Simultaneous Detection of Protein and mRNA in Jurkat and KG‐1a Cells by Mass Cytometry
Bibliographic record
Abstract
Mass cytometry uniquely enables high-dimensional single-cell analysis of complex populations. This recently developed technology is based on inductively coupled time-of-flight mass spectrometry for multiplex proteomic analysis of more than 40 markers per cell. The ability to characterize the transcriptome is critical for the understanding of disease pathophysiology, medical diagnostics, and drug discovery. Current techniques allowing the in situ detection of transcripts in single cells are limited to a small number of simultaneous targets and are generally tedious and labor-intensive. In this report, we present the development of a multiplex method for targeted RNA detection by combining the mass cytometry and RNAscope® platforms. This novel assay, called Metal In Situ Hybridization (MISH), includes the hybridization of RNA-specific target probes followed by signal amplification achieved through a cascade of hybridization events, ending with the binding of amplifier-specific detector probes. The detector probes are tagged with isotopically pure metal atoms used for detection by mass cytometry. Proof-of-principle experiments show the simultaneous detection of three mRNA targets in Jurkat cells in suspension cell assay mode. The localization of transcripts was also investigated using the imaging mass cytometry platform in Jurkat and KG-1a cells. In addition, we optimized the antibody staining procedure to allow the co-detection of mRNA and cell surface markers. Our data demonstrate that MISH can be used to complement protein detection by mass cytometry as well as to investigate gene transcription and translation in single cells. © 2017 International Society for Advancement of Cytometry.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".