Analytical Chemistry

Digital Loop-Mediated Isothermal Amplification-Based Absolute Methylation Quantification Revealed Hypermethylated DAPK1 in Cervical Cancer Patients

The aberrant methylation of many genes has been reported to be associated with various carcinomas. Accurate detection of the methylation level could provide critical insights into the diagnostic analysis of diseases. Here, a sensitive HpaII-edited absolute droplet loop-mediated isothermal amplification (HEADLAMP) method based on methylation-sensitive restriction enzyme (MSRE) HpaII was developed for the digital quantification of DNA methylation. Methylation levels of the death-associated protein kinase 1 (DAPK1) gene that is associated with many cancers were studied using β-actin as an internal reference. DAPK1 (2.5 pM) with 0.01% methylation (250 aM) can be detected with the conventional HpaII-edited LAMP assay. Using HEADLAMP, as low as 1% methylation level can be distinguished with an estimated limit of detection of 5 aM (ca. 3 copies/μL). Moreover, HEADLAMP can detect low levels of methylated DAPK1 in normal L-02 cells, while the conventional assay cannot. Finally, HEADLAMP was applied to the detection of DAPK1 methylation in 20 clinical tissue samples, which revealed hypermethylated DAPK1 in cervical cancer patients. We envisage potential applications of this robust, specific, and sensitive HEADLAMP assay in epigenetic studies and early clinical diagnosis.

Serum-Based Detection of Pancreatic and Ovarian Cancer via a Nanoparticle-Enhanced Fluorescence Array and Machine Learning

Modular and Fast Assembly of Self-Immobilizing Fluorogenic Probes for β-Galactosidase Detection

β-Galactosidase (β-gal) has emerged as a pivotal biomarker in primary ovarian cancer. Despite the existence of numerous fluorescent probes for β-gal activity detection, quinone methide-based immobilizing probes were shown to avoid rapid diffusion of the activated fluorophore and improve the resolution. However, the synthesis of these fluorophores, particularly near-infrared fluorophores, still exhibits lower efficiency. In this study, we introduce modular and rapidly assembled self-immobilizing fluorogenic probes, capitalizing on the proximity labeling properties of quinone methide (QM). Compared to conventional fluorescent probes, these new probes not only exhibit a fluorogenic response but also achieve permanent retention, demonstrating improved detection sensitivity, particularly after cell fixation and in vivo animal model studies. This straightforward synthesis approach holds promise for broader applications in detecting other analytes.

Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification

Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA-SVM, SVM, UMAP-SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.

Deep Learning-Assisted Intelligent Artificial Vision Platform Based on Dual-Luminescence Eu(III)-Functionalized HOF for the Diagnosis of Breast and Ovarian Cancer

Developing an advanced analytical method to detect spermine (Spm) and

Glioma Single-Cell Biomechanical Analysis by Cyclic Conical Constricted Microfluidics

Determining the grade of glioma is a critical step in choosing patients' treatment plans in clinical practices. The pathological diagnosis of patient's glioma samples requires extensive staining and imaging procedures, which are expensive and time-consuming. Current advanced uniform-width-constriction-channel-based microfluidics have proven to be effective in distinguishing cancer cells from normal tissues, such as breast cancer, ovarian cancer, prostate cancer, etc. However, the uniform-width-constriction channels can result in low yields on glioma cells with irregular morphologies and high heterogeneity. In this research, we presented an innovative cyclic conical constricted (CCC) microfluidic device to better differentiate glioma cells from normal glial cells. Compared with the widely used uniform-width-constriction microchannels, the new CCC configuration forces single cells to deform gradually and obtains the biophysical attributes from each deformation. The human-derived glioma cell lines U-87 and U-251, as well as the human-derived normal glial astrocyte cell line HA-1800 were selected as the proof of concept. The results showed that CCC channels can effectively obtain the biomechanical characteristics of different 12-25 μm glial cell lines. The patient glioma samples with WHO grades II, III, and IV were tested by CCC channels and compared between Elastic Net (ENet) and Lasso analysis. The results demonstrated that CCC channels and the ENet can successfully select critical biomechanical parameters to differentiate the grades of single-glioma cells. This CCC device can be potentially further applied to the extensive family of brain tumors at the single-cell level.

Modular and Noncontact Wireless Detection Platform for Ovarian Cancer Markers: Electrochemiluminescent and Photoacoustic Dual-Signal Output Based on Multiresponse Carbon Nano-Onions

An electrochemiluminescent (ECL)-photoacoustic (PA) dual-signal output biosensor based on the modular optimization and wireless nature of a bipolar electrode (BPE) was constructed. To further simplify the detection process, the BPE structure was designed as three separate units: anode ECL collection, cathode catalytic amplification, and intermediate functional sensing units. Specifically, the anode unit was placed with Eosin Yellow, a cheap and effective ECL reagent, and the cathode unit was a laser-induced polyoxometalate-graphene electrode, which was helpful to enhance the anode ECL signal. The intermediate functional sensing unit consisted of a temperature-sensitive conductive film. Further, using a carbon nano-onion nanocomposite with excellent absorption performance in the near-infrared region as a signal tag not only leads to changes in the electrical conductivity of the film through heat transfer and thus affects the ECL signal but also produces a strong PA response. With this design, PA and ECL signals can be output simultaneously. This work not only realizes multiple modularization processes in the design of sensors but also implements the diversification of signal output modes, which will enrich the joint research field of ECL detection technology and other new detection methods.

Quantifying Antibody Binding Kinetics on Fixed Cells and Tissues via Fluorescence Lifetime Imaging

We present a method for monitoring spatially localized antigen-antibody binding events on physiologically relevant substrates (cell and tissue sections) using fluorescence lifetime imaging. Specifically, we use the difference between the fluorescence decay times of fluorescently tagged antibodies in free solution and in the bound state to track the bound fraction over time and hence deduce the binding kinetics. We make use of a microfluidic probe format to minimize the mass transport effects and localize the analysis to specific regions of interest on the biological substrates. This enables measurement of binding constants (

Thiol “Click” Chromene Ring Opening and Subsequent Cascade Nucleophilic Cyclization NIR Fluorescence Imaging Reveal High Levels of Thiol in Drug-Resistant Cells

As the structural unit of natural products, chromene derivatives show a wide range of biological activity and pharmacological activity due to their unique photophysical and chemical properties. Ten years ago, our research group discovered the "thiol-chromene" click reaction, which achieved the selective detection of thiols through the change of the optical spectrum. Afterward, we attempted to develop various chromene-based fluorescent probes for imaging including near-infrared (NIR) probe, ratiometric probe, and multifunctional probe. However, how to integrate the fluorophore and reaction sites into the chromene-based skeleton remains challenging. In this work, we connected the chromene motif with the NIR fluorophore methylene blue utilizing a carbamate spacer to provide a new fluorescent probe (CM-NIR), which is triggered by thiols to open the pyran ring followed by attacking the carbamate by phenolate to releases the methylene blue. This novel cascade mechanism avoids the formation of para-quinone methides, which proved to be toxic to normal cells. CM-NIR also showed the specific imaging of thiols in living cells and mice. More importantly, the thiols level in drug-resistant cancer cells was found to be significantly higher than that in the corresponding cancer cell, which indicated that the thiols level may have an important role in cancer cells developing drug resistance.

Online Quaternized Derivatization Mapping and Glycerides Profiling of Cancer Tissues by Laser Ablation Carbon Fiber Ionization Mass Spectrometry

Mass spectrometry imaging has become a hot research field owing to its ability to reflect the distribution of multiple metabolites in tissue. However, not all kinds of metabolites have great ionization efficiency in mass spectrometry imaging. The mass signals of low polar metabolites like monoglycerides and diglycerides may be seriously suppressed. Many strategies have been proposed to fix the problem, such as on-tissue derivatization and online derivatization. Also, some challenges were encountered when implementing these approaches. Herein, a platform coupled online quaternized derivatization and laser ablation carbon fiber ionization mass spectrometry imaging has been developed. The mass signals of monoglycerides and diglycerides were drastically increased in the platform, and high-quality mass images of these metabolites could be acquired readily. In the platform, metabolites were first desorbed by a laser and then reacted online with a derivatization reagent transmitted by carbon fiber ionization, which also undertook the postionization of derivatization products. Pyridine acted as the main derivatization reagent to target metabolites with hydroxyl groups. Remarkably, the derivatization reaction proceeded rapidly without any catalyst owing to the high energy provided by the laser. The mass images of eight monoglycerides and 21 diglycerides were achieved after applying the platform into human ovarian cancer tissues. Notably, a higher mass intensity of these glycerides was captured in cancerous tissues than in para-cancerous tissues, which might infer aberrations in glyceride metabolisms of cancerous tissues.

Highly Sensitive Detection of Multiple MicroRNAs by High-Performance Liquid Chromatography Coupled with Long and Short Probe-Based Recycling Amplification

This report demonstrated the utility of high-performance liquid chromatography (HPLC)-fluorescence detection for selective separation and sensitive quantification of multiple microRNAs (miRNAs). A duplex specific nuclease (DSN)-assisted target recycling amplification strategy was developed to enhance the signals of miRNAs, which alleviates the low sensitivity of conventional HPLC to nucleic acids. To separate the signals of different miRNAs, DNA probes with different lengths and base sequences were immobilized on magnetic beads. The application of an effective magnetic separation minimized the background signal and extended the dynamic range. This assay achieved a limit of detection of 0.39 fM for miRNA-122, 0.30 fM for miRNA-155, and 0.26 fM for miRNA-21, respectively. The proposed assay was successfully applied to detect simultaneously miRNA-122, miRNA-155, and miRNA-21 in serum samples from healthy persons and cervical cancer patients, and the results were then compared with those of quantitative real-time-polymerase chain reaction amplification.

Anodic Electrochemiluminescence of Carbon Dots Promoted by Nitrogen Doping and Application to Rapid Cancer Cell Detection

In this work, a kind of novel nitrogen doped hydrazide conjugated carbon dots (NHCDs) with strong anodic electrochemiluminescence (ECL) at a low excitation potential were synthesized via a one-step solvothermal approach and applied to construct biosensor for rapid cancer cell detection. The nitrogen doping induced a shift of the highest occupied molecular orbital (HOMO) to the upper energy level thus lowered the anodic ECL excitation potential of carbon dots. Especially, comparing to nondoped hydrazide conjugated carbon dots, NHCDs exhibited 2.5-fold high ECL quantum efficiency because the lower potential could reduce notably the side reactions in the ECL process. Using the high-performance NHCDs to functionalize the electrode surface, a brief ECL biosensor was fabricated to detect the cell-secreted hydrogen peroxide, which could rapidly distinguish cancer cells from normal cells. What is more, the prepared NHCDs, as the combination of low excitation potential, strong ECL emission, and good biocompatibility, were expected to be popular luminophors for clinical diagnose of cancer and monitoring the pharmacodynamics of anticancer drugs.

Elucidating Raman Image-Guided Differential Recognition of Clinically Confirmed Grades of Cervical Exfoliated Cells by Dual Biomarker-Appended SERS-Tag

Ultrasensitive detection of cancer biomarkers via single-cell analysis through Raman imaging is an impending approach that modulates the possibility of early diagnosis. Cervical cancer is one such type that can be monitored for a sufficiently long period toward invasive cancer phenotype. Herein, we report a surface-enhanced Raman scattering (SERS) nanotag (SERS-tag) for the simultaneous detection of p16/K-i67, a dual biomarker persisting in the progression of squamous cell carcinoma of human cervix. A nanoflower-shaped SERS-tag, constituted of hybrid gold nanostar with silver tips to achieve maximum fingerprint enhancement from the incorporated reporter molecule, was further functionalized with the cocktail monoclonal antibodies against p16/K-i67. The recognition by the SERS-tag was first validated in cervical squamous cell carcinoma cell line SiHa as a foot-step study and subsequently implemented to different grades of clinically confirmed exfoliated cells including normal cell (NC), high-grade intra-epithelial lesion (HC), and squamous cell carcinoma (CC) samples of the cervix. Precise Raman mapped images were constituted based on the average intensity gradient of the signature Raman peaks arising from different grades of exfoliated cells. We observed a distinct intensity hike of around 10-fold in the single dysplastic HC and CC samples in comparison to NC specimen, which clearly justify the prevalence of p16/Ki-67. The synthesized probe is able to map the abnormal cells within 20 min with high reproducibility and stability for 1 mm × 1 mm mapping area with good contrast. Amidst the challenges in Raman image-guided modality, the technique was further complemented with the gold standard immunocytochemistry (ICC) dual staining analysis. Even though both are time-consuming techniques, tedious steps can be avoided and real-time readout can be achieved using the SERS mapping unlike immunocytochemistry technique. Therefore, the newly developed Raman image-guided SERS imaging emphasizes the approach of uplifting of SERS in practical utility with further improvement for clinical applications for cervical cancer detection in future.

Detection of 14 High-Risk Human Papillomaviruses Using Digital LAMP Assays on a Self-Digitization Chip

Cervical cancer is the fourth-leading cause of cancer deaths among women worldwide and most cases occur in developing countries. Detection of high-risk (HR) HPV, the etiologic agent of cervical cancer, is a primary screening method for cervical cancer. However, the current gold standard for HPV detection, real-time PCR, is expensive, time-consuming, and instrumentation-intensive. A rapid, low-cost HPV detection method is needed for cervical cancer screening in low-resource settings. We previously developed a digital loop-mediated isothermal amplification (dLAMP) assay for rapid, quantitative detection of nucleic acids without the need for thermocycling. This assay employs a microfluidic self-digitization chip to automatically digitize a sample into an array of nanoliter wells in a simple assay format. Here we evaluate the dLAMP assay and self-digitization chip for detection of the commonly tested 14 high-risk HPVs in clinical samples. The dLAMP platform provided reliable genotyping and quantitative detection of the 14 high-risk HPVs with high sensitivity, demonstrating its potential for simple, rapid, and low-cost diagnosis of HPV infection.

Rapid In Situ Hydrogel LAMP for On-Site Large-Scale Parallel Single-Cell HPV Detection

Rapid human papillomavirus (HPV) screening is urgently needed for preventing and early diagnosis of cervical cancer in rural areas. To date, no HPV nucleic acid test (NAT) can be implemented within a single patient visit starting from clinical samples. Here, we develop a hydrogel loop-mediated isothermal amplification (LAMP) method in a fashion of large-scale parallel (about 1000 cells) in situ HPV DNA detection in clinical cervical exfoliated cells at the single-cell level. It can be used with a hotplate and smartphone to obtain HPV NAT results in less than 30 min, which is especially suitable for the on-site scenario. We apply this rapid HPV NAT on 40 clinical cervical exfoliated cell samples and compare the results to a clinical gold standard quantitative polymerase chain reaction (qPCR) method [area under curve (AUC), 1.00]. Meanwhile, our assay can provide HPV infection information for large-scale parallel single clinical cervical exfoliated cells, which cannot be received from traditional NAT methods. Our findings suggest the potential of in situ hydrogel LAMP as a powerful tool for clinical HPV screening and fundamental research.

Quantitative Detection of Serum Protein-Specific Glycosylation in Ovarian Cancer Based on a Signal-Convertible Mass-Tagged Probe Set

The aberrant expression of sialic acid (Sia) on the surface of serum CA125 protein (CA125-Sia) is closely related to the occurrence of ovarian cancer and may have potential utility for early detection of ovrian cancer. However, the accurate determination of protein-specific glycosylation profiles poses significant analytical challenges, primarily due to the need for simultaneous identification of the target protein and quantification of specific glycosylation as well as their low levels in the early stages of certain diseases. Herein, we report a signal-convertible mass-tagged probe set system for the mass spectrometric detection of serum CA125-Sia. This probe set consists of three functional probes: a capture probe (CP), a labeling probe (LP), and a mass-tagged probe (MP). The serum CA125 protein was first captured by CP, and the terminal Sia was labeled by LP with the help of a heterobifunctional cross-linker. Then, the MP can hybridize with the LP attached to the Sia. Once the hybridization was formed, the MP in the hybridization was hydrolyzed into small fragments in the presence of exonuclease III (Exo III), while the LP reverted to a single-stranded state and could continuously perform the cycle process of hybridization and hydrolysis, thus realizing signal amplification. This strategy has been successfully used to quantify CA125-Sia in serum. It provides a promising platform for the quantification of protein-specific glycoforms in serum samples. Our findings suggest that CA125-Sia may be a novel potential diagnostic marker for the early detection of ovarian cancer.

Serum Fingerprinting-Based Integrative Dual-Omics Machine Learning for Endometriosis-Associated Ovarian Cancer

Dual-omics, by integrating molecular information from two distinct dimensions, can offer more comprehensive perspective for complex disease. Herein, we developed an efficient functionalized mesoporous nanoparticle-coupled laser desorption/ionization mass spectrometry (fMNPLDI-MS) platform capable of extracting high-quality serum metabolic fingerprints (SMFs) and serum peptide fingerprints (SPFs) from trace serum samples within 50 s. Integrating these SMFs and SPFs markedly improved early screening and subtyping of endometriosis-associated ovarian cancer (EAOC) more than single omics. Specifically, leveraging machine learning on the identified 6 metabolites and 6 peptides, the integration strategy attained an area under the receiver operating characteristic curve (AUC) value of 0.989 and accuracy of 93.1%, compared with 0.964/89.7% for metabolomics alone and 0.960/87.9% for peptidomics alone in distinguishing EAOC from benign controls. Similarly, the dual-omics integration strategy achieved an AUC value of 0.875 and accuracy of 86.7%, surpassing individual metabolomics (AUC: 0.869, accuracy: 83.3%) and peptidomics (AUC: 0.733, accuracy: 76.7%), in subtype classification. This high-throughput fMNPLDI-MS dual-omics platform provides a powerful tool for EAOC screening and subtyping, paving the way toward early detection and precision management of this malignancy.

Electron-Shuttling Mechanisms Drive Proximity Labeling to Unveil Tumor Marker Characteristics in Ovarian Cancer from Clinical Samples

In this study, we presented a revolutionary proximity labeling platform, MOF-HRP-Apt, which for the first time integrated electron mediation into the horseradish peroxidase (HRP)-catalyzed proximity labeling system. This novel strategy enabled a single molecular recognition event into multiple covalent labeling events, amplifying spatial signals and enhancing detection sensitivity of the tumor biomarker PTK7. The platform utilized the redox-active iron-based metal-organic framework (MOF) material NH

Ultrasmall Au-GRHa Nanosystem for FL/CT Dual-Mode Imaging-Guided Targeting Photothermal Therapy of Ovarian Cancer

As the most common and lethal cancer of the female gonads, ovarian cancer (OC) has a grave impact on people's health. OC is asymptomatic, insidious in onset, difficult to diagnose and treat, fast-growing, and easy to metastasize and has poor prognosis and high mortality. How to detect OC as early as possible and treat it without side effects has become a challenging medical problem. Herein, the ultrasmall Au-GRHa nanosystem was designed for dual-mode imaging-guided photothermal therapy of OC. The synthesized Au-GRHa nanosystem has ultrasmall size, good biocompatibility, and excellent fluorescence and CT imaging performance, which could detect the OC tumor accurately and intuitively and is expected to provide intraoperative visual navigation for clinical surgery. With its excellent photothermal property, the Au-GRHa nanosystem can be utilized for photothermal therapy of OC, thus providing an alternative to, and reducing the hazards posed by, traditional radiotherapy and chemotherapy. In addition, GnRHa endows the AuNDs with excellent ability to target the Gonadotropin-Releasing Hormone Receptor (GnRH-R), which enhances the uptake of AuNDs by OC tumor cells, improving the targeting accuracy and efficacy of photothermal therapy for OC. This work will facilitate the biomedical applications of the Au-GRHa nanosystem and provide good insights into FL/CT imaging-guided photothermal therapy of OC.

Rapid Hyperspectral Photothermal Mid-Infrared Spectroscopic Imaging from Sparse Data for Gynecologic Cancer Tissue Subtyping

Ovarian cancer detection has traditionally relied on a multistep process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology that, when combined with machine learning algorithms, can eliminate the need for staining and provide quantitative results comparable to traditional histology. However, this technology is slow. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude. This method resolves the longstanding trade-off between imaging resolution and data collection speed, enabling the reconstruction of high-quality, high-resolution images from undersampled data sets and achieving a 10X improvement in data acquisition time. We assessed the performance of our sparse imaging methodology using a variety of quantitative metrics, including mean squared error (MSE), structural similarity index (SSIM), and tissue subtype classification accuracies, employing both random forest and convolutional neural network (CNN) models, accompanied by Receiver Operating Characteristic (ROC) curves. Our statistically robust analysis, based on data from 100 ovarian cancer patient samples and over 65 million data points, demonstrates the method's capability to produce superior image quality and accurately distinguish between different gynecological tissue types with segmentation accuracy exceeding 95%. Our work demonstrates the feasibility of integrating rapid MIR hyperspectral photothermal imaging with machine learning in enhancing ovarian cancer tissue characterization, paving the way for quantitative, label-free, automated histopathology.

Confinement Effect Enhanced Bipolar Electrochemistry: Structural Color Coding Coupled with Wireless Electrochemiluminescence Imaging Technology

In this work, SiO

Sample-Treatment with the Virucidal β-Propiolactone Does Not Preclude Analysis by Large Panel Affinity Proteomics, Including the Discovery of Biomarker Candidates

Virus inactivation is a prerequisite for safe handling of high-risk infectious samples. β-Propiolactone (BPL) is an established reagent with proven virucidal efficacy. BPL primarily reacts with DNA, RNA, and amino acids. The latter may modify antigenic protein epitopes interfering with binding properties of affinity reagents such as antibodies and aptamers used in affinity proteomic screens. We investigated (i) the impact of BPL treatment on the analysis of protein levels in plasma samples using the aptamer-based affinity proteomic platform SomaScan and (ii) effects on protein detection in conditioned medium samples using the proximity extension assay-based Olink Target platform. In the former setup, BPL-treated and native plasma samples from patients with ovarian cancer (

Target-Triggered Aggregation of Modified E. coli for Diagnosis of Ovarian Cancer

Bacteria inherently possess the capability of quorum sensing in response to the environment. In this work, we have proposed a strategy to confer bacteria with the ability to recognize targets with quorum-sensing behavior. Meanwhile, we have successfully achieved artificial control over the target-triggered aggregation of

FREE: Enhanced Feature Representation for Isotopic Envelope Evaluation in Top-Down Mass Spectra Deconvolution

The aim of deconvolution of top-down mass spectra is to recognize monoisotopic peaks from the experimental envelopes in raw mass spectra. So accurate assessment of similarity between theoretical and experimental envelopes is a critical step in mass spectra data deconvolution. Existing evaluation methods primarily rely on intensity differences and

Data Acquisition and Intraoperative Tissue Analysis on a Mobile, Battery-Operated, Orbitrap Mass Spectrometer

Mass spectrometry has been increasingly explored in intraoperative studies as a potential technology to help guide surgical decision making. Yet, intraoperative experiments using high-performance mass spectrometry instrumentation present a unique set of operational challenges. For example, standard operating rooms are often not equipped with the electrical requirements to power a commercial mass spectrometer and are not designed to accommodate their permanent installation. These obstacles can impact progress and patient enrollment in intraoperative clinical studies because implementation of MS instrumentation becomes limited to specific operating rooms that have the required electrical connections and space. To expand our intraoperative clinical studies using the MasSpec Pen technology, we explored the feasibility of transporting and acquiring data on Orbitrap mass spectrometers operating on battery power in hospital buildings. We evaluated the effect of instrument movement including acceleration and rotational speeds on signal stability and mass accuracy by acquiring data using direct infusion electrospray ionization. Data were acquired while rolling the systems in/out of operating rooms and while descending/ascending a freight elevator. Despite these movements and operating the instrument on battery power, the relative standard deviation of the total ion current was <5% and the magnitude of the mass error relative to the internal calibrant never exceeded 5.06 ppm. We further evaluated the feasibility of performing intraoperative MasSpec Pen analysis while operating the Orbitrap mass spectrometer on battery power during an ovarian cancer surgery. We observed that the rich and tissue-specific molecular profile commonly detected from ovarian tissues was conserved when running on battery power. Together, these results demonstrate that Orbitrap mass spectrometers can be operated and acquire data on battery power while in motion and in rotation without losses in signal stability or mass accuracy. Furthermore, Orbitrap mass spectrometers can be used in conjunction to the MasSpec Pen while on battery power for intraoperative tissue analysis.

Stretchable Photonic Crystal-Assisted Glycoprotein Identification for Ovarian Cancer Diagnosis

Photonic crystals with specific wavelengths can realize surface-enhanced excitation and emission intensities of fluorophores and enhance the fluorescence signals of fluorescent molecules. Herein, stretchable photonic crystals with good mechanochromic properties provide continuously adjustable forbidden wavelengths by stretching to change the lattice spacing, with reflectance peaks blue-shifted up to 110 nm to match indicators of different wavelengths and produce differentiated optical enhancement effects. Glycoproteins are significantly identified as clinical markers. However, the wide participation of glycoproteins in various life processes poses enormous complexity and critical challenges for rapid, facile, high-throughput, and accurate clinical analysis or health assessment. In this work, we proposed a stretchable photonic crystal-assisted glycoprotein identification approach for early ovarian cancer diagnosis. Stretchable photonic crystals can provide rich optical information to efficiently identify glycoproteins in complex matrices. A double-indicator fluorescence sensor was designed to respond to the protein trunk and oligosaccharide segment of glycoproteins separately for improved recognition accuracy. Seven typical glycoproteins could be discriminated from proteins, saccharides, or mixture interferents. Clinical ovarian cancer samples for early, intermediate, and advanced ovarian cancer and healthy subjects were verified with 100% accuracy. This strategy of stretchable photonic crystal-assisted glycoprotein identification provides an effective method for accurate, rapid ovarian cancer diagnosis and timely clinical treatment.

Unfolded Protein-Based Sandwich AIE Probe Imparts High Fluorescent Contrast for Pan-Cancer Surgical Navigation

β-Galactosidase-Activated and Red Light-Induced RNA Modification Strategy for Prolonged NIR Fluorescence/PET Bimodality Imaging

Improving the retention of small-molecule-based therapeutic agents in tumors is crucial to achieve precise diagnosis and effective therapy of cancer. Herein, we propose a β-galactosidase (β-Gal)-activated and red light-induced RNA modification (GALIRM) strategy for prolonged tumor imaging. A β-Gal-activatable near-infrared (NIR) fluorescence (FL) and positron emission tomography (PET) bimodal probe

Surface-Enhanced Raman Spectroscopy-Based Detection of EMT-Related Targets in Endometrial Cancer: Potential for Diagnosis and Prognostic Prediction

Epithelial-mesenchymal transformation (EMT) is one of the important mechanisms of malignancy in endometrial cancer, and detection of EMT targets is a key challenge to explore the mechanism of endometrial carcinoma (EC) malignancy and discover novel therapeutic targets. This study attempts to use surface-enhanced Raman spectroscopy (SERS), a highly sensitive, ultrafast, and highly specific analytical technology, to rapidly detect microRNA-200a-3p and ZEB1 in endometrial cancer cell lines. The silver nanoparticles were decorated with iodine and calcium ions, can capture the SERS fingerprints of microRNA-200a-3p and ZEB1 protein, and effectively avoid the interference of impurity signals. At the same time, the method has high sensitivity for the detection of the above EMT targets, and the lowest detection limits for microRNA-200a-3p and ZEB1 are 4.5 pmol/mL and 10 ng/mL, respectively. At the lowest detection concentration, the method still has high stability. In addition, principal component analysis can not only identify microRNA-200a-3p and ZEB1 protein from a variety of EMT-associated microRNA and proteins but also identify them in the total RNA and total protein of endometrial cancer cell lines and normal endometrial epithelial cell lines. This study modified silver nanoparticles with iodine and calcium ions and for the first time captured the fingerprints of EMT-related targets microRNA-200a-3p and ZEB1 at the same time without label, and the method has high sensitivity and stability. This SERS-based method has immense potential for elucidating the molecular mechanisms of EMT-related EC, as well as identifying biomarkers for malignant degree and prognosis prediction.

β-Galactosidase-Activated Near-Infrared Fluorescence and 1 H/ 19 F MRI Trimodal Probe for Real-Time In Vivo Imaging of Ovarian Cancer

β-Galactosidase (β-gal), overexpressed in primary ovarian tumors, serves as an important biomarker for ovarian cancer. Accurate and sensitive detection of β-gal is crucial for cancer diagnosis and therapy. Activatable multimodal probes that show enhancement of multiplex imaging signals upon interaction with their specific molecular target have become powerful tools for rapid and precise imaging of biological processes. Nevertheless, the rational design of such probes remains challenging. Herein, a novel β-gal-activated probe, named Gal-Cy-Gd-F, was designed and synthesized for trimodal imaging of its activity in vivo. Upon activation by β-gal, the probe underwent a unique quinone methide-mediated self-immobilization mechanism that covalently anchored onto the target enzyme or neighboring proteins, thereby effectively prolonging its retention time. Consequently, the longitudinal relaxivity increased from 8.67 ± 0.5 mM

Machine Learning-Driven Extracellular Vesicles Peptidomics Powers Precision Classification of Endometrial Cancer

Endometrial cancer (EC) molecular subtyping is critical for prognosis and treatment but remains hindered by reliance on invasive tissue biopsies and time-consuming genomic sequencing. Here, we present a minimally invasive approach integrating MALDI-TOF mass spectrometry and LC-MS/MS-based peptidomic profiling of plasma extracellular vesicles (EVs) with machine learning for rapid EC screening and subtyping. EVs were isolated from EC patients and controls, and their peptidome fingerprints were analyzed. A machine learning model utilizing 12 discriminative MALDI-TOF MS features, the levels of CA125 and HE4, and clinical features related to cancer risk achieved an AUC of 0.867 in distinguishing EC from the controls. For molecular subtyping (POLE mutant, NSMP, MMRd, P53-abnormal), a multiclassification model demonstrated micro/macro-averaged AUCs of 0.91/0.90. LC-MS/MS identified 7,479 peptides, with fibrinogen α chain (FGA), protease serine 3 (PRSS3), and apolipoprotein A-I (APOA1) emerging as key biomarkers linked to specific subtypes. This study establishes a high-throughput, cost-effective platform for EC management, bridging translational gaps in precision oncology.

Derivatization-Mediated MALDI-MSI and CE-LIF for Collaborative Analysis of Monosaccharides in Endometrial Cancer Patients: From Pathological Tissues to Serum

Abnormal glucose metabolism has been identified as a key characteristic of tumorigenesis. Visualizing the levels of monosaccharides in the lesion tissues and bodies of cancer patients is conducive to uncovering patterns of glucose metabolic reprogramming and assisting in the early diagnosis of cancer. Consequently, based on 7-(diethylamino)coumarin-3-carbohydrazide (DCCH), which has mass spectrometry/optical dual-signal enhancement functionality, both an on-tissue derivatization strategy suitable for matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) analysis and an in-solution derivatization strategy for capillary electrophoresis/laser-induced fluorescence (CE-LIF) analysis have been developed. During the on-tissue derivatization reaction, the nonvolatile MALDI matrix, 2,5-dihydroxybenzoic acid, was innovatively adopted as a proton donor. This approach not only efficiently catalyzes the derivatization process but also significantly minimizes analyte redistribution, and a low-cost airbrush application can achieve satisfying in situ derivatization. For further addressing the issue of the coexistence of multiple conformational isomers in biosamples, a rapid and efficient CE-LIF method was proposed for the separation of five DCCH-labeled monosaccharides (including three conformational isomers) within 8 min. By focusing on endometrial cancer (EC), the synergy of DCCH-mediated MALDI-MSI and CE-LIF analysis successfully achieved high-resolution in situ visualization of an obvious depletion difference in monosaccharides across the focal areas (tumor tissues, peritumoral tissues, and paracancerous tissues), and a comparative analysis was conducted on the serum monosaccharide profiles between patients with EC and those with benign uterine diseases. These analyses yielded novel molecular-level evidence, thereby facilitating a deeper understanding of the monosaccharide metabolic abnormalities associated with EC.

Diagnosis and Biomarker Screening of Endometrial Cancer Enabled by a Versatile Exosome Metabolic Fingerprint Platform

Exosomes have emerged as a revolutionary tool for liquid biopsy (LB), as they carry specific cargo from cells. Profiling the metabolites of exosomes is crucial for cancer diagnosis and biomarker discovery. Herein, we propose a versatile platform for exosomal metabolite assay of endometrial cancer (EC). The platform is based on a nanostructured composite material comprising gold nanoparticle-coated magnetic COF with aptamer modification (Fe

Photoelectrochemical Biosensor Based on TiO2/P5FIn with a Cascade Amplification Strategy for Simultaneous Detection of GSH and E2 Analytes of Ovarian Cancer

In this work, a photoelectrochemical (PEC) sensor for ovarian cancer detection was constructed based on TiO

Ultrasensitive Electrochemiluminescence Nanoprobe for Early Screening and Early Diagnosis of Cervical Cancer

High-risk HPV-16 infection is strongly linked to cervical cancer, making its quantitative detection vital for early screening and early diagnosis. However, accurate detection of HPV-16 DNA is challenging due to the low viral load and biomolecular interference in the cervical microenvironment. Here, we develop an electrochemiluminescence sensor using in situ-grown MXene quantum dots immobilized on N, P-codoped Ti

Thermodynamics-Guided Strand-Displacement-Based DNA Probe for Determination of the Average Methylation Levels of Multiple CpG Sites

Determination of the methylation levels of genes of interest is fundamental for biological and medical research that involves DNA methylation. Using the average methylation levels of multiple CpG sites to represent the methylation levels of the whole gene is much more accurate than using that of only one CpG site. However, current methods that can provide the average methylation levels of several CpG sites are all expensive, time-consuming (several weeks), and labor-intensive. Herein, guided by the unique thermodynamics of the DNA strand-displacement process, we constructed a DNA fluorescent probe for determination of the average methylation levels of multiple CpG sites. Theoretical calculations of the reaction process and proof-of-concepts experiments on two to three CpG sites of synthesized DNA validated the basic principles of our probe. Taking two CpG sites in the promotor regions of

Phosphoproteins Identification on Stretchable Photonic Crystal for Cervical Cancer Assessment

Cervical cancer is one of the diseases that threaten women's health. Early screening and assessment are crucial for disease intervention and treatment. However, existing diagnostic technologies generally have problems such as invasive, high cost, and complex operation, which limit their widespread application in clinical early screening. Therefore, there is an urgent need to develop a rapid, low-cost, minimally invasive, and highly sensitive method for cervical cancer assessment. Protein phosphorylation levels show specific dynamic changes at different stages of cervical cancer development. Herein, we report a phosphoprotein identification method based on a stretchable photonic crystal. Three fluorescent probes were loaded onto the stretchable photonic crystal that constructed a cross-response sensor. The photonic bandgap was dynamically regulated by mechanical stretching to match with the emission wavelengths of the multiple probes, thereby enhancing fluorescence and amplifying signal differences. Without the need for additional probe synthesis and labeling, our strategy can accurately pinpoint the stage of cervical cancer development based on serum samples, with an accuracy rate of up to 94%. This study provides a simple, efficient, and clinically promising detection method for the assessment of cervical cancer.

SiaQuant Unveils Serum α2,3/α2,6 Sialylation Heterogeneities and Predicts Neoadjuvant Chemotherapy Response in Locally Advanced Cervical Cancer

Sialylation significantly influences tumor progression, invasion, and metastasis. Accurately characterizing sialylated

Dual-OR Logic-Gated Lateral Flow Strip Assay Based on Colorimetric-Fluorescence Dual Indication for Screening of HPV16/18 in Multiple Scenarios

The incidence of cervical cancer continues to rise in underdeveloped regions due to low human papillomavirus (HPV) vaccination rates and inadequate screening systems. To achieve convenient, rapid, and accurate detection of HPV, we developed a three-wire lateral flow strip assay system based on dual-OR logic gates for rapid and simultaneous detection of HPV subtypes 16 and 18 in a single test. The system combines three-branch-catalytic hairpin assembly (TCHA)-mediated signal amplification with simple OR logic gate-based signal output to improve detection rates while enabling HPV 16/18 subtype identification. The detection limit of the method was calculated to be 10 aM for the selected target sequences. Meanwhile, the method showed excellent specificity with no false-positive output in real-world detection. The sensitivity of the colorimetric test strips exceeded 90%, while the sensitivity of the fluorescence-based test strips surpassed 95% in detecting clinical samples, demonstrating a high degree of concordance with the results obtained from the real-time quantitative polymerase chain reaction (qPCR). This method provides a simple and easy method for the rapid screening of HPV.

Cancer-Associated Fibroblast Mimetic AIE Probe for Precision Imaging-Guided Full-Cycle Management of Ovarian Cancer Surgery

Fluorescence imaging can improve surgical accuracy in ovarian cancer, but a high signal-to-noise ratio is crucial for tiny metastatic cancers. Meanwhile, intraoperative fluorescent surgical navigation modalities alone are still insufficient to completely remove ovarian cancer lesions, and the recurrence rate remains high. Here, we constructed a cancer-associated fibroblasts (CAFs)-mimetic aggregation-induced emission (AIE) probe to enable full-cycle management of surgery that eliminates recurrence. AIE molecules (P3-PPh

On-Tissue Derivatization with Girard’s Reagent P Enhances N-Glycan Signals for Formalin-Fixed Paraffin-Embedded Tissue Sections in MALDI Mass Spectrometry Imaging

Glycosylation is a major protein post-translational modification whose dysregulation has been associated with many diseases. Herein, an on-tissue chemical derivatization strategy based on positively charged hydrazine reagent (Girard's reagent P) coupled with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was developed for analysis of N-glycans from FFPE treated tissue sections. The performance of the proposed approach was evaluated by analysis of monosaccharides, oligosaccharides, N-glycans released from glycoproteins, as well as MS imaging of N-glycans from human cancer tissue sections. The results demonstrated that the signal-to-noise ratios for target saccharides were notably improved after chemical derivatization, in which signals were enhanced by 230-fold for glucose and over 28-fold for maltooctaose. Improved glycome coverage was obtained for N-glycans derived from glycoproteins and tissue samples after chemical derivatization. Furthermore, on-tissue derivatization was applied for MALDI-MSI of N-glycans from human laryngeal cancer and ovarian cancer tissues. Differentially expressed N-glycans among the tumor region, adjacent normal tissue region, and tumor proximal collagen stroma region were imaged, revealing that high-mannose type N-glycans were predominantly expressed in the tumor region. Overall, our results indicate that the on-tissue labeling strategy coupled with MALDI-MSI shows great potential to spatially characterize N-glycan expression within heterogeneous tissue samples with enhanced sensitivity. This study provides a promising approach to better understand the pathogenesis of cancer related aberrant glycosylation, which is beneficial to the design of improved clinical diagnosis and therapeutic strategies.

Single-Cell ICP-MS in Combination with Fluorescence-Activated Cell Sorting for Investigating the Effects of Nanotransported Cisplatin(IV) Prodrugs

The combined use of fluorescence-activated cell sorting (FACS) and single-cell inductively coupled plasma mass spectrometry (SC-ICP-MS) is reported, for the first time, in this work. It is applied to evaluate the differences between the cellular uptake of ultrasmall iron oxide nanoparticles (FeNPs) loaded with cisplatin(IV) prodrug (FeNPs-Pt(IV)) and cisplatin regarding cell viability. For this aim, FACS is applied to separate viable, apoptotic, and necrotic A2780 ovarian cancer cells after exposing them to the nanotransported prodrug and cisplatin, respectively. The different sorted cell populations are individually analyzed using quantitative SC-ICP-MS to address the intracellular amount of Pt. The highest Pt intracellular content occurs in the apoptotic cell population (about 2.1 fg Pt/cell) with a narrow intercellular distribution when using FeNPs-Pt(IV) nanoprodrug and containing the largest number of cells (75% of the total). In the case of the cisplatin-treated cells, the highest Pt content (about 1.6 fg Pt/cell) could be determined in the viable sorted cell population. The combined methodology, never explored before, permits a more accurate picture of the effect of the intracellular drug content together with the cell death mechanisms associated with the free drug and the nanotransported prodrug, respectively, and opens the door to many possible single-cell experiments in sorted cell populations.

Simultaneous Detection of Ovarian Cancer-Concerned HE4 and CA125 Markers Based on Cu Single-Atom-Triggered CdS QDs and Eu MOF@Isoluminol ECL

Simultaneous detection of different disease markers is significant for clinical diagnosis. In this work, a dual-signal electrochemiluminescence (ECL) immunosensor was constructed for the simultaneous detection of carbohydrate antigen 125 (CA125) and human epithelial protein 4 (HE4) markers of ovarian cancer. The results showed that the Eu metal-organic framework-loaded isoluminol-Au nanoparticles (Eu MOF@Isolu-Au NPs) could generate a strong anodic ECL signal through synergistic interaction; as cathodic luminophore, the composite of carboxyl-functionalized CdS quantum dots and N-doped porous carbon-anchored Cu single-atom catalyst could catalyze H

Chemosensor with Ultra-High Fluorescence Enhancement for Assisting in Diagnosis and Resection of Ovarian Cancer

Fluorescence imaging-guided diagnostics is one of the most promising approaches for facile detection of tumors

Highly Sensitive and Selective Photoelectrochemical Aptasensor for Cancer Biomarker CA125 Based on AuNPs/GaN Schottky Junction

A gold nanoparticle (AuNPs)/gallium nitride (GaN) Schottky junction was fabricated by growing AuNPs in situ on the surface of GaN and then etched by H

EnvCNN: A Convolutional Neural Network Model for Evaluating Isotopic Envelopes in Top-Down Mass-Spectral Deconvolution

Top-down mass spectrometry has become the main method for intact proteoform identification, characterization, and quantitation. Because of the complexity of top-down mass spectrometry data, spectral deconvolution is an indispensable step in spectral data analysis, which groups spectral peaks into isotopic envelopes and extracts monoisotopic masses of precursor or fragment ions. The performance of spectral deconvolution methods relies heavily on their scoring functions, which distinguish correct envelopes from incorrect ones. A good scoring function increases the accuracy of deconvoluted masses reported from mass spectra. In this paper, we present EnvCNN, a convolutional neural network-based model for evaluating isotopic envelopes. We show that the model outperforms other scoring functions in distinguishing correct envelopes from incorrect ones and that it increases the number of identifications and improves the statistical significance of identifications in top-down spectral interpretation.

Specific Near-Infrared Probe for Ultrafast Imaging of Lysosomal β-Galactosidase in Ovarian Cancer Cells

Reactivity based fluorescent probes have been widely investigated as a powerful and noninvasive tool for disease diagnosis in recent years. β-Galactosidase (β-gal), one of the typical lysosomal glycosidases, is reported to be a vital biomarker overexpressed in primary ovarian cancer cells. Fluorescent probes with excellent performance for endogenous β-gal detection offer a unique option for visualization and diagnosis of primary ovarian cancer cells. Herein, a near-infrared fluorescent probe Lyso-Gal with lysosome-targeting ability was developed for lysosomal β-gal detection and imaging in ovarian cancer cells (SKOV-3 cells). Lyso-Gal exhibits weak fluorescence in aqueous solution but emits bright NIR fluorescence at 725 nm after incubation with β-gal. Highly selective imaging of ovarian cancer cells has been achieved upon incubation with Lyso-Gal for only 1 min. The detection time is extremely short. In comparison with a similar hemicyanine probe, Hx-Gal, without lysosome-targeting ability, Lyso-Gal realizes endogenous β-gal visualization in lysosomes and shows brighter fluorescence than Hx-Gal in SKOV-3 cells. This work demonstrates the potential of Lyso-Gal for detection of primary ovarian cancer cells by using β-gal as the biomarker.

Using the Chemical Noise Background in MALDI Mass Spectrometry Imaging for Mass Alignment and Calibration

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is an established tool for the investigation of formalin fixed paraffin embedded (FFPE) tissue samples and shows a high potential for applications in clinical research and histopathological diagnosis. The applicability and accuracy of this method, however, heavily depends on the quality of the acquired data, and in particular mass misalignment in axial time-of-flight (TOF) MSI continues to be a serious issue. We present a mass alignment and recalibration method that is specifically designed to operate on MALDI peptide imaging data. The proposed method exploits statistical properties of the characteristic chemical noise background observed in peptide imaging experiments. By comparing these properties to a theoretical peptide mass model, the effective mass shift of each spectrum is estimated and corrected. The method was evaluated on a cohort of 31 FFPE tissue samples, pursuing a statistical validation approach to estimate both the reduction of relative misalignment, as well as the increase in absolute mass accuracy. Our results suggest that a relative mass precision of approximately 5 ppm and an absolute accuracy of approximately 20 ppm are achievable using our method.

Comparative Assessment of Quantification Methods for Tumor Tissue Phosphoproteomics

With increasing sensitivity and accuracy in mass spectrometry, the tumor phosphoproteome is getting into reach. However, the selection of quantitation techniques best-suited to the biomedical question and diagnostic requirements remains a trial and error decision as no study has directly compared their performance for tumor tissue phosphoproteomics. We compared label-free quantification (LFQ), spike-in-SILAC (stable isotope labeling by amino acids in cell culture), and tandem mass tag (TMT) isobaric tandem mass tags technology for quantitative phosphosite profiling in tumor tissue. Compared to the classic SILAC method, spike-in-SILAC is not limited to cell culture analysis, making it suitable for quantitative analysis of tumor tissue samples. TMT offered the lowest accuracy and the highest precision and robustness toward different phosphosite abundances and matrices. Spike-in-SILAC offered the best compromise between these features but suffered from a low phosphosite coverage. LFQ offered the lowest precision but the highest number of identifications. Both spike-in-SILAC and LFQ presented susceptibility to matrix effects. Match between run (MBR)-based analysis enhanced the phosphosite coverage across technical replicates in LFQ and spike-in-SILAC but further reduced the precision and robustness of quantification. The choice of quantitative methodology is critical for both study design such as sample size in sample groups and quantified phosphosites and comparison of published cancer phosphoproteomes. Using ovarian cancer tissue as an example, our study builds a resource for the design and analysis of quantitative phosphoproteomic studies in cancer research and diagnostics.