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.
The Nanoimager offers the opportunity to see inside the cell with a resolution of under 20 nm using localization-based super-resolution microscopy. This allows quantification of the internal workings of cells with absolute specificity due to fluorescence labeling, at an unprecedented level of detail. Moreover, up to four molecular species can be imaged with the Nanoimager’s four laser lines. The Nanoimager not only presents super-resolved images of cellular features, but offers several tools for quantifying this information and gaining further in-depth analysis of the organization of the labeled molecules.
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 tell when a molecular species is disperse on a membrane for example, or when it forms clusters of molecules in order to perform a function, like the organization of chromatin in the nucleus.
All of these effects could be in response to a stimulus or during a particular stage in the cell cycle, for example, and the Nanoimager can quantitatively discriminate between positive and negative controls. The Nanoimager offers the further advantage of a large field of view and extreme ease of use, drastically reducing the time taken to interpret results from when the sample is ready.
The benefit of super-resolution imaging is highlighted in the figure above and at the top of this page, where the shape of mitochondria in neuroblastoma cells was investigated by labeling TOM20 protein. The TOM20 was labeled with both AlexaFluor555 and AlexaFluor647 as a control. 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 lacking in the conventional image (B). Colocalization analysis confirmed the high degree of correlation between the localizations in the green and the red channel.
In the next figure, clustering analysis was applied to viral proteins in the nucleus of a 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 in a cluster are plotted in white. This helps to characterize the spatial distribution of proteins in the nucleus, and serves to discriminate between different degrees of clustering under different external stimuli. The degree of clustering of proteins could reflect whether or not a particular signalling pathway has been activated, for example.