Sample-induced Aberrations Research Articles

Accurate 3D single-molecule localization via vectorial in situ point spread function retrieval and aberration assessment

Single-molecule localization microscopy (SMLM) gradually plays an important role in deep tissue imaging. However, current SMLM methods primarily rely on fiducial marks, neglecting aberrations introduced by thick samples, thereby resulting in decreased image quality in deep tissues. Here, we introduce vectorial in situ point spread function (PSF) retrieval (VISPR), a method that retrieves a precise PSF model considering both system- and sample-induced aberrations under SMLM conditions. By employing the theory of vectorial PSF model and maximum likelihood estimation (MLE) phase retrieval, VISPR is capable of reconstructing an accurate in situ 3D PSF model achieving the theoretically minimum uncertainty and accurately reflecting three-dimensional information of single molecules. This capability enables accurate 3D super-resolution reconstruction in deep regions away from the coverslips. Additionally, VISPR demonstrates applicability in low signal-to-noise ratio scenarios and compatibility with various SMLM microscope modalities. From both simulations and experiments, we verified the superiority and effectiveness of VISPR. We anticipate that VISPR will become a pivotal tool for advancing deep tissue SMLM imaging.

Posterior approach to correct for focal plane offsets in lattice light-sheet structured illumination microscopy.

Lattice light-sheet structured illumination microscopy (latticeSIM) has proven highly effective in producing three-dimensional images with super resolution rapidly and with minimal photobleaching. However, due to the use of two separate objectives, sample-induced aberrations can result in an offset between the planes of excitation and detection, causing artifacts in the reconstructed images. We introduce a posterior approach to detect and correct the axial offset between the excitation and detection focal planes in latticeSIM and provide a method to minimize artifacts in the reconstructed images. We utilized the residual phase information within the overlap regions of the laterally shifted structured illumination microscopy information components in frequency space to retrieve the axial offset between the excitation and the detection focal planes in latticeSIM. We validated our technique through simulations and experiments, encompassing a range of samples from fluorescent beads to subcellular structures of adherent cells. We also show that using transfer functions with the same axial offset as the one present during data acquisition results in reconstructed images with minimal artifacts and salvages otherwise unusable data. We envision that our method will be a valuable addition to restore image quality in latticeSIM datasets even for those acquired under non-ideal experimental conditions.

Frequency-multiplexed aberration measurement for confocal microscopy

In single-photon confocal fluorescence microscopy, optical aberration affects both excitation and detection light paths, thus can severely degrade image quality. We incorporated an adaptive optics (AO) module into a confocal microscope and used a frequency-multiplexed aberration measurement method to measure and correct sample-induced aberration. We demonstrated that this method can measure aberration using signals from features of different sizes and recover diffraction-limited imaging performance. Applying our AO confocal microscope to imaging through and within living zebrafish larvae, as well as in the mouse brain in vivo, we showed that aberration correction can substantially improve confocal image brightness, resolution, and contrast.

Topography reconstruction of high aspect ratio silicon trench array via near-infrared coherence scanning interferometry

Topography measurement of high aspect ratio trench array using coherence scanning interferometry presents significant challenges because the numerical aperture of detection light is constrained by the trenches. Altering the detection light to penetrate the sample like near-infrared light for silicon could overcome this obstacle, but the trench array spreads the detection light. This study introduces a coherence scanning interferometry model based on three-dimensional point spread function and assuming sample is transparent to detection light, which is realized by integrating rigorous numerical electromagnetic field solution to quantify the modulation aberrations of detection light by transparent trench arrays, and theoretical angular spectrum diffraction utilized for far-field interference imaging. This model facilitates a thorough analysis of the aberrations introduced by trench arrays, encompassing comparisons between trench arrays and a single trench, as well as between the symmetric region of the array and the asymmetric region at the edge. Additionally, an investigation into the impact of unified compensation for low-order aberrations on the topography reconstruction is presented, and we find the sample-induced aberration compensation method utilizing a deformable mirror that we previously proposed for a single trench is still effective confronting trench array. Experimental measurements are performed on silicon trench arrays with the aspect ratio of up to 20:1 and the period of approximately 10 µm to validate the effectiveness of our model and measurement methods, thus providing valuable insights for enhancing high aspect ratio manufacturing.

Second-harmonic generation microscopy with synthetic aperture and computational adaptive optics

Second-harmonic generation (SHG) microscopy is a powerful label-free imaging tool widely used to visualize collagen and muscle in biological tissues. However, traditional laser-scanning SHG microscopy requiring voxel scanning is time-intensive. Wide-field SHG microscopy was designed to bypass this restriction, but its application to deep tissue imaging is limited due to vulnerability to scattering and sample-induced aberrations. We introduce synthetic aperture SHG (SA-SHG) microscopy to attenuate the effect of multiple scattering noises. Our SA-SHG method coherently integrates amplitude and phase maps of wide-field SHG fields taken for different illumination angles, thereby enhancing the signal-to-noise ratio. We also develop computational adaptive optics SHG (CAO-SHG) microscopy to computationally correct the sample-induced aberrations. Our algorithm optimizes SHG fields’ aperture synthesis to identify aberration maps, enabling the restoration of diffraction-limited imaging. We successfully apply this approach to real biological samples, demonstrating its potential for high-resolution imaging in complex biological environments.

Accurate piecewise centroid calculation algorithm for wavefront measurement in adaptive optics.

Adaptive optics using direct wavefront sensing (direct AO) is widely used in two-photon microscopy to correct sample-induced aberrations and restore diffraction-limited performance at high speeds. In general, the direct AO method employs a Sharked-Hartman wavefront sensor (SHWS) to directly measure the aberrations through a spot array. However, the signal-to-noise ratio (SNR) of spots in SHWS varies significantly within deep tissues, presenting challenges for accurately locating spot centroids over a large SNR range, particularly under extremely low SNR conditions. To address this issue, we propose a piecewise centroid calculation algorithm called GCP, which integrates three optimal algorithms for accurate spot centroid calculations under high-, medium-, and low-SNR conditions. Simulations and experiments demonstrate that the GCP can accurately measure aberrations over a large SNR range and exhibits robustness under extremely low-SNR conditions. Importantly, GCP improves the AO working depth by 150 µm compared to the conventional algorithm.

Deep learning-driven adaptive optics for single-molecule localization microscopy

The inhomogeneous refractive indices of biological tissues blur and distort single-molecule emission patterns generating image artifacts and decreasing the achievable resolution of single-molecule localization microscopy (SMLM). Conventional sensorless adaptive optics methods rely on iterative mirror changes and image-quality metrics. However, these metrics result in inconsistent metric responses and thus fundamentally limit their efficacy for aberration correction in tissues. To bypass iterative trial-then-evaluate processes, we developed deep learning-driven adaptive optics for SMLM to allow direct inference of wavefront distortion and near real-time compensation. Our trained deep neural network monitors the individual emission patterns from single-molecule experiments, infers their shared wavefront distortion, feeds the estimates through a dynamic filter and drives a deformable mirror to compensate sample-induced aberrations. We demonstrated that our method simultaneously estimates and compensates 28 wavefront deformation shapes and improves the resolution and fidelity of three-dimensional SMLM through >130-µm-thick brain tissue specimens.

Deep learning-based adaptive optics for light sheet fluorescence microscopy.

Light sheet fluorescence microscopy (LSFM) is a high-speed imaging technique that is often used to image intact tissue-cleared specimens with cellular or subcellular resolution. Like other optical imaging systems, LSFM suffers from sample-induced optical aberrations that decrement imaging quality. Optical aberrations become more severe when imaging a few millimeters deep into tissue-cleared specimens, complicating subsequent analyses. Adaptive optics are commonly used to correct sample-induced aberrations using a deformable mirror. However, routinely used sensorless adaptive optics techniques are slow, as they require multiple images of the same region of interest to iteratively estimate the aberrations. In addition to the fading of fluorescent signal, this is a major limitation as thousands of images are required to image a single intact organ even without adaptive optics. Thus, a fast and accurate aberration estimation method is needed. Here, we used deep-learning techniques to estimate sample-induced aberrations from only two images of the same region of interest in cleared tissues. We show that the application of correction using a deformable mirror greatly improves image quality. We also introduce a sampling technique that requires a minimum number of images to train the network. Two conceptually different network architectures are compared; one that shares convolutional features and another that estimates each aberration independently. Overall, we have presented an efficient way to correct aberrations in LSFM and to improve image quality.

In-silico clearing approach for deep refractive index tomography by partial reconstruction and wave-backpropagation

Refractive index (RI) is considered to be a fundamental physical and biophysical parameter in biological imaging, as it governs light-matter interactions and light propagation while reflecting cellular properties. RI tomography enables volumetric visualization of RI distribution, allowing biologically relevant analysis of a sample. However, multiple scattering (MS) and sample-induced aberration (SIA) caused by the inhomogeneity in RI distribution of a thick sample make its visualization challenging. This paper proposes a deep RI tomographic approach to overcome MS and SIA and allow the enhanced reconstruction of thick samples compared to that enabled by conventional linear-model-based RI tomography. The proposed approach consists of partial RI reconstruction using multiple holograms acquired with angular diversity and their backpropagation using the reconstructed partial RI map, which unambiguously reconstructs the next partial volume. Repeating this operation efficiently reconstructs the entire RI tomogram while suppressing MS and SIA. We visualized a multicellular spheroid of diameter 140 µm within minutes of reconstruction, thereby demonstrating the enhanced deep visualization capability and computational efficiency of the proposed method compared to those of conventional RI tomography. Furthermore, we quantified the high-RI structures and morphological changes inside multicellular spheroids, indicating that the proposed method can retrieve biologically relevant information from the RI distribution. Benefitting from the excellent biological interpretability of RI distributions, the label-free deep visualization capability of the proposed method facilitates a noninvasive understanding of the architecture and time-course morphological changes of thick multicellular specimens.

Adaptive optics for optical microscopy [Invited

Optical microscopy is widely used to visualize fine structures. When applied to bioimaging, its performance is often degraded by sample-induced aberrations. In recent years, adaptive optics (AO), originally developed to correct for atmosphere-associated aberrations, has been applied to a wide range of microscopy modalities, enabling high- or super-resolution imaging of biological structure and function in complex tissues. Here, we review classic and recently developed AO techniques and their applications in optical microscopy.

Fast Adaptive Optics in optically sectioned fluorescence microscopes for functional neuroimaging

We demonstrate how a novel approach for closed-loop Adaptive Optics (AO) specifically adapted to microscopy enables straightforward integration in Light-Sheet and Multiphoton microscopes, as well as fast aberration correction. We present corresponding experimental setups as well as first demonstrations of the benefits of the correction of sample-induced aberrations in zebrafish and mouse brain tissue, with the ultimate goal to enable high-speed, high-sensitivity functional imaging at large depths.

Effects of optical aberrations on localization of MINFLUX super-resolution microscopy.

A novel super-resolution imaging technique based on the minimum photon flux (MINFLUX), can achieve nanometer-scale localization precision and sub-5-nm imaging. However, aberrations can affect the localization performance and degrade the quality of reconstructed images. In this study, we analyze the effects of different low-order aberrations on the MINFLUX system through both theoretical limits and Monte Carlo methods. We report that 1) defocus and spherical aberration have little effect on 2D localization performance, whereas astigmatism and coma have significant negative effects; 2) system aberrations that can be measured in advance cause changes primarily in the magnitude and angular uniformity of localization precision, whereas sample-induced aberrations that cannot be a priori introduce large biases and reduce localization accuracy.

Adaptive optics in single objective inclined light sheet microscopy enables three-dimensional localization microscopy in adult Drosophila brains.

Single-molecule localization microscopy (SMLM) enables the high-resolution visualization of organelle structures and the precise localization of individual proteins. However, the expected resolution is not achieved in tissue as the imaging conditions deteriorate. Sample-induced aberrations distort the point spread function (PSF), and high background fluorescence decreases the localization precision. Here, we synergistically combine sensorless adaptive optics (AO), in-situ 3D-PSF calibration, and a single-objective lens inclined light sheet microscope (SOLEIL), termed (AO-SOLEIL), to mitigate deep tissue-induced deteriorations. We apply AO-SOLEIL on several dSTORM samples including brains of adult Drosophila. We observed a 2x improvement in the estimated axial localization precision with respect to widefield without aberration correction while we used synergistic solution. AO-SOLEIL enhances the overall imaging resolution and further facilitates the visualization of sub-cellular structures in tissue.

Through-skull brain imaging in vivo at visible wavelengths via dimensionality reduction adaptive-optical microscopy.

Compensation of sample-induced optical aberrations is crucial for visualizing microscopic structures deep within biological tissues. However, strong multiple scattering poses a fundamental limitation for identifying and correcting the tissue-induced aberrations. Here, we introduce a label-free deep-tissue imaging technique termed dimensionality reduction adaptive-optical microscopy (DReAM) to selectively attenuate multiple scattering. We established a theoretical framework in which dimensionality reduction of a time-gated reflection matrix can attenuate uncorrelated multiple scattering while retaining a single-scattering signal with a strong wave correlation, irrespective of sample-induced aberrations. We performed mouse brain imaging in vivo through the intact skull with the probe beam at visible wavelengths. Despite the strong scattering and aberrations, DReAM offered a 17-fold enhancement of single scattering–to–multiple scattering ratio and provided high-contrast images of neural fibers in the brain cortex with the diffraction-limited spatial resolution of 412 nanometers and a 33-fold enhanced Strehl ratio.

The Effects of Optical Aberrations to Illumination Beam Thickness in Two-Photon Excitation Microscopes

When performing in vivo imaging of live samples, it is a big challenge to penetrate thick tissues while still maintaining high resolution and a large field of view because of the sample-induced aberrations. These requirements can be met by combining the benefits of two-photon excitation, beam modulation and adaptive optics in an illumination path. However, the relationship between aberrations and the performance of such a microscopy system has never been systematically and comprehensively assessed. Here, two-photon Gaussian and Bessel beams are modulated as illumination beams, and how aberrations affect the thickness of the illumination beams is evaluated. It is found that the thickness variation is highly related to the azimuthal order of Zernike modes. The thickness of the two-photon Gaussian beam is more sensitive to Zernike modes with lower azimuthal order, while the thickness of the two-photon Bessel beam is more sensitive to the higher-azimuthal-order Zernike modes. So, it is necessary to design a new strategy to correct aberrations according to the effects of different Zernike modes in order to maximize the correction capability of correctors and reduce the correction errors for those insensitive Zernike modes. These results may provide important guidance for the design and evaluation of adaptive optical systems in a two-photon excitation microscope.

Quantitative analysis of illumination and detection corrections in adaptive light sheet fluorescence microscopy.

Light-sheet fluorescence microscopy (LSFM) is a high-speed, high-resolution and minimally phototoxic technique for 3D imaging of in vivo and in vitro specimens. LSFM exhibits optical sectioning and when combined with tissue clearing techniques, it facilitates imaging of centimeter scale specimens with micrometer resolution. Although LSFM is ubiquitous, it still faces two main challenges that effect image quality especially when imaging large volumes with high-resolution. First, the light-sheet illumination plane and detection lens focal plane need to be coplanar, however sample-induced aberrations can violate this requirement and degrade image quality. Second, introduction of sample-induced optical aberrations in the detection path. These challenges intensify when imaging whole organisms or structurally complex specimens like cochleae and bones that exhibit many transitions from soft to hard tissue or when imaging deep (> 2 mm). To resolve these challenges, various illumination and aberration correction methods have been developed, yet no adaptive correction in both the illumination and the detection path have been applied to improve LSFM imaging. Here, we bridge this gap, by implementing the two correction techniques on a custom built adaptive LSFM. The illumination beam angular properties are controlled by two galvanometer scanners, while a deformable mirror is positioned in the detection path to correct for aberrations. By imaging whole porcine cochlea, we compare and contrast these correction methods and their influence on the image quality. This knowledge will greatly contribute to the field of adaptive LSFM, and imaging of large volumes of tissue cleared specimens.

Label-free metabolic and structural profiling of dynamic biological samples using multimodal optical microscopy with sensorless adaptive optics

Label-free optical microscopy has matured as a noninvasive tool for biological imaging; yet, it is criticized for its lack of specificity, slow acquisition and processing times, and weak and noisy optical signals that lead to inaccuracies in quantification. We introduce FOCALS (Fast Optical Coherence, Autofluorescence Lifetime imaging, and Second harmonic generation) microscopy capable of generating NAD(P)H fluorescence lifetime, second harmonic generation (SHG), and polarization-sensitive optical coherence microscopy (OCM) images simultaneously. Multimodal imaging generates quantitative metabolic and morphological profiles of biological samples in vitro, ex vivo, and in vivo. Fast analog detection of fluorescence lifetime and real-time processing on a graphical processing unit enables longitudinal imaging of biological dynamics. We detail the effect of optical aberrations on the accuracy of FLIM beyond the context of undistorting image features. To compensate for the sample-induced aberrations, we implemented a closed-loop single-shot sensorless adaptive optics solution, which uses computational adaptive optics of OCM for wavefront estimation within 2 s and improves the quality of quantitative fluorescence imaging in thick tissues. Multimodal imaging with complementary contrasts improves the specificity and enables multidimensional quantification of the optical signatures in vitro, ex vivo, and in vivo, fast acquisition and real-time processing improve imaging speed by 4–40 × while maintaining enough signal for quantitative nonlinear microscopy, and adaptive optics improves the overall versatility, which enable FOCALS microscopy to overcome the limits of traditional label-free imaging techniques.

In vivo volumetric imaging of calcium and glutamate activity at synapses with high spatiotemporal resolution

Studying neuronal activity at synapses requires high spatiotemporal resolution. For high spatial resolution in vivo imaging at depth, adaptive optics (AO) is required to correct sample-induced aberrations. To improve temporal resolution, Bessel focus has been combined with two-photon fluorescence microscopy (2PFM) for fast volumetric imaging at subcellular lateral resolution. To achieve both high-spatial and high-temporal resolution at depth, we develop an efficient AO method that corrects the distorted wavefront of Bessel focus at the objective focal plane and recovers diffraction-limited imaging performance. Applying AO Bessel focus scanning 2PFM to volumetric imaging of zebrafish larval and mouse brains down to 500 µm depth, we demonstrate substantial improvements in the sensitivity and resolution of structural and functional measurements of synapses in vivo. This enables volumetric measurements of synaptic calcium and glutamate activity at high accuracy, including the simultaneous recording of glutamate activity of apical and basal dendritic spines in the mouse cortex.

Optical diffraction tomography from single-molecule localization microscopy

Single-molecule localization microscopy (SMLM) is a powerful method for the imaging of cellular structures. This modality delivers nanoscale resolution by sequentially activating a subset of fluorescent molecules and by extracting their super-resolved positions from the microscope images. The emission patterns of individual molecules can be distorted by the refractive-index (RI) map of the sample, which reduces the accuracy of the molecule localization if not accounted for. In this work, we show that one can exploit those sample-induced aberrations to reveal the structural information of the specimen. Our work is related to the optical diffraction tomography in that we aim to recover the RI map. To that end, we propose an optimization framework in which we reconstruct the RI map and optimize the positions of the molecules in a joint fashion. The benefits of our method are twofold. On one side, we effectively recover the RI map of the sample. On the other side, we further improve the molecule localization—the primary purpose of SMLM. We validate our joint-optimization framework on simulated data. Our results lay the foundation of an exciting and novel extension of SMLM.

Robust adaptive optics for localization microscopy deep in complex tissue

Single-Molecule Localization Microscopy (SMLM) provides the ability to determine molecular organizations in cells at nanoscale resolution, but in complex biological tissues, where sample-induced aberrations hamper detection and localization, its application remains a challenge. Various adaptive optics approaches have been proposed to overcome these issues, but the exact performance of these methods has not been consistently established. Here we systematically compare the performance of existing methods using both simulations and experiments with standardized samples and find that they often provide limited correction or even introduce additional errors. Careful analysis of the reasons that underlie this limited success enabled us to develop an improved method, termed REALM (Robust and Effective Adaptive Optics in Localization Microscopy), which corrects aberrations of up to 1 rad RMS using 297 frames of blinking molecules to improve single-molecule localization. After its quantitative validation, we demonstrate that REALM enables to resolve the periodic organization of cytoskeletal spectrin of the axon initial segment even at 50 μm depth in brain tissue.