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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.

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Estimation of molecular densities and experimental localization precision. (1) Fluorophore dilution (<0.1 μM) leads to very low localization densities (<20 localizations per μm2) allowing the detection of well isolated fluorophores. (2) Grouping localizations from isolated fluorophores was performed with a tracking algorithm. To confirm the detection of isolated fluorophores the tracking radius was varied from 1 to 160 nm for different fluorophore concentrations. (3) Localizations within tracks detected using a tracking radius = 50 nm aligned to the center of mass of each track. (4) For diluted samples the saturation level (tracking radius = 50 nm) indicates the number of localization per track, i.e., the number of localizations per isolated fluorophore. (5) Aligned localizations are used to estimate the experimental localization precision by fitting X and Y projections of the probability density function to a Gauss function.
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Figure 5: Estimation of molecular densities and experimental localization precision. (1) Fluorophore dilution (<0.1 μM) leads to very low localization densities (<20 localizations per μm2) allowing the detection of well isolated fluorophores. (2) Grouping localizations from isolated fluorophores was performed with a tracking algorithm. To confirm the detection of isolated fluorophores the tracking radius was varied from 1 to 160 nm for different fluorophore concentrations. (3) Localizations within tracks detected using a tracking radius = 50 nm aligned to the center of mass of each track. (4) For diluted samples the saturation level (tracking radius = 50 nm) indicates the number of localization per track, i.e., the number of localizations per isolated fluorophore. (5) Aligned localizations are used to estimate the experimental localization precision by fitting X and Y projections of the probability density function to a Gauss function.

Mentions: In dSTORM measurements, localization densities in a certain area of the sample can be directly calculated from the coordinate lists exported by the localization software. Whereas, the number of localizations per unit area can be used to estimate the staining efficiency for different labeling conditions, it only provides relative information on the detected numbers of membrane proteins present. Since organic dyes undergo several photoswitching cycles during a dSTORM measurement, counting molecular numbers with localization microscopy requires further correction for multiple detections of the same molecule. The typical number of localizations recorded per fluorophore under the same optical and chemical conditions can be determined in diluted samples (Figure 5). If the blinking of isolated spots can be unequivocally assigned to single fluorophores, a conversion factor can be extracted to estimate the detected number of labeled membrane proteins (Table 1). For example, we estimate the density of PM proteins labeled with AHA during 4 h 30 min to be approximately ~50 μm−2 and ~125 μm−2 when stained via CuAAC and SPAAC respectively. On the other hand, we detected higher densities of glycans, in the range of ~345 μm−2 and ~280 μm−2, metabolic labeled with Ac4GalNAz during 48 h. It is important to mention that dividing the number of localizations in a region of interest by the average number of localizations detected per isolated fluorophore in reference experiments represents only an average correction value. To prevent over-counting effects in highly dense sample areas, more sophisticated methods based on the temporal and spatial fingerprint of single fluorophore blinking, such as off-time gap (Zhao et al., 2014) and pair correlation function analysis (PCF) (Veatch et al., 2012; Sengupta et al., 2013), can be applied.


Super-Resolution Imaging of Plasma Membrane Proteins with Click Chemistry
Estimation of molecular densities and experimental localization precision. (1) Fluorophore dilution (<0.1 μM) leads to very low localization densities (<20 localizations per μm2) allowing the detection of well isolated fluorophores. (2) Grouping localizations from isolated fluorophores was performed with a tracking algorithm. To confirm the detection of isolated fluorophores the tracking radius was varied from 1 to 160 nm for different fluorophore concentrations. (3) Localizations within tracks detected using a tracking radius = 50 nm aligned to the center of mass of each track. (4) For diluted samples the saturation level (tracking radius = 50 nm) indicates the number of localization per track, i.e., the number of localizations per isolated fluorophore. (5) Aligned localizations are used to estimate the experimental localization precision by fitting X and Y projections of the probability density function to a Gauss function.
© Copyright Policy
Related In: Results  -  Collection

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Figure 5: Estimation of molecular densities and experimental localization precision. (1) Fluorophore dilution (<0.1 μM) leads to very low localization densities (<20 localizations per μm2) allowing the detection of well isolated fluorophores. (2) Grouping localizations from isolated fluorophores was performed with a tracking algorithm. To confirm the detection of isolated fluorophores the tracking radius was varied from 1 to 160 nm for different fluorophore concentrations. (3) Localizations within tracks detected using a tracking radius = 50 nm aligned to the center of mass of each track. (4) For diluted samples the saturation level (tracking radius = 50 nm) indicates the number of localization per track, i.e., the number of localizations per isolated fluorophore. (5) Aligned localizations are used to estimate the experimental localization precision by fitting X and Y projections of the probability density function to a Gauss function.
Mentions: In dSTORM measurements, localization densities in a certain area of the sample can be directly calculated from the coordinate lists exported by the localization software. Whereas, the number of localizations per unit area can be used to estimate the staining efficiency for different labeling conditions, it only provides relative information on the detected numbers of membrane proteins present. Since organic dyes undergo several photoswitching cycles during a dSTORM measurement, counting molecular numbers with localization microscopy requires further correction for multiple detections of the same molecule. The typical number of localizations recorded per fluorophore under the same optical and chemical conditions can be determined in diluted samples (Figure 5). If the blinking of isolated spots can be unequivocally assigned to single fluorophores, a conversion factor can be extracted to estimate the detected number of labeled membrane proteins (Table 1). For example, we estimate the density of PM proteins labeled with AHA during 4 h 30 min to be approximately ~50 μm−2 and ~125 μm−2 when stained via CuAAC and SPAAC respectively. On the other hand, we detected higher densities of glycans, in the range of ~345 μm−2 and ~280 μm−2, metabolic labeled with Ac4GalNAz during 48 h. It is important to mention that dividing the number of localizations in a region of interest by the average number of localizations detected per isolated fluorophore in reference experiments represents only an average correction value. To prevent over-counting effects in highly dense sample areas, more sophisticated methods based on the temporal and spatial fingerprint of single fluorophore blinking, such as off-time gap (Zhao et al., 2014) and pair correlation function analysis (PCF) (Veatch et al., 2012; Sengupta et al., 2013), can be applied.

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.