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A high-throughput gene expression analysis technique using competitive PCR and matrix-assisted laser desorption ionization time-of-flight MS

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Ding, Chunming, Cantor, Charles R.·2003·Proceedings of the National Academy of Sciences

Abstract

We report here an approach for gene expression analysis by combining competitive PCR and matrix-assisted laser desorption ionization time-of-flight MS. A DNA standard is designed with an artificial single nucleotide polymorphism in the gene of interest. The standard is added to the reverse transcription product before PCR. Subsequently, a base extension reaction is carried out at the single nucleotide polymorphism position, and the products are quantified by matrix-assisted laser desorption ionization time-of-flight MS. The approach is capable of relative and absolute quantification of gene expression; it is extremely sensitive (as few as five copies of DNA were quantified) and highly reproducible. It is also capable of simultaneous quantification of both alleles for heterozygotes and alternatively spliced genes. We have incorporated this technique with the homogeneous Mass Extension system (Sequenom) to create a high-throughput, automated gene expression analysis platform where a few hundred genes from 20–500 different samples can be accurately quantified per day.

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Metadata

Authors
Ding, C., & Cantor, C. R.
Venue
Proceedings of the National Academy of Sciences
Published
2003-03-06
Volume
100
Issue
6
Pages
3059-3064
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W1971034474
Cited by
180
References
0
FWCI
7.72
Type
journal article
Language
en
Open access
green

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Ding, C., & Cantor, C. R. (2003). A high-throughput gene expression analysis technique using competitive PCR and matrix-assisted laser desorption ionization time-of-flight MS. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0630494100

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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.