Simplified quantitation of myeloid dendritic cells in peripheral blood using flow cytometry
Bibliographic record
Abstract
BACKGROUND: Recognition of the importance of dendritic cells (DC) in the initiation of T-cell-dependent immune responses has led to increasing interest in methods for the identification of DC within the circulation. We sought to develop a flow cytometric method that would allow the reliable enumeration of absolute myeloid DC counts in minimally manipulated blood samples. METHODS: Myeloid DC were identified by three-color staining of whole blood leukocytes as a discrete population of mononuclear cells expressing high levels of HLA-DR and CD33, yet having little or no expression of CD14 and CD16. This method was analyzed for reproducibility and variation in blood DC number during typical clinical day hours and after exercise. The new method was compared to an established commercial kit method. RESULTS: FACS sorting of the CD33(+) DC showed that they morphologically resembled immature DC, and developed cytoplasmic projections typical of mature DC following overnight culture in granulocyte macrophage-colony stimulating factor (GM-CSF). Within peripheral blood, these DC were found at a mean concentration of 17. 4 +/- 5.4 x 10(6) per liter, corresponding to 0.93 +/- 0.27% of mononuclear cells. Comparison of duplicate samples stained and analyzed in parallel showed that the intrasample variability was very low, with an intraclass correlation coefficient of 0.95. The frequency of CD33(+) myeloid DC and their light scatter characteristics were similar to that of CD11c(+) myeloid cells. Four-color FACS analysis revealed complete identity of CD11c(hi), HLA-DR(+) DC with CD33(+), HLA-DR(+) DC. Only rare CD33(+) DC coexpressed CD123 and HLA-DR. Numbers of blood myeloid DC, identified by CD33 staining, showed no significant variation during standard laboratory hours. However, their numbers rose significantly during vigorous exercise, in parallel to other blood cells. CONCLUSIONS: The method described herein is rapid, reproducible, requires only small volumes of blood, can be readily used by a clinical immunology laboratory, and requires fewer antibodies than a currently available commercial method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".