Digital holography-based 3D particle localisation for single molecule tweezer techniques
Bibliographic record
Abstract
ABSTRACT We present a three-dimensional imaging technique for fast tracking of microscopic objects in a fluid environment. Our technique couples digital holographic microscopy with three-dimensional localisation via parabolic masking. Compared with existing approaches, our method reconstructs 3D volumes from single-plane images, which greatly simplifies image acquisition, reduces the demand on microscope hardware, and facilitates tracking higher densities of microscopic particles while maintaining similar levels of precision. We demonstrate utility of this method in magnetic tweezer experiments, opening their use to multiplexed single-molecule force spectroscopy assays. We propose that our technique will also be useful in other applications that involve the tracking of microscopic objects in three dimensions. SIGNIFICANCE Tracking objects in 3D is a common task in biology, but typically requires the acquisition of image stacks, which is limited by speed, the depth of field of microscope objectives and by the presence of other objects that obscure the illumination. Here we develop HoloMiP ( Holog raphic Mi croscopy with P arabolic masking), which uses digital holography to reconstruct the three-dimensional images from a single plane allowing tracking of light-scattering objects in 3D. HoloMiP outperforms existing methods in precision, speed, simplicity and tolerance to crowding. We show that it is particularly suitable for fast, multiplexed magnetic tweezer experiments, opening new avenues to high-throughput force spectroscopy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".