Abstract
Fluorescence nanoscopy imaging permits the observation of periodic supramolecular protein structures in their natural environment, as well as the unveiling of previously unknown protein periodic structures. Deciphering the biological functions of such protein nanostructures requires systematic and quantitative analysis of large number of images under different experimental conditions and specific stimuli. Here we present a method and an open source software for the automated quantification of protein periodic structures in super-resolved images. Its performance is demonstrated by analyzing the abundance and regularity of the spectrin membrane-associated periodic skeleton (MPS) in hippocampal neurons of 2 to 40 days in vitro, imaged by STED and STORM nanoscopy. The automated analysis reveals that both the abundance and the regularity of the MPS increase over time and reach maximum plateau values after 14 DIV. A detailed analysis of the distributions of correlation coefficients provides indication of dynamical assembly and disassembly of the MPS.
Highlights
Proteins frequently function in the form of regular or periodic, self-assembled supramolecular structures[8], with typical sizes in the range of tens of nanometers
Super-resolution fluorescence imaging enables the visualization of such protein periodic structures in their natural environment, as demonstrated for example in the nuclear pore complex[5,9,10]
A remarkable example is the unveiling of an actin/spectrin membrane-associated periodic skeleton (MPS) first observed in axons of hippocampal neurons in culture by Zhuang and colleagues[6]
Summary
Proteins frequently function in the form of regular or periodic, self-assembled supramolecular structures[8], with typical sizes in the range of tens of nanometers. Quantitative analysis of the MPS has been performed by autocorrelation analysis with manual selection of regions-of-interest aiming to determine the “degree of spectrin periodicity” in different segments of axons[13], for comparing the MPSs of axons and dendrites[12] and for assessing the periodicity of bidimensional protein structures[11]. The code, algorithms and main user functions are described and made available in a public repository
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