Biology subjects

Eckart, H.

Publications and source records attributed to Eckart, H..

5 recordsLinked to original sources

bioRxiv2022

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, we generated transcriptomic (scRNA-seq; 86,708 cells) and regulatory (scATAC-seq; 59,118 cells) profiles of the normal postmenopausal ovary and fallopian tube (FT) at single-cell resolution. In the FT, 22 cell clusters integrated into 11 cell types, including ciliated and secretory epithelial cells, while the ovary had 17 distinct cell clusters defining 6 major cell types. The dominant cell type in both the postmenopausal ovary and FT was stromal cells, which expressed several genes associated with aging. The fimbrial end of the FT had a significant number and variety of immune cells and active communication with the ovary; the ovary contained mostly stromal cells but few immune cells. The epithelial cells of the normal FT expressed multiple ovarian cancer risk-associated genes (CCDC170, RND3, TACC2, STK33, and ADGB). By integrating paired single-cell transcriptomics and chromatin accessibility data we found that the regulatory landscape of the fimbriae was markedly different from the isthmus and ampulla. Intriguingly, several cell types in the FT had comparable gene expression but different transcriptional regulations. Our single-cell transcriptional and regulatory maps allowed us to disentangle the complex cellular makeup of the postmenopausal FT and ovary and will contribute to a better understanding of gynecologic diseases in menopause.

genomics↗

bioRxiv2021

mDrop-seq: Massively parallel single-cell RNA-seq of Saccharomyces cerevisiae and Candida albicans

Advances in high-throughput single-cell mRNA sequencing (scRNA-seq) have been limited till date by technical challenges like tough cell walls and low RNA quantity that prevented transcriptomic profiling of microbial species at throughput. We present microbial Drop-seq or mDrop-seq, a high-throughput scRNA-seq technique that is used 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-shocked 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 yeast species with thicker cell wall compared to S. cerevisiae. Single cell transcriptomes in 39,705 C. albicans cells was 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. These experiments are among the first high throughput single cell RNA-seq on different yeast species and demonstrate mDrop-seq as an affordable, easily implementable, and scalable technique that can quantify the variability in gene expression in different yeast species. We hope that mDrop-seq will lead to better understanding of genetic variation in yeasts in response to stimuli and find immediate applications in investigating drug resistance and infection outcome.

genomics↗

bioRxiv2020

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

Epithelial ovarian cancer is a highly heterogenous, metastatic and lethal disease. The presence of CD8+T cells within ovarian tumors is positively associated with overall patient survival. Determining if a patient has T cells that respond to immunotherapies, their characteristics and how they can be manipulated to target cancer cells is an area of intense investigation in cancer therapy. This study determines the cellular composition and the transcriptional state of immune cells in metastatic ovarian cancer samples from patients using single cell RNA sequencing (scRNA-seq). Hierarchical clustering stratified our patient cohort into 2 main groups: 1) a high T cell infiltration (high Tinf) group and 2) a low T cell infiltration (low Tinf) group. To assess the immune response in these patient samples, we performed an unsupervised clustering of the T cell population in each group. The T cell population clustered into 4 and 3 subpopulations in the high Tinf T cell and low Tinf respectively. A granulysin expressing T cell cluster was identified and unique to the High Tinf group. Interestingly, although both groups had resident memory CD8+T (CD8+Trm) cells, only the CD8+Trm cells in the high Tinf group expressed TOX, a recently described transcription factor. TOX confers longevity to T cells within immunosuppressive environment such as cancer. Interestingly, along with TOX+ T cells, we found a unique plasmablast cluster and an IRF8+ macrophage cluster unique to the high Tinf group. Our comprehensive scRNA-seq study provides important insights in elucidating the immune response in ovarian cancer patients.

cancer biology↗

bioRxiv2019

A Hedgehog-FGF signaling axis patterns anterior mesoderm during gastrulation

The application of single cell technologies to early development holds promise for resolving complex developmental phenotypes. Here we define a novel role for Hedgehog (Hh) signaling for the formation of anterior mesoderm lineages during gastrulation. Single-cell transcriptome analysis of Hh-deficient mesoderm revealed selective deficits in anterior mesoderm populations that later translate to physical defects to anterior embryonic structures including the first pharyngeal arch, heart, and anterior somites. We found that Hh-dependent anterior mesoderm defects were cell non-autonomous to Hh-signal reception. Transcriptional profiling of Hh-deficient mesoderm during gastrulation revealed disruptions to both transcriptional patterning of the mesoderm and a key FGF signaling pathway for mesoderm migration. FGF4 protein application was able to restore cellular migration during gastrulation that was decreased by Hh pathway antagonism. These findings implicate that primitive streak-mediated regulation of anterior mesoderm patterning is controlled by a multicomponent signaling hierarchy activated by Hh signaling and executed by FGF signal transduction.\n\nSIGNIFICANCE STATEMENTHow signaling events during gastrulation pattern the mesoderm is a fascinating developmental process. Although Hedgehog signaling has been implicated in early mesoderm development, its mechanistic role has not been described. We applied single cell sequencing to describe mesodermal defects in Hedgehog pathway mutants--revealing selective defects in anterior mesoderm populations. Transcriptional profiling of gastrulating Hedgehog mutants indicated that several pathways essential for primitive streak function, including FGF, required Hh signaling. Blocking Hedgehog signaling abrogated cell migration during gastrulation, which could be mitigated by addition of FGF4 ligand. This work uncovers a novel Hedgehog to FGF signaling event and describes a unique mechanism by which signals from the node impact to anterior mesoderm formation through the modulation of primitive streak function.

developmental biology↗

bioRxiv2019

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. We compared the detected cell types from primary tissue with iPSC-derived cardiomyocytes profiled with DroNc-seq. 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.

genomics↗