Laser capture microdissection and RNA-seq analysis: High sensitivity approaches to explain histopathological heterogeneity in human glioblastoma FFPE archived tissues

Laser capture microdissection (LCM) coupled with RNA-seq is a powerful tool to identify genes that are differentially expressed in specific histological tumor subtypes. To better understand the role of single tumor cell populations in the complex heterogeneity of glioblastoma, we paired microdissection and NGS technology to study intra-tumoral differences into specific histological regions and cells of human GBM FFPE tumors. We here isolated astrocytes, neurons and endothelial cells in 6 different histological contexts: tumor core astrocytes, pseudopalisading astrocytes, perineuronal astrocytes in satellitosis, neurons with satellitosis, tumor blood vessels, and normal blood vessels. A customized protocol was developed for RNA amplification, library construction, and whole transcriptome analysis of each single portion. We first validated our protocol comparing the obtained RNA expression pattern with the gene expression levels of RNA-seq raw data experiments from the BioProject NCBI database, using Spearman's correlation coefficients calculation. We found a good concordance for pseudopalisading and tumor core astrocytes compartments (0.5 Spearman correlation) and a high concordance for perineuronal astrocytes, neurons, normal, and tumor endothelial cells compartments (0.7 Spearman correlation). Then, Principal Component Analysis and differential expression analysis were employed to find differences between tumor compartments and control tissue and between same cell types into distinct tumor contexts. Data consistent with the literature emerged, in which multiple therapeutic targets significant for glioblastoma (such as Integrins, Extracellular Matrix, transmembrane transport, and metabolic processes) play a fundamental role in the disease progression. Moreover, specific cellular processes have been associated with certain cellular subtypes within the tumor. Our results are promising and suggest a compelling method for studying glioblastoma heterogeneity in FFPE samples and its application in both prospective and retrospective studies.