Single Cell Expression profiling

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July 20, 2012

Mikael Kubista, Professor, TATAA Biocenter

Abstract
There is surprisingly large variation in the copy number of transcripts among even seemingly like cells. Under most conditions for essentially all genes and tissues the variation among cells for any given transcript can be fitted quite well with a log normal distribution. The variation is caused by transcriptional bursting, which is a dynamic process expected to be correlated for genes critical for the same cellular process. This leads to correlated expression levels of related genes among individual cells. Most genes do not have correlated expression on single cell level and it makes no sense normalizing single cell expression data to reference genes. Rather cells can be classified based on the correlated expression of important genes. In my talk I will show how such single cell expression correlation can be used to distinguish cells as well as cell subtypes. Finally, I show how to cope with genomic DNA background in expression profiling studies with the ValidPrime concept.

Drug DiscoveryGenomicsInformatics

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