Despite substantial progress in single-cell RNA-seq (scRNA-seq) data analysis methods, there is still little agreement on how to best normalize such data. Starting from the basic requirements that ...inferred expression states should correct for both biological and measurement sampling noise and that changes in expression should be measured in terms of fold changes, we here derive a Bayesian normalization procedure called Sanity (SAmpling-Noise-corrected Inference of Transcription activitY) from first principles. Sanity estimates expression values and associated error bars directly from raw unique molecular identifier (UMI) counts without any tunable parameters. Using simulated and real scRNA-seq datasets, we show that Sanity outperforms other normalization methods on downstream tasks, such as finding nearest-neighbor cells and clustering cells into subtypes. Moreover, we show that by systematically overestimating the expression variability of genes with low expression and by introducing spurious correlations through mapping the data to a lower-dimensional representation, other methods yield severely distorted pictures of the data.
The origin of functional heterogeneity among macrophages, key innate immune system components, is still debated. While mouse strains differ in their immune responses, the range of gene expression ...variation among their pre‐stimulation macrophages is unknown. With a novel approach to scRNA‐seq analysis, we reveal the gene expression variation in unstimulated macrophage populations from BALB/c and C57BL/6 mice. We show that intrinsic strain‐to‐strain differences are detectable before stimulation and we place the unstimulated single cells within the gene expression landscape of stimulated macrophages. C57BL/6 mice show stronger evidence of macrophage polarization than BALB/c mice, which may contribute to their relative resistance to pathogens. Our computational methods can be generally adopted to uncover biological variation between cell populations.
By analysing the gene expression profiles of individual peritoneal macrophages from BALB/c and C57BL/6 mice in the absence of any specific antigen stimulation, we found intrinsic cell‐to‐cell variability along the previously reported axes of M1/M2 polarization. The gene expression bias was stronger in C57BL/6 compared to BALB/c macrophages.
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.
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.
Display omitted
•A method to infer models of miRNA–target interaction from diverse data sets.•Most predictive model – derived from chimeric miRNA–target sequences.•Chimera–derived general model – ...more predictive than miRNA-specific models.
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.
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.
Synopsis
Understanding brain development requires detailed insight into the temporal transcription dynamics associated with cell lineage determination. This genome‐wide profiling study provides a comprehensive transcriptomic resource of dorsal cerebral cortical lineages, highlighting the dynamics in gene expression from neural stem cells, basal progenitors, and newborn neurons at the population and single‐cell levels.
Neural stem cells (NSCs), basal progenitors (BPs) and newborn neurons (NBN) of the developing cerebral cortex have hallmark gene expression signatures.
Dynamics in gene expression define NSC, BP, and NBN heterogeneity, changes in signaling potential, and transcription factor activity.
Shifts in transcriptional space over developmental time correspond to neural progenitor fate commitment.
Signaling pathways and transcriptional networks during cortical development identify functional nodes and novel gene signatures, implying a ‘cellular logic’ to fundamental cell fate decisions.
An atlas of mouse cerebral cortical cell transcriptome during development reveal patterns of gene expression that correspond to cell fate decisions.
miRNAs 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.
Synopsis
A combination of mathematical modeling and RNA‐seq of single cells with variable miRNA expression provides an approach for inferring the parameters describing the response of miRNA targets expressed in their native context, to variations in miRNA levels.
Cell lines are developed with doxycycline‐inducible expression of specific miRNAs.
Stochastic variation in single‐cell gene expression can be used to infer biochemical parameters of miRNA‐target interaction.
The inferred parameters indicate that in this experimental system, endogenous targets are only marginally affected by the expression of a competing RNA.
These analyses further reveal that at intermediate expression levels, where targets respond ultrasensitively to the miRNA, the correlation in target expression increases 2–3 fold.
A combination of mathematical modeling and RNA‐seq of single cells with variable miRNA expression provides an approach for inferring the parameters describing the response of miRNA targets expressed in their native context, to variations in miRNA levels.
miRNAs 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.
PDF
