scholarly journals FEM: mining biological meaning from cell level in single-cell RNA sequencing data

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12570
Author(s):  
Yunqing Liu ◽  
Na Lu ◽  
Changwei Bi ◽  
Tingyu Han ◽  
Guo Zhuojun ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Mononuclear Cells ◽  
Differentially Expressed Gene ◽  
Biological Significance ◽  
Human Pancreas ◽  
Sequencing Data ◽  
Peripheral Blood Mononuclear ◽  
Single Cell Rna Sequencing ◽  
Expression Matrix

Background One goal of expression data analysis is to discover the biological significance or function of genes that are differentially expressed. Gene Set Enrichment (GSE) analysis is one of the main tools for function mining that has been widely used. However, every gene expressed in a cell is valuable information for GSE for single-cell RNA sequencing (scRNA-SEQ) data and not should be discarded. Methods We developed the functional expression matrix (FEM) algorithm to utilize the information from all expressed genes. The algorithm converts the gene expression matrix (GEM) into a FEM. The FEM algorithm can provide insight on the biological significance of a single cell. It can also integrate with GEM for downstream analysis. Results We found that FEM performed well with cell clustering and cell-type specific function annotation in three datasets (peripheral blood mononuclear cells, human liver, and human pancreas).

scholarly journals Single-Cell RNA Sequencing Analysis of the Immunometabolic Rewiring and Immunopathogenesis of Coronavirus Disease 2019

2021 ◽  
Vol 12 ◽  
Author(s):  
Furong Qi ◽  
Wenbo Zhang ◽  
Jialu Huang ◽  
Lili Fu ◽  
Jinfang Zhao
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Mononuclear Cells ◽  
Plasma Cells ◽  
Severe Disease ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Metabolic Remodeling ◽  
Single Cell Rna Sequencing ◽  
Antibody Secretion

Although immune dysfunction is a key feature of coronavirus disease 2019 (COVID-19), the metabolism-related mechanisms remain elusive. Here, by reanalyzing single-cell RNA sequencing data, we delineated metabolic remodeling in peripheral blood mononuclear cells (PBMCs) to elucidate the metabolic mechanisms that may lead to the progression of severe COVID-19. After scoring the metabolism-related biological processes and signaling pathways, we found that mono-CD14+ cells expressed higher levels of glycolysis-related genes (PKM, LDHA and PKM) and PPP-related genes (PGD and TKT) in severe patients than in mild patients. These genes may contribute to the hyperinflammation in mono-CD14+ cells of patients with severe COVID-19. The mono-CD16+ cell population in COVID-19 patients showed reduced transcription levels of genes related to lysine degradation (NSD1, KMT2E, and SETD2) and elevated transcription levels of genes involved in OXPHOS (ATP6V1B2, ATP5A1, ATP5E, and ATP5B), which may inhibit M2-like polarization. Plasma cells also expressed higher levels of the OXPHOS gene ATP13A3 in COVID-19 patients, which was positively associated with antibody secretion and survival of PCs. Moreover, enhanced glycolysis or OXPHOS was positively associated with the differentiation of memory B cells into plasmablasts or plasma cells. This study comprehensively investigated the metabolic features of peripheral immune cells and revealed that metabolic changes exacerbated inflammation in monocytes and promoted antibody secretion and cell survival in PCs in COVID-19 patients, especially those with severe disease.

scholarly journals Multiplexing droplet-based single cell RNA-sequencing using natural genetic barcodes

2017 ◽  
Author(s):  
Hyun Min Kang ◽  
Meena Subramaniam ◽  
Sasha Targ ◽  
Michelle Nguyen ◽  
Lenka Maliskova ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Mononuclear Cells ◽  
Specific Gene ◽  
Cell Capture ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Peripheral Blood Mononuclear ◽  
Single Cell Rna Sequencing ◽  
Sample Identity

Droplet-based single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes from tens of thousands of cells. Multiplexing samples for single cell capture and library preparation in dscRNA-seq would enable cost-effective designs of differential expression and genetic studies while avoiding technical batch effects, but its implementation remains challenging. Here, we introduce an in-silico algorithm demuxlet that harnesses natural genetic variation to discover the sample identity of each cell and identify droplets containing two cells. These capabilities enable multiplexed dscRNA-seq experiments where cells from unrelated individuals are pooled and captured at higher throughput than standard workflows. To demonstrate the performance of demuxlet, we sequenced 3 pools of peripheral blood mononuclear cells (PBMCs) from 8 lupus patients. Given genotyping data for each individual, demuxlet correctly recovered the sample identity of > 99% of singlets, and identified doublets at rates consistent with previous estimates. In PBMCs, we demonstrate the utility of multiplexed dscRNA-seq in two applications: characterizing cell type specificity and inter-individual variability of cytokine response from 8 lupus patients and mapping genetic variants associated with cell type specific gene expression from 23 donors. Demuxlet is fast, accurate, scalable and could be extended to other single cell datasets that incorporate natural or synthetic DNA barcodes.

scholarly journals IKAP—Identifying K mAjor cell Population groups in single-cell RNA-sequencing analysis

GigaScience ◽  
2019 ◽  
Vol 8 (10) ◽  
Author(s):  
Yun-Ching Chen ◽  
Abhilash Suresh ◽  
Chingiz Underbayev ◽  
Clare Sun ◽  
Komudi Singh ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Peripheral Blood Mononuclear ◽  
Biologically Relevant ◽  
Single Cell Rna Sequencing ◽  
Cell Groups ◽  
Cell Ontology

AbstractBackgroundIn single-cell RNA-sequencing analysis, clustering cells into groups and differentiating cell groups by differentially expressed (DE) genes are 2 separate steps for investigating cell identity. However, the ability to differentiate between cell groups could be affected by clustering. This interdependency often creates a bottleneck in the analysis pipeline, requiring researchers to repeat these 2 steps multiple times by setting different clustering parameters to identify a set of cell groups that are more differentiated and biologically relevant.FindingsTo accelerate this process, we have developed IKAP—an algorithm to identify major cell groups and improve differentiating cell groups by systematically tuning parameters for clustering. We demonstrate that, with default parameters, IKAP successfully identifies major cell types such as T cells, B cells, natural killer cells, and monocytes in 2 peripheral blood mononuclear cell datasets and recovers major cell types in a previously published mouse cortex dataset. These major cell groups identified by IKAP present more distinguishing DE genes compared with cell groups generated by different combinations of clustering parameters. We further show that cell subtypes can be identified by recursively applying IKAP within identified major cell types, thereby delineating cell identities in a multi-layered ontology.ConclusionsBy tuning the clustering parameters to identify major cell groups, IKAP greatly improves the automation of single-cell RNA-sequencing analysis to produce distinguishing DE genes and refine cell ontology using single-cell RNA-sequencing data.

scholarly journals Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1297 ◽  
Author(s):  
Saskia Freytag ◽  
Luyi Tian ◽  
Ingrid Lönnstedt ◽  
Milica Ng ◽  
Melanie Bahlo
Keyword(s):  
Single Cell ◽  
Peripheral Blood Mononuclear Cells ◽  
Gold Standard ◽  
Mononuclear Cells ◽  
Rna Seq ◽  
Sequencing Data ◽  
Peripheral Blood Mononuclear ◽  
Silver Standard ◽  
Single Cell Rna Sequencing ◽  
Blood Mononuclear Cells

Background: The commercially available 10x Genomics protocol to generate droplet-based single cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method to use. Methods: Here we use one gold standard 10x Genomics dataset, generated from the mixture of three cell lines, as well as multiple silver standard 10x Genomics datasets generated from peripheral blood mononuclear cells to examine not only the accuracy but also running time and robustness of a dozen methods. Results: We found that Seurat outperformed other methods, although performance seems to be dependent on many factors, including the complexity of the studied system. Furthermore, we found that solutions produced by different methods have little in common with each other. Conclusions: In light of this we conclude that the choice of clustering tool crucially determines interpretation of scRNA-seq data generated by 10x Genomics. Hence practitioners and consumers should remain vigilant about the outcome of 10x Genomics scRNA-seq analysis.

scholarly journals Scalable full-transcript coverage single cell RNA sequencing with Smart-seq3xpress

2021 ◽  
Author(s):  
Michael Hagemann-Jensen ◽  
Christoph Ziegenhain ◽  
Rickard Sandberg
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Peripheral Blood Mononuclear Cells ◽  
Mononuclear Cells ◽  
Human Peripheral Blood ◽  
Cell Types ◽  
Peripheral Blood Mononuclear ◽  
Single Cell Rna Sequencing ◽  
Blood Mononuclear Cells ◽  
Additional Protein

Plate-based single-cell RNA-sequencing methods with full-transcript coverage typically excel at sensitivity but are more resource and time-consuming. Here, we miniaturized and streamlined the Smart-seq3 protocol for drastically reduced cost and increased throughput. Applying Smart-seq3xpress to 16,349 human peripheral blood mononuclear cells revealed a highly granular atlas complete with both common and rare cell types whose identification previously relied on additional protein measurements or the integration with a reference atlas.

scholarly journals Single-Cell RNA Sequencing of Tocilizumab-Treated Peripheral Blood Mononuclear Cells as an in vitro Model of Inflammation

2021 ◽  
Vol 11 ◽  
Author(s):  
Arya Zarinsefat ◽  
George Hartoularos ◽  
Dmitry Rychkov ◽  
Priyanka Rashmi ◽  
Sindhu Chandran ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Mononuclear Cells ◽  
Cytokine Storm ◽  
Kidney Transplant Recipients ◽  
Peripheral Blood Mononuclear ◽  
Single Cell Rna Sequencing ◽  
Before And After ◽  
Vitro Model

COVID-19 has posed a significant threat to global health. Early data has revealed that IL-6, a key regulatory cytokine, plays an important role in the cytokine storm of COVID-19. Multiple trials are therefore looking at the effects of Tocilizumab, an IL-6 receptor antibody that inhibits IL-6 activity, on treatment of COVID-19, with promising findings. As part of a clinical trial looking at the effects of Tocilizumab treatment on kidney transplant recipients with subclinical rejection, we performed single-cell RNA sequencing of comparing stimulated PBMCs before and after Tocilizumab treatment. We leveraged this data to create an in vitro cytokine storm model, to better understand the effects of Tocilizumab in the presence of inflammation. Tocilizumab-treated cells had reduced expression of inflammatory-mediated genes and biologic pathways, particularly amongst monocytes. These results support the hypothesis that Tocilizumab may hinder the cytokine storm of COVID-19, through a demonstration of biologic impact at the single-cell level.

scholarly journals Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1297 ◽  
Author(s):  
Saskia Freytag ◽  
Luyi Tian ◽  
Ingrid Lönnstedt ◽  
Milica Ng ◽  
Melanie Bahlo
Keyword(s):  
Single Cell ◽  
Peripheral Blood Mononuclear Cells ◽  
Gold Standard ◽  
Mononuclear Cells ◽  
Rna Seq ◽  
Sequencing Data ◽  
Peripheral Blood Mononuclear ◽  
Silver Standard ◽  
Single Cell Rna Sequencing ◽  
Blood Mononuclear Cells

Background: The commercially available 10x Genomics protocol to generate droplet-based single-cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method to use. Methods: Here we use one gold standard 10x Genomics dataset, generated from the mixture of three cell lines, as well as three silver standard 10x Genomics datasets generated from peripheral blood mononuclear cells to examine not only the accuracy but also robustness of a dozen methods. Results: We found that some methods, including Seurat and Cell Ranger, outperform other methods, although performance seems to be dependent on the complexity of the studied system. Furthermore, we found that solutions produced by different methods have little in common with each other. Conclusions: In light of this, we conclude that the choice of clustering tool crucially determines interpretation of scRNA-seq data generated by 10x Genomics. Hence practitioners and consumers should remain vigilant about the outcome of 10x Genomics scRNA-seq analysis.

scholarly journals Single-cell RNA sequencing of peripheral blood mononuclear cells from acute Kawasaki disease patients

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhen Wang ◽  
Lijian Xie ◽  
Guohui Ding ◽  
Sirui Song ◽  
Liqin Chen ◽  
...  
Keyword(s):  
T Cells ◽  
Kawasaki Disease ◽  
Immune Responses ◽  
Single Cell ◽  
Rna Sequencing ◽  
Mononuclear Cells ◽  
Healthy Controls ◽  
Peripheral Blood Mononuclear ◽  
Single Cell Rna Sequencing ◽  
Blood Mononuclear Cells

AbstractKawasaki disease (KD) is the most common cause of acquired heart disease in children in developed countries. Although functional and phenotypic changes of immune cells have been reported, a global understanding of immune responses underlying acute KD is unclear. Here, using single-cell RNA sequencing, we profile peripheral blood mononuclear cells from seven patients with acute KD before and after intravenous immunoglobulin therapy and from three age-matched healthy controls. The most differentially expressed genes are identified in monocytes, with high expression of pro-inflammatory mediators, immunoglobulin receptors and low expression of MHC class II genes in acute KD. Single-cell RNA sequencing and flow cytometry analyses, of cells from an additional 16 KD patients, show that although the percentage of total B cells is substantially decreased after therapy, the percentage of plasma cells among the B cells is significantly increased. The percentage of CD8+ T cells is decreased in acute KD, notably effector memory CD8+ T cells compared with healthy controls. Oligoclonal expansions of both B cell receptors and T cell receptors are observed after therapy. We identify biological processes potentially underlying the changes of each cell type. The single-cell landscape of both innate and adaptive immune responses provides insights into pathogenesis and therapy of KD.

scholarly journals Single-cell RNA sequencing unveils the shared and the distinct cytotoxic hallmarks of human TCRVδ1 and TCRVδ2 γδ T lymphocytes

2019 ◽  
pp. 201818488 ◽  
Author(s):  
Gabriele Pizzolato ◽  
Hannah Kaminski ◽  
Marie Tosolini ◽  
Don-Marc Franchini ◽  
Fréderic Pont ◽  
...  
Keyword(s):  
T Lymphocytes ◽  
Single Cell ◽  
Rna Sequencing ◽  
Mononuclear Cells ◽  
Human Peripheral Blood ◽  
Effector Memory ◽  
In Vitro Proliferation ◽  
Peripheral Blood Mononuclear ◽  
Single Cell Rna Sequencing

γδ T lymphocytes represent ∼1% of human peripheral blood mononuclear cells and even more cells in most tissues of vertebrates. Although they have important anticancer functions, most current single-cell RNA sequencing (scRNA-seq) studies do not identify γδ T lymphocytes because their transcriptomes at the single-cell level are unknown. Here we show that high-resolution clustering of large scRNA-seq datasets and a combination of gene signatures allow the specific detection of human γδ T lymphocytes and identification of their T cell receptor (TCR)Vδ1 and TCRVδ2 subsets in large datasets from complex cell mixtures. In t-distributed stochastic neighbor embedding plots from blood and tumor samples, the few γδ T lymphocytes appear collectively embedded between cytotoxic CD8 T and NK cells. Their TCRVδ1 and TCRVδ2 subsets form close yet distinct subclusters, respectively neighboring NK and CD8 T cells because of expression of shared and distinct cytotoxic maturation genes. Similar pseudotime maturation trajectories of TCRVδ1 and TCRVδ2 γδ T lymphocytes were discovered, unveiling in both subsets an unattended pool of terminally differentiated effector memory cells with preserved proliferative capacity, a finding confirmed by in vitro proliferation assays. Overall, the single-cell transcriptomes of thousands of individual γδ T lymphocytes from different CMV+ and CMV− donors reflect cytotoxic maturation stages driven by the immunological history of donors. This landmark study establishes the rationale for identification, subtyping, and deep characterization of human γδ T lymphocytes in further scRNA-seq studies of complex tissues in physiological and disease conditions.

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