scholarly journals Molecular determinants and heterogeneity of tissue-resident memory CD8+ T lymphocytes revealed by single-cell RNA sequencing

2020 ◽  
Author(s):  
Nadia S. Kurd ◽  
Zhaoren He ◽  
J. Justin Milner ◽  
Kyla D. Omilusik ◽  
Tiani L. Louis ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Microbial Infection ◽  
Molecular Determinants ◽  
Single Cell Rna Sequencing

AbstractDuring an immune response to microbial infection, CD8+ T cells give rise to distinct classes of cellular progeny that coordinately mediate clearance of the pathogen and provide long-lasting protection against reinfection, including a subset of non-circulating tissue-resident memory (TRM) cells that mediate potent protection within non-lymphoid tissues. Here, we utilized single-cell RNA-sequencing to examine the gene expression patterns of individual CD8+ T cells in the spleen and small intestine intraepithelial lymphocyte (siIEL) compartment throughout the course of their differentiation in response to viral infection. These analyses revealed previously unknown transcriptional heterogeneity within the siIEL CD8+ T cell population at several states of differentiation, representing functionally distinct TRM cell subsets as well as a subset of TRM cell precursors within the tissue early in infection. Taken together, these findings may inform strategies to optimize CD8+ T cell responses to protect against microbial infection and cancer.One sentence summaryHere, we applied single-cell RNA-sequencing to elucidate the gene expression patterns of individual CD8+ T cells differentiating throughout the course of infection in the spleen and small intestinal epithelium, which revealed previously unidentified molecular determinants of tissue-resident T cell differentiation as well as functional heterogeneity within the tissue-resident CD8+ T cell population.

scholarly journals Early precursors and molecular determinants of tissue-resident memory CD8+ T lymphocytes revealed by single-cell RNA sequencing

2020 ◽  
Vol 5 (47) ◽  
pp. eaaz6894 ◽  
Author(s):  
Nadia S. Kurd ◽  
Zhaoren He ◽  
Tiani L. Louis ◽  
J. Justin Milner ◽  
Kyla D. Omilusik ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Microbial Infection ◽  
Cell Responses ◽  
Molecular Determinants ◽  
Single Cell Rna Sequencing ◽  
Potent Protection

During an immune response to microbial infection, CD8+ T cells give rise to distinct classes of cellular progeny that coordinately mediate clearance of the pathogen and provide long-lasting protection against reinfection, including a subset of noncirculating tissue-resident memory (TRM) cells that mediate potent protection within nonlymphoid tissues. Here, we used single-cell RNA sequencing to examine the gene expression patterns of individual CD8+ T cells in the spleen and small intestine intraepithelial lymphocyte (siIEL) compartment throughout the course of their differentiation in response to viral infection. These analyses revealed previously unknown transcriptional heterogeneity within the siIEL CD8+ T cell population at several stages of differentiation, representing functionally distinct TRM cell subsets and a subset of TRM cell precursors within the tissue early in infection. Together, these findings may inform strategies to optimize CD8+ T cell responses to protect against microbial infection and cancer.

scholarly journals Cryopreservation of microglia enables single-cell RNA sequencing with minimal effects on disease-related gene expression patterns

iScience ◽  
2021 ◽  
Vol 24 (4) ◽  
pp. 102357
Author(s):  
Brenda Morsey ◽  
Meng Niu ◽  
Shetty Ravi Dyavar ◽  
Courtney V. Fletcher ◽  
Benjamin G. Lamberty ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Related Gene ◽  
Single Cell Rna Sequencing ◽  
Disease Related Gene

scholarly journals scSensitiveGeneDefine: sensitive gene detection in single-cell RNA sequencing data by Shannon entropy

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zechuan Chen ◽  
Zeruo Yang ◽  
Xiaojun Yuan ◽  
Xiaoming Zhang ◽  
Pei Hao
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Shannon Entropy ◽  
Expression Patterns ◽  
Ground Truth ◽  
Unsupervised Clustering ◽  
Gene Expression Patterns ◽  
Data Sets ◽  
Single Cell Rna Sequencing

Abstract Background Single-cell RNA sequencing (scRNA-seq) is the most widely used technique to obtain gene expression profiles from complex tissues. Cell subsets and developmental states are often identified via differential gene expression patterns. Most of the single-cell tools utilized highly variable genes to annotate cell subsets and states. However, we have discovered that a group of genes, which sensitively respond to environmental stimuli with high coefficients of variation (CV), might impose overwhelming influences on the cell type annotation. Result In this research, we developed a method, based on the CV-rank and Shannon entropy, to identify these noise genes, and termed them as “sensitive genes”. To validate the reliability of our methods, we applied our tools in 11 single-cell data sets from different human tissues. The results showed that most of the sensitive genes were enriched pathways related to cellular stress response. Furthermore, we noticed that the unsupervised result was closer to the ground-truth cell labels, after removing the sensitive genes detected by our tools. Conclusion Our study revealed the prevalence of stochastic gene expression patterns in most types of cells, compared the differences among cell marker genes, housekeeping genes (HK genes), and sensitive genes, demonstrated the similarities of functions of sensitive genes in various scRNA-seq data sets, and improved the results of unsupervised clustering towards the ground-truth labels. We hope our method would provide new insights into the reduction of data noise in scRNA-seq data analysis and contribute to the development of better scRNA-seq unsupervised clustering algorithms in the future.

scholarly journals Isoform-level gene expression patterns in single-cell RNA-sequencing data

2018 ◽  
Vol 34 (14) ◽  
pp. 2392-2400 ◽  
Author(s):  
Trung Nghia Vu ◽  
Quin F Wills ◽  
Krishna R Kalari ◽  
Nifang Niu ◽  
Liewei Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

scholarly journals Unique Gene Expression Patterns in Human T-cell Lines Generated from Multiple Sclerosis Patients by Stimulation with a Synthetic MOG Peptide

2005 ◽  
Vol 12 (3) ◽  
pp. 203-209 ◽  
Author(s):  
Mathilda Mandel ◽  
Michael Gurevich ◽  
Gad Lavie ◽  
Irun R. Cohen ◽  
Anat Achiron
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
Cell Lines ◽  
Healthy Subjects ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
T Cell Lines ◽  
Myelin Antigens

Multiple sclerosis (MS) is an autoimmune disease where T-cells activated against myelin antigens are involved in myelin destruction. Yet, healthy subjects also harbor T-cells responsive to myelin antigens, suggesting that MS patient-derived autoimmune T-cells might bear functional differences from T-cells derived from healthy individuals. We addressed this issue by analyzing gene expression patterns of myelin oligodendrocytic glycoprotein (MOG) responsive T-cell lines generated from MS patients and healthy subjects. We identified 150 transcripts that were differentially expressed between MS patients and healthy controls. The most informative 43 genes exhibited >1.5-fold change in expression level. Eighteen genes were up-regulated including BCL2, lifeguard, IGFBP3 and VEGF. Twenty five genes were down-regulated, including apoptotic activators like TNF and heat shock protein genes. This gene expression pattern was unique to MOG specific T-cell lines and was not expressed in T-cell lines reactive to tetanus toxin (TTX). Our results indicate that activation in MS that promotes T-cell survival and expansion, has its own state and that the unique gene expression pattern that characterize autoreactive T-cells in MS represent a constellation of factors in which the chronicity, timing and accumulation of damage make the difference between health and disease.

scholarly journals PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing

2020 ◽  
Vol 36 (13) ◽  
pp. 4021-4029
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Imputation Method ◽  
Supplementary Information ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

Abstract Summary Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Heterogeneity of T Cells in Atherosclerosis Defined by Single-Cell RNA-Sequencing and Cytometry by Time of Flight

2020 ◽  
Author(s):  
Holger Winkels ◽  
Dennis Wolf
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
T Regulatory Cells ◽  
Foam Cell ◽  
Regulatory Cells ◽  
T Helper ◽  
Antigen Specificity ◽  
Single Cell Rna Sequencing

The infiltration and accumulation of pro- and anti-inflammatory leukocytes within the intimal layer of the arterial wall is a hallmark of developing and progressing atherosclerosis. While traditionally perceived as macrophage- and foam cell-dominated disease, it is now established that atherosclerosis is a partial autoimmune disease that involves the recognition of peptides from ApoB (apolipoprotein B), the core protein of LDL (low-density lipoprotein) cholesterol particles, by CD4 + T-helper cells and autoantibodies against LDL and ApoB. Autoimmunity in the atherosclerotic plaque has long been understood as a pathogenic T-helper type-1 driven response with proinflammatory cytokine secretion. Recent developments in high-parametric cell immunophenotyping by mass cytometry, single-cell RNA-sequencing, and in tools exploring antigen-specificity have established the existence of several unforeseen layers of T cell diversity with mixed T H 1 and T regulatory cells transcriptional programs and unpredicted fates. These findings suggest that pathogenic ApoB-reactive T cells evolve from atheroprotective and immunosuppressive CD4 + T regulatory cells that lose their protective properties over time. Here, we discuss T cell heterogeneity in atherosclerosis with a focus on plasticity, antigen-specificity, exhaustion, maturation, tissue residency, and its potential use in clinical prediction.

scholarly journals OMIC-11. SINGLE CELL RNA SEQUENCING FROM THE CSF OF SUBJECTS WITH H3K27M+ DIPG/DMG TREATED WITH GD2 CAR T-CELLULAR THERAPY

2021 ◽  
Vol 23 (Supplement_1) ◽  
pp. i39-i39
Author(s):  
Aaron Mochizuki ◽  
Sneha Ramakrishna ◽  
Zina Good ◽  
Shabnum Patel ◽  
Harshini Chinnasamy ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Product ◽  
Myeloid Suppressor Cells ◽  
Car T Cells ◽  
Car T Cell ◽  
Car T ◽  
Single Cell Rna Sequencing

Abstract Introduction We are conducting a Phase I clinical trial utilizing chimeric antigen receptor (CAR) T-cells targeting GD2 (NCT04196413) for H3K27M-mutant diffuse intrinsic pontine glioma (DIPG) and spinal cord diffuse midline glioma (DMG). Cerebrospinal fluid (CSF) is collected for correlative studies at the time of routine intracranial pressure monitoring via Ommaya catheter. Here we present single cell RNA-sequencing results from the first 3 subjects. Methods Single cell RNA-sequencing was performed utilizing 10X Genomics on cells isolated from CSF at various time points before and after CAR T-cell administration and on the CAR T-cell product. Output was aligned with Cell Ranger and analyzed in R. Results As detailed in the Majzner et al. abstract presented at this meeting, three of four subjects treated at dose-level one exhibited clear radiographic and/or clinical benefit. We have to date completed single cell RNA-sequencing for three of these four subjects (two with benefit, one without). After filtering out low-quality signals and doublets, 89,604 cells across 3 subjects were analyzed. Of these, 4,122 cells represent cells isolated from CSF and 85,482 cells represent CAR T-cell product. Two subjects who demonstrated clear clinical and radiographic improvement exhibited fewer S100A8+S100A9+ myeloid suppressor-cells and CD25+FOXP3+ regulatory T-cells in the CSF pre-infusion compared to the subject who did not derive a therapeutic response. In one subject with DIPG who demonstrated improvement, polyclonal CAR T-cells detectable in CSF at Day +14 demonstrated enrichment of CD8A, GZMA, GNLY and PDCD1 compared to the pre-infusion CAR T-cells by trajectory analysis, suggesting differentiation toward a cytotoxic phenotype; the same subject exhibited increasing numbers of S100A8+S100A9+ myeloid cells and CX3CR1+P2RY12+ microglia over time. Further analyses will be presented as data become available. Conclusions The presence of immunosuppressive myeloid populations, detectable in CSF, may correlate to clinical response in CAR T cell therapy for DIPG/DMG.

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