High-speed cryo-microscopy reveals that ice-nucleating proteins of <i>Pseudomonas syringae</i> trigger freezing at hydrophobic interfaces
Bibliographic record
Abstract
Ice-nucleating proteins (INpro) trigger the freezing of supercooled water droplets relevant to atmospheric, biological, and technological applications. The high ice nucleation activity of INpro isolated from the bacteria Pseudomonas syringae could be linked to the aggregation of proteins at the bacterial membrane or at the air-water interface (AWI) of droplets. Here, we imaged freezing onsets, providing direct evidence of these proposed mechanisms. High-speed cryo-microscopy identified the onset location of freezing in droplets between two protein-repellent glass slides. INpro from sterilized P. syringae (Snomax) statistically favored nucleation at the AWI of the droplets. Removing cellular fragments by filtration or adding surfactants increased the frequency of nucleation events at the AWI. On the other hand, cultivated intact bacteria cells or lipid-free droplets nucleated ice without an affinity to the AWI. Overall, we provide visual evidence that INpro from P. syringae trigger freezing at hydrophobic interfaces, such as the AWI or the bacterial membrane, with important mechanistic implications for applications of INpro.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".