Faculty Opinions recommendation of Proteomic mapping of mitochondria in living cells via spatially restricted enzymatic tagging.
Bibliographic record
Abstract
Microscopy and mass spectrometry (MS) are complementary techniques: the former provides spatiotemporal information in living cells, but only for a handful of recombinant proteins, while the latter can detect thousands of endogenous proteins simultaneously, but only in lysed samples.Here we introduce technology that combines these strengths by offering spatially-and temporallyresolved proteomic maps of endogenous proteins within living cells.The method relies on a genetically-targetable peroxidase enzyme that biotinylates nearby proteins, which are subsequently purified and identified by MS.We used this approach to identify 495 proteins within the human mitochondrial matrix, including 31 not previously linked to mitochondria.The labeling was exceptionally specific and distinguished between inner membrane proteins facing the matrix versus the intermembrane space (IMS).Several proteins previously thought to reside in the IMS or outer membrane, including protoporphyrinogen oxidase, were reassigned to the matrix.The specificity of live-cell peroxidase-mediated proteomic mapping combined with its ease of use offers biologists a powerful tool for understanding the molecular composition of living cells.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.081 |
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".