Single-cell responses to ionizing radiation |
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Authors: | Brian Ponnaiya Sally A. Amundson Shanaz A. Ghandhi Lubomir B. Smilenov Charles R. Geard Manuela Buonanno David J. Brenner |
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Affiliation: | 1. Center for Radiological Research, Columbia University, 630 West 168th Street, VC11-240, New York, NY, 10032, USA
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Abstract: | While gene expression studies have proved extremely important in understanding cellular processes, it is becoming more apparent that there may be differences in individual cells that are missed by studying the population as a whole. We have developed a qRT-PCR protocol that allows us to assay multiple gene products in small samples, starting at 100 cells and going down to a single cell, and have used it to study radiation responses at the single-cell level. Since the accuracy of qRT-PCR depends greatly on the choice of “housekeeping” genes used for normalization, initial studies concentrated on determining the optimal panel of such genes. Using an endogenous control array, it was found that for IMR90 cells, common housekeeping genes tend to fall into one of two categories—those that are relatively stably expressed regardless of the number of cells in the sample, e.g., B2M, PPIA, and GAPDH, and those that are more variable (again regardless of the size of the population), e.g., YWHAZ, 18S, TBP, and HPRT1. Further, expression levels in commonly studied radiation-response genes, such as ATF3, CDKN1A, GADD45A, and MDM2, were assayed in 100, 10, and single-cell samples. It is here that the value of single-cell analyses becomes apparent. It was observed that the expression of some genes such as FGF2 and MDM2 was relatively constant over all irradiated cells, while that of others such as FAS was considerably more variable. It was clear that almost all cells respond to ionizing radiation but the individual responses were considerably varied. The analyses of single cells indicate that responses in individual cells are not uniform and suggest that responses observed in populations are not indicative of identical patterns in all cells. This in turn points to the value of single-cell analyses. |
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