Molecular classification of acute myeloid leukemia (AML) aids prognostic stratification and clinical management. Our aim in this study is to identify transcriptome‐wide mRNAs that are specific to each of the molecular subtypes of AML. We analyzed RNA‐sequencing data of 955 AML samples from three cohorts, including the BeatAML project, the Cancer Genome Atlas, and a cohort of Swedish patients to provide a comprehensive transcriptome‐wide view of subtype‐specific mRNA expression. We identified 729 subtype‐specific mRNAs, discovered in the BeatAML project and validated in the other two cohorts. Using unique proteomics data, we also validated the presence of subtype‐specific mRNAs at the protein level, yielding a rich collection of potential protein‐based biomarkers for the AML community. To enable the exploration of subtype‐specific mRNA expression by the broader scientific community, we provide an interactive resource to the public.

A classic problem in preservation is the microbes can grow in low-moisture foods. In this paper, the water sorption, and thermodynamic properties of glucose/WPI solid matrices were measured, while their molecular mobility was analyzed and associated with the microbial growth of D. Hansenii at various a_w and 30 °C. Although the sorption isotherms, T_g, and relaxation processes of studied matrices were affected by a_w and WPI, the microbial growth showed highly dependent on water mobility rather than a_w. Hence, we introduced water usability (U_w), derived from the mobility difference between system-involved water and liquid pure water explicating from the classical thermodynamic viewpoint, to describe the dynamic changes of water mobility in glucose/WPI matrices. Despite to a_w, the yeast growth rate was enhanced at high U_w matrices concomitantly with a rapid cell doubling time. Therefore, the proposed U_w provides a better understanding of the water relationships of microorganisms in food preservation.

The expression abundance of transcripts in nondiseased breast tissue varies among individuals. The association study of genotypes and imaging phenotypes may help us to understand this individual variation. Since existing reports mainly focus on tumors or lesion areas, the heterogeneity of pathological image features and their correlations with RNA expression profiles for nondiseased tissue are not clear. The aim of this study is to discover the association between the nucleus features and the transcriptome-wide RNAs. We analyzed both microscopic histology images and RNA-sequencing data of 456 breast tissues from the Genotype-Tissue Expression (GTEx) project and constructed an automatic computational framework. We classified all samples into four clusters based on their nucleus morphological features and discovered feature-specific gene sets. The biological pathway analysis was performed on each gene set. The proposed framework evaluates the morphological characteristics of the cell nucleus quantitatively and identifies the associated genes. We found image features that capture population variation in breast tissue associated with RNA expressions, suggesting that the variation in expression pattern affects population variation in the morphological traits of breast tissue. This study provides a comprehensive transcriptome-wide view of imaging-feature-specific RNA expression for healthy breast tissue. Such a framework could also be used for understanding the connection between RNA expression and morphology in other tissues and organs. Pathway analysis indicated that the gene sets we identified were involved in specific biological processes, such as immune processes.

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.