Dataset Information

Defining the Relationship Between Cellular and Extracellular Vesicle (EV) Content in Breast Cancer via an Integrative Multi-Omic Analysis

ABSTRACT: Much recent work has been dedicated to exploring the utility of extracellular vesicles (EVs) as circulating cancer biomarkers. This is driven by the understanding that the molecular cargo of EVs reflects their originating cell, meaning EVs may enable non-invasive tumour surveillance. Few attempts have been made, however, to empirically validate this assumption on the -omic level and define the relationship between the molecular phenotype of cells and EVs. To this end, we present an integrative multi-omic analysis of a panel of breast cancer cell lines and corresponding EVs. Transcriptomic analysis of the cells was first used to confirm that expression of disease-related transcripts remained stable following transition to a low serum EV production medium. Proteomic analysis of the EVs indicated that neither the ER nor PR protein could be directly detected in EVs, but cellular expression of ER could be indirectly inferred from the EV proteome. Transcriptomic analyses generally suggested a direct positive correlation between transcript levels in cells and EVs. Integrative analysis suggested that the EV proteome and transcriptome captured select hallmarks of aberrant pathway activity in the originating cells, supporting the potential use of EVs to non-invasively monitor molecular evolution in breast cancers.

ORGANISM(S): Homo sapiens

PROVIDER: GSE188385 | GEO | 2023/11/03

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Circulating extracellular vesicles (EVs) contain molecular footprints - lipids, proteins, metabolites, RNA, and DNA - from their cell of origin and may provide non-invasive access for detection, characterization, and monitoring of diseases. Consequently, EV-associated RNA and proteins have gained widespread interest in developing liquid-biopsy assays for cancer. Yet, an integrative proteo-transcriptomic landscape of EVs and a direct comparison with their cell of origin remains relatively unexplored. Here, we report that EVs enrich for distinct molecular cargo and their proteo-transcriptome does not linearly correlate with their cell of origin. Our sub-cellular compartment analyses revealed that EV enrich 2-6 times more cytoskeletal and extracellular proteins, while their donor cells cargo 3-10 times more nuclear and mitochondrial proteins. EVs predominantly enrich (40-60%) for small RNA between ~15-200 nucleotides, which include miRNAs, siRNAs, tRNA, 5S rRNAs, and snRNAs, associated with cell differentiation, development, and Wnt signaling signatures. Finally, our integrated proteo-transcriptomic analyses reveal that EVs are enriched with specific small RNAs (RNY3, vtRNA, and MIRLET-7) and their complementary proteins (YBX1, IGF2BP2, SRSF1/2). These analyses suggest that RNA-protein complexes may constitute a functional interaction network inside EVs to protect and regulate access to EV-RNA. To ensure that our analyses are unbiased and independent of the cell of origin, EV isolation methods, site of experimentation, site of data generation, and methodologies applied for RNAseq and mass spectrometry, we have generated and curated comprehensive datasets of proteomics and transcriptomics from a total of 12 cancer cell lines, their EVs, and prostate cancer patientsÕ serum EVs (A detailed description is provided in Fig. 1). These combined analyses demonstrated that irrespective of the cell of origin and other aforementioned conditions, EVs enrich for distinct RNA and protein signatures that do not linearly correlate with their cell of origin.

Project description:Microglia are resident immune cells of the brain that play important roles in mediating inflammatory responses in several neurological diseases via direct and indirect mechanisms. One indirect mechanism may involve extracellular vesicle (EV) release, so that the molecular cargo transported by microglia-derived EVs can have functional effects by facilitating intercellular communication. The molecular composition of microglia-derived EVs, and how microglial activation states impacts EV composition and EV-mediated effects in neuroinflammation, remain poorly understood. We hypothesize that microglia-derived EVs have unique molecular profiles that are determined by microglial activation state. Using size-exclusion chromatography to purify EVs from BV2 microglia, combined with proteomic (label-free quantitative mass spectrometry or LFQ-MS) and transcriptomic (mRNA and non-coding RNA seq) methods, we obtained comprehensive molecular profiles of microglia-derived EVs. LFQ-MS identified several classic EV proteins (tetraspanins, ESCRT machinery, and heat shock proteins), in addition to over 200 proteins not previously reported in the literature. Unique mRNA and microRNA signatures of microglia-derived EVs were also identified. After treating BV2 microglia with lipopolysaccharide (LPS), interleukin-10, or transforming growth factor beta, to mimic pro-inflammatory, anti-inflammatory, or homeostatic states, respectively, LFQ-MS and RNA seq revealed novel state-specific proteomic and transcriptomic signatures of microglia-derived EVs. Particularly, LPS treatment had the most profound impact on proteomic and transcriptomic compositions of microglia-derived EVs. Furthermore, we found that EVs derived from LPS-activated microglia were able to induce pro-inflammatory transcriptomic changes in resting responder microglia, confirming the ability of microglia-derived EVs to relay functionally-relevant inflammatory signals. These comprehensive microglia-EV molecular datasets represent important resources for the neuroscience and glial communities, and provide novel insights into the role of microglia-derived EVs in neuroinflammation.

Dataset Information

Defining the Relationship Between Cellular and Extracellular Vesicle (EV) Content in Breast Cancer via an Integrative Multi-Omic Analysis

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets