Identification of B cells through negative gating—An example of the MIFlowCyt standard applied
Bibliographic record
Abstract
Polychromatic flow cytometric analysis takes advantage of the increasing number of available fluorophores to positively identify and simultaneously assess multiple parameters in the same cell (1). Additional parameters may be analyzed through negative identification (i.e., through exclusion of particular stains or antibodies employed). In this report, we tested whether such negative-gating strategy would identify human B lymphocytes in innate immune phenotyping studies. To this end, B cells were identified as the negatively-stained subpopulation from the CD123 vs. CD11c plot of the CD14(neg-low), MHC II(high) human peripheral blood mononuclear cells. To test the specificity of this negative gating approach, we confirmed that negatively gated B cells indeed expressed CD19, the bona fide marker for human B cells. However, a small number of unidentified cells were contained in the negatively-gated B cells. Furthermore, a small percentage cells expressing markers used to identify monocytes and myeloid dendritic cells (mDC) coexpressed CD19. This identifies such negative B-cell gating approach as potentially problematic. When applied to the analysis of Toll-like receptors (TLR) stimulation experiments, we were however able to interpret the results, as B-cells respond to TLR stimulation in a qualitative different pattern as compared to monocytes and DC. This report is presented in a manner that is fully compliant with the Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard, which was recently adopted by the International Society for Advancement of Cytometry (ISAC) (2) and incorporated in the publishing policies of Cytometry and other journals. We demonstrate how a MIFlowCyt-compliant report can be prepared with minimal effort, and yet provide the reader with a much clearer picture of the portrayed FCM experiment and data.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".