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Quantification of multiple gene expression in individual cells.

ABSTRACT: 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 x 10(9) 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.

SUBMITTER: Peixoto A

PROVIDER: S-EPMC524418 | biostudies-other | 2004 Oct

REPOSITORIES: biostudies-other

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Project description:There is a revolution in the ability to analyze gene expression of single cells in a tissue. To understand this data we must comprehend how cells are distributed in a high-dimensional gene expression space. One open question is whether cell types form discrete clusters or whether gene expression forms a continuum of states. If such a continuum exists, what is its geometry? Recent theory on evolutionary trade-offs suggests that cells that need to perform multiple tasks are arranged in a polygon or polyhedron (line, triangle, tetrahedron and so on, generally called polytopes) in gene expression space, whose vertices are the expression profiles optimal for each task. Here, we analyze single-cell data from human and mouse tissues profiled using a variety of single-cell technologies. We fit the data to shapes with different numbers of vertices, compute their statistical significance, and infer their tasks. We find cases in which single cells fill out a continuum of expression states within a polyhedron. This occurs in intestinal progenitor cells, which fill out a tetrahedron in gene expression space. The four vertices of this tetrahedron are each enriched with genes for a specific task related to stemness and early differentiation. A polyhedral continuum of states is also found in spleen dendritic cells, known to perform multiple immune tasks: cells fill out a tetrahedron whose vertices correspond to key tasks related to maturation, pathogen sensing and communication with lymphocytes. A mixture of continuum-like distributions and discrete clusters is found in other cell types, including bone marrow and differentiated intestinal crypt cells. This approach can be used to understand the geometry and biological tasks of a wide range of single-cell datasets. The present results suggest that the concept of cell type may be expanded. In addition to discreet clusters in gene-expression space, we suggest a new possibility: a continuum of states within a polyhedron, in which the vertices represent specialists at key tasks.

Project description:BACKGROUND: Eukaryotic gene transcription is believed to occur in either a binary or a graded fashion. With binary induction, a transcription activator (TA) regulates the probability with which a gene template is switched from the inactive to the active state without affecting the rate at which RNA molecules are produced from the template. With graded, also called rheostat-like, induction the gene template has continuously varying levels of transcriptional activity, and the TA regulates the rate of RNA production. Support for each of these two mechanisms arises primarily from experimental studies measuring reporter proteins in individual cells, rather than from direct measurement of induction events at the gene template. METHODS AND RESULTS: In this paper, using a computational model of stochastic gene expression, we have studied the biological and experimental conditions under which a binary induction mode operating at the gene template can give rise to differentially expressed "phenotypes" (i.e., binary, hybrid or graded) at the protein level. We have also investigated whether the choice of reporter genes plays a significant role in determining the observed protein expression patterns in individual cells, given the diverse properties of commonly-used reporter genes. Our simulation confirmed early findings that the lifetimes of active/inactive promoters and half-lives of downstream mRNA/protein products are important determinants of various protein expression patterns, but showed that the induction time and the sensitivity with which the expressed genes are detected are also important experimental variables. Using parameter conditions representative of reporter genes including green fluorescence protein (GFP) and beta-galactosidase, we also demonstrated that graded gene expression is more likely to be observed with GFP, a longer-lived protein with low detection sensitivity. CONCLUSION: The choice of reporter genes may determine whether protein expression is binary, graded or hybrid, even though gene induction itself operates in an all-or-none fashion.

Project description:BackgroundAccurate measurements of mRNA expression levels in tissues or cells are crucially dependent on the use of relevant reference genes for normalization of data. In this study we used quantitative real-time PCR and two Excel-based applets (geNorm and BestKeeper) to determine the best reference genes for quantification of target gene mRNA in a complex tissue organ such as the guinea pig cervix.ResultsGene expression studies were conducted in cervical epithelium and stroma during pregnancy and parturition and in cultures of primary cells from this tissue. Among 15 reference gene candidates examined, both geNorm and BestKeeper found CLF1 and CLTC to be the most stable in cervical stroma and cervical epithelium, ACTB and PPIB in primary stroma cells, and CLTC and PPIB in primary epithelial cells. The order of stability among the remaining candidate genes was not in such an agreement. Commonly used reference such as GAPDH and B2M demonstrated lower stability. Determination of pairwise variation values for reference gene combinations using geNorm revealed that the geometric mean of the two most stable genes provides sufficient normalization in most cases. However, for cervical stroma tissue in which many reference gene candidates displayed low stability, inclusion of three reference genes in the geometric mean may improve accuracy of target gene expression level analyses. Using the top ranked reference genes we examined the expression levels of target gene PTGS2 in cervical tissue and cultured cervical cells. We compared the results with PTGS2 expression normalized to the least stable gene and found significant differences in gene expression, up to 10-fold in some samples, emphasizing the importance of appropriately selecting reference genes.ConclusionsWe recommend using the geometric mean of CFL1 and CLTC for normalization of qPCR studies in guinea pig cervical tissue studies, ACTB and PPIB in primary stroma cells and CLTC and PPIB in primary epithelial cells from guinea pig.

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Quantification of multiple gene expression in individual cells.

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