Two-photon excitation selective plane illumination microscopy (2PE-SPIM) of highly scattering samples: characterization and application

In this work we report the advantages provided by two photon excitation (2PE) implemented in a selective plane illumination microscopy (SPIM) when imaging thick scattering samples. In particular, a detailed analysis of the effects induced on the real light sheet excitation intensity distribution is performed. The comparison between single-photon and two-photon excitation profiles shows the reduction of the scattering effects and sample-induced aberrations provided by 2PE-SPIM. Furthermore, uniformity of the excitation distribution and the consequent improved image contrast is shown when imaging scattering phantom samples in depth by 2PE-SPIM. These results show the advantages of 2PE-SPIM and suggest how this combination can further enhance the SPIM performance. Phantom samples have been designed with optical properties compatible with biological applications of interest

Bayesian analysis of data from segmented super-resolution images for quantifying protein clustering

Super-resolution imaging techniques have largely improved our capabilities to visualize nanometric structures in biological systems. Their application further enables one to potentially quantitate relevant parameters to determine the molecular organization and stoichiometry in cells. However, the inherently stochastic nature of the fluorescence emission and labeling strategies imposes the use of dedicated methods to accurately measure these parameters. Here, we describe a Bayesian approach to precisely quantitate the relative abundance of molecular oligomers from segmented images. The distribution of proxies for the number of molecules in a cluster -- such as the number of localizations or the fluorescence intensity -- is fitted via a nested sampling algorithm to compare mixture models of increasing complexity and determine the optimal number of mixture components and their weights. We test the performance of the algorithm on {\it in silico} data as a function of the number of data points, threshold, and distribution shape. We compare these results to those obtained with other statistical methods, showing the improved performance of our approach. Our method provides a robust tool for model selection in fitting data extracted from fluorescence imaging, thus improving the precision of parameter determination. Importantly, the largest benefit of this method occurs for small-statistics or incomplete datasets, enabling accurate analysis at the single image level. We further present the results of its application to experimental data obtained from the super-resolution imaging of dynein in HeLa cells, confirming the presence of a mixed population of cytoplasmatic single motors and higher-order structures.Comment: 17 pages, 6 figure

Quantifying Protein Copy Number in Super-Resolution Using an Imaging Invariant Calibration

The use of super-resolution microscopy in recent years has revealed that proteins often form small assemblies inside cells and are organized in nanoclusters. However, determining the copy number of proteins within these nanoclusters constitutes a major challenge because of unknown labeling stoichiometries and complex fluorophore photophysics. We previously developed a DNA-origami-based calibration approach to extract protein copy number from super-resolution images. However, the applicability of this approach is limited by the fact that the calibration is dependent on the specific labeling and imaging conditions used in each experiment. Hence, the calibration must be repeated for each experimental condition, which is a formidable task. Here, using cells stably expressing dynein intermediate chain fused to green fluorescent protein (HeLa IC74 cells) as a reference sample, we demonstrate that the DNA-origami-based calibration data we previously generated can be extended to super-resolution images taken under different experimental conditions, enabling the quantification of any green-fluorescent-protein-fused protein of interest. To do so, we first quantified the copy number of dynein motors within nanoclusters in the cytosol and along the microtubules. Interestingly, this quantification showed that dynein motors form assemblies consisting of more than one motor, especially along microtubules. This quantification enabled us to use the HeLa IC74 cells as a reference sample to calibrate and quantify protein copy number independently of labeling and imaging conditions, dramatically improving the versatility and applicability of our approach

A DNA Origami Platform for Quantifying Protein Copy Number in Super-Resolution

Single-molecule-based super-resolution microscopy offers researchers a unique opportunity to quantify protein copy number with nanoscale resolution. However, while fluorescent proteins have been characterized for quantitative imaging using calibration standards, similar calibration tools for immunofluorescence with small organic fluorophores are lacking. Here we show that DNA origami, in combination with GFP antibodies, is a versatile platform for calibrating fluorophore and antibody labeling efficiency to quantify protein copy number in cellular contexts using super-resolution microscopy

"Nanoscopium Nominare Libuit": Approaches Towards Optical Nanoscopy and Individual Molecule Localization Microscopy Improvements

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Nanoscale molecular reorganization of the inhibitory postsynaptic density is a determinant of gabaergic synaptic potentiation

Gephyrin is a key scaffold protein mediating the anchoring of GABAA receptors at inhibitory synapses. Here, we exploited superresolution techniques combined with proximity-based clustering analysis and model simulations to investigate the single-molecule gephyrin reorganization during plasticity of inhibitory synapses in mouse hippocampal cultured neurons. This approach revealed that, during the expression of inhibitory LTP, the increase of gephyrin density at postsynaptic sites is associated with the promoted formation of gephyrin nanodomains. We demonstrate that the gephyrin rearrangement in nanodomains stabilizes the amplitude of postsynaptic currents, indicating that, in addition to the number of synaptic GABAA receptors, the nanoscale distribution of GABAA receptors in the postsynaptic area is a crucial determinant for the expression of inhibitory synaptic plasticity. In addition, the methodology implemented here clears the way to the application of the graph-based theory to single-molecule data for the description and quantification of the spatial organization of the synapse at the single-molecule level

Unsupervised spike sorting for large-scale, high-density multielectrode arrays

electrophysiology; high-density multielectrode array; neural cultures; retina; spike sortin

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