2014), in which distinct cell/tissue types cluster according to species of origin, rather than by homology, even when the tissue types are phylogenetically older than the species ( fig. 1C). However, the opposite pattern has also been documented ( Pankey et al. This pattern has been observed in analyses of mature tissues with distinct functions ( Brawand et al. Homologous cell and tissue types are expected to be more closely related to each other than to other tissues ( fig. 1A and B). These are analogous to a gene or species trees, but depict relationships among different cell or tissue types.
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Thus, understanding the evolution of animal complexity requires investigating cell type evolutionary history and homology.Ī promising approach for inferring cell or tissue type homology and evolutionary history is the use of hierarchical clustering or phylogenetic methods with RNA-seq data to reconstruct cell type trees ( fig. 1A and B we used cell types and tissue types interchangeably in this figure, to represent the samples that were frequently collected in comparative RNA-seq studies) ( Pu and Brady 2010 Wang et al. Viewed through the lens of animal phylogeny, this observation suggests that animal complexity increased, in part, via the evolution of new cell types ( Arendt 2008 Wagner 2014 Arendt et al. In contrast, Trichoplax, the morphologically simplest free-living animal, has five to six cell types ( Grell and Ruthmann 1991 Syed and Schierwater 2002), and in sponges only ∼10-18 cell types have been recognized ( Simpson 1984 Valentine et al. For example, there are >400 cell types recognized in humans ( Vickaryous and Hall 2006). Our study provides a statistical method to measure and account for correlated gene expression evolution when interpreting comparative transcriptome data.Ĭomparative transcriptomincs, cell type evolution, gene expression evolution, correlated evolution IntroductionĬomplex organisms, such as birds and mammals, generally have a much higher number of cell types than anatomically “simple” organisms, like sponges and Trichoplax. Removing genes with high LCE allows for accurate reconstruction of evolutionary relationships among tissue types. To identify genes that most strongly contribute to the correlated evolution signal, we performed a gene-wise estimation of LCE on a data set with ten species. Furthermore, we show correlated evolution can alter patterns of hierarchical clustering, causing different tissue types from the same species to cluster together.
#Pervasive 9 serial number skin
Analyzing new data collected from bird skin appendages suggests that LCE decreases with the phylogenetic age of tissues compared, with recently evolved tissues exhibiting the highest LCE. In general, tissues related by morphology or developmental lineage exhibit higher LCE than more distantly related tissues.
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The results reveal pervasive correlated transcriptome evolution among different cell and tissue types. We develop a model to estimate the level of correlated transcriptome evolution (LCE) and apply it to different data sets. This nonindependence can arise for several reasons, such as common regulatory sequences for genes expressed in multiple tissues, that is, pleiotropic effects of mutations. A major challenge for interpreting this data is that cell type transcriptomes may not evolve independently due to correlated changes in gene expression.
![pervasive 9 serial number pervasive 9 serial number](https://www.mdpi.com/sensors/sensors-19-04283/article_deploy/html/images/sensors-19-04283-g018.png)
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Recently, hierarchical clustering and phylogenetic methods have been applied to RNA-seq data to infer cell type evolutionary history and homology. The evolution and diversification of cell types is a key means by which animal complexity evolves.