Davide Risso

Silencing of Odorant Receptor Genes by G Protein βγ Signaling Ensures the Expression of One Odorant Receptor per Olfactory Sensory Neuron

Olfactory sensory neurons express just one out of a possible ∼ 1,000 odorant receptor genes, reflecting an exquisite mode of gene regulation. In one model, once an odorant receptor is chosen for expression, other receptor genes are suppressed by a negative feedback mechanism, ensuring a stable functional identity of the sensory neuron for the lifetime of the cell. The signal transduction mechanism subserving odorant receptor gene silencing remains obscure, however. Here, we demonstrate in the zebrafish that odorant receptor gene silencing is dependent on receptor activity. Moreover, we show that signaling through G protein βγ subunits is both necessary and sufficient to suppress the expression of odorant receptor genes and likely acts through histone methylation to maintain the silenced odorant receptor genes in transcriptionally inactive heterochromatin. These results link receptor activity with the epigenetic mechanisms responsible for ensuring the expression of one odorant receptor per olfactory sensory neuron.

Deficiency of microRNA miR-34a expands cell fate potential in pluripotent stem cells

Limiting potential for totipotency Biological roles for microRNAs are not limited to RNA silencing and posttranscriptional regulation; they have now been shown to also regulate cell pluripotency. Choi et al. eliminated miR-34a from mouse embryonic stem cells and found that the cells exhibited a bidirectional cell fate potential, generating both embryonic and extraembryonic lineages (see the Perspective by Hasuwa and Siomi). During miR-34a deficiency, an endogenous retrovirus was induced, at least in part through Gata2-dependent transcriptional activation. Thus, the interplay of protein-coding genes, noncoding RNAs, and endogenous retroviruses can change cell fate plasticity and the developmental potential of pluripotent stem cells. Science, this issue p. eaag1927; see also p. 581 In mouse pluripotent stem cells, miR-34a deficiency expands developmental potential to both embryonic and extraembryonic lineages. INTRODUCTION Mouse zygotes and early blastomeres have totipotent cell fate potential, generating both embryonic and extraembryonic cell lineages during normal development. This totipotent potential is gradually restricted during development, with the first cell fate specification event being completed by the blastocyst stage. Mouse embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) exhibit a pluripotent potential similar to that of the epiblast in blastocysts, efficiently generating all embryonic cell types but rarely contributing to extraembryonic lineages in the placenta and yolk sac. Experimentally, ESCs and iPSCs can be induced into cells with expanded developmental potential, albeit with low efficiency. Such pluripotent stem cells are characterized by their bidirectional developmental potential (contributing to both embryonic and extraembryonic lineages) and their strong induction of the MuERV-L (MERVL) endogenous retroviruses (ERVs), both of which are features of totipotent two-cell (2C) blastomeres. The low efficiency in generating bipotential ESCs reflects the existence of multiple cellular and molecular impediments that restrict the pluripotent cell fate potential. RATIONALE We identified miR-34a microRNA (miRNA) as a potent regulator that restricts the cell fate potential of ESCs and iPSCs to a pluripotent state. miR-34a−/− pluripotent stem cells exhibit a bidirectional cell fate potential, generating both embryonic and extraembryonic cell lineages in multiple functional assays. Hence, the miR-34a−/− pluripotent stem cells provide a powerful experimental system to dissect the molecular mechanisms that restrict cell fate potential in ESCs and iPSCs. RESULTS miR-34a−/− ESCs and iPSCs exhibited an expanded cell fate potential, generating both embryonic and extraembryonic lineages in teratomas, embryoid bodies, and chimeric embryos. In particular, a single miR-34a−/− ESC injected into a recipient morula could yield progenies in both inner cell mass (ICM) and trophectoderm. Expression profiling studies comparing wild-type and miR-34a−/− pluripotent stem cells revealed a strong and specific induction of the MERVL ERVs, together with many MERVL-proximal genes, in miR-34a−/− ESCs and iPSCs. Whereas wild-type ESCs and iPSCs almost exclusively expressed Oct4, miR-34a−/− ESCs and iPSCs were heterogeneous, containing mutually exclusive populations with either Oct4 expression or MERVL induction. Because MERVL is a specific and highly expressed molecular marker for totipotent 2C blastomeres and for bipotential ESCs, we investigated the mechanism by which miR-34a regulates MERVL expression. We demonstrated that MERVL induction in miR-34a–deficient pluripotent stem cells is regulated at the transcriptional level, at least in part because of an increase of the transcription factor Gata2, a direct target of miR-34a. Knockdown of gata2 in miR-34a–deficient pluripotent stem cells phenocopied miR-34a overexpression, not only down-regulating the expression of MERVL but also abolishing their bipotential cell fate. Thus, miR-34a restricts cell fate potential and represses MERVL induction in pluripotent stem cells, at least in part through down-regulation of Gata2. CONCLUSION We have identified miR-34a as a noncoding RNA that restricts the cell fate potential of ESCs and iPSCs to a pluripotent state. The miR-34a/gata2/MERVL axis plays an essential role in modulating the transition between pluripotent stem cells and bipotential stem cells in culture. Thus, an intricate network of protein-coding genes, noncoding RNAs, and endogenous retroviruses could act cooperatively to define cell fate plasticity and developmental potential in pluripotent stem cells. The miR-34a/Gata2 pathway restricts the cell fate potential of ESCs and iPSCs to a pluripotent state. Pluripotent stem cell cultures contain mutually exclusive populations of pluripotent MERVLlow/Oct4high cells and bipotent MERVLhigh/Oct4low cells. In wild-type ESC and iPSC culture, this equilibrium strongly favors the MERVLlow/Oct4high population at the expense of the MERVLhigh/Oct4low population. miR-34a deficiency increases Gata2-dependent transcription of MERVL endogenous retroviruses, shifting the equilibrium to enable more cells to acquire a bipotential cell fate that yields both embryonic and extraembryonic cell lineages. Embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) efficiently generate all embryonic cell lineages but rarely generate extraembryonic cell types. We found that microRNA miR-34a deficiency expands the developmental potential of mouse pluripotent stem cells, yielding both embryonic and extraembryonic lineages and strongly inducing MuERV-L (MERVL) endogenous retroviruses, similar to what is seen with features of totipotent two-cell blastomeres. miR-34a restricts the acquisition of expanded cell fate potential in pluripotent stem cells, and it represses MERVL expression through transcriptional regulation, at least in part by targeting the transcription factor Gata2. Our studies reveal a complex molecular network that defines and restricts pluripotent developmental potential in cultured ESCs and iPSCs.

How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets

The sequencing of the full transcriptome (RNA-seq) has become the preferred choice for the measurement of genome-wide gene expression. Despite its widespread use, challenges remain in RNA-seq data analysis. One often-overlooked aspect is normalization. Despite the fact that a variety of factors or ‘batch effects’ can contribute unwanted variation to the data, commonly used RNA-seq normalization methods only correct for sequencing depth. The study of gene expression is particularly problematic when it is influenced simultaneously by a variety of biological factors in addition to the one of interest. Using examples from experimental neuroscience, we show that batch effects can dominate the signal of interest; and that the choice of normalization method affects the power and reproducibility of the results. While commonly used global normalization methods are not able to adequately normalize the data, more recently developed RNA-seq normalization can. We focus on one particular method, RUVSeq and show that it is able to increase power and biological insight of the results. Finally, we provide a tutorial outlining the implementation of RUVSeq normalization that is applicable to a broad range of studies as well as meta-analysis of publicly available data.

Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics

BackgroundSingle-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve.ResultsWe introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods.ConclusionsSlingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression.

A general and flexible method for signal extraction from single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) is a powerful high-throughput technique that enables researchers to measure genome-wide transcription levels at the resolution of single cells. Because of the low amount of RNA present in a single cell, some genes may fail to be detected even though they are expressed; these genes are usually referred to as dropouts. Here, we present a general and flexible zero-inflated negative binomial model (ZINB-WaVE), which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. We demonstrate, with simulated and real data, that the model and its associated estimation procedure are able to give a more stable and accurate low-dimensional representation of the data than principal component analysis (PCA) and zero-inflated factor analysis (ZIFA), without the need for a preliminary normalization step.Single-cell RNA sequencing (scRNA-seq) data provides information on transcriptomic heterogeneity within cell populations. Here, Risso et al develop ZINB-WaVE for low-dimensional representations of scRNA-seq data that account for zero inflation, over-dispersion, and the count nature of the data.

ZINB-WaVE: A general and flexible method for signal extraction from single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a powerful technique that enables researchers to measure gene expression at the resolution of single cells. Because of the low amount of RNA present in a single cell, many genes fail to be detected even though they are expressed; these genes are usually referred to as dropouts. Here, we present a general and flexible zero-inflated negative binomial model (ZINB-WaVE), which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. We demonstrate, with simulations and real data, that the model and its associated estimation procedure are able to give a more stable and accurate low-dimensional representation of the data than principal component analysis (PCA) and zero-inflated factor analysis (ZIFA), without the need for a preliminary normalization step.

Bioconductor workflow for single-cell RNA sequencing: Normalization, dimensionality reduction, clustering, and lineage inference.

Novel single-cell transcriptome sequencing assays allow researchers to measure gene expression levels at the resolution of single cells and offer the unprecendented opportunity to investigate at the molecular level fundamental biological questions, such as stem cell differentiation or the discovery and characterization of rare cell types. However, such assays raise challenging statistical and computational questions and require the development of novel methodology and software. Using stem cell differentiation in the mouse olfactory epithelium as a case study, this integrated workflow provides a step-by-step tutorial to the methodology and associated software for the following four main tasks: (1) dimensionality reduction accounting for zero inflation and over dispersion and adjusting for gene and cell-level covariates; (2) cell clustering using resampling-based sequential ensemble clustering; (3) inference of cell lineages and pseudotimes; and (4) differential expression analysis along lineages.

Injury Activates Transient Olfactory Stem Cell States with Diverse Lineage Capacities

Tissue homeostasis and regeneration are mediated by programs of adult stem cell renewal and differentiation. However, the mechanisms that regulate stem cell fates under such widely varying conditions are not fully understood. Using single-cell techniques, we assessed the transcriptional changes associated with stem cell self-renewal and differentiation and followed the maturation of stem cell-derived clones using sparse lineage tracing in the regenerating mouse olfactory epithelium. Following injury, quiescent olfactory stem cells rapidly shift to activated, transient states unique to regeneration and tailored to meet the demands of injury-induced repair, including barrier formation and proliferation. Multiple cell fates, including renewed stem cells and committed differentiating progenitors, are specified during this early window of activation. We further show that Sox2 is essential for cells to transition from the activated to neuronal progenitor states. Our study highlights strategies for stem cell-mediated regeneration that may be conserved in other adult stem cell niches.

Contextual fear conditioning induces differential alternative splicing.

The process of memory consolidation requires transcription and translation to form long-term memories. Significant effort has been dedicated to understanding changes in hippocampal gene expression after contextual fear conditioning. However, alternative splicing by differential transcript regulation during this time period has received less attention. Here, we use RNA-seq to determine exon-level changes in expression after contextual fear conditioning and retrieval. Our work reveals that a short variant of Homer1, Ania-3, is regulated by contextual fear conditioning. The ribosome biogenesis regulator Las1l, small nucleolar RNA Snord14e, and the RNA-binding protein Rbm3 also change specific transcript usage after fear conditioning. The changes in Ania-3 and Las1l are specific to either the new context or the context-shock association, while the changes in Rbm3 occur after context or shock only. Our analysis revealed novel transcript regulation of previously undetected changes after learning, revealing the importance of high throughput sequencing approaches in the study of gene expression changes after learning.

clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets

Clustering of genes and/or samples is a common task in gene expression analysis. The goals in clustering can vary, but an important scenario is that of finding biologically meaningful subtypes within the samples. This is an application that is particularly appropriate when there are large numbers of samples, as in many human disease studies. With the increasing popularity of single-cell transcriptome sequencing (RNA-Seq), many more controlled experiments on model organisms are similarly creating large gene expression datasets with the goal of detecting previously unknown heterogeneity within cells. It is common in the detection of novel subtypes to run many clustering algorithms, as well as rely on subsampling and ensemble methods to improve robustness. We introduce a Bioconductor R package, clusterExperiment, that implements a general and flexible strategy we entitle Resampling-based Sequential Ensemble Clustering (RSEC). RSEC enables the user to easily create multiple, competing clusterings of the data based on different techniques and associated tuning parameters, including easy integration of resampling and sequential clustering, and then provides methods for consolidating the multiple clusterings into a final consensus clustering. The package is modular and allows the user to separately apply the individual components of the RSEC procedure, i.e., apply multiple clustering algorithms, create a consensus clustering or choose tuning parameters, and merge clusters. Additionally, clusterExperiment provides a variety of visualization tools for the clustering process, as well as methods for the identification of possible cluster signatures or biomarkers. The R package clusterExperiment is publicly available through the Bioconductor Project, with a detailed manual (vignette) as well as well documented help pages for each function.