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Mathematical innovation turns blood draw into information gold mine in Stanford study
Date:3/7/2010

the popularity of a piece of legislation. But the rookie analyst has neglected to ask those surveyed which party they lean toward or what state they come from, so doesn't realize the first survey sample had a Democrat-to-Republican ratio of 30:70, while in the second, the ratio was reversed. The analyst might mistakenly infer a huge swing in pre- and post-speech preferences, when in fact the only real change was in the samples' compositions. Meanwhile, a vehement change in support among residents of a small but election-swinging state might go undetected.

In the same way, comparing a gene-expression pattern based on one person's whole-blood sample to another person's, or even the same person's blood over time, isn't very informative with a typical microarray run. Medically significant changes in gene-expression patterns can go unnoticed in those tests, while those that reflect changes in the composition of the sample may trigger false alarms.

While ways of separating whole blood into its constituent cell types do exist, these methods are too tedious, time-consuming and costly for routine clinical diagnostics and, for similar reasons, pose a challenge for research on large groups of subjects.

So the investigators devised an algorithm in this case, a very large number of fairly simple equations. They believed that the simultaneous solution for all these equations enabled the assigning of gene-expression changes to particular cell types in patients' blood samples.

To test their algorithm's accuracy, the researchers obtained whole blood samples from 24 pediatric kidney-transplant patients. Fifteen of the 24 patients were experiencing symptoms of acute transplant rejection, while nine were in stable condition.

Because complete blood counts had been routinely performed on these patients, the frequencies within each sample of five important blood-cell types monocytes, lymphocytes, neutrophils, basophils and eosinophils were k
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Contact: Bruce Goldman
goldmanb@stanford.edu
650-725-2106
Stanford University Medical Center
Source:Eurekalert

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