Self-Functionalized Superlattice Nanosensor Enables Glioblastoma Diagnosis Using Liquid Biopsy

A ratiometric SERS strategy for the prediction of cancer cell proportion and guidance of glioma surgical resection

Rapid and accurate identification of tumor boundaries is critical for the cure of glioma, but it is difficult due to the invasive nature of glioma cells. This paper aimed to explore a rapid diagnostic strategy based on a label-free surface-enhanced Raman scattering (SERS) technique for the quantitative detection of glioma cell proportion intraoperatively. With silver nanoparticles as substrate, an in-depth SERS analysis was performed on simulated clinical samples containing normal brain tissue and different concentrations of patient-derived glioma cells. The results revealed two universal characteristic peaks of 655 and 717 cm-1, which strongly correlated with glioma cell proportion regardless of individual differences. Based on the intensity ratio of the two peaks, a ratiometric SERS strategy for the quantification of glioma cells was established by employing an artificial neuron network model and a polynomial regression model. Such a strategy accurately estimated the proportion of glioma cells in simulated clinical samples (R2 = 0.98) and frozen samples (R2 = 0.85). More importantly, it accurately facilitated the delineation of tumor margins in freshly obtained samples. Taken together, this SERS-based method ensured a rapid and more detailed identification of tumor margins during surgical resection, which could be beneficial for intraoperative decision-making and pathological evaluation.

Pt/Zn-TCPP Nanozyme-Based Flexible Immunoassay for Dual-Mode Pressure-Temperature Monitoring of Low-Abundance Proteins

Pressure and temperature, as common physical parameters, are important for monitoring human health. In contrast, single-mode monitoring is prone to causing experimental errors. Herein, we innovatively designed a dual-mode flexible sensing platform based on a platinum/zinc-meso-tetrakis(4-carboxyphenyl)porphyrin (Pt/Zn-TCPP) nanozyme for the quantitative monitoring of carcinoembryonic antigen (CEA) in biological fluids with pressure and temperature readouts. The Pt/Zn-TCPP nanozyme with catalytic and photothermal efficiencies was synthesized by means of integrating photosensitizers into porous materials. The flexible sensing system after the antigen-antibody reaction recognized the pressure using a flexible skin-like pressure sensor with a digital multimeter readout, whereas the temperature was acquired via the photoheat conversion system of the Pt/Zn-TCPP nanozyme under 808 nm near-infrared (NIR) irradiation using a portable NIR imaging camera on a smartphone. Meanwhile, the dual-mode flexible sensing system was carried out on a homemade three-dimensional (3D)-printed device. Results revealed that the developed dual-mode immunosensing platform could exhibit good pressure and temperature responses within the dynamic range of 0.5-100 ng mL-1 CEA with the detection limits of 0.24 and 0.13 ng mL-1, respectively. In addition, the pressure and temperature were sensed simultaneously without crosstalk interference. Importantly, the dual-mode flexible immunosensing system can effectively avoid false alarms during the measurement, thus providing great potential for simple and low-cost development for point-of-care testing.

Advances of machine learning-assisted small extracellular vesicles detection strategy

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.

A Panorama of Extracellular Vesicle Applications: From Biomarker Detection to Therapeutics

Extracellular vesicles (EVs) secreted by all cell types are involved in the cell-to-cell transfer of regulatory factors that influence cell and tissue phenotypes in normal and diseased tissues. EVs are thus a rich source of biomarker targets for assays that analyze blood and urinary EVs for disease diagnosis. Sensitive biomarker detection in EVs derived from specific cell populations is a key major hurdle when analyzing complex biological samples, but innovative approaches surveyed in this Perspective can streamline EV isolation and enhance the sensitivity of EV detection procedures required for clinical application of EV-based diagnostics and therapeutics, including nanotechnology and microfluidics, to achieve EV characterizations. Finally, this Perspective also outlines opportunities and challenges remaining for clinical translation of EV-based assays.