Microfluidics-Based Bioassays and Imaging of Plant Cells
Bibliographic record
Abstract
Many plant processes occur in the context of and in interaction with a surrounding matrix such as soil (e.g. root growth and root-microbe interactions) or surrounding tissues (e.g. pollen tube growth through the pistil), making it difficult to study them with high-resolution optical microscopy. Over the past decade, microfabrication techniques have been developed to produce experimental systems that allow researchers to examine cell behavior in microstructured environments that mimic geometrical, physical and/or chemical aspects of the natural growth matrices and that cannot be generated using traditional agar plate assays. These microfabricated environments offer considerable design flexibility as well as the transparency required for high-resolution, light-based microscopy. In addition, microfluidic platforms have been used for various types of bioassays, including cellular force assays, chemoattraction assays and electrotropism assays. Here, we review the recent use of microfluidic devices to study plant cells and organs, including plant roots, root hairs, moss protonemata and pollen tubes. The increasing adoption of microfabrication techniques by the plant science community may transform our approaches to investigating how individual plant cells sense and respond to changes in the physical and chemical environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".