CyTOF: An Emerging Technology for Single‐Cell Proteomics in the Mouse
Bibliographic record
Abstract
The ability to analyze the proteome of single cells is critical for the advancement of studies of steady-state and pathological processes. Mass cytometry, or CyTOF, combines principles of mass spectrometry and flow cytometry to enable single-cell analysis of protein expression. CyTOF can simultaneously assess DNA content and proteins and has the capacity to measure 40 to 100 parameters in each cell. Applying this technology to tissues or cells on slides, termed imaging mass cytometry (IMC), allows for visualization of normal and diseased tissues in situ. The high-dimensional proteomic analysis that can be undertaken with CyTOF and IMC has the potential to enhance our understanding of complex and heterogeneous developmental and disease pathways. This article will describe the CyTOF experimental workflow, including reagent selection, sample preparation, and data analysis. CyTOF is compared to flow cytometry, focusing on the strengths and weaknesses of these powerful techniques. Importantly, we review key studies in mouse models of human disease that highlight the strength of CyTOF and IMC to drive discovery research and therapeutic advancement. © 2021 Wiley Periodicals LLC.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".