Hedgehog-FGF signaling axis patterns anterior mesoderm during gastrulation.

The mechanisms used by embryos to pattern tissues across their axes has fascinated developmental biologists since the founding of embryology. Here, using single-cell technology, we interrogate complex patterning defects and define a Hedgehog (Hh)-fibroblast growth factor (FGF) signaling axis required for anterior mesoderm lineage development during gastrulation. Single-cell transcriptome analysis of Hh-deficient mesoderm revealed selective deficits in anterior mesoderm populations, culminating in defects to anterior embryonic structures, including the pharyngeal arches, heart, and anterior somites. Transcriptional profiling of Hh-deficient mesoderm during gastrulation revealed disruptions to both transcriptional patterning of the mesoderm and FGF signaling for mesoderm migration. Mesoderm-specific Fgf4/Fgf8 double-mutants recapitulated anterior mesoderm defects and Hh-dependent GLI transcription factors modulated enhancers at FGF gene loci. Cellular migration defects during gastrulation induced by Hh pathway antagonism were mitigated by the addition of FGF4 protein. These findings implicate a multicomponent signaling hierarchy activated by Hh ligands from the embryonic node and executed by FGF signals in nascent mesoderm to control anterior mesoderm patterning.

Exploring the tumor micro-environment in primary and metastatic tumors of different ovarian cancer histotypes.

Ovarian cancer is a highly heterogeneous disease consisting of at least five different histological subtypes with varying clinical features, cells of origin, molecular composition, risk factors, and treatments. While most single-cell studies have focused on High grade serous ovarian cancer, a comprehensive landscape of the constituent cell types and their interactions within the tumor microenvironment are yet to be established in the different ovarian cancer histotypes. Further characterization of tumor progression, metastasis, and various histotypes are also needed to connect molecular signatures to pathological grading for personalized diagnosis and tailored treatment. In this study, we leveraged high-resolution single-cell RNA sequencing technology to elucidate the cellular compositions on 21 solid tumor samples collected from 12 patients with six ovarian cancer histotypes and both primary (ovaries) and metastatic (omentum, rectum) sites. The diverse collection allowed us to deconstruct the histotypes and tumor site-specific expression patterns of cells in the tumor, and identify key marker genes and ligand-receptor pairs that are active in the ovarian tumor microenvironment. Our findings can be used in improving precision disease stratification and optimizing treatment options.

Single-cell genomics improves the discovery of risk variants and genes of atrial fibrillation.

Genome-wide association studies (GWAS) have linked hundreds of loci to cardiac diseases. However, in most loci the causal variants and their target genes remain unknown. We developed a combined experimental and analytical approach that integrates single cell epigenomics with GWAS to prioritize risk variants and genes. We profiled accessible chromatin in single cells obtained from human hearts and leveraged the data to study genetics of Atrial Fibrillation (AF), the most common cardiac arrhythmia. Enrichment analysis of AF risk variants using cell-type-resolved open chromatin regions (OCRs) implicated cardiomyocytes as the main mediator of AF risk. We then performed statistical fine-mapping, leveraging the information in OCRs, and identified putative causal variants in 122 AF-associated loci. Taking advantage of the fine-mapping results, our novel statistical procedure for gene discovery prioritized 46 high-confidence risk genes, highlighting transcription factors and signal transduction pathways important for heart development. In summary, our analysis provides a comprehensive map of AF risk variants and genes, and a general framework to integrate single-cell genomics with genetic studies of complex traits.

A molecular atlas of the human postmenopausal fallopian tube and ovary from single-cell RNA and ATAC sequencing.

As part of the Human Cell Atlas Initiative, our goal is to generate single-cell transcriptomics (single-cell RNA sequencing [scRNA-seq], 86,708 cells) and regulatory (single-cell assay on transposase accessible chromatin sequencing [scATAC-seq], 59,830 cells) profiles of the normal postmenopausal ovary and fallopian tube (FT). The FT contains 11 major cell types, and the ovary contains 6. The dominating cell type in the FT and ovary is the stromal cell, which expresses aging-associated genes. FT epithelial cells express multiple ovarian cancer risk-associated genes (CCDC170, RND3, TACC2, STK33, and ADGB) and show active communication between fimbrial epithelial cells and ovarian stromal cells. Integrated single-cell transcriptomics and chromatin accessibility data show that the regulatory landscape of the fimbriae is different from other anatomic regions. Cell types with similar gene expression in the FT display transcriptional profiles. These findings allow us to disentangle the cellular makeup of the postmenopausal FT and ovary, advancing our knowledge of gynecologic diseases in menopause.

Hedgehog signaling activates a mammalian heterochronic gene regulatory network controlling differentiation timing across lineages.

Many developmental signaling pathways have been implicated in lineage-specific differentiation; however, mechanisms that explicitly control differentiation timing remain poorly defined in mammals. We report that murine Hedgehog signaling is a heterochronic pathway that determines the timing of progenitor differentiation. Hedgehog activity was necessary to prevent premature differentiation of second heart field (SHF) cardiac progenitors in mouse embryos, and the Hedgehog transcription factor GLI1 was sufficient to delay differentiation of cardiac progenitors in vitro. GLI1 directly activated a de novo progenitor-specific network in vitro, akin to that of SHF progenitors in vivo, which prevented the onset of the cardiac differentiation program. A Hedgehog signaling-dependent active-to-repressive GLI transition functioned as a differentiation timer, restricting the progenitor network to the SHF. GLI1 expression was associated with progenitor status across germ layers, and it delayed the differentiation of neural progenitors in vitro, suggesting a broad role for Hedgehog signaling as a heterochronic pathway.

Characterizing the tumor microenvironment of metastatic ovarian cancer by single-cell transcriptomics.

Understanding the cellular composition of the tumor microenvironment and the interactions of the cells is essential to the development of successful immunotherapies in cancer. We perform single-cell RNA sequencing (scRNA-seq) of 9,885 cells isolated from the omentum in 6 patients with ovarian cancer and identify 9 major cell types, including cancer, stromal, and immune cells. Transcriptional analysis of immune cells stratifies our patient samples into 2 groups: (1) high T cell infiltration (high Tinf) and (2) low T cell infiltration (low Tinf). TOX-expressing resident memory CD8+ T (CD8+ Trm) and granulysin-expressing CD4+ T cell clusters are enriched in the high Tinf group. Concurrently, we find unique plasmablast and plasma B cell clusters, and finally, NR1H2+IRF8+ and CD274+ macrophage clusters, suggesting an anti-tumor response in the high Tinf group. Our scRNA-seq study of metastatic tumor samples provides important insights in elucidating the immune response within ovarian tumors.

mDrop-Seq: Massively Parallel Single-Cell RNA-Seq of Saccharomyces cerevisiae and Candida albicans.

Advances in high-throughput single-cell RNA sequencing (scRNA-seq) have been limited by technical challenges such as tough cell walls and low RNA quantity that prevent transcriptomic profiling of microbial species at throughput. We present microbial Drop-seq or mDrop-seq, a high-throughput scRNA-seq technique that is demonstrated on two yeast species, Saccharomyces cerevisiae, a popular model organism, and Candida albicans, a common opportunistic pathogen. We benchmarked mDrop-seq for sensitivity and specificity and used it to profile 35,109 S. cerevisiae cells to detect variation in mRNA levels between them. As a proof of concept, we quantified expression differences in heat shock S. cerevisiae using mDrop-seq. We detected differential activation of stress response genes within a seemingly homogenous population of S. cerevisiae under heat shock. We also applied mDrop-seq to C. albicans cells, a polymorphic and clinically relevant species of yeast with a thicker cell wall compared to S. cerevisiae. Single-cell transcriptomes in 39,705 C. albicans cells were characterized using mDrop-seq under different conditions, including exposure to fluconazole, a common anti-fungal drug. We noted differential regulation in stress response and drug target pathways between C. albicans cells, changes in cell cycle patterns and marked increases in histone activity when treated with fluconazole. We demonstrate mDrop-seq to be an affordable and scalable technique that can quantify the variability in gene expression in different yeast species. We hope that mDrop-seq will lead to a better understanding of genetic variation in pathogens in response to stimuli and find immediate applications in investigating drug resistance, infection outcome and developing new drugs and treatment strategies.

Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation.

A comprehensive reference map of all cell types in the human body is necessary for improving our understanding of fundamental biological processes and in diagnosing and treating disease. High-throughput single-cell RNA sequencing techniques have emerged as powerful tools to identify and characterize cell types in complex and heterogeneous tissues. However, extracting intact cells from tissues and organs is often technically challenging or impossible, for example in heart or brain tissue. Single-nucleus RNA sequencing provides an alternative way to obtain transcriptome profiles of such tissues. To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3' RNA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes. Clustering of time-series transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics. We then applied DroNc-seq to postmortem heart tissue to test its performance on heterogeneous human tissue samples. Our data confirm that DroNc-seq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.

The DGCR5 long noncoding RNA may regulate expression of several schizophrenia-related genes.

A number of studies indicate that rare copy number variations (CNVs) contribute to the risk of schizophrenia (SCZ). Most of these studies have focused on protein-coding genes residing in the CNVs. Here, we investigated long noncoding RNAs (lncRNAs) within 10 SCZ risk-associated CNV deletion regions (CNV-lncRNAs) and examined their potential contribution to SCZ risk. We used RNA sequencing transcriptome data derived from postmortem brain tissue from control individuals without psychiatric disease as part of the PsychENCODE BrainGVEX and Developmental Capstone projects. We carried out weighted gene coexpression network analysis to identify protein-coding genes coexpressed with CNV-lncRNAs in the human brain. We identified one neuronal function-related coexpression module shared by both datasets. This module contained a lncRNA called DGCR5 within the 22q11.2 CNV region, which was identified as a hub gene. Protein-coding genes associated with SCZ genome-wide association study signals, de novo mutations, or differential expression were also contained in this neuronal module. Using DGCR5 knockdown and overexpression experiments in human neural progenitor cells derived from human induced pluripotent stem cells, we identified a potential role for DGCR5 in regulating certain SCZ-related genes.

Functional assessment of human enhancer activities using whole-genome STARR-sequencing.

BACKGROUND: Genome-wide quantification of enhancer activity in the human genome has proven to be a challenging problem. Recent efforts have led to the development of powerful tools for enhancer quantification. However, because of genome size and complexity, these tools have yet to be applied to the whole human genome. RESULTS:  In the current study, we use a human prostate cancer cell line, LNCaP as a model to perform whole human genome STARR-seq (WHG-STARR-seq) to reliably obtain an assessment of enhancer activity. This approach builds upon previously developed STARR-seq in the fly genome and CapSTARR-seq techniques in targeted human genomic regions. With an improved library preparation strategy, our approach greatly increases the library complexity per unit of starting material, which makes it feasible and cost-effective to explore the landscape of regulatory activity in the much larger human genome. In addition to our ability to identify active, accessible enhancers located in open chromatin regions, we can also detect sequences with the potential for enhancer activity that are located in inaccessible, closed chromatin regions. When treated with the histone deacetylase inhibitor, Trichostatin A, genes nearby this latter class of enhancers are up-regulated, demonstrating the potential for endogenous functionality of these regulatory elements. CONCLUSION: WHG-STARR-seq provides an improved approach to current pipelines for analysis of high complexity genomes to gain a better understanding of the intricacies of transcriptional regulation.