Temporal changes in the gene expression heterogeneity during brain development and aging

Cells in largely non-mitotic tissues such as the brain are prone to stochastic (epi-)genetic alterations that may cause increased variability between cells and individuals over time. Although increased interindividual heterogeneity in gene expression was previously reported, whether this process starts during development or if it is restricted to the aging period has not yet been studied. The regulatory dynamics and functional significance of putative aging-related heterogeneity are also unknown. Here we address these by a meta-analysis of 19 transcriptome datasets from three independent studies, covering diverse human brain regions. We observed a significant increase in inter-individual heterogeneity during aging (20 + years) compared to postnatal development (0 to 20 years). Increased heterogeneity during aging was consistent among different brain regions at the gene level and associated with lifespan regulation and neuronal functions. Overall, our results show that increased expression heterogeneity is a characteristic of aging human brain, and may influence aging-related changes in brain functions

Convolutional Neural Networks for Classification of Colorectal Cancer on Whole Slide Images

Diagnosis of cancer is typically performed by medical pathologist visually inspecting hematoxylin and eosin (H&E) stained whole slide images. Although the early detection of various cancer types is crucial for a successful treatment, it is a challenging work due to inter- and intra-personal variability and it may often result in a disagreement between pathologists. Visual inspection of medical specimen is a well- established method for cancer diagnosis, but digital transformation is now inevitable fact, whereby conventional microscopic examinations with bareeyes have been replaced by artificial learning (AI)- based digital pathology applications in last years. For this reason, AI-driven solutions are now helping to operate on biospecimen images mostly to assist pathologists. Moreover, deep learning-based diagnosis systems have showed a potential for reducing the cost and improving the accuracy. In this work, we aimed to develop a automated pipeline for colorectal cancer diagnoses by applying Convolutional Neural Network (CNN) based models to classify whole slide images in two classes, carcinoma and non-carcinoma. We included several previously developed deep learning classifiers in our pipeline, where a data set with more than 300 annotated and accessible whole-slide images for colorectal cancer will be used to evaluate model performances

Inter-tissue convergence of gene expression during ageing suggests age-related loss of tissue and cellular identity

Developmental trajectories of gene expression may reverse in their direction during ageing, a phenomenon previously linked to cellular identity loss. Our analysis of cerebral cortex, lung, liver, and muscle transcriptomes of 16 mice, covering development and ageing intervals, revealed widespread but tissue-specific ageing-associated expression reversals. Cumulatively, these reversals create a unique phenomenon: mammalian tissue transcriptomes diverge from each other during postnatal development, but during ageing, they tend to converge towards similar expression levels, a process we term Divergence followed by Convergence (DiCo). We found that DiCo was most prevalent among tissue-specific genes and associated with loss of tissue identity, which is confirmed using data from independent mouse and human datasets. Further, using publicly available single-cell transcriptome data, we showed that DiCo could be driven both by alterations in tissue cell-type composition and also by cell-autonomous expression changes within particular cell types