Data from a Multi-year Targeted Proteomics Study of a Longitudinal Birth Cohort of Type 1 Diabetes

Overview

Journal Sci Data

Date 2025 Jan 20

PMID 39833216

Authors

Lisa M Bramer

Ernesto S Nakayasu

Javier E Flores

Jennifer E Van Eyk

Michael J MacCoss

Hemang M Parikh

Thomas O Metz

Bobbie-Jo M Webb-Robertson

Affiliations

Soon will be listed here.

Abstract

The deployment of liquid chromatography-mass spectrometry-based plasma proteomics experiments in a large cohort is sparse, leading to a lack of data available for benchmarking, method development or validation. Comprised of 6,426 plasma analyses, The Environmental Determinants of Diabetes in the Young (TEDDY) proteomics validation study constitutes one of the largest targeted proteomics experiments in the literature to date. The proteomics data from this study were generated over the course of 2.5 years from over 900 study subjects, each providing up to 29 longitudinal samples. The data also includes 916 quality control samples. The targeted mass spectrometry assay was comprised of 694 peptides mapping to 167 proteins and the panel was measured in each subject and QC sample. The targeted proteomic dataset presented here can be used as a resource for new computational methods development, such as for batch correction, as well as for benchmarking and comparing the performance of different methods/tools.

References

Bramer L, Irvahn J, Piehowski P, Rodland K, Webb-Robertson B . A Review of Imputation Strategies for Isobaric Labeling-Based Shotgun Proteomics. J Proteome Res. 2020; 20(1):1-13. PMC: 8996546. DOI: 10.1021/acs.jproteome.0c00123. View

Shen Y, Moore R, Zhao R, Blonder J, Auberry D, Masselon C . High-efficiency on-line solid-phase extraction coupling to 15-150-microm-i.d. column liquid chromatography for proteomic analysis. Anal Chem. 2003; 75(14):3596-3605. DOI: 10.1021/ac0300690. View

Gibbons B, Fillmore T, Gao Y, Moore R, Liu T, Nakayasu E . Rapidly Assessing the Quality of Targeted Proteomics Experiments through Monitoring Stable-Isotope Labeled Standards. J Proteome Res. 2018; 18(2):694-699. PMC: 6537873. DOI: 10.1021/acs.jproteome.8b00688. View

Lee H, Burkhardt B, McLeod W, Smith S, Eberhard C, Lynch K . Biomarker discovery study design for type 1 diabetes in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Diabetes Metab Res Rev. 2013; 30(5):424-34. PMC: 4058423. DOI: 10.1002/dmrr.2510. View

DiMeglio L, Evans-Molina C, Oram R . Type 1 diabetes. Lancet. 2018; 391(10138):2449-2462. PMC: 6661119. DOI: 10.1016/S0140-6736(18)31320-5. View

Webb-Robertson B, Wiberg H, Matzke M, Brown J, Wang J, McDermott J . Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics. J Proteome Res. 2015; 14(5):1993-2001. PMC: 4776766. DOI: 10.1021/pr501138h. View

Vehik K, Fiske S, Logan C, Agardh D, Cilio C, Hagopian W . Methods, quality control and specimen management in an international multicentre investigation of type 1 diabetes: TEDDY. Diabetes Metab Res Rev. 2013; 29(7):557-67. PMC: 3992860. DOI: 10.1002/dmrr.2427. View

Stratton K, Webb-Robertson B, McCue L, Stanfill B, Claborne D, Godinez I . pmartR: Quality Control and Statistics for Mass Spectrometry-Based Biological Data. J Proteome Res. 2019; 18(3):1418-1425. PMC: 6750869. DOI: 10.1021/acs.jproteome.8b00760. View

Pino L, Searle B, Bollinger J, Nunn B, MacLean B, MacCoss M . The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics. Mass Spectrom Rev. 2017; 39(3):229-244. PMC: 5799042. DOI: 10.1002/mas.21540. View

10.

Vehik K, Bonifacio E, Lernmark A, Yu L, Williams A, Schatz D . Hierarchical Order of Distinct Autoantibody Spreading and Progression to Type 1 Diabetes in the TEDDY Study. Diabetes Care. 2020; 43(9):2066-2073. PMC: 7440899. DOI: 10.2337/dc19-2547. View

11.

Piehowski P, Petyuk V, Orton D, Xie F, Moore R, Ramirez-Restrepo M . Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis. J Proteome Res. 2013; 12(5):2128-37. PMC: 3695475. DOI: 10.1021/pr301146m. View

12.

Stacklies W, Redestig H, Scholz M, Walther D, Selbig J . pcaMethods--a bioconductor package providing PCA methods for incomplete data. Bioinformatics. 2007; 23(9):1164-7. DOI: 10.1093/bioinformatics/btm069. View

13.

Zhang Y, Parmigiani G, Johnson W . : batch effect adjustment for RNA-seq count data. NAR Genom Bioinform. 2020; 2(3):lqaa078. PMC: 7518324. DOI: 10.1093/nargab/lqaa078. View

14.

Ziegler A, Rewers M, Simell O, Simell T, Lempainen J, Steck A . Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children. JAMA. 2013; 309(23):2473-9. PMC: 4878912. DOI: 10.1001/jama.2013.6285. View

15.

Rewers M, Hyoty H, Lernmark A, Hagopian W, She J, Schatz D . The Environmental Determinants of Diabetes in the Young (TEDDY) Study: 2018 Update. Curr Diab Rep. 2018; 18(12):136. PMC: 6415767. DOI: 10.1007/s11892-018-1113-2. View

16.

Nakayasu E, Bramer L, Ansong C, Schepmoes A, Fillmore T, Gritsenko M . Plasma protein biomarkers predict the development of persistent autoantibodies and type 1 diabetes 6 months prior to the onset of autoimmunity. Cell Rep Med. 2023; 4(7):101093. PMC: 10394168. DOI: 10.1016/j.xcrm.2023.101093. View

17.

Dunn W, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N . Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc. 2011; 6(7):1060-83. DOI: 10.1038/nprot.2011.335. View

18.

Degnan D, Stratton K, Richardson R, Claborne D, Martin E, Johnson N . : A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes. J Proteome Res. 2023; 22(2):570-576. DOI: 10.1021/acs.jproteome.2c00610. View

19.

Leach D, Stratton K, Irvahn J, Richardson R, Webb-Robertson B, Bramer L . malbacR: A Package for Standardized Implementation of Batch Correction Methods for Omics Data. Anal Chem. 2023; 95(33):12195-12199. DOI: 10.1021/acs.analchem.3c01289. View

20.

Vehik K, Cuthbertson D, Boulware D, Beam C, Rodriguez H, Legault L . Performance of HbA1c as an early diagnostic indicator of type 1 diabetes in children and youth. Diabetes Care. 2012; 35(9):1821-5. PMC: 3425003. DOI: 10.2337/dc12-0111. View