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Quantitative cellular imaging

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Challenge

Understanding how cells work is a primary goal for biological imaging. For a fundamental grasp on the mechanisms that exist within a cell, a detailed knowledge of how the individual molecules are arranged and how organelles facilitate molecular interactions is absolutely necessary. Furthermore, a quantitative interpretation at this level is needed to precisely measure the effects of external stimuli on the cell, such as the cell’s environment, growth conditions, mutations in the genome or treatment with drugs.

Information that supports this level of interpretation could include the organization of molecules on a membrane, the extent of interaction of different types of molecules, the structural preservation of cellular features and the number and density of molecules in a region.

Solution with nanoimager

The Nanoimager offers the opportunity to see inside the cell or look at purified components with a resolution of under 20 nm using localization-based super-resolution microscopy. This allows quantification and understanding of the internal workings of the cell at an unprecedented level of detail. Taking advantage of the absolute specificity of fluorescence labeling, the spatial relationship of up to four molecular species can be imaged with the 4 laser lines of the Nanoimager. The Nanoimager not only presents super-resolved images of cellular features, but offers several tools for quantifying this information.

The characterization tools include colocalization analysis, so you can see which molecules interact with each other and to what extent by quantitating their spatial overlap. The Nanoimager also supports clustering analysis, so you can quantify if a molecular species is disperse on a membrane or came together to form a cluster, an event typically associated with membrane receptors required during cell signaling or uptake and transport of material (see the transferrin image above). Another application would be imaging the organization of nanomaterials in the therapeutic agents themselves using super-resolution, as shown in the figure.

All of these effects could be in response to a stimulus, a change in the cell environment or a particular stage of the cell cycle. The Nanoimager with it’s extreme sensitivity and easy to use analysis tools can quantitatively discriminate between positive and negative controls. The Nanoimager additionally offers the advantage of a large field of view and simultaneous dual channel imaging, which drastically reduces the time for acquisition and interpretation of results.

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Case study

The benefit of super-resolution imaging is highlighted in this figure, where the shape of mitochondria in neuroblastoma cells was investigated by labeling TOM20 protein. TOM20 was labeled with both AlexaFluor555 and AlexaFluor647 as a control (image at the top). The dSTORM image of a mitochondrion (just the AlexaFluor647 component) is shown in A. The histogram along the axis of the mitochondrion (C) shows the width to be around 300-350 nm, and the denser localizations at the edges caused by the 2D projection of the mitochondrion confirm its tubular structure. This level of information is not obtainable in the conventional widefield image (B).

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In the next figure, the clustering analysis tool was applied to viral proteins in the nucleus of an infected mammalian cell. The conventional (lower left) and super-resolved image (upper right) of viral proteins (red channel) and a host protein in the nucleus (green channel) is shown on the left. Note that clustering analysis would be futile with the conventional image. On the right, statistically significant clusters of the proteins are plotted as different colors. Localizations not found in a cluster are plotted in white.

This clustering tool analysis allows us to characterize the spatial distribution of proteins in the nucleus, and assists the discrimination between different degrees of clustering. We can thereby quantify the effect of the viral protein on the host proteins and their level of interaction.

In general, the degree of clustering of proteins could reflect whether or not a particular signaling pathway has been activated or a region of the chromosomes has become accessible and transcriptionally active.