Abstract
Complex sugar polymers known as glycans contribute to the high molecular diversity of the eukaryotic cell surface. The types and levels of glycans on one cell can be sensed by other cells using carbohydrate-specific binding proteins such as lectins, enabling glycans to regulate cell-cell interactions. Glycans are covalently assembled onto proteins and lipids as they traverse the secretory pathway, a tightly regulated process known as glycosylation and carried out by Golgi-resident enzymes. Errors in glycosylation due to the dysfunctional trafficking of enzymes and substrates in the Golgi are implicated in human diseases. Here we ask how much information about Golgi dysfunction is encoded by surface glycans in single cells. This task is challenging due to high cell-to-cell variability in glycan levels. We exploited the loss-of-adhesion driven disorganization of the Golgi in mouse fibroblasts to generate a highly reproducible gradient of Golgi morphology phenotypes, by titrating the Arf1 inhibitor Brefeldin A (BFA). We measured the resulting distribution of cell-surface glycans in single cells using two fluorescently tagged lectin probes (ConA and WGA). A mathematical model of intracellular traffic, parameterized against measurements of Golgi fragmentation and endocytosis, quantitatively explains cell-surface lectin levels across time and BFA concentrations. We used this model to construct an optimal Bayesian decoder and showed that singlecell lectin measurements predict Golgi phenotypes with accuracy far greater than chance. By combining signals from two lectins we further improved prediction accuracy and speed. Such multi-lectin information may be exploited during natural cell-cell communication, and in the development of single-cell diagnostics.
Competing Interest Statement
The authors have declared no competing interest.