Precise DNA Concentration Measurements with Nanopores by Controlled Counting
Bibliographic record
Abstract
Using a solid-state nanopore to measure the concentration of clinically relevant target analytes, such as proteins or specific DNA sequences, is a major goal of nanopore research. This is usually achieved by measuring the capture rate of the target analyte through the pore. However, progress is hindered by sources of systematic error that are beyond the level of control currently achievable with state-of-the-art nanofabrication techniques. In this work, we show that the capture rate process of solid-state nanopores is subject to significant sources of variability, both within individual nanopores over time and between different nanopores of nominally identical size, which are absent from theoretical electrophoretic capture models. We experimentally reveal that these fluctuations are inherent to the nanopore itself and make nanopore-based molecular concentration determination insufficiently precise to meet the standards of most applications. In this work, we present a simple method by which to reduce this variability, increasing the reliability, accuracy, and precision of single-molecule nanopore-based concentration measurements. We demonstrate controlled counting, a concentration measurement technique, which involves measuring the simultaneous capture rates of a mixture of both the target molecule and an internal calibrator of precisely known concentration. Using this method on linear DNA fragments, we show empirically that the requirements for precisely controlling the nanopore properties, including its size, height, geometry, and surface charge density or distribution, are removed while allowing for higher-precision measurements. The quantitative tools presented herein will greatly improve the utility of solid-state nanopores as sensors of target biomolecule concentration.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".