Abstract
Single Molecule Localization Microscopy techniques like PhotoActivated Localization Microscopy, with their sub-diffraction limit spatial resolution, have been popularly used to characterize the spatial organization of membrane proteins, by means of quantitative cluster analysis. However, such quantitative studies remain challenged by the techniques’ inherent sources of errors such as a limited detection efficiency of less than 60%, due to incomplete photo-conversion, and a limited localization precision in the range of 10 – 30nm, varying across the detected molecules, mainly depending on the number of photons collected from each. We provide analytical methods to estimate the effect of these errors in cluster analysis and to correct for them. These methods, based on the Ripley’s L(r) – r or Pair Correlation Function popularly used by the community, can facilitate potentially breakthrough results in quantitative biology by providing a more accurate and precise quantification of protein spatial organization.
Highlights
The spatial organization of most membrane proteins as sub-micrometer spatial clusters might be a key property affecting their functionality [1, 2, 3]
Other SMLM techniques like STochastic Optical Reconstruction Microscopy (STORM) [8] are promising, due to the high specificity provided by PALM originating from the genetic tagging of the protein under study with the fluorescent label, and the relatively lower blinking rate of the Photo-Activable Flourescent Proteins (PA-FPs) and the resulting reduction in the number of repeated localizations of the same fluorophore molecule, we focus on the application of PALM for quantitative analysis, while noting that the methods provided might be applicable to STORM as well
Due to the stochasticity involved in random subsampling, the L(r) − r function corresponding to the subsampled point pattern (i.e localizations corresponding to the detected molecules), called Lsubsampled(r) − r, will be a stochastic quantity
Summary
The spatial organization of most membrane proteins as sub-micrometer spatial clusters might be a key property affecting their functionality [1, 2, 3]. The characteristics of these microdomains, such as the number of proteins per cluster, cluster size and density, are heterogeneous in general, depending on the specific cell type, protein, lipid, cell cycle and environmental conditions. Accurate and precise imaging and quantitative characterization of the spatial microdomain parameters are important tools that can aid these studies. The membrane proteins can be imaged at unprecedented length-scales, and with high specificity and contrast and in their natural environment, using single molecule localization
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