scholarly journals Whole-tissue deconvolution and scRNAseq analysis identify altered endometrial cellular compositions and functionality associated with endometriosis

2021 ◽  
Author(s):  
Daniel Bunis ◽  
Wanxin Wang ◽  
Júlia Vallvé-Juanico ◽  
Sahar Houshdaran ◽  
Sushmita Sen ◽  
...  
Keyword(s):  
Dendritic Cells ◽  
Single Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Cell Type ◽  
Dynamic Changes ◽  
Inflammatory Phenotype ◽  
Full Complement ◽  
Bulk Tissue ◽  
Statistical Metrics

AbstractThe uterine lining (endometrium) exhibits a pro-inflammatory phenotype in women with endometriosis, resulting in pain, infertility, and poor pregnancy outcomes. The full complement of cell types contributing to this phenotype has yet to be identified, as most studies have focused on bulk tissue or select cell populations. Herein, through integrating whole-tissue deconvolution and single cell RNAseq, we comprehensively characterized immune and nonimmune cell types in endometrium of women with or without disease and their dynamic changes across the menstrual cycle. We designed metrics to evaluate specificity of deconvolution signatures that resulted in single cell identification of 13 novel signatures for immune cell subtypes in healthy endometrium. Guided by statistical metrics, we identified contributions of endometrial epithelial, endothelial, plasmacytoid dendritic cells, classical dendritic cells, monocytes, macrophages, and granulocytes to the endometrial pro-inflammatory phenotype, underscoring roles for nonimmune as well as immune cells to the dysfunctionality of this tissue.Teaser SentenceCell type deconvolution and single cell RNAseq analysis identify altered endometrial cellular compositions in women with endometriosis

scholarly journals Whole-Tissue Deconvolution and scRNAseq Analysis Identify Altered Endometrial Cellular Compositions and Functionality Associated With Endometriosis

2022 ◽  
Vol 12 ◽  
Author(s):  
Daniel G. Bunis ◽  
Wanxin Wang ◽  
Júlia Vallvé-Juanico ◽  
Sahar Houshdaran ◽  
Sushmita Sen ◽  
...  
Keyword(s):  
Dendritic Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Dynamic Changes ◽  
Inflammatory Phenotype ◽  
Full Complement ◽  
Bulk Tissue ◽  
Statistical Metrics

The uterine lining (endometrium) exhibits a pro-inflammatory phenotype in women with endometriosis, resulting in pain, infertility, and poor pregnancy outcomes. The full complement of cell types contributing to this phenotype has yet to be identified, as most studies have focused on bulk tissue or select cell populations. Herein, through integrating whole-tissue deconvolution and single-cell RNAseq, we comprehensively characterized immune and nonimmune cell types in the endometrium of women with or without disease and their dynamic changes across the menstrual cycle. We designed metrics to evaluate specificity of deconvolution signatures that resulted in single-cell identification of 13 novel signatures for immune cell subtypes in healthy endometrium. Guided by statistical metrics, we identified contributions of endometrial epithelial, endothelial, plasmacytoid dendritic cells, classical dendritic cells, monocytes, macrophages, and granulocytes to the endometrial pro-inflammatory phenotype, underscoring roles for nonimmune as well as immune cells to the dysfunctionality of this tissue.

scholarly journals Comprehensive multi-omics single-cell data integration reveals greater heterogeneity in the human immune system

2021 ◽  
Author(s):  
Congmin Xu ◽  
Junkai Yang ◽  
Astrid Kosters ◽  
Benjamin R Babcock ◽  
Peng Qiu ◽  
...  
Keyword(s):  
Immune System ◽  
Single Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Cell Receptor ◽  
Surface Proteins ◽  
Cell Type ◽  
Human Immune System ◽  
Receptor Repertoire ◽  
Human Immune

Single-cell transcriptomics enables the definition of diverse human immune cell types across multiple tissue and disease contexts. Still, deeper biological understanding requires comprehensive integration of multiple single-cell omics (transcriptomic, proteomic, and cell receptor repertoire). To improve the identification of diverse cell types and the accuracy of cell-type classification in our multi-omics single-cell datasets, we developed SuPERR-seq, a novel analysis workflow to increase the resolution and accuracy of clustering and allow for the discovery and characterization of previously hidden cell subsets. We show that by incorporating information from cell-surface proteins and immunoglobulin transcript counts, we accurately remove cell doublets and prevent widespread cell-type misclassification. This approach uniquely improves the identification of heterogeneous cell types in the human immune system, including a novel subset of antibody-secreting cells in the bone marrow.

scholarly journals Deciphering the evolution of vertebrate immune cell types with single-cell RNA-seq

2018 ◽  
Author(s):  
Santiago J Carmona ◽  
David Gfeller
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Adaptive Immune System ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Recent Developments ◽  
Species Specific ◽  
Development And Validation ◽  
Whole Transcriptome

Single-cell RNA-seq is revolutionizing our understanding of cell type heterogeneity in many fields of biology, ranging from neuroscience to cancer to immunology. In Immunology, one of the main promises of this approach is the ability to define cell types as clusters in the whole transcriptome space (i.e., without relying on specific surface markers), thereby providing an unbiased classification of immune cell types. So far, this technology has been mainly applied in mouse and human. However, technically it could be used for immune cell-type identification in any species without requiring the development and validation of species-specific antibodies for cell sorting. Here we review recent developments using single-cell RNA-seq to characterize immune cell populations in non-mammalian vertebrates, with a focus on zebrafish (Danio rerio). We advocate that single-cell RNA-seq technology is likely to provide key insights into our understanding of the evolution of the adaptive immune system.

scholarly journals Deciphering the evolution of vertebrate immune cell types with single-cell RNA-seq

2018 ◽  
Author(s):  
Santiago J Carmona ◽  
David Gfeller
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Adaptive Immune System ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Recent Developments ◽  
Species Specific ◽  
Development And Validation ◽  
Whole Transcriptome

Single-cell RNA-seq is revolutionizing our understanding of cell type heterogeneity in many fields of biology, ranging from neuroscience to cancer to immunology. In Immunology, one of the main promises of this approach is the ability to define cell types as clusters in the whole transcriptome space (i.e., without relying on specific surface markers), thereby providing an unbiased classification of immune cell types. So far, this technology has been mainly applied in mouse and human. However, technically it could be used for immune cell-type identification in any species without requiring the development and validation of species-specific antibodies for cell sorting. Here we review recent developments using single-cell RNA-seq to characterize immune cell populations in non-mammalian vertebrates, with a focus on zebrafish (Danio rerio). We advocate that single-cell RNA-seq technology is likely to provide key insights into our understanding of the evolution of the adaptive immune system.

scholarly journals ACTINN: automated identification of cell types in single cell RNA sequencing

2019 ◽  
Author(s):  
Feiyang Ma ◽  
Matteo Pellegrini
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Types ◽  
Mouse Cell ◽  
Supplementary Information ◽  
Cell Type ◽  
Human T Cell ◽  
Single Cell Rna Sequencing

Abstract Motivation Cell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with three hidden layers, trains on datasets with predefined cell types and predicts cell types for other datasets based on the trained parameters. Results We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines. Availability and implementation The codes and datasets are available at https://figshare.com/articles/ACTINN/8967116. Tutorial is available at https://github.com/mafeiyang/ACTINN. All codes are implemented in python. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Cross-tissue immune cell analysis reveals tissue-specific adaptations and clonal architecture across the human body

2021 ◽  
Author(s):  
Conde C Domínguez ◽  
T Gomes ◽  
LB Jarvis ◽  
C Xu ◽  
SK Howlett ◽  
...  
Keyword(s):  
B Cell ◽  
Single Cell ◽  
Immune Cell ◽  
Γδ T Cells ◽  
Cell Types ◽  
Cell Receptor ◽  
Lymphoid Tissues ◽  
Cell Type ◽  
Variable Regions ◽  
Tissue Specific

AbstractDespite their crucial role in health and disease, our knowledge of immune cells within human tissues, in contrast to those circulating in the blood, remains limited. Here, we surveyed the immune compartment of lymphoid and non-lymphoid tissues of six adult donors by single-cell RNA sequencing, including alpha beta T-cell receptor (αβ TCR), gamma delta (γδ) TCR and B-cell receptor (BCR) variable regions. To aid systematic cell type identification we developed CellTypist, a tool for automated and accurate cell type annotation. Using this approach combined with manual curation, we determined the tissue distribution of finely phenotyped immune cell types and cell states. This revealed tissue-specific features within cell subsets, such as a subtype of activated dendritic cells in the airways (expressing CSF2RA, GPR157, CRLF2), ITGAD-expressing γδ T cells in spleen and liver, and ITGAX+ splenic memory B cells. Single cell paired chain TCR analysis revealed cell type-specific biases in VDJ usage, and BCR analysis revealed characteristic patterns of somatic hypermutation and isotype usage in plasma and memory B cell subsets. In summary, our multi-tissue approach lays the foundation for identifying highly resolved immune cell types by leveraging a common reference dataset, tissue-integrated expression analysis and antigen receptor sequencing.

scholarly journals ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells

2019 ◽  
Author(s):  
Samuel A Danziger ◽  
David L Gibbs ◽  
Ilya Shmulevich ◽  
Mark McConnell ◽  
Matthew WB Trotter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Immune Cell ◽  
De Novo ◽  
Cell Types ◽  
Expression Data ◽  
Cell Type ◽  
Data Set ◽  
Rnaseq Data

AbstractImmune cell infiltration of tumors can be an important component for determining patient outcomes, e.g. by inferring immune cell presence by deconvolving gene expression data drawn from a heterogenous mix of cell types. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from cell type purified gene expression data. Many methods of this type have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are hard to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.

scholarly journals Single-cell isoform RNA sequencing (ScISOr-Seq) across thousands of cells reveals isoforms of cerebellar cell types

2018 ◽  
Author(s):  
Ishaan Gupta ◽  
Paul G Collier ◽  
Bettina Haase ◽  
Ahmed Mahfouz ◽  
Anoushka Joglekar ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Full Length ◽  
Cell Of Origin ◽  
Cell Type ◽  
Long Reads ◽  
Long Read ◽  
The Individual ◽  
Bulk Tissue

AbstractFull-length isoform sequencing has advanced our knowledge of isoform biology1–11. However, apart from applying full-length isoform sequencing to very few single cells12,13, isoform sequencing has been limited to bulk tissue, cell lines, or sorted cells. Single splicing events have been described for <=200 single cells with great statistical success14,15, but these methods do not describe full-length mRNAs. Single cell short-read 3’ sequencing has allowed identification of many cell sub-types16–23, but full-length isoforms for these cell types have not been profiled. Using our new method of single-cell-isoform-RNA-sequencing (ScISOr-Seq) we determine isoform-expression in thousands of individual cells from a heterogeneous bulk tissue (cerebellum), without specific antibody-fluorescence activated cell sorting. We elucidate isoform usage in high-level cell types such as neurons, astrocytes and microglia and finer sub-types, such as Purkinje cells and Granule cells, including the combination patterns of distant splice sites6–9,24,25, which for individual molecules requires long reads. We produce an enhanced genome annotation revealing cell-type specific expression of known and 16,872 novel (with respect to mouse Gencode version 10) isoforms (see isoformatlas.com).ScISOr-Seq describes isoforms from >1,000 single cells from bulk tissue without cell sorting by leveraging two technologies in three steps: In step one, we employ microfluidics to produce amplified full-length cDNAs barcoded for their cell of origin. This cDNA is split into two pools: one pool for 3’ sequencing to measure gene expression (step 2) and another pool for long-read sequencing and isoform expression (step 3). In step two, short-read 3’-sequencing provides molecular counts for each gene and cell, which allows clustering cells and assigning a cell type using cell-type specific markers. In step three, an aliquot of the same cDNAs (each barcoded for the individual cell of origin) is sequenced using Pacific Biosciences (“PacBio”)1,2,4,5,26 or Oxford Nanopore3. Since these long reads carry the single-cell barcodes identified in step two, one can determine the individual cell from which each long read originates. Since most single cells are assigned to a named cluster, we can also assign the cell’s cluster name (e.g. “Purkinje cell” or “astrocyte”) to the long read in question (Fig 1A) – without losing the cell of origin of each long read.

scholarly journals Automated identification of Cell Types in Single Cell RNA Sequencing

2019 ◽  
Author(s):  
Feiyang Ma ◽  
Matteo Pellegrini
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Complex Data ◽  
Cell Type ◽  
Human T Cell ◽  
Single Cell Rna Sequencing

AbstractCell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with 3 hidden layers, trains on datasets with predefined cell types, and predicts cell types for other datasets based on the trained parameters. We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines.Author SummarySingle cell RNA sequencing (scRNA-seq) provides high resolution profiling of the transcriptomes of individual cells, which inevitably results in high volumes of data that require complex data processing pipelines. Usually, one of the first steps in the analysis of scRNA-seq is to assign individual cells to known cell types. To accomplish this, traditional methods first group the cells into different clusters, then find marker genes, and finally use these to manually assign cell types for each cluster. Thus these methods require prior knowledge of cell type canonical markers, and some level of subjectivity to make the cell type assignments. As a result, the process is often laborious and requires domain specific expertise, which is a barrier for inexperienced users. By contrast, our neural network ACTINN automatically learns the features for each predefined cell type and uses these features to predict cell types for individual cells. This approach is computationally efficient and requires no domain expertise of the tissues being studied. We believe ACTINN allows users to rapidly identify cell types in their datasets, thus rendering the analysis of their scRNA-seq datasets more efficient.

scholarly journals Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion

2019 ◽  
Author(s):  
Ansuman T. Satpathy ◽  
Jeffrey M. Granja ◽  
Kathryn E. Yost ◽  
Yanyan Qi ◽  
Francesca Meschi ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Immune Cell ◽  
Single Cells ◽  
Cell Types ◽  
Cell Development ◽  
Massively Parallel ◽  
T Cell Exhaustion ◽  
Cell Type ◽  
Cell Exhaustion

AbstractUnderstanding complex tissues requires single-cell deconstruction of gene regulation with precision and scale. Here we present a massively parallel droplet-based platform for mapping transposase-accessible chromatin in tens of thousands of single cells per sample (scATAC-seq). We obtain and analyze chromatin profiles of over 200,000 single cells in two primary human systems. In blood, scATAC-seq allows marker-free identification of cell type-specificcis- andtrans-regulatory elements, mapping of disease-associated enhancer activity, and reconstruction of trajectories of differentiation from progenitors to diverse and rare immune cell types. In basal cell carcinoma, scATAC-seq reveals regulatory landscapes of malignant, stromal, and immune cell types in the tumor microenvironment. Moreover, scATAC-seq of serial tumor biopsies before and after PD-1 blockade allows identification of chromatin regulators and differentiation trajectories of therapy-responsive intratumoral T cell subsets, revealing a shared regulatory program driving CD8+T cell exhaustion and CD4+T follicular helper cell development. We anticipate that droplet-based single-cell chromatin accessibility will provide a broadly applicable means of identifying regulatory factors and elements that underlie cell type and function.

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