The Protein Interactome of a Nanoparticle Population in Whole Cytoplasm under Near‐Native Conditions: A Pilot Study
Bibliographic record
Abstract
Abstract The surface interactions of nanoparticles (NPs) with proteins are intensively studied because they partly determine the biological fate of these reagents. The protein interaction trajectory of NPs delivered from complex biofluids into the cytosol of vertebrate cells however remains poorly characterized. This topology is explored using isolated whole cytoplasm and carboxylated superparamagnetic iron oxide NPs (SPIONs). SPIONs are introduced into the cytoplasm in naked form or with an adsorbed cargo of serum proteins as an example of a complex NP “corona.” By filtering interactome datasets with information about protein localization and absolute cytosolic concentration, the soluble cytoplasmic proteins that contribute to corona formation in the cytoplasm are identified. These include a subset of glycolytic enzymes and proteins in soluble ribosomes. In the cytoplasm, the proteins of the serum corona are quickly removed from SPIONs by a non‐proteolytic mechanism. Although naked SPIONs and SPIONs with a serum corona capture the same 65 cytosolic proteins, their overall interaction trajectory in the cytoplasm is not identical. In particular, they diverge in engagement with macroscale cell parts such as mitochondria. This knowledge of the interaction landscape of NPs in the cytoplasm is expected to support hypothesis development in projects aimed at improving NP reagents for biomedical applications.
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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".