Literature DB >> 17298578

Single nucleotide polymorphism profiling assay to exclude serum sample mix-up.

C J J Huijsmans1, F G C Heilmann, A G M van der Zanden, P M Schneeberger, M H A Hermans.   

Abstract

BACKGROUND AND OBJECTIVES: Sample mix-ups are a threat to the validity of clinical laboratory test results. To detect serum sample mix-ups we developed a single nucleotide polymorphism (SNP) profiling test. SNPs are frequent sequence variations in the human genome. Each individual has a unique combination of these nucleotide variations.
MATERIALS AND METHODS: Predeveloped SNP amplification assays are commercially available. We recently discovered that these SNP assays could be applied to serological samples, which is not self-evident because a key step in serum preparation is removal of white blood cells, the major source of DNA, from blood. DNA was extracted from serum samples. Real-time polymerase chain reaction (PCR) analysis of the purified DNA using a selection of 10 SNP assays provided SNP profiles.
RESULTS: The applicability of the SNP profiling test was demonstrated by means of a case where hepatitis E virus serological determinations of four serum samples of one patient seemed inconsistent. SNP profiling of the samples demonstrated that this was due to the enzyme-linked immunosorbent assay test instead of sample mix-up.
CONCLUSION: We have developed an SNP profiling assay that provides a way to link human serum samples to a source, without post-PCR processing. The chance for two randomly chosen individuals to have an identical profile is 1 in 18 000. Solving potential serum sample mix-ups will secure downstream evaluations and critical decisions concerning the patients involved.

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Year:  2007        PMID: 17298578     DOI: 10.1111/j.1423-0410.2006.00871.x

Source DB:  PubMed          Journal:  Vox Sang        ISSN: 0042-9007            Impact factor:   2.144


  2 in total

1.  Identification and Correction of Sample Mix-Ups in Expression Genetic Data: A Case Study.

Authors:  Karl W Broman; Mark P Keller; Aimee Teo Broman; Christina Kendziorski; Brian S Yandell; Śaunak Sen; Alan D Attie
Journal:  G3 (Bethesda)       Date:  2015-08-19       Impact factor: 3.154

2.  Algorithm-based arterial blood sampling recognition increasing safety in point-of-care diagnostics.

Authors:  Jörg Peter; Wilfried Klingert; Kathrin Klingert; Karolin Thiel; Daniel Wulff; Alfred Königsrainer; Wolfgang Rosenstiel; Martin Schenk
Journal:  World J Crit Care Med       Date:  2017-08-04
  2 in total

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