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Profiling cell-type specific gene expression in post-mortem human brain samples through laser capture microdissection.

Authors :
Almeida, Daniel
Turecki, Gustavo
Source :
Methods. Nov2022, Vol. 207, p3-10. 8p.
Publication Year :
2022

Abstract

• Cell-type-specific enrichment strategies are crucial for tackling the downstream effects of tissue heterogeneity in transcriptomic analyses. • Laser capture microdissection allows for the capture of specific cell types based on size, morphology and/or presence of a specific marker. • Laser capture microdissection has been combined with a wide array of molecular techniques for studying gene expression, including RNA-sequencing. The transcriptome of a cell constitutes an essential piece of cellular identity and contributes to the multifaceted complexity and heterogeneity of cell-types within the mammalian brain. Thus, while a wealth of studies have investigated transcriptomic alterations underlying the pathophysiology of diseases of the brain, their use of bulk-tissue homogenates makes it difficult to tease apart whether observed differences are explained by disease state or cellular composition. Cell-type-specific enrichment strategies are, therefore, crucial in the context of gene expression profiling. Laser capture microdissection (LCM) is one such strategy that allows for the capture of specific cell-types, or regions of interest, under microscopic visualization. In this review, we focus on using LCM for cell-type specific gene expression profiling in post-mortem human brain samples. We begin with a discussion of various LCM systems, followed by a walk-through of each step in the LCM to gene expression profiling workflow and a description of some of the limitations associated with LCM. Throughout the review, we highlight important considerations when using LCM with post-mortem human brain samples. Whenever applicable, commercially available kits that have proven successful in the context of LCM with post-mortem human brain samples are described. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10462023
Volume :
207
Database :
Academic Search Index
Journal :
Methods
Publication Type :
Academic Journal
Accession number :
160214015
Full Text :
https://doi.org/10.1016/j.ymeth.2022.08.013