Fuentes de error, artificios, aceleración y validación del algoritmo de deconvolución con super-resolución para imágenes de microscopía [Not available in English]

Toscani, Micaela; Mazzeo, Alejandro; Martínez, Sandra; Martı́nez, O. E.; Lacapmesure, Axel M.; Vázquez, Guillermo

doi:10.1109/argencon49523.2020.9505479

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Other methods may also impose conditions on the sample to avoid artifacts or to improve the robustness in the presence of noise, background and other experimental conditions. In this sense, the SUPPOSe method is a convolution-based algorithm for improving microscopy images that relies on representing any object under test as a superposition of virtual point sources, all with the same intensity [8][9][10][11]. By knowing the image formation process, SUPPOSe solves an optimization problem that retrieves the optimum set of positions of the virtual sources and their intensity from a single image, resulting in a description of the object with a resolution that is more than three times better the instrument resolution under normal measurement conditions [12,13].…”

Section: Introductionmentioning

confidence: 99%

Improving STED microscopy with SUPPOSe: enhancing resolution from a single-image

Toscani,

Lacapmesure

2024

Methods Appl. Fluoresc.

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Here we apply the gSUPPOSe algorithm on images acquired using Stimulated Emission Depletion (STED) microscopy with the aim of improving the resolution limit achieved. We processed images of the nuclear pore complex (NPC) from cell lines in which the Nup96 nucleoporin was endogenously labeled. This reference protein forms a ring whose diameter is ∽107 nm with 8 corners ∽42 nm apart from each other. The stereotypic arrangement of proteins in the NPC has been used as reference structures to characterize the performance of a variety of microscopy techniques. STED microscopy images resolve the ring arrangement but not the eightfold symmetry of the NPC. After applying the gSUPPOSe algorithm to the STED images, we were able to solve the octagonal structure of the NPC. After processing 562 single NPC, the average radius of the NPC was found to be R = 54.2 +- 2.9 nm, being consistent with the theoretical distances of this structure. To verify that the solutions obtained are compatible with a NPC-type geometry, we rotate the solutions to optimally fit an eightfold symmetric pattern and we count the number of corners that contain at least one localization. Fitting a probabilistic model to the histogram of the number of bright corners gives an effective labeling efficiency of 31%, which is in agreement with the values reported in for other cell lines and ligands used in Single Molecule Localization microscopy, showing that SUPPOSe can reliably retrieve sub-resolution, nanoscale objects from single acquisitions even in noisy conditions.

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Section: Introductionmentioning

confidence: 99%

Improving STED microscopy with SUPPOSe: enhancing resolution from a single-image

Toscani,

Lacapmesure

2024

Methods Appl. Fluoresc.

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“…In fluorescence microscopy, each acquired sample is the result of a noise process acting on the convolution between an underlying object -an arrangement of fluorescent proteins tied to the things we want to see-with the microscope response function -known as Point Spread Function or PSF. SUPPOSe is a convolution-based algorithm for improving microscopy images that relies on representing the object under microscope as a SUPperposition of POint SourcEs with the same intensity [1][2][3][4][5][6][7][8]. By knowing the instrument Point-Spread Function (PSF) and the image formation model, the optimum position of these sources can be retrieved by iteratively solving an optimization problem that results in a description of the object with better resolution than the image itself.…”

Section: Introductionmentioning

confidence: 99%

Improving the resolution of STED microscopy using the SUPPOSe algorithm

Toscani¹,

Lacapmesure²

2023

Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leaders

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Here we apply the gSUPPOSe algorithm on images acquired using Stimulated Emission Depletion (STED) microscopy with the aim of improving the resolution limit achieved. We processed images of the nuclear pore complex (NPC) from cell lines in which the Nup96 nucleoporin was endogenously labeled. This reference protein forms a ring whose diameter is ~107 nm with 8 corners ~42 nm apart from each other. The stereotypic arrangement of proteins in the NPC has been used as reference structures to characterize the performance of a variety of microscopy techniques. STED microscopy images resolve the ring arrangement but not the eightfold symmetry of the NPC. After applying the gSUPPOSe algorithm to the STED images, we were able to solve the octagonal structure of the NPC. After processing 500 Regions Of Interest (ROIs), the average radius of the NPC was found to be R = 54.2 ± 2.9 nm, being consistent with the theoretical distances of this structure. To verify that the solutions obtained are compatible with a NPC-type geometry, we rotate the solutions to optimally fit an eightfold-symmetric pattern and we count the number of corners that contain at least one localization. Fitting a probabilistic model to the histogram of the number of bright corners gives an effective labeling efficiency (ELE) of 31%, which is in agreement with the values reported in for other cell lines and ligands used in STORM images, showing that SUPPOSe can reliably retrieve sub-resolution, nanoscale objects even in such noisy conditions.

show abstract

Fuentes de error, artificios, aceleración y validación del algoritmo de deconvolución con super-resolución para imágenes de microscopía [Not available in English]

Cited by 2 publications

References 15 publications

Improving STED microscopy with SUPPOSe: enhancing resolution from a single-image

Improving STED microscopy with SUPPOSe: enhancing resolution from a single-image

Improving the resolution of STED microscopy using the SUPPOSe algorithm

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