Biology subjects

Zunino, A.

Publications and source records attributed to Zunino, A..

6 recordsLinked to original sources

bioRxiv2026

BrightEyes-Scope: A Reference Implementation for Image Scanning Microscopy with SPAD Array Detection

Image scanning microscopy (ISM) is emerging as the natural evolution of confocal laser-scanning microscopy, combining optical sectioning and a two-fold improvement in lateral resolution with high signal-to-noise ratio. Single-photon avalanche diode (SPAD) array detectors enable an effective implementation of ISM by recording a micro-image at each scan position with single-photon sensitivity, negligible readout noise, and without the fixed frame-readout constraints of conventional cameras. Nevertheless, SPAD array-based ISM remains largely confined to laboratories with expertise in microscope development. Here, we present BrightEyes-Scope, a reference implementation of a SPAD array-based ISM system. We provide step-by-step guidelines for optical design, system construction, alignment, calibration, data acquisition, and image reconstruction. Presented as a complete ISM microscope, BrightEyes-Scope also serves as a reference for upgrading existing laser-scanning microscopes. We anticipate that it will accelerate the adoption of ISM for quantitative super-resolution imaging.

bioengineering↗

bioRxiv2026

Independent noise realizations enable morphologically agnostic image reconstruction in single-photon-sensitive microscopy

Advances in single-photon sensitive detectors are rapidly expanding the adoption of photon-counting fluorescence microscopy. Under the Poisson photon-counting statistics, iterative Richardson-Lucy (RL) -type algorithms are statistically optimal for image deconvolution but suffers from a fundamental semi-convergent behaviour: prolonged iterations inevitably amplify noise, requiring heuristic early stopping or regularisation typically based on assumptions about object morphology. Here we present a morphology-agnostic regularisation framework for RL deconvolution that exploits independent noise realisations instead of structural object priors. Preserving the Poisson statistics of the acquisition process, we formulate the Regularized by Noise (RbN): a regularized variational framework and its corresponding iterative minimization algorithm that exploit the statistical consistency of independent noise realisations to distinguish reproducible image features from stochastic noise. The regularisation strength is selected automatically using the Poisson residual whiteness principle, resulting in a fully data-driven reconstruction without heuristic parameter tuning. Photon-timing-resolved systems naturally provide the independent noise realisations exploited by our framework, whereas computational photon splitting provides a statistically equivalent implementation for photon-counting systems. We validate the approach experimentally using photon-timing-resolved confocal microscopy and image scanning microscopy across eight morphologically distinct subcellular targets, and further demonstrate its applicability to photon-counting microscopes through computational photon splitting. Across imaging modalities, detector technologies, biological structures and signal-to-noise regimes, our method eliminates RL semi-convergence, removes sensitivity to the stopping criterion, and consistently outperforms conventional RL while preserving fine structural detail. More broadly, our results establish independent noise realisations as a general source of morphology-agnostic regularisation. Although demonstrated here for SPAD-based laser-scanning microscopy and Poisson statistics, the underlying principle could be extended to other imaging modalities and, more generally, to statistical inverse problems through appropriate noise-specific formulations.

biophysics↗

bioRxiv2025

Wavefront estimation through structured detection in laser scanning microscopy

Laser scanning microscopy (LSM) is the base of numerous advanced imaging techniques, including confocal laser scanning microscopy (CLSM), a widely used tool in life sciences research. However, its effective resolution is often compromised by optical aberrations, a common challenge in all optical systems. While adaptive optics (AO) can correct these aberrations, current methods face significant limitations: Aberration estimation, which is central to any AO approach, typically requires specialized hardware or prolonged sample exposure, rendering these methods sample-invasive, and less user-friendly. In this study, we introduce a simple and efficient AO approach for CLSM systems equipped with a detector array - the same of super-resolved image-scanning microscopy - and an AO element for beam shaping. We demonstrate that imaging datasets acquired with a detector array inherently encode aberration information. Leveraging this property, we developed a custom convolutional neural network capable of decoding aberrations, up to the 11th Zernike coefficient, directly from a single acquisition. This method enables a new generation of AO implementations for LSM, offering an accessible solution that minimizes sample stress while achieving high-resolution, and aberration-free imaging.

bioengineering↗

bioRxiv2024

Array Detection Enables Large Localization Range for Simple and Robust MINFLUX

The MINFLUX concept significantly enhances the spatial resolution of single-molecule localization microscopy (SMLM) by overcoming the limit imposed by the fluorophores photon counts. Typical MINFLUX microscopes localize the target molecule by scanning a zero-intensity focus around the molecule in a circular trajectory, with smaller trajectory diameters yielding lower localization uncertainties for a given number of photons. Since this approach requires the molecule to be within the scanned trajectory, MINFLUX typically relies on a photon-demanding iterative scheme with decreasing trajectory diameters. Although the iterative procedure does not substantially reduce the photon efficiency of MINFLUX, this approach is prone to misplacements of the trajectory and increases the systems complexity. In this work, we introduce ISM-FLUX, a novel implementation of MINFLUX using image-scanning microscopy (ISM) with a single-photon avalanche diode (SPAD) array detector. ISM-FLUX provides precise MINFLUX localization within the trajectory while maintaining conventional photon-limited uncertainty outside it. The robustness of ISM-FLUX localization results in a larger localization range and greatly simplifies the architecture, which may facilitate broader adoption of MIN-FLUX.

bioengineering↗

bioRxiv2023

A Compact and Effective Photon-Resolved Image Scanning Microscope

Fluorescence confocal laser-scanning microscopy (LSM) is one of the most popular tools for life science research. This popularity is expected to grow thanks to single-photon array detectors tailored for LSM. These detectors offer unique single-photon spatiotemporal information, opening new perspectives for gentle and quantitative super-resolution imaging. However, a flawless recording of this information poses significant challenges for the microscope data-acquisition system. Here, we present a data-acquisition module based on the digital frequency domain principle, able to record photons essential spatial and temporal features. We use this module to extend the capabilities of established imaging techniques based on single-photon avalanche diode (SPAD) array detectors, such as fluorescence lifetime image scanning microscopy. Furthermore, we use the module to introduce a robust multi-species approach encoding the fluorophores excitation spectra in the time domain. Lastly, we combine time-resolved stimulated emission depletion microscopy with image scanning microscopy, boosting spatial resolution. Our results demonstrate how a conventional fluorescence laser scanning microscope can transform into a simple, information-rich, super-resolved imaging system with the simple addition of a SPAD array detector with a tailored data acquisition system. We expected a blooming of advanced single-photon imaging techniques which effectively harness all the sample information encoded in each photon.

biophysics↗

bioRxiv2022

Focus-ISM for Sharp and Gentle Super-Resolved Microscopy

Super-resolution microscopy is routinely used for fixed and thin samples, while its feasibility for imaging live and thick samples is still limited. In the case of stimulated emission depletion (STED) microscopy, the high-intensity illumination required to achieve effective sub-diffraction resolution can introduce photo-damage, thus reducing the compatibility of the technique with live-cell imaging. Moreover, the out-of-focus fluorescence background may overcome the often faint signal stemming from the focal point, thus constraining imaging to thin samples. Here, we combined STED microscopy with image-scanning microscopy (ISM) to mitigate these limitations without any practical disadvantages. We first enhanced a laser scanning microscope (LSM) by introducing a detector array, hence providing access to a set of additional spatial information that is not available with a typical single-element detector. Then, we exploited this extended dataset to implement focus-ISM, a novel method that relaxes the high-intensity requirement of STED microscopy and removes the out-of-focus background. Additionally, we generalized the focus-ISM method to conventional LSM, namely without a STED beam. The proposed approach requires minimal architectural changes compared with conventional STED microscopes but provides substantial advantages for live and thick sample imaging while maintaining all compatibility with all recent advances in STED and confocal microscopy. As such, focus-ISM represents an essential step towards a universal super-resolved LSM technique for subcellular imaging.

biophysics↗