Open-source tools enable accessible and advanced image scanning microscopy data analysis

Zunino, Alessandro; Slenders, Eli; Fersini, Francesco; Bucci, Andrea; Donato, Mattia; Vicidomini, Giuseppe

doi:10.1038/s41566-023-01216-x

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…This capability enables us to conduct fluorescence lifetime measurements. In the context of imaging, we demonstrated the potentiality of the SPAD array detector combined with the BrightEyes-TTM platform, by implementing fluorescence lifetime image scanning microscopy (FLISM): firstly, we used the adaptive pixel-reassignment (APR) algorithm to reconstruct the high spatial resolution and high signal-to-noise ratio image-scanning microscopy (ISM) bi-dimensional intensity image 25 , 42 (Fig. 4 a bottom-left, G3BP1-eGFP expressing cells under oxidative stress) and the three-dimensional ISM time-resolved image, where the third dimension represents the photon arrival time.…”

Section: Resultsmentioning

confidence: 99%

Single-photon microscopy to study biomolecular condensates

Perego,

Zappone,

Castagnetti

et al. 2023

Nat Commun

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Biomolecular condensates serve as membrane-less compartments within cells, concentrating proteins and nucleic acids to facilitate precise spatial and temporal orchestration of various biological processes. The diversity of these processes and the substantial variability in condensate characteristics present a formidable challenge for quantifying their molecular dynamics, surpassing the capabilities of conventional microscopy. Here, we show that our single-photon microscope provides a comprehensive live-cell spectroscopy and imaging framework for investigating biomolecular condensation. Leveraging a single-photon detector array, single-photon microscopy enhances the potential of quantitative confocal microscopy by providing access to fluorescence signals at the single-photon level. Our platform incorporates photon spatiotemporal tagging, which allowed us to perform time-lapse super-resolved imaging for molecular sub-diffraction environment organization with simultaneous monitoring of molecular mobility, interactions, and nano-environment properties through fluorescence lifetime fluctuation spectroscopy. This integrated correlative study reveals the dynamics and interactions of RNA-binding proteins involved in forming stress granules, a specific type of biomolecular condensates, across a wide range of spatial and temporal scales. Our versatile framework opens up avenues for exploring a broad spectrum of biomolecular processes beyond the formation of membrane-less organelles.

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Section: Resultsmentioning

confidence: 99%

Single-photon microscopy to study biomolecular condensates

Perego,

Zappone,

Castagnetti

et al. 2023

Nat Commun

Self Cite

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“…For this reason, we begin our study by assessing the resolution gain provided by our method in non-noisy scenarios at various magnification levels. To this end, we simulate the PSFs of an ideal 5 × 5 array detector at various magnification factors using the BrightEyes-ISM software [28]. Three representative examples of the simulated PSF dataset are presented in figure 3(a), where we can appreciate how, at high magnification, the PSF shape varies very little compared to that of the central detector.…”

Section: Performance Analysis At Different Simulated Magnificationsmentioning

confidence: 99%

“…We simulated the PSFs of the ISM system using the BrightEyes-ISM Python package [28]. The computation is performed using a vectorial diffraction model [20].…”

Section: Numericalmentioning

confidence: 99%

Image scanning microscopy reconstruction by autocorrelation inversion

Ancora,

Zunino,

Vicidomini

et al. 2024

J. Phys. Photonics

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Confocal laser scanning microscopy (CLSM) stands out as one of the most widely used microscopy techniques thanks to its three-dimensional imaging capability and its sub-diffraction spatial resolution, achieved through the closure of a pinhole in front of a single-element detector. However, the pinhole also rejects useful photons, and beating the diffraction limit comes at the price of irremediably compromising the signal-to-noise ratio (SNR) of the data. Image scanning microscopy (ISM) emerged as the rational evolution of CLSM, exploiting a small array detector in place of the pinhole and the single-element detector. Each sensitive element is small enough to achieve sub-diffraction resolution through the confocal effect, but the size of the whole detector is large enough to guarantee excellent collection efficiency and SNR. However, the raw data produced by an ISM setup consists of a 4D dataset, which can be seen as a set of confocal-like images. Thus, fusing the dataset into a single super-resolved image requires a dedicated reconstruction algorithm. Conventional methods are multi-image deconvolution, which requires prior knowledge of the system point spread functions (PSF), or adaptive pixel reassignment (APR), which is effective only on a limited range of experimental conditions. In this work, we describe and validate a novel concept for ISM image reconstruction based on autocorrelation inversion. We leverage unique properties of the autocorrelation to discard low-frequency components and maximize the resolution of the reconstructed image without any assumption on the image or any knowledge of the PSF. Our results push the quality of the ISM reconstruction beyond the level provided by APR and open new perspectives for multi-dimensional image processing.

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“…We set the numerical aperture of the objective lens to NA = 1.4 and the refractive index of the immersion oil to n = 1.5. We performed the simulation of the excitation and emission PSF and the tubulin networks using the BrightEyes-ISM Python package [47]. The computation of the PSFs is performed using a vectorial model [48].…”

Section: Numerical Simulationsmentioning

confidence: 99%

Reconstructing the image scanning microscopy dataset: an inverse problem

2023

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Confocal laser-scanning microscopy (CLSM) is one of the most popular optical architectures for fluorescence imaging. In CLSM, a focused laser beam excites the fluorescence emission from a specific specimen position. Some actuators scan the probed region across the sample and a photodetector collects a single intensity value for each scan point, building a two-dimensional image pixel-by-pixel.Recently, new fast single-photon array detectors have allowed the recording of a full bi-dimensional image of the probed region for each scan point, transforming CLSM into image scanning microscopy (ISM). This latter offers significant improvements over traditional imaging but requires an optimal processing tool to extract a super-resolved image from the four-dimensional dataset.Here we describe the image formation process in ISM from a statistical point of view, and we use the Bayesian framework to formulate a multi-image deconvolution problem. Notably, the single-photon detector suffers exclusively from the photon shot noise, enabling the development of an effective likelihood model. We derive an iterative likelihood maximization algorithm and test it on experimental and simulated data.Furthermore, we demonstrate that the ISM dataset is redundant, enabling the possibility of obtaining reconstruction sampled at twice the scanning step. Our results prove that in ISM, under appropriate conditions, the Nyquist-Shannon sampling criterium is effectively relaxed. This finding can be exploited to speed up the acquisition process by a factor of four, further improving the versatility of ISM systems.

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Open-source tools enable accessible and advanced image scanning microscopy data analysis

Cited by 8 publications

References 9 publications

Single-photon microscopy to study biomolecular condensates

Single-photon microscopy to study biomolecular condensates

Image scanning microscopy reconstruction by autocorrelation inversion

Reconstructing the image scanning microscopy dataset: an inverse problem

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