Bacterial Networks on Hydrophobic Micropillars
Bibliographic record
Abstract
Bacteria have evolved as intelligent microorganisms that can colonize and form highly structured and cooperative multicellular communities with sophisticated singular and collective behaviors. The initial stages of colony formation and intercellular communication are particularly important to understand and depend highly on the spatial organization of cells. Controlling the distribution and growth of bacterial cells at the nanoscale is, therefore, of great interest in understanding the mechanisms of cell-cell communication at the initial stages of colony formation. Staphyloccocus aureus, a ubiquitous human pathogen, is of specific clinical importance due to the rise of antibiotic resistant strains of this species, which can cause life-threatening infections. Although several methods have attempted to pattern bacterial cells onto solid surfaces at single cell resolution, no study has truly controlled the 3D architectures of growing colonies. Herein, we present a simple, low-cost method to pattern S. aureus bacterial colonies and control the architecture of their growth. Using the wetting properties of micropatterened poly(dimethyl siloxane) platforms, with help from the physiological activities of the S. aureus cells, we fabricated connected networks of bacterial microcolonies of various sizes. Unlike conventional heterogeneous growth of biofilms on surfaces, the patterned S. aureus microcolonies in this work grow radially from nanostrings of a few bacterial cells, to form micrometer-thick rods when provided with a nutrient rich environment. This simple, efficient, and low-cost method can be used as a platform for studies of cell-cell communication phenomena, such as quorum sensing, horizontal gene transfer, and metabolic cross-feeding especially during initial stages of colony formation.
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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".