Understanding development and stem cells using single cell-based analyses of gene expression

Assembling the adipose organ: adipocyte lineage segregation and adipogenesis in vivo

Adipose tissue is composed of anatomically distinct depots that mediate several important aspects of energy homeostasis. The past two decades have witnessed increased research effort to elucidate the ontogenetic basis of adipose form and function. In this Review, we discuss advances in our understanding of adipose tissue development with particular emphasis on the embryonic patterning of depot-specific adipocyte lineages and adipocyte differentiation in vivo Micro-environmental cues and other factors that influence cell identity and cell behavior at various junctures in the adipocyte lineage hierarchy are also considered.

Inferring Multidimensional Rates of Aging from Cross-Sectional Data

Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. However, existing datasets are often cross-sectional with each individual observed only once, making it impossible to apply traditional time-series methods. Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data. Our model represents each individual's features over time as a nonlinear function of a low-dimensional, linearly-evolving latent state. We prove that when this nonlinear function is constrained to be order-isomorphic, the model family is identifiable solely from cross-sectional data provided the distribution of time-independent variation is known. On the UK Biobank human health dataset, our model reconstructs the observed data while learning interpretable rates of aging associated with diseases, mortality, and aging risk factors.

Gene Regulatory Networks from Single Cell Data for Exploring Cell Fate Decisions

Single cell experimental techniques now allow us to quantify gene expression in up to thousands of individual cells. These data reveal the changes in transcriptional state that occur as cells progress through development and adopt specialized cell fates. In this chapter we describe in detail how to use our network inference algorithm (PIDC)-and the associated software package NetworkInference.jl-to infer functional interactions between genes from the observed gene expression patterns. We exploit the large sample sizes and inherent variability of single cell data to detect statistical dependencies between genes that indicate putative (co-)regulatory relationships, using multivariate information measures that can capture complex statistical relationships. We provide guidelines on how best to combine this analysis with other complementary methods designed to explore single cell data, and how to interpret the resulting gene regulatory network models to gain insight into the processes regulating cell differentiation.

Trajectory Algorithms to Infer Stem Cell Fate Decisions

Single-cell trajectory analysis is an active research area in single-cell genomics aiming at developing sophisticated algorithms to reconstruct complex cell-state transition trajectories. Here, we present a step-by-step protocol to use CellRouter, a multifaceted single-cell analysis platform that integrates subpopulation identification, gene regulatory networks, and trajectory inference to precisely and flexibly reconstruct complex single-cell trajectories. Subpopulations are either user-defined or identified by a graph-clustering approach in which a k-nearest neighbor graph (kNN) is created from cell-to-cell distances in a low-dimensional embedding. Edges in this graph are weighted by network similarity metrics (e.g., Jaccard index) to robustly encode phenotypic relatedness, creating a representation of single-cell transcriptomes suitable for community detection algorithms to identify clusters of densely connected cells. This subpopulation structure represents a map of putative cell-state transitions. CellRouter implements a flow network algorithm to explore this map and reconstruct cell-state transitions in complex single-cell, multidimensional omics datasets. We describe a step-by-step application of CellRouter to hematopoietic stem and progenitor cell differentiation toward four major lineages-erythrocytes, megakaryocytes, monocytes, and granulocytes-to demonstrate key components of CellRouter for single-cell trajectory analysis.

Electron microscopy snapshots of single particles from single cells

Cryo-electron microscopy (cryo-EM) has become an indispensable tool for structural studies of biological macromolecules. Two additional predominant methods are available for studying the architectures of multiprotein complexes: 1) single-particle analysis of purified samples and 2) tomography of whole cells or cell sections. The former can produce high-resolution structures but is limited to highly purified samples, whereas the latter can capture proteins in their native state but has a low signal-to-noise ratio and yields lower-resolution structures. Here, we present a simple, adaptable method combining microfluidic single-cell extraction with single-particle analysis by EM to characterize protein complexes from individual Caenorhabditis elegans embryos. Using this approach, we uncover 3D structures of ribosomes directly from single embryo extracts. Moreover, we investigated structural dynamics during development by counting the number of ribosomes per polysome in early and late embryos. This approach has significant potential applications for counting protein complexes and studying protein architectures from single cells in developmental, evolutionary, and disease contexts.

Regulatory network characterization in development: challenges and opportunities

Embryonic development and stem cell differentiation, during which coordinated cell fate specification takes place in a spatial and temporal context, serve as a paradigm for studying the orderly assembly of gene regulatory networks (GRNs) and the fundamental mechanism of GRNs in driving lineage determination. However, knowledge of reliable GRN annotation for dynamic development regulation, particularly for unveiling the complex temporal and spatial architecture of tissue stem cells, remains inadequate. With the advent of single-cell RNA sequencing technology, elucidating GRNs in development and stem cell processes poses both new challenges and unprecedented opportunities. This review takes a snapshot of some of this work and its implication in the regulative nature of early mammalian development and specification of the distinct cell types during embryogenesis.

A single cell transcriptional portrait of embryoid body differentiation and comparison to progenitors of the developing embryo

Directed differentiation of pluripotent stem cells provides an accessible system to model development. However, the distinct cell types that emerge, their dynamics, and their relationship to progenitors in the early embryo has been difficult to decipher because of the cellular heterogeneity inherent to differentiation. Here, we used a combination of bulk RNA-Seq, single cell RNA-Seq, and bioinformatics analyses to dissect the cell types that emerge during directed differentiation of mouse embryonic stem cells as embryoid bodies and we compared them to spatially and temporally resolved transcriptional profiles of early embryos. Our single cell analyses of the day 4 embryoid bodies revealed three populations which had retained related yet distinct pluripotent signatures that resemble the pre- or post-implantation epiblast, one population of presumptive neuroectoderm, one population of mesendoderm, and four populations of neural progenitors. By day 6, the neural progenitors predominated the embryoid bodies, but both a small population of pluripotent-like cells and an anterior mesoderm-like Brachyury-expressing population were present. By comparing the day 4 and day 6 populations, we identified candidate differentiation paths, transcription factors, and signaling pathways that mark the in vitro correlate of the transition from the mid-to-late primitive streak stage.

In vivo response of xanthosine on mammary gene expression of lactating Beetal goat

Xanthosine is hypothesized to increase stem cell number by promoting symmetrical cell division. Stem cells, in particular mammary stem/progenitor cells are important for gland growth and tissue repair. Molecular mechanism of xanthosine effects on mammary tissue is very limited therefore, a detailed study is warranted. The objective of this study was to evaluate transcriptomic changes in mammary gland infused/not infused with xanthosine of lactating goat. Seven primiparous Beetal goats on day 5 after kidding, were selected for the study. One gland of each goat was infused with xanthosine (TRT gland) twice daily for 3 days while the other gland did not receive any xanthosine and served as control (CON gland). Biopsy of mammary tissues was taken from TRT and CON glands, 2 days after the last day of treatment that is on day 10 after kidding. Illumina RNA-sequencing (RNA-seq) was performed for global gene expression analysis of contralateral glands. Of 382 differentially expressed genes (DEGs), 372 genes were annotated to the goat genome. Gene ontology analyses revealed majority of the DEGs to be associated with metabolic pathways (glycan and lipid metabolism), biosynthesis of antibiotics and peroxisome proliferator-activated receptor signalling pathways. These molecular pathways are either directly or indirectly involved with lipid metabolism in mammary tissue and host adaptive immune response. Expression of stem cell marker namely aldehyde dehydrogenase enzymes (ALDH1A1, ALDH3B1) were upregulated in the treatment gland. Real-time quantitative PCR (RT-qPCR) analyses of selected DEGs showed their expression profiles to be in agreement with results of RNA-seq. To our knowledge, this is the first study that describes effects of xanthosine on transcriptomic changes of mammary tissue. This information can be used further to dissect the molecular mechanisms underlying effects of xanthosine to improve production potential and udder health.

Quantitative single-cell transcriptomics

Single-cell RNA sequencing (scRNA-seq) is currently transforming our understanding of biology, as it is a powerful tool to resolve cellular heterogeneity and molecular networks. Over 50 protocols have been developed in recent years and also data processing and analyzes tools are evolving fast. Here, we review the basic principles underlying the different experimental protocols and how to benchmark them. We also review and compare the essential methods to process scRNA-seq data from mapping, filtering, normalization and batch corrections to basic differential expression analysis. We hope that this helps to choose appropriate experimental and computational methods for the research question at hand.

Cholesterol signaling in single cells: lessons from STAR and sm-FISH

Cholesterol is an important regulator of cell signaling, both through direct impacts on cell membranes and through oxy-metabolites that activate specific receptors (steroids, hydroxy-cholesterols, bile acids). Cholesterol moves slowly through and between cell membranes with the assistance of specific binding proteins and transfer processes. The prototype cholesterol regulator is the Steroidogenesis Acute Regulatory (STAR), which moves cholesterol into mitochondria, where steroid synthesis is initiated by cytochrome P450 11A1 in multiple endocrine cell types. CYP27A1 generates hydroxyl cholesterol metabolites that activate LXR nuclear receptors to control cholesterol homeostatic and transport mechanisms. LXR regulation of cholesterol transport and storage as cholesterol ester droplets is shared by both steroid-producing cells and macrophage. This cholesterol signaling is crucial to brain neuron regulation by astrocytes and microglial macrophage, mediated by ApoE and sensitive to disruption by β-amyloid plaques. sm-FISH delivers appreciable insights into signaling in single cells, by resolving single RNA molecules as mRNA and by quantifying pre-mRNA at gene loci. sm-FISH has been applied to problems in physiology, embryo development and cancer biology, where single cell features have critical impacts. sm-FISH identifies novel features of STAR transcription in adrenal and testis cells, including asymmetric expression at individual gene loci, delayed splicing and 1:1 association of mRNA with mitochondria. This may represent a functional unit for the translation-dependent cholesterol transfer directed by STAR, which integrates into mitochondrial fusion dynamics. Similar cholesterol dynamics repeat with different players in the cycling of cholesterol between astrocytes and neurons in the brain, which may be abnormal in neurodegenerative diseases.

A Novel Approach to Single Cell RNA-Sequence Analysis Facilitates In Silico Gene Reporting of Human Pluripotent Stem Cell-Derived Retinal Cell Types

Cell type-specific investigations commonly use gene reporters or single-cell analytical techniques. However, reporter line development is arduous and generally limited to a single gene of interest, while single-cell RNA (scRNA)-sequencing (seq) frequently yields equivocal results that preclude definitive cell identification. To examine gene expression profiles of multiple retinal cell types derived from human pluripotent stem cells (hPSCs), we performed scRNA-seq on optic vesicle (OV)-like structures cultured under cGMP-compatible conditions. However, efforts to apply traditional scRNA-seq analytical methods based on unbiased algorithms were unrevealing. Therefore, we developed a simple, versatile, and universally applicable approach that generates gene expression data akin to those obtained from reporter lines. This method ranks single cells by expression level of a bait gene and searches the transcriptome for genes whose cell-to-cell rank order expression most closely matches that of the bait. Moreover, multiple bait genes can be combined to refine datasets. Using this approach, we provide further evidence for the authenticity of hPSC-derived retinal cell types. Stem Cells 2018;36:313-324.

Recent advances in lineage differentiation from stem cells: hurdles and opportunities?

Pluripotent stem cells have the property of long-term self-renewal and the potential to give rise to descendants of the three germ layers and hence all mature cells in the human body. Therefore, they hold the promise of offering insight not only into human development but also for human disease modeling and regenerative medicine. However, the generation of mature differentiated cells that closely resemble their in vivo counterparts remains challenging. Recent advances in single-cell transcriptomics and computational modeling of gene regulatory networks are revealing a better understanding of lineage commitment and are driving modern genome editing approaches. Additional modification of the chemical microenvironment, as well as the use of bioengineering tools to recreate the cellular, extracellular matrix, and physical characteristics of the niche wherein progenitors and mature cells reside, is now being used to further improve the maturation and functionality of stem cell progeny.

Programming Morphogenesis through Systems and Synthetic Biology

Mammalian tissue development is an intricate, spatiotemporal process of self-organization that emerges from gene regulatory networks of differentiating stem cells. A major goal in stem cell biology is to gain a sufficient understanding of gene regulatory networks and cell-cell interactions to enable the reliable and robust engineering of morphogenesis. Here, we review advances in synthetic biology, single cell genomics, and multiscale modeling, which, when synthesized, provide a framework to achieve the ambitious goal of programming morphogenesis in complex tissues and organoids.

Single-cell transcriptome sequencing reveals that cell division cycle 5-like protein is essential for porcine oocyte maturation

The brilliant cresyl blue (BCB) test is used in both basic biological research and assisted reproduction to identify oocytes likely to be developmentally competent. However, the underlying molecular mechanism targeted by the BCB test is still unclear. To explore this question, we first confirmed that BCB-positive porcine oocytes had higher rates of meiotic maturation, better rates of cleavage and development into blastocysts, and lower death rates. Subsequent single-cell transcriptome sequencing on porcine germinal vesicle (GV)-stage oocytes identified 155 genes that were significantly differentially expressed between BCB-negative and BCB-positive oocytes. These included genes such as cdc5l, ldha, spata22, rgs2, paip1, wee1b, and hsp27, which are enriched in functionally important signaling pathways including cell cycle regulation, oocyte meiosis, spliceosome formation, and nucleotide excision repair. In BCB-positive GV oocytes that additionally had a lower frequency of DNA double-strand breaks, the CDC5L protein was significantly more abundant. cdc5l/CDC5L inhibition by short interference (si)RNA or antibody microinjection significantly impaired porcine oocyte meiotic maturation and subsequent parthenote development. Taken together, our single-oocyte sequencing data point to a potential new role for CDC5L in porcine oocyte meiosis and early embryo development, and supports further analysis of this protein in the context of the BCB test.

Reversed graph embedding resolves complex single-cell trajectories

Single-cell trajectories can unveil how gene regulation governs cell fate decisions. However, learning the structure of complex trajectories with multiple branches remains a challenging computational problem. We present Monocle 2, an algorithm that uses reversed graph embedding to describe multiple fate decisions in a fully unsupervised manner. We applied Monocle 2 to two studies of blood development and found that mutations in the genes encoding key lineage transcription factors divert cells to alternative fates.

Human organomics: a fresh approach to understanding human development using single-cell transcriptomics

Innovative methods designed to recapitulate human organogenesis from pluripotent stem cells provide a means to explore human developmental biology. New technologies to sequence and analyze single-cell transcriptomes can deconstruct these 'organoids' into constituent parts, and reconstruct lineage trajectories during cell differentiation. In this Spotlight article we summarize the different approaches to performing single-cell transcriptomics on organoids, and discuss the opportunities and challenges of applying these techniques to generate organ-level, mechanistic models of human development and disease. Together, these technologies will move past characterization to the prediction of human developmental and disease-related phenomena.

Reversed graph embedding resolves complex single-cell trajectories

Single-cell trajectories can unveil how gene regulation governs cell fate decisions. However, learning the structure of complex trajectories with multiple branches remains a challenging computational problem. We present Monocle 2, an algorithm that uses reversed graph embedding to describe multiple fate decisions in a fully unsupervised manner. We applied Monocle 2 to two studies of blood development and found that mutations in the genes encoding key lineage transcription factors divert cells to alternative fates.

Lineage specification of early embryos and embryonic stem cells at the dawn of enabling technologies

Establishment of progenitor cell populations and lineage diversity during embryogenesis and the differentiation of pluripotent stem cells is a fascinating and intricate biological process. Conceptually, an understanding of this developmental process provides a framework to integrate stem-cell pluripotency, cell competence and differentiating potential with the activity of extrinsic and intrinsic molecular determinants. The recent advent of enabling technologies of high-resolution transcriptome analysis at the cellular, population and spatial levels proffers the capability of gaining deeper insights into the attributes of the gene regulatory network and molecular signaling in lineage specification and differentiation. In this review, we provide a snapshot of the emerging enabling genomic technologies that contribute to the study of development and stem-cell biology.

Single-Cell Landscape of Transcriptional Heterogeneity and Cell Fate Decisions during Mouse Early Gastrulation

The mouse inner cell mass (ICM) segregates into the epiblast and primitive endoderm (PrE) lineages coincident with implantation of the embryo. The epiblast subsequently undergoes considerable expansion of cell numbers prior to gastrulation. To investigate underlying regulatory principles, we performed systematic single-cell RNA sequencing (seq) of conceptuses from E3.5 to E6.5. The epiblast shows reactivation and subsequent inactivation of the X chromosome, with Zfp57 expression associated with reactivation and inactivation together with other candidate regulators. At E6.5, the transition from epiblast to primitive streak is linked with decreased expression of polycomb subunits, suggesting a key regulatory role. Notably, our analyses suggest elevated transcriptional noise at E3.5 and within the non-committed epiblast at E6.5, coinciding with exit from pluripotency. By contrast, E6.5 primitive streak cells became highly synchronized and exhibit a shortened G1 cell-cycle phase, consistent with accelerated proliferation. Our study systematically charts transcriptional noise and uncovers molecular processes associated with early lineage decisions.

Advances and challenges in stem cell culture

Stem cells (SCs) hold great promise for cell therapy, tissue engineering, and regenerative medicine as well as pharmaceutical and biotechnological applications. They have the capacity to self-renew and the ability to differentiate into specialized cell types depending upon their source of isolation. However, use of SCs for clinical applications requires a high quality and quantity of cells. This necessitates large-scale expansion of SCs followed by efficient and homogeneous differentiation into functional derivatives. Traditional methods for maintenance and expansion of cells rely on two-dimensional (2-D) culturing techniques using plastic culture plates and xenogenic media. These methods provide limited expansion and cells tend to lose clonal and differentiation capacity upon long-term passaging. Recently, new approaches for the expansion of SCs have emphasized three-dimensional (3-D) cell growth to mimic the in vivo environment. This review provides a comprehensive compendium of recent advancements in culturing SCs using 2-D and 3-D techniques involving spheroids, biomaterials, and bioreactors. In addition, potential challenges to achieve billion-fold expansion of cells are discussed.

Pancreatic Beta Cell Identity in Humans and the Role of Type 2 Diabetes

Pancreatic beta cells uniquely synthetize, store, and release insulin. Specific molecular, functional as well as ultrastructural traits characterize their insulin secretion properties and survival phentoype. In this review we focus on human islet/beta cells, and describe the changes that occur in type 2 diabetes and could play roles in the disease as well as represent possible targets for therapeutical interventions. These include transcription factors, molecules involved in glucose metabolism and insulin granule handling. Quantitative and qualitative insulin release patterns and their changes in type 2 diabetes are also associated with ultrastructural features involving the insulin granules, the mitochondria, and the endoplasmic reticulum.

Single-Cell RNA Sequencing: Unraveling the Brain One Cell at a Time

Single-cell RNA sequencing (scRNA-seq) is an exciting new technology allowing the analysis of transcriptomes from individual cells, and is ideally suited to address the inherent complexity and dynamics of the central nervous system. scRNA-seq has already been applied to the study of molecular taxonomy of the brain. These works have paved the way to expanding our understanding of the nervous system and provide insights into cellular susceptibilities and molecular mechanisms in neurological and neurodegenerative diseases. We discuss recent progress and challenges in applying this technology to advance our understanding of the brain. We advocate the application of scRNA-seq in the discovery of targets and biomarkers as a new approach in developing novel therapeutics for the treatment of neurodegenerative diseases.