Deep autoencoder as an interpretable tool for Raman spectroscopy investigation of chemical and extracellular vesicle mixtures.

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool that provides valuable insight into the molecular contents of chemical and biological samples. However, interpreting Raman spectra from complex or dynamic datasets remains challenging, particularly for highly heterogeneous biological samples like extracellular vesicles (EVs). To overcome this, we developed a tunable and interpretable deep autoencoder for the analysis of several challenging Raman spectroscopy applications, including synthetic datasets, chemical mixtures, a chemical milling reaction, and mixtures of EVs. We compared the results with classical methods (PCA and UMAP) to demonstrate the superior performance of the proposed technique. Our method can handle small datasets, provide a high degree of generalization such that it can fill unknown gaps within spectral datasets, and even quantify relative ratios of cell line-derived EVs to fetal bovine serum-derived EVs within mixtures. This simple yet robust approach will greatly improve the analysis capabilities for many other Raman spectroscopy applications.

The tumour-derived extracellular vesicle proteome varies by endometrial cancer histology and is confounded by an obesogenic environment.

Endometrial cancer, the most common gynaecological cancer worldwide, is closely linked to obesity and metabolic diseases, particularly in younger women. New circulating biomarkers have the potential to improve diagnosis and treatment selections, which could significantly improve outcomes. Our approach focuses on extracellular vesicle (EV) biomarker discovery by directly profiling the proteome of EVs enriched from frozen biobanked endometrial tumours. We analysed nine tissue samples to compare three clinical subgroups-low BMI (Body Mass Index) Endometrioid, high BMI Endometrioid, and Serous (any BMI)-identifying proteins related to histological subtype, BMI, and shared secreted proteins. Using collagenase digestion and size exclusion chromatography, we successfully enriched generous quantities of EVs (range 204.8-1291.0 µg protein: 1.38 × 1011-1.10 × 1012 particles), characterised by their size (∼150 nm), expression of EV markers (CD63/81), and proposed endometrial cancer markers (L1CAM, ANXA2). Mass spectrometry-based proteomic profiling identified 2075 proteins present in at least one of the 18 samples. Compared to cell lysates, EVs were successfully depleted for mitochondrial and blood proteins and enriched for common EV markers and large secreted proteins. Further analysis highlighted significant differences in EV protein profiles between the high BMI subgroup and others, underlining the impact of comorbidities on the EV secretome. Interestingly, proteins differentially abundant in tissue subgroups were largely not also differential in matched EVs. This research identified secreted proteins known to be involved in endometrial cancer pathophysiology and proposed novel diagnostic biomarkers (EIF6, MUC16, PROM1, SLC26A2).

Lactobacillus gasseri and Gardnerella vaginalis produce extracellular vesicles that contribute to the function of the vaginal microbiome and modulate host-Trichomonas vaginalis interactions.

Trichomonas vaginalis is an extracellular protozoan parasite of the human urogenital tract, responsible for a prevalent sexually transmitted infection. Trichomoniasis is accompanied by a dysbiotic microbiome that is characterised by the depletion of host-protective commensals such as Lactobacillus gasseri, and the flourishing of a bacterial consortium that is comparable to the one seen for bacterial vaginosis, including the founder species Gardnerella vaginalis. These two vaginal bacteria are known to have opposite effects on T. vaginalis pathogenicity. Studies on extracellular vesicles (EVs) have been focused on the direction of a microbial producer (commensal or pathogen) to a host recipient, and largely in the context of the gut microbiome. Here, taking advantage of the simplicity of the human cervicovaginal microbiome, we determined the molecular cargo of EVs produced by L. gasseri and G. vaginalis and examined how these vesicles modulate the interaction of T. vaginalis and host cells. We show that these EVs carry a specific cargo of proteins, which functions can be attributed to the opposite roles that these bacteria play in the vaginal biome. Furthermore, these bacterial EVs are delivered to host and protozoan cells, modulating host-pathogen interactions in a way that mimics the opposite effects that these bacteria have on T. vaginalis pathogenicity. This is the first study to describe side-by-side the protein composition of EVs produced by two bacteria belonging to the opposite spectrum of a microbiome and to demonstrate that these vesicles modulate the pathogenicity of a protozoan parasite. Such as in trichomoniasis, infections and dysbiosis co-occur frequently resulting in significant co-morbidities. Therefore, studies like this provide the knowledge for the development of antimicrobial therapies that aim to clear the infection while restoring a healthy microbiome.

Manifold Learning Enables Interpretable Analysis of Raman Spectra from Extracellular Vesicle and Other Mixtures.

Extracellular vesicles (EVs) have emerged as promising diagnostic and therapeutic candidates in many biomedical applications. However, EV research continues to rely heavily on in vitro cell cultures for EV production, where the exogenous EVs present in fetal bovine (FBS) or other required serum supplementation can be difficult to remove entirely. Despite this and other potential applications involving EV mixtures, there are currently no rapid, robust, inexpensive, and label-free methods for determining the relative concentrations of different EV subpopulations within a sample. In this study, we demonstrate that surface-enhanced Raman spectroscopy (SERS) can biochemically fingerprint fetal bovine serum-derived and bioreactor-produced EVs, and after applying a novel manifold learning technique to the acquired spectra, enables the quantitative detection of the relative amounts of different EV populations within an unknown sample. We first developed this method using known ratios of Rhodamine B to Rhodamine 6G, then using known ratios of FBS EVs to breast cancer EVs from a bioreactor culture. In addition to quantifying EV mixtures, the proposed deep learning architecture provides some knowledge discovery capabilities which we demonstrate by applying it to dynamic Raman spectra of a chemical milling process. This label-free characterization and analytical approach should translate well to other EV SERS applications, such as monitoring the integrity of semipermeable membranes within EV bioreactors, ensuring the quality or potency of diagnostic or therapeutic EVs, determining relative amounts of EVs produced in complex co-culture systems, as well as many Raman spectroscopy applications.

Production of Extracellular Vesicles Using a CELLine Adherent Bioreactor Flask.

The efficient production of extracellular vesicles (EVs) from adherent cells in vitro can be challenging when using conventional culture flasks. Issues such as low cell density leading to low EV yield, and the inability to completely remove bovine serum EVs without starvation contribute to this challenge. By comparison, the two-chamber CELLine adherent bioreactor can produce significantly more EVs with improved time, space, and resource efficiency. Furthermore, it is highly accessible and can continually produce EVs using long term cultures without the need for passaging. Lastly, the 10 kDa semipermeable, cellulose acetate membrane separating the cell and media chambers allows for the continual use of bovine serum in the media chamber while preventing bovine EVs from contaminating the conditioned media.

Investigating the consistency of extracellular vesicle production from breast cancer subtypes using CELLine adherent bioreactors.

Extracellular vesicle (EV) research has grown rapidly in recent years, largely due to the potential use of EVs as liquid biopsy biomarkers or therapeutics. However, in-depth characterisation and validation of EVs produced using conventional in vitro cultures can be challenging due to the large area of cell monolayers and volumes of culture media required. To overcome this obstacle, multiple bioreactor designs have been tested for EV production with varying success, but the consistency of EVs produced over time in these systems has not been reported previously. In this study, we demonstrate that several breast cancer cell lines of different subtypes can be cultured simultaneously in space, resource, and time efficient manner using CELLine AD 1000 systems, allowing the consistent production of vast amounts of EVs for downstream experimentation. We report an improved workflow used for inoculating, maintaining, and monitoring the bioreactors, their EV production, and the characterisation of the EVs produced. Lastly, our proteomic analyses of the EVs produced throughout the lifetime of the bioreactors show that core EV-associated proteins are relatively consistent, with few minor variations over time, but that tracking the production of EVs is a convenient method to indirectly monitor the bioreactor and consistency of the yielded EVs. These findings will aid future studies requiring the simultaneous production of large amounts of EVs from several cell lines of different subtypes of a disease and other EV biomanufacturing applications.

Space curvature-inspired nanoplasmonic sensor for breast cancer extracellular vesicle fingerprinting and machine learning classification.

Extracellular vesicles (EVs) are micro and nanoscale lipid-enclosed packages that have shown potential as liquid biopsy targets for cancer because their structure and contents reflect their cell of origin. However, progress towards the clinical applications of EVs has been hindered due to the low abundance of disease-specific EVs compared to EVs from healthy cells; such applications thus require highly sensitive and adaptable characterization tools. To address this obstacle, we designed and fabricated a novel space curvature-inspired surfaced-enhanced Raman spectroscopy (SERS) substrate and tested its capabilities using bioreactor-produced and size exclusion chromatography-purified breast cancer EVs of three different subtypes. Our findings demonstrate the platform's ability to effectively fingerprint and efficiently classify, for the first time, three distinct subtypes of breast cancer EVs following the application of machine learning algorithms on the acquired spectra. This platform and characterization approach will enhance the viability of EVs and nanoplasmonic sensors towards clinical utility for breast cancer and many other applications to improve human health.

Extracellular vesicles produced by the protozoan parasite Trichomonas vaginalis contain a preferential cargo of tRNA-derived small RNAs.

Trichomonas vaginalis is a protozoan parasite that causes trichomoniasis, the most prevalent non-viral sexually transmitted infection worldwide. Trichomonas vaginalis releases extracellular vesicles that play a role in parasite:parasite and parasite:host interactions. The aim of this study was to characterise the RNA cargo of these vesicles. Trichomonas vaginalis extracellular vesicles were found to encapsulate a cargo of RNAs of small size. RNA-seq analysis showed that tRNA-derived small RNAs, mostly 5' tRNA halves, are the main type of small RNA in these vesicles. The tRNA-derived small RNAs in T. vaginalis extracellular vesicles were shown to be derived from the specific processing of tRNAs within cells. The specificity of this RNA cargo in T. vaginalis extracellular vesicles suggests a preference for packaging. The RNA cargo of T. vaginalis was shown to be rapidly internalised by human cells via lipid raft-dependent endocytosis. The potential role of these tsRNAs - an emerging class of small RNAs with regulatory functions - on altering host cellular responses requires further examination, suggesting a new mode of parasite:host communication.

The Protozoan Trichomonas vaginalis Targets Bacteria with Laterally Acquired NlpC/P60 Peptidoglycan Hydrolases.

The human eukaryotic pathogen Trichomonas vaginalis causes trichomoniasis, a prevalent sexually transmitted infection. This extracellular protozoan is intimately associated with the human vaginal mucosa and microbiota, but key aspects of the complex interactions between the parasite and the vaginal bacteria remain elusive. We report that T. vaginalis has acquired, by lateral gene transfer from bacteria, genes encoding peptidoglycan hydrolases of the NlpC/P60 family. Two of the T. vaginalis enzymes were active against bacterial peptidoglycan, retaining the active-site fold and specificity as dl-endopeptidases. The endogenous NlpC/P60 genes are transcriptionally upregulated in T. vaginalis in the presence of bacteria. The overexpression of an exogenous copy enables the parasite to outcompete bacteria from mixed cultures, consistent with the biochemical activity of the enzyme. Our study results highlight the relevance of the interactions of this eukaryotic pathogen with bacteria, a poorly understood aspect of the biology of this important human parasite.IMPORTANCETrichomonas vaginalis is a parasitic protozoan of the human urogenital tract that causes trichomoniasis, a very common sexually transmitted disease. Despite residing extracellularly and in close association with the vaginal bacteria (i.e., the microbiota), very little is known about the nature of the parasite-bacterium interactions. Our study showed that this parasite had acquired genes from bacteria which retained their original function. They produce active enzymes capable of degrading peptidoglycan, a unique polymer of the bacterial cell envelope, helping the parasite to outcompete bacteria in mixed cultures. This study was the first to show that a laterally acquired group of genes enables a eukaryotic mucosal pathogen to control bacterial population. We highlight the importance of understanding the interactions between pathogens and microbiota, as the outcomes of these interactions are increasingly understood to have important implications on health and disease.