Single‐Cell Force Spectroscopy Uncovers Root Zone‐ and Bacteria‐Specific Interactions
Bibliographic record
Abstract
Understanding root-bacteria interactions with plant growth-promoting rhizobacteria (PGPR) is key to developing effective biofertilizers for sustainable agriculture. We performed single-cell force spectroscopy using the atomic force microscope (AFM) to study the primary attachment of two PGPR, Bacillus velezensis and Pseudomonas defensor, to different regions of Arabidopsis thaliana roots. Force measurements with individual cells uncovered distinct attachment strategies by each strain, involving binding via micrometer-long polymers from both bacteria and root surfaces. Flagella differentially affected the binding interactions of each PGPR; their removal altered binding characteristics differently for each strain, highlighting the importance of flagella in early root colonization. Using silica beads to mimic the negatively charged bacteria, we demonstrated the influence of electrostatic forces on root-bacteria interactions. We also examined interactions with abiotic surfaces of varying surface energies, revealing the roles of hydrophilic and hydrophobic forces in initial binding. Our measurements show that differences in the physicochemical properties of bacteria and roots are responsible for variations in primary attachment strategies between PGPR strains and root regions. Parallel fluorescence measurements corroborated our AFM single-cell analysis. Overall, our results provide a nanoscale view of bacterial attachment to roots, offering key insights into how beneficial bacteria colonize roots, crucial for enhancing biofertilizer effectiveness.
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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 | 0.001 | 0.001 |
| 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".