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Comparative, transcriptome analysis of self-organizing optic tissues.

ABSTRACT: Embryonic stem (ES) cells have a remarkable capacity to self-organize complex, multi-layered optic cups in vitro via a culture technique called SFEBq. During both SFEBq and in vivo optic cup development, Rax (Rx) expressing neural retina epithelial (NRE) tissues utilize Fgf and Wnt/?-catenin signalling pathways to differentiate into neural retina (NR) and retinal-pigmented epithelial (RPE) tissues, respectively. How these signaling pathways affect gene expression during optic tissue formation has remained largely unknown, especially at the transcriptome scale. Here, we address this question using RNA-Seq. We generated Rx+ optic tissue using SFEBq, exposed these tissues to either Fgf or Wnt/?-catenin stimulation, and assayed their gene expression across multiple time points using RNA-Seq. This comparative dataset will help elucidate how Fgf and Wnt/?-catenin signaling affect gene expression during optic tissue differentiation and will help inform future efforts to optimize in vitro optic tissue culture technology.

SUBMITTER: Andrabi M

PROVIDER: S-EPMC4477696 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Comparative, transcriptome analysis of self-organizing optic tissues.

Andrabi Munazah M Kuraku Shigehiro S Takata Nozomu N Sasai Yoshiki Y Love Nick R NR

Scientific data 20150623

Embryonic stem (ES) cells have a remarkable capacity to self-organize complex, multi-layered optic cups in vitro via a culture technique called SFEBq. During both SFEBq and in vivo optic cup development, Rax (Rx) expressing neural retina epithelial (NRE) tissues utilize Fgf and Wnt/β-catenin signalling pathways to differentiate into neural retina (NR) and retinal-pigmented epithelial (RPE) tissues, respectively. How these signaling pathways affect gene expression during optic tissue formation ha ...[more]

PMID: 26110066

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Project description:BackgroundParallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs). SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand.ResultsThe method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories (adipose, endocrine, homeostasis, digestion, exocrine, epithelium, sexual reproduction, muscle, immune system and nervous tissues). SOM mapping reduces the dimension of expression data from ten of thousands of genes to a few thousand metagenes, each representing a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of genes related to specific molecular processes in the respective tissue. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering are better represented and provide better signal-to-noise ratios if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues broadly into three clusters containing nervous, immune system and the remaining tissues.ConclusionsThe SOM technique provides a more intuitive and informative global view of the behavior of a few well-defined modules of correlated and differentially expressed genes than the separate discovery of the expression levels of hundreds or thousands of individual genes. The program is available as R-package 'oposSOM'.

Project description:BachgroundEuscaphis konishii Hayata, a member of the Staphyleaceae Family, is a plant that has been widely used in Traditional Chinese Medicine and it has been the source for several types of flavonoids. To identify candidate genes involved in flavonoid biosynthesis and accumulation, we analyzed transcriptome data from three E. konishii tissues (leaf, branch and capsule) using Illumina Hiseq 2000 platform.ResultsA total of 91.7, 100.3 and 100.1million clean reads were acquired for the leaf, branch and capsule, respectively; and 85,342 unigenes with a mean length of 893.60?bp and N50 length of 1307?nt were assembled using Trinity program. BLASTx analysis allowed to annotate 40,218 unigenes using public protein databases, including NR, KOG/COG/eggNOG, Swiss-Prot, KEGG and GO. A total of 14,291 (16.75%) unigenes were assigned to 128 KEGG pathways, and 900 unigenes were annotated into 22 KEGG secondary metabolites, including flavonoid biosynthesis. The structure enzymes involved in flavonoid biosynthesis, such as phenylalanine ammonia lyase, cinnamate 4-hydroxylase, 4-coumarate CoA ligase, shikimate O-hydroxycinnamoyltransferase, coumaroylquinate 3'-monooxygenase, caffeoyl-CoA O-methyltransferase, chalcone synthase, chalcone isomerase, flavanone 3-hydroxylase, flavonoid 3'-hydroxylase, flavonoid 3',5'-hydroxylase, flavonolsynthese, dihydroflavonol 4-reductase, anthocyanidinreductase, leucoanthocyanidin dioxygenase, leucoanthocyanidin reductase, were identified in the transcriptome data, 40 UDP-glycosyltransferase (UGT), 122 Cytochrome P450 (CYP) and 25 O-methyltransferase (OMT) unigenes were also found. A total of 295 unigenes involved in flavonoid transport and 220 transcription factors (97 MYB, 84 bHLH and 39 WD40) were identified. Furthermore, their expression patterns among different tissues were analyzed by DESeq, the differentially expressed genes may play important roles in tissues-specific synthesis, accumulation and modification of flavonoids.ConclusionWe present here the de novo transcriptome analysis of E. konishii and the identification of candidate genes involved in biosynthesis and accumulation of flavonoid. In general, these results are an important resource for further research on gene expression, genomic and functional genomics in E. konishii and other related species.

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Comparative, transcriptome analysis of self-organizing optic tissues.

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Comparative, transcriptome analysis of self-organizing optic tissues.

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