Mass Cytometry: Protocol for Daily Tuning and Running Cell Samples on a CyTOF Mass Cytometer
Bibliographic record
Abstract
In recent years, the rapid analysis of single cells has commonly been performed using flow cytometry and fluorescently-labeled antibodies. However, the issue of spectral overlap of fluorophore emissions has limited the number of simultaneous probes. In contrast, the new CyTOF mass cytometer by DVS Sciences couples a liquid single-cell introduction system to an ICP-MS. Rather than fluorophores, chelating polymers containing highly-enriched metal isotopes are coupled to antibodies or other specific probes. Because of the metal purity and mass resolution of the mass cytometer, there is no "spectral overlap" from neighboring isotopes, and therefore no need for compensation matrices. Additionally, due to the use of lanthanide metals, there is no biological background and therefore no equivalent of autofluorescence. With a mass window spanning atomic mass 103-203, theoretically up to 100 labels could be distinguished simultaneously. Currently, more than 35 channels are available using the chelating reagents available from DVS Sciences, allowing unprecedented dissection of the immunological profile of samples. Disadvantages to mass cytometry include the strict requirement for a separate metal isotope per probe (no equivalent of forward or side scatter), and the fact that it is a destructive technique (no possibility of sorting recovery). The current configuration of the mass cytometer also has a cell transmission rate of only ~25%, thus requiring a higher input number of cells. Optimal daily performance of the mass cytometer requires several steps. The basic goal of the optimization is to maximize the measured signal intensity of the desired metal isotopes (M) while minimizing the formation of oxides (M+16) that will decrease the M signal intensity and interfere with any desired signal at M+16. The first step is to warm up the machine so a hot, stable ICP plasma has been established. Second, the settings for current and make-up gas flow rate must be optimized on a daily basis. During sample collection, the maximum cell event rate is limited by detector efficiency and processing speed to 1000 cells/sec. However, depending on the sample quality, a slower cell event rate (300-500 cells/sec) is usually desirable to allow better resolution between cells events and thus maximize intact singlets over doublets and debris. Finally, adequate cleaning of the machine at the end of the day helps minimize background signal due to free metal.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.047 |
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".