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.

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.

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.

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.

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 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.

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.

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.

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.

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

The human eukaryotic pathogen 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 has acquired, by lateral gene transfer from bacteria, genes encoding peptidoglycan hydrolases of the NlpC/P60 family.