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Record W4362524638 · doi:10.1158/1538-7445.am2023-2001

Abstract 2001: Engineering extracellular vesicle like liposomes with integrin αVβ5 to study its role in cancer metastasis

2023· article· en· W4362524638 on OpenAlexaff
Yunxi Chen, Rubén R. López, Chaymaa Zouggari Ben El Khyat, Thupten Tsering, Vahé Nerguizian, Julia V. Burnier

Bibliographic record

VenueCancer Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsÉcole de Technologie SupérieureMcGill University Health Centre
Fundersnot available
KeywordsLiposomeChemistryExtracellular vesicleNanoparticle tracking analysisMetastasisMicrovesiclesIntegrinVesicleCancer cellBiophysicsDynamic light scatteringBiochemistryCancerCell biologyBiologyNanotechnologyMaterials scienceMembraneNanoparticleCell

Abstract

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Abstract Background: Extracellular vesicles (EVs) are key mediators of cancer metastasis. EV membrane-associated integrins show organotrophic properties by interacting with microenvironmental stromal cells and extracellular matrix. Specifically, integrin αVβ5 is associated with the metastasis of cancers with remarkable liver tropism, including pancreatic cancer, uveal melanoma, and colorectal cancer. Despite the importance of understanding the pathogenic roles of EVs, EV heterogeneity, lengthy isolation, and low yields make it difficult to study the function of EVs at the molecular level. Therefore, we explored engineering EV-like lipid nanoparticles with selected cargo, such as integrin αVβ5, as a tool to study EV biology. Methods: EV-like liposomes (in terms of size and charge) have been produced by our group by nanoprecipitation. Aqueous solvent (proteins in Milli-Q water) and organic solvent (cholesterol and DMPC in ethanol) were mixed in a 3D-printed microfluidic chip. Recombinant integrin αVβ5 was purchased from R&D System and encapsulated into the liposomes in one step. Liposomes with recombinant GFP protein (Thermo Fisher) and liposomes with cellular proteins isolated from 92.1 and A431 cells were produced as proof of concept. Liposomes were produced with dye SP-DilC18 or labelled by PKH after production. Unencapsulated proteins and unbound dye was removed by ultracentrifugation or size exclusion chromatography (qEV, Izon). Stain-free gel (Biorad), flow cytometry, and Tecan Reader were used to detect encapsulated proteins. Hepatocyte uptake of liposomes was analyzed by Incucyte. The liposomes were characterized by transmission emission microscopy (TEM), nanoparticle tracking analysis, and dynamic light scattering. Results: Liposomes with and without protein cargo presented a bi-layer lipid membrane by cryoTEM. 92.1 cellular protein, A431 cellular protein, GFP, and integrin αVβ5 encapsulated by liposomes were detected by stain-free gel. Positive signals from GFP encapsulated by liposomes were detected by flow cytometry and Tecan Reader, indicating encapsulated proteins can remain functional. By Incucyte fluorescence microscopy, we analyzed the uptake of liposomes by hepatocytes and found that different sizes and charges affect the uptake efficiency. Conclusion: In this study, we produced EV-like liposomes with lipid bilayers in a controllable way in terms of size, charges, and functional cargo. We showed that EV-like liposomes could be a novel and easy-to-use model to study the role of specific EV cargo in cancer metastasis. Citation Format: Yunxi Chen, Rubén R. López, Chaymaa Zouggari Ben El Khyat, Thupten Tsering, Vahé Nerguizian, Julia V. Burnier. Engineering extracellular vesicle like liposomes with integrin αVβ5 to study its role in cancer metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2001.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.055
GPT teacher head0.377
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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