An optimized chemical-genetic method for cell-specific metabolic labeling of RNA

Abstract

Tissues and organs are composed of diverse cell types, which poses a major challenge for cell-type-specific profiling of gene expression. Current metabolic labeling methods rely on exogenous pyrimidine analogs that are only incorporated into RNA in cells expressing an exogenous enzyme. This approach assumes that off-target cells cannot incorporate these analogs. We disprove this assumption and identify and characterize the enzymatic pathways responsible for high background incorporation. We demonstrate that mammalian cells can incorporate uracil analogs and characterize the enzymatic pathways responsible for high background incorporation. To overcome these limitations, we developed a new small molecule–enzyme pair consisting of uridine/cytidine kinase 2 and 2′-azidouridine. We demonstrate that 2′-azidouridine is only incorporated in cells expressing uridine/cytidine kinase 2 and characterize selectivity mechanisms using molecular dynamics and X-ray crystallography. Furthermore, this pair can be used to purify and track RNA from specific cellular populations, making it ideal for high-resolution cell-specific RNA labeling. Overall, these results reveal new aspects of mammalian salvage pathways and serve as a new benchmark for designing, characterizing and evaluating methodologies for cell-specific labeling of biomolecules.

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Fig. 1: Schematic of cell-specific metabolic labeling of RNA.
Fig. 2: Mammalian cells are capable of salvaging exogenous uracil analogs for eventual incorporation into cellular RNA.
Fig. 3: Cellular screening shows that the UCK2–2′AzUd pair is suitable for metabolic labeling of RNA.
Fig. 4: Structural analysis of the UCK2–2′AzUd pair reveals the mechanism for selectivity.
Fig. 5: Demonstration of cell-specific metabolic labeling with UCK2–2′AzUd pair.

Data availability

All structures have been deposited in the Protein Data Bank (6N53, 6N54 and 6N55). RNA sequencing datasets have been deposited in the Gene Expression Omnibus (GSE136638).

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Acknowledgements

We thank members of the Spitale laboratory for their careful reading and critique of the manuscript. The Spitale laboratory is supported by startup funds from the University of California, Irvine, and National Institutes of Health (NIH) grants 1DP2GM119164 (to R.C.S.) and 5R21MH113062 (to R.C.S.). R.C.S. is a Pew Biomedical Scholar. S.N. is supported as a Vertex Fellow and NSF BEST-IGERT (DGE-1144901). D.L.M. receives financial support from the NIH (1R01GM108889-01). This material is based on work supported by the National Science Foundation Graduate Research Fellowship under grant DGE-1321846 (to N.L.). C.W.G. acknowledges University of California Cancer Research Coordinating Committee grant CTR-18-522186 and R.Q was supported by MARC (GM-69337) and MBRS+MSD (GM-055246). B.J.C. was supported by a University of California, Irvine Chancellor’s ADVANCE postdoctoral fellowship. We thank the Advanced Light Source at Berkeley National Laboratories and the Stanford Synchrotron Radiation Lightsource for their invaluable help in data collection. This work was also made possible, in part, through access to the Genomics High Throughput Facility Shared Resource of the Cancer Center Support Grant (P30CA-062203) at the University of California, Irvine and NIH shared instrumentation grants 1S10RR025496-01, 1S10OD010794-01 and 1S10OD021718-01.

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Contributions

S.N. and R.C.S. conceived the study. S.N. performed all metabolic labeling studies and UCK1 and UCK2 activity analyses with help from K.S. and K.K. W.E.E. assisted with data analysis. B.J.C., R.Q. and C.W.G. performed X-ray crystallography analysis and binding studies with input from R.C.S. and S.N. N.M.L. and D.L.M. performed molecular dynamics simulations with input from R.C.S. and S.N. The manuscript was written by S.N. and R.C.S. with input from all authors.

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Correspondence to Robert C. Spitale.

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Peer review information Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

41592_2019_726_MOESM3_ESM.mov

This file shows the molecular dynamics calculations for flipping of 2′AzU in the precatalytic state.

Supplementary Information

Supplementary Figs. 1–39 and Supplementary Notes 1–2.

Reporting Summary

Supplementary Video 1

This file shows the molecular dynamics calculations for flipping of 2′AzU in the precatalytic state.

Source data

Source Data Fig. 2

191113_UCK2_Figure_2_Source_Data.xls

Source Data Fig. 3

191113_UCK2_Figure_3_Source_Data.xls

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Nainar, S., Cuthbert, B.J., Lim, N.M. et al. An optimized chemical-genetic method for cell-specific metabolic labeling of RNA. Nat Methods 17, 311–318 (2020). https://doi.org/10.1038/s41592-019-0726-y

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