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Updated: Jun 22, 2025

Assessment of Selective mRNA Translation in Mammalian Cells by Polysome Profiling
Published on: October 28, 2014
Single-nucleus proteomics identifies regulators of protein transport
Jason Derks1,2, Tobias Jonson1, Andrew Leduc1
1Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center, Northeastern University, Boston, MA 02115, USA.
None:
The physiological response of a cell to stimulation depends on its proteome configuration. Therefore, the abundance variation of regulatory proteins across unstimulated single cells can be associatively linked with their response to stimulation. Here we developed an approach that leverages this association across individual cells and nuclei to systematically identify potential regulators of biological processes, followed by targeted validation. Specifically, we applied this approach to identify regulators of nucleocytoplasmic protein transport in macrophages stimulated with lipopolysaccharide (LPS). To this end, we quantified the proteomes of 3,412 individual nuclei, sampling the dynamic response to LPS treatment, and linking functional variability to proteomic variability. Minutes after the stimulation, the protein transport in individual nuclei correlated strongly with the abundance of known protein transport regulators, thus revealing the impact of natural protein variability on functional cellular response. We found that simple biophysical constraints, such as the quantity of nuclear pores, partially explain the variability in LPS-induced nucleocytoplasmic transport. Among the many proteins newly identified to be associated with the response, we selected 16 for targeted validation by knockdown. The knockdown phenotypes confirmed the inferences derived from natural protein and functional variation of single nuclei, thus demonstrating the potential of (sub-)single-cell proteomics to infer functional regulation. We expect this approach to generalize to broad applications and enhance the functional interpretability of single-cell omics data.
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