Glioblastoma Associated Natural Killer Cell EVs Generating Tumour-Specific Signatures: Noninvasive GBM Liquid Biopsy with Self-Functionalized Quantum Probes

Applications of nanotechnology in remodeling the tumour microenvironment for glioblastoma treatment

With the increasing research and deepening understanding of the glioblastoma (GBM) tumour microenvironment (TME), novel and more effective therapeutic strategies have been proposed. The GBM TME involves intricate interactions between tumour and non-tumour cells, promoting tumour progression. Key therapeutic goals for GBM treatment include improving the immunosuppressive microenvironment, enhancing the cytotoxicity of immune cells against tumours, and inhibiting tumour growth and proliferation. Consequently, remodeling the GBM TME using nanotechnology has emerged as a promising approach. Nanoparticle-based drug delivery enables targeted delivery, thereby improving treatment specificity, facilitating combination therapies, and optimizing drug metabolism. This review provides an overview of the GBM TME and discusses the methods of remodeling the GBM TME using nanotechnology. Specifically, it explores the application of nanotechnology in ameliorating immune cell immunosuppression, inducing immunogenic cell death, stimulating, and recruiting immune cells, regulating tumour metabolism, and modulating the crosstalk between tumours and other cells.

Mineralized aggregates based on native protein phase transition for non-destructive diagnosis of seborrheic skin by surface-enhanced Raman spectroscopy

The non-homeostasis of sebum secretion by the sebaceous glands in a complicated microenvironment dramatically impacts the skin health of many people in the world. However, the complexity and hydrophobicity of sebum mean a lack of diagnostic tools, which makes it challenging to determine the reason behind cortical imbalances. Herein, a biomimetic mineralized aggregates (PTL@Au and PTB@Au) strategy has been proposed, which could obtain molecular information about sebum by surface-enhanced Raman spectroscopy (SERS). The breaking of disulfide bonds leads to changes in hydrogen bonding, which transform the natural protein into amyloid-like phase transition protein with β-sheets. It provides sites for the nucleation and crystallization of gold nanocrystals to build mineralized aggregates. The mineralized aggregates show robust adhesion stability at the interfaces of different materials through hydrogen bonding and electrostatic interactions. The stabilization, hydrophobicity (contact angle: 134°), and optical transmission (75%) of the structure could result in superior SERS performance for sebum analysis. It should be noted that this enables the sebum detection of clinical samples while ensuring safety. Such generalized bionic mineralization construction and diagnosis methods also serve as an advanced paradigm for a range of biomedical applications.

Surface-Enhanced Raman Scattering Nanosensing and Imaging in Neuroscience

Monitoring neurochemicals and imaging the molecular content of brain tissues in vitro, ex vivo, and in vivo is essential for enhancing our understanding of neurochemistry and the causes of brain disorders. This review explores the potential applications of surface-enhanced Raman scattering (SERS) nanosensors in neurosciences, where their adoption could lead to significant progress in the field. These applications encompass detecting neurotransmitters or brain disorders biomarkers in biofluids with SERS nanosensors, and imaging normal and pathological brain tissues with SERS labeling. Specific studies highlighting in vitro, ex vivo, and in vivo analysis of brain disorders using fit-for-purpose SERS nanosensors will be detailed, with an emphasis on the ability of SERS to detect clinically pertinent levels of neurochemicals. Recent advancements in designing SERS-active nanomaterials, improving experimentation in biofluids, and increasing the usage of machine learning for interpreting SERS spectra will also be discussed. Furthermore, we will address the tagging of tissues presenting pathologies with nanoparticles for SERS imaging, a burgeoning domain of neuroscience that has been demonstrated to be effective in guiding tumor removal during brain surgery. The review also explores future research applications for SERS nanosensors in neuroscience, including monitoring neurochemistry in vivo with greater penetration using surface-enhanced spatially offset Raman scattering (SESORS), near-infrared lasers, and 2-photon techniques. The article concludes by discussing the potential of SERS for investigating the effectiveness of therapies for brain disorders and for integrating conventional neurochemistry techniques with SERS sensing.

Extracellular vesicles in glioblastoma: Biomarkers and therapeutic tools

Glioblastoma (GBM) is the most aggressive tumor among the gliomas and intracranial tumors and to date prognosis for GBM patients remains poor, with a median survival typically measured in months to a few years depending on various factors. Although standardized therapies are routinely employed, it is clear that these strategies are unable to cope with heterogeneity and invasiveness of GBM. Furthermore, diagnosis and monitoring of responses to therapies are directly dependent on tissue biopsies or magnetic resonance imaging (MRI) techniques. From this point of view, liquid biopsies are arising as key sources of a variety of biomarkers with the advantage of being easily accessible and monitorable. In this context, extracellular vesicles (EVs), physiologically shed into body fluids by virtually all cells, are gaining increasing interest both as natural carriers of biomarkers and as specific signatures even for GBM. What makes these vesicles particularly attractive is they are also emerging as therapeutical vehicles to treat GBM given their native ability to cross the blood-brain barrier (BBB). Here, we reviewed recent advances on the use of EVs as biomarker for liquid biopsy and nanocarriers for targeted delivery of anticancer drugs in glioblastoma.

Label-Free Multiplex Profiling of Exosomal Proteins with a Deep Learning-Driven 3D Surround-Enhancing SERS Platform for Early Cancer Diagnosis

Identification of protein profiling on plasma exosomes by SERS can be a promising strategy for early cancer diagnosis. However, it is still challenging to detect multiple exosomal proteins simultaneously by SERS since the Raman signals of exosomes detected by conventional colloidal nanocrystals or two-dimensional SERS substrates are incomplete and complex. Herein, we develop a novel three-dimensional (3D) surround-enhancing SERS platform, named 3D se-SERS, for the multiplex detection of exosomal proteins. In this 3D se-SERS, proteins and exosomes are covered with "hotspots" generated by the gold nanoparticles, which surround the analytes densely and three-dimensionally, providing sensitive and comprehensive SERS signals. Combining this 3D se-SERS with a deep learning model, we successfully quantitatively profiled seven proteins including CD63, CD81, CD9, CD151, CD171, TSPAN8, and PD-L1 on the surface of plasma exosomes from patients, which can predict the occurrence and advancement of lung cancer. This 3D se-SERS integrating deep learning technique benefits from high sensitivity and significant multiplexing ability for comprehensive analysis of proteins and exosomes, demonstrating the potential of deep learning-driven 3D se-SERS technology for plasma exosome-based early cancer diagnosis.

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.

The Landscape of Biomimetic Nanovesicles in Brain Diseases

Brain diseases, such as brain tumors, neurodegenerative diseases, cerebrovascular diseases, and brain injuries, are caused by various pathophysiological changes, which pose a serious health threat. Brain disorders are often difficult to treat due to the presence of the blood-brain barrier (BBB). Biomimetic nanovesicles (BNVs), including endogenous extracellular vesicles (EVs) derived from various cells and artificial nanovesicles, possess the ability to penetrate the BBB and thus can be utilized for drug delivery to the brain. BNVs, especially endogenous EVs, are widely distributed in body fluids and usually carry various disease-related signal molecules such as proteins, RNA, and DNA, and may also be analyzed to understand the etiology and pathogenesis of brain diseases. This review covers the exhaustive classification and characterization of BNVs and pathophysiological roles involved in various brain diseases, and emphatically focuses on nanotechnology-integrated BNVs for brain disease theranostics, including various diagnosis strategies and precise therapeutic regulations (e.g., immunity regulation, disordered protein clearance, anti-neuroinflammation, neuroregeneration, angiogenesis, and the gut-brain axis regulation). The remaining challenges and future perspectives regarding the nanotechnology-integrated BNVs for the diagnosis and treatment of brain diseases are also discussed and outlined.

Immunoregulatory functions and therapeutic potential of natural killer cell-derived extracellular vesicles in chronic diseases

Extracellular vesicles (EVs) have been proven to play a significant immunoregulatory role in many chronic diseases, such as cancer and immune disorders. Among them, EVs derived from NK cells are an essential component of the immune cell functions. These EVs have been demonstrated to carry a variety of toxic proteins and nucleic acids derived from NK cells and play a therapeutic role in diseases like malignancies, liver fibrosis, and lung injury. However, natural NK-derived EVs (NKEVs) have certain limitations in disease treatment, such as low yield and poor targeting. Concurrently, NK cells exhibit characteristics of memory-like NK cells, which have stronger proliferative capacity, increased IFN-γ production, and enhanced cytotoxicity, making them more advantageous for disease treatment. Recent research has shifted its focus towards engineered extracellular vesicles and their potential to improve the efficiency, specificity, and safety of disease treatments. In this review, we will discuss the characteristics of NK-derived EVs and the latest advancements in disease therapy. Specifically, we will compare different cellular sources of NKEVs and explore the current status and prospects of memory-like NK cell-derived EVs and engineered NKEVs.

Printed Divisional Optical Biochip for Multiplex Visualizable Exosome Analysis at Point-of-Care

Rapid detection of various exosomes is of great significance in early diagnosis and postoperative monitoring of cancers. Here, a divisional optical biochip is reported for multiplex exosome analysis via combining the self-assembly of nanochains and precise surface patterning. Arising from resonance-induced near-field enhancement, the nanochains show distinct color changes after capturing target exosomes for direct visual detection. Then, a series of divisional nanochain-based biochips conjugated with several specific antibodies are fabricated through designed hydrophilic and hydrophobic patterns. Because of the significant wettability difference, one sample droplet is precisely self-splitting into several microdroplets enabling simultaneous identification of multiple target exosomes in 30 min with a sensitivity of 6 × 107 particles mL-1 , which is about two orders lower than enzyme-linked immunosorbent assay. Apart from the trace amount detection, excellent semiquantitative capability is demonstrated to distinguish clinical exosomes from glioblastoma patients and healthy people. This method is simple, versatile, and highly efficient that can be extended as a diagnostic tool for many diseases, promoting the development of liquid biopsy.

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.

The coming of age of liquid biopsy in neuro-oncology

The clinical role of liquid biopsy in oncology is growing significantly. In gliomas and other brain tumours, targeted sequencing of cell-free DNA (cfDNA) from CSF may help differential diagnosis when surgery is not recommended and be more representative of tumour heterogeneity than surgical specimens, unveiling targetable genetic alterations. Given the invasive nature of lumbar puncture to obtain CSF, the quantitative analysis of cfDNA in plasma is a lively option for patient follow-up. Confounding factors may be represented by cfDNA variations due to concomitant pathologies (inflammatory diseases, seizures) or clonal haematopoiesis. Pilot studies suggest that methylome analysis of cfDNA from plasma and temporary opening of the blood-brain barrier by ultrasound have the potential to overcome some of these limitations. Together with this, an increased understanding of mechanisms modulating the shedding of cfDNA by the tumour may help to decrypt the meaning of cfDNA kinetics in blood or CSF.

Assessing Metabolic Markers in Glioblastoma Using Machine Learning: A Systematic Review

Glioblastoma (GBM) is a common and deadly brain tumor with late diagnoses and poor prognoses. Machine learning (ML) is an emerging tool that can create highly accurate diagnostic and prognostic prediction models. This paper aimed to systematically search the literature on ML for GBM metabolism and assess recent advancements. A literature search was performed using predetermined search terms. Articles describing the use of an ML algorithm for GBM metabolism were included. Ten studies met the inclusion criteria for analysis: diagnostic (n = 3, 30%), prognostic (n = 6, 60%), or both (n = 1, 10%). Most studies analyzed data from multiple databases, while 50% (n = 5) included additional original samples. At least 2536 data samples were run through an ML algorithm. Twenty-seven ML algorithms were recorded with a mean of 2.8 algorithms per study. Algorithms were supervised (n = 24, 89%), unsupervised (n = 3, 11%), continuous (n = 19, 70%), or categorical (n = 8, 30%). The mean reported accuracy and AUC of ROC were 95.63% and 0.779, respectively. One hundred six metabolic markers were identified, but only EMP3 was reported in multiple studies. Many studies have identified potential biomarkers for GBM diagnosis and prognostication. These algorithms show promise; however, a consensus on even a handful of biomarkers has not yet been made.

SERS-Based Biosensors Combined with Machine Learning for Medical Application

Surface-enhanced Raman spectroscopy (SERS) has shown strength in non-invasive, rapid, trace analysis and has been used in many fields in medicine. Machine learning (ML) is an algorithm that can imitate human learning styles and structure existing content with the knowledge to effectively improve learning efficiency. Integrating SERS and ML can have a promising future in the medical field. In this review, we summarize the applications of SERS combined with ML in recent years, such as the recognition of biological molecules, rapid diagnosis of diseases, developing of new immunoassay techniques, and enhancing SERS capabilities in semi-quantitative measurements. Ultimately, the possible opportunities and challenges of combining SERS with ML are addressed.