Laser Capture Microdissection and RNA-Seq Analysis: High Sensitivity Approaches to Explain Histopathological Heterogeneity in Human Glioblastoma FFPE Archived Tissues

Abstract

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.

Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis

“…Interestingly, our results suggest that some conventional antipsychotics among drugs mentioned above, including amitriptyline, fluoxetine, and imipramine, may also play a surprising role in the treatment of GBM, which is consistent with previous studies and offers new hope for patients with GBM (12,75,76,80,83,84,104). In addition, Leite et al (105) demonstrated that clomipramine (tricyclic antidepressant drugs such as imipramine) has an impact on GBM growth and has no toxicity in normal cells (astrocytes and microglia).…”

RNA sequencing by direct tagmentation of RNA/DNA hybrids

“…RNA-seq has been widely used in addressing various biological questions, from exploring the pathogenesis of disease (1,2) to constructing transcriptome maps for various species (3,4). RNA-seq provides informative assessments of samples, especially when heterogeneity in a complex biological system (5,6) or time-dependent dynamic processes are being investigated (7)(8)(9). A typical RNA-seq experiment requires a DNA library generated from messenger RNA (mRNA) transcripts.…”

Changing Technologies of RNA Sequencing and Their Applications in Clinical Oncology

“…These overexpressed genes and pathways were known to promote cell survival and infiltrative growth, migration, and resistance to cancer-targeted therapies in GBM. Civita et al (32) also observed a considerable up-regulation of growth factors signaling pathways in pseudopalisading cells compared to the tumor core. The data demonstrate that certain molecular events are region specific and different regions are molecularly interrelated.…”

“…The study established initial feasibility of LCM-RNASeq in a six patient sample population with an aim to identify biomarkers for lung cancer progression. Several studies have recently emerged, here we provide one example that highlights how LCM-RNAseq data can be used to deconvolve multiple cell type-specific gene expression profiles in cancer (32). In this study, the expression profiles of six specific tissue compartments of human glioblastoma (BGM) were analyzed using LCM-RNAseq techniques.…”