Molecular Crosstalk between T Cells and Tumor Uncovers GBM-Specific T Cell Signatures in Blood: Noninvasive GBM Diagnosis Using Immunosensors

NIR-II Light-Driven Genetically Engineered Exosome Nanocatalysts for Efficient Phototherapy against Glioblastoma

Glioblastoma (GBM) poses a significant therapeutic challenge due to its invasive nature and limited drug penetration through the blood-brain barrier (BBB). In response, here we present an innovative biomimetic approach involving the development of genetically engineered exosome nanocatalysts (Mn@Bi2Se3@RGE-Exos) for efficient GBM therapy via improving the BBB penetration and enzyme-like catalytic activities. Interestingly, a photothermally activatable multiple enzyme-like reactivity is observed in such a nanosystem. Upon NIR-II light irradiation, Mn@Bi2Se3@RGE-Exos are capable of converting hydrogen peroxide into hydroxyl radicals, oxygen, and superoxide radicals, providing a peroxidase (POD), oxidase (OXD), and catalase (CAT)-like nanocatalytic cascade. This consequently leads to strong oxidative stresses to damage GBM cells. In vitro, in vivo, and proteomic analysis further reveal the potential of Mn@Bi2Se3@RGE-Exos for the disruption of cellular homeostasis, enhancement of immunological response, and the induction of cancer cell ferroptosis, showcasing a great promise in anticancer efficacy against GBM with a favorable biosafety profile. Overall, the success of this study provides a feasible strategy for future design and clinical study of stimuli-responsive nanocatalytic medicine, especially in the context of challenging brain cancers like GBM.

Raman Scattering Enhancement through Pseudo-Cavity Modes

Raman spectroscopy plays a pivotal role in spectroscopic investigations. The small Raman scattering cross-section of numerous analytes, however, requires enhancement of the signal through specific structuring of the electromagnetic and morphological properties of the underlying surface. This enhancement technique is known as surface-enhanced Raman spectroscopy (SERS). Despite the existence of various proposed alternatives, the approach involving Fabry-Pérot cavities, which constitutes a straightforward method to enhance the electromagnetic field around the analyte, has not been extensively utilized. This is because, for the analyte to experience the maximum electric field, it needs to be embedded within the cavity. Consequently, the top mirror of the cavity will eventually shield it from the external laser source. Recently, an open-cavity configuration has been demonstrated to exhibit properties similar to the classic Fabry-Pérot configuration, with the added advantage of maintaining direct accessibility for the laser source. This paper showcases how such a simple yet innovative configuration can be effectively utilized to achieve remarkable Raman enhancement. The simple structure, coupled with its inexpensive nature and versatility in material selection and scalability, makes it an ideal choice for various analytes and integration into diverse Raman apparatus setups.

Raman spectrum combined with deep learning for precise recognition of Carbapenem-resistant Enterobacteriaceae

Carbapenem-resistant Enterobacteriaceae (CRE) is a major pathogen that poses a serious threat to human health. Unfortunately, currently, there are no effective measures to curb its rapid development. To address this, an in-depth study on the surface-enhanced Raman spectroscopy (SERS) of 22 strains of 7 categories of CRE using a gold silver composite SERS substrate was conducted. The residual networks with an attention mechanism to classify the SERS spectrum from three perspectives (pathogenic bacteria type, enzyme-producing subtype, and sensitive antibiotic type) were performed. The results show that the SERS spectrum measured by the composite SERS substrate was repeatable and consistent. The SERS spectrum of CRE showed varying degrees of species differences, and the strain difference in the SERS spectrum of CRE was closely related to the type of enzyme-producing subtype. The introduced attention mechanism improved the classification accuracy of the residual network (ResNet) model. The accuracy of CRE classification for different strains and enzyme-producing subtypes reached 94.0% and 96.13%, respectively. The accuracy of CRE classification by pathogen sensitive antibiotic combination reached 93.9%. This study is significant for guiding antibiotic use in CRE infection, as the sensitive antibiotic used in treatment can be predicted directly by measuring CRE spectra. Our study demonstrates the potential of combining SERS with deep learning algorithms to identify CRE without culture labels and classify its sensitive antibiotics. This approach provides a new idea for rapid and accurate clinical detection of CRE and has important significance for alleviating the rapid development of resistance to CRE.

Self-Functionalized Superlattice Nanosensor Enables Glioblastoma Diagnosis Using Liquid Biopsy

Glioblastoma (GBM), the most aggressive and lethal brain cancer, is detected only in the advanced stage, resulting in a median survival rate of 15 months. Therefore, there is an urgent need to establish GBM diagnosis tools to identify the tumor accurately. The clinical relevance of the current liquid biopsy techniques for GBM diagnosis remains mostly undetermined, owing to the challenges posed by the blood-brain barrier (BBB) that restricts biomarkers entering the circulation, resulting in the unavailability of clinically validated circulating GBM markers. GBM-specific liquid biopsy for diagnosis and prognosis of GBM has not yet been developed. Here, we introduce extracellular vesicles of GBM cancer stem cells (GBM CSC-EVs) as a previously unattempted, stand-alone GBM diagnosis modality. As GBM CSCs are fundamental building blocks of tumor initiation and recurrence, it is desirable to investigate these reliable signals of malignancy in circulation for accurate GBM diagnosis. So far, there are no clinically validated circulating biomarkers available for GBM. Therefore, a marker-free approach was essential since conventional liquid biopsy relying on isolation methodology was not viable. Additionally, a mechanism capable of trace-level detection was crucial to detecting the rare GBM CSC-EVs from the complex environment in circulation. To break these barriers, we applied an ultrasensitive superlattice sensor, self-functionalized for surface-enhanced Raman scattering (SERS), to obtain holistic molecular profiling of GBM CSC-EVs with a marker-free approach. The superlattice sensor exhibited substantial SERS enhancement and ultralow limit of detection (LOD of attomolar 10-18 M concentration) essential for trace-level detection of invisible GBM CSC-EVs directly from patient serum (without isolation). We detected as low as 5 EVs in 5 μL of solution, achieving the lowest LOD compared to existing SERS-based studies. We have experimentally demonstrated the crucial role of the signals of GBM CSC-EVs in the precise detection of glioblastoma. This was evident from the unique molecular profiles of GBM CSC-EVs demonstrating significant variation compared to noncancer EVs and EVs of GBM cancer cells, thus adding more clarity to the current understanding of GBM CSC-EVs. Preliminary validation of our approach was undertaken with a small amount of peripheral blood (5 μL) derived from GBM patients with 100% sensitivity and 97% specificity. Identification of the signals of GBM CSC-EV in clinical sera specimens demonstrated that our technology could be used for accurate GBM detection. Our technology has the potential to improve GBM liquid biopsy, including real-time surveillance of GBM evolution in patients upon clinical validation. This demonstration of liquid biopsy with GBM CSC-EV provides an opportunity to introduce a paradigm potentially impacting the current landscape of GBM diagnosis.

Application of SERS in In-Vitro Biomedical Detection

Surface-enhanced Raman scattering (SERS), as a rapid and nondestructive biological detection method, holds great promise for clinical on spot and early diagnosis. In order to address the challenging demands of on spot detection of biomedical samples, a variety of strategies has been developed. These strategies include substrate structural and component engineering, data processing techniques, as well as combination with other analytical methods. This report summarizes the recent SERS developments for biomedical detection, and their promising applications in cancer detection, virus or bacterial infection detection, miscarriage spotting, neurological disease screening et al. The first part discusses the frequently used SERS substrate component and structures, the second part reports on the detection strategies for nucleic acids, proteins, bacteria, and virus, the third part summarizes their promising applications in clinical detection in a variety of illnesses, and the forth part reports on recent development of SERS in combination with other analytical techniques. The special merits, challenges, and perspectives are discussed in both introduction and conclusion sections.

Raman spectroscopy: A prospective intraoperative visualization technique for gliomas

The infiltrative growth and malignant biological behavior of glioma make it one of the most challenging malignant tumors in the brain, and how to maximize the extent of resection (EOR) while minimizing the impact on normal brain tissue is the pursuit of neurosurgeons. The current intraoperative visualization assistance techniques applied in clinical practice suffer from low specificity, slow detection speed and low accuracy, while Raman spectroscopy (RS) is a novel spectroscopy technique gradually developed and applied to clinical practice in recent years, which has the advantages of being non-destructive, rapid and accurate at the same time, allowing excellent intraoperative identification of gliomas. In the present work, the latest research on Raman spectroscopy in glioma is summarized to explore the prospect of Raman spectroscopy in glioma surgery.