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

Cardiac specification during gastrulation - The Yellow Brick Road leading to Tinman

The question of how the heart develops, and the genetic networks governing this process have become intense areas of research over the past several decades. This research is propelled by classical developmental studies and potential clinical applications to understand and treat congenital conditions in which cardiac development is disrupted. Discovery of the tinman gene in Drosophila, and examination of its vertebrate homolog Nkx2.5, along with other core cardiac transcription factors has revealed how cardiac progenitor differentiation and maturation drives heart development. Careful observation of cardiac morphogenesis along with lineage tracing approaches indicated that cardiac progenitors can be divided into two broad classes of cells, namely the first and second heart fields, that contribute to the heart in two distinct waves of differentiation. Ample evidence suggests that the fate of individual cardiac progenitors is restricted to distinct cardiac structures quite early in development, well before the expression of canonical cardiac progenitor markers like Nkx2.5. Here we review the initial specification of cardiac progenitors, discuss evidence for the early patterning of cardiac progenitors during gastrulation, and consider how early gene expression programs and epigenetic patterns can direct their development. A complete understanding of when and how the developmental potential of cardiac progenitors is determined, and their potential plasticity, is of great interest developmentally and also has important implications for both the study of congenital heart disease and therapeutic approaches based on cardiac stem cell programming.

An analytical framework for interpretable and generalizable single-cell data analysis

The scaling of single-cell data exploratory analysis with the rapidly growing diversity and quantity of single-cell omics datasets demands more interpretable and robust data representation that is generalizable across datasets. Here, we have developed a 'linearly interpretable' framework that combines the interpretability and transferability of linear methods with the representational power of non-linear methods. Within this framework we introduce a data representation and visualization method, GraphDR, and a structure discovery method, StructDR, that unifies cluster, trajectory and surface estimation and enables their confidence set inference.

Mapping Gene Expression in the Spatial Dimension

The main function and biological processes of tissues are determined by the combination of gene expression and spatial organization of their cells. RNA sequencing technologies have primarily interrogated gene expression without preserving the native spatial context of cells. However, the emergence of various spatially-resolved transcriptome analysis methods now makes it possible to map the gene expression to specific coordinates within tissues, enabling transcriptional heterogeneity between different regions, and for the localization of specific transcripts and novel spatial markers to be revealed. Hence, spatially-resolved transcriptome analysis technologies have broad utility in research into human disease and developmental biology. Here, recent advances in spatially-resolved transcriptome analysis methods are summarized, including experimental technologies and computational methods. Strengths, challenges, and potential applications of those methods are highlighted, and perspectives in this field are provided.

Hemolymph Ecdysteroid Titer Affects Maternal mRNAs during Bombyx mori Oogenesis

Simple Summary

Both maternal genes and ecdysteroids play important roles during embryonic development. In this study, we aimed to characterize the dynamic landscape of maternal mRNAs and the relationship between maternal genes and ecdysteroids during silkworm oogenesis. For the first time, we determined the start of the accumulation of maternal mRNAs in the ovary at the wandering stage during the larval period. We detected the developmental expression profiles of each gene in the ovary or ovariole. We finally confirmed the role of 20-hydroxyecdysone in regulating maternal gene expression. Taken together, our findings expand the understanding of insect oogenesis and provide a perspective on the embryonic development of the silkworm.

Abstract

Silkworm larval–pupal metamorphosis and the first half of pupal–adult development occur during oogenesis from previtellogenesis to vitellogenesis and include two peaks of the hemolymph ecdysteroid titer. Moreover, a rise in 20-hydroxyecdysone titer in early pupae can trigger the first major transition from previtellogenesis to vitellogenesis in silkworm oogenesis. In this study, we first investigated the expression patterns of 66 maternal genes in the ovary at the wandering stage. We then examined the developmental expression profiles in six time-series samples of ovaries or ovarioles by reverse transcription–quantitative PCR. We found that the transcripts of 22 maternal genes were regulated by 20-hydroxyecdysone in the isolated abdomens of the pupae following a single injection of 20-hydroxyecdysone. This study is the first to determine the relationship between 20-hydroxyecdysone and maternal genes during silkworm oogenesis. These findings provide a basis for further research into the embryonic development of Bombyx mori.

Microfluidic single-cell transcriptomics: moving towards multimodal and spatiotemporal omics

Cells are the basic units of life with vast heterogeneity. Single-cell transcriptomics unveils cell-to-cell gene expression variabilities, discovers novel cell types, and uncovers the critical roles of cellular heterogeneity in biological processes. The recent advances in microfluidic technologies have greatly accelerated the development of single-cell transcriptomics with regard to throughput, sensitivity, cost, and automation. In this article, we review state-of-the-art microfluidic single-cell transcriptomics, with a focus on the methodologies. We first summarize six typical microfluidic platforms for isolation and transcriptomic analysis of single cells. Then the on-going trend of microfluidic transcriptomics towards multimodal omics, which integrates transcriptomics with other omics to provide more comprehensive pictures of gene expression networks, is discussed. We also highlight single-cell spatial transcriptomics and single-cell temporal transcriptomics that provide unprecedented spatiotemporal resolution to reveal transcriptomic dynamics in space and time, respectively. The emerging applications of microfluidic single-cell transcriptomics are also discussed. Finally, we discuss the current challenges to be tackled and provide perspectives on the future development of microfluidic single-cell transcriptomics.

Droplet microfluidics on analysis of pathogenic microbes for wastewater-based epidemiology

Infectious diseases caused by pathogenic microbes have posed a major health issue for the public, such as the ongoing COVID-19 global pandemic. In recent years, wastewater-based epidemiology (WBE) is emerging as an effective and unbiased method for monitoring public health. Despite its increasing importance, the advancement of WBE requires more competent and streamlined analytical platforms. Herein we discuss the interactions between WBE and droplet microfluidics, focusing on the analysis of pathogens in droplets, which is hard to be tackled by traditional analytical tools. We highlight research works from three aspects, namely, quantitation of pathogen biomarkers in droplets, single-cell analysis in droplets, and living cell biosensors in droplets, as well as providing future perspectives on the synergy between WBE and droplet microfluidics.

A single-cell guide to retinal development: Cell fate decisions of multipotent retinal progenitors in scRNA-seq

Recent advances in high throughput single-cell RNA sequencing (scRNA-seq) technology have enabled the simultaneous transcriptomic profiling of thousands of individual cells in a single experiment. To investigate the intrinsic process of retinal development, researchers have leveraged this technology to quantify gene expression in retinal cells across development, in multiple species, and from numerous important models of human disease. In this review, we summarize recent applications of scRNA-seq and discuss how these datasets have complemented and advanced our understanding of retinal progenitor cell competence, cell fate specification, and differentiation. Finally, we also highlight the outstanding questions in the field that advances in single-cell data generation and analysis will soon be able to answer.

Seq Your Destiny: Neural Crest Fate Determination in the Genomic Era

Neural crest stem/progenitor cells arise early during vertebrate embryogenesis at the border of the forming central nervous system. They subsequently migrate throughout the body, eventually differentiating into diverse cell types ranging from neurons and glia of the peripheral nervous system to bones of the face, portions of the heart, and pigmentation of the skin. Along the body axis, the neural crest is heterogeneous, with different subpopulations arising in the head, neck, trunk, and tail regions, each characterized by distinct migratory patterns and developmental potential. Modern genomic approaches like single-cell RNA- and ATAC-sequencing (seq) have greatly enhanced our understanding of cell lineage trajectories and gene regulatory circuitry underlying the developmental progression of neural crest cells. Here, we discuss how genomic approaches have provided new insights into old questions in neural crest biology by elucidating transcriptional and posttranscriptional mechanisms that govern neural crest formation and the establishment of axial level identity. Expected final online publication date for the Annual Review of Genetics, Volume 55 is November 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Generalized and scalable trajectory inference in single-cell omics data with VIA

Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the diversity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g., cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset.

Plant Cell Identity in the Era of Single-Cell Transcriptomics

High-throughput single-cell transcriptomic approaches have revolutionized our view of gene expression at the level of individual cells, providing new insights into their heterogeneity, identities, and functions. Recently, technical challenges to the application of single-cell transcriptomics to plants have been overcome, and many plant organs and tissues have now been subjected to analyses at single-cell resolution. In this review, we describe these studies and their impact on our understanding of the diversity, differentiation, and activities of plant cells. We particularly highlight their impact on plant cell identity, including unprecedented views of cell transitions and definitions of rare and novel cell types. We also point out current challenges and future opportunities for the application and analyses of single-cell transcriptomics in plants. Expected final online publication date for the Annual Review of Genetics, Volume 55 is November 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A comparison of deep learning-based pre-processing and clustering approaches for single-cell RNA sequencing data

The emergence of single cell RNA sequencing has facilitated the studied of genomes, transcriptomes and proteomes. As available single-cell RNA-seq datasets are released continuously, one of the major challenges facing traditional RNA analysis tools is the high-dimensional, high-sparsity, high-noise and large-scale characteristics of single-cell RNA-seq data. Deep learning technologies match the characteristics of single-cell RNA-seq data perfectly and offer unprecedented promise. Here, we give a systematic review for most popular single-cell RNA-seq analysis methods and tools based on deep learning models, involving the procedures of data preprocessing (quality control, normalization, data correction, dimensionality reduction and data visualization) and clustering task for downstream analysis. We further evaluate the deep model-based analysis methods of data correction and clustering quantitatively on 11 gold standard datasets. Moreover, we discuss the data preferences of these methods and their limitations, and give some suggestions and guidance for users to select appropriate methods and tools.

A roadmap for the Human Developmental Cell Atlas

The Human Developmental Cell Atlas (HDCA) initiative, which is part of the Human Cell Atlas, aims to create a comprehensive reference map of cells during development. This will be critical to understanding normal organogenesis, the effect of mutations, environmental factors and infectious agents on human development, congenital and childhood disorders, and the cellular basis of ageing, cancer and regenerative medicine. Here we outline the HDCA initiative and the challenges of mapping and modelling human development using state-of-the-art technologies to create a reference atlas across gestation. Similar to the Human Genome Project, the HDCA will integrate the output from a growing community of scientists who are mapping human development into a unified atlas. We describe the early milestones that have been achieved and the use of human stem-cell-derived cultures, organoids and animal models to inform the HDCA, especially for prenatal tissues that are hard to acquire. Finally, we provide a roadmap towards a complete atlas of human development.

Sobp modulates the transcriptional activation of Six1 target genes and is required during craniofacial development

Branchio-oto-renal syndrome (BOR) is a disorder characterized by hearing loss, and craniofacial and/or renal defects. Variants in the transcription factor Six1 and its co-factor Eya1, both of which are required for otic development, are linked to BOR. We previously identified Sobp as a potential Six1 co-factor, and SOBP variants in mouse and humans cause otic phenotypes; therefore, we asked whether Sobp interacts with Six1 and thereby may contribute to BOR. Co-immunoprecipitation and immunofluorescence experiments demonstrate that Sobp binds to and colocalizes with Six1 in the cell nucleus. Luciferase assays show that Sobp interferes with the transcriptional activation of Six1+Eya1 target genes. Experiments in Xenopus embryos that either knock down or increase expression of Sobp show that it is required for formation of ectodermal domains at neural plate stages. In addition, altering Sobp levels disrupts otic vesicle development and causes craniofacial cartilage defects. Expression of Xenopus Sobp containing the human variant disrupts the pre-placodal ectoderm similar to full-length Sobp, but other changes are distinct. These results indicate that Sobp modifies Six1 function and is required for vertebrate craniofacial development, and identify Sobp as a potential candidate gene for BOR.

Single-cell analysis of cell fate bifurcation in the chordate Ciona

BACKGROUND

Inductive signaling interactions between different cell types are a major mechanism for the further diversification of embryonic cell fates. Most blastomeres in the model chordate Ciona robusta become restricted to a single predominant fate between the 64-cell and mid-gastrula stages. The deeply stereotyped and well-characterized Ciona embryonic cell lineages allow the transcriptomic analysis of newly established cell types very early in their divergence from sibling cell states without the pseudotime inference needed in the analysis of less synchronized cell populations. This is the first ascidian study to use droplet scRNAseq with large numbers of analyzed cells as early as the 64-cell stage when major lineages such as primary notochord first become fate restricted.

RESULTS AND CONCLUSIONS

We identify 59 distinct cell states, including new subregions of the b-line neural lineage and the early induction of the tail tip epidermis. We find that 34 of these cell states are directly or indirectly dependent on MAPK-mediated signaling critical to early Ciona patterning. Most of the MAPK-dependent bifurcations are canalized with the signal-induced cell fate lost upon MAPK inhibition, but the posterior endoderm is unique in being transformed into a novel state expressing some but not all markers of both endoderm and muscle. Divergent gene expression between newly bifurcated sibling cell types is dominated by upregulation in the induced cell type. The Ets family transcription factor Elk1/3/4 is uniquely upregulated in nearly all the putatively direct inductions. Elk1/3/4 upregulation together with Ets transcription factor binding site enrichment analysis enables inferences about which bifurcations are directly versus indirectly controlled by MAPK signaling. We examine notochord induction in detail and find that the transition between a Zic/Ets-mediated regulatory state and a Brachyury/FoxA-mediated regulatory state is unexpectedly late. This supports a "broad-hourglass" model of cell fate specification in which many early tissue-specific genes are induced in parallel to key tissue-specific transcriptional regulators via the same set of transcriptional inputs.

A multiscale analysis of early flower development in Arabidopsis provides an integrated view of molecular regulation and growth control

We have analyzed the link between the gene regulation and growth during the early stages of flower development in Arabidopsis. Starting from time-lapse images, we generated a 4D atlas of early flower development, including cell lineage, cellular growth rates, and the expression patterns of regulatory genes. This information was introduced in MorphoNet, a web-based platform. Using computational models, we found that the literature-based molecular network only explained a minority of the gene expression patterns. This was substantially improved by adding regulatory hypotheses for individual genes. Correlating growth with the combinatorial expression of multiple regulators led to a set of hypotheses for the action of individual genes in morphogenesis. This identified the central factor LEAFY as a potential regulator of heterogeneous growth, which was supported by quantifying growth patterns in a leafy mutant. By providing an integrated view, this atlas should represent a fundamental step toward mechanistic models of flower development.

Sex-Specific Development in Haplodiploid Honeybee Is Controlled by the Female-Embryo-Specific Activation of Thousands of Intronic LncRNAs

Embryonic development depends on a highly coordinated shift in transcription programs known as the maternal-to-zygotic transition (MZT). It remains unclear how haploid and diploid embryo coordinate their genomic activation and embryonic development during MZT in haplodiploid animals. Here, we applied a single-embryo RNA-seq approach to characterize the embryonic transcriptome dynamics in haploid males vs. diploid females of the haplodiploid insect honeybee (Apis mellifera). We observed typical zygotic genome activation (ZGA) occurred in three major waves specifically in female honeybee embryos; haploid genome activation was much weaker and occurred later. Strikingly, we also observed three waves of transcriptional activation for thousands of long non-coding transcripts (lncRNA), 73% of which are transcribed from intronic regions and 65% were specific to female honeybee embryos. These findings support a model in which introns encode thousands of lncRNAs that are expressed in a diploid-embryo-specific and ZGA-triggered manner that may have potential functions to regulate gene expression during early embryonic development in the haplodiploid insect honeybee.

LISA2: Learning Complex Single-Cell Trajectory and Expression Trends

Single-cell transcriptional and epigenomics profiles have been applied in a variety of tissues and diseases for discovering new cell types, differentiation trajectories, and gene regulatory networks. Many methods such as Monocle 2/3, URD, and STREAM have been developed for tree-based trajectory building. Here, we propose a fast and flexible trajectory learning method, LISA2, for single-cell data analysis. This new method has two distinctive features: (1) LISA2 utilizes specified leaves and root to reduce the complexity for building the developmental trajectory, especially for some special cases such as rare cell populations and adjacent terminal cell states; and (2) LISA2 is applicable for both transcriptomics and epigenomics data. LISA2 visualizes complex trajectories using 3D Landmark ISOmetric feature MAPping (L-ISOMAP). We apply LISA2 to simulation and real datasets in cerebellum, diencephalon, and hematopoietic stem cells including both single-cell transcriptomics data and single-cell assay for transposase-accessible chromatin data. LISA2 is efficient in estimating single-cell trajectory and expression trends for different kinds of molecular state of cells.

Single-cell RNA analysis identifies pre-migratory neural crest cells expressing markers of differentiated derivatives

The neural crest is a migratory population of stem-like cells that contribute to multiple traits including the bones of the skull, peripheral nervous system, and pigment. How neural crest cells differentiate into diverse cell types is a fundamental question in the study of vertebrate biology. Here, we use single-cell RNA sequencing to characterize transcriptional changes associated with neural crest cell development in the zebrafish trunk during the early stages of migration. We show that neural crest cells are transcriptionally diverse and identify pre-migratory populations already expressing genes associated with differentiated derivatives, specifically in the xanthophore lineage. Further, we identify a population of Rohon–Beard neurons in the data. The data presented identify novel genetic markers for multiple trunk neural crest cell populations and Rohon–Beard neurons providing insight into previously uncharacterized genes critical for vertebrate development.

LineageOT is a unified framework for lineage tracing and trajectory inference

Understanding the genetic and epigenetic programs that control differentiation during development is a fundamental challenge, with broad impacts across biology and medicine. Measurement technologies like single-cell RNA-sequencing and CRISPR-based lineage tracing have opened new windows on these processes, through computational trajectory inference and lineage reconstruction. While these two mathematical problems are deeply related, methods for trajectory inference are not typically designed to leverage information from lineage tracing and vice versa. Here, we present LineageOT, a unified framework for lineage tracing and trajectory inference. Specifically, we leverage mathematical tools from graphical models and optimal transport to reconstruct developmental trajectories from time courses with snapshots of both cell states and lineages. We find that lineage data helps disentangle complex state transitions with increased accuracy using fewer measured time points. Moreover, integrating lineage tracing with trajectory inference in this way could enable accurate reconstruction of developmental pathways that are impossible to recover with state-based methods alone.

COTAN: scRNA-seq data analysis based on gene co-expression

Estimating the co-expression of cell identity factors in single-cell is crucial. Due to the low efficiency of scRNA-seq methodologies, sensitive computational approaches are critical to accurately infer transcription profiles in a cell population. We introduce COTAN, a statistical and computational method, to analyze the co-expression of gene pairs at single cell level, providing the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts’ distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can assess the correlated or anti-correlated expression of gene pairs, providing a new correlation index with an approximate p-value for the associated test of independence. COTAN can evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Similarly to correlation network analysis, it provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions, becoming a new tool to identify cell-identity markers. We assayed COTAN on two neural development datasets with very promising results. COTAN is an R package that complements the traditional single cell RNA-seq analysis and it is available at https://github.com/seriph78/COTAN.

A model explaining mRNA level fluctuations based on activity demands and RNA age

Cellular RNA levels typically fluctuate and are influenced by different transcription rates and RNA degradation rates. However, the understanding of the fundamental relationships between RNA abundance, environmental stimuli, RNA activities, and RNA age distributions is incomplete. Furthermore, the rates of RNA degradation and transcription are difficult to measure in transcriptomic experiments in living organisms, especially in studies involving humans. A model based on activity demands and RNA age was developed to explore the mechanisms of RNA level fluctuations. Using single-cell time-series gene expression experimental data, we assessed the transcription rates, RNA degradation rates, RNA life spans, RNA demand, accumulated transcription levels, and accumulated RNA degradation levels. This model could also predict RNA levels under simulation backgrounds, such as stimuli that induce regular oscillations in RNA abundance, stable RNA levels over time that result from long-term shortage of total RNA activity or from uncontrollable transcription, and relationships between RNA/protein levels and metabolic rates. This information contributes to existing knowledge.

Detected cellular RNA levels usually fluctuate. The understanding of the fundamental relationships between RNA level fluctuations, the rates of RNA degradation and transcription, environmental stimuli, RNA activities, and RNA age distributions is incomplete. In the present research, we developed a model based on the demands of RNA (related to intrinsic and/or extrinsic information), RNA age (determines the survival time and biological activity of an RNA), transcription, and RNA degradation to explain the mechanism underlying intracellular RNA level fluctuations. We also explored applicability of the model for analysing dynamic processes between interacting biomolecules, such as the relationship between RNA and protein level fluctuations. Using single-cell time-series gene expression experimental data, we assessed some biological parameters, such as transcription rates, RNA degradation rates, and RNA life spans. This model could also predict RNA levels under simulation backgrounds, such as stimuli that induce regular oscillations in RNA abundance, stable RNA levels over time that result from long-term shortage of total RNA activity or from uncontrollable transcription, and relationships between RNA/protein levels and metabolic rates. This information contributes to existing knowledge and provides a new perspective for future studies.

Single-Cell Analysis for Whole-Organism Datasets

Cell atlases are essential companions to the genome as they elucidate how genes are used in a cell type-specific manner or how the usage of genes changes over the lifetime of an organism. This review explores recent advances in whole-organism single-cell atlases, which enable understanding of cell heterogeneity and tissue and cell fate, both in health and disease. Here we provide an overview of recent efforts to build cell atlases across species and discuss the challenges that the field is currently facing. Moreover, we propose the concept of having a knowledgebase that can scale with the number of experiments and computational approaches and a new feedback loop for development and benchmarking of computational methods that includes contributions from the users. These two aspects are key for community efforts in single-cell biology that will help produce a comprehensive annotated map of cell types and states with unparalleled resolution.

Temporal transcriptomic profiling reveals dynamic changes in gene expression of Xenopus animal cap upon activin treatment

Activin, a member of the transforming growth factor-β (TGF-β) superfamily of proteins, induces various tissues from the amphibian presumptive ectoderm, called animal cap explants (ACs) in vitro. However, it remains unclear how and to what extent the resulting cells recapitulate in vivo development. To comprehensively understand whether the molecular dynamics during activin-induced ACs differentiation reflect the normal development, we performed time-course transcriptome profiling of Xenopus ACs treated with 50 ng/mL of activin A, which predominantly induced dorsal mesoderm. The number of differentially expressed genes (DEGs) in response to activin A increased over time, and totally 9857 upregulated and 6663 downregulated DEGs were detected. 1861 common upregulated DEGs among all Post_activin samples included several Spemann's organizer genes. In addition, the temporal transcriptomes were clearly classified into four distinct groups in correspondence with specific features, reflecting stepwise differentiation into mesoderm derivatives, and a decline in the regulation of nuclear envelop and golgi. From the set of early responsive genes, we also identified the suppressor of cytokine signaling 3 (socs3) as a novel activin A-inducible gene. Our transcriptome data provide a framework to elucidate the transcriptional dynamics of activin-driven AC differentiation, reflecting the molecular characteristics of early normal embryogenesis.

A Preliminary Single-Cell RNA-Seq Analysis of Embryonic Cells That Express Brachyury in the Amphioxus, Branchiostoma japonicum

Among chordate taxa, the cephalochordates diverged earlier than urochordates and vertebrates; thus, they retain unique, primitive developmental features. In particular, the amphioxus notochord has muscle-like properties, a feature not seen in urochordates or vertebrates. Amphioxus contains two Brachyury genes, Bra1 and Bra2. Bra2 is reportedly expressed in the blastopore, notochord, somites, and tail bud, in contrast to a low level of Bra1 expression only in notochord. To distinguish the expression profiles of the two Brachyury genes at the single-cell level, we carried out single-cell RNA-seq (scRNA-seq) analysis using the amphioxus, Branchiostoma japonicum. This scRNA-seq analysis classified B. japonicum embryonic cells into 15 clusters at developmental stages from midgastrula to early swimming larva. Brachyury was expressed in cells of clusters 4, 5, 8, and 9. We first confirmed that cluster 8 comprises cells that form somites since this cluster specifically expresses four myogenic factor genes. Cluster 9 contains a larger number of cells with high levels of Bra2 expression and a smaller number of cells with Bra1 expression. Simultaneous expression in cluster 9 of tool-kit genes, including FoxA, Goosecoid, and hedgehog, showed that this cluster comprises cells that form the notochord. Expression of Bra2, but not Bra1, in cells of clusters 4 and 5 at the gastrula stage together with expression of Wnt1 and Caudal indicates that clusters 4 and 5 comprise cells of the blastopore, which contiguously form the tail bud. In addition, Hox1, Hox3, and Hox4 were highly expressed in Bra2-expressing clusters 4, 5, 8, and 9 in a temporally coordinated manner, suggesting roles of anterior Hox genes in specification of mesodermal organs, including somites, notochord, and tail bud. This scRNA-seq analysis therefore highlights differences between the two Brachyury genes in relation to embryonic regions in which they are expressed and their levels of expression. Bra2 is the ancestral Brachyury in amphioxus, since expression in the blastopore is shared with other deuterostomes. On the other hand, Bra1 is a duplicate copy and likely evolved a supplementary function in notochord and somite formation in the Branchiostoma lineage.

Embryo-scale, single-cell spatial transcriptomics

Spatial patterns of gene expression manifest at scales ranging from local (e.g., cell-cell interactions) to global (e.g., body axis patterning). However, current spatial transcriptomics methods either average local contexts or are restricted to limited fields of view. Here, we introduce sci-Space, which retains single-cell resolution while resolving spatial heterogeneity at larger scales. Applying sci-Space to developing mouse embryos, we captured approximate spatial coordinates and whole transcriptomes of about 120,000 nuclei. We identify thousands of genes exhibiting anatomically patterned expression, leverage spatial information to annotate cellular subtypes, show that cell types vary substantially in their extent of spatial patterning, and reveal correlations between pseudotime and the migratory patterns of differentiating neurons. Looking forward, we anticipate that sci-Space will facilitate the construction of spatially resolved single-cell atlases of mammalian development.

Control of tissue development and cell diversity by cell cycle-dependent transcriptional filtering

Cell cycle duration changes dramatically during development, starting out fast to generate cells quickly and slowing down over time as the organism matures. The cell cycle can also act as a transcriptional filter to control the expression of long gene transcripts, which are partially transcribed in short cycles. Using mathematical simulations of cell proliferation, we identify an emergent property that this filter can act as a tuning knob to control gene transcript expression, cell diversity, and the number and proportion of different cell types in a tissue. Our predictions are supported by comparison to single-cell RNA-seq data captured over embryonic development. Additionally, evolutionary genome analysis shows that fast-developing organisms have a narrow genomic distribution of gene lengths while slower developers have an expanded number of long genes. Our results support the idea that cell cycle dynamics may be important across multicellular animals for controlling gene transcript expression and cell fate.

CStreet: a computed Cell State trajectory inference method for time-series single-cell RNA sequencing data

MOTIVATION

The increasing amount of time-series single-cell RNA sequencing (scRNA-seq) data raises the key issue of connecting cell states (i.e., cell clusters or cell types) to obtain the continuous temporal dynamics of transcription, which can highlight the unified biological mechanisms involved in cell state transitions. However, most existing trajectory methods are specifically designed for individual cells, so they can hardly meet the needs of accurately inferring the trajectory topology of the cell state, which usually contains cells assigned to different branches.

RESULTS

Here, we present CStreet, a computed Cell State trajectory inference method for time-series scRNA-seq data. It uses time-series information to construct the k-nearest neighbors connections between cells within each time point and between adjacent time points. Then, CStreet estimates the connection probabilities of the cell states and visualizes the trajectory, which may include multiple starting points and paths, using a force-directed graph. By comparing the performance of CStreet with that of six commonly used cell state trajectory reconstruction methods on simulated data and real data, we demonstrate the high accuracy and high tolerance of CStreet.

AVAILABILITY AND IMPLEMENTATION

CStreet is written in Python and freely available on the web at https://github.com/TongjiZhanglab/CStreet and https://doi.org/10.5281/zenodo.4483205.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Stem Cells and Innate Immunity in Aquatic Invertebrates: Bridging Two Seemingly Disparate Disciplines for New Discoveries in Biology

The scopes related to the interplay between stem cells and the immune system are broad and range from the basic understanding of organism's physiology and ecology to translational studies, further contributing to (eco)toxicology, biotechnology, and medicine as well as regulatory and ethical aspects. Stem cells originate immune cells through hematopoiesis, and the interplay between the two cell types is required in processes like regeneration. In addition, stem and immune cell anomalies directly affect the organism's functions, its ability to cope with environmental changes and, indirectly, its role in ecosystem services. However, stem cells and immune cells continue to be considered parts of two branches of biological research with few interconnections between them. This review aims to bridge these two seemingly disparate disciplines towards much more integrative and transformative approaches with examples deriving mainly from aquatic invertebrates. We discuss the current understanding of cross-disciplinary collaborative and emerging issues, raising novel hypotheses and comments. We also discuss the problems and perspectives of the two disciplines and how to integrate their conceptual frameworks to address basic equations in biology in a new, innovative way.

A single-cell-resolution fate map of endoderm reveals demarcation of pancreatic progenitors by cell cycle

A progenitor cell could generate a certain type or multiple types of descendant cells during embryonic development. To make all the descendant cell types and developmental trajectories of every single progenitor cell clear remains an ultimate goal in developmental biology. Characterizations of descendant cells produced by each uncommitted progenitor for a full germ layer represent a big step toward the goal. Here, we focus on early foregut endoderm, which generates foregut digestive organs, including the pancreas, liver, foregut, and ductal system, through distinct lineages. Using unbiased single-cell labeling techniques, we label every individual zebrafish foregut endodermal progenitor cell out of 216 cells to visibly trace the distribution and number of their descendant cells. Hence, single-cell-resolution fate and proliferation maps of early foregut endoderm are established, in which progenitor regions of each foregut digestive organ are precisely demarcated. The maps indicate that the pancreatic endocrine progenitors are featured by a cell cycle state with a long G1 phase. Manipulating durations of the G1 phase modulates pancreatic progenitor populations. This study illustrates foregut endodermal progenitor cell fate at single-cell resolution, precisely demarcates different progenitor populations, and sheds light on mechanistic insights into pancreatic fate determination.

Assessing evolutionary and developmental transcriptome dynamics in homologous cell types

Background

During development, complex organ patterns emerge through the precise temporal and spatial specification of different cell types. On an evolutionary timescale, these patterns can change, resulting in morphological diversification. It is generally believed that homologous anatomical structures are built—largely—by homologous cell types. However, whether a common evolutionary origin of such cell types is always reflected in the conservation of their intrinsic transcriptional specification programs is less clear.

Results

Here, we developed a user‐friendly bioinformatics workflow to detect gene co‐expression modules and test for their conservation across developmental stages and species boundaries. Using a paradigm of morphological diversification, the tetrapod limb, and single‐cell RNA‐sequencing data from two distantly related species, chicken and mouse, we assessed the transcriptional dynamics of homologous cell types during embryonic patterning. With mouse limb data as reference, we identified 19 gene co‐expression modules with varying tissue or cell type‐restricted activities. Testing for co‐expression conservation revealed modules with high evolutionary turnover, while others seemed maintained—to different degrees, in module make‐up, density or connectivity—over developmental and evolutionary timescales.

Conclusions

We present an approach to identify evolutionary and developmental dynamics in gene co‐expression modules during patterning‐relevant stages of homologous cell type specification using single‐cell RNA‐sequencing data.

We present an approach to identify evolutionary and developmental dynamics in gene co‐expression modules during patterning‐relevant stages of homologous cell type specification using single‐cell RNA‐sequencing data.

In Vivo Subcellular Mass Spectrometry Enables Proteo-Metabolomic Single-Cell Systems Biology in a Chordate Embryo Developing to a Normally Behaving Tadpole (X. laevis)*

We report the development of in vivo subcellular high-resolution mass spectrometry (HRMS) for proteo-metabolomic molecular systems biology in complex tissues. With light microscopy, we identified the left-dorsal and left-ventral animal cells in cleavage-stage non-sentient Xenopus laevis embryos. Using precision-translated fabricated microcapillaries, the subcellular content of each cell was double-probed, each time swiftly (<5 s/event) aspirating <5 % of cell volume (≈10 nL). The proteins and metabolites were analyzed by home-built ultrasensitive capillary electrophoresis electrospray ionization employing orbitrap or time-of-flight HRMS. Label-free detection of ≈150 metabolites (57 identified) and 738 proteins found proteo-metabolomic networks with differential quantitative activities between the cell types. With spatially and temporally scalable sampling, the technology preserved the integrity of the analyzed cells, the neighboring cells, and the embryo. 95 % of the analyzed embryos developed into sentient tadpoles that were indistinguishable from their wild-type siblings based on anatomy and visual function in a background color preference assay.

Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box

Understanding and treating heterogeneous brain disorders requires specialized techniques spanning genetics, proteomics, and neuroimaging. Designed to meet this need, NeuroPM-box is a user-friendly, open-access, multi-tool cross-platform software capable of characterizing multiscale and multifactorial neuropathological mechanisms. Using advanced analytical modeling for molecular, histopathological, brain-imaging and/or clinical evaluations, this framework has multiple applications, validated here with synthetic (N > 2900), in-vivo (N = 911) and post-mortem (N = 736) neurodegenerative data, and including the ability to characterize: (i) the series of sequential states (genetic, histopathological, imaging or clinical alterations) covering decades of disease progression, (ii) concurrent intra-brain spreading of pathological factors (e.g., amyloid, tau and alpha-synuclein proteins), (iii) synergistic interactions between multiple biological factors (e.g., toxic tau effects on brain atrophy), and (iv) biologically-defined patient stratification based on disease heterogeneity and/or therapeutic needs. This freely available toolbox ( neuropm-lab.com/neuropm-box.html ) could contribute significantly to a better understanding of complex brain processes and accelerating the implementation of Precision Medicine in Neurology.

Differential gene expression analysis for multi-subject single cell RNA sequencing studies with aggregateBioVar

Motivation

Single-cell RNA-sequencing (scRNA-seq) provides more granular biological information than bulk RNA-sequencing; bulk RNA sequencing remains popular due to lower costs which allows processing more biological replicates and design more powerful studies. As scRNA-seq costs have decreased, collecting data from more than one biological replicate has become more feasible, but careful modeling of different layers of biological variation remains challenging for many users. Here, we propose a statistical model for scRNA-seq gene counts, describe a simple method for estimating model parameters and show that failing to account for additional biological variation in scRNA-seq studies can inflate false discovery rates (FDRs) of statistical tests.

Results

First, in a simulation study, we show that when the gene expression distribution of a population of cells varies between subjects, a naïve approach to differential expression analysis will inflate the FDR. We then compare multiple differential expression testing methods on scRNA-seq datasets from human samples and from animal models. These analyses suggest that a naïve approach to differential expression testing could lead to many false discoveries; in contrast, an approach based on pseudobulk counts has better FDR control.

Availability and implementation

A software package, aggregateBioVar, is freely available on Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/aggregateBioVar.html) to accommodate compatibility with upstream and downstream methods in scRNA-seq data analysis pipelines.

Supplementary information

Raw gene-by-cell count matrices for pig scRNA-seq data are available as GEO accession GSE150211. Supplementary data are available at Bioinformatics online.

Mapping single-cell atlases throughout Metazoa unravels cell type evolution

Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases. Yet, comparison between distant relatives is hindered by complex gene histories and diversifications in expression programs. Previously, we introduced the self-assembling manifold (SAM) algorithm to robustly reconstruct manifolds from single-cell data (Tarashansky et al., 2019). Here, we build on SAM to map cell atlas manifolds across species. This new method, SAMap, identifies homologous cell types with shared expression programs across distant species within phyla, even in complex examples where homologous tissues emerge from distinct germ layers. SAMap also finds many genes with more similar expression to their paralogs than their orthologs, suggesting paralog substitution may be more common in evolution than previously appreciated. Lastly, comparing species across animal phyla, spanning sponge to mouse, reveals ancient contractile and stem cell families, which may have arisen early in animal evolution.

Single-cell transcriptomics of human embryos identifies multiple sympathoblast lineages with potential implications for neuroblastoma origin

Characterization of the progression of cellular states during human embryogenesis can provide insights into the origin of pediatric diseases. We examined the transcriptional states of neural crest- and mesoderm-derived lineages differentiating into adrenal glands, kidneys, endothelium and hematopoietic tissue between post-conception weeks 6 and 14 of human development. Our results reveal transitions connecting the intermediate mesoderm and progenitors of organ primordia, the hematopoietic system and endothelial subtypes. Unexpectedly, by using a combination of single-cell transcriptomics and lineage tracing, we found that intra-adrenal sympathoblasts at that stage are directly derived from nerve-associated Schwann cell precursors, similarly to local chromaffin cells, whereas the majority of extra-adrenal sympathoblasts arise from the migratory neural crest. In humans, this process persists during several weeks of development within the large intra-adrenal ganglia-like structures, which may also serve as reservoirs of originating cells in neuroblastoma.

Discovering a sparse set of pairwise discriminating features in high-dimensional data

MOTIVATION

Recent technological advances produce a wealth of high-dimensional descriptions of biological processes, yet extracting meaningful insight and mechanistic understanding from these data remains challenging. For example, in developmental biology, the dynamics of differentiation can now be mapped quantitatively using single-cell RNA sequencing, yet it is difficult to infer molecular regulators of developmental transitions. Here, we show that discovering informative features in the data is crucial for statistical analysis as well as making experimental predictions.

RESULTS

We identify features based on their ability to discriminate between clusters of the data points. We define a class of problems in which linear separability of clusters is hidden in a low-dimensional space. We propose an unsupervised method to identify the subset of features that define a low-dimensional subspace in which clustering can be conducted. This is achieved by averaging over discriminators trained on an ensemble of proposed cluster configurations. We then apply our method to single-cell RNA-seq data from mouse gastrulation, and identify 27 key transcription factors (out of 409 total), 18 of which are known to define cell states through their expression levels. In this inferred subspace, we find clear signatures of known cell types that eluded classification prior to discovery of the correct low-dimensional subspace.

AVAILABILITY AND IMPLEMENTATION

https://github.com/smelton/SMD.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell profiling for advancing birth defects research and prevention

Cellular analysis of developmental processes and toxicities has traditionally entailed bulk methods (e.g., transcriptomics) that lack single cell resolution or tissue localization methods (e.g., immunostaining) that allow only a few genes to be monitored in each experiment. Recent technological advances have enabled interrogation of genomic function at the single-cell level, providing new opportunities to unravel developmental pathways and processes with unprecedented resolution. Here, we review emerging technologies of single-cell RNA-sequencing (scRNA-seq) to globally characterize the gene expression sets of different cell types and how different cell types emerge from earlier cell states in development. Cell atlases of experimental embryology and human embryogenesis at single-cell resolution will provide an encyclopedia of genes that define key stages from gastrulation to organogenesis. This technology, combined with computational models to discover key organizational principles, was recognized by Science magazine as the "Breakthrough of the year" for 2018 due to transformative potential on the way we study how human cells mature over a lifetime, how tissues regenerate, and how cells change in diseases (e.g., patient-derived organoids to screen disease-specific targets and design precision therapy). Profiling transcriptomes at the single-cell level can fulfill the need for greater detail in the molecular progression of all cell lineages, from pluripotency to adulthood and how cell-cell signaling pathways control progression at every step. Translational opportunities emerge for elucidating pathogenesis of genetic birth defects with cellular precision and improvements for predictive toxicology of chemical teratogenesis.

Identification of a targetable KRAS-mutant epithelial population in non-small cell lung cancer

Lung cancer is the leading cause of cancer deaths. Tumor heterogeneity, which hampers development of targeted therapies, was herein deconvoluted via single cell RNA sequencing in aggressive human adenocarcinomas (carrying Kras-mutations) and comparable murine model. We identified a tumor-specific, mutant-KRAS-associated subpopulation which is conserved in both human and murine lung cancer. We previously reported a key role for the oncogene BMI-1 in adenocarcinomas. We therefore investigated the effects of in vivo PTC596 treatment, which affects BMI-1 activity, in our murine model. Post-treatment, MRI analysis showed decreased tumor size, while single cell transcriptomics concomitantly detected near complete ablation of the mutant-KRAS-associated subpopulation, signifying the presence of a pharmacologically targetable, tumor-associated subpopulation. Our findings therefore hold promise for the development of a targeted therapy for KRAS-mutant adenocarcinomas.

Neural crest multipotency and specification: power and limits of single cell transcriptomic approaches

The neural crest is a unique population of multipotent cells forming in vertebrate embryos. Their vast cell fate potential enables the generation of a diverse array of differentiated cell types in vivo. These include, among others, connective tissue, cartilage and bone of the face and skull, neurons and glia of the peripheral nervous system (including enteric nervous system), and melanocytes. Following migration, these derivatives extensively populate multiple germ layers. Within the competent neural border ectoderm, an area located at the junction between the neural and non-neural ectoderm during embryonic development, neural crest cells form in response to a series of inductive secreted cues including BMP, Wnt, and FGF signals. As cells become progressively specified, they express transcriptional modules conducive with their stage of fate determination or cell state. Those sequential states include the neural border state, the premigratory neural crest state, the epithelium-to-mesenchyme transitional state, and the migratory state to end with post-migratory and differentiation states. However, despite the extensive knowledge accumulated over 150 years of neural crest biology, many key questions remain open, in particular the timing of neural crest lineage determination, the control of potency during early developmental stages, and the lineage relationships between different subpopulations of neural crest cells. In this review, we discuss the recent advances in understanding early neural crest formation using cutting-edge high-throughput single cell sequencing approaches. We will discuss how this new transcriptomic data, from 2017 to 2021, has advanced our knowledge of the steps in neural crest cell lineage commitment and specification, the mechanisms driving multipotency, and diversification. We will then discuss the questions that remain to be resolved and how these approaches may continue to unveil the biology of these fascinating cells.

ACME dissociation: a versatile cell fixation-dissociation method for single-cell transcriptomics

Single-cell sequencing technologies are revolutionizing biology, but they are limited by the need to dissociate live samples. Here, we present ACME (ACetic-MEthanol), a dissociation approach for single-cell transcriptomics that simultaneously fixes cells. ACME-dissociated cells have high RNA integrity, can be cryopreserved multiple times, and are sortable and permeable. As a proof of principle, we provide single-cell transcriptomic data of different species, using both droplet-based and combinatorial barcoding single-cell methods. ACME uses affordable reagents, can be done in most laboratories and even in the field, and thus will accelerate our knowledge of cell types across the tree of life.

Connecting Timescales in Biology: Can Early Dynamical Measurements Predict Long-Term Outcomes?

Prediction of long-term outcomes from short-term measurements remains a fundamental challenge. Quantitative assessment of signaling dynamics, and the resulting transcriptomic and proteomic responses, has yielded fundamental insights into cellular outcomes. However, the utility of these measurements is limited by their short timescale (hours to days), while the consequences of these events frequently unfold over longer timescales. Here, we discuss the predictive power of static and dynamic measurements, drawing examples from fields that have harnessed the predictive capabilities of such measurements. We then explore potential approaches to close this timescale gap using complementary measurements and computational approaches, focusing on the example of dynamic measurements of signaling factors and their impacts on cellular outcomes.

Hemimegalencephaly and intractable seizures associated with the NPRL3 gene variant in a newborn: A case report

Hemimegalencephaly (HME) is a rare hamartomatous congenital malformation of the brain characterized by dysplastic overgrowth of either one of the cerebral hemispheres. HME is associated with early onset seizures, abnormal neurological findings, and with subsequent cognitive and behavioral disabilities. Seizures associated with HME are often refractory to antiepileptic medications. Hemispherectomy is usually necessary to provide effective seizure control. The exact etiology of HME is not fully understood, but involves a disturbance in early brain development and likely involves genes responsible for patterning and symmetry of the brain. We present a female newborn who had refractory seizures due to HME. Whole genome sequencing revealed a novel, likely pathogenic, maternally inherited, 3Kb deletion encompassing exon 5 of the NPRL3 gene (chr16:161898-164745x1). The NPRL3 gene encodes for a nitrogen permease regulator 3-like protein, a subunit of the GATOR complex, which regulates the mTOR signaling pathway. A trial of mTOR inhibitor drug, Sirolimus, did not improve her seizure control. Functional hemispherectomy at 3 months of age resulted in total abatement of clinical seizures.

Tracing cell-type evolution by cross-species comparison of cell atlases

Cell types are the basic building units of multicellular life, with extensive diversities. The evolution of cell types is a crucial layer of comparative cell biology but is thus far not comprehensively studied. We define a compendium of cell atlases using single-cell RNA-seq (scRNA-seq) data from seven animal species and construct a cross-species cell-type evolutionary hierarchy. We present a roadmap for the origin and diversity of major cell categories and find that muscle and neuron cells are conserved cell types. Furthermore, we identify a cross-species transcription factor (TF) repertoire that specifies major cell categories. Overall, our study reveals conservation and divergence of cell types during animal evolution, which will further expand the landscape of comparative genomics.

Reactivation of the pluripotency program precedes formation of the cranial neural crest

During development, cells progress from a pluripotent state to a more restricted fate within a particular germ layer. However, cranial neural crest cells (CNCCs), a transient cell population that generates most of the craniofacial skeleton, have much broader differentiation potential than their ectodermal lineage of origin. Here, we identify a neuroepithelial precursor population characterized by expression of canonical pluripotency transcription factors that gives rise to CNCCs and is essential for craniofacial development. Pluripotency factor Oct4 is transiently reactivated in CNCCs and is required for the subsequent formation of ectomesenchyme. Furthermore, open chromatin landscapes of Oct4⁺ CNCC precursors resemble those of epiblast stem cells, with additional features suggestive of priming for mesenchymal programs. We propose that CNCCs expand their developmental potential through a transient reacquisition of molecular signatures of pluripotency.

Molecular Signatures of Inflammatory Profile and B-Cell Function in Patients with Severe Fever with Thrombocytopenia Syndrome

Dabie bandavirus (severe fever with thrombocytopenia syndrome virus [SFTSV]) induces an immunopathogenic disease with a high fatality rate; however, the mechanisms underlying its clinical manifestations are largely unknown. In this study, we applied targeted proteomics and single-cell transcriptomics to examine the differential immune landscape in SFTS patient blood. Serum immunoprofiling identified low-risk and high-risk clusters of SFTS patients based on inflammatory cytokine levels, which corresponded to disease severity. Single-cell transcriptomic analysis of SFTS patient peripheral blood mononuclear cells (PBMCs) at different infection stages showed pronounced expansion of B cells with alterations in B-cell subsets in fatal cases. Furthermore, plasma cells in which the interferon (IFN) pathway is downregulated were identified as the primary reservoir of SFTSV replication. This study identified not only the molecular signatures of serum inflammatory cytokines and B-cell lineage populations in SFTSV-induced fatalities but also plasma cells as the viral reservoir. Thus, this suggests that altered B-cell function is linked to lethality in SFTSV infections.

Coordinated changes in cellular behavior ensure the lifelong maintenance of the hippocampal stem cell population

Neural stem cell numbers fall rapidly in the hippocampus of juvenile mice but stabilize during adulthood, ensuring lifelong hippocampal neurogenesis. We show that this stabilization of stem cell numbers in young adults is the result of coordinated changes in stem cell behavior. Although proliferating neural stem cells in juveniles differentiate rapidly, they increasingly return to a resting state of shallow quiescence and progress through additional self-renewing divisions in adulthood. Single-cell transcriptomics, modeling, and label retention analyses indicate that resting cells have a higher activation rate and greater contribution to neurogenesis than dormant cells, which have not left quiescence. These changes in stem cell behavior result from a progressive reduction in expression of the pro-activation protein ASCL1 because of increased post-translational degradation. These cellular mechanisms help reconcile current contradictory models of hippocampal neural stem cell (NSC) dynamics and may contribute to the different rates of decline of hippocampal neurogenesis in mammalian species, including humans.

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More proliferating hippocampal stem cells return to shallow quiescence with age

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Dormant stem cells enter deeper quiescence with age

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These changes drive the transition from developmental to adult neurogenesis

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Increasing degradation of ASCL1 protein by HUWE1 coordinates these changes

Phylodynamics for cell biologists

Multicellular organisms are composed of cells connected by ancestry and descent from progenitor cells. The dynamics of cell birth, death, and inheritance within an organism give rise to the fundamental processes of development, differentiation, and cancer. Technical advances in molecular biology now allow us to study cellular composition, ancestry, and evolution at the resolution of individual cells within an organism or tissue. Here, we take a phylogenetic and phylodynamic approach to single-cell biology. We explain how "tree thinking" is important to the interpretation of the growing body of cell-level data and how ecological null models can benefit statistical hypothesis testing. Experimental progress in cell biology should be accompanied by theoretical developments if we are to exploit fully the dynamical information in single-cell data.

Combinatorial transcription factor activities on open chromatin induce embryonic heterogeneity in vertebrates

During vertebrate gastrulation, mesoderm is induced in pluripotent cells, concomitant with dorsal‐ventral patterning and establishing of the dorsal axis. We applied single‐cell chromatin accessibility and transcriptome analyses to explore the emergence of cellular heterogeneity during gastrulation in Xenopus tropicalis. Transcriptionally inactive lineage‐restricted genes exhibit relatively open chromatin in animal caps, whereas chromatin accessibility in dorsal marginal zone cells more closely reflects transcriptional activity. We characterized single‐cell trajectories and identified head and trunk organizer cell clusters in early gastrulae. By integrating chromatin accessibility and transcriptome data, we inferred the activity of transcription factors in single‐cell clusters and tested the activity of organizer‐expressed transcription factors in animal caps, alone or in combination. The expression profile induced by a combination of Foxb1 and Eomes most closely resembles that observed in the head organizer. Genes induced by Eomes, Otx2, or the Irx3‐Otx2 combination are enriched for maternally regulated H3K4me3 modifications, whereas Lhx8‐induced genes are marked more frequently by zygotically controlled H3K4me3. Taken together, our results show that transcription factors cooperate in a combinatorial fashion in generally open chromatin to orchestrate zygotic gene expression.