A novel method for integrating transcriptomics and neuroimaging: Neuroimaging / imaging and genetics. (7th December 2020)

• Record Type: Journal Article
• Authors: Povala, Guilherme; Bastiani, Marco Antônio; Bellaver, Bruna; Ferreira, Pamela C.L.; Souza, Débora Guerini; Brum, Wagner Scheeren; Zatt, Bruno; Zimmer, Eduardo R.
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 4
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.039779
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Positron emission tomography (PET) imaging has been playing a fundamental role in diagnosis of Alzheimer's disease (AD). Also, in the context of AD, blood‐based biomarkers that are capable of predicting PET brain imaging findings are of high interest. Both PET imaging and transcriptomics are rich sources of different biological information. Hence, an interesting strategy to find potential novel blood biomarkers is by integrating PET imaging data with blood transcriptomics. We aim to develop a method for combining blood transcriptomics profile and PET imaging data, which may highlight novel AD biomarkers. Here, we hypothesize that by integrating blood transcriptomics and PET imaging data we will be able to identify clinically relevant novel peripheral biomarkers. Method: Imaging and transcriptomics data were acquired from Alzheimer's Disease Neuroimaging Initiative (ADNI). Microarray gene expression profiling from blood samples of 69 cognitively unimpaired (CU) individuals and 158 mild cognitively impaired (MCI) were submitted to differential expression (DE) analysis using the limma R package. The [F 18 ]FDG‐PET standardized uptake ratio (SUVr), using cerebellum as the reference region, were calculated. Genes obtained from DE analysis were selected to undergo integration with [F 18 ]FDG‐PET images using voxel‐wise generalized linear regressions (GLR) (RMINC package). Results: The DE analysis resulted in 1232 differentially expressed genes (DEGs) (p‐value < 0.05). The GLR computed the associations between gene expression and [F 18 ]FDG for each voxel, resulting in t‐value maps. Afterwards, only gray matter voxels presenting absolute t‐values higher than 2.3 were retained. Then, we transformed t‐value maps into proportion maps. In brief, each volume of interest (VOI) shows the percentage of voxels statistically correlated with gene expression (see Figure 1). Finally, we ranked DEGs according to the amount of VOIs with a proportion higher than 30%. Conclusion: With the use of the proposed method, we were able to integrate blood transcriptomics with PET neuroimaging. The implementation of this method shows great potential in the search for new blood biomarkers, which can accelerate early diagnosis and provides a template for future research in the field.