A specific spectral signature of serum and plasma-derived extracellular vesicles for cancer screening

Head and neck squamous cell carcinoma-derived extracellular vesicles mediate Ca²⁺-dependent platelet activation and aggregation through tissue factor

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is an aggressive malignancy, characterized by poor clinical outcomes, primarily driven by high rate of locoregional recurrence and metastasis. Extensive heterogeneity among the tumor cells as well as modulation of a highly immunosuppressive tumor microenvironment shape cancer progression. Shedding of extracellular vesicles (EVs) derived from tumor cells is a critical mediator of the disease initiating horizontal transfer of tumor components into platelets. This triggers platelet activation and thromboinflammation fueling tumor progression through multiple mechanisms.

METHODS

HNSCC-derived EVs isolated from HNSCC cell lines (SAS, UD-SCC 5) using size exclusion chromatography and characterized via flow cytometry, electron microscopy, nanoparticle tracking analysis and Western blotting, were used to induce platelet activation and aggregation, measured by aggregometry, flow cytometry, as well as the release of chemokines and Adenosine triphosphate, which were quantified using enzyme-linked immunosorbent assays (ELISA). Mechanistic investigations included inhibitor assays, thrombin activity measurements, and proteomic analyses.

RESULTS

We could show that EVs do not activate platelets through the FcγRIIa-IgG axis but platelet activation and aggregation is induced in a calcium-dependent manner, primarily mediated by EV-associated tissue factor. Proteomic analysis confirmed the presence of tissue factor in these vesicles, implicating its involvement in initiating the coagulation cascade, that leads to platelet activation and aggregation. This process was characterized by delayed aggregation kinetics and relied on thrombin activation, as the inhibition of thrombin and its receptors reduced platelet aggregation. HNSCC-derived EVs are pivotal in establishing a prothrombotic environment by promoting platelet activation and aggregation through tissue factor-dependent thrombin generation.

CONCLUSION

These findings indicate a therapeutic potential of targeting EV-mediated pathways as a therapeutic approach to alleviate thrombotic complications in HNSCC patients. Subsequent animal studies will be crucial to validate and extend these observations, providing deeper insight into their clinical implications.

Extracellular vesicles derived from ovarian cancer cell lines discriminated by biochemical and Fourier transform infrared spectroscopy approaches

Ovarian cancer is the most lethal cancer among gynaecological malignancies. Due to the lack of early symptoms and screening tools, patients are diagnosed in advanced stages. Cancer invasion and metastasis through the extracellular matrix (ECM) are enhanced by tumour cell Extracellular Vesicles (EV). The aim of this study was to characterise the EVs derived from two ovarian cancer cell lines (ES2 and SKOV3) using biochemical and vibrational spectroscopic approaches. EVs were prepared by ultracentrifugation and characterised by Nanoparticle Tracking Analysis. Their surface proteins were assessed by MACSPlex EV kit for human exosomes. The presence of MMP14 and integrin subunits was evaluated in EVs and cell protein extracts by Western immunoblotting. Both EVs and cells were measured by Fourier transform infrared spectroscopy (FTIR) and data were analysed by hierarchical cluster analysis (HCA). Spectral differences were observed in the lipids and polysaccharides regions both between the SKOV3 and ES2 cells and their corresponding EVs, which allowed a good delineation by HCA. The differences in the biochemical data were confirmed by similar and specific features exhibited in their respective infrared spectral signatures. ES2 EVs exhibited an enrichment in MMP14 in agreement with the aggressiveness of this ovarian cancer metastatic cell line.

Raman spectroscopy in extracellular vesicles analysis: Techniques, applications and advancements

Raman spectroscopy provides a robust approach for detailed analysis of the chemical and molecular profiles of extracellular vesicles (EVs). Recent advancements in Raman techniques have significantly enhanced the sensitivity and accuracy of EV characterization, enabling precise detection and profiling of molecular components within EV samples. This review introduces and compares various Raman-based techniques for EV characterization. These include Raman spectroscopy (RS), which provides fundamental molecular information; Raman trapping analysis (RTA), which combines optical trapping with Raman scattering for the manipulation and analysis of individual EVs; surface-enhanced Raman spectroscopy (SERS), which enhances the Raman signal through the use of metallic nanostructures, significantly improving sensitivity; and microfluidic SERS, which integrates SERS with microfluidic platforms to allow high-throughput, label-free analysis of EVs in biological fluids. In addition to comparing various Raman techniques, this review provides a comprehensive analysis that includes comparisons of machine learning methods, EV isolation techniques, and characterization strategies. By integrating these approaches, the review presents a holistic perspective on Raman-based EV analysis, covering profiling, purity, heterogeneity and size analysis as well as imaging. The combined assessment of Raman technologies with advanced computational and experimental methodologies supports the development of more robust diagnostic and therapeutic applications involving EVs.

Fourier transform InfraRed spectra analyzed by multivariate and machine learning methods in determination spectroscopy marker of prostate cancer in dried serum

Prostate cancer represents the second most prevalent form of cancer in males globally. In the diagnosis of prostate cancer, the most commonly utilised biomarker is prostate-specific antigen (PSA). It is unfortunate that approximately 25 % of men with elevated PSA levels do not have cancer, and that approximately 20 % of patients with prostate cancer have normal serum PSA levels. Accordingly, a more sensitive methodology must still be identified. It is imperative that new diagnostic methods should be non-invasive, cost-effective, rapid, and highly sensitive. Fourier transform infrared spectroscopy (FTIR) is a technique that fulfils all of the aforementioned criteria. Consequently, the present study used FTIR to assess dried serum samples obtained from a cohort of prostate cancer patients (n = 53) and a control group of healthy individuals (n = 40). Furthermore, this study proposes FTIR markers of prostate cancer obtained from serum. For this purpose, FTIR spectra of dried serum were measured and analysed using statistical, chemometric and machine learning (ML) algorithms including decision trees C5.0, Random Forest (RF), k-Nearest Neighbours (kNN) and Support Vector Machine (SVM). The FTIR spectra of serum collected from patients suffering from prostate cancer exhibited a reduced absorbance values of peaks derived from phospholipids, amides, and lipids. However, these differences were not statistically significant. Furthermore, principal component analysis (PCA) demonstrated that it is challenging to distinguish serum samples from healthy and non-healthy patients. The ML algorithms demonstrated that FTIR was capable of differentiating serum collected from both analysed groups of patients with high accuracy (values between 0.74 and 0.93 for the range from 800 cm-1 to 1800 cm-1 and around 0.70 and 1 for the range from 2800 cm-1 to 3000 cm-1), depending on the ML algorithms used. The results demonstrated that the peaks at 1637 cm-1 and 2851 cm-1 could serve as a FTIR marker for prostate cancer in serum samples. Furthermore, the correlation test indicated a clear correlation between these two wavenumbers and four of the five clinical parameters associated with prostate cancer. However, the relatively small number of samples collected only from patients over the age of 60 indicated that the results should be further investigated using a larger number of serum samples collected from a mean age range. In conclusion, this study demonstrated the potential of FTIR for the detection of prostate cancer in serum samples, highlighting the presence of distinctive spectroscopic markers associated with the analysed cancer type.

The Promise of Infrared Spectroscopy in Liquid Biopsies for Solid Cancer Detection

Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy has shown significant promise in the context of liquid biopsy, offering a potential tool for cancer diagnostics. Unlike traditional tissue biopsies, which may not fully capture the clonal heterogeneity of tumors, liquid biopsy reflects the dynamic state of the disease and its progression more comprehensively. Biofluids such as serum and plasma are low-cost, minimally invasive diagnostic media with well-established clinical uses. This review assesses the use of ATR-FTIR spectroscopy to detect biochemical changes in biofluids linked to various malignancies, including breast, ovarian, endometrial, prostate, bladder, kidney, pancreatic, colorectal, hepatic, esophageal, gastric, lung, and brain cancers. While ATR-FTIR offers the advantages of rapid, minimally invasive detection and real-time disease monitoring, its integration into clinical practice faces challenges, particularly in terms of reproducibility due to variability in sample preparation, spectral acquisition, and data processing. The translation of ATR-FTIR into routine diagnostics will require validation through large-scale cohort studies and multicenter trials to ensure its clinical reliability and effectiveness.

Surface-enhanced infrared spectroscopic study of extracellular vesicles using plasmonic gold nanoparticles

Extracellular vesicles (EVs), sub-micrometer lipid-bound particles released by most cells, are considered a novel area in both biology and medicine. Among characterization methods, infrared (IR) spectroscopy, especially attenuated total reflection (ATR), is a rapidly emerging label-free tool for molecular characterization of EVs. The relatively low number of vesicles in biological fluids (∼1010 particle/mL), however, and the complex content of the EVs' milieu (protein aggregates, lipoproteins, buffer molecules) might result in poor signal-to-noise ratio in the IR analysis of EVs. Exploiting the increment of the electromagnetic field at the surface of plasmonic nanomaterials, surface-enhanced infrared spectroscopy (SEIRS) provides an amplification of characteristic IR signals of EV samples. Negatively charged citrate-capped and positively charged cysteamine-capped gold nanoparticles with around 10 nm diameter were synthesized and tested with blood-derived EVs. Both types of gold nanoparticles contributed to an enhancement of the EVs' IR spectroscopic signature. Joint evaluation of UV-Vis and IR spectroscopic results, supported by FF-TEM images, revealed that proper interaction of gold nanoparticles with EVs is crucial, and an aggregation or clustering of gold nanoparticles is necessary to obtain the SEIRS effect. Positively charged gold nanoparticles resulted in higher enhancement, probably due to electrostatic interaction with EVs, commonly negatively charged.

Platelet activation and blood extracellular vesicles: The influence of venepuncture and short blood storage

Extracellular vesicles (EVs) as membrane-bound particles released by various cells are potential tools for diagnosis and treatment. Blood cells, particularly platelets, are the source of circulating EVs.

MATERIAL

EVs were enriched with gradient ultracentrifugation and measured by nanoparticle tracking assay. A flow cytometric multiplex assay was used for cellular source determination. Activation of platelets was measured as a percentage of CD62p+/CD61+ platelets and correlated with the concentration and size of released EVs.

RESULTS

In general there was no statistically significant correlation between EVs` concentration and degree of platelet activation. EVs from different cellular sources were detected. Comparing different needle thicknesses, there was a decrease in the EVs concentration for the 16G needle versus the 21G needle, but no difference was observed for EVs` size and phenotype or platelets activation. During blood storage, platelet activation increased, but there was no effect on the EVs` concentration, size, or phenotype.

CONCLUSIONS

Preanalytical factors like needle thickness and storage time can affect the MVs' properties. Activation of platelets during blood collection or blood storage occurs; however, it is difficult to determine its effect on the physiological properties of EVs since the mechanisms of EVs` biogenesis and especially clearness are not precisely known.

Label-Free, Noninvasive Bone Cell Classification by Hyperspectral Confocal Raman Microscopy

Characterizing and identifying cells in multicellular in vitro models remain a substantial challenge. Here, we utilize hyperspectral confocal Raman microscopy and principal component analysis coupled with linear discriminant analysis to form a label-free, noninvasive approach for classifying bone cells and osteosarcoma cells. Through the development of a library of hyperspectral Raman images of the K7M2-wt osteosarcoma cell lines, 7F2 osteoblast cell lines, RAW 264.7 macrophage cell line, and osteoclasts induced from RAW 264.7 macrophages, we built a linear discriminant model capable of correctly identifying each of these cell types. The model was cross-validated using a k-fold cross validation scheme. The results show a minimum of 72% accuracy in predicting cell type. We also utilize the model to reconstruct the spectra of K7M2 and 7F2 to determine whether osteosarcoma cancer cells and normal osteoblasts have any prominent differences that can be captured by Raman. We find that the main differences between these two cell types are the prominence of the β-sheet protein secondary structure in K7M2 versus the α-helix protein secondary structure in 7F2. Additionally, differences in the CH2 deformation Raman feature highlight that the membrane lipid structure is different between these cells, which may affect the overall signaling and functional contrasts. Overall, we show that hyperspectral confocal Raman microscopy can serve as an effective tool for label-free, nondestructive cellular classification and that the spectral reconstructions can be used to gain deeper insight into the differences that drive different functional outcomes of different cells.

FTIR Spectroscopy and Blood-Derived Extracellular Vesicles Duo in Alzheimer's Disease

Background

Alzheimer's disease (AD) diagnosis is difficult, and new accurate tools based on peripheral biofluids are urgently needed. Extracellular vesicles (EVs) emerged as a valuable source of biomarker profiles for AD, since their cargo is disease-specific and these can be easily isolated from easily accessible biofluids, as blood. Fourier Transform Infrared (FTIR) spectroscopy can be employed to analyze EVs and obtain the spectroscopic profiles from different regions of the spectra, simultaneously characterizing carbohydrates, nucleic acids, proteins, and lipids.

Objective

The aim of this study was to identify blood-derived EVs (bdEVs) spectroscopic signatures with AD discriminatory potential.

Methods

Herein, FTIR spectra of bdEVs from two biofluids (serum and plasma) and distinct sets of Controls and AD cases were acquired, and EVs' spectra analyzed.

Results

Analysis of bdEVs second derivative peaks area revealed differences between Controls and AD cases in distinct spectra regions, assigned to carbohydrates and nucleic acids, amides, and lipids.

Conclusions

EVs' spectroscopic profiles presented AD discriminatory value, supporting the use of bdEVs combined with FTIR as a screening or complementary tool for AD diagnosis.

Validation of potential RNA biomarkers for prostate cancer diagnosis and monitoring in plasma and urinary extracellular vesicles

Introduction: Prostate cancer (PCa), one of the most prevalent malignancies affecting men worldwide, presents significant challenges in terms of early detection, risk stratification, and active surveillance. In recent years, liquid biopsies have emerged as a promising non-invasive approach to complement or even replace traditional tissue biopsies. Extracellular vesicles (EVs), nanosized membranous structures released by various cells into body fluids, have gained substantial attention as a source of cancer biomarkers due to their ability to encapsulate and transport a wide range of biological molecules, including RNA. In this study, we aimed to validate 15 potential RNA biomarkers, identified in a previous EV RNA sequencing study, using droplet digital PCR. Methods: The candidate biomarkers were tested in plasma and urinary EVs collected before and after radical prostatectomy from 30 PCa patients and their diagnostic potential was evaluated in a test cohort consisting of 20 benign prostate hyperplasia (BPH) and 20 PCa patients' plasma and urinary EVs. Next, the results were validated in an independent cohort of plasma EVs from 31 PCa and 31 BPH patients. Results: We found that the levels of NKX3-1 (p = 0.0008) in plasma EVs, and tRF-Phe-GAA-3b (p < 0.0001) tRF-Lys-CTT-5c (p < 0.0327), piR-28004 (p = 0.0081) and miR-375-3p (p < 0.0001) in urinary EVs significantly decreased after radical prostatectomy suggesting that the main tissue source of these RNAs is prostate and/or PCa. Two mRNA biomarkers-GLO1 and NKX3-1 showed promising diagnostic potential in distinguishing between PCa and BPH with AUC of 0.68 and 0.82, respectively, in the test cohort and AUC of 0.73 and 0.65, respectively, in the validation cohort, when tested in plasma EVs. Combining these markers in a biomarker model yielded AUC of 0.85 and 0.71 in the test and validation cohorts, respectively. Although the PSA levels in the blood could not distinguish PCa from BPH in our cohort, adding PSA to the mRNA biomarker model increased AUC from 0.71 to 0.76. Conclusion: This study identified two novel EV-enclosed RNA biomarkers-NKX3-1 and GLO1-for the detection of PCa, and highlights the complementary nature of GLO1, NKX3-1 and PSA as combined biomarkers in liquid biopsies of PCa.

Extracellular Vesicles: Techniques and Biomedical Applications Related to Single Vesicle Analysis

Extracellular vesicles (EVs) are extensively dispersed lipid bilayer membrane vesicles involved in the delivery and transportation of molecular payloads to certain cell types to facilitate intercellular interactions. Their significant roles in physiological and pathological processes make EVs outstanding biomarkers for disease diagnosis and treatment monitoring as well as ideal candidates for drug delivery. Nevertheless, differences in the biogenesis processes among EV subpopulations have led to a diversity of biophysical characteristics and molecular cargos. Additionally, the prevalent heterogeneity of EVs has been found to substantially hamper the sensitivity and accuracy of disease diagnosis and therapeutic monitoring, thus impeding the advancement of clinical applications. In recent years, the evolution of single EV (SEV) analysis has enabled an in-depth comprehension of the physical properties, molecular composition, and biological roles of EVs at the individual vesicle level. This review examines the sample acquisition tactics prior to SEV analysis, i.e., EV isolation techniques, and outlines the current state-of-the-art label-free and label-based technologies for SEV identification. Furthermore, the challenges and prospects of biomedical applications based on SEV analysis are systematically discussed.

In Situ Raman Study of Neurodegenerated Human Neuroblastoma Cells Exposed to Outer-Membrane Vesicles Isolated from Porphyromonas gingivalis

The aim of this study was to elucidate the chemistry of cellular degeneration in human neuroblastoma cells upon exposure to outer-membrane vesicles (OMVs) produced by Porphyromonas gingivalis (Pg) oral bacteria by monitoring their metabolomic evolution using in situ Raman spectroscopy. Pg-OMVs are a key factor in Alzheimer's disease (AD) pathogenesis, as they act as efficient vectors for the delivery of toxins promoting neuronal damage. However, the chemical mechanisms underlying the direct impact of Pg-OMVs on cell metabolites at the molecular scale still remain conspicuously unclear. A widely used in vitro model employing neuroblastoma SH-SY5Y cells (a sub-line of the SK-N-SH cell line) was spectroscopically analyzed in situ before and 6 h after Pg-OMV contamination. Concurrently, Raman characterizations were also performed on isolated Pg-OMVs, which included phosphorylated dihydroceramide (PDHC) lipids and lipopolysaccharide (LPS), the latter in turn being contaminated with a highly pathogenic class of cysteine proteases, a key factor in neuronal cell degradation. Raman characterizations located lipopolysaccharide fingerprints in the vesicle structure and unveiled so far unproved aspects of the chemistry behind protein degradation induced by Pg-OMV contamination of SH-SY5Y cells. The observed alterations of cells' Raman profiles were then discussed in view of key factors including the formation of amyloid β (Aβ) plaques and hyperphosphorylated Tau neurofibrillary tangles, and the formation of cholesterol agglomerates that exacerbate AD pathologies.

Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.

A review of sensors for classification and subtype discrimination of cancer: Insights into circulating tumor cells and tumor-derived extracellular vesicles

Liquid biopsy can reflect the state of tumors in vivo non-invasively, thus providing a strong basis for the early diagnosis, individualized treatment monitoring and prognosis of tumors. Circulating tumor cells (CTCs) and tumor-derived extracellular vesicles (tdEVs) contain information-rich components, such as nucleic acids and proteins, and they are essential markers for liquid biopsies. Their capture and analysis are of great importance for the study of disease occurrence and development and, consequently, have been the subject of many reviews. However, both CTCs and tdEVs carry the biological characteristics of their original tissue, and few reviews have focused on their function in the staging and classification of cancer. In this review, we focus on state-of-the-art sensors based on the simultaneous detection of multiple biomarkers within CTCs and tdEVs, with clinical applications centered on cancer classification and subtyping. We also provide a thorough discussion of the current challenges and prospects for novel sensors with the ultimate goal of cancer classification and staging. It is hoped that these most advanced technologies will bring new insights into the clinical practice of cancer screening and diagnosis.

Biochemical Characterization of Human Salivary Extracellular Vesicles as a Valuable Source of Biomarkers

Extracellular vesicles (EVs) are natural nanoparticles secreted under physiological and pathological conditions. Thanks to their diagnostic potential, EVs are increasingly being studied as biomarkers of a variety of diseases, including neurological disorders. To date, most studies on EV biomarkers use blood as the source, despite different disadvantages that may cause an impure isolation of the EVs. In the present article, we propose the use of saliva as a valuable source of EVs that could be studied as biomarkers in an easily accessible biofluid. Using a comparable protocol for the isolation of EVs from both liquid biopsies, salivary EVs showed greater purity in terms of co-isolates (evaluated by nanoparticle tracking analysis and Conan test). In addition, Raman spectroscopy was used for the identification of the overall biochemical composition of EVs coming from the two different biofluids. Even considering the limited amount of EVs that can be isolated from saliva, the use of Raman spectroscopy was not hampered, and it was able to provide a comprehensive characterization of EVs in a high throughput and repeatable manner. Raman spectroscopy can thus represent a turning point in the application of salivary EVs in clinics, taking advantage of the simple method of collection of the liquid biopsy and of the quick, sensitive and label-free biophotonics-based approach.

The Convergence of FTIR and EVs: Emergence Strategy for Non-Invasive Cancer Markers Discovery

In conjunction with imaging analysis, pathology-based assessments of biopsied tissue are the gold standard for diagnosing solid tumors. However, the disadvantages of tissue biopsies, such as being invasive, time-consuming, and labor-intensive, have urged the development of an alternate method, liquid biopsy, that involves sampling and clinical assessment of various bodily fluids for cancer diagnosis. Meanwhile, extracellular vesicles (EVs) are circulating biomarkers that carry molecular profiles of their cell or tissue origins and have emerged as one of the most promising biomarkers for cancer. Owing to the biological information that can be obtained through EVs' membrane surface markers and their cargo loaded with biomolecules such as nucleic acids, proteins, and lipids, EVs have become useful in cancer diagnosis and therapeutic applications. Fourier-transform infrared spectroscopy (FTIR) allows rapid, non-destructive, label-free molecular profiling of EVs with minimal sample preparation. Since the heterogeneity of EV subpopulations may result in complicated FTIR spectra that are highly diverse, computational-assisted FTIR spectroscopy is employed in many studies to provide fingerprint spectra of malignant and non-malignant samples, allowing classification with high accuracy, specificity, and sensitivity. In view of this, FTIR-EV approach carries a great potential in cancer detection. The progression of FTIR-based biomarker identification in EV research, the rationale of the integration of a computationally assisted approach, along with the challenges of clinical translation are the focus of this review.

Fast and Deep Diagnosis Using Blood-Based ATR-FTIR Spectroscopy for Digestive Tract Cancers

Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) of liquid biofluids enables the probing of biomolecular markers for disease diagnosis, characterized as a time and cost-effective approach. It remains poorly understood for fast and deep diagnosis of digestive tract cancers (DTC) to detect abundant changes and select specific markers in a broad spectrum of molecular species. Here, we present a diagnostic protocol of DTC in which the in-situ blood-based ATR-FTIR spectroscopic data mining pathway was designed for the identification of DTC triages in 252 blood serum samples, divided into the following groups: liver cancer (LC), gastric cancer (GC), colorectal cancer (CC), and their different three stages respectively. The infrared molecular fingerprints (IMFs) of DTC were measured and used to build a 2-dimensional second derivative spectrum (2D-SD-IR) feature dataset for classification, including absorbance and wavenumber shifts of FTIR vibration peaks. By comparison, the Partial Least-Squares Discriminant Analysis (PLS-DA) and backpropagation (BP) neural networks are suitable to differentiate DTCs and pathological stages with a high sensitivity and specificity of 100% and averaged more than 95%. Furthermore, the measured IMF data was mutually validated via clinical blood biochemistry testing, which indicated that the proposed 2D-SD-IR-based machine learning protocol greatly improved DTC classification performance.

Diagnosis of urogenital cancer combining deep learning algorithms and surface-enhanced Raman spectroscopy based on small extracellular vesicles

PURPOSE

To identify and compare the capacities of serum and serum-derived small extracellular vesicles (EV) in diagnosis of common urogenital cancer combining Surface-enhanced Raman spectroscopy (SERS) and Convolutional Neural Networks (CNN).

MATERIALS AND METHODS

We collected serum samples from 32 patients with prostate cancer (PCa), 33 patients with renal cell cancer (RCC) and 30 patients with bladder cancer (BCa) as well as 35 healthy control (HC), which were thereafter used to enrich extracellular vesicles by ultracentrifuge. Label-free SERS was utilized to collect Raman spectra from serum and matched EV samples. We constructed CNN models to process SERS data for classification of malignant patients and healthy controls (HCs).

RESULTS

We collected 650 and 1206 spectra from serum and serum-derived EV, respectively. CNN models of EV spectra revealed high testing accuracies of 79.3%, 78.7% and 74.2% in diagnosis of PCa, RCC and BCa, respectively. In comparison, serum SERS-based CNN model had testing accuracies of 73.0%, 71.1%, 69.2% in PCa, RCC and BCa, respectively. Moreover, CNN models based on EV SERS data show significantly higher diagnostic capacities than matched serum CNN models with the area under curve (AUC) of 0.80, 0.88 and 0.74 in diagnosis of PCa, RCC and BCa, respectively.

CONCLUSION

Deep learning-based SERS analysis of EV has great potentials in diagnosis of urologic cancer outperforming serum SERS analysis, providing a novel tool in cancer screening.

Asymmetric depth-filtration: A versatile and scalable method for high-yield isolation of extracellular vesicles with low contamination

We developed a novel asymmetric depth filtration (DF) approach to isolate extracellular vesicles (EVs) from biological fluids that outperforms ultracentrifugation and size-exclusion chromatography in purity and yield of isolated EVs. By these metrics, a single-step DF matches or exceeds the performance of multistep protocols with dedicated purification procedures in the isolation of plasma EVs. We demonstrate the selective transit and capture of biological nanoparticles in asymmetric pores by size and elasticity, low surface binding to the filtration medium, and the ability to cleanse EVs held by the filter before their recovery with the reversed flow all contribute to the achieved purity and yield of preparations. We further demonstrate the method's versatility by applying it to isolate EVs from different biofluids (plasma, urine, and cell culture growth medium). The DF workflow is simple, fast, and inexpensive. Only standard laboratory equipment is required for its implementation, making DF suitable for low-resource and point-of-use locations. The method may be used for EV isolation from small biological samples in diagnostic and treatment guidance applications. It can also be scaled up to harvest therapeutic EVs from large volumes of cell culture medium.

Molecular mechanisms and clinical applications of exosomes in prostate cancer

Prostate cancer (PC) is a common tumor in men, and the incidence rate is high worldwide. Exosomes are nanosized vesicles released by all types of cells into multiple biological fluid types. These vesicles contribute to intercellular communication by delivering both nucleic acids and proteins to recipient cells. In recent years, many studies have explored the mechanisms by which exosomes mediate the epithelial-mesenchymal transition, angiogenesis, tumor microenvironment establishment, and drug resistance acquisition in PC, and the mechanisms that have been identified and the molecules involved have provided new perspectives for the possible discovery of novel diagnostic markers in PC. Furthermore, the excellent biophysical properties of exosomes, such as their high stability, high biocompatibility and ability to cross biological barriers, have made exosomes promising candidates for use in novel targeted drug delivery system development. In this review, we summarize the roles of exosomes in the growth and signal transmission in PC and show the promising future of exosome contributions to PC diagnostics and treatment.

Recent advances in optical label-free characterization of extracellular vesicles

Extracellular vesicles (EVs) are complex biological nanoparticles endogenously secreted by all eukaryotic cells. EVs carry a specific molecular cargo of proteins, lipids, and nucleic acids derived from cells of origin and play a significant role in the physiology and pathology of cells, organs, and organisms. Upon release, they may be found in different body fluids that can be easily accessed via noninvasive methodologies. Due to the unique information encoded in their molecular cargo, they may reflect the state of the parent cell and therefore EVs are recognized as a rich source of biomarkers for early diagnostics involving liquid biopsy. However, body fluids contain a mixture of EVs released by different types of healthy and diseased cells, making the detection of the EVs of interest very challenging. Recent research efforts have been focused on the detection and characterization of diagnostically relevant subpopulations of EVs, with emphasis on label-free methods that simplify sample preparation and are free of interfering signals. Therefore, in this paper, we review the recent progress of the label-free optical methods employed for the detection, counting, and morphological and chemical characterization of EVs. We will first briefly discuss the biology and functions of EVs, and then introduce different optical label-free techniques for rapid, precise, and nondestructive characterization of EVs such as nanoparticle tracking analysis, dynamic light scattering, atomic force microscopy, surface plasmon resonance spectroscopy, Raman spectroscopy, and SERS spectroscopy. In the end, we will discuss their applications in the detection of neurodegenerative diseases and cancer and provide an outlook on the future impact and challenges of these technologies to the field of liquid biopsy via EVs.

Diagnosis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome With Partial Least Squares Discriminant Analysis: Relevance of Blood Extracellular Vesicles

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), a chronic disease characterized by long-lasting persistent debilitating widespread fatigue and post-exertional malaise, remains diagnosed by clinical criteria. Our group and others have identified differentially expressed miRNA profiles in the blood of patients. However, their diagnostic power individually or in combinations seems limited. A Partial Least Squares-Discriminant Analysis (PLS-DA) model initially based on 817 variables: two demographic, 34 blood analytic, 136 PBMC miRNAs, 639 Extracellular Vesicle (EV) miRNAs, and six EV features, selected an optimal number of five components, and a subset of 32 regressors showing statistically significant discriminant power. The presence of four EV-features (size and z-values of EVs prepared with or without proteinase K treatment) among the 32 regressors, suggested that blood vesicles carry relevant disease information. To further explore the features of ME/CFS EVs, we subjected them to Raman micro-spectroscopic analysis, identifying carotenoid peaks as ME/CFS fingerprints, possibly due to erythrocyte deficiencies. Although PLS-DA analysis showed limited capacity of Raman fingerprints for diagnosis (AUC = 0.7067), Raman data served to refine the number of PBMC miRNAs from our previous model still ensuring a perfect classification of subjects (AUC=1). Further investigations to evaluate model performance in extended cohorts of patients, to identify the precise ME/CFS EV components detected by Raman and to reveal their functional significance in the disease are warranted.

FT-IR Spectral Signature of Sensitive and Multidrug-Resistant Osteosarcoma Cell-Derived Extracellular Nanovesicles

Osteosarcoma (OS) is the most common primary bone cancer in children and adolescents. Despite aggressive treatment regimens, the outcome is unsatisfactory, and multidrug resistance (MDR) is a pivotal process in OS treatment failure. OS-derived extracellular vesicles (EVs) promote drug resistance to chemotherapy and target therapy through different mechanisms. The aim of this study was to identify subpopulations of osteosarcoma-EVs by Fourier transform infrared spectroscopy (FT-IR) to define a specific spectral signature for sensitive and multidrug-resistant OS-derived EVs. EVs were isolated from sensitive and MDR OS cells as well as from mesenchymal stem cells by differential centrifugation and ultracentrifugation. EVs size, morphology and protein expression were characterized. FT-IR/ATR of EVs spectra were acquired in the region of 400–4000 cm⁻¹ (resolution 4 cm⁻¹, 128 scans). The FT-IR spectra obtained were consistently different in the EVs compared to cells from which they originate. A specific spectral signature, characterized by a shift and a new band (1601 cm⁻¹), permitted to clearly distinguish EVs isolated by sensitive and multidrug-resistant OS cells. Our data suggest that FT-IR spectroscopy allows to characterize and define a specific spectral signature for sensitive and MDR OS-derived EVs.

Infrared and Raman spectroscopy for purity assessment of extracellular vesicles

Extracellular vesicles (EVs) are a complex and heterogeneous population of nanoparticles involved in cell-to-cell communication. Recently, numerous studies have indicated the potential of EVs as therapeutic agents, drug carriers and diagnostic tools. However, the results of these studies are often difficult to evaluate, since different characterization methods are used to assess the purity, physical and biochemical characteristics of the EV samples. In this study, we compared four methods for the EV sample characterization and purity assessment: i) the particle-to-protein ratio based on particle analyses with nanoparticle tracking and protein concentration by bicinchoninic acid assay, ii) Western Blot analysis for specific EV biomarkers, iii) two spectroscopic lipid-to-protein ratios by either the attenuated total reflection Fourier transform infrared (ATR-FTIR) or Raman spectroscopy. The results confirm the value of Raman and ATR-FTIR spectroscopy as robust, fast and operator independent tools that require only a few microliters of EV sample. We propose that the spectroscopic lipid-to-protein (Li/Pr) ratios are reliable parameters for the purity assessment of EV preparations. Moreover, apart from determining protein concentrations, we show that ATR-FTIR spectroscopy can also be used for indirect measurements of EV concentrations. Nevertheless, the Li/Pr ratios do not represent full characterization of the EV preparations. For a complete characterization of selected EV preparations, we recommend also additional use of particle size distribution and EV biomarker analysis.

Extracellular vesicles as a source of prostate cancer biomarkers in liquid biopsies: a decade of research

Prostate cancer is a global cancer burden and considerable effort has been made through the years to identify biomarkers for the disease. Approximately a decade ago, the potential of analysing extracellular vesicles in liquid biopsies started to be envisaged. This was the beginning of a new exciting area of research investigating the rich molecular treasure found in extracellular vesicles to identify biomarkers for a variety of diseases. Vesicles released from prostate cancer cells and cells of the tumour microenvironment carry molecular information about the disease that can be analysed in several biological fluids. Numerous studies document the interest of researchers in this field of research. However, methodological issues such as the isolation of vesicles have been challenging. Remarkably, novel technologies, including those based on nanotechnology, show promise for the further development and clinical use of extracellular vesicles as liquid biomarkers. Development of biomarkers is a long and complicated process, and there are still not many biomarkers based on extracellular vesicles in clinical use. However, the knowledge acquired during the last decade constitutes a solid basis for the future development of liquid biopsy tests for prostate cancer. These are urgently needed to bring prostate cancer treatment to the next level in precision medicine.

Raman spectroscopy of urinary extracellular vesicles to stratify patients with chronic kidney disease in type 2 diabetes

In this study, we verified the hypothesis that Raman signature of urinary extracellular vesicles (UEVs) can be used to stratify patients with diabetes at various stages of chronic kidney disease (CKD). Patients with type 2 diabetes diagnosed with different stages of CKD and healthy subjects were enrolled in the study. UEVs were isolated using low-vacuum filtration followed by ultracentrifugation. Correlation analysis, multiple linear regression and principal component analysis were used to find differences between spectral fingerprints of UEVs derived from both groups of patients. Electron microscopy and nanoparticle tracking analysis were applied to characterize the size and morphology of UEVs. We observed significant correlations between selected Raman bands measured for UEVs and clinical parameters. We found significant differences in the area under the specific bands originating mainly from proteins and lipids between the study groups. Based on the tryptophan and amide III bands, we were able to predict the estimated glomerular filtration rate (eGFR). Principal component analysis, partial least squares regression (PLSR) and correlation analysis of the UEV Raman spectra supported the results obtained from the direct analysis of Raman spectra. Our analysis revealed that PLSR and a regression model including tryptophan and amide III bands allows to estimate the value of eGFR.

β-Sheet Richness of the Circulating Tumor-Derived Extracellular Vesicles for Noninvasive Pancreatic Cancer Screening

Tumor-derived extracellular vesicles (EVs) are under intensive study for their potential as noninvasive diagnosis biomarkers. Most EV-based cancer diagnostic assays trace supernumerary of a single cancer-associated marker or marker signatures. These types of biomarker assays are either subtype-specific or vulnerable to be masked by high background signals. In this study, we introduce using the β-sheet richness (BR) of the tumor-derived EVs as an effective way to discriminate EVs originating from malignant and nonmalignant cells, where EV contents are evaluated as a collective attribute rather than single factors. Circular dichroism, Fourier transform infrared spectroscopy, fluorescence staining assays, and a de novo workflow combining proteomics, bioinformatics, and protein folding simulations were employed to validate the collective attribute at both cellular and EV levels. Based on the BR of the tumorous EVs, we integrated immunoprecipitation and fluorescence labeling targeting the circulating tumor-derived EVs in serum and developed the process into a clinical assay, named EvIPThT. The assay can distinguish patients with and without malignant disease in a pilot cohort, with weak correlations to prognosis biomarkers, suggesting the potential for a cancer screening panel with existing prognostic biomarkers to improve overall performance.

Current and Future Advancements of Raman Spectroscopy Techniques in Cancer Nanomedicine

Raman scattering is one of the most used spectroscopy and imaging techniques in cancer nanomedicine due to its high spatial resolution, high chemical specificity, and multiplexity modalities. The flexibility of Raman techniques has led, in the past few years, to the rapid development of Raman spectroscopy and imaging for nanodiagnostics, nanotherapy, and nanotheranostics. This review focuses on the applications of spontaneous Raman spectroscopy and bioimaging to cancer nanotheranostics and their coupling to a variety of diagnostic/therapy methods to create nanoparticle-free theranostic systems for cancer diagnostics and therapy. Recent implementations of confocal Raman spectroscopy that led to the development of platforms for monitoring the therapeutic effects of anticancer drugs in vitro and in vivo are also reviewed. Another Raman technique that is largely employed in cancer nanomedicine, due to its ability to enhance the Raman signal, is surface-enhanced Raman spectroscopy (SERS). This review also explores the applications of the different types of SERS, such as SERRS and SORS, to cancer diagnosis through SERS nanoprobes and the detection of small-size biomarkers, such as exosomes. SERS cancer immunotherapy and immuno-SERS (iSERS) microscopy are reviewed.

Single Particle Automated Raman Trapping Analysis of Breast Cancer Cell-Derived Extracellular Vesicles as Cancer Biomarkers

Extracellular vesicles (EVs) secreted by cancer cells provide an important insight into cancer biology and could be leveraged to enhance diagnostics and disease monitoring. This paper details a high-throughput label-free extracellular vesicle analysis approach to study fundamental EV biology, toward diagnosis and monitoring of cancer in a minimally invasive manner and with the elimination of interpreter bias. We present the next generation of our single particle automated Raman trapping analysis─SPARTA─system through the development of a dedicated standalone device optimized for single particle analysis of EVs. Our visualization approach, dubbed dimensional reduction analysis (DRA), presents a convenient and comprehensive method of comparing multiple EV spectra. We demonstrate that the dedicated SPARTA system can differentiate between cancer and noncancer EVs with a high degree of sensitivity and specificity (&gt;95% for both). We further show that the predictive ability of our approach is consistent across multiple EV isolations from the same cell types. Detailed modeling reveals accurate classification between EVs derived from various closely related breast cancer subtypes, further supporting the utility of our SPARTA-based approach for detailed EV profiling.

Lipidomic landscape in cancer: Actionable insights for membrane-based therapy and diagnoses

Cancer cells display altered cellular lipid metabolism, including disruption in endogenous lipid synthesis, storage, and exogenous uptake for membrane biogenesis and functions. Altered lipid metabolism and, consequently, lipid composition impacts cellular function by affecting membrane structure and properties, such as fluidity, rigidity, membrane dynamics, and lateral organization. Herein, we provide an overview of lipid membranes and how their properties affect cellular functions. We also detail how the rewiring of lipid metabolism impacts the lipidomic landscape of cancer cell membranes and influences the characteristics of cancer cells. Furthermore, we discuss how the altered cancer lipidome provides cues for developing lipid-inspired innovative therapeutic and diagnostic strategies while improving our limited understanding of the role of lipids in cancer initiation and progression. We also present the arcade of membrane characterization techniques to cement their relevance in cancer diagnosis and monitoring of treatment response.

Nanoparticle surface-enhanced Raman spectroscopy as a noninvasive, label-free tool to monitor hematological malignancy

Aim: Monitoring minimal residual disease remains a challenge to the effective medical management of hematological malignancies; yet surface-enhanced Raman spectroscopy (SERS) has emerged as a potential clinical tool to do so. Materials & methods: We developed a cell-free, label-free SERS approach using gold nanoparticles (nanoSERS) to classify hematological malignancies referenced against two control cohorts: healthy and noncancer cardiovascular disease. A predictive model was built using machine-learning algorithms to incorporate disease burden scores for patients under standard treatment upon. Results: Linear- and quadratic-discriminant analysis distinguished three cohorts with 69.8 and 71.4% accuracies, respectively. A predictive nanoSERS model correlated (MSE = 1.6) with established clinical parameters. Conclusion: This study offers a proof-of-concept for the noninvasive monitoring of disease progression, highlighting the potential to incorporate nanoSERS into translational medicine.

Identification of amyloid beta in small extracellular vesicles via Raman spectroscopy

One of the hallmarks of Alzheimer's disease (AD) pathogenesis is believed to be the production and deposition of amyloid-beta (Aβ) peptide into extracellular plaques. Existing research indicates that extracellular vesicles (EVs) can carry Aβ associated with AD. However, characterization of the EVs-associated Aβ and its conformational variants has yet to be realized. Raman spectroscopy is a label-free and non-destructive method that is able to assess the biochemical composition of EVs. This study reports for the first time the Raman spectroscopic fingerprint of the Aβ present in the molecular cargo of small extracellular vesicles (sEVs). Raman spectra were measured from sEVs isolated from Alzheimer's disease cell culture model, where secretion of Aβ is regulated by tetracycline promoter, and from midbrain organoids. The averaged spectra of each sEV group showed considerable variation as a reflection of the biochemical content of sEVs. Spectral analysis identified more intense Raman peaks at 1650 cm^-1 and 2930 cm^-1 attributable to the Aβ peptide incorporated in sEVs produced by the Alzheimer's cell culture model. Subsequent analysis of the spectra by principal component analysis differentiated the sEVs of the Alzheimer's disease cell culture model from the control groups of sEVs. Moreover, the results indicate that Aβ associated with secreted sEVs has a α-helical secondary structure and the size of a monomer or small oligomer. Furthermore, by analyzing the lipid content of sEVs we identified altered fatty acid chain lengths in sEVs that carry Aβ that may affect the fluidity of the EV membrane. Overall, our findings provide evidence supporting the use of Raman spectroscopy for the identification and characterization of sEVs associated with potential biomarkers of neurological disorders such as toxic proteins.

Using single-vesicle technologies to unravel the heterogeneity of extracellular vesicles

Extracellular vesicles (EVs) are heterogeneous lipid containers with a complex molecular cargo comprising several populations with unique roles in biological processes. These vesicles are closely associated with specific physiological features, which makes them invaluable in the detection and monitoring of various diseases. EVs play a key role in pathophysiological processes by actively triggering genetic or metabolic responses. However, the heterogeneity of their structure and composition hinders their application in medical diagnosis and therapies. This diversity makes it difficult to establish their exact physiological roles, and the functions and composition of different EV (sub)populations. Ensemble averaging approaches currently employed for EV characterization, such as western blotting or 'omics' technologies, tend to obscure rather than reveal these heterogeneities. Recent developments in single-vesicle analysis have made it possible to overcome these limitations and have facilitated the development of practical clinical applications. In this review, we discuss the benefits and challenges inherent to the current methods for the analysis of single vesicles and review the contribution of these approaches to the understanding of EV biology. We describe the contributions of these recent technological advances to the characterization and phenotyping of EVs, examination of the role of EVs in cell-to-cell communication pathways and the identification and validation of EVs as disease biomarkers. Finally, we discuss the potential of innovative single-vesicle imaging and analysis methodologies using microfluidic devices, which promise to deliver rapid and effective basic and practical applications for minimally invasive prognosis systems.

Recent Advances in the Label-Free Characterization of Exosomes for Cancer Liquid Biopsy: From Scattering and Spectroscopy to Nanoindentation and Nanodevices

Exosomes (EXOs) are nano-sized vesicles secreted by most cell types. They are abundant in bio-fluids and harbor specific molecular constituents from their parental cells. Due to these characteristics, EXOs have a great potential in cancer diagnostics for liquid biopsy and personalized medicine. Despite this unique potential, EXOs are not yet widely applied in clinical settings, with two main factors hindering their translational process in diagnostics. Firstly, conventional extraction methods are time-consuming, require large sample volumes and expensive equipment, and often do not provide high-purity samples. Secondly, characterization methods have some limitations, because they are often qualitative, need extensive labeling or complex sampling procedures that can induce artifacts. In this context, novel label-free approaches are rapidly emerging, and are holding potential to revolutionize EXO diagnostics. These methods include the use of nanodevices for EXO purification, and vibrational spectroscopies, scattering, and nanoindentation for characterization. In this progress report, we summarize recent key advances in label-free techniques for EXO purification and characterization. We point out that these methods contribute to reducing costs and processing times, provide complementary information compared to the conventional characterization techniques, and enhance flexibility, thus favoring the discovery of novel and unexplored EXO-based biomarkers. In this process, the impact of nanotechnology is systematically highlighted, showing how the effectiveness of these techniques can be enhanced using nanomaterials, such as plasmonic nanoparticles and nanostructured surfaces, which enable the exploitation of advanced physical phenomena occurring at the nanoscale level.

SP, N. M, Barik AK, Pai KM, Unnikrishnan VK, George SD, Kartha VB, Chidangil S. Photonics of human saliva: potential optical methods for the screening of abnormal health conditions and infections

Human saliva can be treated as a pool of biological markers able to reflect on the state of personal health. Recent years have witnessed an increase in the use of optical devices for the analysis of body fluids. Several groups have carried out studies investigating the potential of saliva as a non-invasive and reliable clinical specimen for use in medical diagnostics. This brief review aims to highlight the optical technologies, mainly surface plasmon resonance (SPR), Raman, and Fourier transform infrared (FTIR) spectroscopy, which are being used for the probing of saliva for diverse biomedical applications. Advances in bio photonics offer the promise of unambiguous, objective and fast detection of abnormal health conditions and viral infections (such as COVID-19) from the analysis of saliva.

Surface-Enhanced Raman Spectroscopy to Characterize Different Fractions of Extracellular Vesicles from Control and Prostate Cancer Patients

Extracellular vesicles (EVs) are membrane-enclosed structures ranging in size from about 60 to 800 nm that are released by the cells into the extracellular space; they have attracted interest as easily available biomarkers for cancer diagnostics. In this study, EVs from plasma of control and prostate cancer patients were fractionated by differential centrifugation at 5000× g, 12,000× g and 120,000× g. The remaining supernatants were purified by ultrafiltration to produce EV-depleted free-circulating (fc) fractions. Spontaneous Raman and surface-enhanced Raman spectroscopy (SERS) at 785 nm excitation using silver nanoparticles (AgNPs) were employed as label-free techniques to collect fingerprint spectra and identify the fractions that best discriminate between control and cancer patients. SERS spectra from 10 µL droplets showed an enhanced Raman signature of EV-enriched fractions that were much more intense for cancer patients than controls. The Raman spectra of dehydrated pellets of EV-enriched fractions without AgNPs were dominated by spectral contributions of proteins and showed variations in S-S stretch, tryptophan and protein secondary structure bands between control and cancer fractions. We conclude that the AgNPs-mediated SERS effect strongly enhances Raman bands in EV-enriched fractions, and the fractions, EV12 and EV120 provide the best separation of cancer and control patients by Raman and SERS spectra.

Comparative Analysis of Platelet-Derived Extracellular Vesicles Using Flow Cytometry and Nanoparticle Tracking Analysis

Growing interest in extracellular vesicles (EVs) has prompted the advancements of protocols for improved EV characterization. As a high-throughput, multi-parameter, and single particle technique, flow cytometry is widely used for EV characterization. The comparison of data on EV concentration, however, is hindered by the lack of standardization between different protocols and instruments. Here, we quantified EV counts of platelet-derived EVs, using two flow cytometers (Gallios and CytoFLEX LX) and nanoparticle tracking analysis (NTA). Phosphatidylserine-exposing EVs were identified by labelling with lactadherin (LA). Calibration with silica-based fluorescent beads showed detection limits of 300 nm and 150 nm for Gallios and CytoFLEX LX, respectively. Accordingly, CytoFLEX LX yielded 40-fold higher EV counts and 13-fold higher counts of LA⁺CD41⁺ EVs compared to Gallios. NTA in fluorescence mode (F-NTA) demonstrated that only 9.5% of all vesicles detected in scatter mode exposed phosphatidylserine, resulting in good agreement of LA⁺ EVs for CytoFLEX LX and F-NTA. Since certain functional characteristics, such as the exposure of pro-coagulant phosphatidylserine, are not equally displayed across the entire EV size range, our study highlights the necessity of indicating the size range of EVs detected with a given approach along with the EV concentration to support the comparability between different studies.

Stability of person-specific blood-based infrared molecular fingerprints opens up prospects for health monitoring

Health state transitions are reflected in characteristic changes in the molecular composition of biofluids. Detecting these changes in parallel, across a broad spectrum of molecular species, could contribute to the detection of abnormal physiologies. Fingerprinting of biofluids by infrared vibrational spectroscopy offers that capacity. Whether its potential for health monitoring can indeed be exploited critically depends on how stable infrared molecular fingerprints (IMFs) of individuals prove to be over time. Here we report a proof-of-concept study that addresses this question. Using Fourier-transform infrared spectroscopy, we have fingerprinted blood serum and plasma samples from 31 healthy, non-symptomatic individuals, who were sampled up to 13 times over a period of 7 weeks and again after 6 months. The measurements were performed directly on liquid serum and plasma samples, yielding a time- and cost-effective workflow and a high degree of reproducibility. The resulting IMFs were found to be highly stable over clinically relevant time scales. Single measurements yielded a multiplicity of person-specific spectral markers, allowing individual molecular phenotypes to be detected and followed over time. This previously unknown temporal stability of individual biochemical fingerprints forms the basis for future applications of blood-based infrared spectral fingerprinting as a multiomics-based mode of health monitoring.

Detection and Investigation of Extracellular Vesicles in Serum and Urine Supernatant of Prostate Cancer Patients

Prostate Cancer (PCa) is one of the most frequently identified urological cancers. PCa patients are often over-diagnosed due to still not highly specific diagnostic methods. The need for more accurate diagnostic tools to prevent overestimated diagnosis and unnecessary treatment of patients with non-malignant conditions is clear, and new markers and methods are strongly desirable. Extracellular vesicles (EVs) hold great promises as liquid biopsy-based markers. Despite the biological and technical issues present in their detection and study, these particles can be found highly abundantly in the biofluid and encompass a wealth of macromolecules that have been reported to be related to many physiological and pathological processes, including cancer onset, metastasis spreading, and treatment resistance. The present study aims to perform a technical feasibility study to develop a new workflow for investigating EVs from several biological sources. Serum and urinary supernatant EVs of PCa, benign prostatic hyperplasia (BPH) patients, and healthy donors were isolated and investigated by a fast, easily performable, and cost-effective cytofluorimetric approach for a multiplex detection of 37 EV-antigens. We also observed significant alterations in serum and urinary supernatant EVs potentially related to BPH and PCa, suggesting a potential clinical application of this workflow.

Infrared Nanospectroscopy of Individual Extracellular Microvesicles

Extracellular vesicles are membrane-delimited structures, involved in several inter-cellular communication processes, both physiological and pathological, since they deliver complex biological cargo. Extracellular vesicles have been identified as possible biomarkers of several pathological diseases; thus, their characterization is fundamental in order to gain a deep understanding of their function and of the related processes. Traditional approaches for the characterization of the molecular content of the vesicles require a large quantity of sample, thereby providing an average molecular profile, while their heterogeneity is typically probed by non-optical microscopies that, however, lack the chemical sensitivity to provide information of the molecular cargo. Here, we perform a study of individual microvesicles, a subclass of extracellular vesicles generated by the outward budding of the plasma membrane, released by two cultures of glial cells under different stimuli, by applying a state-of-the-art infrared nanospectroscopy technique based on the coupling of an atomic force microscope and a pulsed laser, which combines the label-free chemical sensitivity of infrared spectroscopy with the nanometric resolution of atomic force microscopy. By correlating topographic, mechanical and spectroscopic information of individual microvesicles, we identified two main populations in both families of vesicles released by the two cell cultures. Subtle differences in terms of nucleic acid content among the two families of vesicles have been found by performing a fitting procedure of the main nucleic acid vibrational peaks in the 1000–1250 cm⁻¹ frequency range.

Abiotic stressors impact outer membrane vesicle composition in a beneficial rhizobacterium: Raman spectroscopy characterization

Outer membrane vesicles (OMVs) produced by Gram-negative bacteria have roles in cell-to-cell signaling, biofilm formation, and stress responses. Here, the effects of abiotic stressors on OMV contents and composition from biofilm cells of the plant health-promoting bacterium Pseudomonas chlororaphis O6 (PcO6) are examined. Two stressors relevant to this root-colonizing bacterium were examined: CuO nanoparticles (NPs)-a potential fertilizer and fungicide- and H2O2-released from roots during plant stress responses. Atomic force microscopy revealed 40-300 nm diameter OMVs from control and stressed biofilm cells. Raman spectroscopy with linear discriminant analysis (LDA) was used to identify changes in chemical profiles of PcO6 cells and resultant OMVs according to the cellular stressor with 84.7% and 83.3% accuracies, respectively. All OMVs had higher relative concentrations of proteins, lipids, and nucleic acids than PcO6 cells. The nucleic acid concentration in OMVs exhibited a cellular stressor-dependent increase: CuO NP-induced OMVs > H2O2-induced OMVs > control OMVs. Biochemical assays confirmed the presence of lipopolysaccharides, nucleic acids, and protein in OMVs; however, these assays did not discriminate OMV composition according to the cellular stressor. These results demonstrate the sensitivity of Raman spectroscopy using LDA to characterize and distinguish cellular stress effects on OMVs composition and contents.

Fourier Transform Infrared Spectroscopy as a useful tool for the automated classification of cancer cell-derived exosomes obtained under different culture conditions

Exosomes possess great potential as cancer biomarkers in personalized medicine due to their easy accessibility and capability of representing their parental cells. To boost the translational process of exosomes in diagnostics, the development of novel and effective strategies for their label-free and automated characterization is highly desirable. In this context, Fourier Transform Infrared Spectroscopy (FTIR) has great potential as it provides direct access to specific biomolecular bands that give compositional information on exosomes in terms of their protein, lipid and genetic content. Here, we used FTIR spectroscopy in the mid-Infrared (mid-IR) range to study exosomes released from human colorectal adenocarcinoma HT-29 cancer cells cultured in different media. To this purpose, cells were studied in well-fed condition of growth, with 10% of exosome-depleted FBS (EVd-FBS), and under serum starvation with 0.5% EVd-FBS. Our data show the presence of statistically significant differences in the shape of the Amide I and II bands in the two conditions. Based on these differences, we showed the possibility to automatically classify cancer cell-derived exosomes using Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA); we tested the effectiveness of the classifier with a cross-validation approach, obtaining very high accuracy, precision, and recall. Aside from classification purposes, our FTIR data provide hints on the underlying cellular mechanisms responsible for the compositional differences in exosomes, suggesting a possible role of starvation-induced autophagy.

Hybrid Nanoplasmonic Porous Biomaterial Scaffold for Liquid Biopsy Diagnostics Using Extracellular Vesicles

For more effective early-stage cancer diagnostics, there is a need to develop sensitive and specific, non- or minimally invasive, and cost-effective methods for identifying circulating nanoscale extracellular vesicles (EVs). Here, we report the utilization of a simple plasmonic scaffold composed of a microscale biosilicate substrate embedded with silver nanoparticles for surface-enhanced Raman scattering (SERS) analysis of ovarian and endometrial cancer EVs. These substrates are rapidly and inexpensively produced without any complex equipment or lithography. We extensively characterize the substrates with electron microscopy and outline a reproducible methodology for their use in analyzing EVs from in vitro and in vivo biofluids. We report effective chemical treatments for (i) decoration of metal surfaces with cysteamine to nonspecifically pull down EVs to SERS hotspots and (ii) enzymatic cleavage of extraluminal moieties at the surface of EVs that prevent localization of complementary chemical features (lipids/proteins) to the vicinity of the metal-enhanced fields. We observe a major loss of sensitivity for ovarian and endometrial cancer following enzymatic cleavage of EVs’ extraluminal domain, suggesting its critical significance for diagnostic platforms. We demonstrate that the SERS technique represents an ideal tool to assess and measure the high heterogeneity of EVs isolated from clinical samples in an inexpensive, rapid, and label-free assay.

Raman Spectroscopy characterization extracellular vesicles from bovine placenta and peripheral blood mononuclear cells

Placenta-derived extracellular vesicles (EVs) are involved in communication between the placenta and maternal immune cells possibly leading to a modulation of maternal T-cell signaling components. The ability to identify EVs in maternal blood may lead to the development of diagnostic and treatment tools for pregnancy complications. The objective of this work was to differentiate EVs from bovine placenta (trophoblast) and peripheral blood mononuclear cells (PBMC) by a label-free, non-invasive Raman spectroscopy technique. Extracellular vesicles were isolated by ultracentrifugation. Dynamic light scattering (DLS) and scanning electron microscopy (SEM) were applied to verify the presence and the size distribution of EVs. Raman peaks at 728 cm^-1 (collagen) and 1573 cm^-1 (protein) were observed only in PBMC-derived EVs, while the peaks 702 cm^-1 (cholesterol) and 1553 cm^-1 (amide) appeared only in trophoblast-derived EVs. The discrimination of the Raman spectral fingerprints for both types of EVs from different animals was performed by principal component analysis (PCA) and linear discriminant analysis (LDA). The PCA and LDA results clearly segregated the spectral clusters between the two types of EVs. Moreover, the PBMC-derived EVs from different animals were indistinguishable, while the trophoblast-derived EVs from three placental samples of different gestational ages showed separate clusters. This study reports for the first time the Raman characteristic peaks for identification of PBMC and trophoblast-derived EVs. The development of this method also provides a potential tool for further studies investigating the causes and potential treatments for pregnancy complications.

Raman spectroscopy reveals biochemical differences in plasma derived extracellular vesicles from sporadic Amyotrophic Lateral Sclerosis patients

Sporadic amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease for which there is no validated blood based biomarker. Extracellular vesicles (EVs) have the potential to solve this unmet clinical need. However, due to their heterogeneity and complex chemical composition, EVs are difficult to study. Raman spectroscopy (RS) is an optical method that seems particularly well suited to address this task. In fact, RS provides an overview of the biochemical composition of EVs quickly and virtually without any sample preparation. In this work, we studied by RS small extracellular vesicles (sEVs), large extracellular vesicles (lEVs) and blood plasma of sporadic ALS patients and of a matched cohort of healthy controls. The obtained results highlighted lEVs as a particularly promising biomarker for ALS. In fact, their Raman spectra show that sporadic ALS patients have a different lipid content and less intense bands relative to the aromatic amino acid phenylalanine.

Cancer-ID: Toward Identification of Cancer by Tumor-Derived Extracellular Vesicles in Blood

Extracellular vesicles (EVs) have great potential as biomarkers since their composition and concentration in biofluids are disease state dependent and their cargo can contain disease-related information. Large tumor-derived EVs (tdEVs, >1 μm) in blood from cancer patients are associated with poor outcome, and changes in their number can be used to monitor therapy effectiveness. Whereas, small tumor-derived EVs (<1 μm) are likely to outnumber their larger counterparts, thereby offering better statistical significance, identification and quantification of small tdEVs are more challenging. In the blood of cancer patients, a subpopulation of EVs originate from tumor cells, but these EVs are outnumbered by non-EV particles and EVs from other origin. In the Dutch NWO Perspectief Cancer-ID program, we developed and evaluated detection and characterization techniques to distinguish EVs from non-EV particles and other EVs. Despite low signal amplitudes, we identified characteristics of these small tdEVs that may enable the enumeration of small tdEVs and extract relevant information. The insights obtained from Cancer-ID can help to explore the full potential of tdEVs in the clinic.

Reagent-free total protein quantification of intact extracellular vesicles by attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy

Extracellular vesicles (EVs) are lipid bilayer–bounded particles that are actively synthesized and released by cells. The main components of EVs are lipids, proteins, and nucleic acids and their composition is characteristic to their type and origin, and it reveals the physiological and pathological conditions of the parent cells. The concentration and protein composition of EVs closely relate to their functions; therefore, total protein determination can assist in EV-based diagnostics and disease prognosis. Here, we present a simple, reagent-free method based on attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy to quantify the protein content of EV samples without any further sample preparation. After calibration with bovine serum albumin, the protein concentration of red blood cell–derived EVs (REVs) were investigated by ATR-FTIR spectroscopy. The integrated area of the amide I band was calculated from the IR spectra of REVs, which was proportional to the protein quantity in the sample‚ regardless of its secondary structure. A spike test and a dilution test were performed to determine the ability to use ATR-FTIR spectroscopy for protein quantification in EV samples, which resulted in linearity with R² values as high as 0.992 over the concentration range of 0.08 to 1 mg/mL. Additionally, multivariate calibration with the partial least squares (PLS) regression method was carried out on the bovine serum albumin and EV spectra. R² values were 0.94 for the calibration and 0.91 for the validation set. The results indicate that ATR-FTIR measurements provide a reliable method for reagent-free protein quantification of EVs.

Fourier Transform Infrared Spectroscopy as a Cancer Screening and Diagnostic Tool: A Review and Prospects

Infrared spectroscopy has long been used to characterize chemical compounds, but the applicability of this technique to the analysis of biological materials containing highly complex chemical components is arguable. However, recent advances in the development of infrared spectroscopy have significantly enhanced the capacity of this technique in analyzing various types of biological specimens. Consequently, there is an increased number of studies investigating the application of infrared spectroscopy in screening and diagnosis of various diseases. The lack of highly sensitive and specific methods for early detection of cancer has warranted the search for novel approaches. Being more simple, rapid, accurate, inexpensive, non-destructive and suitable for automation compared to existing screening, diagnosis, management and monitoring methods, Fourier transform infrared spectroscopy can potentially improve clinical decision-making and patient outcomes by detecting biochemical changes in cancer patients at the molecular level. Besides the commonly analyzed blood and tissue samples, extracellular vesicle-based method has been gaining popularity as a non-invasive approach. Therefore, infrared spectroscopic analysis of extracellular vesicles could be a useful technique in the future for biomedical applications. In this review, we discuss the potential clinical applications of Fourier transform infrared spectroscopic analysis using various types of biological materials for cancer. Additionally, the rationale and advantages of using extracellular vesicles in the spectroscopic analysis for cancer diagnostics are discussed. Furthermore, we highlight the challenges and future directions of clinical translation of the technique for cancer.