Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.01.27.525833v1?rss=1

Authors: Gonzales, D. T., Schuhmacher, M., Lennartz, H. M., Iglesias-Artola, J. M., Kuhn, S. M., Barahtjan, P., Zechner, C., Nadler, A.

Abstract:
Lipids are key components of cellular signaling networks yet studying the role of molecularly distinct lipid species remains challenging due to the complexity of the cellular lipidome and a scarcity of methods for performing quantitative lipid biochemistry in living cells. We have recently used lipid uncaging to quantify lipid-protein affinities and rates of lipid transbilayer movement and turnover in the diacylglycerol cascade using population average time series data. So far, this approach does not allow to account for the cell-to-cell variability of cellular signaling responses. We here aim to develop a framework that allows to quantitatively determine diacylglycerol-protein affinities and transbilayer movement at the single cell level. A key challenge is that initial uncaging photoreaction yields cannot be measured for single cells and have to be inferred along with the remaining model parameters. We first performed an in silico study on simulated data to understand under which conditions all model parameters are well identifiable. Using profile likelihood analysis, we found that identifiability depends predominantly on the signal-to-noise ratio. The impaired parameter identifiability due to experimental noise can be partially mitigated by increasing the number of single cell trajectories. Using a C1-domain-EGFP fusion protein as a model effector protein in combination with a broad variety of structurally different diacylglycerol species, we acquired multiple sets of single cell signaling trajectories. Using our analytical pipeline, we found that almost all species-specific model parameters are identifiable from experimental data. We find that higher unsaturation degree and longer side chains correlate with faster lipid transbilayer movement and turnover and higher lipid-protein affinities, with the exception of steaoryl-oleoyl glycerol, which noticeably deviated from the general trend. In summary, our work demonstrates how rate parameters and lipid-protein affinities can be quantified from single cell signaling trajectories with sufficient sensitivity to resolve the subtle kinetic differences caused by the chemical diversity of signaling lipid pools.

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