Advancements in DNA-PAINT: applications and challenges in biological imaging and nanoscale metrology
Bibliographic record
Abstract
Super-Resolution Microscopy (SRM) has revolutionized bioimaging by breaking the diffraction limit of light, enabling visualization of structures at the nanometer scale. DNA-PAINT (Point Accumulation for Imaging in Nanoscale Topography) is a versatile SRM technique that leverages the programmability of DNA hybridization to achieve high-resolution and multiplexed imaging of molecular targets. This review examines recent advancements in DNA-PAINT, including improvements in imaging resolution, acquisition speed, and imager design, which have enhanced its applications in biological imaging and nanoscale metrology. DNA-PAINT's unique capacities in programming specific interactions have made it indispensable in a range of biological and non-biological settings, from cellular visualization of structure and function to molecular data storage. Here, we highlight recent advancements in DNA-PAINT and its main practical challenges, focusing on how persistent optimization drives innovation. Addressing these challenges continues to drive its expanding role in biological imaging and broader applications across interdisciplinary fields. This review also highlights the interdependence of DNA-PAINT and other techniques that are fundamental to broadening the impact of SRM and shaping the future of biological and biomedical imaging.
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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) | 0.001 | 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".