Single-cell analysis of gene expression in the nervous system

1996 ◽  
Vol 13 (3) ◽  
pp. 199-211 ◽  
Author(s):  
Diane K. O’Dowd ◽  
Martin A. Smith
Keyword(s):  
Gene Expression ◽  
Nervous System ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Analysis

scholarly journals Single cell analysis of the developing mouse kidney provides deeper insight into marker gene expression and ligand-receptor crosstalk

Development ◽  
2019 ◽  
Vol 146 (12) ◽  
pp. dev178673 ◽  
Author(s):  
Alexander N. Combes ◽  
Belinda Phipson ◽  
Kynan T. Lawlor ◽  
Aude Dorison ◽  
Ralph Patrick ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Marker Gene ◽  
Mouse Kidney ◽  
Cell Analysis ◽  
Marker Gene Expression ◽  
Receptor Crosstalk ◽  
Insight Into

A Fluorescent Reporter for Single Cell Analysis of Gene Expression in Clostridium difficile

2016 ◽  
pp. 69-90 ◽  
Author(s):  
Carolina Piçarra Cassona ◽  
Fátima Pereira ◽  
Mónica Serrano ◽  
Adriano O. Henriques
Keyword(s):  
Gene Expression ◽  
Clostridium Difficile ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Fluorescent Reporter

scholarly journals Single-cell analysis of early antiviral gene expression reveals a determinant of stochasticIFNB1expression

2017 ◽  
Vol 9 (11) ◽  
pp. 857-867 ◽  
Author(s):  
Sultan Doğanay ◽  
Maurice Youzong Lee ◽  
Alina Baum ◽  
Jessie Peh ◽  
Sun-Young Hwang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Decision Making ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Decision Making Process ◽  
Cell Analysis ◽  
Early Expression ◽  
Infected Cells ◽  
Antiviral Gene

Early expression ofRIG-IandMDA5in a subset of infected cells may contribute to the decision making process for turning on theIFNB1expression.

scholarly journals Role of Calcium Signaling Pathway-Related Gene Regulatory Networks in Ischemic Stroke Based on Multiple WGCNA and Single-Cell Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-35
Author(s):  
Weiwei Lin ◽  
Yangxin Wang ◽  
Yisheng Chen ◽  
Qiangwei Wang ◽  
Zhaowen Gu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Calcium Signaling ◽  
Single Cell ◽  
Signaling Pathway ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Kegg Pathways ◽  
Calcium Signaling Pathway ◽  
Core Genes

Background. This study is aimed at investigating the changes in relevant pathways and the differential expression of related gene expression after ischemic stroke (IS) at the single-cell level using multiple weighted gene coexpression network analysis (WGCNA) and single-cell analysis. Methods. The transcriptome expression datasets of IS samples and single-cell RNA sequencing (scRNA-seq) profiles of cerebrovascular tissues were obtained by searching the Gene Expression Omnibus (GEO) database. First, gene pathway scoring was calculated via gene set variation analysis (GSVA) and was imported into multiple WGCNA to acquire key pathways and pathway-related hub genes. Furthermore, SCENIC was used to identify transcription factors (TFs) regulating these core genes using scRNA-seq data. Finally, the pseudotemporal trajectory analysis was used to analyse the role of these TFs on various cell types under hypoxic and normoxic conditions. Results. The scores of 186 KEGG pathways were obtained via GSVA using microarray expression profiles of 40 specimens. WGCNA of the KEGG pathways revealed the two following pathways: calcium signaling pathway and neuroactive ligand-receptor interaction pathways. Subsequently, WGCNA of the gene expression matrix of the samples revealed the calcium signaling pathway-related genes (AC079305.10, BCL10, BCL2A1, BRE-AS1, DYNLL2, EREG, and PTGS2) that were identified as core genes via correlation analysis. Furthermore, SCENIC and pseudotemporal analysis revealed JUN, IRF9, ETV5, and PPARA score gene-related TFs. Jun was found to be associated with hypoxia in endothelial cells, whereas Irf9 and Etv5 were identified as astrocyte-specific TFs associated with oxygen concentration in the mouse cerebral cortex. Conclusions. Calcium signaling pathway-related genes (AC079305.10, BCL10, BCL2A1, BRE-AS1, DYNLL2, EREG, and PTGS2) and TFs (JUN, IRF9, ETV5, and PPARA) were identified to play a key role in IS. This study provides a new perspective and basis for investigating the pathogenesis of IS and developing new therapeutic approaches.

scholarly journals Scattome: A Single-Cell Analysis of Targeted Transcriptome Program to Predict Drug Sensitivity of Single Cells within Human Myeloma Tumors

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4249-4249
Author(s):  
Amit Kumar Mitra ◽  
Ujjal Mukherjee ◽  
Taylor Harding ◽  
Holly Stessman ◽  
Ying Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Lines ◽  
Research Funding ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Pcr Analysis ◽  
Cell Analysis

Abstract Multiple myeloma (MM) is characterized by significant genetic diversity at subclonal levels that likely plays a defining role in the heterogeneity of tumor progression, clinical aggressiveness and drug sensitivity. Such heterogeneity is a driving factor in the evolution of MM, from founder clones through outgrowth of subclonal fractions. DNA Sequencing studies on MM samples have indeed demonstrated such heterogeneity in subclonal architecture at diagnosis based on recurrent mutations in pathologically relevant genes that may ultimately to lead to relapse. However, no study so far has reported a predictive gene expression signature that can identify, distinguish and quantify drug sensitive and drug-resistant subpopulations within a bulk population of myeloma cells. In recent years, our laboratory has successfully developed a gene expression profile (GEP)-based signature that could not only distinguish drug response of MM cell lines, but also was effective in stratifying patient outcomes when applied to GEP profiles from MM clinical trials using proteasome inhibitors (PI) as chemotherapeutic agents. Further, we noted myeloma cell lines that responded to the drug often contained residual sub-population of cells that did not respond, and likely were selectively propagated during drug treatment in vitro, and in patients. In this study, we performed targeted qRT-PCR analysis of single cells using a gene panel that included PI sensitivity genes and gene signatures that could discriminate between low and high-risk myeloma followed by intensive bioinformatics and statistical analysis for the classification and prediction of PI response in individual cells within bulk multiple myeloma tumors. Fluidigm's C1 Single-Cell Auto Prep System was used to perform automated single-cell capture, processing and cDNA synthesis on 576 pre-treatment cells from 12 cell lines representing a wide range of PI-sensitivity and 370 cells from 7 patient samples undergoing PI treatment followed by targeted gene expression profiling of single cells using automated, high-throughput on-chip qRT-PCR analysis using 96.96 Dynamic Array IFCs on the BioMark HD System. Probability of resistance for each individual cell was predicted using a pipeline that employed the machine learning methods Random Forest, Support Vector Machine (radial and sigmoidal), LASSO and kNN (k Nearest Neighbor) for making single-cell GEP data-driven predictions/ decisions. The weighted probabilities from each of the algorithms were used to quantify resistance of each individual cell and plotted using Ensemble forecasting algorithm. Using our drug response GEP signature at the single cell level, we could successfully identify distinct subpopulations of tumor cells that were predicted to be sensitive or resistant to PIs. Subsequently, we developed a R Statistical analysis package (http://cran.r-project.org), SCATTome (Single Cell Analysis of Targeted Transcriptome), that can restructure data obtained from Fluidigm qPCR analysis run, filter missing data, perform scaling of filtered data, build classification models and successfully predict drug response of individual cells and classify each cell's probability of response based on the targeted transcriptome. We will present the program output as graphical displays of single cell response probabilities. This package provides a novel classification method that has the potential to predict subclonal response to a variety of therapeutic agents. Disclosures Kumar: Skyline: Consultancy, Honoraria; BMS: Consultancy; Onyx: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Novartis: Research Funding; Takeda: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.

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