Flow cytometer with mass spectrometer detection for massively multiplexed single-cell biomarker assay
Bibliographic record
Abstract
Abstract This paper describes the development and application of new metal-tagging reagents and a novel mass spectrometer (MS) detector for a flow cytometer that enables highly multiplexed measurement of many biomarkers in individual cells. A new class of tagging reagents, based on an acrylic polymer backbone that incorporates a reproducible number of lanthanide elements, has been developed. When linked to antibodies that specifically recognize target proteins of interest, determination of the tag elements is diagnostic for the presence and quantification of the antigen. The use of enriched stable isotope tags provides the opportunity for multiparametric assay. The new instrument uses inductively coupled plasma (ICP) to vaporize, atomize, and ionize individual cells that have been probed using the metal-labeled antibodies. The elemental composition, specifically of the metal tags, is recorded simultaneously using a time-of-flight (TOF)-MS that has been specifically designed for high-speed analysis during the short transient corresponding to the individual cell event. The detector provides for well-resolved atomic fingerprints of many elemental and isotopic tags, with little overlap of neighboring signals (high abundance sensitivity) and wide dynamic range both for a single antigen and between antigens.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".