Age-related macular degeneration (AMD) is a global leading cause of visual impairment in older populations. ‘Wet’ AMD, the most common subtype of this disease, occurs when pathological angiogenesis infiltrates the subretinal space (choroidal neovascularization), causing hemorrhage and retinal damage. Gold standard anti-vascular endothelial growth factor (VEGF) treatment is an effective therapy, but the long-term prevention of visual decline has not been as successful. This warrants the need to elucidate potential VEGF-independent pathways. We generated blood out-growth endothelial cells (BOECs) from wet AMD and normal control subjects, then induced angiogenic sprouting of BOECs using a fibrin gel bead assay. To deconvolute endothelial heterogeneity, we performed single-cell transcriptomic analysis on the sprouting BOECs, revealing a spectrum of cell states. Our wet AMD BOECs share common pathways with choroidal neovascularization such as extracellular matrix remodeling that promoted proangiogenic phenotype, and our ‘activated’ BOEC subpopulation demonstrated proinflammatory hallmarks, resembling the tip-like cells in vivo. We uncovered new molecular insights that pathological angiogenesis in wet AMD BOECs could also be driven by interleukin signaling and amino acid metabolism. A web-based visualization of the sprouting BOEC single-cell transcriptome has been created to facilitate further discovery research.

Bacterial resistance to antibiotics is a growing problem that is projected to cause more deaths than cancer in 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the bacterial ribosomes, proteins that are involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. In order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct gene co-expression networks, which were used to identify more than a hundred uncharacterized genes that were transcriptionally associated with protein synthesis. We provide the identity of these genes together with the processed gene expression data. The data can be used to identify other vulnerabilities or bacteria, while our approach demonstrates how the processing of gene expression data can be easily crowdsourced.