hCoCena: A toolbox for network-based co-expression analysis and horizontal integration of transcriptomic datasets

Summary: As the number and complexity of transcriptomic datasets increase, there is a rising demand for accessible and user-friendly analysis tools. Here, we present hCoCena (horizontal construction of co-expression networks and analysis), a toolbox facilitating the analysis of a single dataset, as well as the joint analysis of multiple datasets. We describe steps for workspace setup, formatting tables, data processing, and network integration. We then detail procedures for gene clustering, gene set enrichment analysis, and transcription factor enrichment analysis.For complete details on the use and execution of this protocol, please refer to Oestreich et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics

Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

Background!#!The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system.!##!Methods!#!In order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings.!##!Results!#!Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host.!##!Conclusions!#!Our study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity