Cell Imaging Research Articles

CoOOH nanoflake as supporter of CRISPR-Cas12a system for facilitated imaging of miRNA-21 in cells

The aberrant expression of miRNA-21 in cells is frequently associated with the occurrence and metastasis of tumors. The CRISPR-cas12a system has been developed for in vitro detection of miRNA due to its distinctive signal amplification characteristics. But its intricate composition and challenges in cellular delivery have limited its application in cell imaging. In this study, we designed an electrostatic adsorption-based probe by combining positively charged cobalt hydroxyl oxide (CoOOH) nanoflakes with negatively charged CRISPR-cas12a system which allows for detection and intracellular imaging of miRNA-21. This method involves using ascorbic acid to reduce CoOOH nanoflakes into Co2+, which subsequently releases the active form of CRISPR-Cas12 for detecting miRNA-21. The probe has a linear detection range for miRNA-21 from 0.5 pM-50 pM and 100 pM-20 nM, with a detection limit of 0.32 pM. It also shows good selectivity, accuracy, good reproducibility and biocompatibility for miRNA-21 analysis. In subsequent cell experiments, the probe exhibits low cytotoxicity and remarkable cell imaging capabilities. Therefore, this probe offers an innovative approach for miRNA-21 analysis.

Chemisch stabile Diazopeptide als selektive Sonden für Cysteinproteasen in lebenden Zellen

AbstractVon Diazopeptiden wurden schon früher berichtet, sie haben aber aufgrund ihrer hohen Reaktivität bis heute kein breites Einsatzgebiet gefunden. Wir berichten hier von der Herstellung, den Eigenschaften und Anwendungen chemisch stabiler interner Diazopeptide. Peptidylphosphoranylidenester und ‐amide reagieren mit Triflylazid hauptsächlich zu neuartigen 4,5‐disubstituierten 1,2,3‐Triazolylpeptiden. Nonaflylazid hingegen liefert Diazopeptide, die als Amide bei pH 1–14 und als Ester bei pH 1–8 chemisch stabil sind. Somit sind durch Festphasenpeptidsynthese hergestellte Diazopeptide auch während der finalen Entschützung mit 95 % Trifluoressigsäure stabil. Diazopeptide mit der Erkennungssequenz von Caspase‐3 wurden als spezifische, kovalente und irreversible Inhibitoren dieses Enzyms bei niedrigen nanomolaren Konzentrationen identifiziert. Ein fluoreszierendes Diazopeptid drang in lebende Zellen ein und ermöglichte mikroskopische Visualisierung und Quantifizierung von Apoptose durch Durchflusszytometrie. Diazopeptide stellen somit eine neue Klasse aktivitätsbasierter Sonden und Enzyminhibitoren zum Einsatz in der chemischen Biologie und medizinischen Chemie dar.

Chemically Stable Diazo Peptides as Selective Probes of Cysteine Proteases in Living Cells.

Diazo peptides have been described earlier, however, due to their high reactivity have not been broadly used until today. Here, we report the preparation, properties, and applications of chemically stable internal diazo peptides. Peptidyl phosphoranylidene-esters and amides were found to react with triflyl azide primarily to novel 3,4-disubstituted triazolyl-peptides. Nonaflyl azide instead furnished diazo peptides, which are chemically stable from pH 1-14 as amides and from pH 1-8 as esters. Thus, diazo peptides prepared by solid phase peptide synthesis were stable to final deprotection with 95 % trifluoroacetic acid. Diazo peptides with the recognition sequence of caspase-3 were identified as specific, covalent, and irreversible inhibitors of this enzyme at low nanomolar concentrations. A fluorescent diazo peptide entered living cells enabling microscopic imaging and quantification of apoptotic cells via flow cytometry. Thus, internal diazo peptides constitute a novel class of activity-based probes and enzyme inhibitors useful in chemical biology and medicinal chemistry.

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.

New Diagnostic Technology for the Isolation and Analysis of Live Myeloma Cell from the Blood of Multiple Myeloma Patients

Introduction Multiple Myeloma (MM) is characterized by the proliferation of abnormal plasma cells within the bone marrow. Abnormal plasma cells are examined by bone marrow (BM) biopsy but it causes great pain to patients, so it is hard to get BM sample frequently for the monitoring of drug efficacy and minimal residual disease. Therefore, the development of new diagnostic method is absolutely necessary for patient's diagnostic convenience. We are developing new diagnostic system for multiple myeloma to isolate and analyze myeloma cells in peripheral blood (PB) using automated CytoGen Smart Biopsy™ platform. In this study, it was performed the feasibility of a technology to isolate myeloma cells from the PB of MM patients and a cross-analysis with clinical data to explore the possibility of utilization of MM diagnosis. Method Spiking test using MM cell line (RPMI8226, 200 cells) mixed into human blood samples (5mL, healthy donor) was conducted to check the recovery rate of CytoGen Smart BiopsyTM platform. For the clinical evaluation, patient selection was performed on patients who visited the Department of Hematology and Oncology at Samsung Medical Center (Seoul, Korea) based on the MM diagnosis criteria. We evaluated 9 newly diagnosed patients with MM (ISS I, II, and III (3 per group)) and 2 patients with MGUS. The median age of the MM cohort was 71 years (range 51-84) and 54% were females. Live MM cells in PB (7.5 mL) were isolated by CytoGen Smart BiopsyTM Cell Isolator, subsequently the quantification of number of isolated MM cells in PB was accessed with automated CytoGen Smart BiopsyTM Cell Image Analyzer after immunofluorescence (IF) staining with MM specific markers, CD138 and/or CD38. Result In the spiking test with MM cell line, CytoGen Smart BiopsyTM platform showed a high level of recovery rate (over 80%). Clinical evaluation of CytoGen Smart BiopsyTM platform was conducted with 9 MM and 2 MGUS patients. Among the 9 MM patients, more than 100 MM cells in 7.5 mL of PB were observed in all of MM patients except 1 patient with ISS I. In two MGUS, only 3 and 52 cells were observed, respectively. The median value based on ISS staging system was as follows. In stage I: 166 cells, in stage II: 1,739 cells, and in stage III: 2,093 cells were identified. Even a small number of patient samples, an increase tendency in the number of cells by ISS stage was observed. When classified based on R-ISS, the median value for each stage was in stage I: 52 cells, in stage II: 1,737 cells, and in stage III: 5,928 cells, showing a clear tendency to increase by stage. This clinical evaluation clearly demonstrated the possibility of isolation and quantification of MM cells in the PB of MM patients. Conclusions Multiple myeloma is a blood cancer showing a very high recurrence rate even after treatment, so a new diagnostic system replacing BM biopsy is absolutely necessary for patient's convenience. Although various methods have been attempted to identify MM cells in the PB, but it was difficult to use it for diagnosis because they could not isolate live MM cells retaining intact MM markers for accurate analysis. In this study, we verified through clinical evaluation that live MM cells in PB can be isolated and analyzed with CytoGen Smart BiopsyTM platform, a size-based separation technology can isolate live MM cells with a large size compared with other PBMCs. Furthermore, we found an increase tendency of MM cells' number in PB by the stage of MM patients. Expanded clinical study will be performed with more MM samples for clinical validation. We expected that further study will be able to identify the correlation between the number of myeloma cells in the PB and prognostic index such as progression-free survival.

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.

AIE active hydroxycoumarin-anthranilic acid coupled enamine: Sequential detection of Cu2+/S2− ions and live cell imaging application

In this contribution, we designed and developed 7-diethylamino-4-hydroxycoumarin coupled anthranilic acid-based enamine (DHCBA) and its Cu2+ complex (DHCBA-Cu)via simple synthetic strategy. DHCBA shows solvatochromic effect, aggregation induced emission feature at 80 % methanol–water mixture and colorimetric as well as fluorescence turn-off sensing features towards Cu2+ ions. The DHCBA-Cu2+ ensemble has been utilized as a turn-on fluorescence sensor for the detection of S2− through a decomplexation approach. The formation of a 1:1 DHCBA-Cu2+ ensemble was further supported by 1H NMR studies, HRMS analysis and Density Functional Theory (DFT) calculation. The detection limit for Cu2+ was found to be 6.5 nM and then the binding affinity and binding stoichiometry of the probe towards Cu2+ ion was calculated using the online BindFit v0.5 supramolecular software. Moreover, the 1:1 stoichiometric ratio between DHCBA and Cu2+ ions were confirmed via single crystal X-ray analysis. The structural analysis of DHCBA-Cu2+ indicates that it forms an interesting one-dimensional coordination polymeric chain-like structure, further transformed into a two-dimensional polymeric network through hydrogen bonding interactions. The reversible on–off–on emission character of the probe, DHCBA was successfully applied for the construction of the IMPLICATION logic gate as well as on-site detection applications using paper strip and cotton-swab tests. Furthermore, the sensing feature of the enamine against Cu2+/S2− ions were validated by intracellular imaging applications.

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

Sulfur dioxide and its derivatives (HSO3−/SO32-) play a crucial role in maintaining the redox balance of biological systems. Therefore, the development of effective detection methods for sulfur dioxide and its derivatives is of paramount importance. Herein, a novel fluorescent probe, QLN, was designed and synthesized using small molecule quinoline, demonstrating the capacity to recognize HSO3− through Michael addition reaction. The probe QLN displayed good selectivity and anti-interference capabilities for HSO3− recognition, effectively operating within a wide pH range (5–9) and exhibiting a rapid response time (<5 s). Practical applications revealed the potential of probe QLN for detecting HSO3− in living cells and food samples.

Highly selective and sensitive N-amidothiourea-based fluorescence chemosensor for detecting Zn2+ ions and cell Imaging: Potential applications for plasma membrane detection

A “turn-on” fluorescent sensor L was developed for the selective detection of Zn2+ ions in biological settings. This sensor exhibited a specific response to Zn2+ ions, with a detection limit of 39 nM. Analyses through titration and Job’s plot confirmed that the formed complex has a 2:1 stoichiometry (L: Zn2+). The sensor displayed strong selectivity for Zn2+ over other metal ions, demonstrating a robust binding affinity while effectively reducing interference from heavy metals. The identification process was validated by Fourier transform infrared spectroscopy (FT-IR), proton nuclear magnetic resonance (1H NMR) titrations, mass spectrometry (MS), and Density Functional Theory (DFT) calculations. Maximum fluorescence intensity was achieved upon interaction with Zn2+ ions within a physiological pH range of 7.0–7.5. Moreover, successful imaging experiments in HEK293 cells highlighted the sensor’s potential as a powerful tool for monitoring interactions at the plasma membrane.

Fluorescence enhancement of cyanine/hemicyanine dyes with adamantane as an auxochrome through host–guest inclusion with methylated cyclodextrin in water

Fluorescence of cyanine dyes is often quenched in aqueous solutions, limiting their application in water or biological systems. In this work, a novel strategy of host–guest interactions based on cyanine derivatives containing auxochromes was proposed to enhance fluorescence in aqueous solutions and cell imaging. By using the host (methylated β-cyclodextrin, M-β-CD) to inclusion and suppress the TICT effect of the dye, the fluorescence was significantly enhanced at lower host concentrations. Cyanine and hemicyanine dyes (Ad-NH-1a, Ad-NH-1b and Ad-NH-2) with adamantyl group as auxochrome were designed and prepared by using host–guest complexation to inhibit the TICT effect of dyes, thus realizing the purpose of significantly enhancing the fluorescence of dyes at lower concentration. After adding M-β-CD, the fluorescence quantum yields of these dyes increased to 15.9 %, 21.8 %, and 6.08 %, respectively. In comparison, the fluorescence quantum yield of the dyes Ad-CONH-1 and Ad-CONH-2, which contain adamantyl groups, increased only modestly from 1.78 to 2.83 times. Encouraged by these satisfactory results, further cell experiments were conducted with Ad-NH-1a, Ad-NH-1b, and Ad-NH-2. The experiment showed that adding a lower concentration of M-β-CD significantly reduced the clear imaging dosage of the probe (0.3 μM) without altering the organelle targeting of the probes.

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.

Identification of veterinary and medically important blood parasites using contrastive loss-based self-supervised learning

Background and Aim: Zoonotic diseases caused by various blood parasites are important public health concerns that impact animals and humans worldwide. The traditional method of microscopic examination for parasite diagnosis is labor-intensive, time-consuming, and prone to variability among observers, necessitating highly skilled and experienced personnel. Therefore, an innovative approach is required to enhance the conventional method. This study aimed to develop a self-supervised learning (SSL) approach to identify zoonotic blood parasites from microscopic images, with an initial focus on parasite species classification. Materials and Methods: We acquired a public dataset featuring microscopic images of Giemsa-stained thin blood films of trypanosomes and other blood parasites, including Babesia, Leishmania, Plasmodium, Toxoplasma, and Trichomonad, as well as images of both white and red blood cells. The input data were subjected to SSL model training using the Bootstrap Your Own Latent (BYOL) algorithm with Residual Network 50 (ResNet50), ResNet101, and ResNet152 as the backbones. The performance of the proposed SSL model was then compared to that of baseline models. Results: The proposed BYOL SSL model outperformed supervised learning models across all classes. Among the SSL models, ResNet50 consistently achieved high accuracy, reaching 0.992 in most classes, which aligns well with the patterns observed in the pre-trained uniform manifold approximation and projection representations. Fine-tuned SSL models exhibit high performance, achieving 95% accuracy and a 0.960 area under the curve of the receiver operating characteristics (ROC) curve even when fine-tuned with 1% of the data in the downstream process. Furthermore, 20% of the data for training with SSL models yielded ≥95% in all other statistical metrics, including accuracy, recall, precision, specification, F1 score, and ROC curve. As a result, multi-class classification prediction demonstrated that model performance exceeded 91% for the F1 score, except for the early stage of Trypanosoma evansi, which showed an F1 score of 87%. This may be due to the model being exposed to high levels of variation during the developmental stage. Conclusion: This approach can significantly enhance active surveillance efforts to improve disease control and prevent outbreaks, particularly in resource-limited settings. In addition, SSL addresses significant challenges, such as data variability and the requirement for extensive class labeling, which are common in biology and medical fields. Keywords: bootstrap your own latent, fractioned data, microscopic image, pre-trained, self-supervised learning, zoonotic disease.

Urease-powered nanomotor probe for PCA3 ultrasensitive detection in human urine and controllable imaging in live cells

Prostate cancer (PCa) is the second-most common cancer in men. Recent researches indicate that prostate cancer antigen 3 (PCA3) is highly specific for the early screening of PCa. However, conventional techniques for PCA3 detection still remain significant challenges, including complex purification process, long detection time and poor sensitivity. Herein, we present urease-powered Janus nanomotors (JUNMs) to assist the nucleic-acid probe for sensitive urine analysis of prostate cancer (PCa)-related PCA3 and precise in situ imaging of PCa cells. The nanoprobes were synthesized by immobilizing urease and hairpin DNA asymmetrically on the Janus Fe3O4/Au nanoparticles (JUNMs-hDNA). JUNMs-hDNA nanomotors exhibited active motion in the presence of urea to efficiently recognize and capture the PCA3. As a result, the nanoprobes revealed a good linear relationship to PCA3 in the range of 1-100 pM. The total detection time was within 11minutes with a limit of detection (LOD) of 0.6 pM. Subsequently, JUNMs-hDNA nanoprobes have also been successfully applied in the detection of PCA3 in human urine samples. In addition, the JUNMs-hDNA nanoprobes also effectively captured PCa cells and enabled fluorescence imaging, which provided a highly sensitive sensing and imaging platform for PCA3 in body fluids and cellular level. Thus, this strategy offers a new perspective and approach for early, non-invasive and precise clinical diagnosis of PCa.

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.