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.

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.

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.

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.

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.

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.

Neural Network-Based Filter Design for Compressive Raman Classification of Cells.

Cell-based therapies are bound to revolutionize medicine, but significant technical hurdles must be overcome before wider adoption. In particular, nondestructive, label-free methods to characterize cells in real time are needed to optimize the production process and improve quality control. Raman spectroscopy, which provides a fingerprint of a cell's chemical composition, would be an ideal modality but is too slow for high-throughput applications. Compressive Raman techniques, which measure only linear combinations of Raman intensities, can be fast but require careful optimization to deliver high performance. Here, we develop a neural network model to identify optimal parameters for a compressive sensing scheme that reduces measurement time by 2 orders of magnitude. In a data set containing Raman spectra of three different cell types, it achieves up to 90% classification accuracy using only five linear combinations of Raman intensities. Our method thus unlocks the power of Raman spectroscopy for the characterization of cell products.

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.

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.

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.

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.

Generating Neuroimmune Assembloids Using Human Induced Pluripotent Stem Cell (iPSC)-Derived Cortical Organoids and Microglia.

The emergence of brain organoids has revolutionized our understanding of neurodevelopment and neurological diseases by providing an in vitro model system that recapitulates key aspects of human brain development. However, conventional organoid protocols often overlook the role of microglia, the resident immune cells of the central nervous system. Microglia dysfunction is implicated in various neurological disorders, highlighting the need for their inclusion in organoid models. Here, we present a novel method for generating neuroimmune assembloids using human-induced pluripotent stem cell (iPSC)-derived cortical organoids and microglia. Building upon our previous work generating myelinating cortical organoids, we extend our methodology to include the integration of microglia, ensuring their long-term survival and maturation within the organoids. We describe two integration methods: one involving direct addition of microglia progenitors to the organoids and an alternative approach where microglia and dissociated neuronal progenitors are aggregated together in a defined ratio. To facilitate downstream analysis, we also describe a dissociation protocol for single-cell RNA sequencing (scRNA-seq) and provide guidance on fixation, cryosectioning, and immunostaining of assembloid structures. Overall, our protocol provides a comprehensive framework for generating neuroimmune assembloids, offering researchers a valuable tool for studying the interactions between neural cell types and immune cells in the context of neurological diseases.

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.

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.

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.

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.

Microsecond single-molecule tracking (µsSMT).

Here we report on a method to track individual molecules on nanometer length and microsecond timescales using an optical microscope. Our method is based on double-labeling of a molecule with two spectrally distinct fluorophores and illuminating it with laser pulses of different wavelengths that partially overlap temporally. We demonstrate our method by using it to resolve the motion of short DNA oligomers in solution down to a timescale of 100 µs.

Quantification of biological interactions with particle image cross-correlation spectroscopy (PICCS).

A multitude of biological processes that involve multiple interaction partners are observed by two-color microscopy. Here we describe an analysis method for the robust quantification of correlation between signals in different color channels: particle image cross-correlation spectroscopy (PICCS). The method, which exploits the superior positional accuracy obtained in single-object and single-molecule microscopy, can extract the correlation fraction and length scale. We applied PICCS to correlation measurements in living tissues. The morphogen Decapentaplegic (Dpp) was imaged in wing imaginal disks of fruit fly larvae and we quantified what fraction of early endosomes contained Dpp.

Membrane mediated sorting.

Inclusions in biological membranes may interact via deformations they induce on the shape of that very membrane. Such deformations are a purely physical effect, resulting in nonspecific forces between the inclusions. In this Letter we show that this type of interaction can organize membrane domains and hence may play an important biological role. Using a simple analytical model we predict that membrane inclusions sort according to the curvature they impose. We verify this prediction by both numerical simulations and experimental observations of membrane domains in phase separated vesicles.