Abstract
Gene expression is an important process in which genes guide the synthesis of proteins, and molecular-level differences often lead to individual phenotypic variations. Combining molecular information at the nano-level with phenotypic information at the micron level can allow for the identification of a series of gene-level biomarkers related to image phenotypes and provide a more comprehensive way to understand the impact of genes on cell morphology. Currently, most studies in imaging genomics focus on tumors. However, tumor heterogeneity mitigates the reproducibility of gene-micro-correlations. Furthermore, research on the association between imaging features and gene expression patterns in multiple tissues is still lacking. This study aims to explore the correlations between the nuclear features of healthy tissue cells and RNA expression patterns. Based on 4306 samples of 13 organs from the largest human healthy tissue database, the Genotype-Tissue Expression (GTEx) project, a deep learning-based automatic analysis framework was constructed to investigate the geno-micro-correlations across tissues. The proposed framework was used to quantitatively evaluate the nuclear morphological features of each healthy organ and identify gene sets specific to nuclear features in functionally similar organs. It revealed the biological significance of these gene sets through a pathway analysis, including cell growth, development, metabolism, and immunity. The results show that differences in nuclear morphological features of healthy organs are associated with differential RNA expression. By analyzing the correlation of differential patterns in multiple healthy organs, this study revealed the associations between gene expressions and phenotypes in multiple organs.