The shapes of elongating gastruloids are consistent with convergent extension driven by a combination of active cell crawling and differential adhesion.

Gastruloids have emerged as highly useful in vitro models of mammalian gastrulation. One of the most striking features of 3D gastruloids is their elongation, which mimics the extension of the embryonic anterior-posterior axis. Although axis extension is crucial for development, the underlying mechanism has not been fully elucidated in mammalian species. Gastruloids provide an opportunity to study this morphogenic process in vitro. Here, we measure and quantify the shapes of elongating gastruloids and show, by Cellular Potts model simulations based on a novel, optimized algorithm, that convergent extension, driven by a combination of active cell crawling and differential adhesion can explain the observed shapes. We reveal that differential adhesion alone is insufficient and also directly observe hallmarks of convergent extension by time-lapse imaging of gastruloids. Finally, we show that gastruloid elongation can be abrogated by inhibition of the Rho kinase pathway, which is involved in convergent extension in vivo. All in all, our study demonstrates, how gastruloids can be used to elucidate morphogenic processes in embryonic development.

Integration of a multi-omics stem cell differentiation dataset using a dynamical model.

Stem cell differentiation is a highly dynamic process involving pervasive changes in gene expression. The large majority of existing studies has characterized differentiation at the level of individual molecular profiles, such as the transcriptome or the proteome. To obtain a more comprehensive view, we measured protein, mRNA and microRNA abundance during retinoic acid-driven differentiation of mouse embryonic stem cells. We found that mRNA and protein abundance are typically only weakly correlated across time. To understand this finding, we developed a hierarchical dynamical model that allowed us to integrate all data sets. This model was able to explain mRNA-protein discordance for most genes and identified instances of potential microRNA-mediated regulation. Overexpression or depletion of microRNAs identified by the model, followed by RNA sequencing and protein quantification, were used to follow up on the predictions of the model. Overall, our study shows how multi-omics integration by a dynamical model could be used to nominate candidate regulators.

Back in 3D-a report on Genome Informatics 2022.

The annual Genome Informatics conference was held at the Wellcome Genome Campus on September 21-23, 2022. The conference covered a remarkable range of topics of which we highlight a few in this report.

ETV2 Upregulation Marks the Specification of Early Cardiomyocytes and Endothelial Cells During Co-differentiation.

The ability to differentiate human-induced pluripotent stem cells (hiPSCs) efficiently into defined cardiac lineages, such as cardiomyocytes and cardiac endothelial cells, is crucial to study human heart development and model cardiovascular diseases in vitro. The mechanisms underlying the specification of these cell types during human development are not well understood which limits fine-tuning and broader application of cardiac model systems. Here, we used the expression of ETV2, a master regulator of hematoendothelial specification in mice, to identify functionally distinct subpopulations during the co-differentiation of endothelial cells and cardiomyocytes from hiPSCs. Targeted analysis of single-cell RNA-sequencing data revealed differential ETV2 dynamics in the 2 lineages. A newly created fluorescent reporter line allowed us to identify early lineage-predisposed states and show that a transient ETV2-high-state initiates the specification of endothelial cells. We further demonstrated, unexpectedly, that functional cardiomyocytes can originate from progenitors expressing ETV2 at a low level. Our study thus sheds light on the in vitro differentiation dynamics of 2 important cardiac lineages.

Common and rare variant associations with clonal haematopoiesis phenotypes.

Clonal haematopoiesis involves the expansion of certain blood cell lineages and has been associated with ageing and adverse health outcomes1-5. Here we use exome sequence data on 628,388 individuals to identify 40,208 carriers of clonal haematopoiesis of indeterminate potential (CHIP). Using genome-wide and exome-wide association analyses, we identify 24 loci (21 of which are novel) where germline genetic variation influences predisposition to CHIP, including missense variants in the lymphocytic antigen coding gene LY75, which are associated with reduced incidence of CHIP. We also identify novel rare variant associations with clonal haematopoiesis and telomere length. Analysis of 5,041 health traits from the UK Biobank (UKB) found relationships between CHIP and severe COVID-19 outcomes, cardiovascular disease, haematologic traits, malignancy, smoking, obesity, infection and all-cause mortality. Longitudinal and Mendelian randomization analyses revealed that CHIP is associated with solid cancers, including non-melanoma skin cancer and lung cancer, and that CHIP linked to DNMT3A is associated with the subsequent development of myeloid but not lymphoid leukaemias. Additionally, contrary to previous findings from the initial 50,000 UKB exomes6, our results in the full sample do not support a role for IL-6 inhibition in reducing the risk of cardiovascular disease among CHIP carriers. Our findings demonstrate that CHIP represents a complex set of heterogeneous phenotypes with shared and unique germline genetic causes and varied clinical implications.

Why isn't each cell its own cell type? Diminishing returns of increasing cell type diversity can explain cell type allometry.

Since the discovery of cells by Robert Hooke and Antoni van Leeuwenhoek in the 17th century, thousands of different cell types have been identified, most recently by sequencing-based single-cell profiling techniques. Yet, for many organisms we still do not know, how many different cell types they are precisely composed of. A recent survey of experimental data, using mostly morphology as a proxy for cell type, revealed allometric scaling of cell type diversity with organism size. Here, I argue from an evolutionary fitness perspective and suggest that three simple assumptions can explain the observed scaling: Evolving a new cell type has, 1. a fitness cost that increases with organism size, 2. a fitness benefit that also increases with organism size but 3. diminishes exponentially with the number of existing cell types. I will show that these assumptions result in a quantitative model that fits the observed cell type numbers across organisms of all size and explains why we should not expect isometric scaling.

A gastruloid model of the interaction between embryonic and extra-embryonic cell types.

Stem-cell derived in vitro systems, such as organoids or embryoids, hold great potential for modeling in vivo development. Full control over their initial composition, scalability, and easily measurable dynamics make those systems useful for studying specific developmental processes in isolation. Here we report the formation of gastruloids consisting of mouse embryonic stem cells (mESCs) and extraembryonic endoderm (XEN) cells. These XEN-enhanced gastruloids (XEGs) exhibit the formation of neural epithelia, which are absent in gastruloids derived from mESCs only. By single-cell RNA-seq, imaging, and differentiation experiments, we demonstrate the neural characteristics of the epithelial tissue. We further show that the mESCs induce the differentiation of the XEN cells to a visceral endoderm-like state. Finally, we demonstrate that local inhibition of WNT signaling and production of a basement membrane by the XEN cells underlie the formation of the neuroepithelial tissue. In summary, we establish XEGs to explore heterotypic cellular interactions and their developmental consequences in vitro.

Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations.

The ability to discover new cell phenotypes by unsupervised clustering of single-cell transcriptomes has revolutionized biology. Currently, there is no principled way to decide whether a cluster of cells contains meaningful subpopulations that should be further resolved. Here, we present phiclust (ϕclust), a clusterability measure derived from random matrix theory that can be used to identify cell clusters with non-random substructure, testably leading to the discovery of previously overlooked phenotypes.

How a cell decides its own fate: a single-cell view of molecular mechanisms and dynamics of cell-type specification.

On its path from a fertilized egg to one of the many cell types in a multicellular organism, a cell turns the blank canvas of its early embryonic state into a molecular profile fine-tuned to achieve a vital organismal function. This remarkable transformation emerges from the interplay between dynamically changing external signals, the cell's internal, variable state, and tremendously complex molecular machinery; we are only beginning to understand. Recently developed single-cell omics techniques have started to provide an unprecedented, comprehensive view of the molecular changes during cell-type specification and promise to reveal the underlying gene regulatory mechanism. The exponentially increasing amount of quantitative molecular data being created at the moment is slated to inform predictive, mathematical models. Such models can suggest novel ways to manipulate cell types experimentally, which has important biomedical applications. This review is meant to give the reader a starting point to participate in this exciting phase of molecular developmental biology. We first introduce some of the principal molecular players involved in cell-type specification and discuss the important organizing ability of biomolecular condensates, which has been discovered recently. We then review some of the most important single-cell omics methods and relevant findings they produced. We devote special attention to the dynamics of the molecular changes and discuss methods to measure them, most importantly lineage tracing. Finally, we introduce a conceptual framework that connects all molecular agents in a mathematical model and helps us make sense of the experimental data.

Human-iPSC-Derived Cardiac Stromal Cells Enhance Maturation in 3D Cardiac Microtissues and Reveal Non-cardiomyocyte Contributions to Heart Disease.

Cardiomyocytes (CMs) from human induced pluripotent stem cells (hiPSCs) are functionally immature, but this is improved by incorporation into engineered tissues or forced contraction. Here, we showed that tri-cellular combinations of hiPSC-derived CMs, cardiac fibroblasts (CFs), and cardiac endothelial cells also enhance maturation in easily constructed, scaffold-free, three-dimensional microtissues (MTs). hiPSC-CMs in MTs with CFs showed improved sarcomeric structures with T-tubules, enhanced contractility, and mitochondrial respiration and were electrophysiologically more mature than MTs without CFs. Interactions mediating maturation included coupling between hiPSC-CMs and CFs through connexin 43 (CX43) gap junctions and increased intracellular cyclic AMP (cAMP). Scaled production of thousands of hiPSC-MTs was highly reproducible across lines and differentiated cell batches. MTs containing healthy-control hiPSC-CMs but hiPSC-CFs from patients with arrhythmogenic cardiomyopathy strikingly recapitulated features of the disease. Our MT model is thus a simple and versatile platform for modeling multicellular cardiac diseases that will facilitate industry and academic engagement in high-throughput molecular screening.

Deciphering Metabolic Heterogeneity by Single-Cell Analysis.

Single-cell analysis provides insights into cellular heterogeneity and dynamics of individual cells. This Feature highlights recent developments in key analytical techniques suited for single-cell metabolic analysis with a special focus on mass spectrometry-based analytical platforms and RNA-seq as well as imaging techniques that reveal stochasticity in metabolism.

Dynamic Enhancer DNA Methylation as Basis for Transcriptional and Cellular Heterogeneity of ESCs.

Variable levels of DNA methylation have been reported at tissue-specific differential methylation regions (DMRs) overlapping enhancers, including super-enhancers (SEs) associated with key cell identity genes, but the mechanisms responsible for this intriguing behavior are not well understood. We used allele-specific reporters at the endogenous Sox2 and Mir290 SEs in embryonic stem cells and found that the allelic DNA methylation state is dynamically switching, resulting in cell-to-cell heterogeneity. Dynamic DNA methylation is driven by the balance between DNA methyltransferases and transcription factor binding on one side and co-regulated with the Mediator complex recruitment and H3K27ac level changes at regulatory elements on the other side. DNA methylation at the Sox2 and the Mir290 SEs is independently regulated and has distinct consequences on the cellular differentiation state. Dynamic allele-specific DNA methylation at the two SEs was also seen at different stages in preimplantation embryos, revealing that methylation heterogeneity occurs in vivo.

Single-cell transcriptomics reveals gene expression dynamics of human fetal kidney development.

The current understanding of mammalian kidney development is largely based on mouse models. Recent landmark studies revealed pervasive differences in renal embryogenesis between mouse and human. The scarcity of detailed gene expression data in humans therefore hampers a thorough understanding of human kidney development and the possible developmental origin of kidney diseases. In this paper, we present a single-cell transcriptomics study of the human fetal kidney. We identified 22 cell types and a host of marker genes. Comparison of samples from different developmental ages revealed continuous gene expression changes in podocytes. To demonstrate the usefulness of our data set, we explored the heterogeneity of the nephrogenic niche, localized podocyte precursors, and confirmed disease-associated marker genes. With close to 18,000 renal cells from five different developmental ages, this study provides a rich resource for the elucidation of human kidney development, easily accessible through an interactive web application.

Allele-specific repression of Sox2 through the long non-coding RNA Sox2ot.

The transcription factor Sox2 controls the fate of pluripotent stem cells and neural stem cells. This gatekeeper function requires well-regulated Sox2 levels. We postulated that Sox2 regulation is partially controlled by the Sox2 overlapping long non-coding RNA (lncRNA) gene Sox2ot. Here we show that the RNA levels of Sox2ot and Sox2 are inversely correlated during neural differentiation of mouse embryonic stem cells (ESCs). Through allele-specific enhanced transcription of Sox2ot in mouse Sox2eGFP knockin ESCs we demonstrate that increased Sox2ot transcriptional activity reduces Sox2 RNA levels in an allele-specific manner. Enhanced Sox2ot transcription, yielding lower Sox2 RNA levels, correlates with a decreased chromatin interaction of the upstream regulatory sequence of Sox2 and the ESC-specific Sox2 super enhancer. Our study indicates that, in addition to previously reported in trans mechanisms, Sox2ot can regulate Sox2 by an allele-specific mechanism, in particular during development.

Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells.

Gene expression heterogeneity in the pluripotent state of mouse embryonic stem cells (mESCs) has been increasingly well-characterized. In contrast, exit from pluripotency and lineage commitment have not been studied systematically at the single-cell level. Here we measure the gene expression dynamics of retinoic acid driven mESC differentiation from pluripotency to lineage commitment, using an unbiased single-cell transcriptomics approach. We find that the exit from pluripotency marks the start of a lineage transition as well as a transient phase of increased susceptibility to lineage specifying signals. Our study reveals several transcriptional signatures of this phase, including a sharp increase of gene expression variability and sequential expression of two classes of transcriptional regulators. In summary, we provide a comprehensive analysis of the exit from pluripotency and lineage commitment at the single cell level, a potential stepping stone to improved lineage manipulation through timing of differentiation cues.

BMP-SMAD Signaling Regulates Lineage Priming, but Is Dispensable for Self-Renewal in Mouse Embryonic Stem Cells.

Naive mouse embryonic stem cells (mESCs) are in a metastable state and fluctuate between inner cell mass- and epiblast-like phenotypes. Here, we show transient activation of the BMP-SMAD signaling pathway in mESCs containing a BMP-SMAD responsive reporter transgene. Activation of the BMP-SMAD reporter transgene in naive mESCs correlated with lower levels of genomic DNA methylation, high expression of 5-methylcytosine hydroxylases Tet1/2 and low levels of DNA methyltransferases Dnmt3a/b. Moreover, naive mESCs, in which the BMP-SMAD reporter transgene was activated, showed higher resistance to differentiation. Using double Smad1;Smad5 knockout mESCs, we showed that BMP-SMAD signaling is dispensable for self-renewal in both naive and ground state. These mutant mESCs were still pluripotent, but they exhibited higher levels of DNA methylation than their wild-type counterparts and had a higher propensity to differentiate. We showed that BMP-SMAD signaling modulates lineage priming in mESCs, by transiently regulating the enzymatic machinery responsible for DNA methylation.

Differential Stoichiometry among Core Ribosomal Proteins.

Understanding the regulation and structure of ribosomes is essential to understanding protein synthesis and its dysregulation in disease. While ribosomes are believed to have a fixed stoichiometry among their core ribosomal proteins (RPs), some experiments suggest a more variable composition. Testing such variability requires direct and precise quantification of RPs. We used mass spectrometry to directly quantify RPs across monosomes and polysomes of mouse embryonic stem cells (ESC) and budding yeast. Our data show that the stoichiometry among core RPs in wild-type yeast cells and ESC depends both on the growth conditions and on the number of ribosomes bound per mRNA. Furthermore, we find that the fitness of cells with a deleted RP-gene is inversely proportional to the enrichment of the corresponding RP in polysomes. Together, our findings support the existence of ribosomes with distinct protein composition and physiological function.

Studying lineage decision-making in vitro: emerging concepts and novel tools.

Correct and timely lineage decisions are critical for normal embryonic development and homeostasis of adult tissues. Therefore, the search for fundamental principles that underlie lineage decision-making lies at the heart of developmental biology. Here, we review attempts to understand lineage decision-making as the interplay of single-cell heterogeneity and gene regulation. Fluctuations at the single-cell level are an important driving force behind cell-state transitions and the creation of cell-type diversity. Gene regulatory networks amplify such fluctuations and define stable cell types. They also mediate the influence of signaling inputs on the lineage decision. In this review, we focus on insights gleaned from in vitro differentiation of embryonic stem cells. We discuss emerging concepts, with an emphasis on transcriptional regulation, dynamical aspects of differentiation, and functional single-cell heterogeneity. We also highlight some novel tools to study lineage decision-making in vitro.

Transcriptional profiling of cells sorted by RNA abundance.

We have developed a quantitative technique for sorting cells on the basis of endogenous RNA abundance, with a molecular resolution of 10-20 transcripts. We demonstrate efficient and unbiased RNA extraction from transcriptionally sorted cells and report a high-fidelity transcriptome measurement of mouse induced pluripotent stem cells (iPSCs) isolated from a heterogeneous reprogramming culture. This method is broadly applicable to profiling transcriptionally distinct cellular states without requiring antibodies or transgenic fluorescent proteins.