Chemokines as Novel and Versatile Reagents for Flow Cytometry and Cell Sorting
Bibliographic record
Abstract
Cell therapy regimens are frequently compromised by low-efficiency cell homing to therapeutic niches. Improvements in this regard would enhance effectiveness of clinically applicable cell therapy. The major regulators of tissue-specific cellular migration are chemokines, and therefore selection of therapeutic cellular populations for appropriate chemokine receptor expression would enhance tissue-homing competence. A number of practical considerations preclude the use of Abs in this context, and alternative approaches are required. In this study, we demonstrate that appropriately labeled chemokines are at least as effective in detecting their cognate receptors as commercially available Abs. We also demonstrate the utility of biotinylated chemokines as cell-sorting reagents. Specifically, we demonstrate, in the context of CCR7 (essential for lymph node homing of leukocytes), the ability of biotinylated CCL19 with magnetic bead sorting to enrich for CCR7-expressing cells. The sorted cells demonstrate improved CCR7 responsiveness and lymph node-homing capability, and the sorting is effective for both T cells and dendritic cells. Importantly, the ability of chemokines to detect CCR7, and sort for CCR7 positivity, crosses species being effective on murine and human cells. This novel approach to cell sorting is therefore inexpensive, versatile, and applicable to numerous cell therapy contexts. We propose that this represents a significant technological advance with important therapeutic implications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".