Multicolor Immunophenotyping using Flow Cytometry: Evaluation of Multiple Methods for Instrument Optimization
Bibliographic record
Abstract
Abstract A basic tool of the Immunologist, multicolor flow cytometry is a technology that uses multiple fluorescent markers to identify and characterize populations of cells. To obtain high quality fluorescent flow cytometry data, a well-optimized instrument is required. The most common type of detector is the photo-multiplier tube (PMT), which amplifies signal from emitted light photons by applying a voltage to the PMT. As the voltage is increased the fluorescent signal is increasingly separated from background, providing greater resolution of the positive signal. However, at a certain voltage, the increasing separation of fluorescent signal from background will plateau and the separation of fluorescent signal from background will remain constant. This is called the minimum voltage required; an ideal minimal voltage will amplify dim signal above background, but is not so high that the fluorescent signal exceeds the upper range of PMT linearity. In most cases, adjusting voltages above the minimal voltage does not increase the separation, while voltages below the minimal voltage requirement compromise detection of dim fluorescent signal. A variety of methods have been proposed to optimize the PMT voltage, most using a technique called the voltage walk, or voltration. This study compares a variety of techniques and calculations, using different types of hard-dyed beads that are detected in all channels but do not include specific fluorophores used for typical experimentation; both cells and antibody-capture beads labeled with fluorophores specific to a detector; Electronic Noise (EN) of the PMT, and methods combining these approaches. For Research Use Only. Not for use in diagnostic procedures.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".