Neuro-mesodermal progenitors (NMPs): a comparative study between pluripotent stem cells and embryo-derived populations.

The mammalian embryo's caudal lateral epiblast (CLE) harbours bipotent progenitors, called neural mesodermal progenitors (NMPs), that contribute to the spinal cord and the paraxial mesoderm throughout axial elongation. Here, we performed a single cell analysis of different in vitro NMP populations produced either from embryonic stem cells (ESCs) or epiblast stem cells (EpiSCs) and compared them with E8.25 CLE mouse embryos. In our analysis of this region, our findings challenge the notion that NMPs can be defined by the exclusive co-expression of Sox2 and T at mRNA level. We analyse the in vitro NMP-like populations using a purpose-built support vector machine (SVM) based on the embryo CLE and use it as a classification model to compare the in vivo and in vitro populations. Our results show that NMP differentiation from ESCs leads to heterogeneous progenitor populations with few NMP-like cells, as defined by the SVM algorithm, whereas starting with EpiSCs yields a high proportion of cells with the embryo NMP signature. We find that the population from which the Epi-NMPs are derived in culture contains a node-like population, which suggests that this population probably maintains the expression of T in vitro and thereby a source of NMPs. In conclusion, differentiation of EpiSCs into NMPs reproduces events in vivo and suggests a sequence of events for the emergence of the NMP population

An epiblast stem cell-derived multipotent progenitor population for axial extension.

The caudal lateral epiblast of mammalian embryos harbours bipotent progenitors that contribute to the spinal cord and the paraxial mesoderm in concert with the body axis elongation. These progenitors, called neural mesodermal progenitors (NMPs), are identified as cells that co-express Sox2 and T/brachyury, a criterion used to derive NMP-like cells from embryonic stem cells in vitro However, unlike embryonic NMPs, these progenitors do not self-renew. Here, we find that the protocols that yield NMP-like cells in vitro initially produce a multipotent population that, in addition to NMPs, generates progenitors for the lateral plate and intermediate mesoderm. We show that epiblast stem cells (EpiSCs) are an effective source of these multipotent progenitors, which are further differentiated by a balance between BMP and Nodal signalling. Importantly, we show that NMP-like cells derived from EpiSCs exhibit limited self-renewal in vitro and a gene expression signature like their embryonic counterparts.Cambridge Trusts, Cambridge philosophical Societ

Date with Destiny: Genetic and Epigenetic Factors in Cell Fate Decisions in Populations of Multipotent Stem Cells

The governance of cell fate decisions during development is a fundamental biological problem. An important aspect of this is how cells exit a multipotent state and choose their fates in a correct manner and proportion. To tackle an aspect of this problem, I have focused on 2 multipotent models: one infinite self-renewal pluripotency in an artificial environment, and the other, bipotent progenitors in the context of the mouse embryo. The first model aimed to explore the effects of chromatin-associated factors on the ability of pluripotent mouse Embryonic Stem Cells (ESCs) to self-renew, via monitoring gene expression heterogeneity of key genes. The second model focused on Neural Mesodermal Progenitors (NMPs), a bipotent cell population found in the Caudal Lateral Epiblast (CLE) of mammalian embryos, which contributes to the spinal cord and paraxial mesoderm. The aim here was to derive NMPs in vitro which exhibit similar gene expression patterns and function like their mouse embryo counterpart and study their renewal and differentiation in detail. The first multipotent model explores the effects of chromatin remodelling on cell fate decisions, specifically investigating the consequences of inhibiting the histone acetyltransferase Kat2a on the ESCs fate. I found first, that the effect of Kat2a inhibition depends on the pluripotent state of the cells; cells in a ground state exhibit a resistance to Kat2a inhibition and maintain their pluripotency, whereas cells in a naïve state experience destabilization of their pluripotency gene regulatory network and shift towards differentiation. Second, that Kat2a inhibition in the naïve state results in a decline in the gene expression noise strength contributed by the promoter activation operation, which suggests that when ESCs become lineage-primed their transcriptional noise is constrained. In the bipotent model, the NMPs are identified as cells coexpressing Sox2 and T/Brachyury, a criterion used to derive NMP-like cells from ESCs in vitro. Comparison between the different NMPs protocols stresses that Epiblast Stem Cells (EpiSCs) are an effective source for deriving a multipotent population resembling the embryo Caudal Epiblast (CE), that generates NMPs. Furthermore, self-organization of this CE-like population, resulted in axially organized aggregates. Exploiting the mouse embryo CLE as a reference shows that EpiSCs derived NMPs, monolayers and aggregates, consist of a high proportion of cells with the embryo’s NMP signature. Importantly, studying this system in vitro sheds light on the sequence of events which lead to NMP emergence in vivo. On this basis, I conclude that understanding the initial state of cells at a crossroads is important to reveal the limitations it imposes on the cells fate exploration, hence makes it possible to mimic more precisely the fate decision process in vitro.This work was supported by Cambridge Trust, Cambridge Philosophical Society, AJA Karten trust and Fitzwilliam college

Quantifying the Effect of Ribosomal Density on mRNA Stability

<div><p>Gene expression is a fundamental cellular process by which proteins are eventually synthesized based on the information coded in the genes. This process includes four major steps: transcription of the DNA segment corresponding to a gene to mRNA molecules, the degradation of the mRNA molecules, the translation of mRNA molecules to proteins by the ribosome and the degradation of the proteins. We present an innovative quantitative study of the interaction between the gene translation stage and the mRNA degradation stage using large scale genomic data of <i>S. cerevisiae</i>, which include measurements of mRNA levels, mRNA half-lives, ribosomal densities and protein abundances, for thousands of genes. The reported results support the conjecture that transcripts with higher ribosomal density, which is related to the translation stage, tend to have elevated half-lives, and we suggest a novel quantitative estimation of the strength of this relation. Specifically, we show that on average, an increase of <i>78%</i> in ribosomal density yields an increase of <i>25%</i> in mRNA half-life, and that this relation between ribosomal density and mRNA half-life is not function specific. In addition, our analyses demonstrate that ribosomal density along the entire ORF, and not in specific locations, has a significant effect on the transcript half-life. Finally, we show that the reported relation cannot be explained by different expression levels among genes. A plausible explanation for the reported results is that ribosomes tend to protect the mRNA molecules from the exosome complexes degrading them; however, additional non-mutually exclusive possible explanations for the reported relation and experiments for their verifications are discussed in the paper.</p></div

Description of the ribosomal profiling approach

<p>: <b>A</b>. Cells are treated with cycloheximide (for example) to arrest translating ribosomes; <b>B</b>. RNA fragments that are protected from RNases by the ribosomes are isolated and <b>C</b>. processed for Illumina high-throughput sequencing. <b>D</b>. The next steps are computational – reads are mapped to the ORFs of the analyzed organism. Ribosomal footprint reads of a certain codon are generated when the codon is covered by ribosomes. Thus, highly translated genes tend to create a higher number of reads.</p

Biological process GO: RD profile at single nucleotide resolution, of the last <i>600</i> nts when all genes are aligned to the ORFs 3′ end

<p>: <b>A</b>. The RD median of the ORF's 3′ end RD profiles: the red/green bars represent the RD median of the <i>20%</i> of the genes with top/bottom half-life. B. Wilcoxon rank sum test between the ribosomal densities profiles of the genes from the top and bottom <i>20%</i> half-life for different functional genes groups. The x-axes represent the log (Wilcoxon test P-value) and the y-axes represent the functional genes group (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102308#pone.0102308.s016" target="_blank">Table S5</a>). Red bars indicate the <i>P-value ≤0.05</i> whereas green bars indicate the <i>P-value >0.05</i>.</p

Biological process GO: RD profile at single nucleotide resolution, of the first <i>600</i> nts when all genes are aligned to the ORF's 5′ end

<p>: <b>A</b>. The RD median of the ORF's 5′ end RD profiles: the red/green bars represent the RD median of the <i>20%</i> of the genes with top/bottom half-life. <b>B</b>. Wilcoxon rank sum test between the RD profiles of the genes from the top and bottom <i>20%</i> half-life for different functional genes groups. The x-axis represents the log (Wilcoxon test P-value) and the y-axis represents the functional genes group (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102308#pone.0102308.s016" target="_blank">Table S5</a>). Red bars indicate that <i>P-value ≤0.05</i> whereas green bars indicate that <i>P-value >0.05</i>.</p

Ribosomal density profile for highly translated genes at single nucleotide resolution.

<p><b>A</b>. The first <i>600</i> nts; all genes are aligned to the ORF's 5′ end. <b>B</b>. The last <i>600</i> nts; all genes are aligned to the ORF's 3′ end. The y-axes represent the mean RD in logarithmic scale at specific locations along the ORF; the x-axes represent the location of a nucleotide measured as the distance from the ORF's 5′ end (positive numbers at <b>A</b>.) or distance from the ORF's 3′ end (negative number at <b>B</b>.). The red line describes the <i>20%</i> of genes with the longest mRNA half-life; the green line describes the <i>20%</i> of genes with the shortest mRNA half-life, and the blue line describes all the genes. The inset in each plot includes the RD median for each group of genes, from left to right: the <i>20%</i> of the genes with the shortest half-life, all genes, and the <i>20%</i> of the genes with the longest half-life. The number above the arrow is the P-value corresponding to the Wilcoxon rank sum test between the local averaged RD of genes with the <i>20%</i> longest and shortest half-life.</p

mRNA half-life distributions.

<p>Half-life distributions of the genes from the bottom <i>20%</i> RD (green curve), top <i>20%</i> RD (red curve), and of all genes (blue curve). The inset in each plot includes the median of each curve, which is represented by the intersection with the x-axis of a vertical line with the appropriate color: green, red and blue lines that indicate the half-life medians of the genes from the bottom and top <i>20%</i> RD and of all genes respectively; for a better visualization, the graphs are based on the log (mRNA HL) values. The number above the arrow in each inset is the P-value corresponding to the Wilcoxon rank sum test between the mRNA HL of genes with the top and bottom <i>20%</i> RD. <b>A</b>. Half-life distributions at the ORF's 5′ end according to the genes RD average of the first <i>50</i> nucleotides downstream the ORF's 5′ end. <b>B</b>. Half-life distributions at the ORF's 3′ end according to the genes RD average of the last <i>50</i> nucleotides upstream the ORF's 3′ end. <b>C</b>. Half-life distributions according to the genes total average of the ORF's RD.</p

Ribosomal density versus mRNA half-life given the protein abundance (binned data; details in Materials and Methods).

<p>The RD were calculated as: 1) the number of ribosomes on the mRNA <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102308#pone.0102308-Arava1" target="_blank">[30]</a> divided by its length, 2) averaging the ribosomal profiling data from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102308#pone.0102308-Ingolia1" target="_blank">[29]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102308#pone.0102308-Brar1" target="_blank">[32]</a> over the entire ORF, 3) the ORF's 5′ end –the first 50 nt and 4) the ORF's 3′ end – the last 50 nt.</p