Microfluidic On-demand Engineering of Exosomes towards Cancer Immunotherapy
Bibliographic record
Abstract
Extracellular Vesicles (EVs), particularly exosomes (30-150 nm), are an emerging delivery system in mediating cellular communications, which have been observed for priming immune responses by presenting parent cell signaling proteins or tumor antigens to immune cells. Therefore, preparation of antigenic exosomes that can play therapeutic roles, particularly in cancer immunotherapy, is emerging. However, standard benchtop methods (e.g., ultracentrifugation and filtration) lack the ability to purify antigenic exosomes specifically among other microvesicle subtypes, due to the non-selective and time-consuming (>10 h) isolation protocols. Exosome engineering approaches, such as the transfection of parent cells, also suffer from poor yield, low purity, and time-consuming operations. In this paper, we introduce a streamlined microfluidic cell culture platform for integration of harvesting, antigenic modification, and photo-release of surface engineered exosomes in one workflow, which enables the production of intact, MHC peptide surface engineered exosomes for cytolysis activation. The PDMS microfluidic cell culture chip is simply cast from a 3D-printed mold. The proof-of-concept study demonstrated the enhanced ability of harvested exosomes in antigen presentation and T cell activation, by decorating melanoma tumor peptides on the exosome surface (e.g., gp-100, MART-1, MAGE-A3). Such surface engineered antigenic exosomes were harvested in real-time from the on-chip culture of leukocytes isolated from human blood, leading to much faster cellular uptake. The activation of gp100-specific CD8 T cells which were purified from the spleen of 2 Pmel1 transgenic mice was evaluated using surface engineered exosomes prepared from muring antigen presenting cells. Antigen-specific CD8 T cell proliferation was significantly induced by the engineered exosomes compared to native, non-engineered exosomes. This microfluidic platform serves as an automated and highly integrated cell culture device for rapid, and real-time production of therapeutic exosomes that could advance cancer immunotherapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".