Quantitative isoform-profiling of highly diversified recognition molecules

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Abstract

Complex biological systems rely on cell surface cues that govern cellular self-recognition and selective interactions with appropriate partners. Molecular diversification of cell surface recognition molecules through DNA recombination and complex alternative splicing has emerged as an important principle for encoding such interactions. However, the lack of tools to specifically detect and quantify receptor protein isoforms is a major impediment to functional studies. We here developed a workflow for targeted mass spectrometry by selected reaction monitoring (SRM) that permits quantitative assessment of highly diversified protein families. We apply this workflow to dissecting the molecular diversity of the neuronal neurexin receptors and uncover an alternative splicing-dependent recognition code for synaptic ligands.

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Dietmar Schreiner

Biozentrum, University of Basel, Basel, Switzerland

Competing interests
The authors declare that no competing interests exist.
Jovan Simicevic

Biozentrum, University of Basel, Basel, Switzerland

Competing interests
The authors declare that no competing interests exist.
Erik Ahrné

Biozentrum, University of Basel, Basel, Switzerland

Competing interests
The authors declare that no competing interests exist.
Alexander Schmidt

Biozentrum, University of Basel, Basel, Switzerland

Competing interests
The authors declare that no competing interests exist.
Peter Scheiffele

Biozentrum, University of Basel, Basel, Switzerland

For correspondence
peter.scheiffele@unibas.ch

Competing interests
The authors declare that no competing interests exist.

Ethics

Animal experimentation: This work was performed in accordance with the rules for animal experimentation in the Kanton Basel-Stadt, Switzerland. Animals were handled according to animal care and use protocol #2272. This protocol was reviewed and approved by the Kantonales Veterinäramt Basel-Stadt.

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This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.