristic length scale above 30 nm for any clusters of a well-defined size that can be identified. The indicated deviations from complete spatial randomness can have their origin in the various PM deformations e.g., due to the onset of vesicle formation or membrane ruffling. Whereas, it is possible to find small areas with a distribution that perfectly resemble a Neyman-Scott process (with clusters originating only from single emitter blinking), Ripley's h function for data in areas of 5 × 5 μm2 in well-labeled cells under the nucleus (excluding double membrane contributions) typically appear as presented.

View Article: PubMed Central - PubMed

ABSTRACT

Besides its function as a passive cell wall, the plasma membrane (PM) serves as a platform for different physiological processes such as signal transduction and cell adhesion, determining the ability of cells to communicate with the exterior, and form tissues. Therefore, the spatial distribution of PM components, and the molecular mechanisms underlying it, have important implications in various biological fields including cell development, neurobiology, and immunology. The existence of confined compartments in the plasma membrane that vary on many length scales from protein multimers to micrometer-size domains with different protein and lipid composition is today beyond all questions. As much as the physiology of cells is controlled by the spatial organization of PM components, the study of distribution, size, and composition remains challenging. Visualization of the molecular distribution of PM components has been impeded mainly due to two problems: the specific labeling of lipids and proteins without perturbing their native distribution and the diffraction-limit of fluorescence microscopy restricting the resolution to about half the wavelength of light. Here, we present a bioorthogonal chemical reporter strategy based on click chemistry and metabolic labeling for efficient and specific visualization of PM proteins and glycans with organic fluorophores in combination with super-resolution fluorescence imaging by direct stochastic optical reconstruction microscopy (dSTORM) with single-molecule sensitivity.

No MeSH data available.