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Laser Capture Microdissection-Based mRNA Expression Microarrays and Single-Cell RNA Sequencing in Atherosclerosis Research.

Methods Mol Biol. 2022;2419:715-726. doi: 10.1007/978-1-0716-1924-7_43. PMID: 35237997.

Authors/Editors: Zhang X, Wang Z, Zhang C, Li Y, Lu S, Steffens S, Mohanta S, Weber C, Habenicht A, Yin C.
Publication Date: 2022


A major goal of methodologies related to large scale gene expression analyses is to initiate comprehensive information on transcript signatures in single cells within the tissue's anatomy. Until now, this could be achieved in a stepwise experimental approach: (1) identify the majority of transcripts in a single cell (single cell transcriptome); (2) provide information on transcripts on multiple cell subtypes in a complex sample (cell heterogeneity); and (3) give information on each cell's spatial location within the tissue (zonation transcriptomics). Such genetic information will allow construction of functionally relevant gene expression maps of single cells of a given anatomically defined tissue compartment and thus pave the way for subsequent analyses, including their epigenetic modifications. Until today these aims have not been achieved in the area of cardiovascular disease research though steps toward these goals become apparent: laser capture microdissection (LCM)-based mRNA expression microarrays of atherosclerotic plaques were applied to gain information on local gene expression changes during disease progression, providing limited spatial resolution. Moreover, while LCM-derived tissue RNA extracts have been shown to be highly sensitive and covers a range of 10-16,000 genes per array/small amount of RNA, its original promise to isolate single cells from a tissue section turned out not to be practicable because of the inherent contamination of the cell's RNA of interest with RNA from neighboring cells. Many shortcomings of LCM-based analyses have been overcome using single-cell RNA sequencing (scRNA-seq) technologies though scRNA-seq also has several limitations including low numbers of transcripts/cell and the complete loss of spatial information. Here, we describe a protocol toward combining advantages of both techniques while avoiding their flaws.


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