Quantifying the organization and dynamics of <i>M. smegmatis</i> morphology from Long-Term Time-Lapse Atomic Force Microscopy
Bibliographic record
Abstract
Abstract The rod shaped Mycobacterium smegmatis displays complex cell surface morphology, characterized by wave-form cell surface features and driven by asymmetric growth dynamics. To systematically analyze these morphological variations, we developed a comprehensive computational pipeline for automated processing of Long-Term Time-Lapse Atomic Force Microscopy (LTTL-AFM) images of M. smegmatis cells cultured in axenic conditions of growth and stress. Upon running the pipeline to produce large enough datasets of single cell height profiles, we identify and statistically study key features that govern cell surface morphology: We confirm that M. smegmatis cells undergo bi-phasic, asymmetric pole growth with constant elongation rate at the old pole and a shift in the rate of elongation after a lag phase at the new pole. Stable wave-form cell surface peaks and troughs propagate along the long axis of the cell, which emerge as a result of polar elongation. Backtracking in time from cell division, we detect that division-site selection occurs at the wave-trough nearest mid-cell. To reproduce the fundamental cell features observed, we introduced a reaction-diffusion mathematical model on an evolving one-dimensional surface. Our simulations indicate that the dynamic manifestation of wave-form cell surface morphology in M. smegmatis can be explained by the interaction of as few as two “chemical species”, providing a plausible theoretical basis for how molecular determinants may functionally control wave-form morphology in pole-growing bacteria.
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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 | 0.001 |
| 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".