Extending the capabilities of a high-parameter immunophenotyping assay with cytoplasmic staining applications for mass cytometry
Bibliographic record
Abstract
Abstract Maxpar® Direct™ Immune Profiling Assay™ (Cat. No. 201325) is a 30-marker panel for suspension mass cytometry. This panel provides an unprecedented sample-to-answer solution for detecting and analyzing 30 surface markers in a single experiment. The 18 open mass channels in the Maxpar Direct Assay facilitate panel expansion and enable flexibility for higher multiplexity and applications. Among the potential complementary applications, intracellular cytokine staining (ICS) is of particular interest as it may be used to assess infiltrating immune cell phenotypes in the tumor microenvironment. However, for the purpose of assessing cell viability in this workflow, the effectiveness of the Cell-ID™ Intercalator-Rh (103Rh, Cat. No. 201103) included in the Maxpar Direct Assay is in question, as cell permeabilization during ICS can potentially damage the DNA-intercalator bond. In this study, we investigated the compatibility of 103Rh with intracellular staining. We stained either human peripheral blood mononuclear cell or whole blood samples with the Maxpar Direct Assay followed by intracellular staining for the detection of expressed cytokines. We demonstrate that 103Rh provides equivalent functionality as a cell viability indicator during intracellular staining for cytoplasmic proteins compared to the benchmark Cell-ID Cisplatin-194Pt (Cat. No. 201194). This work was designed to support use of the Maxpar Direct Immune Profiling Assay in combination with additional intracellular markers. Overall, these findings expand the applicability of Cell-ID Intercalator-Rh (103Rh) to processes that involve cytoplasmic staining. For Research Use Only. Not for use in diagnostic procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".