Dataset Information

Extracellular vesicle fibrinogen induces encephalitogenic CD8+ T cells in a mouse model of multiple sclerosis

ABSTRACT: Extracellular vesicles isolated from murine and human patient blood plasma was subjected to proteomic analysis to determine the EV proteome in healthy, non-disease murine and human samples and during disease.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human) Mus Musculus (mouse)

TISSUE(S): Blood Plasma

DISEASE(S): Multiple Sclerosis

SUBMITTER: Cory Willis

LAB HEAD: Stephen J. Crocker

PROVIDER: PXD013630 | Pride | 2019-09-27

REPOSITORIES: pride

ACCESS DATA

Dataset's files

Source:

Items per page:

1 - 5 of 26

Publications

Extracellular vesicle fibrinogen induces encephalitogenic CD8+ T cells in a mouse model of multiple sclerosis.

Willis Cory M CM Nicaise Alexandra M AM Menoret Antoine A Ryu Jae Kyu JK Mendiola Andrew S AS Mendiola Andrew S AS Jellison Evan R ER Givogri Maria I MI Han David K DK Bongarzone Ernesto R ER Akassoglou Katerina K Vella Anthony T AT Crocker Stephen J SJ

Proceedings of the National Academy of Sciences of the United States of America 20190508 21

Extracellular vesicles (EVs) are emerging as potent mediators of intercellular communication with roles in inflammation and disease. In this study, we examined the role of EVs from blood plasma (pEVs) in an experimental autoimmune encephalomyelitis mouse model of central nervous system demyelination. We determined that pEVs induced a spontaneous relapsing-remitting disease phenotype in MOG<sub>35-55</sub>-immunized C57BL/6 mice. This modified disease phenotype was found to be driven by CD8+ T ce ...[more]

PMID: 31068461

Similar Datasets

Project description:Background: Epithelial-stromal crosstalk plays a critical role in invasive breast cancer (IBC) pathogenesis; however, little is known on a systems level about how epithelial-stromal interactions evolve during carcinogenesis. Results: We develop a framework for building genome-wide epithelial-stromal co-expression networks composed of pairwise co-expression relationships between mRNA levels of genes expressed in the epithelium and stroma across a population of patients. We apply this method to laser capture micro-dissection expression profiling datasets in the setting of breast carcinogenesis. Our analysis shows that epithelial-stromal co-expression networks undergo extensive re-wiring during carcinogenesis, with the emergence of distinct network hubs in normal breast, ER-positive IBC, and ER-negative IBC, and the emergence of distinct patterns of functional network enrichment. In contrast to normal breast, the strongest epithelial-stromal co-expression relationships in IBC mostly represent self-loops, in which the same gene is co-expressed in epithelial and stromal regions. We validate this observation using an independent laser capture micro-dissection dataset and confirm that self-loop interactions are significantly increased in cancer by performing computational image analysis of epithelial and stromal protein expression using images from the Human Protein Atlas. Conclusions: Epithelial-stromal co-expression network analysis represents a new approach for systems-level analyses of spatially-localized transcriptomic data. The analysis provides new biological insights into the re-wiring of epithelial-stromal co-expression networks and the emergence of epithelial-stromal co-expression self-loops in breast cancer. The approach may facilitate the development of new diagnostics and therapeutics targeting epithelial-stromal interactions in cancer. 36 flash-frozen human primary breast cancer samples were subjected to laser capture microdissection to separately isolate matched tumor epithelial and tumor-associated stromal components. RNA was isolated, subjected to 2 rounds of amplification, and hybridized on Agilent 4x44K microarrays along with a common reference (single round-amplified commercially obtained Universal Human Reference RNA) in a dyeswap design. For two samples of tumor-associated stroma, a second technical replicate was performed. Samples were labelled as ER-positive based on ESR1 gene expression levels in the tumor epithelium, using univariate Gaussian mixture model-based clustering via the mclust package in R.

			Action	DRS
	F502502.dat	Other
	F502503.dat	Other
	F502504.dat	Other
	F502505.dat	Other
	F502506.dat	Other