Optics Express

Digital holographic microscopy systems in a common-path configuration, compared to systems with a separate reference arm, offer a compact design and resistance to disturbances. They can operate with partially coherent illumination, reducing speckle noise. However, they are limited by the overlapping of the object beam and its laterally shifted replica. As a result, images from different regions of the object overlap on the detector, preventing imaging of dense samples. We present the wavelength-scanning replica-removal method, which solves this problem by enabling the separation of information from both replicas and thereby doubling the effective field of view (FOV). The wavelength-scanning multi-shear replica removal algorithm plays a key role in reconstructing the undisturbed phase from a series of holograms recorded with variable shears. The shear value is controlled by changing the illumination wavelength. This enabled the development of two measurement modes: time-domain wavelength scanning for high-quality imaging, and a single-shot mode with frame division into color channels to improve temporal resolution. The method was validated using resolution tests and biological samples - neurons and dynamic yeast cultures. By combining the advantages of the common-path configuration with dense-structure imaging and dynamic processes, the proposed method constitutes a versatile tool for quantitative phase microscopy.

The performance of a laser scanning microscope inevitably depends on the performance of the point detector. As laser scanning approaches aim to penetrate deeper in tissue, there is a commensurate need for detectors that can operate with high sensitivity, bandwidth, and dynamic range at near-infrared wavelengths where scattering is reduced. Here, we demonstrate that fiber optical parametric amplification can be used to boost low-power microscopy signals to levels that can be detected by near-infrared photodiodes without introducing prohibitive noise. We construct amplifiers that achieve >50 dB of parametric gain at wavelengths within the third near-infrared transparency window and have similar sensitivity to near-infrared photomultiplier tubes. Furthermore, these amplifiers outperform detection with a photodiode and subsequent electrical amplification, providing a factor of 10-100-fold improvement in sensitivity. We demonstrate amplifier bandwidths up to ~1.6 GHz, a factor of 10 faster than conventional detectors, including near-infrared photo-multiplier tubes, with sensitivity of ~8 nW (corresponding to ~20 photons/pixel). Finally, the increased performance of the optical amplifier is confirmed in diagnostic imaging experiments where >10x less power is required to achieve the same signal-to-noise ratio and contrast as images using electrical amplification. Accordingly, fiber optical parametric amplification is a new path forward for extending the performance of laser scanning microscopes in the near infrared.

Structured Illumination Microscopy (SIM) provides imaging with spatial super-resolution, as well as optical sectioning capability, without relying on specialized fluorescent dyes. 2D and 3D variants of this method exist, but most bespoke implementations are 2D-SIM, because it is easier to realize and modify than 3D-SIM. 2D-SIM systems, however, often experience reconstruction artifacts, especially when pushing for high lateral spatial resolution in thicker samples. We present enhanced 2D-SIM, an approach to 2D-SIM where both, coarse patterns optimized for removing out-of-focus background, and fine patterns optimized for resolution improvement beyond the diffraction limit are used. In combination, this achieves 2D-SIM reconstructions with high contrast, spatial super-resolution, and significantly reduced reconstruction artifacts. We present the theoretical framework of this technique, and provide enhanced 2D-SIM imaging results of liver sinusoidal endothelial cells stained with fluorophores emitting at visible and near-infrared wavelengths. Quantitative comparisons of power spectral distribution and image resolution are provided.

Sectioning techniques based on patterned illumination have been widely used to obtain well-contrasted images of thick samples using widefield imaging setups. While their application to fluorescence microscopy has been extensively demonstrated and studied, their application to reflection imaging is scarcer and their performance has only been partly characterized. In this paper, we study numerically and analytically two such sectioning techniques, line confocal (LC) and structured illumination (SI), in the context of their application to reflection interference contrast microscopy (RICM), an imaging technique widely use in soft matter and biophysics studies to monitor object-surface interactions, or quantify surface functionalization. Our derivation, however, should provide insight into their use with other reflection methods such as optical coherence tomography (OCT) or scanning laser ophtalmoscope (SLO). We derive approximate analytical equations to relate the performance of sectioning to the optical setup parameters, allowing straightforward understanding of their influence on the achieved image intensity and depth of focus, and we systematically compare our prediction with experimental data. Finally, we quantify the precision and accuracy of each method in typical practical cases, providing guidelines to choose the most appropriate (LC, SI, or a simple background subtraction on a widefield image) for the sample under study.

In contemporary bio-imaging-based research, computer-based assessment is becoming crucial for the characterisation of biological structures, as it minimises the need for time-consuming human annotation, which is prone to human error. Furthermore, it allows for the use of optical techniques that use lower photon intensities, thereby reducing reliance on high-intensity excitation and mitigating adverse effects on their activities. This study details the development and evaluation of sophisticated deep-learning models for amoeba detection using phase-contrast imaging. Using a single-class annotated dataset comprising 88 images and 4,131 annotations, we developed nine object detection models based on Detectron 2 and six variants based on YOLO v10. The diversity of the dataset, acquired under varying setup parameters, facilitated a comprehensive evaluation of the strengths and limitations of each model. A comparative analysis of speed and accuracy was performed to identify the most efficient models for real-time detection, providing critical insights for future microscopic analyses.

Longitudinal live cell imaging is valuable for characterizing dynamic morphological and phenotypic changes in biological systems. However, conventional approaches rely on manual microscope operation, which is labor-intensive, limits imaging frequency, and disrupts the cellular environment. These constraints reduce scalability, increase experimental variability, and restrict both the duration and temporal resolution of continuous imaging. Although automated imaging platforms partially address these limitations, existing solutions are often constrained by the cost, footprint, and inflexibility of in-incubator microscopes or stage-top incubators. Here, we present an automated in-incubator epifluorescence microscope designed for long-term operation. The system features a modular architecture with optional multi-fluorescence imaging, automated plate scanning, configurable light sources, and compatibility with multiple plate formats, including integration with fluidic automation devices. By positioning the light sources and control electronics outside the incubator, the platform improves thermal stability and long-term operational reliability. This approach enables continuous, high-frequency imaging over extended durations, providing a source of rich data for quantifying time-dependent tissue phenotypes, morphological remodeling, and transient biological processes.

Live-cell imaging (LCI) provides researchers the opportunity to understand biological phenomena at a temporal resolution and is achieved using dedicated imaging systems. These studies enable insight into dynamic phenotypic changes occurring in cells, which may otherwise be missed when studying fixed samples. Access to advanced microscopy is disproportionately available to researchers in high-income countries, whereas researchers in low-to middle-income countries (LMICs) are severely underrepresented in the adoption of such technologies. A major barrier to the dissemination of advanced microscopy centres around economic inequalities, with the cost of high-end imaging systems often being prohibitively expensive. Recognition of such disparities has motivated the wider microscopy community to manufacture frugal microscopes that are accessible to researchers in resource-constrained settings. The OpenFlexure Microscope (OFM) is an open source, customisable, 3D-printed microscope suitable for medical research and field-diagnostics. We have made adaptations to the OFM to enable its use for live-cell imaging in humid tissue culture incubators. By moving major electronic components outside of the microscope, we remove the risk of corrosion of the Raspberry Pi and Sangaboard used to operate the instrument. We tested four common 3D-printing polymer materials for increased thermal robustness and found ASA is the best plastic to print the main body of the microscope, offering both durability and image stability in 24- to 48-hour time course experiments. We have also created an optional 3D-printable weighted-hammock system to reduce external vibration artefacts during image acquisition. Critically, electronic modifications included custom extension cables from the motors and camera to the Raspberry Pi and Sangaboard, and the inclusion of 22 ohm ({Omega}) resistors to reduce the current to the stepper motors, preventing detrimental temperature increases inside sealed incubators during prolonged powering of the instrument. To remove dependence on WiFi connections for setting up timelapse experiments, we generated a simple application with a graphical user interface (GUI) that can be installed locally on a Raspberry Pi and is specifically designed for setting up timelapse experiments without extensive computational knowledge or experience. We validated our LCI-OFM adaptations with a 48-hour treatment of MDA-MB-231 breast cancer cells with the chemotherapeutic drug docetaxel, showcasing how the modified microscope can seamlessly feed into established bioimaging pipelines and generate biologically meaningful results. For researchers in LMICs, this adapted LCI-OFM provides new opportunities to study locally-relevant health challenges with timelapse microscopy, enabling deeper insight into biological dynamics and supporting the generation of preliminary data critical for securing grant funding and access to more advanced imaging systems in purpose-built regional imaging hubs.

Spectral information plays a crucial role in biological imaging, yet conventional epifluorescence and histological techniques often rely on RGB image acquisition, limiting the resolution of spectrally overlapping components. Here, we present a phasor-based spectral analysis framework adapted for RGB images, enabling unsupervised segmentation and unmixing without the need for hyperspectral systems or sequential acquisition. By applying a discrete Fourier transform to the red, green, and blue intensities at each pixel, we generate a two-dimensional phasor plot where spectral relationships are encoded in modulation and phase. We demonstrate the utility of this approach across three distinct applications: segmentation of lung histology images stained with hematoxylin and eosin to quantify alveolar collapse, analysis of autofluorescence in skin lesions (nevi and melanoma) to highlight pathological spectral signatures, and spectral unmixing in multicolor-labeled U2OS cells to resolve overlapping fluorophores. Our method improves signal separation, reduces noise, and enhances biological interpretability using standard RGB acquisition. These findings establish RGB phasor analysis as a practical and powerful tool for spectral decomposition and segmentation in microscopy, bridging the gap between conventional imaging and advanced spectral analysis.

Microscopy images are frequently downsampled due to acquisition and computational constraints, requiring reconstruction before downstream analysis. While super-resolution (SR) is typically assessed using pixel-level fidelity metrics, its impact on deep learning (DL) model behavior remains insufficiently understood. In this work, we present a study that examines how different upsampling strategies affect image quality and classification performance. Using the BloodMNIST dataset, we construct matched 224x224 datasets from 64x64 images via bicubic interpolation, SwinIR Classical, and SwinIR RealGAN DL SR models, alongside the original 224 ground-truth images. We evaluate reconstruction quality using the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) scores and assess downstream classification performance using ResNet-50 and Vision Transformer models, with accuracy, macro-F1 score, and a confidence-aware metric, the area under the receiver operating curve for successful prediction (AUPR Success). Our results demonstrate that bicubic interpolation significantly degrades classification performance, whereas SR methods can recover class-relevant information, even better than the ground-truth data. These findings emphasize the importance of confidence-aware evaluation and unambiguous reporting of reconstruction pipelines in microscopy-based DL studies.

The ability to record 3D neuronal activity with cellular resolution, high signal-to-noise ratio (SNR) and millisecond temporal resolution is a major challenge in neuroscience. One powerful method is random-access two-photon microscopy based on acousto-optic deflectors (AODs), which uses a holographically-shaped point spread function (PSF) scanned in 3D to maximize the sampling rate and SNR. However, this approach suffers from greater background contamination due to the holographically shaped PSF than standard two-photon microscopy with diffraction-limited PSF. To overcome this limitation, we implemented a new version of an AOD scanning system, which integrates temporal focusing. The complex spatiotemporal distortions encountered in this configuration, including a significant group delay dispersion associated with the pulse front tilt generated by the AOD, were compensated for by introducing an acousto-optic modulator before the AOD. We designed extended patterns by combining temporal focusing on one direction and holographic wavefront shaping in the perpendicular axis. Taking advantage of the AODs ability to shape the wavefront at the same speed as the scan, we were able to accurately superimpose the spatial and temporal foci over the entire field of view. Finally, we generated complex, extended two-photon excitation patterns by combining temporal focusing in one direction and holographic multiplexing in the perpendicular direction. These patterns provide significantly improved background rejection compared to 2D holographic patterns, thus offering promising prospects for in vivo recordings of neuronal activity in dense samples with improved SNR.

Oblique plane microscopy (OPM) is a light sheet microscopy technique that uses a single high numerical aperture (NA) objective for both illuminating the sample and collecting emission fluorescence from a tilted plane within the specimen. OPM has become indispensable in biological and biomedical research, providing rapid, high-resolution volumetric fluorescence imaging of live cells and tissues while minimising phototoxicity and photobleaching. It also overcomes the sample mounting challenges associated with conventional light sheet microscopes that require two orthogonally placed objectives. However, the application of OPM has been limited by the complex design and the intricate optical alignment and characterisation needed, particularly with the remote-refocusing system (RFS) in the emission path. This protocol offers a detailed, step-by-step guide for constructing an OPM setup using commercially available components and for characterising its performance to ensure optimal imaging quality. We aim to deliver the unique merits of OPM to researchers in life science and medicine, enabling them to visualise the spatiotemporal organisation of key biomolecules, structures, and cells in 3D at high resolutions.

Solid immersion lenses (SILs) enhance the spatial resolution of an optical microscope by increasing the effective numerical aperture (NA) without physical modification of the objective lens. However, SIL application remains limited by cost, fragility, and accessibility. We present a rapid, single-step fabrication process to create optical quality hemispherical SILs using consumer-grade UV-curable transparent resin which reduces material costs by over five orders of magnitude relative to commercial glass counterparts. Our method produced resin SILs within seconds which can be easily implemented into conventional microscopy setups for increasing the effective NA. Quantitative imaging of USAF resolution targets and histology muscle preparations demonstrated a resolution enhancement approaching theoretical limits and comparable performance to N-BK7 glass SILs. This enabled visualisation of features usually below the diffraction limit of low NA dry objectives at a fraction of the cost of otherwise required high-powered objective lenses. To demonstrate accessibility and translational potential, our workflow was taught in a practical tutorial of an international microscopy course, where non-expert participants successfully fabricated, characterised, and applied SILs within a single session, reporting high confidence in independent implementation. We established ultra-low-cost resin SILs as a practical, scalable option to enhance the spatial resolution of routine optical microscopes and as an accessible and cost-effective platform for optics education.

Fourier ptychographic microscopy (FPM) uses sequential multi-angle illumination and iterative phase retrieval to recover a high-resolution complex image from a series of low-resolution brightfield and darkfield images. We present OpenFPM, an open-source FPM platform in which conventional and optomechanical hardware is replaced with compact, low-cost 3D printed components. Illumination, sample and objective positioning, and camera triggering are controlled using a Python-based interface on a Raspberry Pi microcomputer. With a 10 x /0.25 NA objective lens and 636 nm illumination, OpenFPM experimentally achieves amplitude and phase reconstructions with an effective synthetic NA of 0.90 over a 1 mm field-of-view. This platform gives researchers accessible and affordable hardware for developing and testing LED-array microscopy techniques for a range of biomedical imaging applications.

Polarization-resolved second harmonic generation microscopy provides structural information about non-centrosymmetric biological samples such as collagen. It involves illuminating the sample with a focused laser beam having a variable linear polarization angle and recording the second harmonic signal as a function of this angle. However, accurate linear polarization control is challenging due to ellipticity introduced by reflections from mirrors and dichroic mirrors in the optical path. Waveplate-based compensation has emerged as the standard approach to address these distortions, but its effectiveness for quantitative measurements remains incompletely characterized. Here, we attempt to fill this gap by implementing an established automated waveplate compensation method based on a rotating half-waveplate in combination with a compensating quarter-waveplate. This was done on a commercial Leica TCS SP8 MP multiphoton microscope, making various hardware improvements and carefully documenting important experimental details. Despite significant effort, we consistently observed substantial unwanted residual polarization ellipticity, with amplitudes up to 0.25, persisting under optimal waveplate configurations. Our simulation analysis provides evidence that this limitation may arise from wavelength-dependent dichroic mirror birefringence combined with the broad spectral bandwidth (10nm to 20nm full width at half maximum) of femtosecond laser pulses. While the approach investigated here can compensate a single wavelength, different spectral components within the pulse experience different phase retardations from wavelength-dependent optical elements, potentially resulting in residual ellipticity that cannot be eliminated. Our simulations qualitatively reproduced key features of the experimental observations. These findings have important implications for quantitative polarization-resolved second harmonic generation microscopy and suggest that alternative approaches, including specimen rotation or picosecond laser sources with narrower bandwidth, should be investigated for applications requiring precise polarization control. To facilitate community investigation of these effects, we provide open-source analysis code and simulation files.

Ptychographic single-particle analysis (SPA) is a promising technique for high-resolution biological imaging but is still limited by sub-nanometer resolution. In this study, we identified and investigated a critical issue termed sampling mismatch in ptychography that is caused by inaccuracies in the scanning step size and the pixel size of convergent beam electron diffraction (CBED) images. This mismatch induces pixel-size deviations in the reconstructed micrographs and modulates information transfer through a mismatch-induced modulation function (MIMF), which is characterized by phase reversals at specific spatial frequencies of the micrographs. These phase reversals, which vary with the defocus, cause destructive interference when merging micrographs, fundamentally limiting the resolution of SPA. We proposed a correction strategy and demonstrated, on the T. Acidophilum 20S proteasome and apoferritin datasets, that correcting sampling parameters eliminates signal distortions and improves resolution for [~]1.5 [A]. These findings underscore the necessity for the precise control and calibration of the scanning system to achieve high-resolution ptychographic SPA.

Fluorescent reporters such as fluorescent proteins or chemigenetic indicators are indispensable tools for studying biological processes using light microscopy. Choosing an appropriate fluorescent tag is a crucial step in experimental design not only for imaging but also for quantitative measurements such as fluorescence fluctuation spectroscopy. Two key parameters should be considered: Fluorescent brightness and photo-bleaching. Change to fluorescence intensity due to photobleaching is relatively easy to assess in different biological environments, while brightness is more elusive. Here, we develop and employ a fluorescence correlation spectroscopy (FCS) based excitation scan assay that determines fluorescent protein performance and validate it in tissue culture and zebrafish embryos. We employ our FCS pipeline to compare a set of 10 established fluorescent proteins as well as HALO and SNAP tags for both cellular imaging and measurements of diffusion dynamics with FCS. We show that mNeonGreen outperforms mEGFP in tissue culture and zebrafish embryos. We also compare StayGold variants against other green fluorescent proteins and chemigenetic reporters in tissue culture. Overall, we present a broadly applicable approach for determining fluorescent reporter brightness in the living system of interest.

Single-molecule localization microscopy (SMLM) methods enable fluorescence imaging of biological specimens with nanometer-scale resolution. Although fluorophore localization precision is theoretically limited only by photon statistics, in practice the resolution of SMLM images is often degraded by physical drift of the sample and/or the microscope during data acquisition. At present, correcting this effect requires either specialized stabilization systems or computationally intensive post-processing, and established drift correction algorithms based on image cross-correlation suffer from limited temporal resolution. In this study we introduce COMET, a new method for SMLM drift estimation which achieves a substantially higher precision, accuracy, and temporal resolution compared with existing algorithmic approaches. We demonstrate that improved drift estimation translates directly into higher SMLM image resolution, limited by localization precision rather than drift artifacts. COMET is applicable to all types of SMLM data, operating directly on 2D or 3D localization datasets, and is readily integrated into analysis workflows. We benchmark its performance using both simulations and experiments, including STORM, MINFLUX, and Sequential OligoSTORM measurements, where long acquisition times make drift correction particularly challenging. COMET is published as an open-source, Python-based software project and is also available on open cloud-computing platforms.

Super-resolution localization microscopy (SMLM) has become a central tool for nanoscale biological research for its high spatial resolution and compatibility with wide-field microscopy. Achieving quantitative SMLM, however, requires homogeneous high-power illumination, nanometric axial stability, and precise multi-channel detection, features typically restricted to high-end commercial instruments or custom solutions in specialized laboratories. The cost of such microscopes and their technical complexity still limit the accessibility of these advanced imaging techniques. Several home-made single molecule microscopes and their submodules have been demonstrated as opensource, highly-customizable, and cost-effective alternatives for their commercial counterparts. Yet, implementation of such systems often requires expert knowledge in optics, electronics, and control system engineering. We introduce Open Blink, a compact open-source TIRF microscope integrating powerful homogeneous quad-line laser illumination, dual-channel detection, and active focus-lock stabilization for quantitative multi-color super-resolution imaging. Open Blink achieves a localization precision below 10 nm in dSTORM, supports a tunable, large field of view from 105 x 105 {micro}m2 up to 257 x 257 {micro}m2, and maintains axial stability over hours, enabling high-throughput super-resolution acquisition. Built with predominantly off-the-shelf components, and full integration into the open-source software {micro}Manager where metadata registration ensures reproducibility, Open Blink offers a low threshold for adoption by easing implementation, use and maintenance. At a substantially reduced cost of approximately 70 000 Euros, among which the high-power laser combiner alone is less than 20 000 euros, Open Blink greatly improves accessibility for laboratories who wish to implement scalable high performance super-resolution microscopy based on single molecules.

Accurate monitoring of microalgae is essential for assessing marine ecological health and preventing harmful algal blooms in ocean engineering. Current in situ identification methods often suffer from limited discriminative feature extraction and inadequate adaptation to complex underwater imaging conditions. This study introduces a lightweight dual-attention neural network, termed ANMM, designed for real-time, in situ hyperspectral classification of microalgae within integrated underwater monitoring systems. The model strengthens a deepened AlexNet backbone with multi-head latent attention (MLA) and multi-head self-attention (MSA) mechanisms, which jointly enhance local feature refinement and global spectral dependency modeling. An early-stopping strategy is further incorporated to prevent overfitting and ensure robust generalization. Evaluated on a custom dataset of field-collected fluorescence spectra, the model achieves a classification accuracy of 98.91%, outperforming several state-of-the-art deep-learning counterparts. With a compact parameter size of 16.34 M and low-latency inference on edge hardware, the system demonstrates strong potential for deployment on embedded underwater sensing platforms. This work provides a practical and efficient AI-driven solution for continuous marine microalgae monitoring, supporting advances in ocean observation technology and ecological engineering.

Fluorescence imaging with spectrally variant fluorophores allows the spatial mapping of biological structures with exquisite cellular and molecular specificity. However, the ability to robustly discriminate multiple fluorophores in any single imaging experiment is greatly hindered by the broad emission spectra of bio-compatible fluorophores and the large contribution of noise in low-energy regime fluorescence microscopy. In this study, we propose a novel machine learning framework, Bleaching-Excitation-Emission Photodynamics (BEEP) learning, that exploits multiple discriminatory features of fluorescent dyes to greatly expand the number of distinguishable objects in an image by integrating emission spectra, excitation variability, and bleaching dynamics into a unified multi-view, fluorescence unmixing approach. Our method is built upon a rank-one-tensor-based generalized linear model and leverages two biophysically grounded assumptions: consistent spectral and bleaching behaviors under fixed excitation, and invariant fluorophore abundances across excitations. We first extract excitation-specific spectral and bleaching signatures from reference images, and then use them to estimate abundances in complex mixtures. Experimental results on both simulated and real images of microbial populations demonstrate that our approach significantly outperforms conventional and partially multi-view methods, offering improved robustness and accuracy in highly multiplexed fluorescence imaging.