A microfluidic platform for profiling biomechanical properties of bacteria
Bibliographic record
Abstract
The ability to resist mechanical forces is necessary for the survival and division of bacteria and has traditionally been probed using specialized, low-throughput techniques such as atomic force microscopy and optical tweezers. Here we demonstrate a microfluidic technique to profile the stiffness of individual bacteria and populations of bacteria. The approach is similar to micropipette aspiration used to characterize the biomechanical performance of eukaryotic cells. However, the small size and greater stiffness of bacteria relative to eukaryotic cells prevents the use of micropipettes. Here we present devices with sub-micron features capable of applying loads to bacteria in a controlled fashion. Inside the device, individual bacteria are flowed and trapped in tapered channels. Less stiff bacteria undergo greater deformation and therefore travel further into the tapered channel. Hence, the distance traversed by bacteria into a tapered channel is inversely related to cell stiffness. We demonstrate the ability of the device to characterize hundreds of bacteria at a time, measuring stiffness at 12 different applied loads at a time. The device is shown to differentiate between two bacterial species, E. coli (less stiff) and B. subtilis (more stiff), and detect differences between E. coli submitted to antibiotic treatment from untreated cells of the same species/strain. The microfluidic device is advantageous in that it requires only minimal sample preparation, no permanent cell immobilization, no staining/labeling and maintains cell viability. Our device adds detection of biomechanical phenotypes of bacteria to the list of other bacterial phenotypes currently detectable using microchip-based methods and suggests the feasibility of separating/selecting bacteria based on differences in cell stiffness.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".