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Scientists Create Algorithm to Separate Blood Sample into Different Cell Types and Detect Changes
Still, whole blood poses a complication when used as a sample in microarray analyses.
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.
Thus, the investigators devised an algorithm - in this case, a very large number of fairly simple equations.
To test the 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 known.
Analyzing patients' whole blood samples via microarrays without resorting to the new algorithm, the investigators couldn't distinguish any gene-expression pattern differences between the two patient groups.
But when they used the new algorithm, they found hundreds of differences in gene expression.
Those differences could be used to tell which patients were rejecting their transplants and which were not.
In addition, this method let the researchers see that these changes were largely confined to one particular cell type: the monocytes.
Only the new virtual-separation technique made fingering this cellular culprit possible.
The study has been published in Nature Methods.
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