Single-nucleus RNA-seq2 reveals functional crosstalk between liver zonation and ploidy.

Funder: Cancer Research UKSingle-cell RNA-seq reveals the role of pathogenic cell populations in development and progression of chronic diseases. In order to expand our knowledge on cellular heterogeneity, we have developed a single-nucleus RNA-seq2 method tailored for the comprehensive analysis of the nuclear transcriptome from frozen tissues, allowing the dissection of all cell types present in the liver, regardless of cell size or cellular fragility. We use this approach to characterize the transcriptional profile of individual hepatocytes with different levels of ploidy, and have discovered that ploidy states are associated with different metabolic potential, and gene expression in tetraploid mononucleated hepatocytes is conditioned by their position within the hepatic lobule. Our work reveals a remarkable crosstalk between gene dosage and spatial distribution of hepatocytes

Understanding the evolutionary potential of epigenetic variation: a comparison of heritable phenotypic variation in epiRILs, RILs, and natural ecotypes of Arabidopsis thaliana

Increasing evidence for epigenetic variation within and among natural plant populations has led to much speculation about its role in the evolution of plant phenotypes. However, we still have a very limited understanding of the evolutionary potential of epigenetic variation, in particular in comparison to DNA sequence-based variation. To address this question, we compared the magnitudes of heritable phenotypic variation in epigenetic recombinant inbred lines (epiRILs) of Arabidopsis thaliana—lines that mainly differ in DNA methylation but only very little in DNA sequence—with other types of A. thaliana lines that differ strongly also in DNA sequence. We grew subsets of two epiRIL populations with subsets of two genetic RIL populations, of natural ecotype collections, and of lines from a natural population in a common environment and assessed their heritable variation in growth, phenology, and fitness. Among-line phenotypic variation and broad-sense heritabilities tended to be largest in natural ecotypes, but for some traits the variation among epiRILs was comparable to that among RILs and natural ecotypes. Within-line phenotypic variation was generally similar in epiRILs, RILs, and ecotypes. Provided that phenotypic variation in epiRILs is mainly caused by epigenetic differences, whereas in RILs and natural lines it is largely driven by sequence variation, our results indicate that epigenetic variation has the potential to create phenotypic variation that is stable and substantial, and thus of evolutionary significance

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine. © 2021, The Author(s)