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Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments

Nature Methods volume 18, pages 806–815 (2021)Cite this article

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Abstract

Co-fractionation mass spectrometry (CF-MS) has emerged as a powerful technique for interactome mapping. However, there is little consensus on optimal strategies for the design of CF-MS experiments or their computational analysis. Here, we reanalyzed a total of 206 CF-MS experiments to generate a uniformly processed resource containing over 11 million measurements of protein abundance. We used this resource to benchmark experimental designs for CF-MS studies and systematically optimize computational approaches to network inference. We then applied this optimized methodology to reconstruct a draft-quality human interactome by CF-MS and predict over 700,000 protein–protein interactions across 27 eukaryotic species or clades. Our work defines new resources to illuminate proteome organization over evolutionary timescales and establishes best practices for the design and analysis of CF-MS studies.

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Fig. 1: A comprehensive reanalysis of published CF-MS data.
Fig. 2: Benchmarking analysis of individual CF-MS experiments.
Fig. 3: Design of CF-MS experiments.
Fig. 4: Network inference from multiple CF-MS replicates.
Fig. 5: Meta-analysis defines a consensus human CF-MS interactome.
Fig. 6: CF-MS interactomes throughout the eukaryotic evolutionary tree.

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Data availability

A list of all raw mass spectrometry files analyzed in this study and their accession numbers in PRIDE or MassIVE repositories is provided in Supplementary Table 1. All data generated in this study are available at multiple levels of analysis from the following sources: protein chromatograms and protein–protein interaction networks for up to ten proteins can be visualized and downloaded via an interactive web application at http://cf-ms-browser.msl.ubc.ca; processed chromatograms and MaxQuant proteinGroups.txt files are available via Zenodo at https://doi.org/10.5281/zenodo.4499320; complete MaxQuant outputs for all 206 experiments were deposited to the PRIDE repository83 with the dataset identifier PXD022048; predicted interactomes for 27 species and clades, including the consensus human CF-MS interactome, are available via Zenodo at https://doi.org/10.5281/zenodo.4245282. An overview of all publicly available resources generated in this study is provided at the supporting website (https://fosterlab.github.io/CF-MS-analysis).

Code availability

Source code used to download and reanalyze publicly available CF-MS data using MaxQuant is available at https://github.com/skinnider/CF-MS-searches (https://doi.org/10.5281/zenodo.4774750). Source code used to carry out analyses presented in the paper, with relevant intermediate data files, is available at https://github.com/skinnider/CF-MS-analysis (https://doi.org/10.5281/zenodo.4774754). Source code for the CF-MS browser web application is available at https://github.com/skinnider/CF-MS-browser (https://doi.org/10.5281/zenodo.4774752). The CFTK R package is available at https://github.com/fosterlab/CFTK (https://doi.org/10.5281/zenodo.4774771).

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Acknowledgements

This work was supported by funding from Genome Canada and Genome British Columbia (project 214PRO). M.A.S. acknowledges support from a CIHR Vanier Canada Graduate Scholarship, an Izaak Walton Killam Memorial Pre-Doctoral Fellowship, a University of British Columbia (UBC) Four Year Fellowship and a Vancouver Coastal Health–CIHR–UBC MD/PhD Studentship. This work was enabled in part by the support provided by WestGrid and Compute Canada and through computational resources and services provided by Advanced Research Computing at the UBC. We thank Microsoft for the donation of cloud computing resources that enabled part of this work, T. Clark and J. Moon for advice on MaxQuant searches and D. Vavilov for assistance with the web server.

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Authors and Affiliations

  1. Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada

    Michael A. Skinnider & Leonard J. Foster

  2. Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, British Columbia, Canada

    Leonard J. Foster

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  1. Michael A. Skinnider
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Contributions

M.A.S. and L.J.F. designed experiments. M.A.S. performed experiments. M.A.S. and L.J.F. wrote the manuscript.

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Correspondence to Leonard J. Foster.

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Peer review information Nature Methods thanks Fridtjof Lund-Johansen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Arunima Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 A uniformly processed resource of CF–MS data.

a, Approaches to protein quantification employed by published CF–MS experiments. SILAC, stable isotope labelling by amino acids in cell culture; iBAQ, intensity-based absolute quantification. b, Proportion of the organismal proteome quantified in each CF–MS experiment (grey lines, individual datasets; blue line, mean across all datasets). c, Cumulative distribution of the number of proteins quantified per dataset in between one and 25 fractions. d, GO term enrichment among CORUM proteins detected in at least one CF–MS fraction, left, or never detected, right. e, PaxDb consensus protein abundance of mouse proteins detected or never detected by CF–MS. f, Coverage of high, moderate, and low abundance proteins (expressed as a mean proportion of fractions in which these proteins were detected) in published human CF–MS experiments (n = 46). g, As in f, but with CF–MS experiments divided into three groups based on the length of the liquid chromatography gradient. ***, p < 0.001, two-sided Spearman rank correlation. h, PaxDb consensus protein abundance of human proteins in the CORUM database and non-CORUM proteins. i, Difference in the number of protein groups quantified in each CF–MS experiment, compared to the processed chromatogram data accompanying the original publications (grey lines, individual datasets; blue line, mean across all datasets).

Extended Data Fig. 2 Benchmarking computational analysis of individual CF–MS datasets.

a, Measures of association used to quantify the similarity of two protein chromatograms in published CF–MS studies. Bottom row indicates the incorporation of external genomic datasets77. b, Ranks of each measure of association in identifying protein pairs in the same protein complex, left, or annotated to the same GO term, right, across individual CF–MS datasets. c, Number of peaks detected in 20 CF–MS datasets by fitting a mixture of Gaussians to each protein chromatogram. d, Recovery of known protein complexes in the 20 CF–MS datasets from c, scoring only chromatograms that could be fit with a mixture of Gaussians (r2 ≥ 0.5) and comparing the 24 different measures of association shown in Fig. 2 with the co-apex score. Inset text shows the median AUC for each measure of association. e, As in d, but for proteins annotated to the same GO term. f, Recovery of known protein complexes, top, and proportion of originally quantified proteins, bottom, when filtering profiles not detected in some minimum number of fractions, using mutual information as a measure of profile similarity. g, Mean number of protein groups identified, top, and recovery of proteins annotated to the same GO term, bottom, for three approaches to label-free quantification implemented in MaxQuant.

Extended Data Fig. 3 Univariate statistical analysis of computational approaches to individual CF-MS datasets.

a, Difference in the median protein complex AUC between each pair of measures of association. Asterisks indicate pairs of measures of association with a p-value less than 0.05 in a two-sided Brunner-Munzel test. The difference in median AUCs is capped at [–0.1, +0.1] to improve visualization. b, As in a, but for GO terms. c, Difference in the median protein complex AUC between each pair of missing value handling strategies. Asterisks indicate pairs of measures of association with a p-value less than 0.05 in a two-sided Brunner-Munzel test. d, As in c, but for GO terms. e, Difference in the median protein complex AUC between each pair of chromatogram normalization approaches. Asterisks indicate pairs of measures of association with a p-value less than 0.05 in a two-sided Brunner-Munzel test. f, As in e, but for GO terms. g, Difference in the median protein complex AUC between each pair of measures of association, considering only the single best combination of missing value handling and chromatogram normalization for each measure of association. Asterisks indicate pairs of measures of association with a p-value less than 0.05 in a two-sided Brunner-Munzel test. h, As in g, but for GO terms. i, Median difference in the protein complex AUC between matched datasets with label-free protein quantification performed by one of three algorithms within MaxQuant. Asterisks indicate pairs of measures of association with a p-value less than 0.05 in a two-sided paired Brunner-Munzel test. j, As in i, but for GO terms.

Extended Data Fig. 4 Analysis pipelines for individual CF–MS datasets.

a, Recovery of known protein complexes with 163 valid combinations of measures of association, missing value handling, and normalization strategies. b, As in a, but for proteins annotated to the same GO term.

Extended Data Fig. 5 Downsampling analysis of published CF–MS experiments.

a, Recovery of proteins annotated to the same GO term after downsampling CF–MS chromatograms to a fixed number of fractions. b–c, Recovery of protein complexes, b, and GO terms, c, in downsampled CF–MS chromatograms, using mutual information as the measure of profile similarity. d–e, Recovery of protein complexes (AUC, d, and change in AUC, e), in downsampled CF–MS chromatograms, with experiments divided based on the separation method used (IEX, ion exchange chromatography; N-PAGE, native polyacrylamide gel electrophoresis; SEC, size exclusion chromatography). f, Recovery of protein complexes when downsampling windows of adjacent fractions of fixed length, rather than downsampling fractions randomly from the chromatogram matrix. g–h, Comparison of protein complex recovery (AUC, g, and change in AUC, h), in downsampled CF–MS chromatograms when drawing samples of random fractions or adjacent fractions from the chromatogram matrix. a–h, Shaded area shows the standard error.

Extended Data Fig. 6 Downsampling analysis of published CF–MS experiments incorporating multiple biological replicates.

a–b, Recovery of known protein complexes, a, and proteins annotated to the same GO term, b, in downsampled CF–MS chromatograms with fractions sampled from one to five replicates, as shown in Fig. 3b but visualized here as a line graph instead. Error bars show the standard error of the mean. c–d, Recovery of known protein complexes, c, and proteins annotated to the same GO term, d, in downsampled CF–MS chromatograms with fractions sampled from one to five replicates, within individual CF–MS datasets.

Extended Data Fig. 7 Protein quantification and chromatographic separation.

a, Comparison of GO term recovery and proteome coverage between SILAC ratios and iBAQ intensities from individual isotopologue channels in 20 SILAC datasets. Caption and error bars show the mean and standard deviation of the differences in the number of protein groups quantified and the AUC between SILAC ratios and iBAQ intensities. b, Recovery of proteins annotated to the same GO term in CF–MS experiments grouped by fractionation method. c, Difference in the median protein complex AUC between each pair of fractionation methods. Asterisks indicate pairs of measures of association with a p-value less than 0.05 in a two-sided Brunner-Munzel test. d, Regression coefficients for fractionation methods in multivariable statistical analysis, estimated by a linear model fit to the protein complex AUC and including terms for measures of association, missing value handling strategies, approaches to chromatogram normalization, and interactions between them. e, As in c, but for GO terms. f, As in d, but for GO terms. g, Recovery of known protein complexes in published CF–MS experiments grouped by fraction method, with each measure of association shown separately. h, As in g, but for proteins annotated to the same GO term. i, Recovery of individual protein complexes in published CF–MS experiments grouped by fractionation method. j, Number of protein complexes with at least three subunits detected exclusively by one separation method, left, and resolved significantly better by one of the four separation methods, right, across 67 human and mouse CF–MS datasets. k, Examples of protein complexes resolved best by each of the four separation methods. Inset text shows the median AUC.

Extended Data Fig. 8 Machine learning workflows for the integration of multiple CF–MS replicates.

a, Schematic overview of cross-validation approaches for CF–MS data. b, Comparison of cross-validation by protein pairs or individual proteins in network inference from two to four CF–MS experiments using a naive Bayes classifier, with AUCs calculated in cross-validation or in an independent set of held-out protein complexes. c, Impact of feature selection on network inference from two to four CF–MS experiments, comparing between one and six top-performing features, an equivalent number of random features, or five features computed in PrInCE. d, Comparison of top-performing or random features in network inference from two to ten CF–MS experiments, using between one and ten top-performing features. e, Comparison of network inference with features calculated from concatenated matrices of two to four CF–MS experiments, or with features calculated from individual experiments. f, Comparison of network inference from two to four CF–MS experiments using a naive Bayes classifier before and after median imputation of missing values. g, Impact of the number of top-performing or random features provided as input on network inference from two to ten CF–MS experiments. h, Comparison of random forest and naive Bayes classifiers in network inference from two to ten CF–MS replicates, using between one and ten features. i, Network inference from human CF–MS data when integrating varying proportions of SEC and IEX experiments. The total number of CF–MS datasets is shown above the plots, and the number of SEC datasets is shown on the x-axis.

Extended Data Fig. 9 Synergistic and antagonistic feature combinations in network inference from CF–MS data.

a, Performance (AUC, left, and rank, right) of naive Bayes and random forest classifiers trained on 24 measures of association in network inference from combinations of between two and six CF–MS datasets. Each cell reflects the mean AUC from 10 random combinations of datasets. b, Summary of synergistic and antagonistic interactions between features in CF–MS network inference, as shown in detail in panels c–f. Fill reflects the number of times a synergistic (magenta) or antagonistic (cyan) interaction was detected between two features. Network inference was performed using all possible combinations of 24 measures of association from ten random combinations of three or six CF–MS datasets, using either a random forest or naive Bayes classifier. Rows and columns are arranged by the mean performance of individual features across all combinations shown in a (both classifiers, two to six datasets). c, Performance (AUC) of networks inferred from combinations of three CF–MS datasets using a random forest classifier. Rows and columns are arranged by the mean performance of individual features in the same scenario. Text highlights significantly synergistic (+) and antagonistic (–) interactions. Each cell shows the mean AUC from 10 random combinations of datasets. d, As in c, but using a naive Bayes classifier. e, As in c, but for networks inferred from combinations of six CF–MS datasets. f, As in c, but for networks inferred from combinations of six CF–MS datasets, using a naive Bayes classifier.

Extended Data Fig. 10 Saturation analysis of network inference from CF–MS data.

a, Saturation analysis of network inference from two to 40 CF–MS experiments, using variable numbers of top-performing features. Boxplots show n = 10 independent samples. b, Impact of downsampling training set complexes on network inference from two to four CF–MS replicates.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3

Reporting Summary

Supplementary Table 1

Complete list of all CF-MS experiments, a, and raw mass spectrometry files, b, analyzed in this study.

Supplementary Table 2

ROC analysis of protein complexes, a, and GO term membership, b, within individual CF-MS datasets.

Supplementary Table 3

Univariate statistical analysis of computational approaches to individual CF-MS datasets. a,c, protein complexes; b,d, GO term membership.

Supplementary Table 4

Multivariate statistical analysis of computational approaches to individual CF-MS datasets. a, protein complexes; b, GO term membership.

Supplementary Table 5

Univariate and multivariate statistical analysis of methods for protein complex separation.

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Skinnider, M.A., Foster, L.J. Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments. Nat Methods 18, 806–815 (2021). https://doi.org/10.1038/s41592-021-01194-4

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