Quantitative gene expression analysis aims to define the gene expression patterns determining cell behavior. So far, these assessments can only be performed at the population level. Therefore, they determine the average gene expression within a population, overlooking possible cell-to-cell heterogeneity that could lead to different cell behaviors/cell fates. Understanding individual cell behavior requires multiple gene expression analyses of single cells, and may be fundamental for the understanding of all types of biological events and/or differentiation processes. We here describe a new reverse transcription-polymerase chain reaction (RT-PCR) approach allowing the simultaneous quantification of the expression of 20 genes in the same single cell. This method has broad application, in different species and any type of gene combination. RT efficiency is evaluated. Uniform and maximized amplification conditions for all genes are provided. Abundance relationships are maintained, allowing the precise quantification of the absolute number of mRNA molecules per cell, ranging from 2 to 1.28×109 for each individual gene. We evaluated the impact of this approach on functional genetic read-outs by studying an apparently homogeneous population (monoclonal T cells recovered 4 d after antigen stimulation), using either this method or conventional real-time RT-PCR. Single-cell studies revealed considerable cell-to-cell variation: All T cells did not express all individual genes. Gene coexpression patterns were very heterogeneous. mRNA copy numbers varied between different transcripts and in different cells. As a consequence, this single-cell assay introduces new and fundamental information regarding functional genomic read-outs. By comparison, we also show that conventional quantitative assays determining population averages supply insufficient information, and may even be highly misleading
Friday, August 20, 2010
Trisomy 21, or Down syndrome (DS), is the most common genetic cause of mental retardation. Changes in the neuropathology, neurochemistry, neurophysiology, and neuropharmacology of DS patients' brains indicate that there is probably abnormal development and maintenance of central nervous system structure and function. The segmental trisomy mouse (Ts65Dn) is a model of DS that shows analogous neurobehavioral defects. We have studied the global gene expression profiles of normal and Ts65Dn male and normal female mice brains (P30) using the serial analysis of gene expression (SAGE) technique. From the combined sample we collected a total of 152,791 RNA tags and observed 45,856 unique tags in the mouse brain transcriptome. There are 14 ribosomal protein genes (nine underexpressed) among the 330 statistically significant differences between normal male and Ts65Dn male brains, which possibly implies abnormal ribosomal biogenesis in the development and maintenance of DS phenotypes. This study contributes to the establishment of a mouse brain transcriptome and provides the first overall analysis of the differences in gene expression in aneuploid versus normal mammalian brain cells.
Combining congenic mapping with microarray expression profiling offers an opportunity to establish functional links between genotype and phenotype for complex traits such as type 1 diabetes (T1D). We used high-density oligonucleotide arrays to measure the relative expression levels of >39,000 genes and ESTs in the NOD mouse (a murine model of T1D and other autoimmune conditions), four NOD-derived diabetes-resistant congenic strains, and two nondiabetic control strains. We developed a simple, yet general, method for measuring differential expression that provides an objective assessment of significance and used it to identify >400 gene expression differences and eight new candidates for the Idd9.1 locus. We also discovered a potential early biomarker for autoimmune hemolytic anemia that is based on different levels of erythrocyte-specific transcripts in the spleen. Overall, however, our results suggest that the dramatic disease protection conferred by six Idd loci (Idd3,Idd5.1, Idd5.2, Idd9.1, Idd9.2, andIdd9.3) cannot be rationalized in terms of global effects on the noninduced immune system. They also illustrate the degree to which regulatory systems appear to be robust to genetic variation. These observations have important implications for the design of future microarray-based studies in T1D and, more generally, for studies that aim to combine genome-wide expression profiling and congenic mapping.