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Related Experiment Video

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Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

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Published on: February 27, 2020

Tagging and Enriching Proteins Enables Cell-Specific Proteomics.

Thomas S Elliott¹, Ambra Bianco¹, Fiona M Townsley¹

¹Medical Research Council Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK.

Cell Chemical Biology

|July 23, 2016

Summary

We developed a new method called stochastic orthogonal recoding of translation with enrichment (SORT-E) to identify specific proteins within cells. This technique allows for cell-specific proteomics in animals, advancing our understanding of biological processes.

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Area of Science:

Proteomics
Molecular Biology
Biochemistry

Background:

Understanding cell-specific functions requires analyzing proteomes within complex biological systems.
Existing methods face challenges in isolating and identifying proteins from specific cell types in vivo.

Purpose of the Study:

To extend the stochastic orthogonal recoding of translation (SORT) approach for cell-specific proteome tagging.
To develop an enrichment strategy (SORT-E) for efficient recovery and identification of tagged proteins.

Main Methods:

Utilized a cyclopropene-containing amino acid for co-translational tagging in genetically targeted cells.
Developed a tetrazine-biotin probe with a cleavable linker for protein enrichment.
Employed mass spectrometry for the identification of enriched, tagged proteins.

Main Results:

SORT-E efficiently recovers and enriches tagged proteins, including low-abundance ones.
Tagging at distinct codons enriches overlapping but unique protein sets, enhancing proteome coverage.
Successfully performed cell-specific proteomics in the fruit fly (Drosophila melanogaster).

Conclusions:

SORT-E is a powerful tool for cell-specific proteomics in multicellular organisms.
This method enables the definition of cell-specific proteomes during development and disease.
Facilitates research into cellular mechanisms underlying learning and memory.