Dataset Information

Label-free visualization and characterization of extracellular vesicles in breast cancer.

ABSTRACT: Despite extensive interest, extracellular vesicle (EV) research remains technically challenging. One of the unexplored gaps in EV research has been the inability to characterize the spatially and functionally heterogeneous populations of EVs based on their metabolic profile. In this paper, we utilize the intrinsic optical metabolic and structural contrast of EVs and demonstrate in vivo/in situ characterization of EVs in a variety of unprocessed (pre)clinical samples. With a pixel-level segmentation mask provided by the deep neural network, individual EVs can be analyzed in terms of their optical signature in the context of their spatial distribution. Quantitative analysis of living tumor-bearing animals and fresh excised human breast tissue revealed abundance of NAD(P)H-rich EVs within the tumor, near the tumor boundary, and around vessel structures. Furthermore, the percentage of NAD(P)H-rich EVs is highly correlated with human breast cancer diagnosis, which emphasizes the important role of metabolic imaging for EV characterization as well as its potential for clinical applications. In addition to the characterization of EV properties, we also demonstrate label-free monitoring of EV dynamics (uptake, release, and movement) in live cells and animals. The in situ metabolic profiling capacity of the proposed method together with the finding of increasing NAD(P)H-rich EV subpopulations in breast cancer have the potential for empowering applications in basic science and enhancing our understanding of the active metabolic roles that EVs play in cancer progression.

SUBMITTER: You S

PROVIDER: S-EPMC6883786 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Label-free visualization and characterization of extracellular vesicles in breast cancer.

You Sixian S Barkalifa Ronit R Chaney Eric J EJ Tu Haohua H Park Jaena J Sorrells Janet Elise JE Sun Yi Y Liu Yuan-Zhi YZ Yang Lin L Chen Danny Z DZ Marjanovic Marina M Sinha Saurabh S Boppart Stephen A SA

Proceedings of the National Academy of Sciences of the United States of America 20191115 48

Despite extensive interest, extracellular vesicle (EV) research remains technically challenging. One of the unexplored gaps in EV research has been the inability to characterize the spatially and functionally heterogeneous populations of EVs based on their metabolic profile. In this paper, we utilize the intrinsic optical metabolic and structural contrast of EVs and demonstrate in vivo/in situ characterization of EVs in a variety of unprocessed (pre)clinical samples. With a pixel-level segmentat ...[more]

PMID: 31732668

Similar Datasets

Project description:Extracellular vesicles (EVs) that circulate in body fluids possess significant potential for disease diagnosis. Their use in clinical settings, however, has been limited owing to lack of simple and robust isolation methods. To rectify this problem, a centrifugal device for automatic, fast, and efficient isolation of EVs from whole-blood, called Exodisc-B is presented in this paper. Methods: The device comprises a built-in chamber to facilitate plasma separation and two nanoporous filters-one for removing larger particles and the other for enriching EVs. The performance of the device in comparison to ultracentrifugation (UC) was evaluated by analyzing the yield, purity, protein and RNA content of the isolated EVs. Additionally, the EV protein marker expressions were measured by ELISA and statistically analyzed to differentiate prostate cancer patients from healthy donors. Results: Compared with the UC technique, the proposed device is capable of isolating at least an order of magnitude higher number of EVs with about 30-fold higher mRNA count within 40 min. Sandwich ELISA of EV-specific membrane proteins-CD9-CD81-confirmed that Exodisc-B can isolate EVs from a volume of whole blood as low as 30 µL with a capture efficiency exceeding 75%. The device also facilitates temporal monitoring of tumor progression within live mouse xenograft models over a period of 13 weeks while using minimal volumes of weekly collected blood samples. Further, in ELISA analyses of multiple cancer-related proteins, such as prostate-specific antigen (PSA), prostate-specific membrane antigen (PSMA), epithelial cell adhesion molecule (EpCAM), epidermal growth factor receptor 1 (EGFR1), and heat shock protein 90 (HSP90), extracted from EVs isolated from human plasma, 43 patients were differentiated from 30 healthy donors. Conclusion: The results demonstrated the ability of Exodisc-B to provide a rapid, sensitive, and point-of-care-type method for extracting intact EVs from small volumes of clinical blood samples for disease diagnosis and monitoring.

Dataset Information

Label-free visualization and characterization of extracellular vesicles in breast cancer.

Publications

Label-free visualization and characterization of extracellular vesicles in breast cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets