Uncovering common transcriptional features shared by mature peripheral blood PMN-MDSCs and tumor-infiltrating neutrophils in humans
Bibliographic record
Abstract
Human polymorphonuclear-myeloid-derived suppressor cells (PMN-MDSCs) consist of circulating low-density neutrophils (LDNs) characterized by the ability to inhibit T-cell responses. In previous studies, we demonstrated that the mature fraction of PMN-MDSCs (i.e. mPMN-MDSCs) exerts more potent immunosuppressive functions than its immature counterpart. More recently, we defined a specific gene signature of mPMN-MDSCs from cancer patients and G-CSF-treated donors (GDs) by bulk RNA sequencing (RNA-seq) experiments. In this study, by performing single-cell RNA-seq (scRNA-seq) experiments of circulating mPMN-MDSCs from non-small cell lung cancer (NSCLC) patients, we identified a major scRNA-seq cell cluster (arbitrarily named NSCLC c6) specifically displaying immunosuppressive and protumor transcriptomic features. Then, by analyzing publicly available scRNA-seq datasets from human tumor-associated neutrophils (TANs), we uncovered three TAN clusters (arbitrarily named TAN c6-c8) that were found to share with NSCLC c6 several common genes and transcription factor (TF) regulons associated with response to hypoxia, positive regulation of angiogenesis, and metabolic reprogramming. Furthermore, by performing scRNA-seq experiments of GD mPMN-MDSCs, we uncovered four scRNA-seq cell clusters (arbitrarily named GD c4-c7) that were enriched for the same genes and pathways characterizing NSCLC c6 and TAN c6-c8 cells. Altogether, these data uncover that human circulating mPMN-MDSCs and TANs from different cancer types share scRNA-seq cell clusters with transcriptomic similarities, supporting the notion that they might be strictly related.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".