The promising role of proteomes and metabolomes in defining the single‐cell landscapes of plants
Bibliographic record
Abstract
The plant community has a strong track record of RNA sequencing technology deployment, which combined with the recent advent of spatial platforms (e.g. 10× genomics) has resulted in an explosion of single-cell and nuclei datasets that can be positioned in an in situ context within tissues (e.g. a cell atlas). In the genomics era, application of proteomics technologies in the plant sciences has always trailed behind that of RNA sequencing technologies, largely due in part to upfront cost, ease-of-use, and access to expertise. Conversely, the use of early analytical tools for characterizing small molecules (metabolites) from plant systems predates nucleic acid sequencing and proteomics analysis, as the search for plant-based natural products has played a significant role in improving human health throughout history. As the plant sciences field now aims to fully define cell states, cell-specific regulatory networks, metabolic asymmetry and phenotypes, the measurement of proteins and metabolites at the single-cell level will be paramount. As a result of these efforts, the plant community will unlock exciting opportunities to accelerate discovery and drive toward meaningful translational outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".