Identification of unknown cell populations and correlations using single-cell gene expression profiling

Abstract
Single-cell gene expression levels show substantial variations among cells in seemingly homogenous populations. Access to fundamental information about cellular mechanism, such as correlated gene expression, motivates studies of multiple genes expressions in individual cells. Astrocytes perform many control and regulatory functions in the central nervous system. In contrast to neurons, we have limited knowledge about functional diversity of astrocytes and its molecular basis. We will show how subpopulations of cells can be identified at single-cell level using unsupervised algorithms and that gene correlations can be used to identify differences in activity of important transcriptional pathways. We identified two subpopulations of astrocytes with distinct gene expression profiles. One had an expression profile very similar to that of neurosphere cells, whereas
the other showed characteristics of activated astrocytes in vivo. Technical considerations related to reproducible and efficient sampling, lysis, reverse transcription and real-time PCR will also be presented. In addition to astrocytes, single-cell data from tumor cells, beta-cells and embryonic stem cells will be shown.