Single-cell RNA sequencing analysis of mouse follicular somatic cells

2021 ◽  
Author(s):  
Sen Li ◽  
Lei-Ning Chen ◽  
Hai-Jing Zhu ◽  
Xie Feng ◽  
Feng-Yun Xie ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Somatic Cells ◽  
Metabolic Reprogramming ◽  
Follicle Development ◽  
Sequencing Analysis ◽  
Cell Clusters ◽  
New Genes ◽  
Variable Genes ◽  
Cell Cell

Abstract Within the development of ovarian follicle, in addition to cell proliferation and differentiation, sophisticated cell–cell cross talks are established among follicular somatic cells such as granulosa cells (GCs) and theca cells. To systematically reveal the cell differentiation and signal transductions in follicular somatic cells, we collected the mouse follicular somatic cells from secondary to ovulatory stage, and analyzed the single cell transcriptomes. Having data filtered and screened, we found 6883 high variable genes in 4888 single cells. Then follicular somatic cells were clustered into 26 cell clusters, including 18 GC clusters, 4 theca endocrine cell (TEC) clusters, and 4 other somatic cell clusters, which include immune cells and Acta2 positive theca externa cells. From our data, we found there was metabolic reprogramming happened during GC differentiation. We also found both Cyp19a1 and Cyp11a1 could be expressed in TECs. We analyzed the expression patterns of genes associated with cell–cell interactions such as steroid hormone receptor genes, insulin signaling genes, and cytokine/transformation growth factor beta associated genes in all cell clusters. Lastly, we clustered the highly variable genes into 300 gene clusters, which could be used to search new genes involved in follicle development. These transcriptomes of follicular somatic cells provide us potential clues to reveal how mammals regulating follicle development and could help us find targets to improve oocyte quality for women with low fertility.

scholarly journals The Development of Single-Cell Metabolism and Its Role in Studying Cancer Emergent Properties

2022 ◽  
Vol 11 ◽  
Author(s):  
Dingju Wei ◽  
Meng Xu ◽  
Zhihua Wang ◽  
Jingjing Tong
Keyword(s):  
Tumor Cell ◽  
Single Cell ◽  
Immune Escape ◽  
Cell Metabolism ◽  
Single Cells ◽  
Metabolic Reprogramming ◽  
Biological Information ◽  
Emergent Properties ◽  
Anti Cancer ◽  
Cell Metabolomics

Metabolic reprogramming is one of the hallmarks of malignant tumors, which provides energy and material basis for tumor rapid proliferation, immune escape, as well as extensive invasion and metastasis. Blocking the energy and material supply of tumor cells is one of the strategies to treat tumor, however tumor cell metabolic heterogeneity prevents metabolic-based anti-cancer treatment. Therefore, searching for the key metabolic factors that regulate cell cancerous change and tumor recurrence has become a major challenge. Emerging technology––single-cell metabolomics is different from the traditional metabolomics that obtains average information of a group of cells. Single-cell metabolomics identifies the metabolites of single cells in different states by mass spectrometry, and captures the molecular biological information of the energy and substances synthesized in single cells, which provides more detailed information for tumor treatment metabolic target screening. This review will combine the current research status of tumor cell metabolism with the advantages of single-cell metabolomics technology, and explore the role of single-cell sequencing technology in searching key factors regulating tumor metabolism. The addition of single-cell technology will accelerate the development of metabolism-based anti-cancer strategies, which may greatly improve the prognostic survival rate of cancer patients.

Micropillar array embedded system for single cell encapsulation in hydrogel

2015 ◽  
Vol 1724 ◽  
Author(s):  
Kyun Joo Park ◽  
Kyoung G. Lee ◽  
Seunghwan Seok ◽  
Bong Gill Choi ◽  
Seok Jae Lee ◽  
...  
Keyword(s):  
Embedded System ◽  
Single Cell ◽  
Single Cells ◽  
Cell Encapsulation ◽  
Polyethylene Glycol Diacrylate ◽  
Glycol Diacrylate ◽  
Practical Applications ◽  
Cell Clusters ◽  
Single Cell Encapsulation ◽  
Micropillar Array

ABSTRACTA cylindrical-shaped micropillar array embedded microfluidic device was proposed to enhance the dispersion of cell clusters and the efficiency of single cell encapsulation in hydrogel. Different sizes of micropillar arrays act as a sieve to break Escherichia coli (E. coli) aggregates into single cells in polyethylene glycol diacrylate (PEGDA) solution. We applied the external force for the continuous breakup of cell clusters, resulting in the production of more than 70% of single cells into individual hydrogel particles. This proposed strategy and device will be a useful platform to utilize genetically modified microorganisms in practical applications.

scholarly journals ID: 1027 High-Throughput, Single Cell Whole Transcriptome Sequencing Analysis of Cancer Cells with the New BD FACSMelody™ Cell Sorter and BD™ Precise assay

2017 ◽  
Vol 4 (S) ◽  
pp. 102
Author(s):  
Xiaoyang (Alice) Wang ◽  
Chip Lomas ◽  
Craig Betts ◽  
Aaron Walker ◽  
Christina Fan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cancer Cells ◽  
Cell Line ◽  
Selection Criteria ◽  
Single Cells ◽  
Principal Component ◽  
Jurkat Cells ◽  
Sequencing Analysis ◽  
Whole Transcriptome

Gene expression studies performed on bulk samples might obscure the understanding of complex samples. Gene expression analyses performed on single cells, however, can offer a powerful method to resolve sample heterogeneity and reveal hidden biology. Optimal sample preparation is critical to obtain high quality gene expression data from single cells.Historically, single cells or small numbers of cells were isolated and prepared by limiting dilutions, laser capture microdissection, or microfluidics technologies, or fluorescence-activated cell sorting (FACS). FACS sorting enables highthroughput processing of a heterogeneous mixture of cells and ensures the delivery of single cells or a small number ofcells into a chosen receptacle to meet the selection criteria at a purity level that is unmatched by other approaches.Furthermore, by FACS, the single cell selection criteria can be based on surface marker expression, cell size, and granularity(represented by scatter). Sorted cells can be used for any downstream application including next generation sequencing(NGS).In this study, the new, easy-to-use BD FACSMelody™ sorter was applied to sort individual cancer cells. Jurkat cells (a Tleukemia cell line), and T47D cells (a breast cancer cell line) were mixed, stained, analyzed, and sorted on a BD FACSMelody system. The individual cell’s whole transcriptome was interrogated using BD™ Precise Single Cell WTA (whole transcriptome amplification) Assay. Principal component analysis was applied to cluster the sorted Jurkat and T47D-cell populations.

scholarly journals mDia2 formin selectively interacts with catenins and not E-cadherin to regulate Adherens Junction formation

2019 ◽  
Author(s):  
Yuqi Zhang ◽  
Krista M. Pettee ◽  
Kathryn N. Becker ◽  
Kathryn M. Eisenmann
Keyword(s):  
Single Cell ◽  
Actin Polymerization ◽  
Plasma Membranes ◽  
Adherens Junctions ◽  
Single Cells ◽  
Critical Role ◽  
Cell Junctions ◽  
Hek 293 ◽  
E Cadherin ◽  
Cell Cell

AbstractBackgroundEpithelial ovarian cancer (EOC) cells disseminate within the peritoneal cavity, in part, via the peritoneal fluid as single cells, clusters, or spheroids. Initial single cell egress from a tumor can involve disruption of cell-cell adhesions as cells are shed from the primary tumor into the peritoneum. In epithelial cells, Adherens Junctions (AJs) are characterized by homotypic linkage of E-cadherins on the plasma membranes of adjacent cells. AJs are anchored to the intracellular actin cytoskeletal network through a complex involving E-cadherin, p120 catenin, β-catenin, and αE-catenin. However, the specific players involved in the interaction between the junctional E-cadherin complex and the underlying F-actin network remains unclear. Recent evidence indicates that mammalian Diaphanous-related (mDia) formins plays a key role in epithelial cell AJ formation and maintenance through generation of linear actin filaments. Binding of αE-catenin to linear F-actin inhibits association of the branched-actin nucleator Arp2/3, while favoring linear F-actin bundling. We previously demonstrated that loss of mDia2 was associated with invasive single cell egress from EOC spheroids through disruption of junctional F-actin.ResultsIn the current study, we now show that mDia2 has a role at adherens junctions (AJs) in EOC OVCA429 cells and human embryonic kidney (HEK) 293 cells through its association with αE-catenin and β-catenin. mDia2 depletion in EOC cells leads to reduction in actin polymerization and disruption of cell-cell junctions with decreased interaction between β-catenin and E-cadherin.ConclusionsOur results support a necessary role for mDia2 in AJ stability in EOC cell monolayers and indicate a critical role for mDia formins in regulating EOC AJs during invasive transitions.

scholarly journals Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data

2020 ◽  
Author(s):  
Junpeng Zhang ◽  
Lin Liu ◽  
Taosheng Xu ◽  
Wu Zhang ◽  
Chunwen Zhao ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Single Cells ◽  
Small Scale ◽  
Mirna Regulation ◽  
Sequencing Data ◽  
Resolution Level ◽  
Novel Strategy ◽  
Cell Cell

AbstractBackgroundExisting computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation.ResultsIn this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to use single-cell miRNA-mRNA co-sequencing data to identify miRNA regulatory networks at the resolution of individual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks to understand miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. Finally, through exploring cell-cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells to help understand cell-cell crosstalk.ConclusionsTo the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.

PS02.241: SINGLE CELL RNA SEQUENCING REVEALS THE HETEROGENEITY OF NON-CANCER CELL POPULATIONS WITHIN THE ECOSYSTEM OF ESOPHAGEAL ADENOCARCINOMA

2018 ◽  
Vol 31 (Supplement_1) ◽  
pp. 190-190
Author(s):  
Robert Walker ◽  
Maria Secrier ◽  
Jack Harrington ◽  
Rachel Parker ◽  
Jamie Kelly ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Esophageal Adenocarcinoma ◽  
Conflicts Of Interest ◽  
Single Cells ◽  
Micro Particles ◽  
Early Results ◽  
Single Cell Rna Sequencing ◽  
Variable Genes

Abstract Background Esophageal adenocarcinoma (EAC) develops in a complex ecosystem that defines tumour evolution, response to treatment and patient outcomes. We have shown that high levels of cancer associated fibroblast (CAF) in the tumour microenvironment (TME) predicts poor outcome and is inversely related to tumour infiltrating lymphocyte (TILs) abundance. Bulk sequencing studies lack the resolution to dissect the phenotypic and functional heterogeneity and cell-cell interactions of the TME. We have applied single cell RNA sequencing to 12 EAC patients to address this challenge. Methods A total of 24 single-cell suspensions were prepared from resected specimens and paired normal tissue. Single cells and barcoded mRNA-binding micro-particles were combined in droplets containing cell lysis buffer using a custom-built microfluidic platform. Captured mRNA with a cell barcode and unique molecular identifier was reverse transcribed, amplified and sequenced. SEURAT (v2.1, R-package) was used to identify highly-variable genes and perform cell clustering. Results Analysis of 6859 of the highest quality cells using the 4167 most variable genes revealed 46 clusters which were divided into 12 broad populations. Antigen Presenting Cells (n = 189), B Cells (n = 265), Cancer Cells (n = 1449), Endothelial Cells (321), Fibroblasts (1690), Mast Cells (n = 184), Monocytes/Macrophages (n = 254), Plasma Cells (n = 208), Smooth Muscle, (n = 115), Squamous Epithelium (n = 751), T Cells (n = 1433). Analysis of publicly available bulk RNAseq datasets (TCGA) of EAC showed that tumours that were ‘hot’ for a CAF gene signature were ‘cold’ for a T-Cell signature. Subset analysis of the fibroblasts from tumour samples that were enriched for the same CAF signature revealed 3 subtly different clusters. One of these sub-populations differentially expressed genes associated with the Gene Ontology terms GO:00,40011 (locomotion) GO:0,006928 (movement of cell or subcellular component) and GO:00,48870 (cell motility). The two tumours with the highest ratio of this type of CAF to T-Cells were both found to have distant metastasis at resection. Conclusion EAC CAFs are a heterogeneous population with distinct biological functions which may have different implications for prognosis. These early results suggest that we are able to identify candidate biological processes that may describe the mechanisms through which CAFs and TILs influence outcome. Disclosure All authors have declared no conflicts of interest.

dMSCC: a microfluidic platform for microbial single-cell cultivation of Corynebacterium glutamicum under dynamic environmental medium conditions

Lab on a Chip ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 4442-4455
Author(s):  
Sarah Täuber ◽  
Corinna Golze ◽  
Phuong Ho ◽  
Eric von Lieres ◽  
Alexander Grünberger
Keyword(s):  
Single Cell ◽  
Corynebacterium Glutamicum ◽  
Environmental Conditions ◽  
Single Cells ◽  
Dynamic Environment ◽  
Microfluidic System ◽  
Small Cell ◽  
Cell Cultivation ◽  
Microbial Cells ◽  
Cell Clusters

Microbial cells are often exposed to rapidly fluctuating environmental conditions. A novel microfluidic system for the cultivation of single cells and small cell clusters is presented under dynamic environment conditions.

scholarly journals Dissecting the initiation of female meiosis in the mouse at single-cell resolution

2019 ◽  
Author(s):  
Wei Ge ◽  
Jun-Jie Wang ◽  
Rui-Qian Zhang ◽  
Shao-Jing Tan ◽  
Fa-Li Zhang ◽  
...  
Keyword(s):  
Germ Cell ◽  
Single Cell ◽  
Germ Cells ◽  
Molecular Mechanisms ◽  
Gene Expression Pattern ◽  
Cell Populations ◽  
Molecular Pathways ◽  
Cell Clusters ◽  
Prophase I ◽  
New Genes

ABSTRACTGerm cell meiosis is one of the most finely orchestrated events during gametogenesis with distinct developmental patterns in males and females. However, in mammals, the molecular mechanisms involved in this process remain not well known. Here, we report detailed transcriptome analyses of cell populations present in the mouse female gonadal ridges (E11.5) and the embryonic ovaries from E12.5 to E14.5 using single cell RNA sequencing (scRNA seq). These periods correspond with the initiation and progression of meiosis throughout the first stage of prophase I. We identified 13 transcriptionally distinct cell populations and 7 transcriptionally distinct germ cell subclusters that correspond to mitotic (3 clusters) and meiotic (4 clusters) germ cells. By comparing the signature gene expression pattern of 4 meiotic germ cell clusters, we found that the 4 cell clusters correspond to different cell status en route to meiosis progression, and therefore, our research here characterized detailed transcriptome dynamics during meiotic prophase I. Reconstructing the progression of meiosis along pseudotime, we identified several new genes and molecular pathways with potential critical roles in the mitosis/meiosis transition and early meiotic progression. Last, the heterogeneity within somatic cell populations was also discussed and different cellular states were identified. Our scRNA seq analysis here represents a new important resource for deciphering the molecular pathways driving meiosis initiation and progression in female germ cells and ovarian somatic cells.

scholarly journals Comparison of Principal Component Analysis and t-Stochastic Neighbor Embedding with Distance Metric Modifications for Single-cell RNA-sequencing Data Analysis

2017 ◽  
Author(s):  
Haejoon (Ellen) Kwon ◽  
Jean Fan ◽  
Peter Kharchenko
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Component Analysis ◽  
Alternative Methods ◽  
Rna Seq ◽  
Distance Metric ◽  
Single Cell Rna Sequencing ◽  
Reliable Classification ◽  
Variable Genes

AbstractRecent developments in technological tools such as next generation sequencing along with peaking interest in the study of single cells has enabled single-cell RNA-sequencing, in which whole transcriptomes are analyzed on a single-cell level. Studies, however, have been hindered by the ability to effectively analyze these single cell RNA-seq datasets, due to the high-dimensional nature and intrinsic noise in the data. While many techniques have been introduced to reduce dimensionality of such data for visualization and subpopulation identification, the utility to identify new cellular subtypes in a reliable and robust manner remains unclear. Here, we compare dimensionality reduction visualization methods including principle component analysis and t-stochastic neighbor embedding along with various distance metric modifications to visualize single-cell RNA-seq datasets, and assess their performance in identifying known cellular subtypes. Our results suggest that selecting variable genes prior to analysis on single-cell RNA-seq data is vital to yield reliable classification, and that when variable genes are used, the choice of distance metric modification does not particularly influence the quality of classification. Still, in order to take advantage of all the gene expression information, alternative methods must be used for a reliable classification.

Sign in / Sign up

Export Citation Format

Share Document