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 5·10⁵. 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 3.8·10⁵(oligoarrays), 1.1·10⁵(MPSS) and 7.6·10⁴(SAGE), whereas the corresponding sum for all detected transcripts was 1.1·10⁶(oligoarrays), 2.8·10⁵(MPSS) and 3.8·10⁵(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 5·10⁵suggested 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

Coexpression of Normally Incompatible Developmental Pathways in Retinoblastoma Genesis

It is widely believed that the molecular and cellular features of a tumor reflect its cell of origin and can thus provide clues about treatment targets. The retinoblastoma cell of origin has been debated for over a century. Here, we report that human and mouse retinoblastomas have molecular, cellular, and neurochemical features of multiple cell classes, principally amacrine/horizontal interneurons, retinal progenitor cells, and photoreceptors. Importantly, single-cell gene expression array analysis showed that these multiple cell type-specific developmental programs are coexpressed in individual retinoblastoma cells, which creates a progenitor/neuronal hybrid cell. Furthermore, neurotransmitter receptors, transporters, and biosynthetic enzymes are expressed in human retinoblastoma, and targeted disruption of these pathways reduces retinoblastoma growth in vivo and in vitro

Coexpression of Normally Incompatible Developmental Pathways in Retinoblastoma Genesis

It is widely believed that the molecular and cellular features of a tumor reflect its cell of origin and can thus provide clues about treatment targets. The retinoblastoma cell of origin has been debated for over a century. Here, we report that human and mouse retinoblastomas have molecular, cellular, and neurochemical features of multiple cell classes, principally amacrine/horizontal interneurons, retinal progenitor cells, and photoreceptors. Importantly, single-cell gene expression array analysis showed that these multiple cell type-specific developmental programs are coexpressed in individual retinoblastoma cells, which creates a progenitor/neuronal hybrid cell. Furthermore, neurotransmitter receptors, transporters, and biosynthetic enzymes are expressed in human retinoblastoma, and targeted disruption of these pathways reduces retinoblastoma growth in vivo and in vitro.This is a manuscript of an article from Cancer Cell 20 (2011): 260, doi: 10.1016/j.ccr.2011.07.005. Posted with permission.</p

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In (A) the 100 most abundant transcripts for each technology were considered, whereas in (B) the 100 transcripts with the lowest concentration were selected

Number of transcripts per cell for those held in common for all technologies is listed in Table 2.<p><b>Copyright information:</b></p><p>Taken from "Validation of oligoarrays for quantitative exploration of the transcriptome"</p><p>http://www.biomedcentral.com/1471-2164/9/258</p><p>BMC Genomics 2008;9():258-258.</p><p>Published online 30 May 2008</p><p>PMCID:PMC2430212.</p><p></p

N is total number of unique transcripts detected with the respective technologies

<p><b>Copyright information:</b></p><p>Taken from "Validation of oligoarrays for quantitative exploration of the transcriptome"</p><p>http://www.biomedcentral.com/1471-2164/9/258</p><p>BMC Genomics 2008;9():258-258.</p><p>Published online 30 May 2008</p><p>PMCID:PMC2430212.</p><p></p

Catalog of Gene Expression in Adult Retina

<p>The most commonly observed patterns of gene expression in the adult retina are indicated. Data are taken from <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020247#st005" target="_blank">Table S5</a> and cover all genes examined in the adult retina. Genes are placed in a category corresponding to a single cell type if expression is substantially greater in that cell type than in any of the other cell types examined. Genes are placed in categories corresponding to multiple cell types if expression is approximately equal in more than one cell type. The number of genes expressed in photoreceptors and Müller glia differs somewhat from those used in the analysis shown in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020247#pbio-0020247-g005" target="_blank">Figure 5</a>A, since the expression of a large number of photoreceptor-enriched genes was not examined prenatally, and a number of Müller-enriched genes were detectable in Müller glia through the end of the second postnatal week, but not in adult retina. AC, amacrine cells; BC, bipolar cells; GC,ganglion cells; HC, horizontal cells; MG, Müller glia; sAC, subset of amacrine cells; sBC, subset of bipolar cells; sGC, subset of ganglion cells</p