The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution

Matrix viscoelasticity controls spatiotemporal tissue organization

Biomolecular and physical cues of the extracellular matrix environment regulate collective cell dynamics and tissue patterning. Nonetheless, how the viscoelastic properties of the matrix regulate collective cell spatial and temporal organization is not fully understood. Here we show that the passive viscoelastic properties of the matrix encapsulating a spheroidal tissue of breast epithelial cells guide tissue proliferation in space and in time. Matrix viscoelasticity prompts symmetry breaking of the spheroid, leading to the formation of invading finger-like protrusions, YAP nuclear translocation and epithelial-to-mesenchymal transition both in vitro and in vivo in a Arp2/3-complex-dependent manner. Computational modelling of these observations allows us to establish a phase diagram relating morphological stability with matrix viscoelasticity, tissue viscosity, cell motility and cell division rate, which is experimentally validated by biochemical assays and in vitro experiments with an intestinal organoid. Altogether, this work highlights the role of stress relaxation mechanisms in tissue growth dynamics, a fundamental process in morphogenesis and oncogenesis.

Bibliometric analysis of single-cell sequencing researches on immune cells and their application of DNA damage repair in cancer immunotherapy

INTRODUCTION

In recent decades, single-cell sequencing technology has developed rapidly and used widely in various fields of life sciences, especially for the detection of immune cells. A bibliometric analysis of single-cell sequencing research work on immune cells published during the 2011-2021 period should provide new insight on the use of single-cell sequencing.

METHODS

We screened 1,460 publications on single-cell sequencing on immune cells according to the publication date, article type, language, and country.

REULTS

The United States published the first and largest number of articles, while China's research started relatively late, but ranked second in the number of publications. T cells were the most commonly studied immune cells by single-cell sequencing, followed by mononuclear macrophages. Cancer biology was the most common field of immune cell research by single-cell sequencing. Single-cell sequencing studies using γδ T cells were mainly in the fields of cancer biology and cell development, and focused over time from cell surface receptor to cell function. Through in-depth analysis of the articles on single-cell sequencing of T cells in the oncology field, our analysis found that immunotherapy and tumor microenvironment were the most popular research directions in recent years.

DISCUSSION

The combination of DNA damage repair and immunotherapy seems to provide a new strategy for cancer therapy.

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

High-throughput total RNA sequencing in single cells using VASA-seq

Most methods for single-cell transcriptome sequencing amplify the termini of polyadenylated transcripts, capturing only a small fraction of the total cellular transcriptome. This precludes the detection of many long non-coding, short non-coding and non-polyadenylated protein-coding transcripts and hinders alternative splicing analysis. We, therefore, developed VASA-seq to detect the total transcriptome in single cells, which is enabled by fragmenting and tailing all RNA molecules subsequent to cell lysis. The method is compatible with both plate-based formats and droplet microfluidics. We applied VASA-seq to more than 30,000 single cells in the developing mouse embryo during gastrulation and early organogenesis. Analyzing the dynamics of the total single-cell transcriptome, we discovered cell type markers, many based on non-coding RNA, and performed in vivo cell cycle analysis via detection of non-polyadenylated histone genes. RNA velocity characterization was improved, accurately retracing blood maturation trajectories. Moreover, our VASA-seq data provide a comprehensive analysis of alternative splicing during mammalian development, which highlighted substantial rearrangements during blood development and heart morphogenesis.

How does the structure of data impact cell-cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data

Accurately identifying cell-populations is paramount to the quality of downstream analyses and overall interpretations of single-cell RNA-seq (scRNA-seq) datasets but remains a challenge. The quality of single-cell clustering depends on the proximity metric used to generate cell-to-cell distances. Accordingly, proximity metrics have been benchmarked for scRNA-seq clustering, typically with results averaged across datasets to identify a highest performing metric. However, the 'best-performing' metric varies between studies, with the performance differing significantly between datasets. This suggests that the unique structural properties of an scRNA-seq dataset, specific to the biological system under study, have a substantial impact on proximity metric performance. Previous benchmarking studies have omitted to factor the structural properties into their evaluations. To address this gap, we developed a framework for the in-depth evaluation of the performance of 17 proximity metrics with respect to core structural properties of scRNA-seq data, including sparsity, dimensionality, cell-population distribution and rarity. We find that clustering performance can be improved substantially by the selection of an appropriate proximity metric and neighbourhood size for the structural properties of a dataset, in addition to performing suitable pre-processing and dimensionality reduction. Furthermore, popular metrics such as Euclidean and Manhattan distance performed poorly in comparison to several lessor applied metrics, suggesting that the default metric for many scRNA-seq methods should be re-evaluated. Our findings highlight the critical nature of tailoring scRNA-seq analyses pipelines to the dataset under study and provide practical guidance for researchers looking to optimize cell-similarity search for the structural properties of their own data.

Single-cell RNA-sequencing analysis of early sea star development

Echinoderms represent a broad phylum with many tractable features to test evolutionary changes and constraints. Here, we present a single-cell RNA-sequencing analysis of early development in the sea star Patiria miniata, to complement the recent analysis of two sea urchin species. We identified 20 cell states across six developmental stages from 8 hpf to mid-gastrula stage, using the analysis of 25,703 cells. The clusters were assigned cell states based on known marker gene expression and by in situ RNA hybridization. We found that early (morula, 8-14 hpf) and late (blastula-to-mid-gastrula) cell states are transcriptionally distinct. Cells surrounding the blastopore undergo rapid cell state changes that include endomesoderm diversification. Of particular import to understanding germ cell specification is that we never see Nodal pathway members within Nanos/Vasa-positive cells in the region known to give rise to the primordial germ cells (PGCs). The results from this work contrast the results of PGC specification in the sea urchin, and the dataset presented here enables deeper comparative studies in tractable developmental models for testing a variety of developmental mechanisms.

Single-cell technologies: From research to application

In recent years, more and more single-cell technologies have been developed. A vast amount of single-cell omics data has been generated by large projects, such as the Human Cell Atlas, the Mouse Cell Atlas, the Mouse RNA Atlas, the Mouse ATAC Atlas, and the Plant Cell Atlas. Based on these single-cell big data, thousands of bioinformatics algorithms for quality control, clustering, cell-type annotation, developmental inference, cell-cell transition, cell-cell interaction, and spatial analysis are developed. With powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence, the molecular landscape at the single-cell level can be revealed. With spatial transcriptomics and single-cell multi-omics, even the spatial dynamic multi-level regulatory mechanisms can be deciphered. Such single-cell technologies have many successful applications in oncology, assisted reproduction, embryonic development, and plant breeding. We not only review the experimental and bioinformatics methods for single-cell research, but also discuss their applications in various fields and forecast the future directions for single-cell technologies. We believe that spatial transcriptomics and single-cell multi-omics will become the next booming business for mechanism research and commercial industry.

Canalized gene expression during development mediates caste differentiation in ants

Ant colonies are higher-level organisms consisting of specialized reproductive and non-reproductive individuals that differentiate early in development, similar to germ-soma segregation in bilateral Metazoa. Analogous to diverging cell lines, developmental differentiation of individual ants has often been considered in epigenetic terms but the sets of genes that determine caste phenotypes throughout larval and pupal development remain unknown. Here, we reconstruct the individual developmental trajectories of two ant species, Monomorium pharaonis and Acromyrmex echinatior, after obtaining >1,400 whole-genome transcriptomes. Using a new backward prediction algorithm, we show that caste phenotypes can be accurately predicted by genome-wide transcriptome profiling. We find that caste differentiation is increasingly canalized from early development onwards, particularly in germline individuals (gynes/queens) and that the juvenile hormone signalling pathway plays a key role in this process by regulating body mass divergence between castes. We quantified gene-specific canalization levels and found that canalized genes with gyne/queen-biased expression were enriched for ovary and wing functions while canalized genes with worker-biased expression were enriched in brain and behavioural functions. Suppression in gyne larvae of Freja, a highly canalized gyne-biased ovary gene, disturbed pupal development by inducing non-adaptive intermediate phenotypes between gynes and workers. Our results are consistent with natural selection actively maintaining canalized caste phenotypes while securing robustness in the life cycle ontogeny of ant colonies.

Connecting past and present: single-cell lineage tracing

Central to the core principle of cell theory, depicting cells' history, state and fate is a fundamental goal in modern biology. By leveraging clonal analysis and single-cell RNA-seq technologies, single-cell lineage tracing provides new opportunities to interrogate both cell states and lineage histories. During the past few years, many strategies to achieve lineage tracing at single-cell resolution have been developed, and three of them (integration barcodes, polylox barcodes, and CRISPR barcodes) are noteworthy as they are amenable in experimentally tractable systems. Although the above strategies have been demonstrated in animal development and stem cell research, much care and effort are still required to implement these methods. Here we review the development of single-cell lineage tracing, major characteristics of the cell barcoding strategies, applications, as well as technical considerations and limitations, providing a guide to choose or improve the single-cell barcoding lineage tracing.

Recent advances in understanding cell types during human gastrulation

Gastrulation is a fundamental process during embryonic development, conserved across all multicellular animals [1]. In the majority of metazoans, gastrulation is characterised by large scale morphogenetic remodeling, leading to the conversion of an early pluripotent embryonic cell layer into the three primary 'germ layers': an outer ectoderm, inner endoderm and intervening mesoderm layer. The morphogenesis of these three layers of cells is closely coordinated with cellular diversification, laying the foundation for the generation of the hundreds of distinct specialized cell types in the animal body. The process of gastrulation has for a long time attracted tremendous attention in a broad range of experimental systems ranging from sponges to mice. In humans the process of gastrulation starts approximately 14 days after fertilization and continues for slightly over a week. However our understanding of this important process, as it pertains to human, is limited. Donations of human fetal material at these early stages are exceptionally rare, making it nearly impossible to study human gastrulation directly. Therefore, our understanding of human gastrulation is predominantly derived from animal models such as the mouse [2,3] and from studies of limited collections of fixed whole samples and histological sections of human gastrulae [4-7], some of which date back to over a century ago. More recently we have been gaining valuable molecular insights into human gastrulation using in vitro models of hESCs [8-12] and increasingly, in vitro cultured human and non-human primate embryos [13-16]. However, while methods have been developed to culture human embryos into this stage (and probably beyond), current ethical standards prohibit the culture of human embryos past 14 days again limiting our ability to experimentally probe human gastrulation. This review discusses recent molecular insights from the study of a rare CS 7 human gastrula obtained as a live sample and raises several questions arising from this recent study that it will be interesting to address in the future using emerging models of human gastrulation.

Integrative insights and clinical applications of single-cell sequencing in cancer immunotherapy

Recently, immunotherapy has gained increasing popularity in oncology. Several immunotherapies obtained remarkable clinical effects, but the efficacy varied, and only subsets of cancer patients benefited. Breaking the constraints and improving immunotherapy efficacy is extremely important in precision medicine. Whereas traditional sequencing approaches mask the characteristics of individual cells, single-cell sequencing provides multiple dimensions of cellular characterization at the single-cell level, including genomic, transcriptomic, epigenomic, proteomic, and multi-omics. Hence, the complexity of the tumor microenvironment, the universality of tumor heterogeneity, cell composition and cell-cell interactions, cell lineage tracking, and tumor drug resistance mechanisms are revealed in-depth. However, the clinical transformation of single-cell technology is not to the point of in-depth study, especially in the application of immunotherapy. The newly discovered vital cells and tremendous biomarkers facilitate the development of more efficient individualized therapeutic regimens to guide clinical treatment and predict prognosis. This review provided an overview of the progress in distinct single-cell sequencing methods and emerging strategies. For perspective, the expanding utility of combining single-cell sequencing and other technologies was discussed.

Quantitative analysis of transcriptome dynamics provides novel insights into developmental state transitions

BACKGROUND

During embryogenesis, the developmental potential of initially pluripotent cells becomes progressively restricted as they transit to lineage restricted states. The pluripotent cells of Xenopus blastula-stage embryos are an ideal system in which to study cell state transitions during developmental decision-making, as gene expression dynamics can be followed at high temporal resolution.

RESULTS

Here we use transcriptomics to interrogate the process by which pluripotent cells transit to four different lineage-restricted states: neural progenitors, epidermis, endoderm and ventral mesoderm, providing quantitative insights into the dynamics of Waddington's landscape. Our findings provide novel insights into why the neural progenitor state is the default lineage state for pluripotent cells and uncover novel components of lineage-specific gene regulation. These data reveal an unexpected overlap in the transcriptional responses to BMP4/7 and Activin signaling and provide mechanistic insight into how the timing of signaling inputs such as BMP are temporally controlled to ensure correct lineage decisions.

CONCLUSIONS

Together these analyses provide quantitative insights into the logic and dynamics of developmental decision making in early embryos. They also provide valuable lineage-specific time series data following the acquisition of specific lineage states during development.

Human stem cell models to study placode development, function and pathology

Placodes are embryonic structures originating from the rostral ectoderm that give rise to highly diverse organs and tissues, comprising the anterior pituitary gland, paired sense organs and cranial sensory ganglia. Their development, including the underlying gene regulatory networks and signalling pathways, have been for the most part characterised in animal models. In this Review, we describe how placode development can be recapitulated by the differentiation of human pluripotent stem cells towards placode progenitors and their derivatives, highlighting the value of this highly scalable platform as an optimal in vitro tool to study the development of human placodes, and identify human-specific mechanisms in their development, function and pathology.

Pluripotency factors are repurposed to shape the epigenomic landscape of neural crest cells

Yamanaka factors are essential for establishing pluripotency in embryonic stem cells, but their function in multipotent stem cell populations is poorly understood. Here, we show that OCT4 and SOX2 cooperate with tissue-specific transcription factors to promote neural crest formation. By assessing avian and human neural crest cells at distinct developmental stages, we characterized the epigenomic changes that occur during their specification, migration, and early differentiation. This analysis determined that the OCT4-SOX2 dimer is required to establish a neural crest epigenomic signature that is lost upon cell fate commitment. The OCT4-SOX2 genomic targets in the neural crest differ from those of embryonic stem cells, indicating the dimer displays context-specific functions. Binding of OCT4-SOX2 to neural crest enhancers requires pioneer factor TFAP2A, which physically interacts with the dimer to modify its genomic targets. Our results demonstrate how Yamanaka factors are repurposed in multipotent cells to control chromatin organization and define their developmental potential.

Accurate prediction of functional states of cis-regulatory modules reveals common epigenetic rules in humans and mice

BACKGROUND

Predicting cis-regulatory modules (CRMs) in a genome and their functional states in various cell/tissue types of the organism are two related challenging computational tasks. Most current methods attempt to simultaneously achieve both using data of multiple epigenetic marks in a cell/tissue type. Though conceptually attractive, they suffer high false discovery rates and limited applications. To fill the gaps, we proposed a two-step strategy to first predict a map of CRMs in the genome, and then predict functional states of all the CRMs in various cell/tissue types of the organism. We have recently developed an algorithm for the first step that was able to more accurately and completely predict CRMs in a genome than existing methods by integrating numerous transcription factor ChIP-seq datasets in the organism. Here, we presented machine-learning methods for the second step.

RESULTS

We showed that functional states in a cell/tissue type of all the CRMs in the genome could be accurately predicted using data of only 1~4 epigenetic marks by a variety of machine-learning classifiers. Our predictions are substantially more accurate than the best achieved so far. Interestingly, a model trained on a cell/tissue type in humans can accurately predict functional states of CRMs in different cell/tissue types of humans as well as of mice, and vice versa. Therefore, epigenetic code that defines functional states of CRMs in various cell/tissue types is universal at least in humans and mice. Moreover, we found that from tens to hundreds of thousands of CRMs were active in a human and mouse cell/tissue type, and up to 99.98% of them were reutilized in different cell/tissue types, while as small as 0.02% of them were unique to a cell/tissue type that might define the cell/tissue type.

CONCLUSIONS

Our two-step approach can accurately predict functional states in any cell/tissue type of all the CRMs in the genome using data of only 1~4 epigenetic marks. Our approach is also more cost-effective than existing methods that typically use data of more epigenetic marks. Our results suggest common epigenetic rules for defining functional states of CRMs in various cell/tissue types in humans and mice.

Regulation of Oct4 in stem cells and neural crest cells

During embryonic development, cells gradually restrict their developmental potential as they exit pluripotency and differentiate into various cell types. The POU transcription factor Oct4 (encoded by Pou5f1) lies at the center of the pluripotency machinery that regulates stemness and differentiation in stem cells, and is required for reprogramming of somatic cells into induced pluripotent stem cells (iPSCs). Several studies have revealed that Oct4 and other stemness genes are also expressed in multipotent cell populations such as neural crest cells (NCCs), and are required to expand the NCC developmental potential. Transcriptional regulation of Oct4 has been studied extensively in stem cells during early embryonic development and reprogramming, but not in NCCs. Here, we review how Oct4 is regulated in pluripotent stem cells, and address some of the gaps in knowledge about regulation of the pluripotency network in NCCs.

Embryo model completes gastrulation to neurulation and organogenesis

Embryonic stem (ES) cells can undergo many aspects of mammalian embryogenesis in vitro1-5, but their developmental potential is substantially extended by interactions with extraembryonic stem cells, including trophoblast stem (TS) cells, extraembryonic endoderm stem (XEN) cells and inducible XEN (iXEN) cells6-11. Here we assembled stem cell-derived embryos in vitro from mouse ES cells, TS cells and iXEN cells and showed that they recapitulate the development of whole natural mouse embryo in utero up to day 8.5 post-fertilization. Our embryo model displays headfolds with defined forebrain and midbrain regions and develops a beating heart-like structure, a trunk comprising a neural tube and somites, a tail bud containing neuromesodermal progenitors, a gut tube, and primordial germ cells. This complete embryo model develops within an extraembryonic yolk sac that initiates blood island development. Notably, we demonstrate that the neurulating embryo model assembled from Pax6-knockout ES cells aggregated with wild-type TS cells and iXEN cells recapitulates the ventral domain expansion of the neural tube that occurs in natural, ubiquitous Pax6-knockout embryos. Thus, these complete embryoids are a powerful in vitro model for dissecting the roles of diverse cell lineages and genes in development. Our results demonstrate the self-organization ability of ES cells and two types of extraembryonic stem cells to reconstitute mammalian development through and beyond gastrulation to neurulation and early organogenesis.

The role of single-cell genomics in human genetics

Single-cell sequencing is a powerful approach that can detect genetic alterations and their phenotypic consequences in the context of human development, with cellular resolution. Humans start out as single-cell zygotes and undergo fission and differentiation to develop into multicellular organisms. Before fertilisation and during development, the cellular genome acquires hundreds of mutations that propagate down the cell lineage. Whether germline or somatic in nature, some of these mutations may have significant genotypic impact and lead to diseased cellular phenotypes, either systemically or confined to a tissue. Single-cell sequencing enables the detection and monitoring of the genotype and the consequent molecular phenotypes at a cellular resolution. It offers powerful tools to compare the cellular lineage between 'normal' and 'diseased' conditions and to establish genotype-phenotype relationships. By preserving cellular heterogeneity, single-cell sequencing, unlike bulk-sequencing, allows the detection of even small, diseased subpopulations of cells within an otherwise normal tissue. Indeed, the characterisation of biopsies with cellular resolution can provide a mechanistic view of the disease. While single-cell approaches are currently used mainly in basic research, it can be expected that applications of these technologies in the clinic may aid the detection, diagnosis and eventually the treatment of rare genetic diseases as well as cancer. This review article provides an overview of the single-cell sequencing technologies in the context of human genetics, with an aim to empower clinicians to understand and interpret the single-cell sequencing data and analyses. We discuss the state-of-the-art experimental and analytical workflows and highlight current challenges/limitations. Notably, we focus on two prospective applications of the technology in human genetics, namely the annotation of the non-coding genome using single-cell functional genomics and the use of single-cell sequencing data for in silico variant prioritisation.

Single-Cell Mass Spectrometry of Metabolites and Proteins for Systems and Functional Biology

Molecular composition is intricately intertwined with cellular function, and elucidation of this relationship is essential for understanding life processes and developing next-generational therapeutics. Technological innovations in capillary electrophoresis (CE) and liquid chromatography (LC) mass spectrometry (MS) provide previously unavailable insights into cellular biochemistry by allowing for the unbiased detection and quantification of molecules with high specificity. This chapter presents our validated protocols integrating ultrasensitive MS with classical tools of cell, developmental, and neurobiology to assess the biological function of important biomolecules. We use CE and LC MS to measure hundreds of metabolites and thousands of proteins in single cells or limited populations of tissues in chordate embryos and mammalian neurons, revealing molecular heterogeneity between identified cells. By pairing microinjection and optical microscopy, we demonstrate cell lineage tracing and testing the roles the dysregulated molecules play in the formation and maintenance of cell heterogeneity and tissue specification in frog embryos (Xenopus laevis). Electrophysiology extends our workflows to characterizing neuronal activity in sections of mammalian brain tissues. The information obtained from these studies mutually strengthen chemistry and biology and highlight the importance of interdisciplinary research to advance basic knowledge and translational applications forward.

The continuum of Drosophila embryonic development at single-cell resolution

Drosophila melanogaster is a powerful, long-standing model for metazoan development and gene regulation. We profiled chromatin accessibility in almost 1 million and gene expression in half a million nuclei from overlapping windows spanning the entirety of embryogenesis. Leveraging developmental asynchronicity within embryo collections, we applied deep neural networks to infer the age of each nucleus, resulting in continuous, multimodal views of molecular and cellular transitions in absolute time. We identify cell lineages; infer their developmental relationships; and link dynamic changes in enhancer usage, transcription factor (TF) expression, and the accessibility of TFs' cognate motifs. With these data, the dynamics of enhancer usage and gene expression can be explored within and across lineages at the scale of minutes, including for precise transitions like zygotic genome activation.

Multi-layered transcriptional control of cranial neural crest development

The neural crest (NC) is an emblematic population of embryonic stem-like cells with remarkable migratory ability. These distinctive attributes have inspired the curiosity of developmental biologists for over 150 years, however only recently the regulatory mechanisms controlling the complex features of the NC have started to become elucidated at genomic scales. Regulatory control of NC development is achieved through combinatorial transcription factor binding and recruitment of associated transcriptional complexes to distal cis-regulatory elements. Together, they regulate when, where and to what extent transcriptional programmes are actively deployed, ultimately shaping ontogenetic processes. Here, we discuss how transcriptional networks control NC ontogeny, with a special emphasis on the molecular mechanisms underlying specification of the cephalic NC. We also cover emerging properties of transcriptional regulation revealed in diverse developmental systems, such as the role of three-dimensional conformation of chromatin, and how they are involved in the regulation of NC ontogeny. Finally, we highlight how advances in deciphering the NC transcriptional network have afforded new insights into the molecular basis of human diseases.

Vertebrate Cell Differentiation, Evolution, and Diseases: The Vertebrate-Specific Developmental Potential Guardians VENTX/NANOG and POU5/OCT4 Enter the Stage

During vertebrate development, embryonic cells pass through a continuum of transitory pluripotent states that precede multi-lineage commitment and morphogenesis. Such states are referred to as “refractory/naïve” and “competent/formative” pluripotency. The molecular mechanisms maintaining refractory pluripotency or driving the transition to competent pluripotency, as well as the cues regulating multi-lineage commitment, are evolutionarily conserved. Vertebrate-specific “Developmental Potential Guardians” (vsDPGs; i.e., VENTX/NANOG, POU5/OCT4), together with MEK1 (MAP2K1), coordinate the pluripotency continuum, competence for multi-lineage commitment and morphogenesis in vivo. During neurulation, vsDPGs empower ectodermal cells of the neuro-epithelial border (NEB) with multipotency and ectomesenchyme potential through an “endogenous reprogramming” process, giving rise to the neural crest cells (NCCs). Furthermore, vsDPGs are expressed in undifferentiated-bipotent neuro-mesodermal progenitor cells (NMPs), which participate in posterior axis elongation and growth. Finally, vsDPGs are involved in carcinogenesis, whereby they confer selective advantage to cancer stem cells (CSCs) and therapeutic resistance. Intriguingly, the heterogenous distribution of vsDPGs in these cell types impact on cellular potential and features. Here, we summarize the findings about the role of vsDPGs during vertebrate development and their selective advantage in evolution. Our aim to present a holistic view regarding vsDPGs as facilitators of both cell plasticity/adaptability and morphological innovation/variation. Moreover, vsDPGs may also be at the heart of carcinogenesis by allowing malignant cells to escape from physiological constraints and surveillance mechanisms.

Cell landscape of larval and adult Xenopus laevis at single-cell resolution

The rapid development of high-throughput single-cell RNA sequencing technology offers a good opportunity to dissect cell heterogeneity of animals. A large number of organism-wide single-cell atlases have been constructed for vertebrates such as Homo sapiens, Macaca fascicularis, Mus musculus and Danio rerio. However, an intermediate taxon that links mammals to vertebrates of more ancient origin is still lacking. Here, we construct the first Xenopus cell landscape to date, including larval and adult organs. Common cell lineage-specific transcription factors have been identified in vertebrates, including fish, amphibians and mammals. The comparison of larval and adult erythrocytes identifies stage-specific hemoglobin subtypes, as well as a common type of cluster containing both larval and adult hemoglobin, mainly at NF59. In addition, cell lineages originating from all three layers exhibits both antigen processing and presentation during metamorphosis, indicating a common regulatory mechanism during metamorphosis. Overall, our study provides a large-scale resource for research on Xenopus metamorphosis and adult organs.

Mesenchymal-epithelial interaction regulates gastrointestinal tract development in mouse embryos

After gut tube patterning in early embryos, the cellular and molecular changes of developing stomach and intestine remain largely unknown. Here, combining single-cell RNA sequencing and spatial RNA sequencing, we construct a spatiotemporal transcriptomic landscape of the mouse stomach and intestine during embryonic days E9.5-E15.5. Several subpopulations are identified, including Lox⁺ stomach mesenchyme, Aldh1a3⁺ small-intestinal mesenchyme, and Adamdec1⁺ large-intestinal mesenchyme. The regionalization and heterogeneity of both the epithelium and the mesenchyme can be traced back to E9.5. The spatiotemporal distributions of cell clusters and the mesenchymal-epithelial interaction analysis indicate that a coordinated development of the epithelium and mesenchyme contribute to the stomach regionalization, intestine segmentation, and villus formation. Using the gut tube-derived organoids, we find that the cell fate of the foregut and hindgut can be switched by the regional niche factors, including fibroblast growth factors (FGFs) and retinoic acid (RA). This work lays a foundation for further dissection of the mechanisms governing this process.

Systematic identification of cell-fate regulatory programs using a single-cell atlas of mouse development

Waddington's epigenetic landscape is a metaphor frequently used to illustrate cell differentiation. Recent advances in single-cell genomics are altering our understanding of the Waddington landscape, yet the molecular mechanisms of cell-fate decisions remain poorly understood. We constructed a cell landscape of mouse lineage differentiation during development at the single-cell level and described both lineage-common and lineage-specific regulatory programs during cell-type maturation. We also found lineage-common regulatory programs that are broadly active during the development of invertebrates and vertebrates. In particular, we identified Xbp1 as an evolutionarily conserved regulator of cell-fate determinations across different species. We demonstrated that Xbp1 transcriptional regulation is important for the stabilization of the gene-regulatory networks for a wide range of mouse cell types. Our results offer genetic and molecular insights into cellular gene-regulatory programs and will serve as a basis for further advancing the understanding of cell-fate decisions.

Developing immortal cell lines from Xenopus embryos, four novel cell lines derived from Xenopus tropicalis

The diploid anuran Xenopus tropicalis has emerged as a key research model in cell and developmental biology. To enhance the usefulness of this species, we developed methods for generating immortal cell lines from Nigerian strain (NXR_1018, RRID:SCR_013731) X. tropicalis embryos. We generated 14 cell lines that were propagated for several months. We selected four morphologically distinct lines, XTN-6, XTN-8, XTN-10 and XTN-12 for further characterization. Karyotype analysis revealed that three of the lines, XTN-8, XTN-10 and XTN-12 were primarily diploid. XTN-6 cultures showed a consistent mixed population of diploid cells, cells with chromosome 8 trisomy, and cells containing a tetraploid content of chromosomes. The lines were propagated using conventional culture methods as adherent cultures at 30°C in a simple, diluted L-15 medium containing fetal bovine serum without use of a high CO₂ incubator. Transcriptome analysis indicated that the four lines were distinct lineages. These methods will be useful in the generation of cell lines from normal and mutant strains of X. tropicalis as well as other species of Xenopus.

Multiomic atlas with functional stratification and developmental dynamics of zebrafish cis-regulatory elements

Zebrafish, a popular organism for studying embryonic development and for modeling human diseases, has so far lacked a systematic functional annotation program akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium and created a central repository to store and process zebrafish developmental functional genomic data. Our data coordination center ( https://danio-code.zfin.org ) combines a total of 1,802 sets of unpublished and re-analyzed published genomic data, which we used to improve existing annotations and show its utility in experimental design. We identified over 140,000 cis-regulatory elements throughout development, including classes with distinct features dependent on their activity in time and space. We delineated the distinct distance topology and chromatin features between regulatory elements active during zygotic genome activation and those active during organogenesis. Finally, we matched regulatory elements and epigenomic landscapes between zebrafish and mouse and predicted functional relationships between them beyond sequence similarity, thus extending the utility of zebrafish developmental genomics to mammals.

Concordant and Heterogeneity of Single-Cell Transcriptome in Cardiac Development of Human and Mouse

Normal heart development is vital for maintaining its function, and the development process is involved in complex interactions between different cell lineages. How mammalian hearts develop differently is still not fully understood. In this study, we identified several major types of cardiac cells, including cardiomyocytes (CMs), fibroblasts (FBs), endothelial cells (ECs), ECs/FBs, epicardial cells (EPs), and immune cells (macrophage/monocyte cluster, MACs/MONOs), based on single-cell transcriptome data from embryonic hearts of both human and mouse. Then, species-shared and species-specific marker genes were determined in the same cell type between the two species, and the genes with consistent and different expression patterns were also selected by constructing the developmental trajectories. Through a comparison of the development stage similarity of CMs, FBs, and ECs/FBs between humans and mice, it is revealed that CMs at e9.5 and e10.5 of mice are most similar to those of humans at 7 W and 9 W, respectively. Mouse FBs at e10.5, e13.5, and e14.5 are correspondingly more like the same human cells at 6, 7, and 9 W. Moreover, the e9.5-ECs/FBs of mice are most similar to that of humans at 10W. These results provide a resource for understudying cardiac cell types and the crucial markers able to trace developmental trajectories among the species, which is beneficial for finding suitable mouse models to detect human cardiac physiology and related diseases.

Joint profiling of gene expression and chromatin accessibility during amphioxus development at single-cell resolution

Vertebrate evolution was accompanied by two rounds of whole-genome duplication followed by functional divergence in terms of regulatory circuits and gene expression patterns. As a basal and slow-evolving chordate species, amphioxus is an ideal paradigm for exploring the origin and evolution of vertebrates. Single-cell sequencing has been widely used to construct the developmental cell atlas of several representative species of vertebrates (human, mouse, zebrafish, and frog) and tunicates (sea squirts). Here, we perform single-nucleus RNA sequencing (snRNA-seq) and single-cell assay for transposase accessible chromatin sequencing (scATAC-seq) for different stages of amphioxus (covering embryogenesis and adult tissues). With the datasets generated, we constructed a developmental tree for amphioxus cell fate commitment and lineage specification and characterize the underlying key regulators and genetic regulatory networks. The data are publicly available on the online platform AmphioxusAtlas.

Cellular plasticity in the neural crest and cancer

In vertebrates, neural crest cells (NCCs) are a multipotent embryonic population generating both neural/neuronal and mesenchymal derivatives, and thus the neural crest (NC) is often referred to as the fourth germ layer. NC development is a dynamic process, where NCCs possess substantial plasticity in transcriptional and epigenomic profiles. Recent technical advances in single-cell and low-input sequencing have empowered fine-resolution characterisation of NC development. In this review, we summarise the latest models underlying NC-plasticity acquirement and cell-fate restriction, outline the connections between NC plasticity and NC-derived cancer and envision the new opportunities in studying NC plasticity and its link to cancer.

Energy landscape decomposition for cell differentiation with proliferation effect

Complex interactions between genes determine the development and differentiation of cells. We establish a landscape theory for cell differentiation with proliferation effect, in which the developmental process is modeled as a stochastic dynamical system with a birth-death term. We find that two different energy landscapes, denoted U and V, collectively contribute to the establishment of non-equilibrium steady differentiation. The potential U is known as the energy landscape leading to the steady distribution, whose metastable states stand for cell types, while V indicates the differentiation direction from pluripotent to differentiated cells. This interpretation of cell differentiation is different from the previous landscape theory without the proliferation effect. We propose feasible numerical methods and a mean-field approximation for constructing landscapes U and V. Successful applications to typical biological models demonstrate the energy landscape decomposition's validity and reveal biological insights into the considered processes.

Single cell atlas of developing mouse dental germs reveals populations of CD24+ and Plac8+ odontogenic cells

The spatiotemporal relationships in high-resolution during odontogenesis remain poorly understood. We report a cell lineage and atlas of developing mouse teeth. We performed a large-scale (92,688 cells) single cell RNA sequencing, tracing the cell trajectories during odontogenesis from embryonic days 10.5 to 16.5. Combined with an assay for transposase-accessible chromatin with high-throughput sequencing, our results suggest that mesenchymal cells show the specific transcriptome profiles to distinguish the tooth types. Subsequently, we identified key gene regulatory networks in teeth and bone formation and uncovered spatiotemporal patterns of odontogenic mesenchymal cells. CD24⁺ and Plac8⁺ cells from the mesenchyme at the bell stage were distributed in the upper half and preodontoblast layer of the dental papilla, respectively, which could individually induce nonodontogenic epithelia to form tooth-like structures. Specifically, the Plac8⁺ tissue we discovered is the smallest piece with the most homogenous cells that could induce tooth regeneration to date. Our work reveals previously unknown heterogeneity and spatiotemporal patterns of tooth germs that may lead to tooth regeneration for regenerative dentistry.

Dynamical landscapes of cell fate decisions

The generation of cellular diversity during development involves differentiating cells transitioning between discrete cell states. In the 1940s, the developmental biologist Conrad Waddington introduced a landscape metaphor to describe this process. The developmental path of a cell was pictured as a ball rolling through a terrain of branching valleys with cell fate decisions represented by the branch points at which the ball decides between one of two available valleys. Here we discuss progress in constructing quantitative dynamical models inspired by this view of cellular differentiation. We describe a framework based on catastrophe theory and dynamical systems methods that provides the foundations for quantitative geometric models of cellular differentiation. These models can be fit to experimental data and used to make quantitative predictions about cellular differentiation. The theory indicates that cell fate decisions can be described by a small number of decision structures, such that there are only two distinct ways in which cells make a binary choice between one of two fates. We discuss the biological relevance of these mechanisms and suggest the approach is broadly applicable for the quantitative analysis of differentiation dynamics and for determining principles of developmental decisions.

Connecting the DOTs on Cell Identity

DOT1-Like (DOT1L) is the sole methyltransferase of histone H3K79, a modification enriched mainly on the bodies of actively transcribing genes. DOT1L has been extensively studied in leukemia were some of the most frequent onco-fusion proteins contain portions of DOT1L associated factors that mislocalize H3K79 methylation and drive oncogenesis. However, the role of DOT1L in non-transformed, developmental contexts is less clear. Here we assess the known functional roles of DOT1L both in vitro cell culture and in vivo models of mammalian development. DOT1L is evicted during the 2-cell stage when cells are totipotent and massive epigenetic and transcriptional alterations occur. Embryonic stem cell lines that are derived from the blastocyst tolerate the loss of DOT1L, while the reduction of DOT1L protein levels or its catalytic activity greatly enhances somatic cell reprogramming to induced pluripotent stem cells. DOT1L knockout mice are embryonically lethal when organogenesis commences. We catalog the rapidly increasing studies of total and lineage specific knockout model systems that show that DOT1L is broadly required for differentiation. Reduced DOT1L activity is concomitant with increased developmental potential. Contrary to what would be expected of a modification that is associated with active transcription, loss of DOT1L activity results in more upregulated than downregulated genes. DOT1L also participates in various epigenetic networks that are both cell type and developmental stage specific. Taken together, the functions of DOT1L during development are pleiotropic and involve gene regulation at the locus specific and global levels.

Advances in Single-Cell Multi-Omics and Application in Cardiovascular Research

With the development of ever more powerful and versatile high-throughput sequencing techniques and innovative ways to capture single cells, mapping the multicellular tissues at the single-cell level is becoming routine practice. However, it is still challenging to depict the epigenetic landscape of a single cell, especially the genome-wide chromatin accessibility, histone modifications, and DNA methylation. We summarize the most recent methodologies to profile these epigenetic marks at the single-cell level. We also discuss the development and advancement of several multi-omics sequencing technologies from individual cells. Advantages and limitations of various methods to compare and integrate datasets obtained from different sources are also included with specific practical notes. Understanding the heart tissue at single-cell resolution and multi-modal levels will help to elucidate the cell types and states involved in physiological and pathological events during heart development and disease. The rich information produced from single-cell multi-omics studies will also promote the research of heart regeneration and precision medicine on heart diseases.

Droplet Microfluidics for High-Resolution Virology

Microfluidics has enabled a new era of cellular and molecular assays due to the small length scales, parallelization, and the modularity of various analysis and actuation functions. Droplet microfluidics, in particular, has been instrumental in providing new tools for biology with its ability to quickly and reproducibly generate drops that act as individual reactors. A notable beneficiary of this technology has been single-cell RNA sequencing, which has revealed new heterogeneities and interactions for the fundamental unit of life. However, viruses far surpass the diversity of cellular life, affect the dynamics of all ecosystems, and are a chronic source of global health crises. Despite their impact on the world, high-throughput and high-resolution viral profiling has been difficult, with conventional methods being limited to population-level averaging, large sample volumes, and few cultivable hosts. Consequently, most viruses have not been identified and studied. Droplet microfluidics holds the potential to address many of these limitations and offers new levels of sensitivity and throughput for virology. This Feature highlights recent efforts that have applied droplet microfluidics to the detection and study of viruses, including for diagnostics, virus-host interactions, and cell-independent virus assays. In combination with traditional virology methods, droplet microfluidics should prove a potent tool toward achieving a better understanding of the most abundant biological species on Earth.

Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis

A major challenge in understanding vertebrate embryogenesis is the lack of topographical transcriptomic information that can help correlate microenvironmental cues within the hierarchy of cell-fate decisions. Here, we employed Stereo-seq to profile 91 zebrafish embryo sections covering six critical time points during the first 24 h of development, obtaining a total of 152,977 spots at a resolution of 10 × 10 × 15 μm³ (close to cellular size) with spatial coordinates. Meanwhile, we identified spatial modules and co-varying genes for specific tissue organizations. By performing the integrated analysis of the Stereo-seq and scRNA-seq data from each time point, we reconstructed the spatially resolved developmental trajectories of cell-fate transitions and molecular changes during zebrafish embryogenesis. We further investigated the spatial distribution of ligand-receptor pairs and identified potentially important interactions during zebrafish embryo development. Our study constitutes a fundamental reference for further studies aiming to understand vertebrate development.

Cell-Lineage Guided Mass Spectrometry Proteomics in the Developing (Frog) Embryo

Characterization of molecular events as cells give rise to tissues and organs raises a potential to better understand normal development and design efficient remedies for diseases. Technologies enabling accurate identification and quantification of diverse types and large numbers of proteins would provide still missing information on molecular mechanisms orchestrating tissue and organism development in space and time. Here, we present a mass spectrometry-based protocol that enables the measurement of thousands of proteins in identified cell lineages in Xenopus laevis (frog) embryos. The approach builds on reproducible cell-fate maps and established methods to identify, fluorescently label, track, and sample cells and their progeny (clones) from this model of vertebrate development. After collecting cellular contents using microsampling or isolating cells by dissection or fluorescence-activated cell sorting, proteins are extracted and processed for bottom-up proteomic analysis. Liquid chromatography and capillary electrophoresis are used to provide scalable separation for protein detection and quantification with high-resolution mass spectrometry (HRMS). Representative examples are provided for the proteomic characterization of neural-tissue fated cells. Cell-lineage-guided HRMS proteomics is adaptable to different tissues and organisms. It is sufficiently sensitive, specific, and quantitative to peer into the spatio-temporal dynamics of the proteome during vertebrate development.

Emi2 enables centriole amplification during multiciliated cell differentiation

Massive centriole amplification during multiciliated cell (MCC) differentiation is a notable example of organelle biogenesis. This process is thought to be enabled by a derived cell cycle state, but the key cell cycle components required for centriole amplification in MCC progenitors remain poorly defined. Here, we show that emi2 (fbxo43) expression is up-regulated and acts in MCC progenitors after cell cycle exit to transiently inhibit anaphase-promoting complex/cyclosome (APC/C)cdh1 activity. We find that this inhibition is required for the phosphorylation and activation of a key cell cycle kinase, plk1, which acts, in turn, to promote different steps required for centriole amplification and basal body formation, including centriole disengagement, apical migration, and maturation into basal bodies. This emi2-APC/C-plk1 axis is also required to down-regulate gene expression essential for centriole amplification after differentiation is complete. These results identify an emi2-APC/C-plk1 axis that promotes and then terminates centriole assembly and basal body formation during MCC differentiation.

Integrated single-cell transcriptomic and epigenetic study of cell state transition and lineage commitment in embryonic mouse cerebellum

Recent studies using single-cell RNA-sequencing have revealed cellular heterogeneity in the developing mammalian cerebellum, yet the regulatory logic underlying this cellular diversity remains to be elucidated. Using integrated single-cell RNA and ATAC analyses, we resolved developmental trajectories of cerebellar progenitors and identified putative trans- and cis-elements that control cell state transition. We reverse engineered gene regulatory networks (GRNs) of each cerebellar cell type. Through in silico simulations and in vivo experiments, we validated the efficacy of GRN analyses and uncovered the molecular control of a posterior transitory zone (PTZ), a distinct progenitor zone residing immediately anterior to the morphologically defined rhombic lip (RL). We showed that perturbing cell fate specification in the PTZ and RL causes posterior cerebellar vermis hypoplasia, the most common cerebellar birth defect in humans. Our study provides a foundation for comprehensive studies of developmental programs of the mammalian cerebellum.

Applications of Single-Cell Sequencing Technology to the Enteric Nervous System

With recent technical advances and diminishing sequencing costs, single-cell sequencing modalities have become commonplace. These tools permit analysis of RNA expression, DNA sequence, chromatin structure, and cell surface antigens at single-cell resolution. Simultaneous measurement of numerous parameters can resolve populations including rare cells, thus revealing cellular diversity within organs and permitting lineage reconstruction in developing tissues. Application of these methods to the enteric nervous system has yielded a wealth of data and biological insights. We review recent papers applying single-cell sequencing tools to the nascent neural crest and to the developing and mature enteric nervous system. These studies have shown significant diversity of enteric neurons and glia, suggested paradigms for neuronal specification, and revealed signaling pathways active during development. As technology evolves and multiome techniques combining two or more of transcriptomic, genomic, epigenetic, and proteomic data become prominent, we anticipate these modalities will become commonplace in ENS research and may find a role in diagnostic testing and personalized therapeutics.

Modeling bursty transcription and splicing with the chemical master equation

Splicing cascades that alter gene products posttranscriptionally also affect expression dynamics. We study a class of processes and associated distributions that emerge from models of bursty promoters coupled to directed acyclic graphs of splicing. These solutions provide full time-dependent joint distributions for an arbitrary number of species with general noise behaviors and transient phenomena, offering qualitative and quantitative insights about how splicing can regulate expression dynamics. Finally, we derive a set of quantitative constraints on the minimum complexity necessary to reproduce gene coexpression patterns using synchronized burst models. We validate these findings by analyzing long-read sequencing data, where we find evidence of expression patterns largely consistent with these constraints.

The Ribosomal Protein L5 Functions During Xenopus Anterior Development Through Apoptotic Pathways

Ribosomal biogenesis is a fundamental process necessary for cell growth and division. Ribosomal protein L5 (Rpl5) is part of the large ribosomal subunit. Mutations in this protein have been associated with the congenital disease Diamond Blackfan anemia (DBA), a so called ribosomopathy. Despite of the ubiquitous need of ribosomes, clinical manifestations of DBA include tissue-specific symptoms, e.g., craniofacial malformations, eye abnormalities, skin pigmentation failure, cardiac defects or liver cirrhosis. Here, we made use of the vertebrate model organism Xenopus laevis and showed a specific expression of rpl5 in the developing anterior tissue correlating with tissues affected in ribosomopathies. Upon Rpl5 knockdown using an antisense-based morpholino oligonucleotide approach, we showed different phenotypes affecting anterior tissue, i.e., defective cranial cartilage, malformed eyes, and microcephaly. Hence, the observed phenotypes in Xenopus laevis resemble the clinical manifestations of DBA. Analyses of the underlying molecular basis revealed that the expression of several marker genes of neural crest, eye, and brain are decreased during induction and differentiation of the respective tissue. Furthermore, Rpl5 knockdown led to decreased cell proliferation and increased cell apoptosis during early embryogenesis. Investigating the molecular mechanisms underlying Rpl5 function revealed a more than additive effect between either loss of function of Rpl5 and loss of function of c-Myc or loss of function of Rpl5 and gain of function of Tp53, suggesting a common signaling pathway of these proteins. The co-injection of the apoptosis blocking molecule Bcl2 resulted in a partial rescue of the eye phenotype, supporting the hypothesis that apoptosis is one main reason for the phenotypes occurring upon Rpl5 knockdown. With this study, we are able to shed more light on the still poorly understood molecular background of ribosomopathies.

EMBEDR: Distinguishing signal from noise in single-cell omics data

Single-cell “omics”-based measurements are often high dimensional so that dimensionality reduction (DR) algorithms are necessary for data visualization and analysis. The lack of methods for separating signal from noise in DR outputs has limited their utility in generating data-driven discoveries in single-cell data. In this work we present EMBEDR, which assesses the output of any DR algorithm to distinguish evidence of structure from algorithm-induced noise in DR outputs. We apply EMBEDR to DR-generated representations of single-cell omics data of several modalities to show where they visually show real—not spurious—structure. EMBEDR generates a “p” value for each sample, allowing for direct comparisons of DR algorithms and facilitating optimization of algorithm hyperparameters. We show that the scale of a sample’s neighborhood can thus be determined and used to generate a novel “cell-wise optimal” embedding. EMBEDR is available as a Python package for immediate use.

An overview of the benefits and difficulties of dimensionality reduction

A novel algorithm for quantifying and identifying quality within embeddings of data

Quality can be optimized to find data scales and set algorithm parameters

A cell-wise view of quality generates robust and interpretable representations of data

Modern technologies have enabled biologists to construct enormous datasets containing millions of observations of thousands of measurements. These datasets push the limits of traditional analysis techniques, leaving doubts about the quality and fidelity of these methods. In this work, we present a sort of meta-algorithm, called EMBEDR, which seeks to evaluate when a certain class of methods, known as dimensionality reduction methods, are generating high-quality representations of data. We show that EMBEDR allows for visualizations of even large datasets to be interpreted with confidence. Furthermore, we show how asking about the method quality itself can lead to improved analyses of data. These improved analyses may directly impact our understanding of cellular biology, including how cells behave, grow, and respond to stimuli.

The transcriptional portraits of the neural crest at the individual cell level

Since the discovery of this cell population by His in 1850, the neural crest has been under intense study for its important role during vertebrate development. Much has been learned about the function and regulation of neural crest cell differentiation, and as a result, the neural crest has become a key model system for stem cell biology in general. The experiments performed in embryology, genetics, and cell biology in the last 150 years in the neural crest field has given rise to several big questions that have been debated intensely for many years: "How does positional information impact developmental potential? Are neural crest cells individually multipotent or a mixed population of committed progenitors? What are the key factors that regulate the acquisition of stem cell identity, and how does a stem cell decide to differentiate towards one cell fate versus another?" Recently, a marriage between single cell multi-omics, statistical modeling, and developmental biology has shed a substantial amount of light on these questions, and has paved a clear path for future researchers in the field.

Modeling Human Genetic Disorders with CRISPR Technologies in Xenopus

Combining the power of Xenopus developmental biology with CRISPR-based technologies promises great discoveries in understanding and treating human genetic disorders. Here we provide a practical pipeline for how to go from known disease gene(s) or risk gene(s) of interest to methods for gaining functional insight into the contribution of these genes to disorder etiology in humans.

Systematic reconstruction of cellular trajectories across mouse embryogenesis

Mammalian embryogenesis is characterized by rapid cellular proliferation and diversification. Within a few weeks, a single-cell zygote gives rise to millions of cells expressing a panoply of molecular programs. Although intensively studied, a comprehensive delineation of the major cellular trajectories that comprise mammalian development in vivo remains elusive. Here, we set out to integrate several single-cell RNA-sequencing (scRNA-seq) datasets that collectively span mouse gastrulation and organogenesis, supplemented with new profiling of ~150,000 nuclei from approximately embryonic day 8.5 (E8.5) embryos staged in one-somite increments. Overall, we define cell states at each of 19 successive stages spanning E3.5 to E13.5 and heuristically connect them to their pseudoancestors and pseudodescendants. Although constructed through automated procedures, the resulting directed acyclic graph (TOME (trajectories of mammalian embryogenesis)) is largely consistent with our contemporary understanding of mammalian development. We leverage TOME to systematically nominate transcription factors (TFs) as candidate regulators of each cell type's specification, as well as 'cell-type homologs' across vertebrate evolution.

Simultaneous cellular and molecular phenotyping of embryonic mutants using single-cell regulatory trajectories

Developmental progression and cellular diversity are largely driven by transcription factors (TFs); yet, characterizing their loss-of-function phenotypes remains challenging and often disconnected from their underlying molecular mechanisms. Here, we combine single-cell regulatory genomics with loss-of-function mutants to jointly assess both cellular and molecular phenotypes. Performing sci-ATAC-seq at eight overlapping time points during Drosophila mesoderm development could reconstruct the developmental trajectories of all major muscle types and reveal the TFs and enhancers involved. To systematically assess mutant phenotypes, we developed a single-nucleus genotyping strategy to process embryo pools of mixed genotypes. Applying this to four TF mutants could identify and quantify their characterized phenotypes de novo and discover new ones, while simultaneously revealing their regulatory input and mode of action. Our approach is a general framework to dissect the functional input of TFs in a systematic, unbiased manner, identifying both cellular and molecular phenotypes at a scale and resolution that has not been feasible before.

A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants

In all multicellular organisms, transcriptional networks orchestrate organ development. The Arabidopsis root, with its simple structure and indeterminate growth, is an ideal model for investigating the spatiotemporal transcriptional signatures underlying developmental trajectories. To map gene expression dynamics across root cell types and developmental time, we built a comprehensive, organ-scale atlas at single-cell resolution. In addition to estimating developmental progressions in pseudotime, we employed the mathematical concept of optimal transport to infer developmental trajectories and identify their underlying regulators. To demonstrate the utility of the atlas to interpret new datasets, we profiled mutants for two key transcriptional regulators at single-cell resolution, shortroot and scarecrow. We report transcriptomic and in vivo evidence for tissue trans-differentiation underlying a mixed cell identity phenotype in scarecrow. Our results support the atlas as a rich community resource for unraveling the transcriptional programs that specify and maintain cell identity to regulate spatiotemporal organ development.

A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants

In all multicellular organisms, transcriptional networks orchestrate organ development. The Arabidopsis root, with its simple structure and indeterminate growth, is an ideal model for investigating the spatiotemporal transcriptional signatures underlying developmental trajectories. To map gene expression dynamics across root cell types and developmental time, we built a comprehensive, organ-scale atlas at single-cell resolution. In addition to estimating developmental progressions in pseudotime, we employed the mathematical concept of optimal transport to infer developmental trajectories and identify their underlying regulators. To demonstrate the utility of the atlas to interpret new datasets, we profiled mutants for two key transcriptional regulators at single-cell resolution, shortroot and scarecrow. We report transcriptomic and in vivo evidence for tissue trans-differentiation underlying a mixed cell identity phenotype in scarecrow. Our results support the atlas as a rich community resource for unraveling the transcriptional programs that specify and maintain cell identity to regulate spatiotemporal organ development.