Extracellular vesicles (EVs) actively participate in intercellular communication and pathological processes. Studying the molecular signatures of EVs is the key to reveal their biological functions and clinical values, which, however, is greatly hindered by their sub-100-nm dimensions, the low quantities of biomolecules each EV carries, and the large population heterogeneity. Here, we report the single-EV Flow Cytometry Analysis technique that realizes single EV counting and phenotyping in a conventional flow cytometer for the first time, enabled by Target-Initiated Engineering (TIE) of DNA nanostructures on each EV. By illuminating multiple markers on single EVs, we reveal statistically significant differences among the molecular signatures of EVs originated from several breast cancer cell lines, and successfully recognize the cancer cell-derived EVs among the heterogeneous EV populations. Thus, our approach holds great potential for various biological and biomedical applications.

Duan and Y. Liu. W. Zhong directed the review layout design, coordinated the writing efforts, and prepared the sections of Introduction and outlook and opportunities sections.

Exosomes play important roles in mediating intercellular communication and regulating a variety of biological processes, but clear understanding of their functions and biogenesis has not been achieved, due to the high technical difficulties involved in analysis of small vesicular structures that contain a high proportion of membrane structures. Herein, we designed a novel approach to integrate two nanomaterials carrying varied surface properties, the hydrophilic, macroporous graphene foam (GF) and the amphiphilic periodic mesoporous organosilica (PMO), for efficient exosome isolation from human serum and effective protein profiling. The high specific surface area of GF, after modification with the antibody against the exosomal protein marker, CD63, allowed highly specific isolation of exosomes from complex biological samples with high recovery. Since the organic solvent, methanol, turned out to be the most effective lysis solution for releasing the exosomal proteins, the amphiphilic PMO was employed to rapidly recover the exosomal proteins, including the highly hydrophobic membrane proteins. The fine pores of PMO also acted as the nanoreactors to accelerate protein digestion that produced peptides subject to liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. A total of 334 proteins with 111 membrane proteins [31% of these contained >2 transmembrane domains (TMD)] were identified using the integrated GF/PMO platform. In contrast, with the commercial exosome isolation kit and the in-solution protein digestion method, only 151 proteins were found, with 28 being membrane proteins (only one contained three TMDs). Our results support that the integrated GF/PMO platform is of great value to facilitate the comprehensive characterization of exosomal proteins for better understanding of their functions and for identification of more exosome-based disease markers.

Effective in silico methods to predict protein corona compositions on engineered nanomaterials (ENMs) could help elucidate the biological outcomes of ENMs in biosystems without the need for conducting lengthy experiments for corona characterization. However, the physicochemical properties of ENMs, used as the descriptors in current modeling methods, are insufficient to represent the complex interactions between ENMs and proteins. Herein, we utilized the fluorescence change (FC) from fluorescamine labeling on a protein, with or without the presence of the ENM, as a novel descriptor of the ENM to build machine learning models for corona formation. FCs were significantly correlated with the abundance of the corresponding proteins in the corona on diverse classes of ENMs, including metal and metal oxides, nanocellulose, and 2D ENMs. Prediction models established by the random forest algorithm using FCs as the ENM descriptors showed better performance than the conventional descriptors, such as ENM size and surface charge, in the prediction of corona formation. Moreover, they were able to predict protein corona formation on ENMs with very heterogeneous properties. We believe this novel descriptor can improve in silico studies of corona formation, leading to a better understanding on the protein adsorption behaviors of diverse ENMs in different biological matrices. Such information is essential for gaining a comprehensive view of how ENMs interact with biological systems in ENM safety and sustainability assessments.

Abstract. Licorice has been shown to affect the activities of several cytochrome P450 enzymes. This study aims to identify the key constituents in licorice which may affect these activities. Bioactivity assay was combined with metabolic profiling to identify these compounds in several complex licorice extracts. Firstly, the inhibition potencies of 40 pure licorice compounds were tested using an liquid chromatography/tandem mass spectrometry cocktail method. Significant inhibitors of human P450 isozymes 1A2, 2C9, 2C19, 2D6, and 3A4 were then selected for examination of their structural features by molecular docking to determine their molecular interaction with several P450 isozymes. Based on the present in vitro inhibition findings, along with our previous in vivo metabolic studies and the prevalence of individual compounds in licorice extract, we identified several licorice constituents, viz., liquiritigenin, isoliquiritigenin, together with seven isoprenylated flavonoids and arylcoumarins, which could be key components responsible for the herb-drug interaction between cytochrome P450 and licorice. In addition, hydrophilic flavonoid glycosides and saponins may be converted into these P450 inhibitors in vivo. These studies represent a comprehensive examination of the potential effects of licorice components on the metabolic activities of P450 enzymes.