Development of a metal-based detection method for simultaneous protein and gene expression analysis in single cells by mass cytometry
Bibliographic record
Abstract
Abstract The Fluidigm® CyTOF system is a mass cytometer that uniquely enables high-dimensional single-cell analysis of complex populations. Mass cytometry is based on inductively coupled plasma time-of-flight mass spectrometry used for multiplex proteomic analysis. In this approach, metal-conjugated affinity reagents are used to tag the components of cells. The cells are nebulized and sent to an argon plasma, ionizing the multi-atom metal tags, which are then analyzed by a time-of-flight mass spectrometer. Gene expression can be finely tuned through the synthesis of RNA and through the control of its stability and location. Misregulation of gene expression can have severe consequences, such as developmental disorders, cancer and autoimmune diseases. The ability to acquire complete spatial-temporal profiles of gene expression is therefore critical for the understanding of disease pathophysiology, medical diagnostics and drug discovery. In this presentation, we discuss the development of a multiplex method for targeted RNA detection using the Fluidigm CyTOF® and Advanced Cell Diagnostics RNAscope® platforms. This novel assay includes the hybridization of RNA-specific target probes, followed by signal amplification and ending with the binding of amplifier-specific metal-labelled probes. Presently, we are able to detect four different mRNA probes in a single-cell analysis setting. The detection of RNA is compatible with Fluidigm products such as Maxpar® antibodies. Future developments will include high-dimensional detection that enables researchers to investigate the simultaneous expression profile of RNA and protein across millions of 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".