scholarly journals Decipher the Molecular Response of Plant Single Cell Types to Environmental Stresses

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Mehrnoush Nourbakhsh-Rey ◽  
Marc Libault
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Environmental Stresses ◽  
Molecular Response ◽  
Cell Type ◽  
Abiotic And Biotic Stresses ◽  
Biotic Stresses ◽  
Root Hair Cell ◽  
Accurate Picture ◽  
Single Cell Type

The analysis of the molecular response of entire plants or organs to environmental stresses suffers from the cellular complexity of the samples used. Specifically, this cellular complexity masks cell-specific responses to environmental stresses and logically leads to the dilution of the molecular changes occurring in each cell type composing the tissue/organ/plant in response to the stress. Therefore, to generate a more accurate picture of these responses, scientists are focusing on plant single cell type approaches. Several cell types are now considered as models such as the pollen, the trichomes, the cotton fiber, various root cell types including the root hair cell, and the guard cell of stomata. Among them, several have been used to characterize plant response to abiotic and biotic stresses. In this review, we are describing the various -omic studies performed on these different plant single cell type models to better understand plant cell response to biotic and abiotic stresses.

scholarly journals Estimating and Correcting for Off-Target Cellular Contamination in Brain Cell Type Specific RNA-Seq Data

2021 ◽  
Vol 14 ◽  
Author(s):  
Jordan Sicherman ◽  
Dwight F. Newton ◽  
Paul Pavlidis ◽  
Etienne Sibille ◽  
Shreejoy J. Tripathy
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Target Cell ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Brain Cell ◽  
Cell Type ◽  
Cell Purification ◽  
Single Cell Type

Transcriptionally profiling minor cellular populations remains an ongoing challenge in molecular genomics. Single-cell RNA sequencing has provided valuable insights into a number of hypotheses, but practical and analytical challenges have limited its widespread adoption. A similar approach, which we term single-cell type RNA sequencing (sctRNA-seq), involves the enrichment and sequencing of a pool of cells, yielding cell type-level resolution transcriptomes. While this approach offers benefits in terms of mRNA sampling from targeted cell types, it is potentially affected by off-target contamination from surrounding cell types. Here, we leveraged single-cell sequencing datasets to apply a computational approach for estimating and controlling the amount of off-target cell type contamination in sctRNA-seq datasets. In datasets obtained using a number of technologies for cell purification, we found that most sctRNA-seq datasets tended to show some amount of off-target mRNA contamination from surrounding cells. However, using covariates for cellular contamination in downstream differential expression analyses increased the quality of our models for differential expression analysis in case/control comparisons and typically resulted in the discovery of more differentially expressed genes. In general, our method provides a flexible approach for detecting and controlling off-target cell type contamination in sctRNA-seq datasets.

The Cytology of the Digestive and Salivary Glands of the Limpet, Patella

1963 ◽  
Vol s3-104 (65) ◽  
pp. 23-37
Author(s):  
D. PUGH
Keyword(s):  
Salivary Gland ◽  
Single Cell ◽  
Salivary Glands ◽  
Cell Types ◽  
The Other ◽  
Cell Type ◽  
Food Material ◽  
Tubular Gland ◽  
Single Cell Type ◽  
Reserve Food

As reported by earlier investigators, the epithelium of the digestive tubules is composed of two cell-types. One type of cell is glandular, the other type is absorptive and digestive, and to a lesser extent secretory. The latter type of cell also contains glycogen and numerous lipid globules, so that the digestive gland as a whole contains a large quantity of reserve food material. The epithelium of the digestive duct possesses a single cell-type; the cells are ciliated and heavily pigmented, and they produce a viscous secretion. The salivary gland is a compound tubular gland. The cells elaborate a secretion containing protein and probably some carbohydrate.

scholarly journals A Holistic Analysis of the Intestinal Stem Cell Niche Network

2019 ◽  
Author(s):  
Darrick M. Hansen ◽  
Paloma Ivon Meneses Giles ◽  
Xi C. He ◽  
Shiyuan Chen ◽  
Ariel Paulson ◽  
...  
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Intestinal Mucosa ◽  
Cell Types ◽  
Intestinal Stem Cell ◽  
Mucosal Cell ◽  
Cell Type ◽  
Regulatory Pathways ◽  
Cell Niche ◽  
Single Cell Type

SummaryAlthough many studies into the intestinal stem cell (ISC) niche have been carried out, they have focused on the role of a single cell type or molecular signal. However, no holistic comparisons of the predominant cell types and signals present within the intestinal mucosa have been conducted to date. We utilize bulk RNA sequencing to profile 20 different mucosal cell types covering four major cell categories: epithelial, stromal, endothelial and immune. We further examined the stromal signaling environment using scRNAseq to provide a more comprehensive view of the signaling microenvironment within the intestinal mucosa. We identified the primary signals for the major ISC regulatory pathways and their respective cellular sources. Our analysis suggests that a ‘niche network’ exists, with no single cell type being responsible for ISC self-renewal, proliferation, or differentiation; rather, each cell type within the network carries out specific functions in a highly cooperative and coordinated manner.

scholarly journals A global overview of single-cell type selectivity and pleiotropy in complex diseases and traits

2020 ◽  
Author(s):  
Chao Xue ◽  
Lin Jiang ◽  
Qihan Long ◽  
Ying Chen ◽  
Xiangyi Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Genetics ◽  
Expression Profiles ◽  
Association Studies ◽  
Cell Types ◽  
Genome Wide Association Studies ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Complex Phenotypes ◽  
Single Cell Type

AbstractAfter centuries of genetic studies, one of the most fundamental questions, i.e. in what cell types do DNA mutations regulate a phenotype, remains unanswered for most complex phenotypes. The current availability of hundreds of genome-wide association studies (GWASs) and single-cell RNA sequencing (scRNA-seq) of millions of cells provides a unique opportunity to address the question. In the present study, we firstly constructed an association landscape between over 20,000 single cell clusters and 997 complex phenotypes by a cross annotation framework with scRNA-seq expression profiles and GWAS summary statistics. We then performed an extensive overview of cell-type specificity and pleiotropy in human phenotypes and found most phenotypes (>90%) were moderately selectively associated with a limited number of cell types while a small fraction cell types (<10%) had strong pleiotropy in multiple phenotypes (~100). Moreover, we identified three cell type-phenotype mutual pleiotropy blocks in the landscape. The application of the single cell type-phenotype cross annotation framework (named SPA) also explained the T cell biased lymphopenia and suggested important supporting genes in severe COVID-19 from human genetics angle. All the cell type-phenotype association results can be queried and visualized at http://pmglab.top/spa.

scholarly journals A single–cell type transcriptomics map of human tissues

2021 ◽  
Vol 7 (31) ◽  
pp. eabh2169
Author(s):  
Max Karlsson ◽  
Cheng Zhang ◽  
Loren Méar ◽  
Wen Zhong ◽  
Andreas Digre ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Protein Profiling ◽  
Human Tissues ◽  
Cell Type ◽  
Protein Coding ◽  
Expression Of Genes ◽  
Transcriptomics Data ◽  
Single Cell Type ◽  
Resolution Single

Advances in molecular profiling have opened up the possibility to map the expression of genes in cells, tissues, and organs in the human body. Here, we combined single-cell transcriptomics analysis with spatial antibody-based protein profiling to create a high-resolution single–cell type map of human tissues. An open access atlas has been launched to allow researchers to explore the expression of human protein-coding genes in 192 individual cell type clusters. An expression specificity classification was performed to determine the number of genes elevated in each cell type, allowing comparisons with bulk transcriptomics data. The analysis highlights distinct expression clusters corresponding to cell types sharing similar functions, both within the same organs and between organs.

scholarly journals Fully-automated cell-type identification with specific markers extracted from single-cell transcriptomic data

2019 ◽  
Author(s):  
Aleksandr Ianevski ◽  
Anil K Giri ◽  
Tero Aittokallio
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Computational Method ◽  
Marker Genes ◽  
Cell Type ◽  
Cell Clusters ◽  
Mouse Tissues ◽  
Single Cell Type ◽  
The Individual ◽  
Selection Of

AbstractSingle-cell transcriptomics enables systematic charting of cellular composition of complex tissues. Identification of cell populations often relies on unsupervised clustering of cells based on the similarity of the scRNA-seq profiles, followed by manual annotation of cell clusters using established marker genes. However, manual selection of marker genes for cell-type annotation is a laborious and error-prone task since the selected markers must be specific both to the individual cell clusters and various cell types. Here, we developed a computational method, termed ScType, which enables data-driven selection of marker genes based solely on given scRNA-seq data. Using a compendium of 7 scRNA-seq datasets from various human and mouse tissues, we demonstrate how ScType enables unbiased, accurate and fully-automated single-cell type annotation by guaranteeing the specificity of marker genes both across cell clusters and cell types. The widely-applicable method is implemented as an interactive web-tool (https://sctype.fimm.fi), connected with comprehensive database of specific markers.

scholarly journals CyAnno: A semi-automated approach for cell type annotation of mass cytometry datasets

2020 ◽  
Author(s):  
Abhinav Kaushik ◽  
Diane Dunham ◽  
Ziyuan He ◽  
Monali Manohar ◽  
Manisha Desai ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Cell Types ◽  
Training Data ◽  
Live Cells ◽  
Cell Type ◽  
Mixed Cell ◽  
Learning Framework ◽  
Single Cell Type ◽  
Rare Cells

AbstractFor immune system monitoring in large-scale studies at the single-cell resolution using CyTOF, (semi-)automated computational methods are applied for annotating live cells of mixed cell types. Here, we show that the live cell pool can be highly enriched with undefined heterogeneous cells, i.e. ‘ungated’ cells, and that current (semi-)automated approaches ignore their modeling resulting in misclassified annotations. Therefore, we introduce ‘CyAnno’, a novel semi-automated approach for deconvoluting the unlabeled cytometry dataset based on a machine learning framework utilizing manually gated training data that allows the integrative modeling of ‘gated’ cell types and the ‘ungated’ cells. By applying this framework on several CyTOF datasets, we demonstrated that including the ‘ungated’ cells can lead to a significant increase in the prediction accuracy of the ‘gated’ cell types. CyAnno can be used to identify even a single cell type, including rare cells, with higher efficacy than current state-of-the-art semi-automated approaches.

scholarly journals Author Correction: iLoF: An intelligent Lab on Fiber Approach for Human Cancer Single-Cell Type Identification

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Joana S. Paiva ◽  
Pedro A. S. Jorge ◽  
Rita S. R. Ribeiro ◽  
Meritxell Balmaña ◽  
Diana Campos ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Cancer ◽  
Cell Type ◽  
Single Cell Type

Differential calcium signalling by m2 and m3 muscarinic acetylcholine receptors in a single cell type

1995 ◽  
Vol 352 (5) ◽  
pp. 469-476 ◽  
Author(s):  
Martina Schmidt ◽  
Christine Bienek ◽  
Chris J. van Koppen ◽  
Martin C. Michel ◽  
Karl H. Jakobs
Keyword(s):  
Single Cell ◽  
Acetylcholine Receptors ◽  
Calcium Signalling ◽  
Muscarinic Acetylcholine Receptors ◽  
Cell Type ◽  
Muscarinic Acetylcholine ◽  
Single Cell Type

scholarly journals Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

2020 ◽  
Author(s):  
Feng Tian ◽  
Fan Zhou ◽  
Xiang Li ◽  
Wenping Ma ◽  
Honggui Wu ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Human Cell ◽  
Expression Profiles ◽  
Single Cells ◽  
Cell Types ◽  
List Type ◽  
Cell Type ◽  
Genomic Architecture ◽  
Gene Modules

SummaryBy circumventing cellular heterogeneity, single cell omics have now been widely utilized for cell typing in human tissues, culminating with the undertaking of human cell atlas aimed at characterizing all human cell types. However, more important are the probing of gene regulatory networks, underlying chromatin architecture and critical transcription factors for each cell type. Here we report the Genomic Architecture of Cells in Tissues (GeACT), a comprehensive genomic data base that collectively address the above needs with the goal of understanding the functional genome in action. GeACT was made possible by our novel single-cell RNA-seq (MALBAC-DT) and ATAC-seq (METATAC) methods of high detectability and precision. We exemplified GeACT by first studying representative organs in human mid-gestation fetus. In particular, correlated gene modules (CGMs) are observed and found to be cell-type-dependent. We linked gene expression profiles to the underlying chromatin states, and found the key transcription factors for representative CGMs.HighlightsGenomic Architecture of Cells in Tissues (GeACT) data for human mid-gestation fetusDetermining correlated gene modules (CGMs) in different cell types by MALBAC-DTMeasuring chromatin open regions in single cells with high detectability by METATACIntegrating transcriptomics and chromatin accessibility to reveal key TFs for a CGM

Sign in / Sign up

Export Citation Format

Share Document