CE–MS Metabolomic and LC–MS Proteomic Analyses of Breast Cancer Exosomes Reveal Alterations in Purine and Carnitine Metabolism
Bibliographic record
Abstract
A nanosheath-flow capillary electrophoresis mass spectrometry (CE-MS) system with electrospray ionization was used to profile cationic metabolite cargo in exosomes secreted by nontumorigenic MCF-10A and tumorigenic MDA-MB-231 breast epithelial cells. An in-house-produced sheath liquid interface was developed and machined from PEEK to enable nanoflow volumes. Normalization of CE-MS peak areas to the total UV signal was employed to enhance quantitative accuracy and reduce variability. CE-MS-based metabolomics revealed increased purine synthesis intermediates and increased carnitine synthesis metabolites in MDA-MB-231-derived exosomes, with pathway enrichment indicating the activation of de novo purine pathways and upregulation of carnitine metabolism. In addition, nano-LC-MS-based proteomics revealed differential expression of ecto-5'-nucleotidase (NT5E) and mitochondrial aldehyde dehydrogenase (ALDH9A1), demonstrating metabolic alterations in related enzymatic steps. This study demonstrates the application of nanosheath-flow CE-MS for comprehensive and quantitative exosome metabolomics, uncovering metabolic reprogramming in purine and carnitine pathways between normal and cancerous breast cell lines and providing insight into exosome-mediated signaling of breast cancer metabolism.
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 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".