scholarly journals Transcriptional landscape of highly lignified poplar stems at single-cell resolution

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yang Chen ◽  
Shaofei Tong ◽  
Yuanzhong Jiang ◽  
Fandi Ai ◽  
Yanlin Feng ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Networks ◽  
Vascular Development ◽  
Secondary Growth ◽  
Cell Types ◽  
Protoplast Isolation ◽  
Marker Genes ◽  
Specific Marker ◽  
Cell Clusters ◽  
Transcriptional Landscape

Abstract Background Plant secondary growth depends on the activity of the vascular cambium, which produces xylem and phloem. Wood derived from xylem is the most abundant form of biomass globally and has played key socio-economic and subsistence roles throughout human history. However, despite intensive study of vascular development, the full diversity of cell types and the gene networks engaged are still poorly understood. Results Here, we have applied an optimized protoplast isolation protocol and RNA sequencing to characterize the high-resolution single-cell transcriptional landscape of highly lignified poplar stems. We identify 20 putative cell clusters with a series of novel cluster-specific marker genes and find that these cells are highly heterogeneous based on the transcriptome. Analysis of these marker genes’ expression dynamics enables reconstruction of the cell differentiation trajectories involved in phloem and xylem development. We find that different cell clusters exhibit distinct patterns of phytohormone responses and emphasize the use of our data to predict potential gene redundancy and identify candidate genes related to vascular development in trees. Conclusions These findings establish the transcriptional landscape of major cell types of poplar stems at single-cell resolution and provide a valuable resource for investigating basic principles of vascular cell specification and differentiation in trees.

scholarly journals JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

2020 ◽  
Author(s):  
Mohit Goyal ◽  
Guillermo Serrano ◽  
Ilan Shomorony ◽  
Mikel Hernaez ◽  
Idoia Ochoa
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Rna Seq ◽  
Batch Effects ◽  
Cell Type ◽  
Latent Space ◽  
Cell Type Specific ◽  
Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

scholarly journals Revealing immune responses in the Mycobacterium avium subsp. paratuberculosis-infected THP-1 cells using single cell RNA-sequencing

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254194
Author(s):  
Hong-Tae Park ◽  
Woo Bin Park ◽  
Suji Kim ◽  
Jong-Sung Lim ◽  
Gyoungju Nah ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Single Cell ◽  
Mycobacterium Avium ◽  
Expression Patterns ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Cell Type ◽  
Cytokines And Chemokines

Mycobacterium avium subsp. paratuberculosis (MAP) is a causative agent of Johne’s disease, which is a chronic and debilitating disease in ruminants. MAP is also considered to be a possible cause of Crohn’s disease in humans. However, few studies have focused on the interactions between MAP and human macrophages to elucidate the pathogenesis of Crohn’s disease. We sought to determine the initial responses of human THP-1 cells against MAP infection using single-cell RNA-seq analysis. Clustering analysis showed that THP-1 cells were divided into seven different clusters in response to phorbol-12-myristate-13-acetate (PMA) treatment. The characteristics of each cluster were investigated by identifying cluster-specific marker genes. From the results, we found that classically differentiated cells express CD14, CD36, and TLR2, and that this cell type showed the most active responses against MAP infection. The responses included the expression of proinflammatory cytokines and chemokines such as CCL4, CCL3, IL1B, IL8, and CCL20. In addition, the Mreg cell type, a novel cell type differentiated from THP-1 cells, was discovered. Thus, it is suggested that different cell types arise even when the same cell line is treated under the same conditions. Overall, analyzing gene expression patterns via scRNA-seq classification allows a more detailed observation of the response to infection by each cell type.

scholarly journals Decontamination of ambient RNA in single-cell RNA-seq with DecontX

2019 ◽  
Author(s):  
Shiyi Yang ◽  
Sean E. Corbett ◽  
Yusuke Koga ◽  
Zhe Wang ◽  
W. Evan Johnson ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Marker Genes ◽  
Specific Marker ◽  
Aberrant Expression ◽  
Cell Type Specific ◽  
Hierarchical Bayesian Method ◽  
Assess Quality

ABSTRACTDroplet-based microfluidic devices have become widely used to perform single-cell RNA sequencing (scRNA-seq) and discover novel cellular heterogeneity in complex biological systems. However, ambient RNA present in the cell suspension can be incorporated into these droplets and aberrantly counted along with a cell’s native mRNA. This results in cross-contamination of transcripts between different cell populations and can potentially decrease the precision of downstream analyses. We developed a novel hierarchical Bayesian method called DecontX to estimate and remove contamination in individual cells from scRNA-seq data. DecontX accurately predicted the proportion of contaminated counts in a mixture of mouse and human cells. Decontamination of PBMC datasets removed aberrant expression of cell type specific marker genes from other cell types and improved overall separation of cell clusters. In general, DecontX can be incorporated into scRNA-seq workflows to assess quality of dissociation protocols and improve downstream analyses.

scholarly journals Single cell analysis of the effects of developmental lead (Pb) exposure on the hippocampus

2019 ◽  
Author(s):  
Kelly M. Bakulski ◽  
John F. Dou ◽  
Robert C. Thompson ◽  
Christopher Lee ◽  
Lauren Y. Middleton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Differential Gene Expression ◽  
Cell Types ◽  
Marker Genes ◽  
Cell Type ◽  
Cell Clusters ◽  
Differential Gene ◽  
Pb Exposure ◽  
And Behavior

AbstractBackgroundLead (Pb) exposure is ubiquitous and has permanent developmental effects on childhood intelligence and behavior and adulthood risk of dementia. The hippocampus is a key brain region involved in learning and memory, and its cellular composition is highly heterogeneous. Pb acts on the hippocampus by altering gene expression, but the cell type-specific responses are unknown.ObjectiveExamine the effects of perinatal Pb treatment on adult hippocampus gene expression, at the level of individual cells, in mice.MethodsIn mice perinatally exposed to control water (n=4) or a human physiologically-relevant level (32 ppm in maternal drinking water) of Pb (n=4), two weeks prior to mating through weaning, we tested for gene expression and cellular differences in the hippocampus at 5-months of age. Analysis was performed using single cell RNA-sequencing of 5,258 cells from the hippocampus by 10x Genomics Chromium to 1) test for gene expression differences averaged across all cells by treatment; 2) compare cell cluster composition by treatment; and 3) test for gene expression and pathway differences within cell clusters by treatment.ResultsGene expression patterns revealed 12 cell clusters in the hippocampus, mapping to major expected cell types (e.g. microglia, astrocytes, neurons, oligodendrocytes). Perinatal Pb treatment was associated with 12.4% more oligodendrocytes (P=4.4×10−21) in adult mice. Across all cells, differential gene expression analysis by Pb treatment revealed cluster marker genes. Within cell clusters, differential gene expression with Pb treatment (q<0.05) was observed in endothelial, microglial, pericyte, and astrocyte cells. Pathways up-regulated with Pb treatment were protein folding in microglia (P=3.4×10−9) and stress response in oligodendrocytes (P=3.2×10−5).ConclusionBulk tissue analysis may be confounded by changes in cell type composition and may obscure effects within vulnerable cell types. This study serves as a biological reference for future single cell studies of toxicant or neuronal complications, to ultimately characterize the molecular basis by which Pb influences cognition and behavior.

scholarly journals ASAP: A web-based platform for the analysis and interactive visualization of single-cell RNA-seq data

2016 ◽  
Author(s):  
Vincent Gardeux ◽  
Fabrice David ◽  
Adrian Shajkofci ◽  
Petra C Schwalie ◽  
Bart Deplancke
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Complete Analysis ◽  
Marker Genes ◽  
Specific Marker ◽  
Rna Seq ◽  
Web Based ◽  
Wide Range

AbstractMotivationSingle-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of thousands of individual cells, enabling the molecular exploration of tissues at the cellular level. Such analytical capacity is of great interest to many research groups in the world, yet, these groups often lack the expertise to handle complex scRNA-seq data sets.ResultsWe developed a fully integrated, web-based platform aimed at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering, and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. This Automated Single-cell Analysis Pipeline (ASAP) combines a wide range of commonly used algorithms with sophisticated visualization tools. Compared with existing scRNA-seq analysis platforms, researchers (including those lacking computational expertise) are able to interact with the data in a straightforward fashion and in real time. Furthermore, given the overlap between scRNA-seq and bulk RNA-seq analysis workflows, ASAP should conceptually be broadly applicable to any RNA-seq dataset. As a validation, we demonstrate how we can use ASAP to simply reproduce the results from a single-cell study of 91 mouse cells involving five distinct cell types.AvailabilityThe tool is freely available at http://[email protected]

scholarly journals Single-Cell Analysis of the Gene Expression Effects of Developmental Lead (Pb) Exposure on the Mouse Hippocampus

2020 ◽  
Vol 176 (2) ◽  
pp. 396-409
Author(s):  
Kelly M Bakulski ◽  
John F Dou ◽  
Robert C Thompson ◽  
Christopher Lee ◽  
Lauren Y Middleton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Expression Patterns ◽  
Cell Types ◽  
Cell Cluster ◽  
Marker Genes ◽  
Cell Type ◽  
Cell Clusters ◽  
Pb Exposure

Abstract Lead (Pb) exposure is ubiquitous with permanent neurodevelopmental effects. The hippocampus brain region is involved in learning and memory with heterogeneous cellular composition. The hippocampus cell type-specific responses to Pb are unknown. The objective of this study is to examine perinatal Pb treatment effects on adult hippocampus gene expression, at the level of individual cells. In mice perinatally exposed to control water or a human physiologically relevant level (32 ppm in maternal drinking water) of Pb, 2 weeks prior to mating through weaning, we tested for hippocampus gene expression and cellular differences at 5 months of age. We sequenced RNA from 5258 hippocampal cells to (1) test for treatment gene expression differences averaged across all cells, (2) compare cell cluster composition by treatment, and (3) test for treatment gene expression and pathway differences within cell clusters. Gene expression patterns revealed 12 hippocampus cell clusters, mapping to major expected cell types (eg, microglia, astrocytes, neurons, and oligodendrocytes). Perinatal Pb treatment was associated with 12.4% more oligodendrocytes (p = 4.4 × 10−21) in adult mice. Across all cells, Pb treatment was associated with expression of cell cluster marker genes. Within cell clusters, Pb treatment (q &lt; 0.05) caused differential gene expression in endothelial, microglial, pericyte, and astrocyte cells. Pb treatment upregulated protein folding pathways in microglia (p = 3.4 × 10−9) and stress response in oligodendrocytes (p = 3.2 × 10−5). Bulk tissue analysis may be influenced by changes in cell type composition, obscuring effects within vulnerable cell types. This study serves as a biological reference for future single-cell toxicant studies, to ultimately characterize molecular effects on cognition and behavior.

scholarly journals scClustViz – Single-cell RNAseq cluster assessment and visualization

F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 1522 ◽  
Author(s):  
Brendan T. Innes ◽  
Gary D. Bader
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Clustering Algorithms ◽  
Expression Patterns ◽  
Software Tool ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Cell Type ◽  
Single Experiment

Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data. The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types. scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.

scholarly journals Acoel single-cell transcriptomics: cell type analysis of a deep branching bilaterian

2020 ◽  
Author(s):  
Jules Duruz ◽  
Cyrielle Kaltenrieder ◽  
Peter Ladurner ◽  
Rémy Bruggmann ◽  
Pedro Martìnez ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Whole Body ◽  
Marker Genes ◽  
Specific Marker ◽  
Cell Type ◽  
Distinct Cell ◽  
Organ Systems ◽  
Major Cell Type ◽  
Different Cell Types

Abstract Bilaterian animals display a wide variety of cell types, organized into defined anatomical structures and organ systems, which are mostly absent in pre-bilaterian animals. Xenacoelomorpha are an early-branching bilaterian phylum displaying an apparently relatively simple anatomical organization that have greatly diverged from other bilaterian clades. In this study, we use whole-body single-cell transcriptomics on the acoel Isodiametra pulchra to identify and characterize different cell types. Our analysis identifies the existence of ten major cell type categories in acoels all contributing to main biological functions of the organism: metabolism, locomotion and movements, behavior, defense and development. Interestingly, while most cell clusters express core fate markers shared with other animal clades, we also describe a surprisingly large number of clade-specific marker genes, suggesting the emergence of clade-specific common molecular machineries functioning in distinct cell types. Together, these results provide novel insight into the evolution of bilaterian cell types and open the door to a better understanding of the origins of the bilaterian body plan and their constitutive cell types.

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 scClustViz – Single-cell RNAseq cluster assessment and visualization

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1522 ◽  
Author(s):  
Brendan T. Innes ◽  
Gary D. Bader
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Clustering Algorithms ◽  
Expression Patterns ◽  
Software Tool ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Cell Type ◽  
Single Experiment

Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data. The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types. scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.

Sign in / Sign up

Export Citation Format

Share Document