Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 506

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Folding-Based Electrochemical Biosensors: The Case for Responsive Nucleic Acid Architectures

Open access
Lubin, Arica A., Plaxco, Kevin W.·2010·Accounts of Chemical Research

Abstract

Biomolecular recognition is versatile, specific, and high affinity, qualities that have motivated decades of research aimed at adapting biomolecules into a general platform for molecular sensing. Despite significant effort, however, so-called "biosensors" have almost entirely failed to achieve their potential as reagentless, real-time analytical devices; the only quantitative, reagentless biosensor to achieve commercial success so far is the home glucose monitor, employed by millions of diabetics. The fundamental stumbling block that has precluded more widespread success of biosensors is the failure of most biomolecules to produce an easily measured signal upon target binding. Antibodies, for example, do not change their shape or dynamics when they bind their recognition partners, nor do they emit light or electrons upon binding. It has thus proven difficult to transduce biomolecular binding events into a measurable output signal, particularly one that is not readily spoofed by the binding of any of the many potentially interfering species in typical biological samples. Analytical approaches based on biomolecular recognition are therefore mostly cumbersome, multistep processes relying on analyte separation and isolation (such as Western blots, ELISA, and other immunochemical methods); these techniques have proven enormously useful, but are limited almost exclusively to laboratory settings. In this Account, we describe how we have refined a potentially general solution to the problem of signal detection in biosensors, one that is based on the binding-induced "folding" of electrode-bound DNA probes. That is, we have developed a broad new class of biosensors that employ electrochemistry to monitor binding-induced changes in the rigidity of a redox-tagged probe DNA that has been site-specifically attached to an interrogating electrode. These folding-based sensors, which have been generalized to a wide range of specific protein, nucleic acid, and small-molecule targets, are rapid (responding in seconds to minutes), sensitive (detecting sub-picomolar to micromolar concentrations), and reagentless. They are also greater than 99% reusable, are supported on micrometer-scale electrodes, and are readily fabricated into densely packed sensor arrays. Finally, and critically, their signaling is linked to a binding-specific change in the physics of the probe DNA, and not simply to adsorption of the target onto the sensor head. Accordingly, they are selective enough to be employed directly in blood, crude soil extracts, cell lysates, and other grossly contaminated clinical and environmental samples. Indeed, we have recently demonstrated the ability to quantitatively monitor a specific small molecule in real-time directly in microliters of flowing, unmodified blood serum. Because of their sensitivity, substantial background suppression, and operational convenience, these folding-based biosensors appear potentially well suited for electronic, on-chip applications in pathogen detection, proteomics, metabolomics, and drug discovery.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/ar900165x

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Cited by

See all 506

Metadata

Authors
Lubin, A. A., & Plaxco, K. W.
Venue
Accounts of Chemical Research
Published
2010-03-04
Volume
43
Issue
4
Pages
496-505
ISSN
  • 0001-4842 (ISSN-L)
  • 1520-4898
OpenAlex
W2091826118
Cited by
506
References
2
FWCI
15.12
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Folding-Based Electrochemical Biosensors: The Case for Responsive Nucleic Acid Architectures. https://app.cochat.ai/papers/10.1021/ar900165x
The paper
Lubin, A. A., & Plaxco, K. W. (2010). Folding-Based Electrochemical Biosensors: The Case for Responsive Nucleic Acid Architectures. Accounts of Chemical Research. https://doi.org/10.1021/ar900165x

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.