Collecting and analysing pig mucosal scraping samples for mass spectrometry-based proteomics: A study on factors causing variation in proteomic data

Abstract

In this study, we evaluated the effects of experimenter handling and batch effects on proteomic analyses of mucosal scrapings in pigs. We analyzed mucosal samples from twelve piglets that were fed two distinct diets. Mucosal samples were obtained from a 60 cm segment collected from the jejunum, which was divided into six equal segments. Scrapings were collected by two experimenters, who each handled three of the six segments per pig. Both experimenters were given the exact same instructions for performing the mucosal scrapings. Samples were then randomized and processed in two separate groups on different days to examine batch effects. Protein extraction and digestion was carried out using Midi S-Traps (ProtiFi) and peptides were analyzed by DIA-MS. Data processing and statistical analysis were performed using DIA-NN and RStudio. Our analyses revealed no significant experimenter effects, but we observed small batch effects across datasets. Our findings emphasize the importance of standardized protocols to minimize experimenter-induced variability and the necessity of correcting for batch effects during data analysis. Our study provides a framework for minimizing unwanted variability in future proteomic studies, ensuring more reliable and reproducible findings. Significance: With this study, we aimed to contribute to the improvement of the reliability and reproducibility of proteomic analyses in intestinal mucosal research. By systematically evaluating the effects of experimenter handling and batch processing on mucosal scraping samples, the study highlights key sources of variability that can influence proteomic outcomes. While experimenter effects were minimal, the identification of batch effects underscores the necessity of standardized protocols and appropriate corrections during data analysis. These findings provide a framework for minimizing unwanted variability in future studies, ultimately enhancing the accuracy of proteomic data interpretation and advancing research in intestinal health and disease.