scholarly journals Ontology based molecular signatures for immune cell types via gene expression analysis

2013 ◽  
Vol 14 (1) ◽  
pp. 263 ◽  
Author(s):  
Terrence F Meehan ◽  
Nicole A Vasilevsky ◽  
Christopher J Mungall ◽  
David S Dougall ◽  
Melissa A Haendel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Immune Cell ◽  
Cell Types ◽  
Molecular Signatures

scholarly journals Control of impulsivity by Gi-protein signalling in layer-5 pyramidal neurons of the anterior cingulate cortex

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Bastiaan van der Veen ◽  
Sampath K. T. Kapanaiah ◽  
Kasyoka Kilonzo ◽  
Peter Steele-Perkins ◽  
Martin M. Jendryka ◽  
...  
Keyword(s):  
Gene Expression ◽  
Anterior Cingulate Cortex ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Pyramidal Neurons ◽  
Treatment Options ◽  
Pyramidal Cells ◽  
Cingulate Cortex ◽  
Cell Types ◽  
Anterior Cingulate

AbstractPathological impulsivity is a debilitating symptom of multiple psychiatric diseases with few effective treatment options. To identify druggable receptors with anti-impulsive action we developed a systematic target discovery approach combining behavioural chemogenetics and gene expression analysis. Spatially restricted inhibition of three subdivisions of the prefrontal cortex of mice revealed that the anterior cingulate cortex (ACC) regulates premature responding, a form of motor impulsivity. Probing three G-protein cascades with designer receptors, we found that the activation of Gi-signalling in layer-5 pyramidal cells (L5-PCs) of the ACC strongly, reproducibly, and selectively decreased challenge-induced impulsivity. Differential gene expression analysis across murine ACC cell-types and 402 GPCRs revealed that - among Gi-coupled receptor-encoding genes - Grm2 is the most selectively expressed in L5-PCs while alternative targets were scarce. Validating our approach, we confirmed that mGluR2 activation reduced premature responding. These results suggest Gi-coupled receptors in ACC L5-PCs as therapeutic targets for impulse control disorders.

Gene expression analysis of osteoblasts and calcifying vascular cells: Similar cell types or similar mechanisms?

Bone ◽  
2011 ◽  
Vol 48 ◽  
pp. S119
Author(s):  
R.D.A.M. Alves⁎ ◽  
M. Koedam ◽  
J. van de Peppel ◽  
M. Eijken ◽  
J.P.T.M. van Leeuwen
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Cell Types ◽  
Vascular Cells ◽  
Similar Cell ◽  
Calcifying Vascular Cells

scholarly journals Differential gene expression analysis of HNSCC tumors deciphered tobacco dependent and independent molecular signatures

Oncotarget ◽  
2019 ◽  
Vol 10 (58) ◽  
pp. 6168-6183 ◽  
Author(s):  
Inayatullah Shaikh ◽  
Afzal Ansari ◽  
Garima Ayachit ◽  
Monika Gandhi ◽  
Priyanka Sharma ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Molecular Signatures ◽  
Differential Gene Expression Analysis ◽  
Differential Gene

From Tissues to Cell Types and Back: Single-Cell Gene Expression Analysis of Tissue Architecture

2018 ◽  
Vol 1 (1) ◽  
pp. 29-51 ◽  
Author(s):  
Xi Chen ◽  
Sarah A. Teichmann ◽  
Kerstin B. Meyer
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Three Dimensional ◽  
Cell Types ◽  
Biological Knowledge ◽  
Tissue Architecture ◽  
Cell Gene Expression ◽  
Cell Gene

With the recent transformative developments in single-cell genomics and, in particular, single-cell gene expression analysis, it is now possible to study tissues at the single-cell level, rather than having to rely on data from bulk measurements. Here we review the rapid developments in single-cell RNA sequencing (scRNA-seq) protocols that have the potential for unbiased identification and profiling of all cell types within a tissue or organism. In addition, novel approaches for spatial profiling of gene expression allow us to map individual cells and cell types back into the three-dimensional context of organs. The combination of in-depth single-cell and spatial gene expression data will reveal tissue architecture in unprecedented detail, generating a wealth of biological knowledge and a better understanding of many diseases.

scholarly journals Gene expression analysis method integration and co-expression module detection applied to rare glucide metabolism disorders using ExpHunterSuite

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fernando M. Jabato ◽  
José Córdoba-Caballero ◽  
Elena Rojano ◽  
Carlos Romá-Mateo ◽  
Pascual Sanz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Immune Cell ◽  
Disease Model ◽  
Simulated Data ◽  
Model Organisms ◽  
Link Type ◽  
Module Detection ◽  
Expression Module

AbstractHigh-throughput gene expression analysis is widely used. However, analysis is not straightforward. Multiple approaches should be applied and methods to combine their results implemented and investigated. We present methodology for the comprehensive analysis of expression data, including co-expression module detection and result integration via data-fusion, threshold based methods, and a Naïve Bayes classifier trained on simulated data. Application to rare-disease model datasets confirms existing knowledge related to immune cell infiltration and suggest novel hypotheses including the role of calcium channels. Application to simulated and spike-in experiments shows that combining multiple methods using consensus and classifiers leads to optimal results. ExpHunter Suite is implemented as an R/Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite. It can be applied to model and non-model organisms and can be run modularly in R; it can also be run from the command line, allowing scalability with large datasets. Code and reports for the studies are available from https://github.com/fmjabato/ExpHunterSuiteExamples.

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