Single recipient cell tracking of tellurium-labeled extracellular vesicle proteomes (TeLEV) identifies EV-driven immunomodulation
Bibliographic record
Abstract
Abstract Extracellular vesicles (EVs) mediate tumor-immune cell communication by carrying protein cargo that can immediately modulate signaling and antigen presentation. Yet mapping the uptake of primary EV proteomes by human immune cells at single-cell resolution has been constrained by a lack of labeling strategies. We show here that TeLEV, a tellurium-based metabolic mass tagging approach that incorporates L-2-tellurienylalanine (TePhe) into EV proteomes, can produce a biologically rare monoisotopic signal, which is detectable by mass cytometry, imaging mass cytometry, and nanoscale SIMS, without perturbing EV morphology, yield, or proteome composition. We applied TeLEV to label primary malignant B-cell-derived EVs (MBC-EVs) from chronic lymphocytic leukemia (CLL) patients and could follow EV uptake by distinct cell populations of healthy donor peripheral blood mononuclear cells. MBC-EV uptake occurred predominantly in cells of myeloid lineages. In direct control experiments with matched secreted proteins, a machine learning approach identified CD123, CD127, and CD25 as key markers distinguishing primary MBC-EV recipients from matched secreted protein recipient cells. Nanoscale imaging enabled localization of EV-delivered proteins within heterochromatin, whereas Te-labeled secreted proteins accumulated in the cytoplasm of recipient cells. We then generated a pan-immune EV uptake atlas by tracing the uptake of primary and cell-line EVs from nine cell lines and six donors with chronic lymphocytic leukemia into 2,977,094 recipient cells across 43 cell types and subpopulations. We found that the uptake of MBC-EVs by myeloid recipients induced monocyte-derived dendritic-cell polarization characterized by the co-expression of the interleukin-receptor triad (IL-RT: CD123, CD127, CD25) identified above. Time-resolved EV uptake analysis showed a rapid, transient expression of CD123/CD127 followed by CD25, both tightly coupled to MBC-EV uptake by myeloid cells. The intensity of IL-RT expression correlated with that of PD-L1 and BCL-2. Using different STAT degraders to bidirectionally modify the EV-induced STAT5 signal, we observed that MBC-EV uptake and IL-RT, PD-L1, and BCL-2 expression increased with STAT3 degradation and decreased with STAT5 degradation. To investigate the functional consequences of the MBC-EV-induced changes, we showed that MBC-EVs in the presence of IL-2 induced a high-CD25 immune state with low cytotoxic and high B cell proliferation. Taken together, TeLEV represents a novel tool for single-cell tracking of EV proteomes, revealing STAT5-dependent immune remodeling of recipient cells.
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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".