Cell Imaging Research Articles

Metal-Responsive Fluorophore and Amikacin-Conjugated Heparin for Bacterial Cell Imaging and Antibacterial Applications

Metal-Responsive Fluorophore and Amikacin-Conjugated Heparin for Bacterial Cell Imaging and Antibacterial Applications

A Spectroscopic Study on the Amyloid-β Interaction with Clicked Peptide-Porphyrin Conjugates: a Vision Toward the Detection of Aβ Peptides in Aqueous Solution.

Alzheimer's disease (AD) is a multifactorial form of dementia mainly affecting people in the elderly, but no effective cure is available. According to the amyloid hypothesis the aggregation of Amyloid-β (Aβ) into oligomeric toxic species is believed to concur with the onset and progression of the disease heavily. By using a click chemistry approach, we conjugated a suitable designed peptide sequence to a metalloporphyrin moiety to obtain three hybrid peptide systems to be studied for their interaction with Amyloid-β peptides. The aim is to get new tools for the diagnosis and therapy in AD. The results described in this study, which were obtained through spectroscopic techniques (UV-Vis, CD, bis-Ans and intrinsic porphyrin Fluorescence), Microfluidics (GCI) and cell biology (MTT, Live cell imaging and flow cytometry), reveal interesting features about the structure-activity relationships connecting these conjugates with the interaction with Aβ, as well as on their potential use as sensing systems. In our opinion the data reported in this paper make the porphyrin-peptide conjugates highly compelling for further exploration as spectroscopic probes to detect Aβ biomarkers in biological fluids.

Clickable HaloTag ligands for live cell labeling and imaging

Self-labeling protein (SLP) tags, such as HaloTag, have gained considerable interest as advanced tools for live cell labeling. However, the chloroalkane-based substrates that can be directly used for protein labeling are limited. Here, we report two bioorthogonal small molecule linkers, chloroalkane-tetrazine (CA-Tz) and chloroalkane-azide (CA-N3), which can penetrate cell membranes and facilitate click chemistry-based labeling in live cells. We compare their labeling capability using two clickable silicon rhodamine dyes (SiR-PEG3-TCO and SiR-PEG4-DBCO). Confocal imaging results demonstrate that using CA-Tz and SiR-PEG3-TCO dye exhibits superior intracellular labeling with low nonspecific signals. We subsequently compared the photostability of SiR dyes with that of green fluorescent proteins (mEmerald). Total internal reflection fluorescence (TIRF) imaging indicates that SiR dyes exhibit superior photostability under identical excitation conditions, making them suitable for long-term cell imaging. Furthermore, SiR dyes labeling also shows high structure retention for the fourth-order super-resolution optical fluctuation imaging (SOFI) compared to fluorescent proteins. This study presents clickable HaloTag linkers as effective tools for live cell labeling and imaging, highlighting the high-quality labeling of chloroalkane linkers and clickable dyes for live cell imaging.

LASCA-Based Monitoring of Drug Impact and Classification using Machine Learning for Biospeckle Images of Melanoma Cells

LASCA-Based Monitoring of Drug Impact and Classification using Machine Learning for Biospeckle Images of Melanoma Cells

Long-Axial-Range Double-Helix Point Spread Functions for 3D Volumetric Super-Resolution Imaging.

Single-molecule localization microscopy (SMLM) is a powerful tool for observing structures beyond the diffraction limit of light. Combining SMLM with engineered point spread functions (PSFs) enables 3D imaging over an extended axial range, as has been demonstrated for super-resolution imaging of various cellular structures. However, super-resolving structures in 3D in thick samples, such as whole mammalian cells, remains challenging as it typically requires acquisition and postprocessing stitching of multiple slices to cover the entire sample volume or more complex analysis of the data. Here, we demonstrate how the imaging and analysis workflows can be simplified by 3D single-molecule super-resolution imaging with long-axial-range double-helix (DH)-PSFs. First, we experimentally benchmark the localization precisions of short- and long-axial-range DH-PSFs at different signal-to-background ratios by imaging fluorescent beads. The performance of the DH-PSFs in terms of achievable resolution and imaging speed was then quantified for 3D single-molecule super-resolution imaging of mammalian cells by DNA-PAINT imaging of nuclear lamina protein lamin B1 in U-2 OS cells. Furthermore, we demonstrate how the use of a deep-learning-based algorithm allows the localization of dense emitters, drastically improving the achievable imaging speed and resolution. Our data demonstrate that using long-axial-range DH-PSFs offers stitching-free, 3D super-resolution imaging of whole mammalian cells, simplifying the experimental and analysis procedures for obtaining volumetric nanoscale structural information.

A Portable Zn2+ Fluorescence Sensor for Information Storage and Bio-Imaging in Living Cells.

A fluorescence probe (Probe-ITR) was designed and synthesized for the rapid detection of Zn2+ based on the excited state intramolecular proton transfer (ESIPT) mechanism. The specific recognition ability of Probe-ITR to Zn2+ was tested using UV-VIS and fluorescence emission spectroscopy. The results demonstrated a rapid response within 40s upon addition of an appropriate amount of Zn2+ into the mixed solution of the target probe molecules (DMSO/PBS = 5/5). Under 365nm ultraviolet light irradiation, the colorless solution changed to yellow-green fluorescence with a 150-folds increase in intensity. Furthermore, the detection limit for specific recognition of Zn2+ by the probe molecule is only 17.3 nmol/L, indicating high sensitivity. The practical application potential of the probe molecules was enhanced by employing on information storage and conducting cell imaging experiments.

Iodinated PSMA Ligands as XFI Tracers for Targeted Cell Imaging and Characterization of Nanoparticles

Prostate cancer is the second most commonly diagnosed cancer in men worldwide. Despite this, current diagnostic tools are still not satisfactory, lacking sensitivity for early-stage or single-cell diagnosis. This study describes the development of small-molecule tracers for the well-known tumor marker prostate-specific membrane antigen (PSMA). These tracers contain a urea motif for PSMA-targeting and iodinated aromatic moieties to allow detection via X-ray fluorescence imaging (XFI). Tracers with a triiodobenzoyl moiety allowed the specific targeting and successful imaging of PSMA+ cell lines with XFI. The XFI-measured uptake of 7.88 × 10−18 mol iodine (I) per cell is consistent with the uptake of known PSMA tracers measured by other techniques such as inductively coupled plasma mass spectrometry (ICP-MS). This is the first successful application of XFI to tumor cell targeting with a small-molecule tracer. In addition, iodinated tracers were used for the characterization of quantum dots (QDs) conjugated to PSMA-targeting urea motifs. The resulting targeted QD conjugates were shown to selectively bind PSMA+ cell lines via confocal microscopy. The immobilized iodinated targeting vectors allowed the determination of the tracer/QD ratio via XFI and ICP-MS. This ratio is a key property of targeted particles and difficult to measure by other techniques.

Trimethyl Lock Based Tools for Drug Delivery and Cell Imaging - Synthesis and Properties.

Trimethyl lock (TML) systems have become increasingly important in medicinal and bioorganic chemistry, particularly for their roles in the targeted delivery of therapeutic agents and as integral components in fluorogenic probes for cellular imaging. The simplicity and efficiency of their synthesis have established TML systems as versatile platforms for the controlled release of active molecules under particular physiological conditions. This review consolidates recent advancements in the application of TML systems, with a focus on their use in drug delivery, cellular imaging, and other areas where precise molecular release is crucial. Additionally, we discuss the synthetic strategies employed to construct TML-based conjugates, underscoring their potential to enhance the specificity and efficacy of bioactive compounds in various biomedical applications.

Cumulative learning based segmentation aided cell mixtures classification in digital holographic microscopy

The tumor microenvironment consists of several components like tumor cells, normal healthy cells, extracellular matrix, secreted factors, and other non-cellular components. The interaction among these components is crucial for tumor progression and metastasis. Identifying the presence of tumor cells among the normal healthy cells during the initial stages of cancer will aid in its early detection and treatment. One of the main features that differentiates a cancer cell from a normal healthy cell distinctively is its morphology. In this study, the effectiveness of machine learning algorithms is evaluated in classifying cells co-cultured in a Petri dish. To achieve this, digital holographic microscopy is used to capture the quantitative phase images of human dermal fibroblast and A375 human melanoma cancer cells co-cultured at 1:1, 1:2, and 2:1 ratios. Cell segmentation was performed using a cumulative learning approach. Subsequently, morphological and phase-based features were extracted to train the machine learning algorithms for binary classification of cells. Phase-based features in addition to the morphological features were found to provide improved performance of the classifier. The classifier demonstrated an average F1-score of 0.9 during testing.

Epac activation reduces trans-endothelial migration of undifferentiated neuroblastoma cells and cellular differentiation with a CDK inhibitor further enhances Epac effect.

Neuroblastoma (NB) is the most common solid extracranial neoplasm found in children and is derived from primitive sympathoadrenal neural precursor. The disease accounts for 15% of all cancer deaths in children. The mortality rate is high in patients presenting with a metastatic tumour even with extensive treatments. This signifies the need for further research towards the development of new additional therapies that can combat not only tumour growth but metastasis, especially amongst the high-risk groups. During metastasis, primary tumour cells become migratory and travel towards a capillary within the tumour. They then degrade the matrix surrounding the pericytes and endothelial cells traversing the endothelial barrier twice to establish a secondary. This led to the hypothesis that modulation of the endothelial cell junctional stability could have an influence on tumour metastasis. To test this hypothesis, agents that modulate endothelial permeability on NB cell line migration and invasion were assessed in vitro in a tissue culture model. The cAMP agonist and its antagonists were found to have no obvious effect on both SK-N-BE2C and SK-N-AS migration, invasion and proliferation. Next, NB cells were cocultured with HDMEC cells and live cell imaging was used to assess the effect of an Epac agonist on trans-endothelial cell migration of NB cells. Epac1 agonist remarkably reduced the trans-endothelial migration of both SK-N-BE2C and SK-N-AS cells. These results demonstrate that an Epac1 agonist may perhaps serve as an adjuvant to currently existing therapies for the high-risk NB patients.

Heat application in live cell imaging.

Thermal heating of biological samples allows to reversibly manipulate cellular processes with high temporal and spatial resolution. Manifold heating techniques in combination with live-cell imaging were developed, commonly tailored to customized applications. They include Peltier elements and microfluidics for homogenous sample heating as well as infrared lasers and radiation absorption by nanostructures for spot heating. A prerequisite of all techniques is that the induced temperature changes are measured precisely which can be the main challenge considering subcellular structures or multicellular organisms as target regions. This article discusses heating and temperature sensing techniques for live-cell imaging, leading to future applications in cell biology.

An integrated portable system for laser speckle contrast imaging and digital holographic microscopy

A multimodal quantitative phase imaging platform combining digital holographic microscopy (DHM) with laser speckle contrast imaging (LSCI) is demonstrated for imaging weakly scattering and transparent samples in a label-free manner. It uses the principle of coherence of the light source for interferometric detection of phase of transparent and semi-transparent samples. The speckle formation resulting from the coherence property of the laser source is used to track dynamic activities in the regions of interest in the sample. Integration of these two techniques onto a microfluidic chip leads to an optofluidic real-time microscope for live cell imaging applications. In this work, we have developed an integrated multimodal system combining digital holographic microscopy and laser speckle contrast imaging system for Optofluidics and in vitro studies. The sample flowing through the microfluidic channel is imaged to record holograms and video of intensity images at the same time. This enables to map the flow within a microfluidic channel and quantify the channel as well as the particle flow through the channel. The channel morphology along with the particles flowing through the channel are quantified using DHM. The two-dimensional speckle contrast images map the flow of the dynamic microbeads and cells with very high contrast in almost real time. A low-cost portable multimodal quantitative phase microscope combining DHM and LSCI has been demonstrated for real time imaging with applications in optofluidics.

A quinolinium-based fluorescent probe for ultrafast detection of bisulfite and its applications in food detection, SO2 gas detection, and cell imaging

A quinolinium-based fluorescent probe for ultrafast detection of bisulfite and its applications in food detection, SO2 gas detection, and cell imaging

Galla chinensis residue-derived carbon dots for sensitive detection of doxorubicin hydrochloride and cell imaging

As an antibiotic, the imbalance of the concentration of doxycycline hydrochloride (DOX) can cause many harms, so real-time detection of the DOX content is crucial. In this study, Galla chinensis residue (GCR) was used as a carbon source, m-phenylenediamine was used as an N-doping agent, and blue-green fluorescent carbon dots (N-GCRCDs) were prepared by a simple hydrothermal synthesis method, with a high quantum yield of 27.31 %. Under optimized conditions, a specific fluorescent probe was established to detect DOX. The addition of DOX resulted in a significant decrease in fluorescence intensity, with a detection range of 5.7–114.9 μM and the detection limit was 1.38 μM. Finally, the feasibility of this method was validated in human urine and the experimental results exhibited a DOX recovery rate of 97.45–104.41 % and an RSD of 0.91–2.64 %. The quenching mechanism was mainly attributed to the inner filter effect and static quenching. Furthermore, the potential application of N-GCRCDs in cells was studied and biocompatibility was evaluated using HepG2 cells, which showed low toxicity at concentrations of 0–50 μg/mL and successful application for HepG2 cell imaging. This study provides reference for resource utilization of GCR and quantitative detection of antibiotics.

A Novel Cortex Phellodendri Chinensis-Based Carbon Dots Platform for Remarkable Analgesia for Clinical Pain Management.

In this study, we explored the eco-friendly synthesis of photoluminescent CCDs employing a direct one-step pyrolysis process, utilizing natural Cortex Phellodendri Chinensis as the precursor material and studied their analgesic effect in mice. The synthesized carbon dots underwent comprehensive characterization through a range of spectroscopic and microscopic techniques. These included UV-Vis, FTIR, fluorescence spectroscopy and HR-TEM, DLS instruments. HR-TEM results exhibited the presence of homogenous spherical-shaped C-dots of about 3.3nm without aggregates. Furthermore, the prepared CCDs were studied for their in vivo analgesic effect in mice by performing tail-immersion, hot plate and acetic acid writhing tests. Also, an MTT assay was performed to assess the in vitro cytotoxicity of CCDs against L929 cells. In vitro cytotoxicity studies revealed that L929 cells exhibited higher cell viability when treated with prepared CCDs. The cellular uptake studies revealed the phase contrast images of MG-63 cells at wavelength 488nm clearly depicted the aggregation of green, fluorescent CCDs within the cells while leaving nuclei unobscured. In addition, to the best of our understanding, the results presented in this paper showed that CCDs exhibited an important analgesic effect and enhanced anti-nociceptive activity, which may be due to stimulation of the opioidergic system. Consequently, CCDs appear to be a viable analgesic alternative for traditional analgesic candidates in pain management.

Hybrid Wavelet‐Deep Learning Framework for Fluorescence Microscopy Images Enhancement

ABSTRACTFluorescence microscopy is a powerful tool for visualizing cellular structures, but it faces challenges such as noise, low contrast, and autofluorescence that can hinder accurate image analysis. To address these limitations, we propose a novel hybrid image enhancement method that combines wavelet‐based denoising, linear contrast enhancement, and convolutional neural network‐based autofluorescence correction. Our automated method employs Haar wavelet transform for noise reduction and a series of adaptive linear transformations for pixel value adjustment, effectively enhancing image quality while preserving crucial details. Furthermore, we introduce a semantic segmentation approach using CNNs to identify and correct autofluorescence in cellular aggregates, enabling targeted mitigation of unwanted background signals. We validate our method using quantitative metrics, such as signal‐to‐noise ratio (SNR) and peak signal‐to‐noise ratio (PSNR), demonstrating superior performance compared to both mathematical and deep learning‐based techniques. Our method achieves an average SNR improvement of 8.5 dB and a PSNR increase of 4.2 dB compared with the original images, outperforming state‐of‐the‐art methods such as BM3D and CLAHE. Extensive testing on diverse datasets, including publicly available human‐derived cardiosphere and fluorescence microscopy images of bovine endothelial cells stained for mitochondria and actin filaments, showcases the flexibility and robustness of our approach across various acquisition conditions and artifacts. The proposed method significantly improves fluorescence microscopy image quality, facilitating more accurate and reliable analysis of cellular structures and processes, with potential applications in biomedical research and clinical diagnostics.

A novel adjunctive diagnostic method for bone cancer: Osteosarcoma cell segmentation based on Twin Swin Transformer with multi-scale feature fusion

BackgroundOsteosarcoma, the most common primary bone tumor originating from osteoblasts, poses a significant challenge in medical practice, particularly among adolescents. Conventional diagnostic methods heavily rely on manual analysis of magnetic resonance imaging (MRI) scans, which often fall short in providing accurate and timely diagnosis. This underscores the critical need for advancements in medical imaging technologies to improve the detection and characterization of osteosarcoma. MethodsIn this study, we sought to address the limitations of current diagnostic approaches by leveraging Hoechst-stained images of osteosarcoma cells obtained via fluorescence microscopy. Our primary objective was to enhance the segmentation of osteosarcoma cells, a crucial step in precise diagnosis and treatment planning. Recognizing the shortcomings of existing feature extraction networks in capturing detailed cellular structures, we propose a novel approach utilizing a twin swin transformer architecture for osteosarcoma cell segmentation, with a focus on multi-scale feature fusion. ResultsThe experimental findings demonstrate the effectiveness of the proposed Twin Swin Transformer with multi-scale feature fusion in significantly improving osteosarcoma cell segmentation. Compared to conventional techniques, our method achieves superior segmentation performance, highlighting its potential utility in clinical settings. ConclusionThe development of our Twin Swin Transformer with multi-scale feature fusion method represents a significant advancement in medical imaging technology, particularly in the field of osteosarcoma diagnosis. By harnessing advanced computational techniques and leveraging high-resolution imaging data, our approach offers enhanced accuracy and efficiency in osteosarcoma cell segmentation, ultimately facilitating better patient care and clinical decision-making.

High-Precision Defect Detection in Solar Cells Using YOLOv10 Deep Learning Model

This study presents an advanced defect detection approach for solar cells using the YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell images annotated with 12 distinct defect types, our model integrates Compact Inverted Blocks (CIBs) and Partial Self-Attention (PSA) modules to enhance feature extraction and classification accuracy. Training on the Viking cluster with state-of-the-art GPUs, our model achieved remarkable results, including a mean Average Precision (mAP@0.5) of 98.5%. Detailed analysis of the model’s performance revealed exceptional precision and recall rates for most defect classes, notably achieving 100% accuracy in detecting black core, corner, fragment, scratch, and short circuit defects. Even for challenging defect types such as a thick line and star crack, the model maintained high performance, with accuracies of 94% and 96%, respectively. The Recall–Confidence and Precision–Recall curves further demonstrate the model’s robustness and reliability across varying confidence thresholds. This research not only advances the state of automated defect detection in photovoltaic manufacturing but also underscores the potential of YOLOv10 for real-time applications. Our findings suggest significant implications for improving the quality control process in solar cell production. Although the model demonstrates high accuracy across most defect types, certain subtle defects, such as thick lines and star cracks, remain challenging, indicating potential areas for further optimization in future work.

Potential treatment approaches for malignant peritoneal mesothelioma: in vivo and in vitro experimental study of natural killer cell immunotherapy.

Malignant peritoneal mesothelioma (MPM) is a rare primary malignant tumor with an extremely poor prognosis that currently lacks effective treatment options. This study investigated the in vitro and in vivo efficacy of natural killer (NK) cells for treatment of MPM. An in vitro study was conducted to assess the cytotoxicity of NK cells from umbilical cord blood to MPM cells with the use of a high-content imaging analysis system, the Cell Counting Kit-8 assay, and Wright-Giemsa staining. The level of NK cell effector molecule expression was detected by flow cytometry and enzyme-linked immunosorbent assays. The ability of NK cells to kill MPM cells was determined based on live cell imaging, transmission electron microscopy, and scanning electron microscopy. An in vivo study was conducted to assess the efficacy and safety of NK cell therapy based on the experimental peritoneal cancer index, small animal magnetic resonance imaging, and conventional histopathologic, cytologic, and hematologic studies. NK cells effectively killed MPM cells through the release of effector molecules (granzyme B, perforin, interferon-γ, and tumor necrosis factor-α) in a dose- and density-dependent manner. The NK cell killing process potentially involved four dynamic steps: chemotaxis; hitting; adhesion; and penetration. NK cells significantly reduced the tumor burden, diminished ascites production, and extended survival with no significant hematologic toxicity or organ damage in NOG mice. NK cell immunotherapy inhibited proliferation of MPM cells in vitro and in vivo with a good safety profile.

Improving the generalizability of white blood cell classification with few-shot domain adaptation

The morphological classification of nucleated blood cells is fundamental for the diagnosis of hematological diseases. Many Deep Learning algorithms have been implemented to automatize this classification task, but most of the time they fail to classify images coming from different sources. This is known as “domain shift”. Whereas some research has been conducted in this area, domain adaptation techniques are often computationally expensive and can introduce significant modifications to initial cell images. In this article, we propose an easy-to-implement workflow where we trained a model to classify images from two datasets, and tested it on images coming from eight other datasets. An EfficientNet model was trained on a source dataset comprising images from two different datasets. It was afterwards fine-tuned on each of the eight target datasets by using 100 or less-annotated images from these datasets. Images from both the source and the target dataset underwent a color transform to put them into a standardized color style. The importance of color transform and fine-tuning was evaluated through an ablation study and visually assessed with scatter plots, and an extensive error analysis was carried out. The model achieved an accuracy higher than 80% for every dataset and exceeded 90% for more than half of the datasets. The presented workflow yielded promising results in terms of generalizability, significantly improving performance on target datasets, whereas keeping low computational cost and maintaining consistent color transformations. Source code is available at: https://github.com/mc2295/WBC_Generalization