131 RNA-Seq TRANSCRIPTOME PROFILING OF INDIVIDUAL RHESUS MACAQUE OOCYTES AND PRE-IMPLANTATION EMBRYOS

2014 ◽  
Vol 26 (1) ◽  
pp. 179 ◽  
Author(s):  
J. L. Chitwood ◽  
V. R. Burruel ◽  
S. A. Meyers ◽  
P. J. Ross
Keyword(s):  
Gene Expression ◽  
Rhesus Macaque ◽  
Hierarchical Clustering ◽  
Intracytoplasmic Sperm Injection ◽  
Developmental Stages ◽  
Polar Body ◽  
Germinal Vesicle ◽  
Principal Component ◽  
Primate Species ◽  
Rna Seq

The objective of this study was to perform whole transcriptome sequencing (RNA-Seq) on individual rhesus macaque oocytes and intracytoplasmic sperm injection-derived embryos. Nine developmental stages were assayed (n = 2–3 per stage): germinal vesicle (GV), meiosis I (MI), and meiosis II (MII) oocytes and pronuclear (PN), 2-cell (2C), 4-cell (4C), 8-cell (8C), morula (MO), and blastocyst (BL) stage embryos. Rhesus females were superovulated with hrFSH and oocytes collected by follicular aspiration of ovaries recovered at necropsy 35 h after hCG treatment. Based on GV and polar body (PB) presence/absence, oocytes were classified as GV, MI, and MII. Embryos were produced by intracytoplasmic sperm injection of MII oocytes and cultured in HECM-9 media. Individual oocytes and embryos were collected at different developmental stages and frozen until use. The Clontech SMARTer Ultra Low Input RNA Kit for Illumina Sequencing was used for cDNA synthesis (oligo dT) and amplification. Sequencing libraries were created from fragmented cDNA with the Illumina TruSeq DNA kit. Single 100-bp reads were produced with a HiSEqn 2000 apparatus. Read alignment was done in CLC Genomics Workbench software to the RhesusBase genome annotation (53 788 genes). A gene was considered expressed if reads per kilobase of transcript model per million mapped reads (RPKM) was >0.4. Principal component analysis (PCA), hierarchical clustering, and differential gene expression analyses were performed with the DESEqn 2 package in R. On average, 22 311 310 reads were produced per sample with 63% aligning to the rhesus transcriptome. A total of 14 527 genes were detected in all replicates of at least one stage, with an average of 8855 genes per stage. The PCA and hierarchical clustering of all expressed genes discriminated between samples of different stages, except for 2C and PN, which were indistinguishable. All oocytes grouped to the same subcluster and close to PN-4C embryos, forming another subcluster. The 8C embryos constituted their own subcluster, which was closer to MO and BL than to other pre-implantation stages. Of genes expressed exclusively in embryo samples (n = 2823), 42% were expressed beginning at the 8C stage, 39% began expression at MO or BL, and 18% started expression between PN to 4C stage. The highest numbers of differentially expressed (DE) genes between consecutive stages were for MI-MII, MII-PN, 4C-8C, and 8C-MO, with an average of 5306 DE genes, whereas only 1060 were found in other comparisons. When gene expression was compared relative to the MII stage, 8C, MO, and BL had the highest numbers of DE genes (8965, 9635, and 9100, respectively, v. an average of 4713 for all other embryo stages). The high proportion of embryo-specific genes beginning expression at the 8C stage along with large numbers of DE genes observed between MII and 8C and the isolation of 8C samples to a unique expression cluster with PCA/hierarchical clustering indicates that major embryonic genome activation occurs at the 8C stage in the rhesus macaque. Overall, the dataset represents a comprehensive resource for analysis of polyadenylated transcript levels throughout early development in a nonhuman primate species.

scholarly journals Circular RNAs of the Nucleophosmin 1 (NPM1) Gene in Acute Myeloid Leukemia Patients

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 2695-2695
Author(s):  
Susanne Hirsch ◽  
Tamara J. Blätte ◽  
Sarah Grasedieck ◽  
Arefeh Rouhi ◽  
Mojca Jongen-Lavrencic ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Myeloid Leukemia ◽  
Principal Component ◽  
Circular Rnas ◽  
Rna Seq ◽  
Leukemic Transformation ◽  
Healthy Control ◽  
Tlr Signaling Pathway ◽  
Control Samples

Abstract Background: The nucleophosmin 1 (NPM1) gene is not only commonly mutated in acute myeloid leukemia (AML), but also encodes several linear splice isoforms, one of which was recently shown to be of prognostic importance. Furthermore, circular RNAs (circRNAs) are transcribed from the NPM1 gene which demands further investigation with regard to function in normal hematopoiesis and impact on leukemogenesis. Aims: We aimed to investigate circRNAs derived from NPM1 and gain insights into their regulation and function. Additionally, we wanted to determine changes in the circular RNAome in the course of hematopoietic differentiation and leukemic transformation. Methods: Circular NPM1 transcripts were detected by PCR and sequenced in leukemic cell lines (n=7) and healthy control samples (n=3, peripheral blood-derived mononuclear cells). Expression of hsa_circ_0075001 and total NPM1 was measured in a cohort of 23 NPM1 wildtype (NPM1wt) and 23 NPM1 mutated (NPM1mut) AML patients via quantitative real-time PCR (qPCR), and Affymetrix U133plus2 microarray data was set in relation to the expression levels. Principal component analysis (PCA) was conducted to identify groups with similarities in gene expression patterns and differentially expressed genes were subjected to pathway analysis. Next, ribosomal RNA-depleted RNA-seq was performed for 5 NPM1mut and 5 NPM1wt AML cases, as well as 10 healthy control samples derived from 4 FACS-sorted myeloid differentiation stages (myeloblasts, promyelocytes, metamyelocytes and neutrophils). PCA and unsupervised hierarchical clustering were performed based on circRNA expression. Results: We detected and sequenced multiple circular NPM1 transcripts (n=23) in leukemic as well as in healthy control cells. As hsa_circ_0075001 showed differential expression between different AML cell lines in a semi-quantitative PCR analysis, quantification in 46 AML patients via qPCR was performed. This analysis revealed that total NPM1 and hsa_circ_0075001 expression were independent of the NPM1 mutational status. Furthermore, the hsa_circ_0075001 expression status defined distinct leukemia subgroups characterized by similarities in gene expression as determined by PCA. For example, differentially expressed genes between high versus low hsa_circ_0075001 expression groups (dichotomized at the median) were significantly enriched in components of the Toll-like receptor (TLR) signaling pathway, which was downregulated in patients with high hsa_circ_0075001 expression. Expression of hsa_circ_0075001 correlated positively with total NPM1 expression, and RNA-seq analysis further revealed a global correlation of circRNA and parental gene expression. In total, in our cohort circRNAs were found for 19 % of all expressed genes. PCA based on circRNA expression illustrated that immature and mature hematopoietic cells, as well as NPM1wt and NPM1mut AML samples, exhibit distinct circRNA signatures (Figure 1). Thus, circRNA expression seems to play a role during differentiation of normal hematopoietic cells, but also seems to be severely deregulated in AML. Figure 1: Altered circular RNA expression in AML patients compared to healthy control samples. Principal component analysis (PCA) of circRNA expression data of 5 NPM1mut patients (red), 5 NPM1wt patients (green), and 10 healthy control samples, of which 4 were derived from immature (blue) and 6 from more mature myeloid differentiation stages (purple). Data was generated via RNA-Seq and reads derived from circRNAs were aligned and quantified using STAR, and normalized and transformed using DESeq2. PCA was performed based on 500 genes with the highest variance of circRNA expression across all samples. Conclusions: circRNAs transcribed from the NPM1 gene showed differential expression in AML cell lines and healthy cells, and higher hsa_circ_0075001 expression defined an AML subgroup characterized by downregulation of the TLR signaling pathway. These findings provide evidence for the relevance of circular NPM1 transcripts and add another level of complexity to the multifaceted gene NPM1. In general, circRNA expression seems to be involved in the regulation of hematopoietic differentiation, which is in line with previous observations, but, based on distinct circRNA expression profiles in AML, they might also play a significant pathogenic role in leukemic transformation. Figure 1 Figure 1. Disclosures Paschka: Celgene: Honoraria; Pfizer Pharma GmbH: Honoraria; Bristol-Myers Squibb: Honoraria; Medupdate GmbH: Honoraria; Novartis: Consultancy; ASTEX Pharmaceuticals: Consultancy.

RNA-sequencing indicates age-dependent shifts in the cardiac fibroblast transcriptome between fetal, neonatal, and adult developmental ages

2021 ◽  
Author(s):  
Luke R Perreault ◽  
Thanh T Le ◽  
Madeleine J Oudin ◽  
Lauren Deems Black
Keyword(s):  
Gene Expression ◽  
Immune Cell ◽  
Cardiac Fibroblasts ◽  
Principal Component ◽  
Phenotypic Heterogeneity ◽  
Gene Set Enrichment Analysis ◽  
Rna Seq ◽  
Adult Rats ◽  
Chemokine Production ◽  
Developmental Age

Background: Cardiac fibroblasts are responsible for extracellular matrix turnover and repair in the cardiac environment and serve to help facilitate immune responses. However, it is well established that they have significant phenotypic heterogeneity with respect to location, physiological conditions, and developmental age. The goal of this study was to provide an in-depth transcriptomic profile of cardiac fibroblasts derived from rat hearts at fetal, neonatal, and adult developmental ages to ascertain variations in gene expression that may drive functional differences in these cells at these specific stages of development. Results: We performed RNA-seq of cardiac fibroblasts isolated from fetal, neonatal, and adult rats and compared to the rat genome. Principal component analysis of RNA-seq data suggested data variance was predominantly due to developmental age. Differential expression and Gene set enrichment analysis against Gene Ontology and Kyoto Encyclopedia of Genes and Genomes datasets indicated an array of differences across developmental ages, including significant decreases in cardiac development and cardiac function-associated genes with age, and a significant increase in immune and inflammatory-associated functions - particularly immune cell signaling, and cytokine and chemokine production - with respect to increasing developmental age. Conclusion: These results reinforce established evidence of diverse phenotypic heterogeneity of fibroblasts with respect to developmental age. Further, based on our analysis of gene expression, age-specific alterations in cardiac fibroblasts may play a crucial role in observed differences in cardiac inflammation and immune response observed across developmental ages.

scholarly journals Optimized Hybrid Heuristic Based Dimensionality Reduction Methods for Malaria Vector Using KNN Classifier

2020 ◽  
Author(s):  
Micheal Olaolu Arowolo ◽  
Marion Olubunmi Adebiyi ◽  
Ayodele Ariyo Adebiyi ◽  
Oludayo Olugbara
Keyword(s):  
Gene Expression ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Feature Space ◽  
Component Analysis ◽  
Rna Seq ◽  
Knn Classifier ◽  
Data Dimensionality Reduction ◽  
Reduction Methods ◽  
Mosquito Anopheles Gambiae

Abstract RNA-Seq data are utilized for biological applications and decision making for the classification of genes. A lot of works in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in the transformation of these data. In this study, a novel optimized hybrid investigative approach is proposed. It combines an optimized genetic algorithm with Principal Component Analysis and Independent Component Analysis (GA-O-PCA and GAO-ICA), which are used to identify an optimum subset and latent correlated features, respectively. The classifier uses KNN on the reduced mosquito Anopheles gambiae dataset, to enhance the accuracy and scalability in the gene expression analysis. The proposed algorithm is used to fetch relevant features based on the high-dimensional input feature space. A fast algorithm for feature ranking is used to select relevant features. The performances of the model are evaluated and validated using the classification accuracy to compare existing approaches in the literature. The achieved experimental results prove to be promising for selecting relevant genes and classifying pertinent gene expression data analysis by indicating that the approach is a capable addition to prevailing machine learning methods.

scholarly journals Enhanced Dimensionality Reduction Methods for Classifying Malaria Vector Dataset using Decision Tree

2021 ◽  
Vol 50 (9) ◽  
pp. 2579-2589
Author(s):  
Micheal Olaolu Arowolo ◽  
Marion Olubunmi Adebiyi ◽  
Ayodele Ariyo Adebiyi
Keyword(s):  
Gene Expression ◽  
Decision Tree ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Feature Space ◽  
Relevant Information ◽  
Component Analysis ◽  
Rna Seq ◽  
Reduction Methods ◽  
Mosquito Anopheles Gambiae

RNA-Seq data are utilized for biological applications and decision making for classification of genes. Lots of work in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in fetching relevant information in a given data. In this study, a novel optimized dimensionality reduction algorithm is proposed, by combining an optimized genetic algorithm with Principal Component Analysis and Independent Component Analysis (GA-O-PCA and GAO-ICA), which are used to identify an optimum subset and latent correlated features, respectively. The classifier uses Decision tree on the reduced mosquito anopheles gambiae dataset to enhance the accuracy and scalability in the gene expression analysis. The proposed algorithm is used to fetch relevant features based from the high-dimensional input feature space. A feature ranking and earlier experience are used. The performances of the model are evaluated and validated using the classification accuracy to compare existing approaches in the literature. The achieved experimental results prove to be promising for feature selection and classification in gene expression data analysis and specify that the approach is a capable accumulation to prevailing data mining techniques.

scholarly journals Transcriptomic profiling of human orbital fat and differentiating orbital fibroblasts

2021 ◽  
Author(s):  
Dong Won Kim ◽  
Kamil Taneja ◽  
Thanh Hoang ◽  
Clayton Pio Santiago ◽  
Timothy James McCulley ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Time Course ◽  
Principal Component ◽  
Developmental Time ◽  
Rna Seq ◽  
Orbital Fat ◽  
Orbital Fibroblasts ◽  
Compare Gene Expression

Purpose: Orbital fat hyperplasia has a central role in the manifestations of thyroid-associated orbitopathy (TAO). To better understand the pathways involved in adipogenesis in TAO, we have used transcriptomic methods to analyze gene expression in control and TAO patients, as well as in differentiating orbital fibroblasts (OFs). Methods: We performed bulk RNA sequencing (RNA-Seq) on intraconal orbital fat to compare gene expression in control and TAO patients. We treated cultured OFs derived from TAO patients with media containing dexamethasone, insulin, rosiglitazone, and isobutylmethylxanthine (IBMX) to induce adipogenesis. We used single nuclear RNA-Seq (snRNA-Seq) profiling of treated OFs to compare gene expression over time in order to identify pathways that are involved in orbital adipogenesis in vitro and compared the dynamic patterns of gene expression identify differences in gene expression in control and TAO orbital fat. Results: Orbital fat from TAO and control patients segregate with principal component analysis (PCA). Numerous signaling pathways are enriched in orbital fat isolated from TAO patients. SnRNA-Seq of orbital fibroblasts undergoing adipogenesis reveals differential expression of adipocyte-specific genes over the developmental time course. Furthermore, genes that are enriched in TAO orbital fat are also upregulated in orbital adipocytes that differentiate in vitro, while genes that are enriched in control orbital fat are enriched in orbital fibroblasts prior to differentiation. Conclusions: Differentiating orbital fibroblasts serve as a model to study orbital fat hyperplasia seen in TAO. We demonstrate that the insulin-like growth factor-1 receptor (IGF-1R) and Wnt signaling pathways are differentially expressed early in orbital adipogenesis.

Comprehensive immune and mutational profile of melanoma.

2018 ◽  
Vol 36 (5_suppl) ◽  
pp. 182-182 ◽  
Author(s):  
Jeffrey M. Conroy ◽  
Sarabjot Pabla ◽  
Marc S. Ernstoff ◽  
Igor Puzanov ◽  
Mary Nesline ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hierarchical Clustering ◽  
Immune Escape ◽  
Principal Component ◽  
Wild Type ◽  
Immune Phenotype ◽  
Mutational Burden ◽  
Mutational Status ◽  
Immune Phenotypes ◽  
Highly Correlated

182 Background: The association between tumor mutational profiles and immune signatures has not been well-characterized. Methods: 306 melanoma samples were tested by NGS using a comprehensive cancer panel for mutational status and an immune response panel which interrogates the expression profile of 54 validated immune-related genes. The ranking of gene expression, mutational burden and 7 immune phenotypes was compared to a reference population. 38% cases were positive for activating BRAF mutations, 12% for RAS, and 6% for NF1. The remaining 44% were considered triple wild type. Principal component analysis (PCA) followed by hierarchical clustering was performed to determine association of BRAF/RAS/NF1 mutations and triple wild type with immune phenotypes, mutational burden and gene expression as measured by the NGS panels. Results: PCA showed that the first and second dimension explain 86% of the variation in the mutation profiles of the 306 melanomas. The first principal component highly correlated with BRAF positive status (pval < 0.001), the second highly correlated with RAS positive status (pval < 0.001), and the third principal component, although not informative, highly correlated with NF1 status (pval < 0.001) and Mutation Burden (pval < 0.001). Hierarchical clustering of the samples resulted in 4 distinct clusters: RAS positive, BRAF Positive, NF1 positive and triple wild type. The RAS positive cluster demonstrated significantly lower expression of ICOSLG, ICOS, CD4, C10orf54, CD40 and CD244 genes. Similarly, the BRAF positive cluster under-expresses immune escape and proinflammatory immune phenotypes, but over-expressed OX40L. The NF1 positive cluster had significantly higher mutational burden than other clusters. On the contrary, the triple wild type cluster over-expressed 6 out of 7 immune phenotypes. Conclusions: BRAF/RAS/NF1 mutation status are immunophenotypically distinct and do not associate with a typical immune phenotype in the tumor microenvironment. Triple wild type samples present with an overall activated immune phenotype, representative of an inflamed tumor. Additional studies are necessary to include additional activating or loss of function mutations to expand these findings.

scholarly journals Manganese Treatment Alleviates Zinc Deficiency Symptoms in Arabidopsis Seedlings

2020 ◽  
Vol 61 (10) ◽  
pp. 1711-1723
Author(s):  
Sayuri Nakayama ◽  
Shigeo S Sugano ◽  
Haruna Hirokawa ◽  
Izumi C Mori ◽  
Hiroyuki Daimon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Basal Medium ◽  
Principal Component ◽  
Growth Conditions ◽  
Zn Deficiency ◽  
Rna Seq ◽  
Short Term ◽  
Chlorophyll Contents ◽  
Deficient Medium

Abstract Plant phenotypes caused by mineral deficiencies differ depending on growth conditions. We recently reported that the growth of Arabidopsis thaliana was severely inhibited on MGRL-based zinc (Zn)-deficient medium but not on Murashige–Skoog-based Zn-deficient medium. Here, we explored the underlying reason for the phenotypic differences in Arabidopsis grown on the different media. The root growth and chlorophyll contents reduced by Zn deficiency were rescued by the addition of extra manganese (Mn) during short-term growth (10 or 14 d). However, this treatment did not affect the growth recovery after long-term growth (38 d). To investigate the reason for plant recovery from Zn deficiency, we performed the RNA-seq analysis of the roots grown on the Zn-basal medium and the Zn-depleted medium with/without additional Mn. Principal component analysis of the RNA-seq data showed that the gene expression patterns of plants on the Zn-basal medium were similar to those on the Zn-depleted medium with Mn, whereas those on the Zn-depleted medium without Mn were different from the others. The expression of several transcription factors and reactive oxygen species (ROS)-related genes was upregulated in only plants on the Zn-depleted medium without Mn. Consistent with the gene expression data, ROS accumulation in the roots grown on this medium was higher than those grown in other conditions. These results suggest that plants accumulate ROS and reduce their biomass under undesirable growth conditions, such as Zn depletion. Taken together, this study shows that the addition of extra Mn to the Zn-depleted medium induces transcriptional changes in ROS-related genes, thereby alleviating short-term growth inhibition due to Zn deficiency.

scholarly journals Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Micheal Olaolu Arowolo ◽  
Marion Olubunmi Adebiyi ◽  
Ayodele Ariyo Adebiyi ◽  
Oludayo Olugbara
Keyword(s):  
Gene Expression ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Feature Space ◽  
Component Analysis ◽  
Rna Seq ◽  
Knn Classifier ◽  
Data Dimensionality Reduction ◽  
Reduction Methods ◽  
Mosquito Anopheles Gambiae

AbstractRNA-Seq data are utilized for biological applications and decision making for the classification of genes. A lot of works in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in the transformation of these data. In this study, a novel optimized hybrid investigative approach is proposed. It combines an optimized genetic algorithm with Principal Component Analysis and Independent Component Analysis (GA-O-PCA and GAO-ICA), which are used to identify an optimum subset and latent correlated features, respectively. The classifier uses KNN on the reduced mosquito Anopheles gambiae dataset, to enhance the accuracy and scalability in the gene expression analysis. The proposed algorithm is used to fetch relevant features based on the high-dimensional input feature space. A fast algorithm for feature ranking is used to select relevant features. The performances of the model are evaluated and validated using the classification accuracy to compare existing approaches in the literature. The achieved experimental results prove to be promising for selecting relevant genes and classifying pertinent gene expression data analysis by indicating that the approach is capable of adding to prevailing machine learning methods.

scholarly journals Transposable element exaptation is the primary source of novelty in the primate gene regulatory landscape

2016 ◽  
Author(s):  
Marco Trizzino ◽  
YoSon Park ◽  
Marcia Holsbach-Beltrame ◽  
Katherine Aracena ◽  
Katelyn Mika ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Phenotypic Diversity ◽  
Regulatory Element ◽  
Critical Role ◽  
Primary Source ◽  
Primate Species ◽  
Rna Seq ◽  
Cell Type Specificity ◽  
Regulatory Landscape

AbstractGene regulation plays a critical role in the evolution of phenotypic diversity. We investigated the evolution of liver promoters and enhancers in six primate species. We performed ChlP-seq for two histone modifications and RNA-seq to profile cis-regulatory element (CRE) activity and gene expression. The primate regulatory landscape is largely conserved across the lineage. Conserved CRE function is associated with sequence conservation, proximity to coding genes, cell type specificity of CRE function, and transcription factor binding. Newly evolved CREs are enriched in immune response and neurodevelopmental functions, while conserved CREs bind master regulators. Transposable elements (TEs) are the primary source of novelty in primate gene regulation. Newly evolved CREs are enriched in young TEs that affect gene expression. However, only 17% of conserved CREs overlap a TE, suggesting that target gene expression is under strong selection. Finally, we identified specific genomic features driving the functional recruitment of newly inserted TEs.

scholarly journals Pervasive correlated evolution in gene expression shapes cell type transcriptomes

2016 ◽  
Author(s):  
Cong Liang ◽  
Jacob M. Musser ◽  
Alison Cloutier ◽  
Richard O. Prum ◽  
Günter P. Wagner
Keyword(s):  
Gene Expression ◽  
Hierarchical Clustering ◽  
Cell Types ◽  
Regulatory Sequences ◽  
Rna Seq ◽  
Cell Type ◽  
Correlated Evolution ◽  
Phylogenetic Methods ◽  
Expression Evolution ◽  
Gene Expression Evolution

AbstractThe evolution and diversification of cell types is a key means by which animal complexity evolves. Recently, hierarchical clustering and phylogenetic methods have been applied to RNA-seq data to infer cell type evolutionary history and homology. A major challenge for interpreting this data is that cell type transcriptomes may not evolve independently due to correlated changes in gene expression. This non-independence can arise for several reasons, such as when different tissues share common regulatory sequences for regulating genes expressed in multiple tissues, i.e. pleiotropic effects of mutations. We develop a model to estimate the level of correlated transcriptome evolution (LCE) and apply it to different datasets. The results reveal pervasive correlated transcriptome evolution among different cell and tissue types. In general, tissues related by morphology or developmental lineage exhibit higher LCE than more distantly related tissues. 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. Furthermore, we show correlated evolution can alter patterns of hierarchical clustering, causing different tissue types from the same species to cluster together. Using a dataset with sufficient taxon sampling, we performed a gene-wise estimation of LCE, identifying genes that most strongly contribute to the correlated evolution signal. Removing genes with high LCE allows for accurate reconstruction of evolutionary relationships among tissue types. Our study provides a statistical method to measure and account for correlated gene expression evolution when interpreting comparative transcriptome data.

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