The molecular anatomy of mammalian upper lip and primary palate fusion at single cell resolution

The mammalian lip and primary palate form when coordinated growth and morphogenesis bring the nasal and maxillary processes into contact, and the epithelia co-mingle, remodel and clear from the fusion site to allow mesenchyme continuity. Although several genes required for fusion have been identified, an integrated molecular and cellular description of the overall process is lacking. Here, we employ single cell RNA sequencing of the developing mouse face to identify ectodermal, mesenchymal and endothelial populations associated with patterning and fusion of the facial prominences. This analysis indicates that key cell populations at the fusion site exist within the periderm, basal epithelial cells and adjacent mesenchyme. We describe the expression profiles that make each population unique, and the signals that potentially integrate their behaviour. Overall, these data provide a comprehensive high-resolution description of the various cell populations participating in fusion of the lip and primary palate, as well as formation of the nasolacrimal groove, and they furnish a powerful resource for those investigating the molecular genetics of facial development and facial clefting that can be mined for crucial mechanistic information concerning this prevalent human birth defect.

Single cell expression analysis reveals anatomical and cell cycle-dependent transcriptional shifts during heart development

The heart is a complex organ composed of multiple cell and tissue types. Cardiac cells from different regions of the growing embryonic heart exhibit distinct patterns of gene expression, which are thought to contribute to heart development and morphogenesis. Single cell RNA sequencing allows genome-wide analysis of gene expression at the single cell level. Here, we have analyzed cardiac cells derived from early stage developing hearts by single cell RNA-seq and identified cell cycle gene expression as a major determinant of transcriptional variation. Within cell cycle stage-matched CMs from a given heart chamber, we found that CMs in the G2/M phase downregulated sarcomeric and cytoskeletal markers. We also identified cell location-specific signaling molecules that may influence the proliferation of other nearby cell types. Our data highlight how variations in cell cycle activity selectively promote cardiac chamber growth during development, reveal profound chamber-specific cell cycle-linked transcriptional shifts, and open the way to deeper understanding of pathogenesis of congenital heart disease.

Expression dynamics of repetitive DNA in early human embryonic development

BACKGROUND

The last decade witnessed a number of genome-wide studies on human pre-implantation, which mostly focused on genes and provided only limited information on repeats, excluding the satellites. Considering the fact that repeats constitute a large portion of our genome with reported links to human physiology and disease, a thorough understanding of their spatiotemporal regulation during human embryogenesis will give invaluable clues on chromatin dynamics across time and space. Therefore, we performed a detailed expression analysis of all repetitive DNA elements including the satellites across stages of human pre-implantation and embryonic stem cells.

RESULTS

We uncovered stage-specific expressions of more than a thousand repeat elements whose expressions fluctuated with a mild global decrease at the blastocyst stage. Most satellites were highly expressed at the 4-cell level and expressions of ACRO1 and D20S16 specifically peaked at this point. Whereas all members of the SVA elements were highly upregulated at 8-cell and morula stages, other transposons and small RNA repeats exhibited a high level of variation among their specific subtypes. Our repeat enrichment analysis in gene promoters coupled with expression correlations highlighted potential links between repeat expressions and nearby genes, emphasising mostly 8-cell and morula specific genes together with SVA_D, LTR5_Hs and LTR70 transposons. The DNA methylation analysis further complemented the understanding on the mechanistic aspects of the repeatome's regulation per se and revealed critical stages where DNA methylation levels are negatively correlating with repeat expression.

CONCLUSIONS

Taken together, our study shows that specific expression patterns are not exclusive to genes and long non-coding RNAs but the repeatome also exhibits an intriguingly dynamic pattern at the global scale. Repeats identified in this study; particularly satellites, which were historically associated with heterochromatin, and those with potential links to nearby gene expression provide valuable insights into the understanding of key events in genomic regulation and warrant further research in epigenetics, genomics and developmental biology.

Maturation of heart valve cell populations during postnatal remodeling

Heart valve cells mediate extracellular matrix (ECM) remodeling during postnatal valve leaflet stratification, but phenotypic and transcriptional diversity of valve cells in development is largely unknown. Single cell analysis of mouse heart valve cells was used to evaluate cell heterogeneity during postnatal ECM remodeling and leaflet morphogenesis. The transcriptomic analysis of single cells from postnatal day (P)7 and P30 murine aortic (AoV) and mitral (MV) heart valves uncovered distinct subsets of melanocytes, immune and endothelial cells present at P7 and P30. By contrast, interstitial cell populations are different from P7 to P30. P7 valve leaflets exhibit two distinct collagen- and glycosaminoglycan-expressing interstitial cell clusters, and prevalent ECM gene expression. At P30, four interstitial cell clusters are apparent with leaflet specificity and differential expression of complement factors, ECM proteins and osteogenic genes. This initial transcriptomic analysis of postnatal heart valves at single cell resolution demonstrates that subpopulations of endothelial and immune cells are relatively constant throughout postnatal development, but interstitial cell subpopulations undergo changes in gene expression and cellular functions in primordial and mature valves.

VASC: Dimension Reduction and Visualization of Single-cell RNA-seq Data by Deep Variational Autoencoder

Single-cell RNA sequencing (scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities at the single cell level. It is an important step for studying cell sub-populations and lineages, with an effective low-dimensional representation and visualization of the original scRNA-Seq data. At the single cell level, the transcriptional fluctuations are much larger than the average of a cell population, and the low amount of RNA transcripts will increase the rate of technical dropout events. Therefore, scRNA-seq data are much noisier than traditional bulk RNA-seq data. In this study, we proposed the deep variational autoencoder for scRNA-seq data (VASC), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. VASC can explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on over 20 datasets, VASC shows superior performances in most cases and exhibits broader dataset compatibility compared to four state-of-the-art dimension reduction and visualization methods. In addition, VASC provides better representations for very rare cell populations in the 2D visualization. As a case study, VASC successfully re-establishes the cell dynamics in pre-implantation embryos and identifies several candidate marker genes associated with early embryo development. Moreover, VASC also performs well on a 10× Genomics dataset with more cells and higher dropout rate.

Elucidating the developmental trajectories of GABAergic cortical interneuron subtypes

GABAergic interneurons in the neocortex play pivotal roles in the feedforward and feedback inhibition that control higher order information processing and thus, malfunction in the inhibitory circuits often leads to neurodevelopmental disorders. Very interestingly, a large diversity of morphology, synaptic targeting specificity, electrophysiological properties and molecular expression profiles are found in cortical interneurons, which originate within the distantly located embryonic ganglionic eminences. Here, I will review the still ongoing effort to understand the developmental trajectories of GABAergic cortical interneuron subtypes.

Dgcr8 deletion in the primitive heart uncovered novel microRNA regulating the balance of cardiac-vascular gene program

Primitive mammalian heart transforms from a single tube to a four-chambered muscular organ during a short developmental window. We found that knocking out global microRNA by deleting Dgcr8 microprocessor in Mesp1 cardiovascular progenitor cells lead to the formation of extremely dilated and enlarged heart due to defective cardiomyocyte (CM) differentiation. Transcriptome analysis revealed unusual upregulation of vascular gene expression in Dgcr8 cKO hearts. Single cell RNA sequencing study further confirmed the increase of angiogenesis genes in single Dgcr8 cKO CM. We also performed global microRNA profiling of E9.5 heart for the first time, and identified that miR-541 was transiently highly expressed in E9.5 hearts. Interestingly, introducing miR-541 back into microRNA-free CMs partially rescued their defects, downregulated angiogenesis genes and significantly upregulated cardiac genes. Moreover, miR-541 can target Ctgf and inhibit endothelial function. Our results suggest that microRNAs are required to suppress abnormal angiogenesis gene program to maintain CM differentiation.

Single cell transcriptome sequencing: A new approach for the study of mammalian sex determination

Mammalian sex determination is a highly complex developmental process that is particularly difficult to study due to the limited number of gonadal cells present at the bipotential stage, the large cellular heterogeneity in both testis and ovaries and the rapid sex-dependent differentiation processes. Single-cell RNA-sequencing (scRNA-seq) circumvents the averaging artifacts associated with methods traditionally used to profile bulk populations of cells. It is a powerful tool that allows the identification and classification of cell populations in a comprehensive and unbiased manner. In particular, scRNA-seq enables the tracing of cells along developmental trajectories and characterization of the transcriptional dynamics controlling their differentiation. In this review, we describe the current state-of-the-art experimental methods used for scRNA-seq and discuss their strengths and limitations. Additionally, we summarize the multiple key insights that scRNA-seq has provided to the understanding of mammalian sex determination. Finally, we briefly discuss the future of this technology, as well as complementary applications in single cell -omics in the context of mammalian sex determination.

A UNIFIED STATISTICAL FRAMEWORK FOR SINGLE CELL AND BULK RNA SEQUENCING DATA

Recent advances in technology have enabled the measurement of RNA levels for individual cells. Compared to traditional tissue-level bulk RNA-seq data, single cell sequencing yields valuable insights about gene expression profiles for different cell types, which is potentially critical for understanding many complex human diseases. However, developing quantitative tools for such data remains challenging because of high levels of technical noise, especially the "dropout" events. A "dropout" happens when the RNA for a gene fails to be amplified prior to sequencing, producing a "false" zero in the observed data. In this paper, we propose a Unified RNA-Sequencing Model (URSM) for both single cell and bulk RNA-seq data, formulated as a hierarchical model. URSM borrows the strength from both data sources and carefully models the dropouts in single cell data, leading to a more accurate estimation of cell type specific gene expression profile. In addition, URSM naturally provides inference on the dropout entries in single cell data that need to be imputed for downstream analyses, as well as the mixing proportions of different cell types in bulk samples. We adopt an empirical Bayes' approach, where parameters are estimated using the EM algorithm and approximate inference is obtained by Gibbs sampling. Simulation results illustrate that URSM outperforms existing approaches both in correcting for dropouts in single cell data, as well as in deconvolving bulk samples. We also demonstrate an application to gene expression data on fetal brains, where our model successfully imputes the dropout genes and reveals cell type specific expression patterns.

High-Throughput Single-Cell RNA Sequencing and Data Analysis

Understanding biological systems at a single cell resolution may reveal several novel insights which remain masked by the conventional population-based techniques providing an average readout of the behavior of cells. Single-cell transcriptome sequencing holds the potential to identify novel cell types and characterize the cellular composition of any organ or tissue in health and disease. Here, we describe a customized high-throughput protocol for single-cell RNA-sequencing (scRNA-seq) combining flow cytometry and a nanoliter-scale robotic system. Since scRNA-seq requires amplification of a low amount of endogenous cellular RNA, leading to substantial technical noise in the dataset, downstream data filtering and analysis require special care. Therefore, we also briefly describe in-house state-of-the-art data analysis algorithms developed to identify cellular subpopulations including rare cell types as well as to derive lineage trees by ordering the identified subpopulations of cells along the inferred differentiation trajectories.