Accelerating biophysical studies and applications by label-free nanopore sensing
Bibliographic record
Abstract
Label-free single-molecule sensing technologies are attractive tools for investigating the properties of biological molecules via the understanding of molecular functionality.Among these technologies, nanopore sensing has become one of the growing technologies [1,2].Nanopore sensing operates in the principle of resistive pulse sensing, where sensing molecules, such as DNA, RNA, and protein, pass through a pore under the electrical field, resulting in a blockade current due to the molecular occupation in a pore.The physical properties of sensing entities were obtained by analyzing blockade current, which can provide a fingerprint of sensing molecules (Figure 1) [3].In this commentary article, we review the eight presentations at the symposium "Innovative label-free nanopore sensing toward biophysical studies and applications" of the 60th Annual Meeting of the Biophysical Society of Japan held in September 2022 and introduce how this sensing technology can be used as a tool to open new biophysical science or applications other than DNA sequencing.Kyle Briggs at Ottawa University/ Northern Nanopore Instruments talked about an automated method of electricalbased nanopore fabrication, which is one of the gold standard fabrication methods in the lab, and introduced how to accelerate solid-state nanopore research using this method [4,5].He also presented the automated muti-pore fabrication tools having multi-channels fluidic flow cells with multi-membrane chips.Finally, he showed the nanopore trace analysis software, Nanolyzer, which has multiple functions such as multi-level blockade current fitting, overlay translocation events, kernel density estimation, etc.Kan Shoji at Nagaoka University of Technology presented a probe-type planer bilayer lipid membrane (pBLM) system [6,7] and its application for scanning ion conductance microscopy (SICM) [8].In this system, pBLMs can be repeatedly formed at the tip of probes by inserting probes into a layered bath solution of an oil/lipid mixture and electrolyte.He mounted the probe into a SICM setup and demonstrated spatially-resolved chemical sensing by manipulating the probe.Additionally, he introduced an efficient current measurement system for synthetic DNA nanopores.Although DNA nanopore structures are expected to be applied for nanopore sensing, it is challenging to efficiently insert DNA nanopores into pBLMs.He prepared DNA nanopore-tethered gold electrodes and formed pBLMs on the surface of electrodes by inserting electrodes into the bath solution.Resultantly, efficient insertions of DNA nanopores were observed, and this method potentially accelerates applications of DNA nanopores for nanopore sensing.Figure 1 The fundamental working principle of nanopore sensing
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".