Biological Imaging Research Articles

An intramolecular charge transfer based fluorescent probe for imaging of OCl–

The discovery and utilization of new fluorescent chromophore is indispensable to exploit high performance probes for biological research. Stokes shift is one of the most important properties of chromophore accounting for super-resolution fluorescence imaging. Intramolecular charge transfer (ICT) is one of the fundamental mechanisms for fluorescence that accompanied by large Stokes shifts. Based on the conformational changes between ground and excited states, ICT models can be divided into two types: conformation-steady ICT, whose conformation remains unchanged, and conformation-changeable ICT, which is characterized by the rotation of the chromophore around an axis upon excitation. Herein, we report a new chromophore whose donor and acceptor parts took a butterfly geometry with a dihedral angle of 21° in ground state and a planar conformation upon photo excitation. The bent conformation might be ascribed to the extra conjugated double bond, which made the coplanarity of the chromophore in ground state get worse. The chromophore shows a remarkable Stokes shift over 150 nm and a high fluorescence quantum yieldof 0.62. The limit of detection is 41 nM, which enabled the imaging of basal as well as induced OCl– in different cells. Moreover, the pronounced spectroscopic properties ensure the in vivo monitoring of OCl– in arthritic mice. This finding would shed light on the exploitation of small molecule probes based on new fluorescence chromophore for precise biological imaging.

Enhanced light-sheet illumination by a multi-layered stair-step phase mask

Light-sheet microscopy is a valuable tool for live biological imaging, offering high speed, high spatial resolution, and low phototoxicity imaging. This study explores a multi-layered stair-step phase mask to further improve illumination of this microscopy. By inserting the mask into the illumination optics, we can modify the Gaussian beam to extend its length by approximately 1.6-fold, which increases the field of view in light-sheet microscopy while keeping resolution reduction small. We demonstrate this improvement through in vivo imaging of medaka fish embryos, highlighting the mask’s potential to enhance the visualization of multicellular organisms at the cellular level.

Evaluating deep neural networks in optimizing drug discovery and precision medicine: A review

Introduction/Background: Deep neural networks have shown great promise in advancing drug discovery and precision medicine. By leveraging large amounts of complex biomedical and chemical data, deep learning approaches can identify novel targets, predict drug-target and drug-drug interactions, generate new molecular structures, and assist in personalized treatment selection and development. However, fully utilizing deep learning techniques for optimization across the drug development pipeline remains an ongoing challenge. Materials and Methods: A comprehensive literature review was conducted using major bibliographic databases including PubMed, Web of Science, and Scopus. Search terms included combinations of "deep learning", "drug discovery", "precision medicine", "biomedical data", and "neural networks". Over 200 papers published between 2010-2023 related to deep learning applications in pharmacology and genomics were identified and reviewed. Results: Deep learning has been widely applied at various stages of the drug discovery process including target identification/prioritization, lead generation/optimization, and prediction of molecular properties. Convolutional neural networks are commonly used for the representation and classification of biological sequence and image data for tasks such as gene expression analysis and pathogen detection from microscopy images. Graph neural networks effectively model compound structures and interactome networks to predict molecular bindings and disease associations. Multi-modal neural networks integrate diverse data types for personalized treatment response prediction and biomarker discovery. Challenges remain around data and model interpretation, generalization to new targets/diseases, and integration across domains. Discussion: While deep learning has shown promise, rigorous benchmarking and validation on real-world clinical endpoints are still needed to establish usefulness in decision-making. Data and model transparency must be improved to enable scientific insights. Privacy and security risks accompanying "real world" biomedical big data will require ethical practices. Standardization and sharing of resources/protocols could accelerate progress by enabling comparison of techniques. Combining deep learning with other AI paradigms like causal inference may further improve utility in drug discovery and precision healthcare. Conclusion: Deep neural networks demonstrate potential for optimizing drug development and precision medicine applications. Continued advancement relies on addressing challenges around data, models, validation, and ethics. Multi-disciplinary collaborations integrating machine learning, molecular biology, medicine, and other domains are needed to fully realize benefits to patients.

Small molecule probes with strong one and two-photon excited fluorescence for bioimaging

Two-photon excited fluorescence (TPEF) is widely sought after in bioimaging applications due to its exceptional deep tissue penetration and excellent high spatiotemporal resolution. In this paper, benzothiazole and benzimidazole are linked to N, N-diphenylaniline, respectively, through pyridine vinyl. Two new D-π-A (electron donor-π conjugated bridges-electron acceptor) probes (P1, P2) were designed and synthesized. The molecular structures were completely characterized through FT-IR, 1H NMR, 13C NMR, ESI-MS, and X-ray single crystal diffraction. The probes have good one-photon excited fluorescence and two-photon excited fluorescence. Experimental test results indicate that they can be fluorescence probes for biological imaging of HePG2 and juvenile zebrafish. In addition, the fluorescent probes also have the ability to target and locate cancer cell mitochondria.

Feasibility study of YSO/SiPM based detectors for virtual monochromatic image synthesis.

The development of photon-counting CT systems has focused on semiconductor detectors like cadmium zinc telluride (CZT) and cadmium telluride (CdTe). However, these detectors face high costs and charge-sharing issues, distorting the energy spectrum. Indirect detection using Yttrium Orthosilicate (YSO) scintillators with silicon photomultiplier (SiPM) offers a cost-effective alternative with high detection efficiency, low dark count rate, and high sensor gain. This work aims to demonstrate the feasibility of the YSO/SiPM detector (DexScanner L103) based on the Multi-Voltage Threshold (MVT) sampling method as a photon-counting CT detector by evaluating the synthesis error of virtual monochromatic images. In this study, we developed a proof-of-concept benchtop photon-counting CT system, and employed a direct method for empirical virtual monochromatic image synthesis (EVMIS) by polynomial fitting under the principle of least square deviation without X-ray spectral information. The accuracy of the empirical energy calibration techniques was evaluated by comparing the reconstructed and actual attenuation coefficients of calibration and test materials using mean relative error (MRE) and mean square error (MSE). In dual-material imaging experiments, the overall average synthesis error for three monoenergetic images of distinct materials is 2.53% ±2.43%. Similarly, in K-edge imaging experiments encompassing four materials, the overall average synthesis error for three monoenergetic images is 4.04% ±2.63%. In rat biological soft-tissue imaging experiments, we further predicted the densities of various rat tissues as follows: bone density is 1.41±0.07 g/cm3, adipose tissue density is 0.91±0.06 g/cm3, heart tissue density is 1.09±0.04 g/cm3, and lung tissue density is 0.32±0.07 g/cm3. Those results showed that the reconstructed virtual monochromatic images had good conformance for each material. This study indicates the SiPM-based photon-counting detector could be used for monochromatic image synthesis and is a promising method for developing spectral computed tomography systems.

A novel fluorescent probe for fast detection of sulfur dioxide derivatives in water, soil, food samples and its applications in biological imaging

Sulfur dioxide (SO2) has a wide range of applications in food additives and industrial production, and it is one of the main substances that form acid rain, causing serious harm to ecosystems and human health. Hence, it is necessary to construct an effective tool to quickly and accurately detect SO2 derivatives in environmental, food, and biological samples. In this study, fluorescent probe NPMQ was built to detect SO2 derivatives from nopinone with the merits of superior water solubility, high sensitivity (12nM), excellent specificity, large Stokes shift (180nm), and rapid response time (within 5s). NPMQ was used to qualitatively and quantitatively detect SO2 derivatives in environmental water, soil and food samples. In addition, an electrospinning film was prepared with the probe NPMQ to image SO2 derivatives, and test strips are capable of rapidly, sensitively, and selectively detecting SO2 derivatives with the naked eye. Moreover, the probe NPMQ was used to visualize endogenous SO2 derivatives in Arabidopsis thaliana under Cd2+ stress. Furthermore, the probe NPMQ was employed to image exogenous and endogenous SO2 derivatives in living Hela, HepG-2 cells, and zebrafish. This study develops an effective tool for monitoring SO2 derivatives in the environmental, food, and biological systems.

Bifocal lenses with adjustable intensities enabled by bilayer liquid crystal structures.

In this paper, we propose bifocal lenses based on bilayer structures composed of a liquid crystal (LC) cell and LC polymer, and the relative intensity of two foci can be adjusted arbitrarily through applying an external voltage. Two LC layers have different light modulation functions: when circularly polarized light passes through the first layer, part of the outgoing light is converted with PB phase modulation and another part is not converted; followed by the second layer, PB modulation of these two parts would be simultaneously realized but with opposite signs; thus the transmitted left- and right-handed circularly polarized (LCP and RCP) light can be independently controlled. As proof-of-concept examples, longitudinal and transverse bifocal lenses are designed to split an incident LCP light into two convergent beams with orthogonal helicity, and the position of the two foci can be flexibly arranged. Benefitting from the electrically controlled polarization conversion efficiency (PCE) of the LC cell, the relative intensity of the two foci can be adjusted arbitrarily. Experimental results agree well with theoretical calculations. Besides, a broadband polarization and an edge imaging system based on the proposed bifocal LC lenses have also been demonstrated. This paper presents a simple method to design a functional multilayer LC device and the proposed bifocal lenses may have potentials in the optical interconnection, biological imaging, and optical computing.

Study on the Influence of Host-Guest Structure and Polymer Introduction on the Afterglow Properties of Doped Crystals.

Due to their low cost, good biocompatibility, and ease of structural modification, organic long-persistent luminescence (LPL) materials have garnered significant attention in organic light-emitting diodes, biological imaging, information encryption, and chemical sensing. Efficient charge separation and carrier migration by the host-guest structure or using polymers and crystal to build rigid environments are effective ways of preparing high-performance materials with long-lasting afterglow. In this study, four types of crystalline materials (MODPA: DDF-O, MODPA: DDF-CHO, MODPA: DDF-Br, and MODPA: DDF-TRC) were prepared by a convenient host-guest doping method at room temperature under ambient conditions, i.e., in the presence of oxygen. The first three types exhibited long-lived charge-separated (CS) states and achieved visible LPL emissions with durations over 7, 4, and 2 s, respectively. More surprisingly, for the DDF-O material prepared with PMMA as the polymer substrate, the afterglow time of DDF-O: PMMA was longer than 10 s. The persistent room-temperature phosphorescence effect caused by different CS state generation efficiencies and rigid environment were the main reason for the difference in LPL duration. The fourth crystalline material was without charge separation and exhibited no LPL because it was not a D-A system. The research results indicate that the CS state generation efficiency and a rigid environment are the key factors affecting the LPL properties. This work provides new understandings in designing organic LPL materials.

A clustering tool for generating biological geometries for computational modeling in radiobiology.

To develop a computational tool that converts biological images into geometries compatible with computational software dedicated to the Monte Carlo simulation of radiation transport (TOPAS), and subsequent biological tissue responses (CompuCell3D). The depiction of individual biological entities from segmentation images is essential in computational radiobiological modeling for two reasons: image pixels or voxels representing a biological structure, like a cell, should behave as a single entity when simulating biological processes, and the action of radiation in tissues is described by the association of biological endpoints to physical quantities, as radiation dose, scored the entire group of voxels assembling a cell.
Approach: The tool is capable of cropping and resizing the images and performing clustering of image voxels to create independent entities (clusters) by assigning a unique identifier to these voxels conforming to the same cluster. The clustering algorithm is based on the adjacency of voxels with image values above an intensity threshold to others already assigned to a cluster. The performance of the tool to generate geometries that reproduced original images was evaluated by the Dice Similarity Coefficient (DSC), and by the number of individual entities in both geometries. A set of tests consisting of segmentation images of cultured neuroblastoma cells, two cell nucleus populations, and the vasculature of a mouse brain were used.
Main results: The DSC was 1.0 in all images, indicating that original and generated geometries were identical, and the number of individual entities in both geometries agreed, proving the ability of the tool to cluster voxels effectively following user-defined specifications. The potential of this tool in computational radiobiological modeling, was shown by evaluating the spatial distribution of DNA Double-Strand-Breaks after microbeam irradiation in a segmentation image of a cell culture.
Significance: This tool enables the use of realistic biological geometries in computational radiobiological studies.

Nonclassical Spin‐Multiplexing Metasurfaces Enabled Multifunctional Meta‐Scope

AbstractDielectric metasurfaces have emerged as attractive devices for advanced imaging systems because of their high efficiency, ability of wavefront manipulation, and lightweight. The classical spin‐multiplexing metasurfaces can only provide two orthogonal circular polarization channels and require high phase contrast which limits their applications. Here, metasurfaces with arbitrary three independent channels are demonstrated by proposing a nonclassical spin‐multiplexing approach exploring the low refractive index meta‐atoms. A zoom microscope with on‐axis tri‐foci and a synchronous achiral‐chiral microscope with in‐plane tri‐foci based on silicon nitride metasurfaces are experimentally demonstrated. Based on the on‐axis tri‐foci metasurface, singlet zoom imaging with three magnifications and a broadband response (blue to red) based on a single metasurface is first demonstrated. A compact microscope (meta‐scope) consisting of two metasurfaces with three magnifications of 9.5, 10, and 29X with diffraction‐limited resolutions is further constructed, respectively. Utilizing the in‐plane tri‐foci metasurface, a singlet microscope with three achiral‐chiral channels is demonstrated. It offers a magnification of 53X and a diffraction‐limited resolution, enabling simultaneous imaging of an object's achiral and chiral properties. Our multifunctional metasurfaces and meta‐scope approaches could boost the applications in biological imaging and machine vision.

Ultrabroadband chromaticity-programmable random lasing based on waveguide-assisted pumping strategy

Chromaticity-tunable random lasers (RLs) have wide applications in laser display and imaging. However, the achievable chromaticity range poses challenges due to their inherent randomness and the inevitable loss. Here, an ultrabroadband chromaticity-programmable RL has been demonstrated via waveguide-assisted pumping strategy. The unique configuration with destroyed waveguide supplies an excellent platform for achieving full color random lasing through the cascade pumping process. Random lasing with tunable wavelengths spanning the entire visible range is achieved via side-pumping schemes. The eight acceptor RLs can be simultaneously pumped to obtain chromaticity-programmable random lasing, showcasing a Commission Internationale de l'Eclairage (CIE) color map with 155% more perceptible colors than the standard red-green-blue space. This opens the possibilities for programmable RLs with potential applications in biological imaging and smart sensing.

Fast X-ray trimodal computed tomography with an improved diffraction enhanced imaging method

The rapid acquisition of projective images with low radiation dose is essential in computed tomography with diffraction enhanced imaging to extract absorption, refraction, and scattering images from weakly absorbing specimens. This plays a critical role in applying diffraction enhanced imaging to biological and medical imaging. In this study, an improved diffraction enhanced imaging method is proposed to rapidly implement X-ray trimodal computed tomography. This method positions the sample in a specific region near the analyzer crystal, allowing the simultaneous acquisition of transmitted and diffracted images in a single exposure. When combined with computed tomography, three different sample properties, known as absorption, phase and scattering, can be reconstructed simultaneously by collecting the projective images as the sample rotates from 0° to 360°. The experimental results demonstrate that this straightforward method enables the quantitative extraction of sample information and simplifies the data acquisition procedure in computed tomography with diffraction enhanced imaging. Therefore, this novel method offers the advantages of straightforward imaging device, rapid CT data acquisition, low radiation dose and more comprehensive sample information extraction.

Enabling robust near-infrared afterglow polymers through cascade energy transfer

Near-infrared afterglow materials, which can significantly reduce background fluorescence and improve penetration in living organisms, are vital for precision imaging. However, the practicability of near-infrared afterglow materials has been hindered by their short lifetime and low luminescence quantum efficiency. Herein, we propose an effective strategy for doping water-soluble afterglow polymer host materials with fluorescent dyes, leveraging the step-by-step Förster energy transfer principle to realize near-infrared afterglow. Benefiting from the ultralong lifetime of 4.2 s and high quantum efficiency of 20.1 % for the blue host PAMCz as well as efficient cascade energy transfer efficiency of 83.3 %, the developed NIR afterglow exhibits luminescence peaked at 750 nm, a lifetime of 1.4 s, and a quantum efficiency of 17.1 %. A successful demonstration of near-infrared afterglow biological imaging with a penetration depth of 3.4 mm and signal-to-background ratio of 48.3, has been established. These advancements expand the scope of near-infrared afterglow materials by combining an ultralong lifetime with high quantum efficiency, providing a roadmap for designing robust near-infrared materials.

Theoretical Insight into the Fluorescence Spectral Tuning Mechanism: A Case Study of Flavin-Dependent Bacterial Luciferase.

Bioluminescence of bacteria is widely applied in biological imaging, environmental toxicant detection, and many other situations. Understanding the spectral tuning mechanism not only helps explain the diversity of colors observed in nature but also provides principles for bioengineering new color variants for practical applications. In this study, time-dependent density functional theory (TD-DFT) and quantum mechanics and molecular mechanics (QM/MM) calculations have been employed to understand the fluorescence spectral tuning mechanism of bacterial luciferase with a focus on the electrostatic effect. The spectrum can be tuned by both a homogeneous dielectric environment and oriented external electric fields (OEEFs). Increasing the solvent polarity leads to a redshift of the fluorescence emission maximum, λF, accompanied by a substantial increase in density. In contrast, applying an OEEF along the long axis of the isoalloxazine ring (X-axis) leads to a significant red- or blue-shift in λF, depending on the direction of the OEEF, yet with much smaller changes in intensity. The effect of polar solvents is directionless, and the red-shifts can be attributed to the larger dipole moment of the S1 state compared with that of the S0 state. However, the effect of OEEFs directly correlates with the difference dipole moment between the S1 and S0 states, which is directional and is determined by the charge redistribution upon deexcitation. Moreover, the electrostatic effect of bacterial luciferase is in line with the presence of an internal electric field (IEF) pointing in the negative X direction. Finally, the key residues that contribute to this IEF and strategies for modulating the spectrum through site-directed point mutations are discussed.

Making use of manufacturing process variations: Machine learning approaches for efficient medical and biological study-based image compression and lossless transmission

This research paper explores advanced machine learning techniques for compressing and transmitting medical and biological study-based images without loss of critical diagnostic information. We investigate deep learning architectures including autoencoders, generative adversarial networks (GANs) and transformer models optimized for various medical and biological imaging modalities. Our proposed hybrid compression pipeline combines semantic segmentation, region-adaptive encoding, and learned post-processing to achieve state-of-the-art compression ratios while preserving clinically relevant features. Extensive experiments on large-scale datasets of X-rays, CT scans, MRI scans and microscopy images demonstrate the efficacy of our approach in terms of compression performance, reconstruction quality, and computational efficiency. We also present a novel blockchain-based system for secure and lossless transmission of the compressed medical and biological data. Our findings indicate that machine learning-driven compression can enable more efficient storage and sharing of medical and biological images in resource-constrained healthcare and research environments.

A 1,3,4-thiadiazole based turn-on probe with large Stoke’s shift and ESIPT effect for fluorescent detection of H2S and its applications

Based on a useful 1,3,4-thiadiazole scaffold in our lab, a turn-on fluorescent probe for sensing H2S in environment, biological imaging and food analysis was rationally designed and synthesized. In this probe, 1,3,4-thiadiazole unit was used as fluorophore with ESIPT effect and 2,4-dinitrophenyl moiety was used as recognition fragment and fluorescent bursting agent to inhibt ESIPT. Thus, a PET process was formed and an “off–on” procedure arose after added target analyte. These brought the probe could specifically detect H2S without being interfered by other biological thiols. It showed high sensitivity (LOD=60.8 nM), large Stoke’s shift (133 nm) and had a stable performance over pH range of 6.0–9.0 in H2S monitoring system. More importantly, probe could be used for detection of H2S in real water, three beer samples and red wine sample. Furthermore, probe was successfully utilized for visualizing H2S in HeLa cells and applied for real-time monitoring of H2S during food spoilage by “naked eye” and smartphone colorimetric analysis.

Computer-aided diagnosis of breast cancer from mammogram images using deep learning algorithms

Even though accurate detection of dangerous malignancies from mammogram images is mostly dependent on radiologists' experience, specialists occasionally differ in their assessments. Computer-aided diagnosis provides a better solution for image diagnosis that can help experts make more reliable decisions. In medical applications for diagnosing cancerous growths from mammogram images, computerized and accurate classification of breast cancer mammogram images is critical. The deep learning approach has been widely applied in medical image processing and has had considerable success in biological image classification. The Convolutional Neural Network (CNN), Inception, and EfficientNet are proposed in this paper. The proposed models attain better performance compared to the conventional CNN. The models are used to automatically classify breast cancer mammogram images from Kaggle into benign and malignant. Simulation results demonstrated that EfficientNet, with an accuracy between 97.13 and 99.27%, and overall accuracy of 98.29%, perform better than the other models in this paper.

Using neural networks for image analysis in general physiology.

An article with three goals, namely, to (1) provide the set of ideas and information needed to understand, at a basic level, the application of convolutional neural networks (CNNs) to analyze images in biology; (2) trace a path to adopting and adapting, at code level, the applications of machine learning (ML) that are freely available and potentially applicable in biology research; (3) by using as examples the networks described in the recent article by Ríos et al. (2024. https://doi.org/10.1085/jgp.202413595), add logic and clarity to their description.

Sub-60-nm isotropic 3D super-resolution microscopy through self-interference field excitation

Due to its unique optical sectioning capability, confocal laser scanning microscopy (CLSM) can provide highly sensitive, highly specific imaging of specimens in three dimensions and has been recognized as an indispensable tool for biological and medical studies. Nonetheless, the spatial resolution of CLSM is constrained by the diffraction nature, with λ/2 resolution laterally (xy) and 1.5λ resolution axially (z). To improve the imaging resolution beyond the diffraction limit as well as to achieve its isotropy, we present a strategy of mirror-assisted self-interference field excitation (SIEx) highly nonlinear microscopy. The imaging principle has been theoretically modeled and investigated in accordance with the Wolf vector diffraction theory. The experimental demonstration of isotropic three-dimensional SIEx nanoscopy, assisted with the ultrahigh-order optical nonlinearity of photon avalanching nanoparticles, was achieved utilizing a common laser-scanning microscope configuration, resulting in a lateral resolution of 54 nm (λ/15) and an axial resolution of 57 nm (λ/15) with one single beam from a low-power, continuous-wave, near-infrared laser (19kW⋅cm−2). We further extended the applicability of the SIEx scheme to biological imaging and demonstrated super-resolution imaging for immunolabeled actin filaments of BSC-1 cells with an isotropic full width at half maximum of ∼67nm (λ/13). Our facile SIEx methodology can, in principle, be seamlessly integrated with the existing and widely available laser-scanning fluorescence microscopes without adding any complexity, thereby enabling their capability of 3D isotropic super-resolution imaging.

Edta-assisted rapid synthesis of highly monodisperse and water-soluble ZnSe quantum dots colloids by electron-beam irradiation

ZnSe quantum dots (QDs) have attracted attentions due to small size and strong fluorescence. However, ZnSe QDs are easy to agglomerate and insoluble in aqueous solutions, which limits their practical applications. Herein, we report a novel strategy for synthesizing highly dispersed and water-soluble ZnSe QDs by electron beam radiation with the aid of EDTA within only a few minutes at room temperature without any reduction reagents. In the reaction system, EDTA plays an important role in controlling the quantum size of crystal growth, inhibiting the agglomeration of ZnSe QDs and improving the water solubility. ZnSe QDs exhibit a large bandgap of 4.3 eV, significantly blue shifted compared with bulk ZnSe due to the quantum confinement effect. Constructed the structural model by MS, the nucleation reaction and growth mechanism of ZnSe QDs are proposed. Electron-beam irradiation can be widely used to synthesize water-soluble semiconductor QDs with promising application in biological imaging.