Quantification of Au Nanoparticle Biouptake and Distribution to Freshwater Algae Using Single Cell – ICP-MS
Bibliographic record
Abstract
Quantifying metal and nanoparticle (NP) biouptake and distribution on an individual cellular basis has previously been impossible, given available techniques which provide qualitative data that are laborious to acquire and prone to artifacts. Quantifying metal and metal NP uptake and loss processes in environmental organisms will lead to mechanistic understanding of biouptake and improved understanding of potential hazards and risks of metals and NPs. In this work, we present a new technique, single cell inductively coupled plasma mass spectrometry (SC-ICP-MS), which allows quantification of metal concentrations on an individual cell basis down to the attogram (ag) per cell level. We present data validating the novel method, along with the mass of metal per cell. Finally, we use SC-ICP-MS, with ancillary cell counting methods, to quantify the biouptake and strong sorption and distribution of both dissolved Au and Au NPs in a freshwater alga (Cyptomonas ovate). The data suggests differences between dissolved and NP uptake and loss. In the case of NPs, there was a dose and time dependent uptake, but individual cellular variations; at the highest realistic exposure conditions used in this study up to 40-50% of cells contained NPs, while 50-60% of cells did not.
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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 | 0.000 |
| 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) | 0.001 | 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 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".