Biological Imaging Research Articles

Organic luminogens (OLs) with piezochromic (PC) properties have attracted significant attention for their varied applications in chemical sensors, organic optoelectronic devices, biological imaging, etc. In this work, we designed and synthesized three donor–acceptor–donor- or donor–acceptor-structured OLs with different donor or acceptor moieties. Their photophysical properties in both dilute solution and aggregated states were studied through various spectroscopic analytical methods, and their PC properties were investigated under mechanical grinding (MG) conditions. The OLs containing cyanostilbene moiety exhibited a photoemission shift up to ~45 nm after simple grinding, while that was only ~10 nm for cyanostyrene-containing OL. Combined with the powder X-ray diffraction analysis, the incorporation of the cyanostilbene moiety is inferred to play an important role in inducing the apparent PC properties. Our study not only reports novel OLs with good PC properties, but also discusses the structure–property relationships in order to provide guidance for future rational design and the development of novel PC materials.

Abstract Bioorthogonal Raman probes offer significant advantages for multiplexed, background‐free biological imaging due to their unique vibrational fingerprints and photostability. However, their widespread application is limited by the inherently weak Raman scattering signal, which creates a pressing need for high‐sensitivity Raman probes. Here, chemically stable diacetylene (─C≡C─C≡C─) functionalized β‐ketoenamine covalent organic frameworks (BDDA COFs) are synthesized as Raman probes for in vivo imaging of bone cracks. The BDDA COFs exhibited significantly enhanced Raman intensity at 2205 cm −1 (up to ≈10 5 fold vs 5‐ethynyl‐2′‐deoxyuridine) compared to conventional alkyne‐based probes. Moreover, polydopamine (PDA)‐coated COFs (BDDA@PDA COFs) are prepared by utilizing the high affinity of PDA for calcium ions exposed at bone injury sites, thereby enabling highly sensitive and specific ex vivo and in vivo Raman imaging of bone cracks. The development of COFs containing alkyne groups as strong Raman imaging probes in the silent region provides a new paradigm for in vivo Raman imaging materials and also establishes a technical foundation for future applications in disease diagnosis and precision medicine.

Introduction: In recent years carbon dots (CDs) have attracted researchers due to their unique physicochemical and fluorescent (FL) features, which can be applied in many fields such as battery materials, fluorescence sensing, display, biological imaging and photocatalysis. Method: We prepared CDs by using a facile one-step sintering method. The fluorescent properties and the application of pH detection were measured and analyzed. Results: The results show that CDs emit bright purplish-blue light centred at 425 nm excited by 355 nm UV light. FL intensity shows a linear relationship with pH values at 1~4 and 7~11, respectively. The reasonable mechanism of the tested effective pH sensitivity is discussed. Conclusion: Our study shows that the CDs prepared by the one-step sintering method have great potential to be used as pH sensors for physiochemical measurements.

A novel ESIPT fluorescent probe based on 1,3,4-thiadiazole and coumarin for sequential detection of Cu2+ and H2S and its application in food samples.

Rapid detection of hydrogen sulfide utilizing dinitrophenyl ether-based fluorescent probes incorporating aldehyde functional groups.

Evaluation of the deep learning-based detection of dopaminergic neurons in primary culture: A practical alternative to manual counting.

Near-infrared (NIR)-emitting trivalent lanthanides (LnIII) are attractive for applications such as biological imaging and telecommunications. However, their sensitization remains challenging, particularly at excitation wavelengths corresponding to low energies. To address this challenge, a new coumarin-bearing isophthalate-based bridging ligand (C-ip2-), in which the coumarin sensitizer is directly attached to the bridge, was synthesized. In the corresponding [Ln2Ga8(shi)8(C-ip)4]2- metallacrowns (MCs), the sensitization of the NIR-emitting NdIII, ErIII, and YbIII cations was achieved upon excitation of appended coumarins in the visible range (λexc = 435 nm). Compared to the previous generation of [Ln2Ga8(shi)8(C-mip)4]2- MCs, the coumarin antennas are about 5 Å closer to the LnIII emissive centers in this new series of [Ln2Ga8(shi)8(C-ip)4]2- MCs, resulting in an increase in the values by factors of 6.2, 9.4, and 2.7 for NdIII, ErIII, and YbIII analogues, respectively. This allows the detection of their NIR emissions through tissue-mimicking phantoms of 1 mm thickness, validating the potential of this design for NIR imaging applications.

In materials science, plant biology, agriculture, and environmental research, the automated analysis of high‐magnification, complex microscopy images, such as those generated by freeze‐fracture electron microscopy (FF‐TEM), remains a critical challenge that limits the scalability of data interpretation. We present a deep learning computer vision pipeline for high‐throughput detection and morphological characterization analysis of cellulose synthase complexes (CSCs, or rosettes) in FF‐TEM images. The pipeline integrates preprocessing, detection, human‐in‐the‐loop verification, and semantic segmentation to quantify features such as rosette diameter and inter‐lobe spacing. The approach was trained and tested on a curated dataset of high‐resolution FF‐TEM micrographs of Physcomitrium patens , expanded via strategic tiling and augmentation to over 650 images. We compare YOLOv8 and YOLOv9 architectures and demonstrate that YOLOv9 achieves superior performance in both localization accuracy (mAP50‐95 = 0.854) and inference speed. The resulting distributions revealed biological variability consistent with prior manual studies, validating the approach for high‐throughput applications. Our results show that the pipeline achieves human‐expert level accuracy while dramatically reducing analysis time, enabling scalable, reproducible structural characterization of intramembrane protein complexes. The pipeline is broadly applicable to other domains requiring precise interpretation of complex microscopy data and establishes a foundation for future artificial intelligence (AI)‐assisted workflows in biological imaging.

Fluorescence imaging is an indispensable tool for noninvasive visualization of biomolecules in living systems, offering high sensitivity for disease diagnosis and image-guided therapy. Compared to conventional "always-on" fluorescent probes, small molecule single-locked probes achieve higher detection sensitivity and imaging quality by specifically triggering optical signals upon encountering target biomarkers. However, their utility is often compromised by false-positive signals generated in healthy tissues due to the expression of nontarget biomarkers. In contrast, dual-locked fluorescent probes, designed to detect two biomarkers simultaneously, provide significantly enhanced specificity for identifying precise bio molecule events or pathological conditions. This review discusses recent progress in developing dual-locked fluorescent probes for biological imaging, with a focus on their diverse chemical structures and design strategies. Finally, we discuss current challenges and potential future directions for applying these probes in complex biological systems.

Advanced electrochemical sensors offer great opportunities to detect active pharmaceutical ingredients using interactions between nanomaterials and target analytes. Miniaturization of these sensors, wireless data transmission, and sensitivity are important research areas. The integration of quantum computing and artificial intelligence can provide significant improvements in areas such as electrochemical sensing, materials science, and nanofiber fabrication. Furthermore, electrochemical sensors and related techniques (such as voltammetry, amperometry, impedance, and chronoamperometry) and the roles of different electrode types in pharmaceutical drug analysis are discussed. Other methods used to detect these drugs include optical and microfabricated methods. The advantages and disadvantages of these different techniques are illustrated and evaluated with future perspectives. These technologies enable personalized medicine that rapidly assesses drug efficacy and patient-specific responses, while also enabling the development of sustainable electronic systems and more efficient sensors. Research on artificial intelligence is also increasing in the pharmaceutical industry. This study highlights the advantages and future promise of various technological applications of AI technology in drug design and development, which have varying effects. Furthermore, it offers potential in the medical field, particularly through rapid testing and the study of drug interactions using electrochemical sensor technology. Electrochemical biosensors, in particular, are crucial in biological imaging, electrochemical analysis, and drug delivery due to their high specificity, selectivity, and intercycling stability. This review focuses on recent advances in electrochemical devices for healthcare applications, detailing their fabrication, analytical performance, and clinical applications.

Photosensitive fluorophores, whose emission can be controlled using light, are essential for advanced biological imaging, enabling precise spatiotemporal tracking of molecular features and facilitating super-resolution microscopy techniques. Although irreversibly photoactivatable fluorophores are well established, reversible reporters that can be reactivated multiple times remain scarce, and only a few have been applied in living cells using generalizable protein labeling methods. To address these limitations, we introduce chemigenetic photoswitchable fluorophores, leveraging the self-labeling HaloTag protein with fluorogenic rhodamine dye ligands. By incorporating a light-responsive protein domain into HaloTag, we engineer a tunable, photoswitchable HaloTag (psHaloTag), which can reversibly modulate the fluorescence of a bound dye-ligand via a light-induced conformational change. Our best performing psHaloTag variants show excellent performance in living cells, with large, reversible, deep-red fluorescence turn-on upon 450nm illumination across various biomolecular targets and SMLM compatibility. Together, this work establishes the chemigenetic approach as a versatile platform for the design of photoswitchable reporters, tunable through both genetic and synthetic modifications, with promising applications for dynamic imaging.

Lanthanide-ion-activated nanoparticles stimulated by 808 or 980 nm lasers present promising applications in biological imaging. This contribution reveals their physicochemical properties and explores their potential as near-infrared-II (NIR-II) fluorescent agents for bioimaging. Specifically, the NIR-IIb window (1500-1700 nm) has the advantages of low scattering and less autofluorescence from the tissues, which makes this region suitable for imaging with greater clarity. Lanthanides offer diverse emission possibilities due to their rich energy levels, which make them highly effective nanoprobes. This study focuses on gadolinium oxide (Gd2O3) as the host material due to its facile fabrication and low toxicity. The Gd2O3 system is doped with Yb3+ and Er3+ ions and achieves a high quantum efficiency of 22.8% in the NIR-IIx and NIR-IIb windows. Moreover, the superior penetrability of the NIR-IIb window is unveiled by the penetration depth testing and in vivo imaging studies. Additionally, Gd3+ ions exhibit magnetic properties, which support their application in magnetic resonance imaging (MRI). This work reveals the high brightness and high energy transfer efficiency of the Yb3+-Er3+ system and explores the feasibility of Gd2O3 nanoparticles in MRI. Therefore, we believe that this work provides a superior and biocompatible candidate for understanding the dynamics of MRI/NIR-II imaging of the nanophosphor for clinical applications.

This study introduces a microscopic computational imaging system capable of multidimensional spatial, spectral, and polarization detection. The system utilizes four optical encoders and a neural network to reconstruct 36 high-resolution images (1280×960 pixels) across nine spectral bands (400–800 nm), each captured at four polarization angles (0°, 45°, 90°, and 135°), achieving a 1:9 reconstruction ratio. The system performance was evaluated through biological imaging applications, achieving high-resolution visualization of paramecia nuclei, pine pollen surface topography, and mouse muscle fiber birefringence. The integrated design addresses the limitations of conventional single-modality microscopy, providing a versatile tool for biomedical and material analysis.

Super-resolution microscopy has pushed the limits of biological imaging. However, achieving isotropic resolution across all spatial dimensions remains a challenge and often requires a complex and highly sensitive optical setup. Herein, we introduce axial interference speckle illumination-engineered structured illumination microscopy (AXIS-SIM), a minimal-modification approach that utilizes constructive interference from a simple back-reflecting mirror to enhance the axial resolution without additional phase control or complex beam shaping. AXIS-SIM provides superior optical sectioning and improves axial resolution beyond the typical axial resolution of conventional 3D-structured illumination microscopy (~300 nm), achieving lateral and axial resolutions of 108.5 and 140.1 nm, respectively. Furthermore, its robustness against alignment errors and sample-induced aberrations enables high-throughput 3D super-resolution imaging of diverse biological specimens. We demonstrate its potential by visualizing the 3D morphology of cell membranes, resolving the nanoscale distribution of lysosomes and microtubules and tracking lysosomal movements with enhanced axial clarity.

Fluorescent nanodiamonds (FNDs) containing nitrogen-vacancy (NV) defects are useful probes for biological imaging and nanoscale sensing applications. Here, we explore the effect of chemical surface modifications and core-shell structures on the T1 relaxation times of 100 nm FNDs hosting nitrogen-vacancy ensembles. The results show that surface oxidation and silica coating of FNDs using the Stöber method can dramatically increase the spin relaxation time from T1 = 320 ± 9 μs to T1 = 1.00 ± 0.06 ms. Using FT-IR and NEXAFS measurements conducted on air oxidized particles, we find that changes to surface functional groups and sp2 carbon density may be responsible for the observed enhancements to the spin relaxation rate. Finally, we use a Monte Carlo model to numerically investigate the relationship between chemical sensitivity and shell thickness and find that a shell thickness on the order of 1 nm should provide the highest sensitivity. Our findings demonstrate that the surface of FNDs can be engineered to exhibit bulk-like T1 relaxation times, in the absence of complex quantum control sequences, which is crucial to advancing biosensing and imaging applications where surface spin noise currently limits measurement precision.

Precise assessment of toxicological effects remains a key bottleneck in biomedical and environmental health assessments. Traditional toxicology relies on macroscopic end points and manual image analysis, which limit sensitivity to structural damage and introduce subjective bias. We developed an automated deep learning approach based on U-Net for the precise assessment of toxic effects and established a general framework for objective toxicological analysis. Our U-Net model can perform pixel-level segmentation and morphological quantification on thousands of biological images in 1 min without bias. This developed model was then applied to distinguish size-dependent developmental toxicity induced by Ag+, 15 nm, and 100 nm silver nanoparticles (AgNPs) in zebrafish, including the photoreceptor cell layer, inner plexiform layer, skeletal muscle, and spinal cord, which revealed previously undetectable size-dependent and organ-specific toxicity disparities that conventional analytical approaches failed to resolve. The method has the potential to be widely applied to the toxicity assessment of other emerging materials and contaminants. Our model displays great potential to improve toxicity assessment accuracy, efficiency, and reproducibility, providing a scalable application for precise toxicological assessments, including imaging analysis and standardization of assessment processes.

Scanning Ion Conductance Microscopy (SICM) provides high-resolution, nanoscale imaging of living cells, but it is generally limited by a slow scan rate, making it challenging to capture dynamic processes in real time. To tackle this challenge, an integrated data acquisition and computational framework is proposed that improves the temporal resolution of SICM by selectively skipping certain scan lines. A partial convolutional neural network (Partial-CNN) model is developed and trained on SICM images and their corresponding masks to reconstruct the complete images from the undersampled data, ensuring the retention of structural integrity. This approach significantly reduces the image acquisition time (i.e., by 30-63%) without compromising quality, as validated through multiple quantitative metrics. Compared to conventional deep learning methods, the Partial-CNN demonstrates higher accuracy in reconstructing fine details and maintaining consistent height maps across skipped regions. It is shown that this method provides an increased temporal resolution and retains image fidelity, making it suitable for real-time dynamic SICM imaging and improving the smart scanning microscopy applications in time-resolved biological imaging.

The rapid pace of innovation in biological microscopy has produced increasingly large images, putting pressure on data storage and impeding efficient data sharing, management and visualization. This trend necessitates new, efficient compression solutions, as traditional coder-decoder methods often struggle with the diversity of bioimages, leading to suboptimal results. Here we show an adaptive compression workflow based on implicit neural representation that addresses these challenges. Our approach enables application-specific compression, supports images of varying dimensionality and allows arbitrary pixel-wise decompression. On a wide range of real-world microscopy images, we demonstrate that our workflow achieves high, controllable compression ratios while preserving the critical details necessary for downstream scientific analysis.

Optical microscopy constitutes an essential cornerstone in the life sciences, facilitating detailed investigations into the structural and dynamic complexities of biological systems. Nonetheless, classical optical microscopy encounters significant challenges in probing the intricate complexities of cellular and molecular systems, particularly due to the diffraction limit of light and limitations posed by detection noise. Although significant advances in optical microscopy have realized super-resolution, high signal-to-noise ratios, and high-speed imaging, these methods frequently require high-intensity illumination, potentially inducing photodamage and photobleaching in biological samples. Quantum-twinned photons, characterized by their unique properties of quantum entanglement, quantum correlation, and quantum interference at the single photon level, present transformative solutions to these limitations. Several imaging modalities have been developed that utilize quantum-twinned photons, encompassing quantum correlation imaging, quantum entanglement imaging, and quantum interference imaging. These techniques exhibit quantum-enhanced imaging capabilities that markedly outperform classical methods, with diverse applications in cellular, tissue, and organism imaging. Centered on this theme, here we present a comprehensive review of quantum biological imaging leveraging the three pivotal quantum properties of quantum-twinned photons. The review encompasses the physical principles underlying these methods, recent experimental advancements, and an exploration of future prospects and challenges in the practical implementation of quantum bio-imaging.

Magnetic Resonance Imaging (MRI) is a non-invasive technique that provides high-resolution tissue imaging, making it a potential tool for hepatocellular carcinoma (HCC) imaging diagnosis. However, effective visualization of HCC-related molecular changes requires advanced nanoscale contrast agents with surface modifications for specific biomarker binding. Iron-platinum nanoparticles (FePt NPs) are widely used for T2-weighted MRI contrast but are rapidly degraded by macrophages, limiting their accumulation and signal enhancement in vivo. To address this issue, metal-organic frameworks (MOFs) can encapsulate FePt NPs to improve stability and imaging contrast. Additionally, red blood cell membrane (RBC-m) coating enhances tumor tissue accumulation, enabling real-time tracking and diagnosis of HCC. Initial studies have demonstrated the effectiveness of this technology in HCC imaging diagnosis, contributing to disease monitoring and treatment evaluation. With further optimization, these nanocomposite probes have the potential to enhance MRI-based HCC diagnostics, bridging molecular biology and clinical imaging to advance personalized medicine.