Cryopreserved whole blood for the quantification of monocyte, T‐cell and NK‐cell subsets, and monocyte receptor expression by multi‐color flow cytometry: A methodological study based on participants from the canadian longitudinal study on aging
Bibliographic record
Abstract
Immunophenotyping by multi-color flow cytometry is arguably the best tool to identify and quantify distinct cell lineages from the peripheral blood and other biological fluids/tissues. Effective in both clinical and research settings, it can be used to estimate the frequency of a given cell type or measure its phenotypic or functional properties. Normally, immunophenotyping is performed in fresh or fractionated blood (i.e., PBMCs) the same day, or within 24 hours of collection; however, this may not be feasible for all study designs. We have previously shown that cryopreserved blood, a biospecimen that is simple and inexpensive to prepare, is comparable to fresh blood for the enumeration of major leukocyte cell types. For the following study, we sought to extend these observations to distinct subsets of: monocytes (classical, intermediate, and non-classical), T-cells (CD4/CD8 naïve, central and effector memory, senescent, and terminally differentiated, and regulatory T-cells), and NK-cells (CD56 bright and dim); we also examined the expression of monocyte cell-surface receptors CX3CR1, CCR2, TLR2, and TLR4. Our results indicate that cryopreserved blood is comparable to fresh blood; with exception to relatively rare subsets and lowly expressed receptors, the absolute or relative frequency of cell subsets generally correlated >0.80 between blood types, while monocyte receptor expressed was mostly >0.70. Furthermore, the day-to-day coefficient of variation for most cell subsets and parameters was below 20%. Given these findings, we suggest that cryopreserved peripheral blood be given greater consideration for studies in which the quantification of distinct leukocyte subsets is required. © 2018 International Society for Advancement of Cytometry.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".