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Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis

Nature Biotechnology volume 28, pages 83–89 (2010)Cite this article

Abstract

Replicate mass spectrometry (MS) measurements and the use of multiple analytical methods can greatly expand the comprehensiveness of shotgun proteomic profiling of biological samples1,2,3,4,5. However, the inherent biases and variations in such data create computational and statistical challenges for quantitative comparative analysis6. We developed and tested a normalized, label-free quantitative method termed the normalized spectral index (SIN), which combines three MS abundance features: peptide count, spectral count and fragment-ion (tandem MS or MS/MS) intensity. SIN largely eliminated variances between replicate MS measurements, permitting quantitative reproducibility and highly significant quantification of thousands of proteins detected in replicate MS measurements of the same and distinct samples. It accurately predicts protein abundance more often than the five other methods we tested. Comparative immunoblotting and densitometry further validate our method. Comparative quantification of complex data sets from multiple shotgun proteomics measurements is relevant for systems biology and biomarker discovery.

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Figure 1: Statistical analysis of replicate MS measurement variation before and after normalization.
Figure 2: Correlation of SIN with protein abundance.
Figure 3: Statistical analysis of normalization methods applied to variable protein load and distinct sample data sets.
Figure 4: Comparative analysis of proteins quantified by SDS-PAGE and MS analysis.

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Acknowledgements

This work was supported by National Institute of Health grants (to J.E.S): RO1HL074063, R33CA118602 and P01CA104898.

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Authors and Affiliations

  1. Proteogenomics Research Institute for Systems Medicine, San Diego, California, USA

    Noelle M Griffin, Jingyi Yu, Fred Long, Phil Oh, Sabrina Shore, Yan Li & Jan E Schnitzer

  2. Sidney Kimmel Cancer Center, San Diego, California, USA

    Noelle M Griffin, Jingyi Yu, Fred Long, Phil Oh, Sabrina Shore, Yan Li & Jan E Schnitzer

  3. The Scripps Research Institute, La Jolla, California, USA

    Jim A Koziol

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  1. Noelle M Griffin
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  6. Yan Li
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Contributions

N.M.G. designed, developed and analyzed the methods, provided some of the mass spectrometry data, performed the spiking experiments and analysis and wrote the manuscript; J.Y. initiated the project, designed, tested and implemented the methods; F.L. developed the scripts for data extraction; P.O. performed western blot analysis and densitometry; S.S. performed western blot analysis; Y.L. provided key mass spectrometry data; J.A.K. provided direction for statistical analysis; J.E.S supervised the project, designed specific tests and helped to write the manuscript. All authors have read and agreed to all the content in this manuscript.

Corresponding author

Correspondence to Jan E Schnitzer.

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Supplementary Figs. 1–7, Supplementary Table 1, Supplementary Notes, Supplementary Methods, Supplementary Data and Supplementary Discussion (PDF 827 kb)

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Griffin, N., Yu, J., Long, F. et al. Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis. Nat Biotechnol 28, 83–89 (2010). https://doi.org/10.1038/nbt.1592

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