No abstract

Dagmar iber 3,4 , christian Beisel 5 , erik van nimwegen 2 & Verdon taylor 1* Neural stem cells (NSCs) generate neurons of the cerebral cortex with distinct morphologies and functions. How specific neuron production, differentiation and migration are orchestrated is unclear. Hippo signaling regulates gene expression through Tead transcription factors (TFs). We show that Hippo transcriptional coactivators Yap1/Taz and the Teads have distinct functions during cortical development. Yap1/Taz promote NSC maintenance and Satb2 + neuron production at the expense of Tbr1 + neuron generation. However, Teads have moderate effects on NSC maintenance and do not affect Satb2 + neuron differentiation. Conversely, whereas Tead2 blocks Tbr1 + neuron formation, Tead1 and Tead3 promote this early fate. In addition, we found that Hippo effectors regulate neuronal migration to the cortical plate (CP) in a reciprocal fashion, that ApoE, Dab2 and Cyr61 are Tead targets, and these contribute to neuronal fate determination and migration. Our results indicate that multifaceted Hippo signaling is pivotal in different aspects of cortical development. NSCs of the developing cerebral cortex form the ventricular zone (VZ) lining the lumen of the neural tube 1-5. NSCs in the dorsal anterior forebrain are the major source of the projection neurons of the cerebral cortex 4,5. The mechanisms controlling the patterning and cell fate specification of these stem cells during early brain development are not clearly understood. Although various signaling pathways including Notch, Wnt, Shh, FGFs, TGF-β, Retinoic acid, Reelin and Hippo are known to regulate NSC proliferation and to control fate decisions, neurogenesis, and gliogenesis; the crosstalk between the different signaling pathways and the integration of these signals on target genes governing complex cell fate choices is unclear 1-3. Hippo signaling is evolutionarily conserved and a regulator of organ size control and tissue homeostasis 6-9. The pathway is regulated by numerous stimuli including G-protein coupled receptor signaling, mechanical stress, cellular energy status, cell-cell contact and cell-extra-cellular matrix interactions 6-8. Hippo signaling employs a cascade of phosphorylation steps mediated by the kinases Mst1/2 and Lats1/2 8-10. Lats1/2 phosphorylate the transcriptional coregulators Yap1 and Taz to promote cytoplasmic retention and subsequent degradation 6-8. When Hippo signaling is inactive, Yap1/Taz translocate to the nucleus and form multiple complexes with different DNA binding partners including TEADs, SMADs, and Runx TFs (Fig. S1a) 8-10. The Teads are major regulators of Hippo target genes in many systems including cancer 8,11,12. Fat4 and Dchs are receptor and ligand, respectively, of the Hippo pathway in embryonic NSCs. Knockdown of Fat4 results in increased proliferation in the developing nervous system and reduction of neuronal differentiation 13,14. Mutations in FAT4 and DCHS cause Van Maldergem syndrome in humans, an autosomal-recessive disorder characterized by in...

AbstractmiRNAs are small RNAs that regulate gene expression post‐transcriptionally. By repressing the translation and promoting the degradation of target mRNAs, miRNAs may reduce the cell‐to‐cell variability in protein expression, induce correlations between target expression levels, and provide a layer through which targets can influence each other's expression as “competing RNAs” (ceRNAs). However, experimental evidence for these behaviors is limited. Combining mathematical modeling with RNA sequencing of individual human embryonic kidney cells in which the expression of two distinct miRNAs was induced over a wide range, we have inferred parameters describing the response of hundreds of miRNA targets to miRNA induction. Individual targets have widely different response dynamics, and only a small proportion of predicted targets exhibit high sensitivity to miRNA induction. Our data reveal for the first time the response parameters of the entire network of endogenous miRNA targets to miRNA induction, demonstrating that miRNAs correlate target expression and at the same time increase the variability in expression of individual targets across cells. The approach is generalizable to other miRNAs and post‐transcriptional regulators to improve the understanding of gene expression dynamics in individual cell types.

We quantify the strength of miRNA-target interactions with MIRZA, a recently introduced biophysical model. We show that computationally predicted energies of interaction correlate strongly with the energies of interaction estimated from biochemical measurements of Michaelis-Menten constants. We further show that the accuracy of the MIRZA model can be improved taking into account recently emerged experimental data types. In particular, we use chimeric miRNA-mRNA sequences to infer a MIRZA-CHIMERA model and we provide a framework for inferring a similar model from measurements of rate constants of miRNA-mRNA interaction in the context of Argonaute proteins. Finally, based on a simple model of miRNA-based regulation, we discuss the importance of interaction energy and its variability between targets for the modulation of miRNA target expression in vivo.

In spite of a large investment in the development of methodologies for analysis of singlecell RNA-seq data, there is still little agreement on how to best normalize such data, i.e. how to quantify gene expression states of single cells from such data. Starting from a few basic requirements such as that inferred expression states should correct for both intrinsic biological fluctuations and measurement noise, and that changes in expression state should be measured in terms of fold-changes rather than changes in absolute levels, we here derive a unique Bayesian procedure for normalizing single-cell RNA-seq data from first principles. Our implementation of this normalization procedure, called Sanity (SAmpling Noise corrected Inference of Transcription activitY), estimates log expression values and associated errors bars directly from raw UMI counts without any tunable parameters.Comparison of Sanity with other recent normalization methods on a selection of scRNAseq datasets shows that Sanity outperforms other methods on basic downstream processing tasks such as clustering cells into subtypes and identification of differentially expressed genes. More importantly, we show that all other normalization methods present severely distorted pictures of the data. By failing to account for biological and technical Poisson noise, many methods systematically predict the lowest expressed genes to be most variable in expression, whereas in reality these genes provide least evidence of true biological variability. In addition, by confounding noise removal with lower-dimensional representation of the data, many methods introduce strong spurious correlations of expression levels with the total UMI count of each cell as well as spurious co-expression of genes.

The cerebral cortex contains billions of neurons, and their disorganization or misspecification leads to neurodevelopmental disorders. Understanding how the plethora of projection neuron subtypes are generated by cortical neural stem cells (NSCs) is a major challenge. Here, we focused on elucidating the transcriptional landscape of murine embryonic NSCs, basal progenitors (BPs), and newborn neurons (NBNs) throughout cortical development. We uncover dynamic shifts in transcriptional space over time and heterogeneity within each progenitor population. We identified signature hallmarks of NSC, BP, and NBN clusters and predict active transcriptional nodes and networks that contribute to neural fate specification. We find that the expression of receptors, ligands, and downstream pathway components is highly dynamic over time and throughout the lineage implying differential responsiveness to signals. Thus, we provide an expansive compendium of gene expression during cortical development that will be an invaluable resource for studying neural developmental processes and neurodevelopmental disorders.

Gene regulatory networks are ultimately encoded by the sequence-specific binding of (TFs) to short DNA segments. Although it is customary to represent the binding specificity of a TF by a position-specific weight matrix (PSWM), which assumes each position within a site contributes independently to the overall binding affinity, evidence has been accumulating that there can be significant dependencies between positions. Unfortunately, methodological challenges have so far hindered the development of a practical and generally-accepted extension of the PSWM model. On the one hand, simple models that only consider dependencies between nearest-neighbor positions are easy to use in practice, but fail to account for the distal dependencies that are observed in the data. On the other hand, models that allow for arbitrary dependencies are prone to overfitting, requiring regularization schemes that are difficult to use in practice for non-experts. Here we present a new regulatory motif model, called dinucleotide weight tensor (DWT), that incorporates arbitrary pairwise dependencies between positions in binding sites, rigorously from first principles, and free from tunable parameters. We demonstrate the power of the method on a large set of ChIP-seq data-sets, showing that DWTs outperform both PSWMs and motif models that only incorporate nearest-neighbor dependencies. We also demonstrate that DWTs outperform two previously proposed methods. Finally, we show that DWTs inferred from ChIP-seq data also outperform PSWMs on HT-SELEX data for the same TF, suggesting that DWTs capture inherent biophysical properties of the interactions between the DNA binding domains of TFs and their binding sites. We make a suite of DWT tools available at dwt.unibas.ch, that allow users to automatically perform ‘motif finding’, i.e. the inference of DWT motifs from a set of sequences, binding site prediction with DWTs, and visualization of DWT ‘dilogo’ motifs.

MiRNAs are posttranscriptional repressors of gene expression that may additionally reduce the celltocell variability in protein expression, induce correlations between target expression levels and provide a layer through which targets can influence each other's expression as 'competing RNAs' (ceRNAs). Here we combined single cell sequencing of human embryonic kidney cells in which the expression of two distinct miRNAs was induced over a wide range, with mathematical modeling, to estimate MichaelisMenten ( K M )type constants for hundreds of evolutionarily conserved miRNA targets. These parameters, which we inferred here for the first time in the context of the entire network of endogenous miRNA targets, vary over~2 orders of magnitude. They reveal an in vivo hierarchy of miRNA targets, defined by the concentration of miRNAArgonaute complexes at which the targets are most sensitively downregulated. The data further reveals miRNAinduced correlations in target expression at the single cell level, as well as the response of target noise to the miRNA concentration. The approach is generalizable to other miRNAs and posttranscriptional regulators and provides a deeper understanding of gene expression dynamics.