Single-Cell Gene-Expression Analysis by Quantitative RT-PCR
The problem of development has long been one of the key issues in biology. With stem-cell therapies on the horizon, the “reverse engineering” of developmental programs promises to become a task of great practical significance. We now understand the general schemes by which transcriptional networks r...
| Summary: | The problem of development has long been one of the key issues in biology. With stem-cell therapies on the horizon, the “reverse engineering” of developmental programs promises to become a task of great practical significance. We now understand the general schemes by which transcriptional networks regulate cellular differentiation and morphogenesis. These genetic circuits function as complex state machines which, over the course of development, undergo sequenced transitions that bring cells to specific end states. A variety of different gene-expression assays can be used to follow these transitions. The sensitivity of the assays now in common use limits the resolution with which we can follow the activity of genetic-regulatory networks. This thesis describes two projects aimed at refining an established gene-profiling method, quantitative RT-PCR, so that it can be used to profile transcriptional-network states cross-sectionally within developing cell populations at single-cell resolution. Two advanced qRT-PCR protocols were developed to support these projects, one based on microfluidic “digital PCR,” the other based on multiplexed “preamplification PCR.” These protocols were used to measure transcription-factor expression in hematopoietic progenitor cells, and to evaluate the effects of aging on the stability of gene regulation. In their current form, the digital PCR and preamplification techniques will permit the analysis of perhaps a few hundred to a few thousand cells in a single-cell survey. By combining microfluidic-chip assays with the preamplification method, it will soon be possible to scale up to the analysis of many thousands of cells, while profiling many different transcription factors in each individual cell. This should facilitate the modeling of the transcriptional networks which control cellular differentiation as we press forward into the era of “tissue engineering." |
|---|