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 SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

2020 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

Abstract Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

scholarly journals Implication of specific retinal cell-type involvement and gene expression changes in AMD progression using integrative analysis of single-cell and bulk RNA-seq profiling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yafei Lyu ◽  
Randy Zauhar ◽  
Nicholas Dana ◽  
Christianne E. Strang ◽  
Jian Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Age Related Macular Degeneration ◽  
Specific Gene ◽  
Cell Type ◽  
Adult Human ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific ◽  
Cell Data

AbstractAge‐related macular degeneration (AMD) is a blinding eye disease with no unifying theme for its etiology. We used single-cell RNA sequencing to analyze the transcriptomes of ~ 93,000 cells from the macula and peripheral retina from two adult human donors and bulk RNA sequencing from fifteen adult human donors with and without AMD. Analysis of our single-cell data identified 267 cell-type-specific genes. Comparison of macula and peripheral retinal regions found no cell-type differences but did identify 50 differentially expressed genes (DEGs) with about 1/3 expressed in cones. Integration of our single-cell data with bulk RNA sequencing data from normal and AMD donors showed compositional changes more pronounced in macula in rods, microglia, endothelium, Müller glia, and astrocytes in the transition from normal to advanced AMD. KEGG pathway analysis of our normal vs. advanced AMD eyes identified enrichment in complement and coagulation pathways, antigen presentation, tissue remodeling, and signaling pathways including PI3K-Akt, NOD-like, Toll-like, and Rap1. These results showcase the use of single-cell RNA sequencing to infer cell-type compositional and cell-type-specific gene expression changes in intact bulk tissue and provide a foundation for investigating molecular mechanisms of retinal disease that lead to new therapeutic targets.

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 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 uncovers the excitatory/inhibitory synaptic unbalance in the retrosplenial cortex after peripheral nerve injury

2021 ◽  
Author(s):  
Jing-Hua Wang ◽  
Cheng Wu ◽  
Yan-Na Lian ◽  
Li Liu ◽  
Wei Chen ◽  
...  
Keyword(s):  
Neuropathic Pain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Nerve Injury ◽  
Retrosplenial Cortex ◽  
Electrophysiological Recording ◽  
Specific Gene ◽  
Cell Type ◽  
Protein Levels ◽  
Single Cell Rna Sequencing

Nerve injury in the somatosensory pathway may induce maladaptive changes in the transcriptional or protein levels that contribute to the development and maintenance of neuropathic pain. The retrosplenial cortex (RSC) processes nociceptive information, and it presents structural and molecular changes after nerve injury, in contrast, the detailed transcriptional changes in the RSC were unknown. Here we first confirm the involvements of RSC in the regulation of pain sensation and observe that the same peripheral stimulation activated more splenial neurons after nerve injury; decreasing the activities of CamkIIa+ splenial cells relieves peripheral pain hypersensitivity. Using a single-cell RNA-sequencing (scRNA-seq) approach, we identify the cell-type-specific gene expressional changes after nerve injury. By analyzing the expression of ligand-gated ion channels, we observe the decreased Gabar1a but increased Gria1in the CamkIIa+ neurons; consistently, we confirm the unbalanced excitatory/inhibitory synaptic transmissions by using the electrophysiological recording approach. Our data, therefore, provide information of the cell type-dependent transcriptomic changes in the RSC after nerve injury, which would help to understand the mechanisms of mediating neuropathic pain.

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 SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References

2019 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M. Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

AbstractRecent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

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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