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

Unveiling the Electrical Properties of Hyaluronan-Coated Cancer Extracellular Vesicles Using Correlative Scanning Probe Microscopy-Based Nano-Electrical Modes

Cancer cells produce extracellular vesicles (EVs) coated with an anionic sugar polymer, hyaluronan (HA), in the extracellular matrix. Hyaluronan is an established cancer biomarker in several cancer types. In this work, we thoroughly investigated the electrical properties of HA-coated EVs using advanced scanning probe microscopy (SPM) based nanoelectrical modes, which include EFM (electrostatic force microscopy), KPFM (Kelvin probe force microscopy), PFM (piezoresponse force microscopy) and C-AFM (conductive atomic force microscopy). Analyses revealed distinct properties for different sets of EVs regarding surface potential, charge distribution, and piezoelectric electro-mechanical response at the single-vesicle resolution. The typical electron transport capabilities are primarily driven by ions in sandwiched EV junctions. This correlative approach essentially could distinguish HA-coated cancer EVs (CEVs) from normal EV (NEVs) counterparts. The combined SPM-based nanoelectrical modes offered a multiplexed one-stop label-free solution for EV's electrical property assessments. This strategy is useful in developing EV-based bioelectronic sensors.

Automating the amino acid identification in elliptical dichroism spectrometer with Machine Learning

Amino acid identification is crucial across various scientific disciplines, including biochemistry, pharmaceutical research, and medical diagnostics. However, traditional methods such as mass spectrometry require extensive sample preparation and are time-consuming, complex and costly. Therefore, this study presents a pioneering Machine Learning (ML) approach for automatic amino acid identification by utilizing the unique absorption profiles from an Elliptical Dichroism (ED) spectrometer. Advanced data preprocessing techniques and ML algorithms to learn patterns from the absorption profiles that distinguish different amino acids were investigated to prove the feasibility of this approach. The results show that ML can potentially revolutionize the amino acid analysis and detection paradigm.

Characterizing biomolecular structure features through an innovative elliptical dichroism spectrometry for cancer detection

This research introduces a novel method for evaluating the structural features of biomolecules, utilizing our innovative Elliptical Dichroism (ED) spectrometer specifically designed for stereochemical analysis. By integrating ED spectrometry with autocorrelation (AC) analysis, we investigate the conformational characteristics of biological molecules such as amino acids, proteins, and extracellular vesicles (EVs) induced by elliptically polarized UV absorption. Our streamlined approach offers a cost-effective and portable solution with minimal sample consumption and supports multiple working modes to efficiently characterize biomolecular structures. The insight from this new approach demonstrates potential applications in using biomolecular characterization for cancer detection.

Deciphering extracellular vesicles protein cargo in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) presents a significant therapeutic challenge as it is frequently diagnosed at advanced inoperable stages. Therefore, the development of a reliable screening tool for PDAC is crucial for effective prevention and treatment. Extracellular vesicles (EVs), characterized by their cup-shaped lipid bilayer structure and ubiquitous release from various cell types, offer notable advantages as an emerging liquid biopsy technique that is rapid, minimally invasive, easily sampled, and cost-effective. While EVs play a substantial role in cancer progression, EV proteins serve as direct mediators of diverse cellular behaviors and have immense potential as biomarkers for PDAC diagnosis and prognostication. This review provides an overview of EV proteins regarding PDAC diagnosis and prognostic implications as well as disease progression.

Exploring novel circulating biomarkers for liver cancer through extracellular vesicle characterization with infrared spectroscopy and plasmonics

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common form of liver cancer, with cirrhosis being a major risk factor. Traditional blood markers like alpha-fetoprotein (AFP) demonstrate limited efficacy in distinguishing between HCC and cirrhosis, underscoring the need for more effective diagnostic methodologies. In this context, extracellular vesicles (EVs) have emerged as promising candidates; however, their practical diagnostic application is restricted by the current lack of label-free methods to accurately profile their molecular content. To address this gap, our study explores the potential of mid-infrared (mid-IR) spectroscopy, both alone and in combination with plasmonic nanostructures, to detect and characterize circulating EVs.

RESULTS

EVs were extracted from HCC and cirrhotic patients. Mid-IR spectroscopy in the Attenuated Total Reflection (ATR) mode was utilized to identify potential signatures for patient classification, highlighting significant changes in the Amide I-II region (1475-1700 cm-1). This signature demonstrated diagnostic performance comparable to AFP and surpassed it when the two markers were combined. Further investigations utilized a plasmonic metasurface suitable for ultrasensitive spectroscopy within this spectral range. This device consists of two sets of parallel rod-shaped gold nanoantennas (NAs); the longer NAs produced an intense near-field amplification in the Amide I-II bands, while the shorter NAs were utilized to provide a sharp reflectivity edge at 1800-2200 cm-1 for EV mass-sensing. A clinically relevant subpopulation of EVs was targeted by conjugating NAs with an antibody specific to Epithelial Cell Adhesion Molecule (EpCAM). This methodology enabled the detection of variations in the quantity of EpCAM-presenting EVs and revealed changes in the Amide I-II lineshape.

SIGNIFICANCE

The presented results can positively impact the development of novel laboratory methods for the label-free characterization of EVs, based on the combination between mid-IR spectroscopy and plasmonics. Additionally, data obtained by using HCC and cirrhotic subjects as a model system, suggest that this approach could be adapted for monitoring these conditions.

Integrated proteomic profiling identifies amino acids selectively cytotoxic to pancreatic cancer cells

Pancreatic adenocarcinoma (PDAC) is one of the most deadly cancers, characterized by extremely limited therapeutic options and a poor prognosis, as it is often diagnosed during late disease stages. Innovative and selective treatments are urgently needed, since current therapies have limited efficacy and significant side effects. Through proteomics analysis of extracellular vesicles, we discovered an imbalanced distribution of amino acids secreted by PDAC tumor cells. Our findings revealed that PDAC cells preferentially excrete proteins with certain preferential amino acids, including isoleucine and histidine, via extracellular vesicles. These amino acids are associated with disease progression and can be targeted to elicit selective toxicity to PDAC tumor cells. Both in vitro and in vivo experiments demonstrated that supplementation with these specific amino acids effectively eradicated PDAC cells. Mechanistically, we also identified XRN1 as a potential target for these amino acids. The high selectivity of this treatment method allows for specific targeting of tumor metabolism with very low toxicity to normal tissues. Furthermore, we found this treatment approach is easy-to-administer and with sustained tumor-killing effects. Together, our findings reveal that exocytosed amino acids may serve as therapeutic targets for designing treatments of intractable PDAC and potentially offer alternative treatments for other types of cancers.

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.

Advancements in Mid-Infrared spectroscopy of extracellular vesicles

Extracellular vesicles (EVs) are lipid vesicles secreted by all cells into the extracellular space and act as nanosized biological messengers among cells. They carry a specific molecular cargo, composed of lipids, proteins, nucleic acids, and carbohydrates, which reflects the state of their parent cells. Due to their remarkable structural and compositional heterogeneity, characterizing EVs, particularly from a biochemical perspective, presents complex challenges. In this context, mid-infrared (IR) spectroscopy is emerging as a valuable tool, providing researchers with a comprehensive and label-free spectral fingerprint of EVs in terms of their specific molecular content. This review aims to provide an up-to-date critical overview of the major advancements in mid-IR spectroscopy of extracellular vesicles, encompassing both fundamental and applied research achievements. We also systematically emphasize the new possibilities offered by the integration of emerging cutting-edge IR technologies, such as tip-enhanced and surface-enhanced spectroscopy approaches, along with the growing use of machine learning for data analysis and spectral interpretation. Additionally, to assist researchers in navigating this intricate subject, our manuscript includes a wide and detailed collection of the spectral peaks that have been assigned to EV molecular constituents up to now in the literature.

EvIPqPCR, Target Circulating Tumorous Extracellular Vesicles for Detection of Pancreatic Cancer

Pancreatic cancer patients predominantly present with advanced disease at diagnosis, contributing to its high mortality. A noninvasive, fast screening method to detect this disease is an unmet need. Tumor-derived extracellular vesicles (tdEVs) bearing information from parental cells have emerged as a promising cancer diagnostic biomarker. However, most tdEV-based assays have impractical sample volumes and time-consuming, complex, and costly techniques. To overcome these limitations, we developed a novel diagnostic method for pancreatic cancer screening. Our approach utilizes the mitochondrial DNA to nuclear DNA ratio of EVs as a collective cell-specific characteristic. We introduce EvIPqPCR, a fast method that combines immunoprecipitation (IP) and qPCR quantification to detect tumor-derived EVs directly from serum. Importantly, our method employs DNA isolation-free and duplexing probes for qPCR, saving at least 3 h. This technique has the potential to serve as a translational assay for cancer screening with a weak correlation to prognosis biomarkers and sufficient discriminatory power among healthy controls, pancreatitis, and pancreatic cancer cases.

A portable elliptical dichroism spectrometer targeting secondary structural features of tumorous protein for pancreatic cancer detection

Stereochemical analysis is essential for understanding the complex function of biomolecules. Various direct and indirect approaches can be used to explore the allosteric configuration. However, the size, cost, and delicate nature of these systems limit their biomedical usage. Here, we constructed elliptical dichroism (ED) spectrometer for biomedical applications, whose performance is validated by experiment and theoretical simulation (Jones/Mueller calculus and time-dependent density-functional theory). Instead of complicated control of circular polarization, ED spectrometer adopted the absorbance of left- and right-oriented elliptically polarized light. With a simplified design, we demonstrated the potential of ED spectrometry as an alternative for secondary structural analysis of biomolecules, their conformation and chirality. It not only provides a portable, low-cost alternative to the sophisticated instruments currently used for structural analysis of biomolecules but also provides superior translational features: low sample consumption(200 μl), easy operation, and multiple working modes, for noninvasive cancer detection.

Variabilities in global DNA methylation and β-sheet richness establish spectroscopic landscapes among subtypes of pancreatic cancer

PURPOSE

Knowledge about pancreatic cancer (PC) biology has been growing rapidly in recent decades. Nevertheless, the survival of PC patients has not greatly improved. The development of a novel methodology suitable for deep investigation of the nature of PC tumors is of great importance. Molecular imaging techniques, such as Fourier transform infrared (FTIR) spectroscopy and Raman hyperspectral mapping (RHM) combined with advanced multivariate data analysis, were useful in studying the biochemical composition of PC tissue.

METHODS

Here, we evaluated the potential of molecular imaging in differentiating three groups of PC tumors, which originate from different precursor lesions. Specifically, we comprehensively investigated adenocarcinomas (ACs): conventional ductal AC, intraductal papillary mucinous carcinoma, and ampulla of Vater AC. FTIR microspectroscopy and RHM maps of 24 PC tissue slides were obtained, and comprehensive advanced statistical analyses, such as hierarchical clustering and nonnegative matrix factorization, were performed on a total of 211,355 Raman spectra. Additionally, we employed deep learning technology for the same task of PC subtyping to enable automation. The so-called convolutional neural network (CNN) was trained to recognize spectra specific to each PC group and then employed to generate CNN-prediction-based tissue maps. To identify the DNA methylation spectral markers, we used differently methylated, isolated DNA and compared the observed spectral differences with the results obtained from cellular nuclei regions of PC tissues.

RESULTS

The results showed significant differences among cancer tissues of the studied PC groups. The main findings are the varying content of β-sheet-rich proteins within the PC cells and alterations in the relative DNA methylation level. Our CNN model efficiently differentiated PC groups with 94% accuracy. The usage of CNN in the classification task did not require Raman spectral data preprocessing and eliminated the need for extensive knowledge of statistical methodologies.

CONCLUSIONS

Molecular spectroscopy combined with CNN technology is a powerful tool for PC detection and subtyping. The molecular fingerprint of DNA methylation and β-sheet cytoplasmic proteins established by our results is different for the main PC groups and allowed the subtyping of pancreatic tumors, which can improve patient management and increase their survival. Our observations are of key importance in understanding the variability of PC and allow translation of the methodology into clinical practice by utilizing liquid biopsy testing.

Nanostructures and Nanotechnologies for the Detection of Extracellular Vesicle

Liquid biopsy has been taken as a minimally invasive examination and a promising surrogate to the clinically applied tissue-based test for the diagnosis and molecular analysis of cancer. Extracellular vesicles (EVs) carry complex molecular information from the tumor, allowing for the multicomponent analysis of cancer and would be beneficial to personalized medicine. In this review, the advanced nanomaterials and nanotechniques for the detection and molecular profiling of EVs, highlight the advantages of nanotechnology in the high-purity isolation and the high-sensitive and high-specific identification of EVs, are summarized. An outlook on the clinical application of nanotechnology-based liquid biopsy in the diagnosis, prognostication, and surveillance of cancer is also provided. It provides information for developing liquid biopsy based on EVs by discussing the advantages and challenges of functionalized nanomaterials and various nanotechnologies.

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.

Environmental noise reduction for tunable resistive pulse sensing of extracellular vesicles

Extracellular vesicles (EVs) bearing biomolecules from parental cells can represent a novel source of disease biomarkers and are under intensive study for their clinical potential. Tunable resistive pulse sensing (TRPS) quantifies the magnitude of a small ionic resistive pulse current to determine the size, concentration, and zeta potential of EVs. Environmental noise is a common limiting factor that affects the precision of sensing devices. TRPS is particularly vulnerable to environmental noise, including both mechanical and electrical. The upper detection limit of the TRPS relies on the physical size of the elastomeric tunable nanopore. The lower limit relies on the electrical signal-to-noise ratio. Guided by simulation, we designed an external device to suppress environmental noise for TRPS measurement. Both mechanical and electrical environmental noise reductions were observed after using the shield. The study also validated the noise reduction function of the shield by quantifying EVs from different cell origins. Detection of EVs smaller than 200 nm was improved by using the shield; which was reported challenging for conventional quantification methods. The study highlighted a feasible approach to solve environmental noise challenges for TRPS based EV quantification.

High Blood Concentration of Leukocyte-Derived Extracellular Vesicles Is Predictive of Favorable Clinical Outcomes in Patients with Pancreatic Cancer: Results from a Multicenter Prospective Study

Pancreatic cancer (PC) is one of the leading causes of cancer-related death worldwide. Identification of novel tumor biomarkers is highly advocated in PC to optimize personalized treatment algorithms. Blood-circulating extracellular vesicles hold promise for liquid biopsy application in cancer. We used an optimized flow cytometry protocol to study leukocyte-derived EVs (CD45+) and PD-L1+ EVs in blood from 56 pancreatic cancer patients and 48 healthy controls (HCs). Our results show that PC patients presented higher blood levels of total EVs (p = 0.0003), leukocyte-derived EVs (LEVs) (p = 0.001) and PD-L1+ EVs (p = 0.01), as compared with HCs. Interestingly, a blood concentration of LEVs at baseline was independently associated with improved overall survival in patients with borderline resectable or primary unresectable PC (HR = 0.17; 95% CI 0.04-0.79; p = 0.02). Additionally, increased blood-based LEVs were independently correlated with prolonged progression-free survival (HR = 0.10; 95% CI 0.01-0.82; p = 0.03) and significantly associated with higher disease control rate (p = 0.02) in patients with advanced PC receiving standard chemotherapy. Notably, a strong correlation between a decrease in blood LEVs concentration during chemotherapy and disease control was observed (p = 0.005). These intriguing findings point to the potential of LEVs as novel blood-based EV biomarkers for improved personalized medicine in patients affected by PC.

Cellulose nanofibers as Scaffold-forming materials for thin film drug delivery systems

We explored the potential of cellulose nanofiber (CNF) for designing prolonged-release, thin-film drug delivery systems (TF-DDS). These delivery systems can be used as locally deployable drug-releasing scaffolds for achieving spatial and temporal control over therapeutic concentration in target tissues. Using doxorubicin (DOX) as a model anticancer drug, CNF-based TF-DDS were prepared using different film-formation processes, such as solvent casting and lyophilization. Formulations were prepared with or without the incorporation of additional macromolecular additives, such as gelatin, to include further biomechanical functionality. We studied the films for their mechanical properties, thermal stability, wettability, porosity and in vitro drug release properties. Our experimental results showed that CNF-based films, when prepared via solvent casting method, showed optimized performance in terms of DOX loading, and prolonged-release than those prepared via lyophilization-based fabrication processes. Scanning electron microscopy (SEM) analysis of the CNF-based films showed uniform distribution of fiber entanglement, which provided the scaffolds with sufficient porosity and tortuosity contributing to the sustained release of the drug from the delivery system. We also observed that surface layering of gelatin on CNF films via dip-coating significantly increased the mechanical strength and reduced the wettability of the films, and as such, affected drug release kinetics. The performance of the TF-DDS was evaluated in-vitro against two pancreatic cancer cell lines, i.e. MIA PaCa-2 and PANC-1. We observed that, along with the enhancement of mean dissolution time (MDT) of DOX, CNF-based TF-DDS were able to suppress the proliferation of pancreatic cancer cells in a time-dependent fashion, indicating that the drug liberated from the films were therapeutically active against cancer cells. Additionally, TF-DDS were also tested ex-vivo on patient-derived xenograft (PDX) model of pancreatic ductal adenocarcinoma (PDAC). We observed that DOX released from the TF-DDS was able to reduce Ki-67 positive, pancreatic cancer cells in these models.

Microfluidic Approaches for Affinity-Based Exosome Separation

As a subspecies of extracellular vesicles (EVs), exosomes have provided promising results in diagnostic and theranostic applications in recent years. The nanometer-sized exosomes can be extracted by liquid biopsy from almost all body fluids, making them especially suitable for mainly non-invasive point-of-care (POC) applications. To achieve this, exosomes must first be separated from the respective biofluid. Impurities with similar properties, heterogeneity of exosome characteristics, and time-related biofouling complicate the separation. This practical review presents the state-of-the-art methods available for the separation of exosomes. Furthermore, it is shown how new separation methods can be developed. A particular focus lies on the fabrication and design of microfluidic devices using highly selective affinity separation. Due to their compactness, quick analysis time and portable form factor, these microfluidic devices are particularly suitable to deliver fast and reliable results for POC applications. For these devices, new manufacturing methods (e.g., laminating, replica molding and 3D printing) that use low-cost materials and do not require clean rooms are presented. Additionally, special flow routes and patterns that increase contact surfaces, as well as residence time, and thus improve affinity purification are displayed. Finally, various analyses are shown that can be used to evaluate the separation results of a newly developed device. Overall, this review paper provides a toolbox for developing new microfluidic affinity devices for exosome separation.

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.