Evaluation of sted super-resolution image quality by image correlation spectroscopy (QuICS)

Cerutti, Elena; D'Amico, Morgana; Cainero, Isotta; Dellino, Gaetano Ivan; Faretta, Mario; Vicidomini, Giuseppe; Pelicci, Pier Giuseppe; Bianchini, Paolo; Diaspro, Alberto; Lanzanò, Luca

doi:10.1038/s41598-021-00301-x

Cited by 10 publications

(5 citation statements)

References 38 publications

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“…To evaluate quantitatively the improvement provided by the SPLIT-PIN software, we applied the quality by image correlation spectroscopy (QuICS) algorithm 23 to the images. QuICS is based on the calculation of a radial spatial autocorrelation function (ACF), i.e.…”

Section: Resultsmentioning

confidence: 99%

“…The quality of the generated SPLIT-PIN images was analyzed the Quality by Image Correlation Spectroscopy (QuICS) algorithm 23 . The QuICS analysis was performed in MATLAB using the code available at https://github.com/llanzano/QuICS .…”

Section: Methodsmentioning

confidence: 99%

“…The parameters R, B, N have been calculated as: where we have indicated I av as the average intensity value over all the pixels of the image. R is the width of the autocorrelation function, related to the spatial resolution; B is the brightness, related to the image contrast; N is the relative noise variance, related to the signal-to-noise ratio of the image 23 .…”

Section: Methodsmentioning

confidence: 99%

“…We evaluate the improvement provided by the SPLIT-PIN software using the recently introduced quality by image correlation spectroscopy (QuICS) algorithm 23 . QuICS quantifies the resolution, the contrast and the noise level in the images.…”

Section: Introductionmentioning

confidence: 99%

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SPLIT-PIN software enabling confocal and super-resolution imaging with a virtually closed pinhole

Franco

Costantino

Cerutti

et al. 2023

Sci Rep

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In point-scanning microscopy, optical sectioning is achieved using a small aperture placed in front of the detector, i.e. the detection pinhole, which rejects the out-of-focus background. The maximum level of optical sectioning is theoretically obtained for the minimum size of the pinhole aperture, but this is normally prevented by the dramatic reduction of the detected signal when the pinhole is closed, leading to a compromise between axial resolution and signal-to-noise ratio. We have recently demonstrated that, instead of closing the pinhole, one can reach a similar level of optical sectioning by tuning the pinhole size in a confocal microscope and by analyzing the resulting image series. The method, consisting in the application of the separation of photons by lifetime tuning (SPLIT) algorithm to series of images acquired with tunable pinhole size, is called SPLIT-pinhole (SPLIT-PIN). Here, we share and describe a SPLIT-PIN software for the processing of series of images acquired at tunable pinhole size, which generates images with reduced out-of-focus background. The software can be used on series of at least two images acquired on available commercial microscopes equipped with a tunable pinhole, including confocal and stimulated emission depletion (STED) microscopes. We demonstrate applicability on different types of imaging modalities: (1) confocal imaging of DNA in a non-adherent cell line; (2) removal of out-of-focus background in super-resolved STED microscopy; (3) imaging of live intestinal organoids stained with a membrane dye.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SPLIT-PIN software enabling confocal and super-resolution imaging with a virtually closed pinhole

Franco

Costantino

Cerutti

et al. 2023

Sci Rep

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“…Since GluA1 is also expressed by striatal astrocytes and interneurons in addition to SPNs, we examined whether the enhanced sGluA1 seen in whole striatum reflected postsynaptic sites in SPNs. For this we used τ-STED (τ- s timulation e mission d epletion m icroscopy), a super-resolution approach (20), to quantify sGluA subunits (labeled prior to permeabilization) within a region defined by the postsynaptic density marker PSD95 in dissociated corticostriatal co-cultures. SPNs were identified and segmented using labeling for DARPP-32, a cytoplasmic protein that fills SPNs ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Parkinson’s-linked LRRK2-G2019S derails AMPAR trafficking, mobility and composition in striatum with cell-type and subunit specificity

Gupta,

Guevara,

Tielemans

et al. 2023

Preprint

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Parkinson's (PD) is a multi-factorial disease that affects multiple brain systems and circuits. While defined by motor symptoms caused by degeneration of brainstem dopamine neurons, debilitating non-motor abnormalities in fronto-striatal based cognitive function are common, appear early and are initially independent of dopamine. Young adult mice expressing the PD-associated G2019S missense mutation in Lrrk2 also exhibit deficits in fronto-striatal-based cognitive tasks. In mice and humans, cognitive functions require dynamic adjustments in glutamatergic synapse strength through cell-surface trafficking of AMPA-type glutamate receptors (AMPARs), but it is unknown how LRRK2 mutation impacts dynamic features of AMPAR trafficking in striatal projection neurons (SPNs). Here, we used Lrrk2G2019S knockin mice to show that surface AMPAR subunit stoichiometry is altered biochemically and functionally in mutant SPNs to favor incorporation of GluA1 over GluA2. GluA1-containing AMPARs were resistant to internalization from the cell surface, leaving an excessive accumulation of GluA1 on the surface within and outside synapses. This negatively impacted trafficking dynamics that normally support synapse strengthening, as GluA1-containing AMPARs failed to increase at synapses in response to a potentiating stimulus and showed significantly reduced surface mobility. Surface GluA2-containing AMPARs were expressed at normal levels in synapses, indicating subunit-selective impairment. Abnormal surface accumulation of GluA1 was independent of PKA activity and was limited to D1R SPNs. Since LRRK2 mutation is thought to be part of a common PD pathogenic pathway, our data suggest that sustained, striatal cell-type specific changes in AMPAR composition and trafficking contribute to cognitive or other impairments associated with PD.

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Machine Learning‐Based Estimation of Experimental Artifacts and Image Quality in Fluorescence Microscopy

Corbetta,

Bocklitz

2024

Advanced Intelligent Systems

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Reliable characterization of image data is fundamental for imaging applications, FAIR data management, and an objective evaluation of image acquisition, processing, and analysis steps in an image‐based investigation of biological samples. Image quality assessment (IQA) often relies on human visual perception, which is not objective, or reference ground truth images, which are not often available. This study presents a method for a comprehensive IQA of microscopic images, which solves these issues by employing a set of reference‐free metrics that estimate the presence of experimental artifacts. The metrics are jointly validated on a semisynthetic dataset and are tested on experimental images. Finally, the metrics are employed in a machine learning model, demonstrating their effectiveness for automatic artifact classification through multimarker IQA. This work provides a reliable reference‐free method for IQA in optical microscopy, which can be integrated into the experimental workflow and tuned to address specific artifact detection tasks.

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Evaluation of sted super-resolution image quality by image correlation spectroscopy (QuICS)

Cited by 10 publications

References 38 publications

SPLIT-PIN software enabling confocal and super-resolution imaging with a virtually closed pinhole

SPLIT-PIN software enabling confocal and super-resolution imaging with a virtually closed pinhole

Parkinson’s-linked LRRK2-G2019S derails AMPAR trafficking, mobility and composition in striatum with cell-type and subunit specificity

Machine Learning‐Based Estimation of Experimental Artifacts and Image Quality in Fluorescence Microscopy

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