Design Parameters for a Mass Cytometry Detectable HaloTag Ligand
Bibliographic record
Abstract
Mass cytometry permits the high dimensional analysis of complex biological samples; however, some techniques are not yet integrated into the mass cytometry workflow due to reagent availability. The use of self-labeling protein systems, such as HaloTag, are one such application. Here, we describe the design and implementation of the first mass cytometry ligands for use with HaloTag. “Click”-amenable HaloTag warheads were first conjugated onto poly( l -lysine) or poly(acrylic acid) polymers that were then functionalized with diethylenetriaminepentaacetic acid (DTPA) lutetium metal chelates. Kinetic analysis of the HaloTag labeling rates demonstrated that the structure appended to the 1-chlorohexyl warhead was key to success. A construct with a diethylene glycol spacer appended to a benzamide gave similar rates ( k obs ∼ 10 2 M –1 s –1 ), regardless of the nature of the polymer. Comparison of the polymer with a small molecule chelate having rapid HaloTag labeling kinetics ( k obs ∼ 10 4 M –1 s –1 ) suggests the polymers significantly reduced the HaloTag labeling rate. HEK293T cells expressing surface-exposed GFP-HaloTag fusions were labeled with the polymeric constructs and 175 Lu content measured by cytometry by time-of-flight (CyTOF). Robust labeling was observed; however, significant nonspecific binding of the constructs to cells was also present. Heavily pegylated polymers demonstrated that nonspecific binding could be reduced to allow cells bearing the HaloTag protein to be distinguished from nonexpressing cells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".