Single-cell transcriptional profiling: a window into embryonic cell-type specification

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

Synthetic symmetry breaking and programmable multicellular structure formation

During development, cells undergo symmetry breaking into differentiated subpopulations that self-organize into complex structures.1,2,3,4,5 However, few tools exist to recapitulate these behaviors in a controllable and coupled manner.6,7,8,9 Here, we engineer a stochastic recombinase genetic switch tunable by small molecules to induce programmable symmetry breaking, commitment to downstream cell fates, and morphological self-organization. Inducers determine commitment probabilities, generating tunable subpopulations as a function of inducer dosage. We use this switch to control the cell-cell adhesion properties of cells committed to each fate.10,11 We generate a wide variety of 3D morphologies from a monoclonal population and develop a computational model showing high concordance with experimental results, yielding new quantitative insights into the relationship between cell-cell adhesion strengths and downstream morphologies. We expect that programmable symmetry breaking, generating precise and tunable subpopulation ratios and coupled to structure formation, will serve as an integral component of the toolbox for complex tissue and organoid engineering.

Single-cell RNA sequencing to identify cellular heterogeneity and targets in cardiovascular diseases: from bench to bedside

The mechanisms of cardiovascular diseases (CVDs) remain incompletely elucidated. Single-cell RNA sequencing (scRNA-seq) has enabled the profiling of single-cell transcriptomes at unprecedented resolution and throughput, which is critical for deciphering cardiovascular cellular heterogeneity and underlying disease mechanisms, thereby facilitating the development of therapeutic strategies. In this review, we summarize cellular heterogeneity in cardiovascular homeostasis and diseases as well as the discovery of potential disease targets based on scRNA-seq, and yield new insights into the promise of scRNA-seq technology in precision medicine and clinical application.

HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses

Understanding gene expression patterns across different human cell types is crucial for investigating mechanisms of cell type differentiation, disease occurrence and progression. The recent development of single-cell RNA-seq (scRNA-seq) technologies significantly boosted the characterization of cell type heterogeneities in different human tissues. However, the huge number of datasets in the public domain also posed challenges in data integration and reuse. We present Human Universal Single Cell Hub (HUSCH, http://husch.comp-genomics.org), an atlas-scale curated database that integrates single-cell transcriptomic profiles of nearly 3 million cells from 185 high-quality human scRNA-seq datasets from 45 different tissues. All the data in HUSCH were uniformly processed and annotated with a standard workflow. In the single dataset module, HUSCH provides interactive gene expression visualization, differentially expressed genes, functional analyses, transcription regulators and cell-cell interaction analyses for each cell type cluster. Besides, HUSCH integrated different datasets in the single tissue module and performs data integration, batch correction, and cell type harmonization. This allows a comprehensive visualization and analysis of gene expression within each tissue based on single-cell datasets from multiple sources and platforms. HUSCH is a flexible and comprehensive data portal that enables searching, visualizing, analyzing, and downloading single-cell gene expression for the human tissue atlas.

Spatial molecular anatomy of germ layers in the gastrulating cynomolgus monkey embryo

During mammalian embryogenesis, spatial regulation of gene expression and cell signaling are functionally coupled with lineage specification, patterning of tissue progenitors, and germ layer morphogenesis. While the mouse model has been instrumental for understanding mammalian development, comparatively little is known about human and non-human primate gastrulation due to the restriction of both technical and ethical issues. Here, we present a spatial and temporal survey of the molecular dynamics of cell types populating the non-human primate embryos during gastrulation. We reconstructed three-dimensional digital models from serial sections of cynomolgus monkey (Macaca fascicularis) gastrulating embryos at 1-day temporal resolution from E17 to E21. Spatial transcriptomics identifies gene expression profiles unique to the germ layers. Cross-species comparison reveals a developmental coordinate of germ layer segregation between mouse and primates, and species-specific transcription programs during gastrulation. These findings offer insights into evolutionarily conserved and divergent processes during mammalian gastrulation.

ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks

MOTIVATION

With the advancement of technology, we can generate and access large-scale, high dimensional and diverse genomics data, especially through single-cell RNA sequencing (scRNA-seq). However, integrative downstream analysis from multiple scRNA-seq datasets remains challenging due to batch effects.

RESULTS

In this article, we propose a light-structured deep learning framework called ResPAN for scRNA-seq data integration. ResPAN is based on Wasserstein Generative Adversarial Network (WGAN) combined with random walk mutual nearest neighbor pairing and fully skip-connected autoencoders to reduce the differences among batches. We also discuss the limitations of existing methods and demonstrate the advantages of our model over seven other methods through extensive benchmarking studies on both simulated data under various scenarios and real datasets across different scales. Our model achieves leading performance on both batch correction and biological information conservation and maintains scalable to datasets with over half a million cells.

AVAILABILITY AND IMPLEMENTATION

An open-source implementation of ResPAN and scripts to reproduce the results can be downloaded from: https://github.com/AprilYuge/ResPAN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cell Trafficking at the Intersection of the Tumor-Immune Compartments

Migration is an essential cellular process that regulates human organ development and homeostasis as well as disease initiation and progression. In cancer, immune and tumor cell migration is strongly associated with immune cell infiltration, immune escape, and tumor cell metastasis, which ultimately account for more than 90% of cancer deaths. The biophysics and molecular regulation of the migration of cancer and immune cells have been extensively studied separately. However, accumulating evidence indicates that, in the tumor microenvironment, the motilities of immune and cancer cells are highly interdependent via secreted factors such as cytokines and chemokines. Tumor and immune cells constantly express these soluble factors, which produce a tightly intertwined regulatory network for these cells' respective migration. A mechanistic understanding of the reciprocal regulation of soluble factor-mediated cell migration can provide critical information for the development of new biomarkers of tumor progression and of tumor response to immuno-oncological treatments. We review the biophysical andbiomolecular basis for the migration of immune and tumor cells and their associated reciprocal regulatory network. We also describe ongoing attempts to translate this knowledge into the clinic.

The Promise of Single-cell Technology in Providing New Insights Into the Molecular Heterogeneity and Management of Acute Lymphoblastic Leukemia

Drug resistance and treatment failure in pediatric acute lymphoblastic leukemia (ALL) are in part driven by tumor heterogeneity and clonal evolution. Although bulk tumor genomic analyses have provided some insight into these processes, single-cell sequencing has emerged as a powerful technique to profile individual cells in unprecedented detail. Since the introduction of single-cell RNA sequencing, we now have the capability to capture not only transcriptomic, but also genomic, epigenetic, and proteomic variation between single cells separately and in combination. This rapidly evolving field has the potential to transform our understanding of the fundamental biology of pediatric ALL and guide the management of ALL patients to improve their clinical outcome. Here, we discuss the impact single-cell sequencing has had on our understanding of tumor heterogeneity and clonal evolution in ALL and provide examples of how single-cell technology can be integrated into the clinic to inform treatment decisions for children with high-risk disease.

Engineering a niche supporting hematopoietic stem cell development using integrated single-cell transcriptomics

Hematopoietic stem cells (HSCs) develop from hemogenic endothelium within embryonic arterial vessels such as the aorta of the aorta-gonad-mesonephros region (AGM). To identify the signals responsible for HSC formation, here we use single cell RNA-sequencing to simultaneously analyze the transcriptional profiles of AGM-derived cells transitioning from hemogenic endothelium to HSCs, and AGM-derived endothelial cells which provide signals sufficient to support HSC maturation and self-renewal. Pseudotemporal ordering reveals dynamics of gene expression during the hemogenic endothelium to HSC transition, identifying surface receptors specifically expressed on developing HSCs. Transcriptional profiling of niche endothelial cells identifies corresponding ligands, including those signaling to Notch receptors, VLA-4 integrin, and CXCR4, which, when integrated in an engineered platform, are sufficient to support the generation of engrafting HSCs. These studies provide a transcriptional map of the signaling interactions necessary for the development of HSCs and advance the goal of engineering HSCs for therapeutic applications.

Single-cell sequencing reveals lineage-specific dynamic genetic regulation of gene expression during human cardiomyocyte differentiation

Dynamic and temporally specific gene regulatory changes may underlie unexplained genetic associations with complex disease. During a dynamic process such as cellular differentiation, the overall cell type composition of a tissue (or an in vitro culture) and the gene regulatory profile of each cell can both experience significant changes over time. To identify these dynamic effects in high resolution, we collected single-cell RNA-sequencing data over a differentiation time course from induced pluripotent stem cells to cardiomyocytes, sampled at 7 unique time points in 19 human cell lines. We employed a flexible approach to map dynamic eQTLs whose effects vary significantly over the course of bifurcating differentiation trajectories, including many whose effects are specific to one of these two lineages. Our study design allowed us to distinguish true dynamic eQTLs affecting a specific cell lineage from expression changes driven by potentially non-genetic differences between cell lines such as cell composition. Additionally, we used the cell type profiles learned from single-cell data to deconvolve and re-analyze data from matched bulk RNA-seq samples. Using this approach, we were able to identify a large number of novel dynamic eQTLs in single cell data while also attributing dynamic effects in bulk to a particular lineage. Overall, we found that using single cell data to uncover dynamic eQTLs can provide new insight into the gene regulatory changes that occur among heterogeneous cell types during cardiomyocyte differentiation.

Single-cell mapping of DNA G-quadruplex structures in human cancer cells

G-quadruplexes (G4s) are four-stranded DNA secondary structures that form in guanine-rich regions of the genome. G4s have important roles in transcription and replication and have been implicated in genome instability and cancer. Thus far most work has profiled the G4 landscape in an ensemble of cell populations, therefore it is critical to explore the structure-function relationship of G4s in individual cells to enable detailed mechanistic insights into G4 function. With standard ChIP-seq methods it has not been possible to determine if G4 formation at a given genomic locus is variable between individual cells across a population. For the first time, we demonstrate the mapping of a DNA secondary structure at single-cell resolution. We have adapted single-nuclei (sn) CUT&Tag to allow the detection of G4s in single cells of human cancer cell lines. With snG4-CUT&Tag, we can distinguish cellular identity from a mixed cell-type population solely based on G4 features within individual cells. Our methodology now enables genomic investigations on cell-to-cell variation of a DNA secondary structure that were previously not possible.

Transposable elements shape the evolution of mammalian development

Transposable elements (TEs) promote genetic innovation but also threaten genome stability. Despite multiple layers of host defence, TEs actively shape mammalian-specific developmental processes, particularly during pre-implantation and extra-embryonic development and at the maternal-fetal interface. Here, we review how TEs influence mammalian genomes both directly by providing the raw material for genetic change and indirectly via co-evolving TE-binding Krüppel-associated box zinc finger proteins (KRAB-ZFPs). Throughout mammalian evolution, individual activities of ancient TEs were co-opted to enable invasive placentation that characterizes live-born mammals. By contrast, the widespread activity of evolutionarily young TEs may reflect an ongoing co-evolution that continues to impact mammalian development.

Human embryonic development: from peri-implantation to gastrulation

The basic body plan of the mammalian embryo is established through gastrulation, a pivotal early postimplantation event during which the three major germ layers (endoderm, ectoderm, and mesoderm) are specified with cellular and spatial diversity. Despite its basic and clinical importance, human embryo development from peri-implantation to gastrulation remains shrouded in mystery. Recent advances in the elongated in vitro culture of rodent and non-primate embryos and the construction of embryo-like structures have helped to improve understanding of the mechanisms of human early embryonic development. Here, we review the recent advances and possible future directions in the development of in vitro models to better understand human embryogenesis from peri-implantation to gastrulation.

Single-Cell Sequencing Methodologies: From Transcriptome to Multi-Dimensional Measurement

Cells are the basic building blocks of biological systems, with inherent unique molecular features and development trajectories. The study of single cells facilitates in-depth understanding of cellular diversity, disease processes, and organization of multicellular organisms. Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for the interrogation of gene expression patterns and the dynamics of single cells, allowing cellular heterogeneity to be dissected at unprecedented resolution. Nevertheless, measuring at only transcriptome level or 1D is incomplete; the cellular heterogeneity reflects in multiple dimensions, including the genome, epigenome, transcriptome, spatial, and even temporal dimensions. Hence, integrative single cell analysis is highly desired. In addition, the way to interpret sequencing data by virtue of bioinformatic tools also exerts critical roles in revealing differential gene expression. Here, a comprehensive review that summarizes the cutting-edge single-cell transcriptome sequencing methodologies, including scRNA-seq, spatial and temporal transcriptome profiling, multi-omics sequencing and computational methods developed for scRNA-seq data analysis is provided. Finally, the challenges and perspectives of this field are discussed.

Laser Targeted Oligo Ligation (LTOL) to Identify DNA Sequences in the Vicinity of a Single Subnuclear Structure in a Single Cell

Gene loci are organized around nuclear substructures, forming gene hubs which provide a level of transcriptional control. To date, most techniques used to investigate the genes in these hubs have been based on using material from bulk cells. This makes identifying specific gene associations difficult. Here we describe the Laser Targeted Oligo Ligation (LTOL) technique that was developed to identify DNA sequences around a single subnuclear structure on a single-cell basis by targeting these regions with two-photon irradiation.

iMAP: integration of multiple single-cell datasets by adversarial paired transfer networks

The integration of single-cell RNA-sequencing datasets from multiple sources is critical for deciphering cell-to-cell heterogeneities and interactions in complex biological systems. We present a novel unsupervised batch effect removal framework, called iMAP, based on both deep autoencoders and generative adversarial networks. Compared with current methods, iMAP shows superior, robust, and scalable performance in terms of both reliably detecting the batch-specific cells and effectively mixing distributions of the batch-shared cell types. Applying iMAP to tumor microenvironment datasets from two platforms, Smart-seq2 and 10x Genomics, we find that iMAP can leverage the powers of both platforms to discover novel cell-cell interactions.

In vivo and ex vivo haematopoietic stem cell expansion

PURPOSE OF REVIEW

Haematopoietic stem cells (HSCs) are characterized by two key features: self-renewal ability and multilineage differentiation potential. Through these cellular activities, HSCs sustain blood and immune system homeostasis throughout life and can also reconstitute the entire haematopoietic system within a bone marrow ablated recipient. This approach of HSC transplantation is used clinically as a curative treatment option for numerous haematological diseases, both malignant and nonmalignant.

RECENT FINDINGS

Elucidation of the mechanism of HSC expansion represents a major focus within haematology. Here, we review the recent progress towards understanding HSC expansion in vivo and ex vivo, including a discussion of recent clonal transplantation assays and the development of novel ex vivo culture systems.

SUMMARY

Recent findings provide exciting promise for improving the safety and efficacy of current HSC-based therapies as well as for the development of new therapeutic paradigms.

Understanding generation and regeneration of pancreatic β cells from a single-cell perspective

Understanding the mechanisms that underlie the generation and regeneration of β cells is crucial for developing treatments for diabetes. However, traditional research methods, which are based on populations of cells, have limitations for defining the precise processes of β-cell differentiation and trans-differentiation, and the associated regulatory mechanisms. The recent development of single-cell technologies has enabled re-examination of these processes at a single-cell resolution to uncover intermediate cell states, cellular heterogeneity and molecular trajectories of cell fate specification. Here, we review recent advances in understanding β-cell generation and regeneration, in vivo and in vitro, from single-cell technologies, which could provide insights for optimization of diabetes therapy strategies.

Single-cell chromatin accessibility maps reveal regulatory programs driving early mouse organogenesis

During mouse embryonic development, pluripotent cells rapidly divide and diversify, yet the regulatory programs that define the cell repertoire for each organ remain ill-defined. To delineate comprehensive chromatin landscapes during early organogenesis, we mapped chromatin accessibility in 19,453 single nuclei from mouse embryos at 8.25 days post-fertilization. Identification of cell-type-specific regions of open chromatin pinpointed two TAL1-bound endothelial enhancers, which we validated using transgenic mouse assays. Integrated gene expression and transcription factor motif enrichment analyses highlighted cell-type-specific transcriptional regulators. Subsequent in vivo experiments in zebrafish revealed a role for the ETS factor FEV in endothelial identity downstream of ETV2 (Etsrp in zebrafish). Concerted in vivo validation experiments in mouse and zebrafish thus illustrate how single-cell open chromatin maps, representative of a mammalian embryo, provide access to the regulatory blueprint for mammalian organogenesis.

Integrated single-cell analysis of multicellular immune dynamics during hyperacute HIV-1 infection

Cellular immunity is critical for controlling intracellular pathogens, but individual cellular dynamics and cell-cell cooperativity in evolving human immune responses remain poorly understood. Single-cell RNA-sequencing (scRNA-seq) represents a powerful tool for dissecting complex multicellular behaviors in health and disease1,2 and nominating testable therapeutic targets3. Its application to longitudinal samples could afford an opportunity to uncover cellular factors associated with the evolution of disease progression without potentially confounding inter-individual variability4. Here, we present an experimental and computational methodology that uses scRNA-seq to characterize dynamic cellular programs and their molecular drivers, and apply it to HIV infection. By performing scRNA-seq on peripheral blood mononuclear cells from four untreated individuals before and longitudinally during acute infection5, we were powered within each to discover gene response modules that vary by time and cell subset. Beyond previously unappreciated individual- and cell-type-specific interferon-stimulated gene upregulation, we describe temporally aligned gene expression responses obscured in bulk analyses, including those involved in proinflammatory T cell differentiation, prolonged monocyte major histocompatibility complex II upregulation and persistent natural killer (NK) cell cytolytic killing. We further identify response features arising in the first weeks of infection, for example proliferating natural killer cells, which potentially may associate with future viral control. Overall, our approach provides a unified framework for characterizing multiple dynamic cellular responses and their coordination.

Reconstructing temporal and spatial dynamics from single-cell pseudotime using prior knowledge of real scale cell densities

Modern cytometry methods allow collecting complex, multi-dimensional data sets from heterogeneous cell populations at single-cell resolution. While methods exist to describe the progression and order of cellular processes from snapshots of such populations, these descriptions are limited to arbitrary pseudotime scales. Here we describe MAPiT, an universal transformation method that recovers real-time dynamics of cellular processes from pseudotime scales by utilising knowledge of the distributions on the real scales. As use cases, we applied MAPiT to two prominent problems in the flow-cytometric analysis of heterogeneous cell populations: (1) recovering the kinetics of cell cycle progression in unsynchronised and thus unperturbed cell populations, and (2) recovering the spatial arrangement of cells within multi-cellular spheroids prior to spheroid dissociation for cytometric analysis. Since MAPiT provides a theoretic basis for the relation of pseudotime values to real temporal and spatial scales, it can be used broadly in the analysis of cellular processes with snapshot data from heterogeneous cell populations.

When blood development meets single-cell transcriptomics

Blood cells arise during embryonic development by three temporally distinct waves. Belonging to the third wave, hematopoietic stem cells (HSCs) are generated from hemogenic endothelium via endothelial-to-hematopoietic transition in mid-gestational embryos. Recently, studies combined with single-cell transcriptomics have provided massive new insights into the molecular evolutions and the underlying mechanisms of distinct waves of hematopoietic specification. In this review, we discuss the current single-cell profiling techniques, the most recent novel findings involved in the generation of distinct waves of blood cells, especially the HSCs, using single-cell transcriptional profiling combined with functional evaluations, and the perspectives to use the accumulating huge single-cell transcriptional data sets to study developmental hematopoiesis.

Concepts and limitations for learning developmental trajectories from single cell genomics

Single cell genomics has become a popular approach to uncover the cellular heterogeneity of progenitor and terminally differentiated cell types with great precision. This approach can also delineate lineage hierarchies and identify molecular programmes of cell-fate acquisition and segregation. Nowadays, tens of thousands of cells are routinely sequenced in single cell-based methods and even more are expected to be analysed in the future. However, interpretation of the resulting data is challenging and requires computational models at multiple levels of abstraction. In contrast to other applications of single cell sequencing, where clustering approaches dominate, developmental systems are generally modelled using continuous structures, trajectories and trees. These trajectory models carry the promise of elucidating mechanisms of development, disease and stimulation response at very high molecular resolution. However, their reliable analysis and biological interpretation requires an understanding of their underlying assumptions and limitations. Here, we review the basic concepts of such computational approaches and discuss the characteristics of developmental processes that can be learnt from trajectory models.

Molecular recording of mammalian embryogenesis

Ontogeny describes the emergence of complex multicellular organisms from single totipotent cells. This field is particularly challenging in mammals, owing to the indeterminate relationship between self-renewal and differentiation, variation in progenitor field sizes, and internal gestation in these animals. Here we present a flexible, high-information, multi-channel molecular recorder with a single-cell readout and apply it as an evolving lineage tracer to assemble mouse cell-fate maps from fertilization through gastrulation. By combining lineage information with single-cell RNA sequencing profiles, we recapitulate canonical developmental relationships between different tissue types and reveal the nearly complete transcriptional convergence of endodermal cells of extra-embryonic and embryonic origins. Finally, we apply our cell-fate maps to estimate the number of embryonic progenitor cells and their degree of asymmetric partitioning during specification. Our approach enables massively parallel, high-resolution recording of lineage and other information in mammalian systems, which will facilitate the construction of a quantitative framework for understanding developmental processes.

Recent advances in single-cell analysis by mass spectrometry

Cells are the most basic structural units that play vital roles in the functioning of living organisms. Analysis of the chemical composition and content of a single cell plays a vital role in ensuring precise investigations of cellular metabolism, and is a crucial aspect of lipidomic and proteomic studies. In addition, structural knowledge provides a better understanding of cell behavior as well as the cellular and subcellular mechanisms. However, single-cell analysis can be very challenging due to the very small size of each cell as well as the large variety and extremely low concentrations of substances found in individual cells. On account of its high sensitivity and selectivity, mass spectrometry holds great promise as an effective technique for single-cell analysis. Numerous mass spectrometric techniques have been developed to elucidate the molecular profiles at the cellular level, including electrospray ionization mass spectrometry (ESI-MS), secondary ion mass spectrometry (SIMS), laser-based mass spectrometry and inductively coupled plasma mass spectrometry (ICP-MS). In this review, the recent advances in single-cell analysis by mass spectrometry are summarized. The strategies of different ionization modes to achieve single-cell analysis are classified and discussed in detail.

Defining multistep cell fate decision pathways during pancreatic development at single-cell resolution

The generation of terminally differentiated cell lineages during organogenesis requires multiple, coordinated cell fate choice steps. However, this process has not been clearly delineated, especially in complex solid organs such as the pancreas. Here, we performed single-cell RNA-sequencing in pancreatic cells sorted from multiple genetically modified reporter mouse strains at embryonic stages E9.5-E17.5. We deciphered the developmental trajectories and regulatory strategies of the exocrine and endocrine pancreatic lineages as well as intermediate progenitor populations along the developmental pathways. Notably, we discovered previously undefined programs representing the earliest events in islet α- and β-cell lineage allocation as well as the developmental pathway of the "first wave" of α-cell generation. Furthermore, we demonstrated that repressing ERK pathway activity is essential for inducing both α- and β-lineage differentiation. This study provides key insights into the regulatory mechanisms underlying cell fate choice and stepwise cell fate commitment and can be used as a resource to guide the induction of functional islet lineage cells from stem cells in vitro.

A single-cell molecular map of mouse gastrulation and early organogenesis

Across the animal kingdom, gastrulation represents a key developmental event during which embryonic pluripotent cells diversify into lineage-specific precursors that will generate the adult organism. Here we report the transcriptional profiles of 116,312 single cells from mouse embryos collected at nine sequential time points ranging from 6.5 to 8.5 days post-fertilization. We construct a molecular map of cellular differentiation from pluripotency towards all major embryonic lineages, and explore the complex events involved in the convergence of visceral and primitive streak-derived endoderm. Furthermore, we use single-cell profiling to show that Tal1-/- chimeric embryos display defects in early mesoderm diversification, and we thus demonstrate how combining temporal and transcriptional information can illuminate gene function. Together, this comprehensive delineation of mammalian cell differentiation trajectories in vivo represents a baseline for understanding the effects of gene mutations during development, as well as a roadmap for the optimization of in vitro differentiation protocols for regenerative medicine.

RNA Structure and Cellular Applications of Fluorescent Light-Up Aptamers

The cellular functions of RNA are not limited to their role as blueprints for protein synthesis. In particular, noncoding RNA, such as, snRNAs, lncRNAs, miRNAs, play important roles. With increasing numbers of RNAs being identified, it is well known that the transcriptome outnumbers the proteome by far. This emphasizes the great importance of functional RNA characterization and the need to further develop tools for these investigations, many of which are still in their infancy. Fluorescent light-up aptamers (FLAPs) are RNA sequences that can bind nontoxic, cell-permeable small-molecule fluorogens and enhance their fluorescence over many orders of magnitude upon binding. FLAPs can be encoded on the DNA level using standard molecular biology tools and are subsequently transcribed into RNA by the cellular machinery, so that they can be used as fluorescent RNA tags (FLAP-tags). In this Minireview, we give a brief overview of the fluorogens that have been developed and their binding RNA aptamers, with a special focus on published crystal structures. A summary of current and future cellular FLAP applications with an emphasis on the study of RNA-RNA and RNA-protein interactions using split-FLAP and Förster resonance energy transfer (FRET) systems is given.

Integrative single-cell analysis of transcriptome, DNA methylome and chromatin accessibility in mouse oocytes

Oocyte growth is a key step in forming mature eggs that are ready to be fertilized. The states and modifications of chromatin represent critical sources of information for this process. However, the dynamics and interrelations of these chromatin characteristics remain elusive. In this study, we developed an improved scCOOL-seq technique (iscCOOL-seq), which is a multi-omics, single-cell and single-base resolution method with high mapping rates, and explored the chromatin accessibility landscape and its relationship to DNA methylation in growing mouse oocytes. The most dramatic change in chromatin accessibility occurs during oocyte growth initiation, accompanied with prominent transcriptome alterations and an elevated variation in DNA methylation levels among individual oocytes. Unlike CpG islands (CGIs), partially methylated domains (PMDs) are associated with a low density of nucleosome-depleted regions (NDRs) during the whole maturation period. Surprisingly, highly expressed genes are usually associated with NDRs at their transcriptional end sites (TESs). In addition, genes with de novo methylated gene bodies during oocyte maturation are already open at their promoters before oocyte growth initiation. Furthermore, epigenetic and transcription factors that might be involved in oocyte maturation are identified. Our work paves the way for dissecting the complex, yet highly coordinated, epigenetic alterations during mouse oocyte growth and the establishment of totipotency.

Combining Optical Approaches with Human Inducible Pluripotent Stem Cells in G Protein-Coupled Receptor Drug Screening and Development

Drug discovery for G protein-coupled receptors (GPCRs) stands at an interesting juncture. Screening programs are slowly moving away from model heterologous cell systems such as human embryonic kidney (HEK) 293 cells to more relevant cellular, tissue and whole animal platforms. Investigators are now developing analytical approaches as means to undertake different aspects of drug discovery by scaling into increasingly more relevant models all the way down to the single cell level. Such approaches include cellular, tissue slice and whole animal models where biosensors that track signaling events and receptor conformational profiles can be used. Here, we review aspects of biosensor-based imaging approaches that might be used in inducible pluripotent stem cell (iPSC) and organoid models, and focus on how such models must be characterized in order to apply them in drug screening.