Project description:Transcriptome analysis of somatic stem cells and their progeny is fundamental to identify new factors controlling proliferation versus differentiation during tissue formation. Here, we generated a combinatorial, fluorescent reporter mouse line to isolate proliferating neural stem cells, differentiating progenitors and newborn neurons that coexist as intermingled cell populations during brain development. Transcriptome sequencing revealed numerous novel long non-coding (lnc)RNAs and uncharacterized protein-coding transcripts identifying the signature of neurogenic commitment. Importantly, most lncRNAs overlapped neurogenic genes and shared with them a nearly identical expression pattern suggesting that lncRNAs control corticogenesis by tuning the expression of nearby cell fate determinants. We assessed the power of our approach by manipulating lncRNAs and protein-coding transcripts with no function in corticogenesis reported to date. This led to several evident phenotypes in neurogenic commitment and neuronal survival, indicating that our study provides a remarkably high number of uncharacterized transcripts with hitherto unsuspected roles in brain development. Finally, we focussed on one lncRNA, Miat, whose manipulation was found to trigger pleiotropic effects on brain development and aberrant splicing of Wnt7b. Hence, our study suggests that lncRNA-mediated alternative splicing of cell fate determinants controls stem-cell commitment during neurogenesis.
Project description:Transcriptome analysis of somatic stem cells and their progeny is fundamental to identify new factors controlling proliferation versus differentiation during tissue formation. Here we generated a combinatorial, fluorescent reporter mouse line to isolate proliferating neural stem cells, differentiating progenitors and newborn neurons that coexist as intermingled cell populations during brain development. Transcriptome sequencing revealed numerous novel long non-coding (lnc)RNAs and uncharacterized protein-coding transcripts identifying the signature of neurogenic commitment. Importantly, most lncRNAs overlapped neurogenic genes and shared with them a nearly identical expression pattern suggesting that lncRNAs control corticogenesis by tuning the expression of nearby cell fate determinants. We assessed the power of our approach by manipulating lncRNAs and protein-coding transcripts with no function in corticogenesis reported to date. This led to several evident phenotypes in neurogenic commitment and neuronal survival indicating that our study provides a remarkably high number of uncharacterized transcripts with hitherto unsuspected roles in brain development. Finally, we focussed on one lncRNA, Miat, whose manipulation was found to trigger pleiotropic effects on brain development and aberrant splicing of Wnt7b. Hence, our study suggests that lncRNA-mediated alternative splicing of cell fate determinants controls stem cell commitment during neurogenesis. “LncRNAs control neurogenesis” Aprea, Prenninger, Dori, Monasor, Wessendof, Zocher, Massalini, Ghosh, Alexopoulou, Lesche, Dahl, Groszer, Hiller, Calegari, The EMBO Journal (In Press) mRNA profiles of Proliferating Progenitors, Differentiating Progenitors and Neurons from lateral cortex of E14.5 mouse embryos. Each cell type in three biological replicates.
Project description:Long non-coding (lnc)RNAs play key roles in many biological processes. Elucidating the function of lncRNAs in cell type specification during organ development requires knowledge about their expression in individual progenitor types rather than in whole tissues. To achieve this during cortical development, we used a dual-reporter mouse line to isolate coexisting proliferating neural stem cells, differentiating neurogenic progenitors and newborn neurons and assessed the expression of lncRNAs by paired-end, high-throughput sequencing. We identified 379 genomic loci encoding novel lncRNAs and performed a comprehensive assessment of cell-specific expression patterns for all, annotated and novel, lncRNAs described to date. Our study provides a powerful new resource for studying these elusive transcripts during stem cell commitment and neurogenesis.
Project description:BACKGROUND:Leucine-rich repeats and immunoglobulin-like domains 1 (Lrig1) regulates stem cell quiescence. As a marker, it identifies stem cells in multiple organs of the mouse. We had detected Lrig1 expression in cultured Id1high neural stem cells obtained from the lateral walls lining the lateral ventricles of the adult mouse brain. Thus, we investigated whether Lrig1 expression also identifies stem cells in that region in vivo. METHODS:Publicly available single cell RNA sequencing datasets were analyzed with Seurat and Monocle. The Lrig1+ cells were lineage traced in vivo with a novel non-disruptive co-translational Lrig1T2A-iCreERT2 reporter mouse line. RESULTS:Analysis of single cell RNA sequencing datasets suggested Lrig1 was highly expressed in the most primitive stem cells of the neurogenic lineage in the lateral wall of the adult mouse brain. In support of their neurogenic stem cell identity, cell cycle entry was only observed in two morphologically distinguishable Lrig1+ cells that could also be induced into activation by Ara-C infusion. The Lrig1+ neurogenic stem cells were observed throughout the lateral wall. Neuroblasts and neurons were lineage traced from Lrig1+ neurogenic stem cells at 1 year after labeling. CONCLUSIONS:We identified Lrig1 as a marker of long-term neurogenic stem cells in the lateral wall of the mouse brain. Lrig1 expression revealed two morphotypes of the Lrig1+ cells that function as long-term neurogenic stem cells. The spatial distribution of the Lrig1+ neurogenic stem cells suggested all subtypes of the adult neurogenic stem cells were labeled.
Project description:To elucidate the transcriptional 'landscape' that regulates human lymphoid commitment during postnatal life, we used RNA sequencing to assemble the long non-coding transcriptome across human bone marrow and thymic progenitor cells spanning the earliest stages of B lymphoid and T lymphoid specification. Over 3,000 genes encoding previously unknown long non-coding RNAs (lncRNAs) were revealed through the analysis of these rare populations. Lymphoid commitment was characterized by lncRNA expression patterns that were highly stage specific and were more lineage specific than those of protein-coding genes. Protein-coding genes co-expressed with neighboring lncRNA genes showed enrichment for ontologies related to lymphoid differentiation. The exquisite cell-type specificity of global lncRNA expression patterns independently revealed new developmental relationships among the earliest progenitor cells in the human bone marrow and thymus.
Project description:Genes that encode proteins playing a role in more than one biological process are frequently dependent on their tissue context, and human diseases result from the altered interplay of tissue- and cell-specific processes. In this work, we performed a computational approach that identifies tissue-specific co-expression networks by integrating miRNAs, long-non-coding RNAs, and mRNAs in more than eight thousands of human samples from thirty normal tissue types. Our analysis (1) shows that long-non coding RNAs and miRNAs have a high specificity, (2) confirms several known tissue-specific RNAs, and (3) identifies new tissue-specific co-expressed RNAs that are currently still not described in the literature. Some of these RNAs interact with known tissue-specific RNAs or are crucial in key cancer functions, suggesting that they are implicated in tissue specification or cell differentiation.
Project description:MicroRNA (miR)-128-3p is a brain-enriched miRNA that participates in the regulation of neural cell differentiation and the protection of neurons, but the mechanisms by which miR-128-3p regulates its target and downstream genes to influence cell fate from adult stem cells are poorly understood. In this study, we show down-regulation of miR-128-3p during all-trans retinoic acid (ATRA)-induced neurogenic differentiation from amniotic epithelial cells (AECs). We investigated miR-128-3p in both the Notch pathway and in the expression of neuron-specific genes predicted to be involved in miR-128-3p signaling to elucidate its role in the genetic regulation of downstream neurogenic differentiation. Our results demonstrate that miR-128-3p is a negative regulator for the transcription of the neuron-specific genes ? III-tubulin, neuron-specific enolase (NSE), and polysialic acid-neural cell adhesion molecule (PSA-NCAM) via targeting Jagged 1 to inhibit activation of the Notch signaling pathway. We also used bioinformatics algorithms to screen for miR-128-3p interactions with long non-coding (lnc) RNA and circular RNA as competing endogenous RNAs to further elucidate underlying down-regulated molecular mechanisms. The lncRNA maternally expressed 3 is up-regulated by the ATRA/cAMP/CREB pathway, and it, in turn, is directly down-regulated by miR-128-3p to increase the amount of neuron differentiation. Endogenous miRNAs are, therefore, involved in neurogenic differentiation from AECs and should be considered during the development of effective cell transplant therapies for the treatment of neurodegenerative disease.
Project description:Two thirds of the human genome is composed of repetitive sequences. Despite their prevalence, DNA repeats are largely ignored. The vast majority of our genome is transcribed to produce non protein-coding RNAs. Among these, long non protein-coding RNAs represent the most prevalent and functionally diverse class. The relevance of the non protein-coding genome to human disease has mainly been studied regarding the altered microRNA expression and function in human cancer. On the contrary, the elucidation of the involvement of long non-coding RNAs in disease is only in its infancy. We have recently found that a chromatin associated, long non protein-coding RNA regulates a Polycomb/Trithorax epigenetic switch at the basis of the repeat associated facioscapulohumeral muscular dystrophy, a common muscle disorder. Based on this, we propose that long non-coding RNAs produced by repetitive sequences contribute in shaping the epigenetic landscape in normal human physiology and in disease.
Project description:We used RNA sequencing to analyze transcript profiles of ten autopsy brain regions from ten subjects. RNA sequencing techniques were designed to detect both coding and non-coding RNA, splice isoform composition, and allelic expression. Brain regions were selected from five subjects with a documented history of smoking and five non-smokers. Paired-end RNA sequencing was performed on SOLiD instruments to a depth of >40 million reads, using linearly amplified, ribosomally depleted RNA. Sequencing libraries were prepared with both poly-dT and random hexamer primers to detect all RNA classes, including long non-coding (lncRNA), intronic and intergenic transcripts, and transcripts lacking poly-A tails, providing additional data not previously available. The study was designed to generate a database of the complete transcriptomes in brain region for gene network analyses and discovery of regulatory variants.Of 20,318 protein coding and 18,080 lncRNA genes annotated from GENCODE and lncipedia, 12 thousand protein coding and 2 thousand lncRNA transcripts were detectable at a conservative threshold. Of the aligned reads, 52 % were exonic, 34 % intronic and 14 % intergenic. A majority of protein coding genes (65 %) was expressed in all regions, whereas ncRNAs displayed a more restricted distribution. Profiles of RNA isoforms varied across brain regions and subjects at multiple gene loci, with neurexin 3 (NRXN3) a prominent example. Allelic RNA ratios deviating from unity were identified in?>?400 genes, detectable in both protein-coding and non-coding genes, indicating the presence of cis-acting regulatory variants. Mathematical modeling was used to identify RNAs stably expressed in all brain regions (serving as potential markers for normalizing expression levels), linked to basic cellular functions. An initial analysis of differential expression analysis between smokers and nonsmokers implicated a number of genes, several previously associated with nicotine exposure.RNA sequencing identifies distinct and consistent differences in gene expression between brain regions, with non-coding RNA displaying greater diversity between brain regions than mRNAs. Numerous RNAs exhibit robust allele selective expression, proving a means for discovery of cis-acting regulatory factors with potential clinical relevance.
Project description:In plants, long non-protein coding RNAs are believed to have essential roles in development and stress responses. However, relative to advances on discerning biological roles for long non-protein coding RNAs in animal systems, this RNA class in plants is largely understudied. With comparatively few validated plant long non-coding RNAs, research on this potentially critical class of RNA is hindered by a lack of appropriate prediction tools and databases. Supervised learning models trained on data sets of mostly non-validated, non-coding transcripts have been previously used to identify this enigmatic RNA class with applications largely focused on animal systems. Our approach uses a training set comprised only of empirically validated long non-protein coding RNAs from plant, animal, and viral sources to predict and rank candidate long non-protein coding gene products for future functional validation.Individual stochastic gradient boosting and random forest classifiers trained on only empirically validated long non-protein coding RNAs were constructed. In order to use the strengths of multiple classifiers, we combined multiple models into a single stacking meta-learner. This ensemble approach benefits from the diversity of several learners to effectively identify putative plant long non-coding RNAs from transcript sequence features. When the predicted genes identified by the ensemble classifier were compared to those listed in GreeNC, an established plant long non-coding RNA database, overlap for predicted genes from Arabidopsis thaliana, Oryza sativa and Eutrema salsugineum ranged from 51 to 83% with the highest agreement in Eutrema salsugineum. Most of the highest ranking predictions from Arabidopsis thaliana were annotated as potential natural antisense genes, pseudogenes, transposable elements, or simply computationally predicted hypothetical protein. Due to the nature of this tool, the model can be updated as new long non-protein coding transcripts are identified and functionally verified.This ensemble classifier is an accurate tool that can be used to rank long non-protein coding RNA predictions for use in conjunction with gene expression studies. Selection of plant transcripts with a high potential for regulatory roles as long non-protein coding RNAs will advance research in the elucidation of long non-protein coding RNA function.