Heterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos.

The major and essential objective of pre-implantation development is to establish embryonic and extra-embryonic cell fates. To address when and how this fundamental process is initiated in mammals, we characterize transcriptomes of all individual cells throughout mouse pre-implantation development. This identifies targets of master pluripotency regulators Oct4 and Sox2 as being highly heterogeneously expressed between blastomeres of the 4-cell embryo, with Sox21 showing one of the most heterogeneous expression profiles. Live-cell tracking demonstrates that cells with decreased Sox21 yield more extra-embryonic than pluripotent progeny. Consistently, decreasing Sox21 results in premature upregulation of the differentiation regulator Cdx2, suggesting that Sox21 helps safeguard pluripotency. Furthermore, Sox21 is elevated following increased expression of the histone H3R26-methylase CARM1 and is lowered following CARM1 inhibition, indicating the importance of epigenetic regulation. Therefore, our results indicate that heterogeneous gene expression, as early as the 4-cell stage, initiates cell-fate decisions by modulating the balance of pluripotency and differentiation.

Measuring and Modeling Single-Cell Heterogeneity and Fate Decision in Mouse Embryos.

Cellular heterogeneity is a property of any living system; however, its relationship with cellular fate decision remains an open question. Recent technological advances have enabled valuable insights, especially in complex systems such as the mouse embryo. In this review, we discuss recent studies that characterize cellular heterogeneity at different levels during mouse development, from the two-cell stage up to gastrulation. In addition to key experimental findings, we review mathematical modeling approaches that help researchers interpret these findings. Disentangling the role of heterogeneity in cell fate decision will likely rely on the refined integration of experiments, large-scale omics data, and mathematical modeling, complemented by the use of synthetic embryos and gastruloids as promising in vitro models.

Single-cell transcriptomic characterization of a gastrulating human embryo.

Gastrulation is the fundamental process in all multicellular animals through which the basic body plan is first laid down1-4. It is pivotal in generating cellular diversity coordinated with spatial patterning. In humans, gastrulation occurs in the third week after fertilization. Our understanding of this process in humans is relatively limited and based primarily on historical specimens5-8, experimental models9-12 or, more recently, in vitro cultured samples13-16. Here we characterize in a spatially resolved manner the single-cell transcriptional profile of an entire gastrulating human embryo, staged to be between 16 and 19 days after fertilization. We use these data to analyse the cell types present and to make comparisons with other model systems. In addition to pluripotent epiblast, we identified primordial germ cells, red blood cells and various mesodermal and endodermal cell types. This dataset offers a unique glimpse into a central but inaccessible stage of our development. This characterization provides new context for interpreting experiments in other model systems and represents a valuable resource for guiding directed differentiation of human cells in vitro.

CIARA: a cluster-independent algorithm for identifying markers of rare cell types from single-cell sequencing data.

A powerful feature of single-cell genomics is the possibility of identifying cell types from their molecular profiles. In particular, identifying novel rare cell types and their marker genes is a key potential of single-cell RNA sequencing. Standard clustering approaches perform well in identifying relatively abundant cell types, but tend to miss rarer cell types. Here, we have developed CIARA (Cluster Independent Algorithm for the identification of markers of RAre cell types), a cluster-independent computational tool designed to select genes that are likely to be markers of rare cell types. Genes selected by CIARA are subsequently integrated with common clustering algorithms to single out groups of rare cell types. CIARA outperforms existing methods for rare cell type detection, and we use it to find previously uncharacterized rare populations of cells in a human gastrula and among mouse embryonic stem cells treated with retinoic acid. Moreover, CIARA can be applied more generally to any type of single-cell omic data, thus allowing the identification of rare cells across multiple data modalities. We provide implementations of CIARA in user-friendly packages available in R and Python.

Multiplex-GAM: genome-wide identification of chromatin contacts yields insights overlooked by Hi-C.

Technology for measuring 3D genome topology is increasingly important for studying gene regulation, for genome assembly and for mapping of genome rearrangements. Hi-C and other ligation-based methods have become routine but have specific biases. Here, we develop multiplex-GAM, a faster and more affordable version of genome architecture mapping (GAM), a ligation-free technique that maps chromatin contacts genome-wide. We perform a detailed comparison of multiplex-GAM and Hi-C using mouse embryonic stem cells. When examining the strongest contacts detected by either method, we find that only one-third of these are shared. The strongest contacts specifically found in GAM often involve 'active' regions, including many transcribed genes and super-enhancers, whereas in Hi-C they more often contain 'inactive' regions. Our work shows that active genomic regions are involved in extensive complex contacts that are currently underestimated in ligation-based approaches, and highlights the need for orthogonal advances in genome-wide contact mapping technologies.

Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data.

MOTIVATION: In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms' ability to capture structural properties of networks, which are fundamental, e.g., for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs. RESULTS: To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four inference algorithms and provide guidance on which algorithm should be used depending on the global network property of interest. AVAILABILITY AND IMPLEMENTATION: STREAMLINE is available at https://github.com/ScialdoneLab/STREAMLINE. The data generated in this study are available at https://doi.org/10.5281/zenodo.10710444.

Single molecule MATAC-seq reveals key determinants of DNA replication origin efficiency.

Stochastic origin activation gives rise to significant cell-to-cell variability in the pattern of genome replication. The molecular basis for heterogeneity in efficiency and timing of individual origins is a long-standing question. Here, we developed Methylation Accessibility of TArgeted Chromatin domain Sequencing (MATAC-Seq) to determine single-molecule chromatin accessibility of four specific genomic loci. MATAC-Seq relies on preferential modification of accessible DNA by methyltransferases combined with Nanopore-Sequencing for direct readout of methylated DNA-bases. Applying MATAC-Seq to selected early-efficient and late-inefficient yeast replication origins revealed large heterogeneity of chromatin states. Disruption of INO80 or ISW2 chromatin remodeling complexes leads to changes at individual nucleosomal positions that correlate with changes in their replication efficiency. We found a chromatin state with an accessible nucleosome-free region in combination with well-positioned +1 and +2 nucleosomes as a strong predictor for efficient origin activation. Thus, MATAC-Seq identifies the large spectrum of alternative chromatin states that co-exist on a given locus previously masked in population-based experiments and provides a mechanistic basis for origin activation heterogeneity during eukaryotic DNA replication. Consequently, our single-molecule chromatin accessibility assay will be ideal to define single-molecule heterogeneity across many fundamental biological processes such as transcription, replication, or DNA repair in vitro and ex vivo.

EBNA2-EBF1 complexes promote MYC expression and metabolic processes driving S-phase progression of Epstein-Barr virus-infected B cells.

Epstein-Barr virus (EBV) is a human tumor virus which preferentially infects resting human B cells. Upon infection in vitro, EBV activates and immortalizes these cells. The viral latent protein EBV nuclear antigen 2 (EBNA2) is essential for B cell activation and immortalization; it targets and binds the cellular and ubiquitously expressed DNA-binding protein CBF1, thereby transactivating a plethora of viral and cellular genes. In addition, EBNA2 uses its N-terminal dimerization (END) domain to bind early B cell factor 1 (EBF1), a pioneer transcription factor specifying the B cell lineage. We found that EBNA2 exploits EBF1 to support key metabolic processes and to foster cell cycle progression of infected B cells in their first cell cycles upon activation. The α1-helix within the END domain was found to promote EBF1 binding. EBV mutants lacking the α1-helix in EBNA2 can infect and activate B cells efficiently, but activated cells fail to complete the early S phase of their initial cell cycle. Expression of MYC, target genes of MYC and E2F, as well as multiple metabolic processes linked to cell cycle progression are impaired in EBVΔα1-infected B cells. Our findings indicate that EBF1 controls B cell activation via EBNA2 and, thus, has a critical role in regulating the cell cycle of EBV-infected B cells. This is a function of EBF1 going beyond its well-known contribution to B cell lineage specification.

Erratum: Pluripotent state transitions coordinate morphogenesis in mouse and human embryos.

This corrects the article DOI: 10.1038/nature24675.

The role of cell geometry and cell-cell communication in gradient sensing.

Cells can measure shallow gradients of external signals to initiate and accomplish a migration or a morphogenetic process. Recently, starting from mathematical models like the local-excitation global-inhibition (LEGI) model and with the support of empirical evidence, it has been proposed that cellular communication improves the measurement of an external gradient. However, the mathematical models that have been used have over-simplified geometries (e.g., they are uni-dimensional) or assumptions about cellular communication, which limit the possibility to analyze the gradient sensing ability of more complex cellular systems. Here, we generalize the existing models to study the effects on gradient sensing of cell number, geometry and of long- versus short-range cellular communication in 2D systems representing epithelial tissues. We find that increasing the cell number can be detrimental for gradient sensing when the communication is weak and limited to nearest neighbour cells, while it is beneficial when there is long-range communication. We also find that, with long-range communication, the gradient sensing ability improves for tissues with more disordered geometries; on the other hand, an ordered structure with mostly hexagonal cells is advantageous with nearest neighbour communication. Our results considerably extend the current models of gradient sensing by epithelial tissues, making a step further toward predicting the mechanism of communication and its putative mediator in many biological processes.

Complex multi-enhancer contacts captured by genome architecture mapping.

The organization of the genome in the nucleus and the interactions of genes with their regulatory elements are key features of transcriptional control and their disruption can cause disease. Here we report a genome-wide method, genome architecture mapping (GAM), for measuring chromatin contacts and other features of three-dimensional chromatin topology on the basis of sequencing DNA from a large collection of thin nuclear sections. We apply GAM to mouse embryonic stem cells and identify enrichment for specific interactions between active genes and enhancers across very large genomic distances using a mathematical model termed SLICE (statistical inference of co-segregation). GAM also reveals an abundance of three-way contacts across the genome, especially between regions that are highly transcribed or contain super-enhancers, providing a level of insight into genome architecture that, owing to the technical limitations of current technologies, has previously remained unattainable. Furthermore, GAM highlights a role for gene-expression-specific contacts in organizing the genome in mammalian nuclei.

Pluripotent state transitions coordinate morphogenesis in mouse and human embryos.

The foundations of mammalian development lie in a cluster of embryonic epiblast stem cells. In response to extracellular matrix signalling, these cells undergo epithelialization and create an apical surface in contact with a cavity, a fundamental event for all subsequent development. Concomitantly, epiblast cells transit through distinct pluripotent states, before lineage commitment at gastrulation. These pluripotent states have been characterized at the molecular level, but their biological importance remains unclear. Here we show that exit from an unrestricted naive pluripotent state is required for epiblast epithelialization and generation of the pro-amniotic cavity in mouse embryos. Embryonic stem cells locked in the naive state are able to initiate polarization but fail to undergo lumenogenesis. Mechanistically, exit from naive pluripotency activates an Oct4-governed transcriptional program that results in expression of glycosylated sialomucin proteins and the vesicle tethering and fusion events of lumenogenesis. Similarly, exit of epiblasts from naive pluripotency in cultured human post-implantation embryos triggers amniotic cavity formation and developmental progression. Our results add tissue-level architecture as a new criterion for the characterization of different pluripotent states, and show the relevance of transitions between these states during development of the mammalian embryo.

Resolving early mesoderm diversification through single-cell expression profiling.

In mammals, specification of the three major germ layers occurs during gastrulation, when cells ingressing through the primitive streak differentiate into the precursor cells of major organ systems. However, the molecular mechanisms underlying this process remain unclear, as numbers of gastrulating cells are very limited. In the mouse embryo at embryonic day 6.5, cells located at the junction between the extra-embryonic region and the epiblast on the posterior side of the embryo undergo an epithelial-to-mesenchymal transition and ingress through the primitive streak. Subsequently, cells migrate, either surrounding the prospective ectoderm contributing to the embryo proper, or into the extra-embryonic region to form the yolk sac, umbilical cord and placenta. Fate mapping has shown that mature tissues such as blood and heart originate from specific regions of the pre-gastrula epiblast, but the plasticity of cells within the embryo and the function of key cell-type-specific transcription factors remain unclear. Here we analyse 1,205 cells from the epiblast and nascent Flk1(+) mesoderm of gastrulating mouse embryos using single-cell RNA sequencing, representing the first transcriptome-wide in vivo view of early mesoderm formation during mammalian gastrulation. Additionally, using knockout mice, we study the function of Tal1, a key haematopoietic transcription factor, and demonstrate, contrary to previous studies performed using retrospective assays, that Tal1 knockout does not immediately bias precursor cells towards a cardiac fate.

Epstein-Barr virus reprograms human B lymphocytes immediately in the prelatent phase of infection.

Epstein-Barr virus (EBV) is a human tumor virus and a model of herpesviral latency. The virus efficiently infects resting human B lymphocytes and induces their continuous proliferation in vitro, which mimics certain aspects of EBV's oncogenic potential in vivo. How lymphoblastoid cell lines (LCLs) evolve from the infected lymphocytes is uncertain. We conducted a systematic time-resolved longitudinal study of cellular functions and transcriptional profiles of newly infected naïve primary B lymphocytes. EBV reprograms the cells comprehensively and globally. Rapid and extensive transcriptional changes occur within 24 h and precede any metabolic and phenotypic changes. Within 72 h, the virus activates the cells, changes their phenotypes with respect to cell size, RNA, and protein content, and induces metabolic pathways to cope with the increased demand for energy, supporting an efficient cell cycle entry on day 3 postinfection. The transcriptional program that EBV initiates consists of 3 waves of clearly discernable clusters of cellular genes that peak on day 2, 3, or 4 and regulate RNA synthesis, metabolic pathways, and cell division, respectively. Upon onset of cell doublings on day 4, the cellular transcriptome appears to be completely reprogrammed to support the proliferating cells, but 3 additional clusters of EBV-regulated genes fine-tune cell signaling, migration, and immune response pathways, eventually. Our study reveals that more than 11,000 genes are regulated upon EBV infection as naïve B cells exit quiescence to enter a germinal center-like differentiation program, which culminates in immortalized, proliferating cells that partially resemble plasmablasts and early plasma cells.

COMUNET: a tool to explore and visualize intercellular communication.

MOTIVATION: Intercellular communication plays an essential role in multicellular organisms and several algorithms to analyze it from single-cell transcriptional data have been recently published, but the results are often hard to visualize and interpret. RESULTS: We developed Cell cOmmunication exploration with MUltiplex NETworks (COMUNET), a tool that streamlines the interpretation of the results from cell-cell communication analyses. COMUNET uses multiplex networks to represent and cluster all potential communication patterns between cell types. The algorithm also enables the search for specific patterns of communication and can perform comparative analysis between two biological conditions. To exemplify its use, here we apply COMUNET to investigate cell communication patterns in single-cell transcriptomic datasets from mouse embryos and from an acute myeloid leukemia patient at diagnosis and after treatment. AVAILABILITY AND IMPLEMENTATION: Our algorithm is implemented in an R package available from https://github.com/ScialdoneLab/COMUNET, along with all the code to perform the analyses reported here. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Automatic identification of relevant genes from low-dimensional embeddings of single-cell RNA-seq data.

MOTIVATION: Dimensionality reduction is a key step in the analysis of single-cell RNA-sequencing data. It produces a low-dimensional embedding for visualization and as a calculation base for downstream analysis. Nonlinear techniques are most suitable to handle the intrinsic complexity of large, heterogeneous single-cell data. However, with no linear relation between gene and embedding coordinate, there is no way to extract the identity of genes driving any cell's position in the low-dimensional embedding, making it difficult to characterize the underlying biological processes. RESULTS: In this article, we introduce the concepts of local and global gene relevance to compute an equivalent of principal component analysis loadings for non-linear low-dimensional embeddings. Global gene relevance identifies drivers of the overall embedding, while local gene relevance identifies those of a defined sub-region. We apply our method to single-cell RNA-seq datasets from different experimental protocols and to different low-dimensional embedding techniques. This shows our method's versatility to identify key genes for a variety of biological processes. AVAILABILITY AND IMPLEMENTATION: To ensure reproducibility and ease of use, our method is released as part of destiny 3.0, a popular R package for building diffusion maps from single-cell transcriptomic data. It is readily available through Bioconductor. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Normalizing single-cell RNA sequencing data: challenges and opportunities.

Single-cell transcriptomics is becoming an important component of the molecular biologist's toolkit. A critical step when analyzing data generated using this technology is normalization. However, normalization is typically performed using methods developed for bulk RNA sequencing or even microarray data, and the suitability of these methods for single-cell transcriptomics has not been assessed. We here discuss commonly used normalization approaches and illustrate how these can produce misleading results. Finally, we present alternative approaches and provide recommendations for single-cell RNA sequencing users.

Using single-cell genomics to understand developmental processes and cell fate decisions.

High-throughput -omics techniques have revolutionised biology, allowing for thorough and unbiased characterisation of the molecular states of biological systems. However, cellular decision-making is inherently a unicellular process to which "bulk" -omics techniques are poorly suited, as they capture ensemble averages of cell states. Recently developed single-cell methods bridge this gap, allowing high-throughput molecular surveys of individual cells. In this review, we cover core concepts of analysis of single-cell gene expression data and highlight areas of developmental biology where single-cell techniques have made important contributions. These include understanding of cell-to-cell heterogeneity, the tracing of differentiation pathways, quantification of gene expression from specific alleles, and the future directions of cell lineage tracing and spatial gene expression analysis.

Interspecific variation of olfactory preferences in flies, mice, and humans.

Aiming to unravel interspecific differences in olfactory preferences, we performed comparative studies of odor valence in flies, mice, and humans. Our analysis suggests a model where flies and mice share similar olfactory preferences, but neither species share odor preferences with humans. This model contrasts with a previous study by Mandairon et al., which suggested that the olfactory preferences of mice and humans are similar. A probabilistic examination revealed that underpowered studies can result in spurious significant correlations, which can account for the differences between both studies. Future analyses aimed at dissecting the olfactory preferences across species need to test large numbers of odorants to stress-test the model proposed here and identify robust associations.