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Designing a Comparative Proteomics Experiment: Retention-time Alignment and Imputation Algorithms Affect Statistical Comparisons Between Samples

ABSTRACT: Comparative proteomics experiments reveal biomarkers by using statistical tests to determine proteins expressed with higher abundance in one sample versus another. However, comparative experiments can be complicated by variability from all aspects of proteomics workflows. To account for variability, software for database searching contains retention-time alignment and imputation algorithms to correct for retention-time shifts and assign abundances to missing proteins. While these algorithms improve quantification and reduce processing time, we hypothesize that they alter statistical comparisons between samples when samples are searched together. Herein, we search different cleanup methods or single proteins separately versus together in Progenesis Qi for proteomics database searching software. Our results show that searching samples together increases the number of identifications by each sample, enhances protein similarity between samples, and leads to false transfers. Further, we demonstrate that searching samples together affects protein abundance, differentially expressed proteins, and confidence scores due to retention-time alignment and imputation algorithms. Ultimately, we highlight that careful consideration of the search design is necessary to determine biomarkers in comparative proteomics experiments.

INSTRUMENT(S): Synapt G2-S HDMS, nanoACQUITY UPLC

ORGANISM(S): Equine Heart Bovine Erythrocytes Bovine Blood Chicken Egg White Homo Sapiens (ncbitaxon:9606) Equine Skeletal Muscle

SUBMITTER: Elyssia Gallagher

PROVIDER: MSV000096112 | MassIVE | Wed Oct 16 11:35:00 GMT+01:00 2024

SECONDARY ACCESSION(S): PXD056868

REPOSITORIES: MassIVE

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Project description:Bacterial and fungal exometabolites were harvested from the MetaFlowTrain system, where bacteria and fungi were cultivated in various peat washes. Two milliliters of exometabolites were collected from the MetaFlowTrain output at two distinct time points: 24 hours and 62 hours. These exometabolites were subsequently extracted (for detailed methods, refer to the associated publication). Data Overview: 221208 Folders: Data were used to create Figures 3 and 4 and were acquired in Full-MS/ddMS2 (Top10). 230530 Folders: Data contributed to Figure 2 and were acquired in Full-MS. File Naming Convention: Sample number_Microbial inoculum in the first chamber_Microbial inoculum in the second chamber_Peat wash used For example, 01_F_B_Peat1 refers to sample 1, fungi in the first chamber, bacteria in the second chamber, and peat wash 1. Abbreviations: F: Fungi, B: Bacteria, X: Mock Additional Files: TraceFinder Raw Tables These tables are provided for the four files (221208_Guillaume_Chambers_AA, 221208_Guillaume_Chambers_TCA&Gly, 230530_Guillaume_Chambers, 230530_Guillaume_Chambers_AA) and include key parameters for all samples, pools, and standards: - Retention Time (Actual RT) - Accurate Mass Measurements (m/z Apex / m/z Delta) - Ion Abundance (Area) - Ion Height (Height) Summary Table (Level 1 identification): A summary table is also included, detailing targeted retention times for all metabolites and the spectra for the TCA&Gly identification: - RT Sample Average: The average retention time for samples in the set. - RT Sample %RSD: The percentage relative standard deviation (%RSD) of sample retention times, indicating variability. - RT Pool Average: The average retention time for pooled samples. - RT Pool %RSD: The %RSD for pooled samples, showing variability. - RT Std Average: The average retention time for standard samples. - RT Std %RSD: The %RSD for standard retention times. - RT Difference Pool-Std: The difference in retention times between pooled and standard samples.

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Designing a Comparative Proteomics Experiment: Retention-time Alignment and Imputation Algorithms Affect Statistical Comparisons Between Samples

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