Filter selection for five color flow cytometric analysis with a single laser
Bibliographic record
Abstract
Flow cytometry has evolved from single- and two-color analysis to the current use of 11-16 colors. The relatively bright excitation spectra of most fluorochromes have made color compensation a challenge especially when performed manually. We describe how by choosing filters with narrower bandwidths results in the color compensation values between FITC, PE, PE-TxR (ECD), PE-Cy5, and PE-Cy7 that range from 0 % to 50% depending on the combination of fluorochromes. Peripheral blood mononuclear cells were stained with alpha-CD4-FITC, alpha-CD27-PE, alpha-CD62L-ECD, alpha-CD45RA-PE-Cy5 and alpha-CD3-PE-Cy7. The samples were acquired on a MO Flo. The initial (first) and second filter sets for our experiments consisted of 530/30 or 519/20 for FITC, 580/30 or 575/20for PE, 630/30 or 630/22 for PE-TxR (ECD), 670/30 or 675/20 for PE-Cy5 and 740LP or 780/40 for PE-Cy7. Nonstained cells were used to adjust the threshold values of detection for each photo multiplier tube (PMT) for each filter set. The mean fluorescent intensity (MFI) of each fluorochrome was not reduced to any great extent by either filter set. However, the compensation value between PE and PE-TxR (ECD) with the first filter selection ranged from 84% to 89% and with the second set of filters it was 25-36%. In addition, the compensation between PE-TxR (ECD) and PE-Cy5 were reduced to 30.2% from 44.2% with the second filter set. The reduction of filter bandwidths that results in minimizing spectral overlaps without lost of signal provides a method by which discrimination of signals between PE containing fluorochromes can be achieved.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".