The DOI matches its Crossref record.
High-Sensitivity Detection of DNA Hybridization on Microarrays Using Resonance Light Scattering
Abstract
The application of resonance light scattering (RLS) particles for high-sensitivity detection of DNA hybridization on cDNA microarrays is demonstrated. Arrays composed of approximately 2000 human genes ("targets") were hybridized with colabeled (Cy3 and biotin) human lung cDNA probes at concentrations ranging from 8.3 ng/microL to 16.7 pg/microL. After hybridization, the arrays were imaged using a fluorescence scanner. The arrays were then treated with 80-nm-diameter gold RLS Particles coated with anti-biotin antibodies and imaged in a white light, CCD-based imaging system. At low probe concentrations, significantly more genes were detected by RLS compared to labeling by Cy3. For example, for hybridizations with a probe concentration of 83.3 pg/microL, approximately 1150 positive genes were detected using RLS compared to approximately 110 positive genes detected with Cy3. In a differential gene expression experiment using human lung and leukemia RNA samples, similar differential expression profiles were obtained for labeling by RLS and fluorescence technologies. The use of RLS Particles is particularly attractive for detection and identification of low-abundance mRNAs and for those applications in which the amount of sample is limited.
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.
Integrity signals
Registry
Cites
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.
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
Cited by
Nanostructures in Biodiagnostics
Rosi, N. L., & Mirkin, C. A. (2005) · Chemical Reviews · doi:10.1021/cr030067f · Cited by 4,603
A unified view of propagating and localized surface plasmon resonance biosensors
Haes, A. J., & Van Duyne, R. P. (2004) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-004-2708-9 · Cited by 666
Penn, S. G., He, L., & Natan, M. J. (2003) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2003.08.013 · Cited by 512
Stears, R. L., Martinsky, T., & Schena, M. (2003) · Nature Medicine · doi:10.1038/nm0103-140 · Cited by 279
Metadata
- Authors
- Bao, P., Frutos, A. G., Greef, C., Lahiri, J., Muller, U., Peterson, T. C., Warden, L., & Xie, X.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2002-03-09
- Volume
- 74
- Issue
- 8
- Pages
- 1792-1797
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2082737617
- Cited by
- 184
- References
- 0
- FWCI
- 6.37
- Type
- journal article
- Language
- en
- Open access
- closed
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
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.