Madsen, C. T. | Search bioRxiv

bioRxiv2026

Suspension culture drives metabolic rewiring in human pluripotent stem cells

Human pluripotent stem cells (hPSCs) are increasingly expanded in 3D suspension culture to support scalable cell culturing, yet how the transition from 2D to 3D cultures affects hPSCs remains poorly understood. Here, we compare hPSCs in 2D and 3D cultures using transcriptomics, proteomics, spent medium analysis, and stable-isotope tracing. hPSCs maintained pluripotency in both formats, although suspension culture showed altered growth dynamics and increased initial cell loss. Multi-omics analysis identified culture format as the dominant driver of molecular variation and revealed changes in adhesion, cytoskeletal organization, glycolysis, and oxidative phosphorylation. Functional analyses showed increased lactate production from glucose and stable-isotope tracing revealed increased glucose-derived pyruvate formation and altered partitioning of glucose-derived carbon within the TCA cycle in 3D suspension cultures. Glutamine contribution to the TCA cycle was reduced, whereas relative reductive carboxylation was increased in 3D suspension cultures. Together, these findings show that transition from 2D to 3D suspension culture induces metabolic rewiring in hPSCs.

cell biology↗

bioRxiv2022

Identifying endogenous peptide receptors bycombining structure and transmembrane topologyprediction

Many secreted endogenous peptides rely on signalling pathways to exert their function in the body. While peptides can be discovered through high throughput technologies, their cognate receptors typically cannot, hindering the understanding of their mode of action. We investigate the use of AlphaFold-Multimer for identifying the cognate receptors of secreted endogenous peptides in human receptor libraries without any prior knowledge about likely candidates. We find that AlphaFolds predicted confidence metrics have strong performance for prioritizing true peptide-receptor interactions. By applying transmembrane topology prediction using DeepTMHMM, we further improve performance by detecting and filtering biologically implausible predicted interactions. In a library of 1112 human receptors, the method ranks true receptors in the top percentile on average for 11 benchmark peptide-receptor pairs.

bioinformatics↗