Spatio-temporal mRNA tracking in the early zebrafish embryo

Advances in Spatial Omics Technologies

Rapidly developing spatial omics technologies provide us with new approaches to deeply understanding the diversity and functions of cell types within organisms. Unlike traditional approaches, spatial omics technologies enable researchers to dissect the complex relationships between tissue structure and function at the cellular or even subcellular level. The application of spatial omics technologies provides new perspectives on key biological processes such as nervous system development, organ development, and tumor microenvironment. This review focuses on the advancements and strategies of spatial omics technologies, summarizes their applications in biomedical research, and highlights the power of spatial omics technologies in advancing the understanding of life sciences related to development and disease.

Fish Evo-Devo: Moving Toward Species-Specific and Knowledge-Based Interactome

A knowledge-based interactome maps interactions among proteins and molecules within a cell using experimental data, computational predictions, and literature mining. These interactomes are vital for understanding cellular functions, pathways, and the evolutionary conservation of protein interactions. They reveal how interactions regulate growth, differentiation, and development. Transitioning to functionally validated interactomes is crucial in evolutionary developmental biology (Evo-Devo), especially for non-model species, to uncover unique regulatory networks, evolutionary novelties, and reliable gene interaction models. This enhances our understanding of complex trait evolution across species. The European Evo-Devo 2024 conference in Helsinki hosted the first fish-specific Evo-Devo symposium, highlighting the growing interest in fish models. Advances in genome annotation, genome editing, imaging, and molecular screening are expanding fish Evo-Devo research. High-throughput molecular data have enabled the deduction of gene regulatory networks. The next steps involve creating species-specific interactomes, validating them functionally, and integrating additional molecular data to deepen the understanding of complex regulatory interactions in fish Evo-Devo. This short review aims to address the logical steps for this transition, as well as the necessities and limitations of this journey.

The maternal-to-zygotic transition: reprogramming of the cytoplasm and nucleus

A fertilized egg is initially transcriptionally silent and relies on maternally provided factors to initiate development. For embryonic development to proceed, the oocyte-inherited cytoplasm and the nuclear chromatin need to be reprogrammed to create a permissive environment for zygotic genome activation (ZGA). During this maternal-to-zygotic transition (MZT), which is conserved in metazoans, transient totipotency is induced and zygotic transcription is initiated to form the blueprint for future development. Recent technological advances have enhanced our understanding of MZT regulation, revealing common themes across species and leading to new fundamental insights about transcription, mRNA decay and translation.

Decoding the blueprints of embryo development with single-cell and spatial omics

Embryonic development is a complex and intricately regulated process that encompasses precise control over cell differentiation, morphogenesis, and the underlying gene expression changes. Recent years have witnessed a remarkable acceleration in the development of single-cell and spatial omic technologies, enabling high-throughput profiling of transcriptomic and other multi-omic information at the individual cell level. These innovations offer fresh and multifaceted perspectives for investigating the intricate cellular and molecular mechanisms that govern embryonic development. In this review, we provide an in-depth exploration of the latest technical advancements in single-cell and spatial multi-omic methodologies and compile a systematic catalog of their applications in the field of embryonic development. We deconstruct the research strategies employed by recent studies that leverage single-cell sequencing techniques and underscore the unique advantages of spatial transcriptomics. Furthermore, we delve into both the current applications, data analysis algorithms and the untapped potential of these technologies in advancing our understanding of embryonic development. With the continuous evolution of multi-omic technologies, we anticipate their widespread adoption and profound contributions to unraveling the intricate molecular foundations underpinning embryo development in the foreseeable future.

Spatiotemporal control of translation in live zebrafish embryos via photoprotected mRNAs

Translation of mRNA into protein is a fundamental process and tightly controlled during development. Several mechanisms acting on the mRNA level regulate when and where an mRNA is expressed. To explore the effects of conditional and transient gene expression in a developing organism, it is vital to experimentally enable abrogation and restoration of translation. We recently developed the FlashCaps technology allowing preparation of translationally muted mRNAs and their controlled activation by light. Here, we validate its functionality in vivo. We demonstrate that translation of FlashCap-eGFP-mRNA can be triggered in zebrafish embryos with spatiotemporal control. The injected FlashCap-mRNA is stable for hours and remains muted. Light-mediated activation up to 24 h post fertilization produces visible amounts of eGFP and can be restricted to distinct parts of the embryo. This methodology extends the toolbox for vertebrate models by enabling researchers to locally activate mRNA translation at different timepoints during development.

Transcriptome and Temporal Transcriptome Analyses in Single Cells

Transcriptome analysis in single cells, enabled by single-cell RNA sequencing, has become a prevalent approach in biomedical research, ranging from investigations of gene regulation to the characterization of tissue organization. Over the past decade, advances in single-cell RNA sequencing technology, including its underlying chemistry, have significantly enhanced its performance, marking notable improvements in methodology. A recent development in the field, which integrates RNA metabolic labeling with single-cell RNA sequencing, has enabled the profiling of temporal transcriptomes in individual cells, offering new insights into dynamic biological processes involving RNA kinetics and cell fate determination. In this review, we explore the chemical principles and design improvements that have enhanced single-molecule capture efficiency, improved RNA quantification accuracy, and increased cellular throughput in single-cell transcriptome analysis. We also illustrate the concept of RNA metabolic labeling for detecting newly synthesized transcripts and summarize recent advancements that enable single-cell temporal transcriptome analysis. Additionally, we examine data analysis strategies for the precise quantification of newly synthesized transcripts and highlight key applications of transcriptome and temporal transcriptome analyses in single cells.

Storm: Incorporating transient stochastic dynamics to infer the RNA velocity with metabolic labeling information

The time-resolved scRNA-seq (tscRNA-seq) provides the possibility to infer physically meaningful kinetic parameters, e.g., the transcription, splicing or RNA degradation rate constants with correct magnitudes, and RNA velocities by incorporating temporal information. Previous approaches utilizing the deterministic dynamics and steady-state assumption on gene expression states are insufficient to achieve favorable results for the data involving transient process. We present a dynamical approach, Storm (Stochastic models of RNA metabolic-labeling), to overcome these limitations by solving stochastic differential equations of gene expression dynamics. The derivation reveals that the new mRNA sequencing data obeys different types of cell-specific Poisson distributions when jointly considering both biological and cell-specific technical noise. Storm deals with measured counts data directly and extends the RNA velocity methodology based on metabolic labeling scRNA-seq data to transient stochastic systems. Furthermore, we relax the constant parameter assumption over genes/cells to obtain gene-cell-specific transcription/splicing rates and gene-specific degradation rates, thus revealing time-dependent and cell-state-specific transcriptional regulations. Storm will facilitate the study of the statistical properties of tscRNA-seq data, eventually advancing our understanding of the dynamic transcription regulation during development and disease.

CellRank 2: unified fate mapping in multiview single-cell data

Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature we use to recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm development. Moreover, we enable estimating cell-specific transcription and degradation rates from metabolic-labeling data, which we apply to an intestinal organoid system to delineate differentiation trajectories and pinpoint regulatory strategies.

Cell-type-specific mRNA transcription and degradation kinetics in zebrafish embryogenesis from metabolically labeled single-cell RNA-seq

During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the relative contributions of mRNA transcription and degradation to shaping those profiles remains challenging, especially within embryos with diverse cellular identities. Here, we combine single-cell RNA-Seq and metabolic labeling to capture temporal cellular transcriptomes of zebrafish embryos where newly-transcribed (zygotic) and pre-existing (maternal) mRNA can be distinguished. We introduce kinetic models to quantify mRNA transcription and degradation rates within individual cell types during their specification. These models reveal highly varied regulatory rates across thousands of genes, coordinated transcription and destruction rates for many transcripts, and link differences in degradation to specific sequence elements. They also identify cell-type-specific differences in degradation, namely selective retention of maternal transcripts within primordial germ cells and enveloping layer cells, two of the earliest specified cell types. Our study provides a quantitative approach to study mRNA regulation during a dynamic spatio-temporal response.

Dissecting the spatiotemporal diversity of adult neural stem cells

Adult stem cells are important for tissue turnover and regeneration. However, in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here, we dissected the diversity of neural stem cells in the adult zebrafish brain, an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single-cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze the regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells, with some subtypes being restricted to a single brain region, while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation, with different states being involved in proliferative and non-proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for the functional manipulation of neural stem cell subtypes.

Transcription bodies regulate gene expression by sequestering CDK9

The localization of transcriptional activity in specialized transcription bodies is a hallmark of gene expression in eukaryotic cells. It remains unclear, however, if and how transcription bodies affect gene expression. Here we disrupted the formation of two prominent endogenous transcription bodies that mark the onset of zygotic transcription in zebrafish embryos and analysed the effect on gene expression using enriched SLAM-seq and live-cell imaging. We find that the disruption of transcription bodies results in the misregulation of hundreds of genes. Here we focus on genes that are upregulated. These genes have accessible chromatin and are poised to be transcribed in the presence of the two transcription bodies, but they do not go into elongation. Live-cell imaging shows that disruption of the two large transcription bodies enables these poised genes to be transcribed in ectopic transcription bodies, suggesting that the large transcription bodies sequester a pause release factor. Supporting this hypothesis, we find that CDK9-the kinase that releases paused polymerase II-is highly enriched in the two large transcription bodies. Overexpression of CDK9 in wild-type embryos results in the formation of ectopic transcription bodies and thus phenocopies the removal of the two large transcription bodies. Taken together, our results show that transcription bodies regulate transcription by sequestering machinery, thereby preventing genes elsewhere in the nucleus from being transcribed.

miR-430 regulates zygotic mRNA during zebrafish embryogenesis

BACKGROUND

Early embryonic developmental programs are guided by the coordinated interplay between maternally inherited and zygotically manufactured RNAs and proteins. Although these processes happen concomitantly and affecting gene function during this period is bound to affect both pools of mRNAs, it has been challenging to study their expression dynamics separately.

RESULTS

By employing SLAM-seq, a nascent mRNA labeling transcriptomic approach, in a developmental time series we observe that over half of the early zebrafish embryo transcriptome consists of maternal-zygotic genes, emphasizing their pivotal role in early embryogenesis. We provide an hourly resolution of de novo transcriptional activation events and follow nascent mRNA trajectories, finding that most de novo transcriptional events are stable throughout this period. Additionally, by blocking microRNA-430 function, a key post transcriptional regulator during zebrafish embryogenesis, we directly show that it destabilizes hundreds of de novo transcribed mRNAs from pure zygotic as well as maternal-zygotic genes. This unveils a novel miR-430 function during embryogenesis, fine-tuning zygotic gene expression.

CONCLUSION

These insights into zebrafish early embryo transcriptome dynamics emphasize the significance of post-transcriptional regulators in zygotic genome activation. The findings pave the way for future investigations into the coordinated interplay between transcriptional and post-transcriptional landscapes required for the establishment of animal cell identities and functions.

Mapping Cellular Interactions from Spatially Resolved Transcriptomics Data

Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently, through the introduction of spatially resolved transcriptomics technologies (SRTs), especially those that achieve single cell resolution. However, significant challenges remain to analyze such highly complex data properly. Here, we introduce a Bayesian multi-instance learning framework, spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates, and most importantly the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of spacia for all three commercialized single cell resolution ST technologies: MERSCOPE/Vizgen, CosMx/Nanostring, and Xenium/10X. Spacia unveiled how endothelial cells, fibroblasts and B cells in the tumor microenvironment contribute to Epithelial-Mesenchymal Transition and lineage plasticity in prostate cancer cells. We deployed spacia in a set of pan-cancer datasets and showed that B cells also participate in PDL1/PD1 signaling in tumors. We demonstrated that a CD8+ T cell/PDL1 effectiveness signature derived from spacia analyses is associated with patient survival and response to immune checkpoint inhibitor treatments in 3,354 patients. We revealed differential spatial interaction patterns between γδ T cells and liver hepatocytes in healthy and cancerous contexts. Overall, spacia represents a notable step in advancing quantitative theories of cellular communications.

Characterization of human pluripotent stem cell differentiation by single-cell dual-omics analyses

In vivo differentiation of human pluripotent stem cells (hPSCs) has unique advantages, such as multilineage differentiation, angiogenesis, and close cell-cell interactions. To systematically investigate multilineage differentiation mechanisms of hPSCs, we constructed the in vivo hPSC differentiation landscape containing 239,670 cells using teratoma models. We identified 43 cell types, inferred 18 cell differentiation trajectories, and characterized common and specific gene regulation patterns during hPSC differentiation at both transcriptional and epigenetic levels. Additionally, we developed the developmental single-cell Basic Local Alignment Search Tool (dscBLAST), an R-based cell identification tool, to simplify the identification processes of developmental cells. Using dscBLAST, we aligned cells in multiple differentiation models to normally developing cells to further understand their differentiation states. Overall, our study offers new insights into stem cell differentiation and human embryonic development; dscBLAST shows favorable cell identification performance, providing a powerful identification tool for developmental cells.

Dissecting mammalian reproduction with spatial transcriptomics

BACKGROUND

Mammalian reproduction requires the fusion of two specialized cells: an oocyte and a sperm. In addition to producing gametes, the reproductive system also provides the environment for the appropriate development of the embryo. Deciphering the reproductive system requires understanding the functions of each cell type and cell-cell interactions. Recent single-cell omics technologies have provided insights into the gene regulatory network in discrete cellular populations of both the male and female reproductive systems. However, these approaches cannot examine how the cellular states of the gametes or embryos are regulated through their interactions with neighboring somatic cells in the native tissue environment owing to tissue disassociations. Emerging spatial omics technologies address this challenge by preserving the spatial context of the cells to be profiled. These technologies hold the potential to revolutionize our understanding of mammalian reproduction.

OBJECTIVE AND RATIONALE

We aim to review the state-of-the-art spatial transcriptomics (ST) technologies with a focus on highlighting the novel biological insights that they have helped to reveal about the mammalian reproductive systems in the context of gametogenesis, embryogenesis, and reproductive pathologies. We also aim to discuss the current challenges of applying ST technologies in reproductive research and provide a sneak peek at what the field of spatial omics can offer for the reproduction community in the years to come.

SEARCH METHODS

The PubMed database was used in the search for peer-reviewed research articles and reviews using combinations of the following terms: 'spatial omics', 'fertility', 'reproduction', 'gametogenesis', 'embryogenesis', 'reproductive cancer', 'spatial transcriptomics', 'spermatogenesis', 'ovary', 'uterus', 'cervix', 'testis', and other keywords related to the subject area. All relevant publications until April 2023 were critically evaluated and discussed.

OUTCOMES

First, an overview of the ST technologies that have been applied to studying the reproductive systems was provided. The basic design principles and the advantages and limitations of these technologies were discussed and tabulated to serve as a guide for researchers to choose the best-suited technologies for their own research. Second, novel biological insights into mammalian reproduction, especially human reproduction revealed by ST analyses, were comprehensively reviewed. Three major themes were discussed. The first theme focuses on genes with non-random spatial expression patterns with specialized functions in multiple reproductive systems; The second theme centers around functionally interacting cell types which are often found to be spatially clustered in the reproductive tissues; and the thrid theme discusses pathological states in reproductive systems which are often associated with unique cellular microenvironments. Finally, current experimental and computational challenges of applying ST technologies to studying mammalian reproduction were highlighted, and potential solutions to tackle these challenges were provided. Future directions in the development of spatial omics technologies and how they will benefit the field of human reproduction were discussed, including the capture of cellular and tissue dynamics, multi-modal molecular profiling, and spatial characterization of gene perturbations.

WIDER IMPLICATIONS

Like single-cell technologies, spatial omics technologies hold tremendous potential for providing significant and novel insights into mammalian reproduction. Our review summarizes these novel biological insights that ST technologies have provided while shedding light on what is yet to come. Our review provides reproductive biologists and clinicians with a much-needed update on the state of art of ST technologies. It may also facilitate the adoption of cutting-edge spatial technologies in both basic and clinical reproductive research.

A ligand-receptor interactome atlas of the zebrafish

Studies in zebrafish can unravel the functions of cellular communication and thus identify novel bench-to-bedside drugs targeting cellular communication signaling molecules. Due to the incomplete annotation of zebrafish proteome, the knowledge of zebrafish receptors, ligands, and tools to explore their interactome is limited. To address this gap, we de novo predicted the cellular localization of zebrafish reference proteome using deep learning algorithm. We combined the predicted and existing annotations on cellular localization of zebrafish proteins and created repositories of zebrafish ligands, membrane receptome, and interactome as well as associated diseases and targeting drugs. Unlike other tools, our interactome atlas is based on both the physical interaction data of zebrafish proteome and existing human ligand-receptor pair databases. The resources are available as R and Python scripts. DanioTalk provides a novel resource for researchers interested in targeting cellular communication in zebrafish, as we demonstrate in applications studying synapse and axo-glial interactome. DanioTalk methodology can be applied to build and explore the ligand-receptor atlas of other non-mammalian model organisms.

Single-cell temporal dynamics reveals the relative contributions of transcription and degradation to cell-type specific gene expression in zebrafish embryos

During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the underlying regulation of mRNA transcription and degradation remains a challenge, especially within whole embryos with diverse cellular identities. Here, we collect temporal cellular transcriptomes of zebrafish embryos, and decompose them into their newly-transcribed (zygotic) and pre-existing (maternal) mRNA components by combining single-cell RNA-Seq and metabolic labeling. We introduce kinetic models capable of quantifying regulatory rates of mRNA transcription and degradation within individual cell types during their specification. These reveal different regulatory rates between thousands of genes, and sometimes between cell types, that shape spatio-temporal expression patterns. Transcription drives most cell-type restricted gene expression. However, selective retention of maternal transcripts helps to define the gene expression profiles of germ cells and enveloping layer cells, two of the earliest specified cell-types. Coordination between transcription and degradation restricts expression of maternal-zygotic genes to specific cell types or times, and allows the emergence of spatio-temporal patterns when overall mRNA levels are held relatively constant. Sequence-based analysis links differences in degradation to specific sequence motifs. Our study reveals mRNA transcription and degradation events that control embryonic gene expression, and provides a quantitative approach to study mRNA regulation during a dynamic spatio-temporal response.

SLAMseq resolves the kinetics of maternal and zygotic gene expression during early zebrafish embryogenesis

The maternal-to-zygotic transition (MZT) is a key developmental process in metazoan embryos that involves the activation of zygotic transcription (ZGA) and degradation of maternal transcripts. We employed metabolic mRNA sequencing (SLAMseq) to deconvolute the compound embryonic transcriptome in zebrafish. While mitochondrial zygotic transcripts prevail prior to MZT, we uncover the spurious transcription of hundreds of short and intron-poor genes as early as the 2-cell stage. Upon ZGA, most zygotic transcripts originate from thousands of maternal-zygotic (MZ) genes that are transcribed at rates comparable to those of hundreds of purely zygotic genes and replenish maternal mRNAs at distinct timescales. Rapid replacement of MZ transcripts involves transcript decay features unrelated to major maternal degradation pathways and promotes de novo synthesis of the core gene expression machinery by increasing poly(A)-tail length and translation efficiency. SLAMseq hence provides insights into the timescales, molecular features, and regulation of MZT during zebrafish embryogenesis.

Revealing the History and Mystery of RNA-Seq

Advances in RNA-sequencing technologies have led to the development of intriguing experimental setups, a massive accumulation of data, and high demand for tools to analyze it. To answer this demand, computational scientists have developed a myriad of data analysis pipelines, but it is less often considered what the most appropriate one is. The RNA-sequencing data analysis pipeline can be divided into three major parts: data pre-processing, followed by the main and downstream analyses. Here, we present an overview of the tools used in both the bulk RNA-seq and at the single-cell level, with a particular focus on alternative splicing and active RNA synthesis analysis. A crucial part of data pre-processing is quality control, which defines the necessity of the next steps; adapter removal, trimming, and filtering. After pre-processing, the data are finally analyzed using a variety of tools: differential gene expression, alternative splicing, and assessment of active synthesis, the latter requiring dedicated sample preparation. In brief, we describe the commonly used tools in the sample preparation and analysis of RNA-seq data.

A single-cell RNA labeling strategy for measuring stress response upon tissue dissociation

Tissue dissociation, a crucial step in single-cell sample preparation, can alter the transcriptional state of a sample through the intrinsic cellular stress response. Here we demonstrate a general approach for measuring transcriptional response during sample preparation. In our method, transcripts made during dissociation are labeled for later identification upon sequencing. We found general as well as cell-type-specific dissociation response programs in zebrafish larvae, and we observed sample-to-sample variation in the dissociation response of mouse cardiomyocytes despite well-controlled experimental conditions. Finally, we showed that dissociation of the mouse hippocampus can lead to the artificial activation of microglia. In summary, our approach facilitates experimental optimization of dissociation procedures as well as computational removal of transcriptional perturbation response.

Nascent transcriptome reveals orchestration of zygotic genome activation in early embryogenesis

Early embryo development requires maternal-to-zygotic transition, during which transcriptionally silent nuclei begin widespread gene expression during zygotic genome activation (ZGA).1-3 ZGA is vital for early cell fating and germ-layer specification,3,4 and ZGA timing is regulated by multiple mechanisms.1-5 However, controversies remain about whether these mechanisms are interrelated and vary among species6-10 and whether the timing of germ-layer-specific gene activation is temporally ordered.11,12 In some embryonic models, widespread ZGA onset is spatiotemporally graded,13,14 yet it is unclear whether the transcriptome follows this pattern. A major challenge in addressing these questions is to accurately measure the timing of each gene activation. Here, we metabolically label and identify the nascent transcriptome using 5-ethynyl uridine (5-EU) in Xenopus blastula embryos. We find that EU-RNA-seq outperforms total RNA-seq in detecting the ZGA transcriptome, which is dominated by transcription from maternal-zygotic genes, enabling improved ZGA timing determination. We uncover discrete spatiotemporal patterns for individual gene activation, a majority following a spatial pattern of ZGA that is correlated with a cell size gradient.14 We further reveal that transcription necessitates a period of developmental progression and that ZGA can be precociously induced by cycloheximide, potentially through elongation of interphase. Finally, most ectodermal genes are activated earlier than endodermal genes, suggesting a temporal orchestration of germ-layer-specific genes, potentially linked to the spatially graded pattern of ZGA. Together, our study provides fundamental new insights into the composition and dynamics of the ZGA transcriptome, mechanisms regulating ZGA timing, and its role in the onset of early cell fating.

Evolutionary conservation of maternal RNA localization in fishes and amphibians revealed by TOMO-Seq

Asymmetrical localization of biomolecules inside the egg, results in uneven cell division and establishment of many biological processes, cell types and the body plan. However, our knowledge about evolutionary conservation of localized transcripts is still limited to a few models. Our goal was to compare localization profiles along the animal-vegetal axis of mature eggs from four vertebrate models, two amphibians (Xenopus laevis, Ambystoma mexicanum) and two fishes (Acipenser ruthenus, Danio rerio) using the spatial expression method called TOMO-Seq. We revealed that RNAs of many known important transcripts such as germ layer determinants, germ plasm factors and members of key signalling pathways, are localized in completely different profiles among the models. It was also observed that there was a poor correlation between the vegetally localized transcripts but a relatively good correlation between the animally localized transcripts. These findings indicate that the regulation of embryonic development within the animal kingdom is highly diverse and cannot be deduced based on a single model.

Single cell- and spatial 'Omics revolutionize physiology

Single cell multi- 'Omics and Spatial Transcriptomics are prominent technological highlights of recent years, and both fields still witness a ceaseless firework of novel approaches for high resolution profiling of additional omics layers. As all life processes in organs and organisms are based on the functions of their fundamental building blocks, the individual cells and their interactions, these methods are of utmost worth for the study of physiology in health and disease. Recent discoveries on embryonic development, tumor immunology, the detailed cellular composition and function of complex tissues like for example the kidney or the brain, different roles of the same cell type in different organs, the oncogenic program of individual tumor entities, or the architecture of immunopathology in infected tissue are based on single cell and spatial transcriptomics experiments. In this review, we will give a broad overview of technological concepts for single cell and spatial analysis, showing both advantages and limitations, and illustrate their impact with some particularly impressive case studies.

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.

Mapping transcriptomic vector fields of single cells

Single-cell (sc)-RNA-seq, together with RNA-velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo, that infers absolute RNA velocity, reconstructs continuous vector-field functions that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo’s power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically-labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1–GATA1 circuit. Leveraging the Least-Action-Path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo thus represents an important step in advancing quantitative and predictive theories of cell-state transitions.

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

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

Insights Into Central Nervous System Glial Cell Formation and Function From Zebrafish

The term glia describes a heterogenous collection of distinct cell types that make up a large proportion of our nervous system. Although once considered the glue of the nervous system, the study of glial cells has evolved significantly in recent years, with a large body of literature now highlighting their complex and diverse roles in development and throughout life. This progress is due, in part, to advances in animal models in which the molecular and cellular mechanisms of glial cell development and function as well as neuron-glial cell interactions can be directly studied in vivo in real time, in intact neural circuits. In this review we highlight the instrumental role that zebrafish have played as a vertebrate model system for the study of glial cells, and discuss how the experimental advantages of the zebrafish lend themselves to investigate glial cell interactions and diversity. We focus in particular on recent studies that have provided insight into the formation and function of the major glial cell types in the central nervous system in zebrafish.

When Bigger Is Better: 3D RNA Profiling of the Developing Head in the Catshark Scyliorhinus canicula

We report the adaptation of RNA tomography, a technique allowing spatially resolved, genome-wide expression profiling, to a species occupying a key phylogenetic position in gnathostomes, the catshark Scyliorhinus canicula. We focused analysis on head explants at an embryonic stage, shortly following neural tube closure and of interest for a number of developmental processes, including early brain patterning, placode specification or the establishment of epithalamic asymmetry. As described in the zebrafish, we have sequenced RNAs extracted from serial sections along transverse, horizontal and sagittal planes, mapped the data onto a gene reference taking advantage of the high continuity genome recently released in the catshark, and projected read counts onto a digital model of the head obtained by confocal microscopy. This results in the generation of a genome-wide 3D atlas, containing expression data for most protein-coding genes in a digital model of the embryonic head. The digital profiles obtained for candidate forebrain regional markers along antero-posterior, dorso-ventral and left-right axes reproduce those obtained by in situ hybridization (ISH), with expected relative organizations. We also use spatial autocorrelation and correlation as measures to analyze these data and show that they provide adequate statistical tools to extract novel expression information from the model. These data and tools allow exhaustive searches of genes exhibiting any predefined expression characteristic, such a restriction to a territory of interest, thus providing a reference for comparative analyses across gnathostomes. This methodology appears best suited to species endowed with large embryo or organ sizes and opens novel perspectives to a wide range of evo-devo model organisms, traditionally counter-selected on size criterion.