Extracellular Vesicles Define Discrete Nano‐Based Niches Within the Human Haematopoietic System
Bibliographic record
Abstract
Stem cell niches are complex multi-signalling networks comprised of molecular cues and physical interactions, orchestrated by niche-resident cells and the extracellular factors they produce. The bone niche specifically houses haematopoietic stem cells (HSCs), a critical cell type responsible for producing all blood and immune cells throughout life. Currently, how niches facilitate an ideal environment with simultaneously coordinating both intrinsic and extrinsic cellular signals is unknown. Studies presented here identify the existence of unique extracellular vesicle (EV)-defined niches within the haematopoietic system of human individuals. Bridging studies using proteomic signatures, nanoparticle characterization at single-vesicle resolution and machine learning-based techniques reveal that EVs can be grouped by blood, bone marrow and trabeculae within a human individual. Stem cell assays demonstrate that these niche-defined EVs impart functional effects on stem cells/progenitors based on location within the haematopoietic system. Finally, using single-cell transcriptomic analyses, results identify for the first time how niche-sourced EVs differentially affect the most primitive human HSCs and progenitors. This study highlights the significance of nanoparticles on human immunity and blood production and provides evidence for a new role for EVs, namely the demarcation of distinct nano-niches within biological systems.
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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 | 0.001 | 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".