Towards single cell ICP-MS normalized quantitative experiments using certified selenized yeast
Bibliographic record
Abstract
In the search for a normalized procedure to replicate and compare single cell-inductively coupled plasma-mass spectrometry (SC-ICP-MS) experiments, SELM-1, a certified reference material containing selenium enriched yeast cells has been used. Selenium concentrations (both, intra- and extracellular) have been measured using either sequential or simultaneous procedures. Regarding quantitative results, the sequential procedure involving cell washing followed by freeze drying of the washed material and intracellular Se quantification using SC-ICP-MS provided best results. In this case, intracellular Se accounted for 1304 ± 48 mg kg−1 (corresponding to 64% of the certified Se content). The average mass of Se per yeast cell was 41.6 fg Se with a dispersion of 1.6–279 fg Se/cell. In the isolated extracellular Se fraction, the Se concentration accounted for 412 ± 48 mg kg−1 (about 21% of the total Se). Thus, the sequential procedure provided a total Se recovery of about 85% with respect to the certified value. The direct dilution and simultaneous measurement of intra- and extracellular Se by SC-ICP-MS provided results of 1024 ± 42 mg kg−1 for intracellular and 316 ± 30 mg kg−1 for extracellular Se representing a total recovery of about 66%. In both cases, an initial thorough characterization of the cell density per solid weighed material was conducted by flow cytometry and the cell integrity ensured using confocal microscopy. These results clearly demonstrated that with appropriate sample preparation, SC-ICP-MS is a unique tool, which is capable of providing quantitative information about intracellular and extracellular Se. In addition, SELM-1 seems the ideal tool to enable data normalization at the single cell level to replicate, benchmark, and improve new SC-ICP-MS studies by using the same material for data validation.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".