Cited by 5,180Advanced Proteomics Techniques and ApplicationsBioinformatics and Genomic NetworksAdvanced Biosensing Techniques and Applications

The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index; none of the 16 sources its claims cite is checked for support yet.

Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ

Open access

Abstract

Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

MaxLFQ is a label-free LC-MS quantification method combining delayed normalization with maximal extraction of peptide ratio information across samples. In benchmark datasets it accurately recovered known protein mixing ratios, including 3:1 proteome mixes and UPS dynamic-range ratios over several orders of magnitude.

Key findings

  • In a HeLa/E. coli proteome benchmark, MaxLFQ produced narrower ratio distributions and less overlap between human and E. coli proteins than spectral counting or summed peptide intensities, and most accurately recapitulated the expected 3-fold change; 3453 human and 1556 E. coli proteins were quantified.
  • Statistical testing by t test outperformed fold-change ranking: at precision 0.98, 72% of E. coli proteins were recalled, and at precision 95%, 88% were retrieved with the Welch modified t test.
  • For simulated smaller ratios, at 10% FDR almost all truly changing proteins were recovered at ratio 3, about half at ratio 1.6, with a strong drop below 1.6; the Q = 0.25 half-coverage point was around ratio 1.4.
  • In the UPS dynamic-range benchmark, MaxLFQ was close to expected values within 2 orders of magnitude and showed increased scatter but no systematic error above 100-fold ratios; summed intensities slightly underestimated very large ratios, and spectral counts covered 2 orders of magnitude less and systematically underestimated ratios.

Methods

  • Two benchmark datasets were used: (1) trypsin-digested HeLa and E. coli lysates mixed at 1:1 and 3:1 E. coli ratios, separated by isoelectric focusing into 24 fractions and analyzed in three replicates on an LTQ Orbitrap; (2) UPS1 and UPS2 standards spiked into E. coli peptides and analyzed in four replicates on a Q Exactive.
  • Raw data were processed with MaxQuant/Andromeda at 1% peptide and protein FDR; match-between-runs transferred identifications across LC-MS runs, increasing quantifiable isotope patterns more than 2-fold in the proteome benchmark and by 38% in the UPS benchmark.
  • MaxLFQ delayed normalization by optimizing run-specific normalization coefficients via Levenberg–Marquardt minimization of squared log fold changes, then reconstructed protein abundance profiles from pair-wise peptide ratios using median peptide ratios and least-squares fitting.

Limitations

  • Stated: MaxLFQ requires a dominant population of proteins that change minimally between conditions; in the benchmark the changing E. coli population was 31% of measured proteins, which still worked but shifted the non-changing population slightly negative.
  • Stated: sample-handling variability across months, machines, or laboratories remains a challenge; studies needing ultimate ratio accuracy or individual peptide quantification, such as post-translational modifications, will still rely on isotope labels, and clinical applications may benefit from spike-in references.
  • Stated: computational effort grows quadratically with sample number, though a fast normalization heuristic using a graph with minimum three and average six nearest neighbors gives linear scaling; large ratios show increased scatter above 100-fold. Evident from the text: validation rests on two benchmark datasets plus an in silico downscaling experiment, not broad clinical or diverse biological validation.

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Integrity signals

Registry

Crossref · 10.1074/mcp.m113.031591

Cites

80 of 80 references verified0 citation errors · 0 not checkable

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44 Own result · 34 Own interpretation · 20 No basis given · 11 Not checked yet · 2 Own method

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  • It may state two separate claims as one. 31
  • Probably not a substantial claim. 27
  • The quote only partly states this claim. 3
55 of 55
  • The authors state this as an assumption.

    A majority population of proteins exists that is not changing between the samples, so average behavior can be used as a relative standard.

    “We exploit the fact that the majority of the proteome typically does not change between any two conditions so that the average behavior can be used as a relative standard.”
    “MaxLFQ has the prerequisite that a majority population of proteins exists that is not changing between the samples.”
    “normalization was based on the assumption of a dominating population of non-changing proteins.”
  • The authors state this as an assumption.

    A dominant population of proteins changes minimally between experimental conditions.

    “The only prerequisite is a dominant population of proteins that change minimally between experimental conditions.”
  • The authors state this as an assumption.

    A large overlap of peptides between samples can be interpreted as a small distance between them.

    “A large overlap of peptides between each pair of nodes was interpreted as a small distance between them.”
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Metadata

Authors
Cox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N., & Mann, M.
Affiliations
Max Planck Institute of Biochemistry (DE)
Corresponding author
  • Cox, Jürgen
  • Mann, Matthias
Venue
Molecular & Cellular Proteomics
Publisher
Elsevier BV
Published
2014-09
Volume
13
Issue
9
Pages
2513-2526
ISSN
1535-9476 (ISSN-L)
OpenAlex
W2170776626
Cited by
5,180
References
80
Citations by year
2026: 369 · 2025: 513 · 2024: 583 · 2023: 558 · 2022: 512 · 2021: 561 · 2020: 550 · 2019: 448 · 2018: 402 · 2017: 299 · 2016: 245 · 2015: 111 · 2014: 24
FWCI
97.15
Type
journal article
Language
en
Open access
Locations
  • Molecular & Cellular Proteomics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Max Planck Digital Library (submittedVersion, other-oa)
  • MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
Topics
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
Keywords
label-free quantification, MaxQuant
MeSH terms
Algorithms, Escherichia coli, HeLa Cells, Humans, Peptides, Proteins, Software, Proteome, Proteomics

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