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 Cell-type-specific analysis of alternative polyadenylation using single-cell transcriptomics data

2019 ◽  
Vol 47 (19) ◽  
pp. 10027-10039 ◽  
Author(s):  
Eldad David Shulman ◽  
Ran Elkon
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
Alternative Polyadenylation ◽  
Cell Types ◽  
Protein Coding ◽  
Multiple Datasets ◽  
Transcriptomics Data ◽  
Cell Type Specific ◽  
Transcriptional Gene Regulation ◽  
Different Cell Types

AbstractAlternative polyadenylation (APA) is emerging as an important layer of gene regulation because the majority of mammalian protein-coding genes contain multiple polyadenylation (pA) sites in their 3′ UTR. By alteration of 3′ UTR length, APA can considerably affect post-transcriptional gene regulation. Yet, our understanding of APA remains rudimentary. Novel single-cell RNA sequencing (scRNA-seq) techniques allow molecular characterization of different cell types to an unprecedented degree. Notably, the most popular scRNA-seq protocols specifically sequence the 3′ end of transcripts. Building on this property, we implemented a method for analysing patterns of APA regulation from such data. Analyzing multiple datasets from diverse tissues, we identified widespread modulation of APA in different cell types resulting in global 3′ UTR shortening/lengthening and enhanced cleavage at intronic pA sites. Our results provide a proof-of-concept demonstration that the huge volume of scRNA-seq data that accumulates in the public domain offers a unique resource for the exploration of APA based on a very broad collection of cell types and biological conditions.

scholarly journals Comprehensive benchmarking of computational deconvolution of transcriptomics data

2020 ◽  
Author(s):  
Francisco Avila Cobos ◽  
José Alquicira-Hernandez ◽  
Joseph Powell ◽  
Pieter Mestdagh ◽  
Katleen De Preter
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cell Type ◽  
Factors Affecting ◽  
Marker Selection ◽  
Cell Type Composition ◽  
Type Composition ◽  
Comparable Performance ◽  
Transcriptomics Data ◽  
Combined Impact

AbstractMany computational methods to infer cell type proportions from bulk transcriptomics data have been developed. Attempts comparing these methods revealed that the choice of reference marker signatures is far more important than the method itself. However, a thorough evaluation of the combined impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the results is still lacking.Using different single-cell RNA-sequencing (scRNA-seq) datasets, we generated hundreds of pseudo-bulk mixtures to evaluate the combined impact of these factors on the deconvolution results. Along with methods to perform deconvolution of bulk RNA-seq data we also included five methods specifically designed to infer the cell type composition of bulk data using scRNA-seq data as reference.Both bulk and single-cell deconvolution methods perform best when applied to data in linear scale and the choice of normalization can have a dramatic impact on the performance of some, but not all methods. Overall, single-cell methods have comparable performance to the best performing bulk methods and bulk methods based on semi-supervised approaches showed higher error and lower correlation values between the computed and the expected proportions. Moreover, failure to include cell types in the reference that are present in a mixture always led to substantially worse results, regardless of any of the previous choices. Taken together, we provide a thorough evaluation of the combined impact of the different factors affecting the computational deconvolution task across different datasets and propose general guidelines to maximize its performance.

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.

scholarly journals Spatial mapping of cell types by integration of transcriptomics data

2019 ◽  
Author(s):  
Alma Andersson ◽  
Joseph Bergenstråhle ◽  
Michaela Asp ◽  
Ludvig Bergenstråhle ◽  
Aleksandra Jurek ◽  
...  
Keyword(s):  
Single Cell ◽  
Probabilistic Method ◽  
Cell Types ◽  
Spatial Mapping ◽  
Transcriptional Expression ◽  
Cell Type ◽  
Single Cell Rna Sequencing ◽  
Different Types ◽  
Transcriptomics Data ◽  
Spatial Cell

Spatial transcriptomics and single cell RNA-sequencing offer complementary insights into the transcriptional expression landscape. We here present a probabilistic method that integrates data from both techniques, leveraging their respective strengths in such a way that we are able to spatially map cell types to a tissue. The method is applied to several different types of tissue where the spatial cell type topographies are successfully delineated.

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 Single-cell RNA profiling links ncRNAs to spatiotemporal gene expression during C. elegans embryogenesis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yan Sun ◽  
Qichao Yu ◽  
Lei Li ◽  
Zhanlong Mei ◽  
Biaofeng Zhou ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Specific Protein ◽  
Embryonic Cells ◽  
Cell Type ◽  
Gene Expressions ◽  
Protein Coding ◽  
C Elegans ◽  
Protein Coding Genes ◽  
Cell Type Specific

Abstract Recent studies show that non-coding RNAs (ncRNAs) can regulate the expression of protein-coding genes and play important roles in mammalian development. Previous studies have revealed that during C. elegans (Caenorhabditis elegans) embryo development, numerous genes in each cell are spatiotemporally regulated, causing the cell to differentiate into distinct cell types and tissues. We ask whether ncRNAs participate in the spatiotemporal regulation of genes in different types of cells and tissues during the embryogenesis of C. elegans. Here, by using marker-free full-length high-depth single-cell RNA sequencing (scRNA-seq) technique, we sequence the whole transcriptomes from 1031 embryonic cells of C. elegans and detect 20,431 protein-coding genes, including 22 cell-type-specific protein-coding markers, and 9843 ncRNAs including 11 cell-type-specific ncRNA markers. We induce a ncRNAs-based clustering strategy as a complementary strategy to the protein-coding gene-based clustering strategy for single-cell classification. We identify 94 ncRNAs that have never been reported to regulate gene expressions, are co-expressed with 1208 protein-coding genes in cell type specific and/or embryo time specific manners. Our findings suggest that these ncRNAs could potentially influence the spatiotemporal expression of the corresponding genes during the embryogenesis of C. elegans.

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 literature-derived knowledge graph augments the interpretation of single cell RNA-seq datasets

2021 ◽  
Author(s):  
Deeksha Doddahonnaiah ◽  
Patrick Lenehan ◽  
Travis Hughes ◽  
David Zemmour ◽  
Enrique Garcia-Rivera ◽  
...  
Keyword(s):  
Single Cell ◽  
Language Processing ◽  
Retinal Pigment ◽  
Cell Types ◽  
Retinal Pigment Epithelial Cells ◽  
Knowledge Graph ◽  
Cell Type ◽  
Protein Coding ◽  
Cellular Identity ◽  
Complementary Set

Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have rapidly advanced in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type terms across over 26 million biomedical documents. The resultant gene-cell type associations (GCAs) are significantly stronger between a curated set of matched cell type-marker pairs than the complementary set of mismatched pairs (Mann Whitney p < 6.15x10-76, r = 0.24; Cohens D = 2.6). Building on this, we developed an augmented annotation algorithm that leverages GCAs to categorize cell clusters identified in scRNA-seq datasets, and we tested its ability to predict the cellular identity of 185 clusters in 13 datasets from human blood, pancreas, lung, liver, kidney, retina, and placenta. With the optimized settings, the true cellular identity matched the top prediction in 66% of tested clusters and was present among the top five predictions for 94% of clusters. Further, contextualization of differential expression analyses with these GCAs highlights poorly characterized markers of established cell types, such as CLIC6 and DNASE1L3 in retinal pigment epithelial cells and endothelial cells, respectively. Taken together, this study illustrates for the first time how the systematic application of a literature derived knowledge graph can expedite and enhance the annotation and interpretation of scRNA-seq data.

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.

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