Transcriptomic dissection of myogenic differentiation signature in caprine by RNA-Seq

Tripathi, Ajai K.; Patel, Amrutlal K.; Shah, Rohan J.; Patel, Anand; Shah, Tejas; Bhatt, Vaibhav D.; Joshi, Chaitanya G.

doi:10.1016/j.mod.2014.01.001

Cited by 19 publications

(16 citation statements)

References 42 publications

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“…Quantitative transcriptome analysis identified 549 upregulated genes and 614 downregulated genes during early stages of neurodevelopment. As expected, and in agreement with changes observed in other differentiating cell types, GO term enrichment analysis showed strong downregulation of genes related to cell proliferation during terminal differentiation Gao, Yourick, and Sprando 2014;Tripathi et al 2014). Simultaneously, genes related to neurodevelopmental processes such as neurite formation and axonogenesis are upregulated, which is in line with changes previously observed in mouse iPSCs and human ESCs (Wu et al 2010;Chen et al 2013).…”

Section: Quantitative Profiles Of Transcriptomic and Proteomic Of Earsupporting

confidence: 89%

Quantitative mapping of transcriptome and proteome dynamics during polarization of human iPSC-derived neurons

Lindhout

Kooistra

Portegies

et al. 2020

Preprint

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Early neuronal development is a well-coordinated process in which neuronal stem cells differentiate into polarized neurons. This process has been well studied in classical non-human model systems, but to what extent this is recapitulated in human neurons remains unclear. To study neuronal polarization in human neurons, we cultured human iPSC-derived neurons, characterized early developmental stages, measured electrophysiological responses, and systematically profiled transcriptomic and proteomic dynamics during these steps. We found extensive remodeling of the neuron transcriptome and proteome, with altered mRNA expression of ~1,100 genes and different expression profiles of ~1,500 proteins during neuronal differentiation and polarization. We also identified a distinct stage in axon development marked by an increase in microtubule remodeling and apparent relocation of the axon initial segment from the distal to proximal axon. Our comprehensive characterization and quantitative map of transcriptome and proteome dynamics provides a solid framework for studying polarization in human neurons.(FOM, #16NEPH05, CJW). AUTHOR CONTRIBUTIONSFWL, RK and SP designed the research, conducted experiments, and wrote the article together with LJH. RS conducted and analyzed the mass spectrometry experiments supervised by MA, LJH performed and interpreted the electrophysiology data supervised by CJW, RK and BLS provided and interpreted the RNA sequencing analysis. HDM edited the article. CCH designed the experimental plan, supervised the research and wrote the article.

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Section: Quantitative Profiles Of Transcriptomic and Proteomic Of Earsupporting

confidence: 89%

Quantitative mapping of transcriptome and proteome dynamics during polarization of human iPSC-derived neurons

Lindhout

Kooistra

Portegies

et al. 2020

Preprint

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“…In goat skeletal muscle, the main focus has been on genes and miRNAs rather than on lncRNAs [44, 45, 50, 51]. In the present study, we identified a total of 3981 multiple exon lncRNAs in fetal and postnatal goat skeletal muscle.…”

Section: Discussionmentioning

confidence: 83%

Genome-wide identification and characterization of long non-coding RNAs in developmental skeletal muscle of fetal goat

et al. 2016

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BackgroundLong non-coding RNAs (lncRNAs) have been studied extensively over the past few years. Large numbers of lncRNAs have been identified in mouse, rat, and human, and some of them have been shown to play important roles in muscle development and myogenesis. However, there are few reports on the characterization of lncRNAs covering all the development stages of skeletal muscle in livestock.ResultsRNA libraries constructed from developing longissimus dorsi muscle of fetal (45, 60, and 105 days of gestation) and postnatal (3 days after birth) goat (Capra hircus) were sequenced. A total of 1,034,049,894 clean reads were generated. Among them, 3981 lncRNA transcripts corresponding to 2739 lncRNA genes were identified, including 3515 intergenic lncRNAs and 466 anti-sense lncRNAs. Notably, in pairwise comparisons between the libraries of skeletal muscle at the different development stages, a total of 577 transcripts were differentially expressed (P < 0.05) which were validated by qPCR using randomly selected six lncRNA genes. The identified goat lncRNAs shared some characteristics, such as fewer exons and shorter length, with the lncRNAs in other mammals. We also found 1153 lncRNAs genes were neighbored 1455 protein-coding genes (<10 kb upstream and downstream) and functionally enriched in transcriptional regulation and development-related processes, indicating they may be in cis-regulatory relationships. Additionally, Pearson’s correlation coefficients of co-expression levels suggested 1737 lncRNAs and 19,422 mRNAs were possibly in trans-regulatory relationships (r > 0.95 or r < −0.95). These co-expressed mRNAs were enriched in development-related biological processes such as muscle system processes, regulation of cell growth, muscle cell development, regulation of transcription, and embryonic morphogenesis.ConclusionsThis study provides a catalog of goat muscle-related lncRNAs, and will contribute to a fuller understanding of the molecular mechanism underpinning muscle development in mammals.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3009-3) contains supplementary material, which is available to authorized users.

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“…Regardless, we have shown that RNA-seq quality metrics and bias removal can successfully be utilized on multiple platforms. Because many aspects of library preparation and normalization are universal aspects of working with RNA, including isolation, purification, priming, amplification, reagent batch and kit version, the recommendations and most of our observations presented here will likely be applicable to any sequencing platform used for RNA-seq 46,47 .…”

Section: Discussionmentioning

confidence: 99%

Detecting and correcting systematic variation in large-scale RNA sequencing data

Li¹,

Łabaj²,

et al. 2014

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High-throughput RNA sequencing (RNA-seq) enables comprehensive scans of entire transcriptomes, but best practices for analyzing RNA-seq data have not been fully defined, particularly for data collected with multiple sequencing platforms or at multiple sites. Here we used standardized RNA samples with built-in controls to examine sources of error in large-scale RNA-seq studies and their impact on the detection of differentially expressed genes (DEGs). Analysis of variations in guanine-cytosine content, gene coverage, sequencing error rate and insert size allowed identification of methods that produce more false positives or are less reproducible across sites. Moreover, commonly used methods fornormalization (cqn, EDASeq, RUV2, sva, PEER) varied in their ability to remove these systematic biases, depending on sample complexity and initial data quality. Normalization methods that combine data from genes across sites are strongly recommended to identify and remove site-specific effects, and can substantially improve RNA-seq studies.

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Transcriptomic dissection of myogenic differentiation signature in caprine by RNA-Seq

Cited by 19 publications

References 42 publications

Quantitative mapping of transcriptome and proteome dynamics during polarization of human iPSC-derived neurons

Quantitative mapping of transcriptome and proteome dynamics during polarization of human iPSC-derived neurons

Genome-wide identification and characterization of long non-coding RNAs in developmental skeletal muscle of fetal goat

Detecting and correcting systematic variation in large-scale RNA sequencing data

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