scholarly journals Gene expression-based identification of antigen-responsive CD8+ T cells on a single-cell level

2019 ◽  
Author(s):  
Yannick F. Fuchs ◽  
Virag Sharma ◽  
Anne Eugster ◽  
Gloria Kraus ◽  
Robert Morgenstern ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
T Cells ◽  
Single Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
Large Gene ◽  
Gene Sets ◽  
Cell Gene Expression ◽  
Cell Gene

AbstractCD8+ T cells are important effectors of adaptive immunity against pathogens, tumors and self antigens. Here, we asked how human cognate antigen-responsive CD8+ T cells and their receptors could be identified in unselected single-cell gene expression data. Single-cell RNA sequencing and qPCR of dye-labelled antigen-specific cells identified large gene sets that were congruently up- or downregulated in virus-responsive CD8+ T cells under different antigen presentation conditions. Combined expression of TNFRSF9, XCL1, XCL2, and CRTAM was the most distinct marker of virus-responsive cells on a single-cell level. Using transcriptomic data, we developed a machine learning-based classifier that provides sensitive and specific detection of virus-responsive CD8+ T cells from unselected populations. Gene response profiles of CD8+ T cells specific for the autoantigen islet-specific glucose-6-phosphatase catalytic subunit-related protein differed markedly from virus-specific cells. These findings provide single-cell gene expression parameters for comprehensive identification of rare antigen-responsive cells and T cell receptors.One-sentence summaryIdentification of genes, gene sets, and development of a machine learning-based classifier that distinguishes antigen-responsive CD8+ T cells on a single-cell level.

scholarly journals A bead-based microfluidic approach to integrated single-cell gene expression analysis by quantitative RT-PCR

RSC Advances ◽  
2015 ◽  
Vol 5 (7) ◽  
pp. 4886-4893 ◽  
Author(s):  
Hao Sun ◽  
Tim Olsen ◽  
Jing Zhu ◽  
Jianguo Tao ◽  
Brian Ponnaiya ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Single Cell Level ◽  
Rt Pcr ◽  
Cell Level ◽  
P Gene ◽  
Cell Gene Expression ◽  
Cell Gene

Gene expression analysis at the single-cell level is critical to understanding variations among cells in heterogeneous populations.

scholarly journals In situelectro-sequencing in three-dimensional tissues

2021 ◽  
Author(s):  
Qiang Li ◽  
Zuwan Lin ◽  
Ren Liu ◽  
Xin Tang ◽  
Jiahao Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Three Dimensional ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Single Cell Level ◽  
Cell Level ◽  
Cell Gene Expression ◽  
Cell Gene

AbstractPairwise mapping of single-cell gene expression and electrophysiology in intact three-dimensional (3D) tissues is crucial for studying electrogenic organs (e.g., brain and heart)1–5. Here, we introducein situelectro-sequencing (electro-seq), combining soft bioelectronics within situRNA sequencing to stably map millisecond-timescale cellular electrophysiology and simultaneously profile a large number of genes at single-cell level across 3D tissues. We appliedin situelectro-seq to 3D human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) patches, precisely registering the CM gene expression with electrophysiology at single-cell level, enabling multimodalin situanalysis. Such multimodal data integration substantially improved the dissection of cell types and the reconstruction of developmental trajectory from spatially heterogeneous tissues. Using machine learning (ML)-based cross-modal analysis,in situelectro-seq identified the gene-to-electrophysiology relationship over the time course of cardiac maturation. Further leveraging such a relationship to train a coupled autoencoder, we demonstrated the prediction of single-cell gene expression profile evolution using long-term electrical measurement from the same cardiac patch or 3D millimeter-scale cardiac organoids. As exemplified by cardiac tissue maturation,in situelectro-seq will be broadly applicable to create spatiotemporal multimodal maps and predictive models in electrogenic organs, allowing discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.

scholarly journals Prediction of single-cell gene expression for transcription factor analysis

GigaScience ◽  
2020 ◽  
Vol 9 (11) ◽  
Author(s):  
Fatemeh Behjati Ardakani ◽  
Kathrin Kattler ◽  
Tobias Heinen ◽  
Florian Schmidt ◽  
David Feuerborn ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Single Cell ◽  
Cell Types ◽  
Single Cell Level ◽  
Cell Level ◽  
Novel Approach ◽  
Cell Gene Expression ◽  
Specific Regulation ◽  
Cell Gene

Abstract Background Single-cell RNA sequencing is a powerful technology to discover new cell types and study biological processes in complex biological samples. A current challenge is to predict transcription factor (TF) regulation from single-cell RNA data. Results Here, we propose a novel approach for predicting gene expression at the single-cell level using cis-regulatory motifs, as well as epigenetic features. We designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single-cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. TFs identified using these models could be validated by the literature. Conclusion Our proposed method allows us to identify distinct TFs that show cell type–specific regulation. This approach is not limited to TFs but can use any type of data that can potentially be used in explaining gene expression at the single-cell level to study factors that drive differentiation or show abnormal regulation in disease. The implementation of our workflow can be accessed under an MIT license via https://github.com/SchulzLab/Triangulate.

scholarly journals Single-cell gene-expression profiling reveals qualitatively distinct CD8 T cells elicited by different gene-based vaccines

2011 ◽  
Vol 108 (14) ◽  
pp. 5724-5729 ◽  
Author(s):  
L. Flatz ◽  
R. Roychoudhuri ◽  
M. Honda ◽  
A. Filali-Mouhim ◽  
J.-P. Goulet ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Single Cell ◽  
Gene Expression Profiling ◽  
Cd8 T Cells ◽  
Expression Profiling ◽  
Cell Gene Expression ◽  
Cell Gene

ID: 135: SINGLE CELL GENE EXPRESSION STUDIES IN LUPUS MONOCYTES REVEAL A UNIQUE ANTII-INFLAMMATORY NON-CLASSICAL MONOCYTE POPULATION ASSOCIATED WITH CLINICAL QUIESCENCE

2016 ◽  
Vol 64 (4) ◽  
pp. 977.2-977
Author(s):  
Z Jin ◽  
MA Jensen ◽  
JM Dorschner ◽  
DM Vsetecka ◽  
S Amin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Capture ◽  
Gene Set ◽  
Inflammatory Gene ◽  
Gene Sets ◽  
Cell Gene Expression ◽  
Induced Genes ◽  
Cell Gene ◽  
Classical Monocytes

BackgroundOur previous studies have shown that different cell types from the same blood sample demonstrate diverse gene expression parameters. In follow up work, it seems that this diversity extends to cells of the same type from the same blood sample. In this study, we examine single cell gene expression in SLE patient monocytes and determine correlations with clinical features.MethodsCD14++CD16− classical monocytes (CLs) and CD14dimCD16+ non-classical monocytes (NCLs) from SLE patients were purified by magnetic separation. The Fluidigm single cell capture and pre-amplification system was used for single cell capture and target gene pre-amplification. Fluidigm Biomark system (Rt-PCR system) was used to quantify expression of 87 monocyte-related genes. IFN-induced genes in monocytes were identified by culturing monocytes isolated from whole blood of healthy controls with or without IFN-α. Genes significant up-regulated by IFN were identified as IFN-induced genes in current study. An individual cell IFN score was given based upon the sum of expression of IFN-induced genes.ResultsBoth CLs and NCLs demonstrated a wide range of expression of IFN-induced genes, and NCL monocytes had higher IFN scores than CL monocytes. Using unsupervised hierarchical clustering, we found four gene sets that clustered monocytes functionally. These included an IFN-induced gene set, two inflammatory gene sets, and one immunosuppressive gene set. Interestingly, we could define a large subset of NCL monocytes with upregulation of suppressive transcripts (including TGF-β and PDL1) and IFN-induced transcripts were also upregulated, while the two inflammatory gene sets were down-regulated. These cells were highly over-represented in a patient with inactive disease who was on immunosuppressants at the time of blood draw. The proportion of anti-inflammatory gene set expressing NCLs was inversely correlated with anti-dsDNA titers (rho=−0.77, p=0.0051) and positively correlated with C3 complement (rho=0.68, p=0.030) in the SLE patient group, suggesting that these cells are also associated with serological quiescence.ConclusionUsing single cell gene expression, we have identified a unique population of NCL monocytes in SLE patients with upregulation of a combination of anti-inflammatory and IFN-induced transcripts. These cells correspond with clinical and serological quiescence.

scholarly journals Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning

2020 ◽  
Vol 48 (20) ◽  
pp. 11335-11346
Author(s):  
Nikolaos-Kosmas Chlis ◽  
Lisa Rausch ◽  
Thomas Brocker ◽  
Jan Kranich ◽  
Fabian J Theis
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Imaging Flow Cytometry ◽  
Myeloid Progenitor ◽  
Myeloid Progenitor Cells ◽  
Cell Gene Expression ◽  
Cell Gene

Abstract High-content imaging and single-cell genomics are two of the most prominent high-throughput technologies for studying cellular properties and functions at scale. Recent studies have demonstrated that information in large imaging datasets can be used to estimate gene mutations and to predict the cell-cycle state and the cellular decision making directly from cellular morphology. Thus, high-throughput imaging methodologies, such as imaging flow cytometry can potentially aim beyond simple sorting of cell-populations. We introduce IFC-seq, a machine learning methodology for predicting the expression profile of every cell in an imaging flow cytometry experiment. Since it is to-date unfeasible to observe single-cell gene expression and morphology in flow, we integrate uncoupled imaging data with an independent transcriptomics dataset by leveraging common surface markers. We demonstrate that IFC-seq successfully models gene expression of a moderate number of key gene-markers for two independent imaging flow cytometry datasets: (i) human blood mononuclear cells and (ii) mouse myeloid progenitor cells. In the case of mouse myeloid progenitor cells IFC-seq can predict gene expression directly from brightfield images in a label-free manner, using a convolutional neural network. The proposed method promises to add gene expression information to existing and new imaging flow cytometry datasets, at no additional cost.

Sign in / Sign up

Export Citation Format

Share Document