“RADIOTRANSCRIPTOMICS”: A synergy of imaging and transcriptomics in clinical assessment

Abstract

Recent advances in quantitative imaging and “omics” technology have generated a wealth of mineable biological “big data”. With the push towards a P4 “predictive, preventive, personalized, and participatory” approach to medicine, researchers began integrating complementary tools to further tune existing diagnostic and therapeutic models. The field of radiogenomics has long pioneered such multidisciplinary investigations in neuroscience and oncology, correlating genotypic and phenotypic signatures to study structural and functional changes in relation to altered molecular behavior. Given the innate dynamic nature of complex disorders and the role of environmental and epigenetic factors in pathogenesis, the transcriptome can further elucidate serial modifications undetected at the genome level. We therefore propose “radiotranscriptomics” as a new member of the P4 medicine initiative, combining transcriptome information, including gene expression and isoform variation, and quantitative image annotations.

Personalized medicine in sarcoidosis: unravelling biomarkers for targeted care

“…Likewise, radiotranscriptomics, a combination of the analysis of the expression of different genes with different measurable radiological manifestations, has gained increasing interest in lung cancer. Raiotranscriptomics may be helpful in identifying surrogate systemic markers of lung damage to improve disease management and to predict prognosis [47,48].…”

Deep phenotyping for precision medicine in Parkinson's disease

“…Analogously to imaging genetics, imaging transcriptomics deals with the integrated analysis of brain imaging and gene expression data to gain insights into the molecular changes associated with neurodegeneration. As omics offer a dynamic dimension, disease progression can be followed by using imaging transcriptomics ( Katrib et al, 2016 ). A common approach is to correlate gene expression with IDPs through shared defined regions of interest ( Mroczek et al, 2021 ).…”

Single-Cell Transcriptomics Bioinformatics and Computational Challenges

“…Previous multi-omics bioinformatics tools applied to bulk samples could be leveraged. The use of graphs and tensor approaches that integrate heterogeneous features in bulk samples may be good starting points for multi-dimensional single cell data (Li et al, 2009 ; Levine et al, 2015 ; Katrib et al, 2016 ; Zhu et al, 2016 ). Efforts should also be made toward developing computational methods to make use of spatial information (possibly guided by imaging) in combination of scRNA-Seq (Pettit et al, 2014 ; Satija et al, 2015 ).…”