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

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

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.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

Transcription Factor Cascade in Photoreceptor Development

<p>Transcription factors that are selectively expressed in developing rods (and possibly cones as well) are shown. The schematic diagram integrates gene expression data from previously identified photoreceptor-enriched transcription factors and from genes explored in this study. The genes shown are <i>Mm.193526/Yboxbp4, Mm.3499/Rax, Mm. 89623/mCas, Mm.1635/PIAS3,</i> and <i>Mm.235550/ERRβ</i>. See Figure S6 for images of the developmental expression patterns of previously characterized transcription factors. Sections were from central retina. Cellular laminae of both the developing and mature retina are indicated with colored bars. All pictures were taken at 200x. See Table S5 for a full list of probes used.</p

Precursor Patterns for Major Retinal Cell Types

<p>Genes that are selectively expressed in immature subtypes of retinal cells. From the top, the differentiating cell types that express the genes in question are ganglion cells <i>(Mm.45753/KIAA0013),</i> rod photoreceptors <i>(Mm.103742/Cdc42GAP),</i> bipolar cells <i>(Mm.29496/Zf-1),</i> horizontal cells <i>(Mm.2214 /septin 4),</i> amacrine cells <i>(Mm.23916),</i> and Müller glia <i>(Mm.29729/Tweety1).</i> Sections were from central retina. Cellular laminae of both the developing and mature retina are indicated with colored bars. All pictures were taken at 200x. See Table S5 for a full list of probes used.</p

Median Plot of SAGE Tag <i>K-</i>Means Cluster Analysis Using 24 Clusters

<p>Tags present at greater than 0.1% in one or more of the ten wild-type total retina libraries are considered. SAGE libraries are plotted on the x-axis, and tag abundance, plotted as a fraction of the total tags for a gene in the library in question, is shown on the y-axis. A full list of tags and their abundance levels used for the analysis is detailed in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020247#st003" target="_blank">Table S3</a>.</p