14 research outputs found
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Individual Retinal Progenitor Cells Display Extensive Heterogeneity of Gene Expression
The development of complex tissues requires that mitotic progenitor cells integrate information from the environment. The highly varied outcomes of such integration processes undoubtedly depend at least in part upon variations among the gene expression programs of individual progenitor cells. To date, there has not been a comprehensive examination of these differences among progenitor cells of a particular tissue. Here, we used comprehensive gene expression profiling to define these differences among individual progenitor cells of the vertebrate retina. Retinal progenitor cells (RPCs) have been shown by lineage analysis to be multipotent throughout development and to produce distinct types of daughter cells in a temporal, conserved order. A total of 42 single RPCs were profiled on Affymetrix arrays. In situ hybridizations performed on both retinal sections and dissociated retinal cells were used to validate the results of the microarrays. An extensive amount of heterogeneity in gene expression among RPCs, even among cells isolated from the same developmental time point, was observed. While many classes of genes displayed heterogeneity of gene expression, the expression of transcription factors constituted a significant amount of the observed heterogeneity. In contrast to previous findings, individual RPCs were found to express multiple bHLH transcription factors, suggesting alternative models to those previously developed concerning how these factors may be coordinated. Additionally, the expression of cell cycle related transcripts showed differences among those associated with G2 and M, versus G1 and S phase, suggesting different levels of regulation for these genes. These data provide insights into the types of processes and genes that are fundamental to cell fate choices, proliferation decisions, and, for cells of the central nervous system, the underpinnings of the formation of complex circuitry
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Clustering Analysis of SAGE Data using a Poisson Approach
Serial analysis of gene expression (SAGE) data have been poorly exploited by clustering analysis owing to the lack of appropriate statistical methods that consider their specific properties. We modeled SAGE data by Poisson statistics and developed two Poisson-based distances. Their application to simulated and experimental mouse retina data show that the Poisson-based distances are more appropriate and reliable for analyzing SAGE data compared to other commonly used distances or similarity measures such as Pearson correlation or Euclidean distance.Statistic
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Vesicular Stomatitis Virus with the Rabies Virus Glycoprotein Directs Retrograde Transsynaptic Transport Among Neurons In Vivo
Defining the connections among neurons is critical to our understanding of the structure and function of the nervous system. Recombinant viruses engineered to transmit across synapses provide a powerful approach for the dissection of neuronal circuitry in vivo. We recently demonstrated that recombinant vesicular stomatitis virus (VSV) can be endowed with anterograde or retrograde transsynaptic tracing ability by providing the virus with different glycoproteins. Here we extend the characterization of the transmission and gene expression of recombinant VSV (rVSV) with the rabies virus glycoprotein (RABV-G), and provide examples of its activity relative to the anterograde transsynaptic tracer form of rVSV. rVSV with RABV-G was found to drive strong expression of transgenes and to spread rapidly from neuron to neuron in only a retrograde manner. Depending upon how the RABV-G was delivered, VSV served as a polysynaptic or monosynaptic tracer, or was able to define projections through axonal uptake and retrograde transport. In animals co-infected with rVSV in its anterograde form, rVSV with RABV-G could be used to begin to characterize the similarities and differences in connections to different areas. rVSV with RABV-G provides a flexible, rapid, and versatile tracing tool that complements the previously described VSV-based anterograde transsynaptic tracer
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The Cis-regulatory Logic of the Mammalian Photoreceptor Transcriptional Network
The photoreceptor cells of the retina are subject to a greater number of genetic diseases than any other cell type in the human body. The majority of more than 120 cloned human blindness genes are highly expressed in photoreceptors. In order to establish an integrative framework in which to understand these diseases, we have undertaken an experimental and computational analysis of the network controlled by the mammalian photoreceptor transcription factors, Crx, Nrl, and Nr2e3. Using microarray and in situ hybridization datasets we have produced a model of this network which contains over 600 genes, including numerous retinal disease loci as well as previously uncharacterized photoreceptor transcription factors. To elucidate the connectivity of this network, we devised a computational algorithm to identify the photoreceptor-specific cis-regulatory elements (CREs) mediating the interactions between these transcription factors and their target genes. In vivo validation of our computational predictions resulted in the discovery of 19 novel photoreceptor-specific CREs near retinal disease genes. Examination of these CREs permitted the definition of a simple cis-regulatory grammar rule associated with high-level expression. To test the generality of this rule, we used an expanded form of it as a selection filter to evolve photoreceptor CREs from random DNA sequences in silico. When fused to fluorescent reporters, these evolved CREs drove strong, photoreceptor-specific expression in vivo. This study represents the first systematic identification and in vivo validation of CREs in a mammalian neuronal cell type and lays the groundwork for a systems biology of photoreceptor transcriptional regulation
Comparison of hybridization-based and sequencing-based gene expression technologies on biological replicates
<p>Abstract</p> <p>Background</p> <p>High-throughput systems for gene expression profiling have been developed and have matured rapidly through the past decade. Broadly, these can be divided into two categories: hybridization-based and sequencing-based approaches. With data from different technologies being accumulated, concerns and challenges are raised about the level of agreement across technologies. As part of an ongoing large-scale cross-platform data comparison framework, we report here a comparison based on identical samples between one-dye DNA microarray platforms and MPSS (Massively Parallel Signature Sequencing).</p> <p>Results</p> <p>The DNA microarray platforms generally provided highly correlated data, while moderate correlations between microarrays and MPSS were obtained. Disagreements between the two types of technologies can be attributed to limitations inherent to both technologies. The variation found between pooled biological replicates underlines the importance of exercising caution in identification of differential expression, especially for the purposes of biomarker discovery.</p> <p>Conclusion</p> <p>Based on different principles, hybridization-based and sequencing-based technologies should be considered complementary to each other, rather than competitive alternatives for measuring gene expression, and currently, both are important tools for transcriptome profiling.</p
Validation of oligoarrays for quantitative exploration of the transcriptome
<p>Abstract</p> <p>Background</p> <p>Oligoarrays have become an accessible technique for exploring the transcriptome, but it is presently unclear how absolute transcript data from this technique compare to the data achieved with tag-based quantitative techniques, such as massively parallel signature sequencing (MPSS) and serial analysis of gene expression (SAGE). By use of the TransCount method we calculated absolute transcript concentrations from spotted oligoarray intensities, enabling direct comparisons with tag counts obtained with MPSS and SAGE. The tag counts were converted to number of transcripts per cell by assuming that the sum of all transcripts in a single cell was 5路10<sup>5</sup>. Our aim was to investigate whether the less resource demanding and more widespread oligoarray technique could provide data that were correlated to and had the same absolute scale as those obtained with MPSS and SAGE.</p> <p>Results</p> <p>A number of 1,777 unique transcripts were detected in common for the three technologies and served as the basis for our analyses. The correlations involving the oligoarray data were not weaker than, but, similar to the correlation between the MPSS and SAGE data, both when the entire concentration range was considered and at high concentrations. The data sets were more strongly correlated at high transcript concentrations than at low concentrations. On an absolute scale, the number of transcripts per cell and gene was generally higher based on oligoarrays than on MPSS and SAGE, and ranged from 1.6 to 9,705 for the 1,777 overlapping genes. The MPSS data were on same scale as the SAGE data, ranging from 0.5 to 3,180 (MPSS) and 9 to1,268 (SAGE) transcripts per cell and gene. The sum of all transcripts per cell for these genes was 3.8路10<sup>5 </sup>(oligoarrays), 1.1路10<sup>5 </sup>(MPSS) and 7.6路10<sup>4 </sup>(SAGE), whereas the corresponding sum for all detected transcripts was 1.1路10<sup>6 </sup>(oligoarrays), 2.8路10<sup>5 </sup>(MPSS) and 3.8路10<sup>5 </sup>(SAGE).</p> <p>Conclusion</p> <p>The oligoarrays and TransCount provide quantitative transcript concentrations that are correlated to MPSS and SAGE data, but, the absolute scale of the measurements differs across the technologies. The discrepancy questions whether the sum of all transcripts within a single cell might be higher than the number of 5路10<sup>5 </sup>suggested in the literature and used to convert tag counts to transcripts per cell. If so, this may explain the apparent higher transcript detection efficiency of the oligoarrays, and has to be clarified before absolute transcript concentrations can be interchanged across the technologies. The ability to obtain transcript concentrations from oligoarrays opens up the possibility of efficient generation of universal transcript databases with low resource demands.</p
The Cis-regulatory Logic of the Mammalian Photoreceptor Transcriptional Network
The photoreceptor cells of the retina are subject to a greater number of genetic diseases than any other cell type in the human body. The majority of more than 120 cloned human blindness genes are highly expressed in photoreceptors. In order to establish an integrative framework in which to understand these diseases, we have undertaken an experimental and computational analysis of the network controlled by the mammalian photoreceptor transcription factors, Crx, Nrl, and Nr2e3. Using microarray and in situ hybridization datasets we have produced a model of this network which contains over 600 genes, including numerous retinal disease loci as well as previously uncharacterized photoreceptor transcription factors. To elucidate the connectivity of this network, we devised a computational algorithm to identify the photoreceptor-specific cis-regulatory elements (CREs) mediating the interactions between these transcription factors and their target genes. In vivo validation of our computational predictions resulted in the discovery of 19 novel photoreceptor-specific CREs near retinal disease genes. Examination of these CREs permitted the definition of a simple cis-regulatory grammar rule associated with high-level expression. To test the generality of this rule, we used an expanded form of it as a selection filter to evolve photoreceptor CREs from random DNA sequences in silico. When fused to fluorescent reporters, these evolved CREs drove strong, photoreceptor-specific expression in vivo. This study represents the first systematic identification and in vivo validation of CREs in a mammalian neuronal cell type and lays the groundwork for a systems biology of photoreceptor transcriptional regulation