Electronically subtracting expression patterns from a mixed cell population.
BACKGROUND
Biological samples frequently contain multiple cell-types that each can play a crucial role in the development and/or regulation of adjacent cells or tissues. The search for biomarkers, or expression patterns of, one cell-type in those samples can be a complex and time-consuming process. Ordinarily, extensive laboratory bench work must be performed to separate the mixed cell population into its subcomponents, such that each can be accurately characterized.


RESULTS
We have developed a methodology to electronically subtract gene expression in one or more components of a mixed cell population from a mixture, to reveal the expression patterns of other minor or difficult to isolate components. Examination of simulated data indicates that this procedure can reliably determine the expression patterns in cell-types that contribute as little as 5% of the total expression in a mixed cell population. We re-analyzed microarray expression data from the viral infection of macrophages and from the T-cells of wild type and Foxp3 deletion mice. Using our subtraction methodology, we were able to substantially improve the identification of genes involved in processes of subcomponent portions of these samples.
Gosink
Mark M
MM
Scientific Computing, Scripps Florida, 5353 Parkside Dr Jupiter, FL 33458, USA. gosink@scripps.edu
Petrie
Howard T
HT
Tsinoremas
Nicholas F
NF
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D002468:Cell Physiological Phenomena; D018920:Coculture Techniques / Q000379:methods*; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D020543:Proteome / Q000378:metabolism*
D020543:Proteome