Single-Cell Gene Expression Analysis: Implications for Neurodegenerative and Neuropsychiatric Disorders View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2004-06

AUTHORS

Stephen D. Ginsberg, Irina Elarova, Marc Ruben, Fengzhu Tan, Scott E. Counts, James H. Eberwine, John Q. Trojanowski, Scott E. Hemby, Elliott J. Mufson, Shaoli Che

ABSTRACT

Technical and experimental advances in microaspiration techniques, RNA amplification, quantitative real-time polymerase chain reaction (qPCR), and cDNA microarray analysis have led to an increase in the number of studies of single-cell gene expression. In particular, the central nervous system (CNS) is an ideal structure to apply single-cell gene expression paradigms. Unlike an organ that is composed of one principal cell type, the brain contains a constellation of neuronal and noneuronal populations of cells. A goal is to sample gene expression from similar cell types within a defined region without potential contamination by expression profiles of adjacent neuronal subpopulations and noneuronal cells. The unprecedented resolution afforded by single-cell RNA analysis in combination with cDNA microarrays and qPCR-based analyses allows for relative gene expression level comparisons across cell types under different experimental conditions and disease states. The ability to analyze single cells is an important distinction from global and regional assessments of mRNA expression and can be applied to optimally prepared tissues from animal models as well as postmortem human brain tissues. This focused review illustrates the potential power of single-cell gene expression studies within the CNS in relation to neurodegenerative and neuropsychiatric disorders such as Alzheimer's disease (AD) and schizophrenia, respectively. More... »

PAGES

1053-1064

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/b:nere.0000023593.77052.f7

DOI

http://dx.doi.org/10.1023/b:nere.0000023593.77052.f7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1040892632

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/15176463


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HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1023/b:nere.0000023593.77052.f7'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1023/b:nere.0000023593.77052.f7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1023/b:nere.0000023593.77052.f7'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1023/b:nere.0000023593.77052.f7'


 

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