UC San Diego
Metabolic analysis of single cell gene expression data: What can we learn?
- Author(s): Zhou, Yuchen
- Advisor(s): Palsson, Bernhard Ø.
- et al.
The advent of single cell profiling technologies has brought unprecedented resolution to cell heterogeneity in key human tissues. A significant remaining challenge is to interpret this cell variation in terms of meaningful functional differences and interactions between cell types. In this study, we perform a metabolic reconstruction-based assessment of transcriptional heterogeneity in the human brain and kidney. We focus specifically on transporters as their expression is associated with both metabolic interactions between cell types and uptake of drugs. We find that: 1) we are able to identify drug-transporter relationships through structural homology between drugs and native transporter substrates, 2) we observe concomitant brain-region specific expression differences in key transporters that may impact therapeutic impact, 3) upstream metabolic genes have expression that correlates with the transporter expression, suggesting that transporter differences are likely physiologically significant, 4) examination of kidney data shows differential expression across different regions and cell types in the human kidney, 5) metabolic reconstructions provide useful interpretation to help understand the significance of single cell gene expression differences. Native metabolic activity of transporters is postulated through expression of metabolic genes with activities that are correlated as determined by metabolic flux modeling. This work illustrates the types of higher order interactions that can be elucidated from single cell profiling data and paves the way to clinical interventions targeting particular cell types and cell interactions.