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High-quality and robust protein quantification in large clinical/pharmaceutical cohorts with IonStar proteomics investigation

Abstract

Robust, reliable quantification of large sample cohorts is often essential for meaningful clinical or pharmaceutical proteomics investigations, but it is technically challenging. When analyzing very large numbers of samples, isotope labeling approaches may suffer from substantial batch effects, and even with label-free methods, it becomes evident that low-abundance proteins are not reliably measured owing to unsufficient reproducibility for quantification. The MS1-based quantitative proteomics pipeline IonStar was designed to address these challenges. IonStar is a label-free approach that takes advantage of the high sensitivity/selectivity attainable by ultrahigh-resolution (UHR)-MS1 acquisition (e.g., 120–240k full width at half maximum at m/z = 200) which is now widely available on ultrahigh-field Orbitrap instruments. By selectively and accurately procuring quantitative features of peptides within precisely defined, very narrow m/z windows corresponding to the UHR-MS1 resolution, the method minimizes co-eluted interferences and substantially enhances signal-to-noise ratio of low-abundance species by decreasing noise level. This feature results in high sensitivity, selectivity, accuracy and precision for quantification of low-abundance proteins, as well as fewer missing data and fewer false positives. This protocol also emphasizes the importance of well-controlled, robust experimental procedures to achieve high-quality quantification across a large cohort. It includes a surfactant cocktail-aided sample preparation procedure that achieves high/reproducible protein/peptide recoveries among many samples, and a trapping nano-liquid chromatography–mass spectrometry strategy for sensitive and reproducible acquisition of UHR-MS1 peptide signal robustly across a large cohort. Data processing and quality evaluation are illustrated using an example dataset (http://proteomecentral.proteomexchange.org), and example results from pharmaceutical project and one clinical project (patients with acute respiratory distress syndrome) are shown. The complete IonStar pipeline takes ~1–2 weeks for a sample cohort containing ~50–100 samples.

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Fig. 1: The scheme of the IonStar protocol.
Fig. 2: IonStar substantially improves the specificity, accuracy and precision for quantification of low-abundance proteins by taking advantage of the high sensitivity and selectivity attainable by UHR-MS1 (e.g., 120–240k FWHM at m/z = 200), which is now widely available on Orbitrap instruments.
Fig. 3: An illustration of the design of LC–MS analysis sequence for a relatively large cohort, using a project containing 80 analytical samples (AS, eight groups, N = 10 biological subjects per group) as the example.
Fig. 4: Performance evaluation of proteomic quantification by IonStar and several popular label-free quantitative approaches.
Fig. 5: Comparison of the quantitative accuracy, precision and discovery of significantly different proteins for proteins with the lowest 25% abundances by IonStar (IS) and several popular label-free quantitative approaches, including spectral counting (SpC, NSAF), the newest version of MaxQuant (MQ) and PEAKS (PK) using the 25-sample, mixed-proteome benchmark set (N = 5 per group, five groups).
Fig. 6: Comparison of quantitative accuracy, precision and discovery of TPs for all proteins by IonStar (IS) and several popular label-free quantitative approaches, including spectral counting (SpC, NSAF), MaxQuant (MQ) and PEAKS (PK).
Fig. 7: Application of IonStar in one preclinical project involving 100 rat brain tissue samples.
Fig. 8: Application of IonStar in one clinical project involving serum samples from 60 human subjects with ARDS.

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Data availability

The MS proteomics data for 25-sample technical evaluation sample set that associated with Figs. 46 have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository106 with the dataset identifier PXD030780. The data that support the anticipated results of rat brain samples (Fig. 7) are available through the original publication47. The data that support the anticipated results of ARDS patients (Fig. 8) have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository106 with the dataset identifier PXD036822 and https://doi.org/10.6019/PXD036822.

Code availability

The R Shiny interactive web application package ‘UHR-IonStar’ is available at https://github.com/JunQu-Lab/UHRIonStarApp.

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Acknowledgements

This work is partially supported by NIH grants DK124020 (J.Q.), AG068168 (J.Q.), CA224434 (J.Q.) and HL103411 (J.Q.), a DOD grant W81XWH1910805 (J.Q. and S. Sethi) and a CPT consortium grant. Data presented in this work were obtained via the University at Buffalo Center for Proteomics.

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Authors and Affiliations

Authors

Contributions

S. Shen, X.W. and J.Q. conceptualized, designed and developed the IonStar protocol over the past 5 years. S. Shen, X.W., M.Z., S.Q., M.Q., C.H., L.J., Y.T., J.W., C.T. and J.Q. composed the protocol and reviewed the previous experimental data. M.M., X.Z. and S.R. validated previous results in the application section. M.Q. and S. Sethi designed and led the human ARDS study and provided the samples. D.P. designed and led the rat preclinical study and provided the samples. S. Shen, X.W., S.R., M.M., S.H., S.Q., X.Z. and J.Q. wrote and organized the manuscript. M.Q., Y.T., S. Sethi and J.W. helped to revise parts of the manuscript.

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Correspondence to Jun Qu.

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The authors declare no competing interests.

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Nature Protocols thanks Cheng Chang, Feng Zhu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Key references using this protocol

Shen, X. et al. Proc. Natl Acad. Sci. USA 115, E4767–E4776 (2018): https://doi.org/10.1073/pnas.1800541115

Shen, S. et al. Anal. Chem. 90, 10350–10359 (2018): https://doi.org/10.1021/acs.analchem.8b02172

Shen, X. et al. J. Proteome Res. 16, 2445–2456 (2017): https://doi.org/10.1021/acs.jproteome.7b00061

Wang, X. et al. Anal. Chem. 93, 4884–4893 (2021): https://doi.org/10.1021/acs.analchem.0c05002

Shen, S. et al. Int. J. Mol. Sci. 22, 2246 (2021): https://doi.org/10.3390/ijms22052246

Key data used in this protocol

Wang, X. et al. Anal. Chem. 93, 4884–4893 (2021): https://doi.org/10.1021/acs.analchem.0c05002

Shen, S. et al. Int. J. Mol. Sci. 22, 2246 (2021): https://doi.org/10.3390/ijms22052246

Extended data

Extended Data Fig. 1 Graphic scheme of the trapping nano-LC system.

A large-i.d. trapping column (300 μm i.d.) and 65-cm-long, 75 μm i.d. C18 separation column, heated at 50 °C, are connected and operated by a six-port valve. The unique selective trapping-delivery strategy consists of three stages, including sample injection, cleanup and delivery. The red lines and arrows indicate the flow of the peptide sample in each stage.

Extended Data Fig. 2 Schematic illustration of the IonStar data processing workflow.

The IonStar data processing workflow encompasses the following components: first, generation of precisely defined UHR-MS1 quantitative features with an extremely narrow m/z window; second, accurate assignment of peptide IDs to the quantitative features after peptide/protein identification; third, a stringent post-feature-generation QC procedure to eliminate low-quality quantitative data at both feature and peptide level, followed by aggregation of the data to protein level; finally, a series of postprocessing data analysis tools for assessment of data quality, visualization of the quantitative results, statistical analysis and discovery of significantly changed proteins.

Extended Data Fig. 3 Quantitative precision and accuracy of proteins uniquely quantified by IonStar and of the lowest 25% abundance.

a, Intragroup CV (i.e., precision) of proteins that are uniquely quantified by IonStar that are of the lowest 25% in abundances. The numbers above the boxplots denote the number of proteins with the lowest 25% abundances that are uniquely quantified by IonStar. b, Relative quantitative errors of protein ratios (i.e., accuracy) of E. coli proteins with the lowest 25% abundance that uniquely quantified by IonStar. The number above the boxplots denotes the number of E. coli proteins uniquely quantified by IonStar and with the lowest 25% abundance.

Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1–4 and Tables 1–3

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Shen, S., Wang, X., Zhu, X. et al. High-quality and robust protein quantification in large clinical/pharmaceutical cohorts with IonStar proteomics investigation. Nat Protoc 18, 700–731 (2023). https://doi.org/10.1038/s41596-022-00780-w

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