Microfluidic micropipette aspiration for measuring the deformability of single cells
Bibliographic record
Abstract
We present a microfluidic technique for measuring the deformability of single cells using the pressure required to deform such cells through micrometre-scale tapered constrictions. Our technique is equivalent to whole-cell micropipette aspiration, but involves considerably simpler operation, less specialized equipment, and less technical skill. Single cells are infused into a microfluidic channel, and then deformed through a series of funnel-shaped constrictions. The constriction openings are sized to create a temporary seal with each cell as it passes through the constriction, replicating the interaction with the orifice of a micropipette. Precisely controlled deformation pressures are generated using an external source and then attenuated 100 : 1 using an on-chip microfluidic circuit. Our apparatus is capable of generating precisely controlled pressures as small as 0.3 Pa in a closed microchannel network, which is impervious to evaporative losses that normally limit the precision of such equipment. Intrinsic cell deformability, expressed as cortical tension, is determined from the threshold deformation pressure using the liquid-drop model. We measured the deformability of several types of nucleated cells and determined the optimal range of constriction openings. The cortical tension of passive human neutrophils was measured to be 37.0 ± 4.8 pN μm(-1), which is consistent with previous micropipette aspiration studies. The cortical tensions of human lymphocytes, RT4 human bladder cancer cells, and L1210 mouse lymphoma cells were measured to be 74.7 ± 9.8, 185.4 ± 25.3, and 235.4 ± 31.0 pN μm(-1) respectively. The precision and usability of our technique demonstrates its potential as a biomechanical assay for wide-spread use in biological and clinical laboratories.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".