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Isoform-Level Interpretation of High-Throughput Proteomics Data Enabled by Deep Integration with RNA-seq.

Becky C Carlyle1, Robert R Kitchen1,2, Jing Zhang2

  • 1Department of Psychiatry , Yale School of Medicine, Connecticut Mental Health Center , 34 Park Street , New Haven , Connecticut 06519 , United States.

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Summary

This study integrates RNA-seq and LC-MS/MS proteomics data to directly estimate protein isoform abundances. This approach successfully identifies dominant isoforms in over 80% of gene products in cell culture and 70% in human brain tissue.

Keywords:
HEK293RNA-seqbrainexpectation maximizationintegrative analysisisoformsmass spectrometrypeptidesproteogenomicsribosome profiling

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Area of Science:

  • Molecular Biology
  • Proteomics
  • Genomics

Background:

  • Cellular gene expression is regulated at multiple levels, with protein isoforms playing a key role in cellular function.
  • Current proteomic methods like LC-MS/MS have limitations in sensitivity and coverage for isoform detection due to enzymatic digestion.
  • RNA-sequencing (RNA-seq) offers higher sensitivity for transcriptomic profiling.

Purpose of the Study:

  • To develop a method for directly estimating protein isoform abundances from LC-MS/MS data.
  • To leverage RNA-seq data to improve the accuracy of protein isoform identification.
  • To explore the integration of translatome data for enhanced isoform definition.

Main Methods:

  • Utilized transcript-level expression data from RNA-seq to set prior likelihoods for protein isoform abundance estimation.
  • Applied a novel approach to directly estimate protein isoform abundances from LC-MS/MS data.
  • Integrated ribosome profiling (translatome) data to further refine isoform identification.

Main Results:

  • Successfully identified a principal protein isoform in over 80% of gene products in HEK293 cell culture.
  • Identified a principal protein isoform in over 70% of detected proteins in complex human brain tissue.
  • Demonstrated that integrating translatome data further refines the process of isoform definition.

Conclusions:

  • Deep integration of RNA-seq and LC-MS/MS data enables direct estimation of protein isoform abundances.
  • This integrated approach significantly enhances the ability to identify dominant protein isoforms across different biological samples.
  • Defining isoforms using matched transcriptomic, translatomic, and proteomic data increases the functional relevance of experimental datasets and deepens understanding of gene expression regulation.