Imaging Of Biological Samples Research Articles

Breaking Abbe's diffraction limit with harmonic deactivation microscopy.

Nonlinear optical microscopy provides elegant means for label-free imaging of biological samples and condensed matter systems. The widespread areas of application could even be increased if resolution was improved, which the famous Abbe diffraction limit now restrains. Super-resolution techniques can break the diffraction limit but most rely on fluorescent labeling. This makes them incompatible with (sub)femtosecond temporal resolution and applications that demand the absence of labeling. Here, we introduce harmonic deactivation microscopy (HADES) for breaking the diffraction limit in nonfluorescent samples. By controlling the harmonic generation process on the quantum level with a second donut-shaped pulse, we confine the third-harmonic generation to three times below the original focus size of a scanning microscope. We demonstrate that resolution improvement by deactivation is more efficient for higher harmonic orders and only limited by the maximum applicable deactivation-pulse fluence. This provides a route toward sub-100-nanometer resolution in a regular nonlinear microscope.

Superconducting nanowire single-photon detector enhanced near-infrared II portable confocal microscopy for tissue imaging with indocyanine green.

In this Letter a novel, to our knowledge, approach for near-infrared (NIR) fluorescence portable confocal microscopy is introduced, aiming to enhance fluorescence imaging of biological samples in the NIR-II window. By integrating a superconducting nanowire single-photon detector (SNSPD) into a confocal microscopy, we have significantly leveraged the detection efficiency of the NIR-II fluorescence signal from indocyanine green (ICG), an FDA-approved dye known for its NIR-II fluorescence capabilities. The SNSPD, characterized by its extremely low dark count rate and optimized NIR system detection efficiency, enables the excitation of ICG with 1 mW and the capture of low-light fluorescence signals from deep regions (up to 512 µm). Consequently, our technique was able to produce high-resolution images of bio samples with a superior signal-to-noise ratio, making a substantial advancement in the field of fluorescence microscopy and offering a promising opportunity for future clinical study.

Multifunctional computational fluorescence self-interference holographic microscopy

Fluorescence microscopy is crucial in various fields such as biology, medicine, and life sciences. Fluorescence self-interference holographic microscopy has great potential in bio-imaging owing to its unique wavefront coding characteristics; thus, it can be employed as three-dimensional (3D) scanning-free super-resolution microscopy. However, the available approaches are limited to low optical efficiency, complex optical setups, and single imaging functions. The geometric phase lens can efficiently manipulate the optical field’s amplitude, phase, and polarization. Inspired by geometric phase and self-interference holography, a self-interference fluorescent holographic microscope-based geometric phase lens is proposed. This system allows for wide-field, 3D fluorescence holographic imaging, and edge-enhancement from the reconstruction of only one complex-valued hologram. Experiments demonstrate the effectiveness of our method in imaging biological samples, with improved resolution and signal-to-noise ratio. Furthermore, its simplicity and convenience make it easily compatible with existing optical microscope setups, making it a powerful tool for observing biological samples and detecting industrial defects.

LiveLattice: Real-time visualisation of tilted light-sheet microscopy data using a memory-efficient transformation algorithm.

Light-sheet fluorescence microscopy (LSFM), a prominent fluorescence microscopy technique, offers enhanced temporal resolution for imaging biological samples in four dimensions (4D; x, y, z, time). Some of the most recent implementations, including inverted selective plane illumination microscopy (iSPIM) and lattice light-sheet microscopy (LLSM), move the sample substrate at an oblique angle relative to the detection objective's optical axis. Data from such tilted-sample-scan LSFMs require subsequent deskewing and rotation for proper visualisation and analysis. Such data preprocessing operations currently demand substantial memory allocation and pose significant computational challenges for large 4D dataset. The consequence is prolonged data preprocessing time compared to data acquisition time, which limits the ability for live-viewing the data as it is being captured by the microscope. To enable the fast preprocessing of large light-sheet microscopy datasets without significant hardware demand, we have developed WH-Transform, a memory-efficient transformation algorithm for deskewing and rotating the raw dataset, significantly reducing memory usage and the run time by more than 10-fold for large image stacks. Benchmarked against the conventional method and existing software, our approach demonstrates linear runtime compared to the cubic and quadratic runtime of the other approaches. Preprocessing a raw 3D volume of 2 GB (512 × 1536 × 600 pixels) can be accomplished in 3 s using a GPU with 24 GB of memory on a single workstation. Applied to 4D LLSM datasets of human hepatocytes, lung organoid tissue and brain organoid tissue, our method provided rapid and accurate preprocessing within seconds. Importantly, such preprocessing speeds now allow visualisation of the raw microscope data stream in real time, significantly improving the usability of LLSM in biology. In summary, this advancement holds transformative potential for light-sheet microscopy, enabling real-time, on-the-fly data preprocessing, visualisation, and analysis on standard workstations, thereby revolutionising biological imaging applications for LLSM and similar microscopes.

A deep learning method for simultaneous denoising and missing wedge reconstruction in cryogenic electron tomography

Cryogenic electron tomography is a technique for imaging biological samples in 3D. A microscope collects a series of 2D projections of the sample, and the goal is to reconstruct the 3D density of the sample called the tomogram. Reconstruction is difficult as the 2D projections are noisy and can not be recorded from all directions, resulting in a missing wedge of information. Tomograms conventionally reconstructed with filtered back-projection suffer from noise and strong artefacts due to the missing wedge. Here, we propose a deep-learning approach for simultaneous denoising and missing wedge reconstruction called DeepDeWedge. The algorithm requires no ground truth data and is based on fitting a neural network to the 2D projections using a self-supervised loss. DeepDeWedge is simpler than current state-of-the-art approaches for denoising and missing wedge reconstruction, performs competitively and produces more denoised tomograms with higher overall contrast.

DMD and microlens array as a switchable module for illumination angle scanning in optical diffraction tomography.

Optical diffraction tomography (ODT) enables label-free and morphological 3D imaging of biological samples using refractive-index (RI) contrast. To accomplish this, ODT systems typically capture multiple angular-specific scattering measurements, which are used to computationally reconstruct a sample's 3D RI. Standard ODT systems employ scanning mirrors to generate angular illuminations. However, scanning mirrors are limited to illuminating the sample from only one angle at a time. Furthermore, when operated at high speeds, these mirrors may exhibit mechanical instabilities that compromise image quality and measurement speed. Recently, newer ODT systems have been introduced that utilize digital-micromirror devices (DMD), spatial light modulators (SLMs), or LED arrays to achieve switchable angle-scanning with no physically-scanning components. However, these systems associate with power inefficiencies and/or spurious diffraction orders that can also limit imaging performance. In this work, we developed a novel non-interferometric ODT system that utilizes a fully switchable module for angle scanning composed of a DMD and microlens array (MLA). Compared to other switchable ODT systems, this module enables each illumination angle to be generated fully independently from every other illumination angle (i.e., no spurious diffraction orders) while also optimizing the power efficiency based on the required density of illumination angles. We validate the quantitative imaging capability of this system using calibration microspheres. We also demonstrate its capability for imaging multiple-scattering samples by imaging an early-stage zebrafish embryo.

Three Photon Excited Image Scanning Microscopy for in-Depth Super-Resolution Studies of Biological Samples

Multiphoton laser scanning microscopy is a powerful tool for deep imaging of thick biological samples. Image scanning microscopy (ISM) has demonstrated significant improvements in the signal-to-noise ratio in confocal laser scanning microscopy, while at the same time improving upon the effectively attainable resolution. Two-photon excitation (2PE), combined with ISM, has been shown to allow for deep tissue imaging with enhanced resolution compared to 2PE microscopy. Three-photon excitation (3PE) has enabled record imaging depth and contrast for multiphoton imaging, due to the superior suppression of out-of-focus signal generation. In this paper, we demonstrate super-resolution 3PE ISM. This is achieved using a single-photon avalanche detector array, and 1040-nm pulses for 3PE of blue fluorescence. This method enables subdiffraction limited resolution imaging of biological samples stained with blue fluorescent markers, such as mouse myocardial and spinal cord tissues stained with 4′,6-diamidino-2-phenylindole. Deconvolution improves the resolving power further and allows for imaging with better than λ/8 resolution with respect to the 3PE wavelength λ. With the ISM pixel reassignment procedure, we demonstrate a resolution enhancement of ∼1.6 laterally, compared to the resolution attained using a photomultiplier tube in a non-descanned detection arrangement, and a factor of ∼1.8 enhancement in axial resolution. The experimentally measured three-dimensional point spread function volume is shrunk ∼4.4-fold, which is close to the theoretically expected enhancement. Published by the American Physical Society 2024

Nitrogen-doped MoS2 QDs as fluorescent probes for sensitive detection of curcumin and cell imaging

BackgroundCurcumin has been used in traditional medicine because of its pharmacological activity, including antioxidant, antibacterial, anticancer, and anticarcinogenic properties. Therefore, sensitive and selective monitoring of curcumin is highly demand for practical application. ResultsIn this study, we describe the construction of a fluorescence method for curcumin assay based on nitrogen-doped MoS2 quantum dots (N–MoS2 QDs). The N–MoS2 QDs are constructed by a solvothermal method using sodium molybdate and Cys as precursors. With the addition of curcumin, the bright blue fluorescence of N–MoS2 QDs is quenched by the inner filter effect (IFE). The QDs emitted bright blue fluorescence and could be quenched by the addition of curcumin via IFE. The dynamic range is the range of 0.1–10 μM for curcumin detection, with a detection limit of 59 nM. N–MoS2 QDs were applied for curcumin assay in real samples with good recovery. In addition, the N–MoS2 QDs exhibited relative low cytotoxicity and could be applied for fluorescence-based imaging in biological samples. SignificanceOur study indicates that the sensor possesses good selectivity to monitor curcumin in water samples, human urine samples, ginger powder samples, mustard samples, and curry samples with satisfactory recoveries. The N–MoS2 QDs possess less cytotoxicity with excellent biocompatibility and were applied for in vitro cell imaging.

OsO2 as the Contrast-Generating Chemical Species of Osmium-Stained Biological Tissues in Electron Microscopy.

Electron imaging of biological samples stained with heavy metals has enabled visualization of subcellular structures critical in chemical-, structural-, and neuro-biology. In particular, osmium tetroxide (OsO4) has been widely adopted for selective lipid imaging. Despite the ubiquity of its use, the osmium speciation in lipid membranes and the process for contrast generation in electron microscopy (EM) have continued to be open questions, limiting efforts to improve staining protocols and therefore high-resolution nanoscale imaging of biological samples. Following our recent success using photoemission electron microscopy (PEEM) to image mouse brain tissues with synaptic resolution, we have used PEEM to determine the nanoscale electronic structure of Os-stained biological samples. Os(IV), in the form of OsO2, generates nanoaggregates in lipid membranes, leading to a strong spatial variation in the electronic structure and electron density of states. OsO2 has a metallic electronic structure that drastically increases the electron density of states near the Fermi level. Depositing metallic OsO2 in lipid membranes allows for strongly enhanced EM signals and conductivity of biological materials. The identification of the chemical species and understanding of the membrane contrast mechanism of Os-stained biological specimens provides a new opportunity for the development of staining protocols for high-resolution, high-contrast EM imaging.

Sparse-angle optical projection tomography based on multi-layer sparsity and deep image priors

Optical projection tomography (OPT) is a computational imaging technique to acquire the volumetric images of biological samples ranging from millimeters to centimeters. For in-vivo OPT, it is essential to minimize the inspection time to reduce the adverse impacts on organisms, including the anesthetic side effect and phototoxicity. It can be achieved by projecting the samples from equally spaced sparse angles, but this method will induce radial artifacts in the reconstructed tomographic images. This paper develops a high-quality reconstruction method for sparse-angle OPT by jointly exploiting the multi-layer sparsity prior and deep image prior (DIP) on the volumetric images. The DIP module works in an unsupervised manner without requirement on a training dataset. This method can also address the inter-layer correlation within the samples, and process multi-layer images in parallel to improve the reconstruction accuracy and efficiency. Simulations and experiments demonstrate the superiority of the proposed method over some widely used reconstruction algorithms for sparse-angle OPT.

Clinical boundary conditions for propagation-based X-ray phase contrast imaging: from bio-sample models targeting to clinical applications.

Typical propagation-based X-ray phase contrast imaging (PB-PCI) experiments using polyenergetic sources are tested in very ideal conditions: low-energy spectrum (mainly characteristic X-rays), small thickness and homogeneous materials considered weakly absorbing objects, large object-to-detector distance, long exposure times and non-clinical detector. Explore PB-PCI features using boundary conditions imposed by a low power polychromatic X-ray source (X-ray spectrum without characteristic X-rays), thick and heterogenous materials and a small area imaging detector with high low-detection radiation threshold, elements commonly found in a clinical scenario. A PB-PCI setup implemented using a microfocus X-ray source and a dental imaging detector was characterized in terms of different spectra and geometric parameters on the acquired images. Test phantoms containing fibers and homogeneous materials with close attenuation characteristics and animal bone and mixed soft tissues (bio-sample models) were analyzed. Contrast to Noise Ratio (CNR), system spatial resolution and Kerma values were obtained for all images. Phase contrast images showed CNR up to 15% higher than conventional contact images. Moreover, it is better seen when large magnifications (>3) and object-to-detector distances (>13 cm) were used. The influence of the spectrum was not appreciable due to the low efficiency of the detector (thin scintillator screen) at high energies. Despite the clinical boundary condition used in this work, regarding the X-ray spectrum, thick samples, and detection system, it was possible to acquire phase contrast images of biological samples.

Nanoscale Quantitative Imaging of Single Nuclear Pore Complexes by Scanning Electrochemical Microscopy.

The nuclear pore complex (NPC) is a proteinaceous nanopore that solely and selectively regulates the molecular transport between the cytoplasm and nucleus of a eukaryotic cell. The ∼50 nm-diameter pore of the NPC perforates the double-membrane nuclear envelope to mediate both passive and facilitated molecular transport, thereby playing paramount biological and biomedical roles. Herein, we visualize single NPCs by scanning electrochemical microscopy (SECM). The high spatial resolution is accomplished by employing ∼25 nm-diameter ion-selective nanopipets to monitor the passive transport of tetrabutylammonium at individual NPCs. SECM images are quantitatively analyzed by employing the finite element method to confirm that this work represents the highest-resolution nanoscale SECM imaging of biological samples. Significantly, we apply the powerful imaging technique to address the long-debated origin of the central plug of the NPC. Nanoscale SECM imaging demonstrates that unplugged NPCs are more permeable to the small probe ion than are plugged NPCs. This result supports the hypothesis that the central plug is not an intrinsic transporter, but is an impermeable macromolecule, e.g., a ribonucleoprotein, trapped in the nanopore. Moreover, this result also supports the transport mechanism where the NPC is divided into the central pathway for RNA export and the peripheral pathway for protein import to efficiently mediate the bidirectional traffic.

An enhanced multimode phase imaging method based on the transport of intensity equation.

Label-free biological cell imaging relies on rapid multimode phase imaging of biological samples in natural settings. To improve image contrast, phase is encoded into intensity information using the differential interference contrast (DIC) and Zernike phase contrast (ZPC) techniques. To enable multimode contrast-enhanced observation of unstained specimens, this paper proposes an improved multimode phase imaging method based on the transport of intensity equation (TIE), which combines conventional microscopy with computational imaging. The ZPC imaging module based on adaptive aperture adjustment is applied when the quantitative phase results of biological samples have been obtained by solving the TIE. Simultaneously, a rotationally symmetric shear-based technique is used that can yield isotropic DIC. In this paper, we describe numerical simulation and optical experiments carried out to validate the accuracy and viability of this technology. The calculated Michelson contrast of the ZPC image in the resolution plate experiment increased from 0.196 to 0.394.

LiveLattice: Real-time visualization of tilted light-sheet microscopy data using a memory-efficient transformation algorithm.

Light-sheet fluorescence microscopy (LSFM), a prominent fluorescence microscopy technique, offers enhanced temporal resolution for imaging biological samples in four dimensions (4D; x, y, z, time). Some of the most recent implementations, including inverted selective plane illumination microscopy (iSPIM) and lattice light-sheet microscopy (LLSM), rely on a tilting of the sample plane with respect to the light sheet of 30-45 degrees to ease sample preparation. Data from such tilted-sample-plane LSFMs require subsequent deskewing and rotation for proper visualization and analysis. Such transformations currently demand substantial memory allocation. This poses computational challenges, especially with large datasets. The consequence is long processing times compared to data acquisition times, which currently limits the ability for live-viewing the data as it is being captured by the microscope. To enable the fast preprocessing of large light-sheet microscopy datasets without significant hardware demand, we have developed WH-Transform, a novel GPU-accelerated memory-efficient algorithm that integrates deskewing and rotation into a single transformation, significantly reducing memory requirements and reducing the preprocessing run time by at least 10-fold for large image stacks. Benchmarked against conventional methods and existing software, our approach demonstrates linear scalability. Processing large 3D stacks of up to 15 GB is now possible within one minute using a single GPU with 24 GB of memory. Applied to 4D LLSM datasets of human hepatocytes, human lung organoid tissue, and human brain organoid tissue, our method outperforms alternatives, providing rapid, accurate preprocessing within seconds. Importantly, such processing speeds now allow visualization of the raw microscope data stream in real time, significantly improving the usability of LLSM in biology. In summary, this advancement holds transformative potential for light-sheet microscopy, enabling real-time, on-the-fly data processing, visualization, and analysis on standard workstations, thereby revolutionizing biological imaging applications for LLSM, SPIM and similar light microscopes.

Single‐Walled Carbon Nanotube‐Based Optical Nano/Biosensors for Biomedical Applications: Role in Bioimaging, Disease Diagnosis, and Biomarkers Detection

AbstractThe convergence of advanced nanotechnology with disease diagnosis has ushered in a transformative era in healthcare, empowering early and accurate detection of diseases and paving the way for timely interventions, improved treatment outcomes, and enhanced patient well‐being. The development of novel materials is frequently the impetus behind significant advancements in sensor technology. Among them, single‐walled carbon nanotubes (SWCNTSs) have emerged as promising nanomaterials for developing biosensors. Their unique optical, electrical, and biocompatibility properties make them promising candidates for enhancing the sensitivity and real‐time monitoring capabilities of biosensors, as well as for enabling various bioimaging techniques. Recent studies have demonstrated the utility of SWCNTS‐based biosensors in the real‐time monitoring of biological analytes, such as nitric oxide and hydrogen peroxide (H2O2), with potential implications for disease understanding and therapeutic response assessment. Moreover, SWCNTSs have shown promise in bioimaging applications, including fluorescence, Raman spectroscopy, and photoluminescence imaging of biological samples. This article delves into the core principles, design strategies, and operational mechanisms that underpin SWCNTS‐bioimaging techniques‐based biosensors. It emphasizes on their unique properties and versatile functionalization of carbon nanotubes, laying the foundation for their integration into biosensor platforms and applications aimed at diagnosing a wide spectrum of diseases including infectious diseases, cancer, neurological disorders, and metabolic conditions.

Scanning Electron Microscopy Imaging of Large DNA Molecules Using a Metal-Free Electro-Stain Composed of DNA-Binding Proteins and Synthetic Polymers.

This paper presents the first scanning electron microscopy (SEM)-based DNA imaging in biological samples. This novel approach incorporates a metal-free electro-stain reagent, formulated by combining DNA-binding proteins and synthetic polymers to enhance the visibility of 2-nm-thick DNA under SEM. Notably, DNA molecules stain with proteins and polymers appear as dark lines under SEM. The resulting DNA images exhibit a thickness of 15.0±4.0nm. As SEM is the primary platform, it integrates seamlessly with various chemically functionalized large surfaces with the aid of microfluidic devices. The approach allows high-resolution imaging of various DNA structures including linear, circular, single-stranded DNA and RNA, originating from nuclear and mitochondrial genomes. Furthermore, quantum dots are successfully visualized as bright labels that are sequence-specifically incorporated into DNA molecules, which highlights the potential for SEM-based optical DNA mapping. In conclusion, DNA imaging using SEM with the novel electro-stain offers electron microscopic resolution with the ease of optical microscopy.

Single Extracellular Vesicle Imaging and Computational Analysis Identifies Inherent Architectural Heterogeneity.

Evaluating the heterogeneity of extracellular vesicles (EVs) is crucial for unraveling their complex actions and biodistribution. Here, we identify consistent architectural heterogeneity of EVs using cryogenic transmission electron microscopy (cryo-TEM), which has an inherent ability to image biological samples without harsh labeling methods while preserving their native conformation. Imaging EVs isolated using different methodologies from distinct sources, such as cancer cells, normal cells, immortalized cells, and body fluids, we identify a structural atlas of their dominantly consistent shapes. We identify EV architectural attributes by utilizing a segmentation neural network model. In total, 7,576 individual EVs were imaged and quantified by our computational pipeline. Across all 7,576 independent EVs, the average eccentricity was 0.5366 ± 0.2, and the average equivalent diameter was 132.43 ± 67 nm. The architectural heterogeneity was consistent across all sources of EVs, independent of purification techniques, and compromised of single spherical, rod-like or tubular, and double shapes. This study will serve as a reference foundation for high-resolution images of EVs and offer insights into their potential biological impact.

Pretraining a foundation model for generalizable fluorescence microscopy-based image restoration.

Fluorescence microscopy-based image restoration has received widespread attention in the life sciences and has led to significant progress, benefiting from deep learning technology. However, most current task-specific methods have limited generalizability to different fluorescence microscopy-based image restoration problems. Here, we seek to improve generalizability and explore the potential of applying a pretrained foundation model to fluorescence microscopy-based image restoration. We provide a universal fluorescence microscopy-based image restoration (UniFMIR) model to address different restoration problems, and show that UniFMIR offers higher image restoration precision, better generalization and increased versatility. Demonstrations on five tasks and 14 datasets covering a wide range of microscopy imaging modalities and biological samples demonstrate that the pretrained UniFMIR can effectively transfer knowledge to a specific situation via fine-tuning, uncover clear nanoscale biomolecular structures and facilitate high-quality imaging. This work has the potential to inspire and trigger new research highlights for fluorescence microscopy-based image restoration.

Multimode fiber-coupled VIPA spectrometer for high-throughput Brillouin imaging of biological samples

Slow acquisition time and instrument stability are the two major limitations for the application of confocal Brillouin microscopy to biological materials. Although overlooked, coupling the microscope to the spectrometer with a multimode fiber (MMF) is a simple yet viable solution to increase both the detection efficiency and the stability of the classical single-mode fiber-coupled virtually imaged phase array (VIPA) instruments. Here we implement the first successful MMF-coupled VIPA spectrometer for confocal Brillouin applications and present a dimensioning strategy to optimize its collected power. The use of an MMF brings a tremendous improvement on the stability of the spectrometer that allows performing experiments over several weeks without realignment of the device. For instance, we map the Brillouin shift and linewidth in growing ductal and acinar organoids with a spatial resolution of 1×1×6μm3 and 50ms dwell time. Our results clearly reveal the formation of a lumen in these organoids. Careful examination of the data also suggests an increase in the viscosity of the cells of the assembly.

High-resolution X-Ray imaging of small animal samples based on Commercial-Off-The-Shelf CMOS image sensors.

An automated system for acquiring microscopic-resolution radiographic images of biological samples was developed. Mass-produced, low-cost, and easily automated components were used, such as Commercial-Off-The-Self CMOS image sensors (CIS), stepper motors, and control boards based on Arduino and RaspberryPi. System configuration, imaging protocols, and Image processing (filtering and stitching) were defined to obtain high-resolution images and for successful computational image reconstruction. Radiographic images were obtained for animal samples including the widely used animal models zebrafish (Danio rerio) and the fruit-fly (Drosophila melanogaster), as well as other small animal samples. The use of phosphotungstic acid (PTA) as a contrast agent was also studied. Radiographic images with resolutions of up to (7±0.6)μm were obtained, making this system comparable to commercial ones. This work constitutes a starting point for the development of more complex systems such as X-ray attenuation micro-tomography systems based on low-cost off-the-shelf technology. It will also bring the possibility to expand the studies that can be carried out with small animal models at many institutions (mostly those working on tight budgets), particularly those on the effects of ionizing radiation and absorption of heavy metal contaminants in animal tissues.