A photoacoustic technique to measure the properties of single cells
Bibliographic record
Abstract
We demonstrate a new technique to non-invasively determine the diameter and sound speed of single cells using a combined ultrasonic and photoacoustic technique. Two cell lines, B16-F1 melanoma cells and MCF7 breast cancer cells were examined using this technique. Using a 200 MHz transducer, the ultrasound backscatter from a single cell in suspension was recorded. Immediately following, the cell was irradiated with a 532 nm laser and the resulting photoacoustic wave recorded by the same transducer. The melanoma cells contain optically absorbing melanin particles, which facilitated photoacoustic wave generation. MCF7 cells have negligible optical absorption at 532 nm; the cells were permeabilized and stained with trypan blue prior to measurements. The measured ultrasound and photoacoustic power spectra were compared to theoretical equations with the cell diameter and sound speed as variables (Anderson scattering model for ultrasound, and a thermoelastic expansion model for photoacoustics). The diameter and sound speed were extracted from the models where the spectral shape matched the measured signals. However the photoacoustic spectrum for the melanoma cell did not match theory, which is likely because melanin particles are located around the cytoplasm, and not within the nucleus. Therefore a photoacoustic finite element model of a cell was developed where the central region was not used to generate a photoacoustic wave. The resulting power spectrum was in better agreement with the measured signal than the thermoelastic expansion model. The MCF7 cell diameter obtained using the spectral matching method was 17.5 μm, similar to the optical measurement of 16 μm, while the melanoma cell diameter obtained was 22 μm, similar to the optical measurement of 21 μm. The sound speed measured from the MCF7 and melanoma cell was 1573 and 1560 m/s, respectively, which is within acceptable values that have been published in literature.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".