Electrical detection and actuation of single biological cells with application to deformability cytometry for markerless diagnostics
Bibliographic record
Abstract
An all-electrical system is developed to actuate and detect single biological cells in a microfluidic channel for diagnostic applications. Interdigitated electrodes fabricated on the channel floor transfer a high frequency signal for capacitance detection and a low frequency signal for dielectrophoretic actuation. In the fluid-filled channel, a pressure-driven flow propels single biological cells, which induce time-dependent capacitance signatures as they pass over the electrodes. With a sub-attofarad (~0.15 aF RMS, 53 Hz bandwidth) capacitance resolution, this system detects biological cells (e.g., 1 yeast cell ~ 50 aF) and their deflections (1 micrometer ~ 5 aF) from exerted dielectrophoretic forces (> 5 pN). Electrical detection of cell actuation by strong DEP forces provides an avenue for both inducing and monitoring the deformation of viscoelastic cells. A strong and repulsive dielectrophoretic force can be used to press a biological cell into a channel wall. When this occurs, the mechanical properties of the cell can be investigated by capacitively monitoring the cell-to-wall interaction. The nature of the resulting interaction is shown to depend on the mechanical properties of the cell (surface morphology and viscoelastic properties). Various mammalian cell types such as Chinese Hamster Ovary (CHO) cells, mouse fibroblasts, human blood cells, human breast cells and their tumorogenic phenotypes are investigated using this system. Between these populations, the effective Young's modulus varies widely from 20 Pa (neutrophils) to 1-2 GPa (polystyrene microspheres). The viability and phenotype of a biological cell are known to reflect its mechanical and electrical properties. Consequently, this work investigates whether dielectrophoretically induced cell deformations are correlated with corresponding variations in capacitance, which could be used for discriminating cell phenotypes in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".