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.

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.

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.