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 Mapping single-cell atlases throughout Metazoa unravels cell type evolution

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Alexander J Tarashansky ◽  
Jacob M Musser ◽  
Margarita Khariton ◽  
Pengyang Li ◽  
Detlev Arendt ◽  
...  
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Cell Types ◽  
The Self ◽  
Cell Type ◽  
Germ Layers ◽  
Animal Evolution ◽  
Self Assembling ◽  
Animal Phyla ◽  
Cell Data

Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases. Yet, comparison between distant relatives is hindered by complex gene histories and diversifications in expression programs. Previously, we introduced the self-assembling manifold (SAM) algorithm to robustly reconstruct manifolds from single-cell data (Tarashansky et al., 2019). Here, we build on SAM to map cell atlas manifolds across species. This new method, SAMap, identifies homologous cell types with shared expression programs across distant species within phyla, even in complex examples where homologous tissues emerge from distinct germ layers. SAMap also finds many genes with more similar expression to their paralogs than their orthologs, suggesting paralog substitution may be more common in evolution than previously appreciated. Lastly, comparing species across animal phyla, spanning mouse to sponge, reveals ancient contractile and stem cell families, which may have arisen early in animal evolution.

scholarly journals A single cell atlas reveals unanticipated cell type complexity in Drosophila ovaries

2021 ◽  
Author(s):  
Maija Slaidina ◽  
Selena Gupta ◽  
Ruth Lehmann
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Spatial Organization ◽  
Follicle Cell ◽  
Cell Types ◽  
Cell Type ◽  
Organ Function ◽  
Relative Paucity ◽  
Germ Cell Differentiation ◽  
Cell Type Specific

AbstractOrgan function relies on the spatial organization and functional coordination of numerous cell types. The Drosophila ovary is a widely used model system to study the cellular activities underlying organ function, including stem cell regulation, cell signaling and epithelial morphogenesis. However, the relative paucity of cell type specific reagents hinders investigation of molecular functions at the appropriate cellular resolution.Here, we used single cell RNA sequencing to characterize all cell types of the stem cell compartment and early follicles of the Drosophila ovary. We computed transcriptional signatures and identified specific markers for nine states of germ cell differentiation, and 23 somatic cell types and subtypes. We uncovered an unanticipated diversity of escort cells, the somatic cells that directly interact with differentiating germline cysts. Three escort cell subtypes reside in discrete anatomical positions, and express distinct sets of secreted and transmembrane proteins, suggesting that diverse micro-environments support the progressive differentiation of germ cells. Finally, we identified 17 follicle cell subtypes, and characterized their transcriptional profiles. Altogether, we provide a comprehensive resource of gene expression, cell type specific markers, spatial coordinates and functional predictions for 34 ovarian cell types and subtypes.

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 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 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 atlas of the developing Drosophila ovary identifies follicle stem cell progenitors

2019 ◽  
Author(s):  
Maija Slaidina ◽  
Torsten U. Banisch ◽  
Selena Gupta ◽  
Ruth Lehmann
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Cell Types ◽  
Lineage Tracing ◽  
Cell Type ◽  
Systematic Analysis ◽  
Adult Cell ◽  
Cell Type Specific ◽  
Follicle Stem Cells ◽  
Drosophila Ovary

AbstractAddressing the complexity of organogenesis at a system-wide level requires a complete understanding of adult cell types, their origin and precursor relationships. The Drosophila ovary has been a model to study how coordinated stem cell units, germline and somatic follicle stem cells, maintain and renew an organ. However, lack of cell-type specific tools have limited our ability to study the origin of individual cell types and stem cell units. Here, we use a single cell RNA sequencing approach to uncover all known cell types of the developing ovary, reveal transcriptional signatures, and identify cell type specific markers for lineage tracing. Our study identifies a novel cell type corresponding to the elusive follicle stem cell precursors and predicts sub-types of known cell types. Altogether, we reveal a previously unanticipated complexity of the developing ovary, and provide a comprehensive resource for the systematic analysis of ovary morphogenesis.

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.

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