Raman Spectral Signatures of Serum-Derived Extracellular Vesicle-Enriched Isolates May Support the Diagnosis of CNS Tumors

Toward the nanoscale chemical and physical probing of milk-derived extracellular vesicles using Raman and tip-enhanced Raman spectroscopy

Tip-enhanced Raman spectroscopy (TERS) is an advanced technique to perform local chemical analysis of the surface of a sample through the improvement of the sensitivity and the spatial resolution of Raman spectroscopy by plasmonic enhancement of the electromagnetic signal in correspondence with the nanometer-sized tip of an atomic force microscope (AFM). In this work, TERS is demonstrated to represent an innovative and powerful approach for studying extracellular vesicles, in particular bovine milk-derived extracellular vesicles (mEVs), which are nanostructures with considerable potential in drug delivery and therapeutic applications. Raman spectroscopy has been used to analyze mEVs at the micrometric and sub-micrometric scales to obtain a detailed Raman spectrum in order to identify the 'signature' of mEVs in terms of their characteristic molecular vibrations and, therefore, their chemical compositions. With the ability to improve lateral resolution, TERS has been used to study individual mEVs, demonstrating the possibility of investigating a single mEV selected on the surface of the sample and, moreover, analyzing specific locations on the selected mEV with nanometer lateral resolution. TERS potentially allows one to reveal local differences in the composition of mEVs providing new insights into their structure. Also, thanks to the intrinsic properties of TERS to acquire the signal from only the first few nanometers of the surface, chemical investigation of the lipid membrane in correspondence with the various locations of the selected mEV could be performed by analyzing the peaks of the Raman shift in the relevant range of the spectrum (2800-3000 cm-1). Despite being limited to mEVs, this work demonstrates the potential of TERS in the analysis of extracellular vesicles.

Innovation in Non-Invasive Diagnosis and Disease Monitoring for Meningiomas

Meningiomas are tumors of the central nervous system that vary in their presentation, ranging from benign and slow-growing to highly aggressive. The standard method for diagnosing and classifying meningiomas involves invasive surgery and can fail to provide accurate prognostic information. Liquid biopsy methods, which exploit circulating tumor biomarkers such as DNA, extracellular vesicles, micro-RNA, proteins, and more, offer a non-invasive and dynamic approach for tumor classification, prognostication, and evaluating treatment response. Currently, a clinically approved liquid biopsy test for meningiomas does not exist. This review provides a discussion of current research and the challenges of implementing liquid biopsy techniques for advancing meningioma patient care.

Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumor-specificity and predictive potential of extracellular vesicles for cell invasion and proliferation - A meta-analysis

BACKGROUND

Although interest in the role of extracellular vesicles (EV) in oncology is growing, not all potential aspects have been investigated. In this meta-analysis, data regarding (i) the EV proteome and (ii) the invasion and proliferation capacity of the NCI-60 tumor cell lines (60 cell lines from nine different tumor types) were analyzed using machine learning methods.

METHODS

On the basis of the entire proteome or the proteins shared by all EV samples, 60 cell lines were classified into the nine tumor types using multiple logistic regression. Then, utilizing the Least Absolute Shrinkage and Selection Operator, we constructed a discriminative protein panel, upon which the samples were reclassified and pathway analyses were performed. These panels were validated using clinical data (n = 4,665) from Human Protein Atlas.

RESULTS

Classification models based on the entire proteome, shared proteins, and discriminative protein panel were able to distinguish the nine tumor types with 49.15%, 69.10%, and 91.68% accuracy, respectively. Invasion and proliferation capacity of the 60 cell lines were predicted with R2 = 0.68 and R2 = 0.62 (p < 0.0001). The results of the Reactome pathway analysis of the discriminative protein panel suggest that the molecular content of EVs might be indicative of tumor-specific biological processes.

CONCLUSION

Integrating in vitro EV proteomic data, cell physiological characteristics, and clinical data of various tumor types illuminates the diagnostic, prognostic, and therapeutic potential of EVs. Video Abstract.

Label-free atherosclerosis diagnosis through a blood drop of apolipoprotein E knockout mouse model using surface-enhanced Raman spectroscopy validated by machine learning algorithm

The direct preventative detection of flow-induced atherosclerosis remains a significant challenge, impeding the development of early treatments and prevention measures. This study proposes a method for diagnosing atherosclerosis in the carotid artery using nanometer biomarker measurements through surface-enhanced Raman spectroscopy (SERS) from single-drop blood samples. Atherosclerotic acceleration is induced in apolipoprotein E knockout mice which underwent a partial carotid ligation and were fed a high-fat diet to rapidly induce disturbed flow-induced atherosclerosis in the left common carotid artery while using the unligated, contralateral right carotid artery as control. The progressive atherosclerosis development of the left carotid artery was verified by micro-magnetic resonance imaging (micro-MRI) and histology in comparison to the right carotid artery. Single-drop blood samples are deposited on chips of gold-coated ZnO nanorods grown on silicon wafers that filter the nanometer markers and provide strong SERS signals. A diagnostic classifier was established based on principal component analysis (PCA), which separates the resultant spectra into the atherosclerotic and control groups. Scoring based on the principal components enabled the classification of samples into control, mild, and severe atherosclerotic disease. The PCA-based analysis was validated against an independent test sample and compared against the PCA-PLS-DA machine learning algorithm which is known for applicability to Raman diagnosis. The accuracy of the PCA modification-based diagnostic criteria was 94.5%, and that of the machine learning algorithm 97.5%. Using a mouse model, this study demonstrates that diagnosing and classifying the severity of atherosclerosis is possible using a single blood drop, SERS technology, and machine learning algorithm, indicating the detectability of biomarkers and vascular factors in the blood which correlate with the early stages of atherosclerosis development.

Is There a Role for Machine Learning in Liquid Biopsy for Brain Tumors? A Systematic Review

The paucity of studies available in the literature on brain tumors demonstrates that liquid biopsy (LB) is not currently applied for central nervous system (CNS) cancers. The purpose of this systematic review focused on the application of machine learning (ML) to LB for brain tumors to provide practical guidance for neurosurgeons to understand the state-of-the-art practices and open challenges. The herein presented study was conducted in accordance with the PRISMA-P (preferred reporting items for systematic review and meta-analysis protocols) guidelines. An online literature search was launched on PubMed/Medline, Scopus, and Web of Science databases using the following query: "((Liquid biopsy) AND (Glioblastoma OR Brain tumor) AND (Machine learning OR Artificial Intelligence))". The last database search was conducted in April 2023. Upon the full-text review, 14 articles were included in the study. These were then divided into two subgroups: those dealing with applications of machine learning to liquid biopsy in the field of brain tumors, which is the main aim of this review (n = 8); and those dealing with applications of machine learning to liquid biopsy in the diagnosis of other tumors (n = 6). Although studies on the application of ML to LB in the field of brain tumors are still in their infancy, the rapid development of new techniques, as evidenced by the increase in publications on the subject in the past two years, may in the future allow for rapid, accurate, and noninvasive analysis of tumor data. Thus making it possible to identify key features in the LB samples that are associated with the presence of a brain tumor. These features could then be used by doctors for disease monitoring and treatment planning.

Extracellular vesicles as source for the identification of minimally invasive molecular signatures in glioblastoma

The analysis of extracellular vesicles (EVs) as a source of cancer biomarkers is an emerging field since low-invasive biomarkers are highly demanded. EVs constitute a heterogeneous population of small membrane-contained vesicles that are present in most of body fluids. They are released by all cell types, including cancer cells and their cargo consists of nucleic acids, proteins and metabolites and varies depending on the biological-pathological state of the secretory cell. Therefore, EVs are considered as a potential source of reliable biomarkers for cancer. EV biomarkers in liquid biopsy can be a valuable tool to complement current medical technologies for cancer diagnosis, as their sampling is minimally invasive and can be repeated over time to monitor disease progression. In this review, we highlight the advances in EV biomarker research for cancer diagnosis, prognosis, and therapy monitoring. We especially focus on EV derived biomarkers for glioblastoma. The diagnosis and monitoring of glioblastoma still relies on imaging techniques, which are not sufficient to reflect the highly heterogenous and invasive nature of glioblastoma. Therefore, we discuss how the use of EV biomarkers could overcome the challenges faced in diagnosis and monitoring of glioblastoma.

The role of the metabolite cargo of extracellular vesicles in tumor progression

Metabolomic reprogramming in tumor and stroma cells is a hallmark of cancer but understanding its effects on the metabolite composition and function of tumor-derived extracellular vesicles (EVs) is still in its infancy. EVs are membrane-bound sacs with a complex molecular composition secreted by all living cells. They are key mediators of intercellular communication both in normal and pathological conditions and play a crucial role in tumor development. Although lipids are major components of EVs, most of the EV cargo studies have targeted proteins and nucleic acids. The potential of the EV metabolome as a source for biomarker discovery has gained recognition recently, but knowledge on the biological activity of tumor EV metabolites still remains limited. Therefore, we aimed (i) to compile the list of metabolites identified in tumor EVs isolated from either clinical specimens or in vitro samples and (ii) describe their role in tumor progression through literature search and pathway analysis.

A systematic update to circulating extracellular vesicles proteome; transcriptome and small RNA-ome as glioma diagnostic, prognostic and treatment-response biomarkers

Brain gliomas are major neurosurgical challenges due to high mortality and morbidity. Hence, development of novel biomarkers is of great value to plan appropriate treatment strategy. Evaluation of the molecular content of extracellular vesicles (EVs) as novel non-invasive biomarker repertoires can provide a real-time portrait of disease status. This study aims to provide a systematic, comprehensive and critical report of the diagnostic and prognostic significance of EV biomarkers (proteins, DNAs and RNAs) for brain gliomas, discuss their biogenesis and passage through the blood brain barrier, and also highlight the high throughput methods used for EV biomarker discovery; as well as discussing potential limitations of EV isolation and characterization methods as glioma diagnostic, prognostic or treatment response biomarkers. Moreover, we critically appraise the bias risk in the previous studies, discuss the limitations EV biomarker discovery faces to enter neurosurgical practice in the future, and highlight the need for more optimized protocols for EV isolation and biomarker discovery in high throughput studies. The current systematic review was conducted upon PRISMA guidelines [10].

Characterization of ovarian cancer-derived extracellular vesicles by surface-enhanced Raman spectroscopy

Ovarian cancer is the most lethal gynecological malignancy, owing to the fact that most cases are diagnosed at a late stage. To improve prognosis and reduce mortality, we must develop methods for the early diagnosis of ovarian cancer. A step towards early and non-invasive cancer diagnosis is through the utilization of extracellular vesicles (EVs), which are nanoscale, membrane-bound vesicles that contain proteins and genetic material reflective of their parent cell. Thus, EVs secreted by cancer cells can be thought of as cancer biomarkers. In this paper, we present gold nanohole arrays for the capture of ovarian cancer (OvCa)-derived EVs and their characterization by surface-enhanced Raman spectroscopy (SERS). For the first time, we have characterized EVs isolated from two established OvCa cell lines (OV-90, OVCAR3), two primary OvCa cell lines (EOC6, EOC18), and one human immortalized ovarian surface epithelial cell line (hIOSE) by SERS. We subsequently determined their main compositional differences by principal component analysis and were able to discriminate the groups by a logistic regression-based machine learning method with ∼99% accuracy, sensitivity, and specificity. The results presented here are a great step towards quick, facile, and non-invasive cancer diagnosis.