An optimized protocol for pig intestinal mucosa proteomics.

The overall well-being of organisms is widely recognized to be closely intertwined with their intestinal health. The intestinal mucosal layer plays a pivotal role in ensuring the proper functioning of the intestine, a fact observed not only in humans but also in animals like pigs. Any alterations to the mucosal layer of a pig's intestine can potentially disrupt its functionality, thereby impacting the animal's health and productivity. Mass spectrometry-based proteome analysis serves as a valuable tool in investigating the intricate dynamics of the proteome within the intestinal mucosa. Such studies hold promise in uncovering causal relationships between mucosal changes and overall health outcomes in pigs. It is anticipated that insights gathered from proteome studies will inform future strategies aimed at enhancing the health and productivity of pigs. However, the research field lacks a standardized and detailed method to extract proteins from pig intestinal mucosa and prepare proteins for proteome analysis. In the present study, we evaluated three alternative S-Trap-based protocols for analyzing ileal mucosal scrapings from pigs. Samples were either freeze-dried and treated as solid samples or ground in liquid nitrogen, categorized as either solid or liquid samples. In our analysis, a total of 2840 proteins were identified across all samples. Through statistical analysis and gene ontology examinations, we investigated potential differences between the three approaches. Even though our findings revealed no significant differences among the three methods, we propose the use of the protocol wherein samples are freeze-dried and treated as solid for protein extraction. This protocol stands out as the most convenient and practical option, offering ease of use and ensuring consistent and reliable results. By establishing a standardized approach, we aim to advance research efforts in understanding pig intestinal health. SIGNIFICANCE: The development of an optimized protocol for protein extraction of intestinal mucosal scrapings in pigs addresses a gap in the field and enhances future research on pig intestinal health. By use of the protocol and mass spectrometry-based proteome analysis, valuable insights for improving the health and productivity of pigs can be presented. Studying the complex dynamics of the proteome within the intestinal mucosa, potentially identifying links between mucosal changes and health outcomes, provides us with information about the critical connection between intestinal health and the overall well-being and productivity of pigs. By creating a standardized approach, consistent, reliable, and reproducible results can be obtained for this type of research.

Benefit of In Silico Predicted Spectral Libraries in Data-Independent Acquisition Data Analysis Workflows.

Data-independent acquisition (DIA) has become a well-established method for MS-based proteomics. However, the list of options to analyze this type of data is quite extensive, and the use of spectral libraries has become an important factor in DIA data analysis. More specifically the use of in silico predicted libraries is gaining more interest. By working with a differential spike-in of human standard proteins (UPS2) in a constant yeast tryptic digest background, we evaluated the sensitivity, precision, and accuracy of the use of in silico predicted libraries in data DIA data analysis workflows compared to more established workflows. Three commonly used DIA software tools, DIA-NN, EncyclopeDIA, and Spectronaut, were each tested in spectral library mode and spectral library-free mode. In spectral library mode, we used independent spectral library prediction tools PROSIT and MS2PIP together with DeepLC, next to classical data-dependent acquisition (DDA)-based spectral libraries. In total, we benchmarked 12 computational workflows for DIA. Our comparison showed that DIA-NN reached the highest sensitivity while maintaining a good compromise on the reproducibility and accuracy levels in either library-free mode or using in silico predicted libraries pointing to a general benefit in using in silico predicted libraries.

High-Throughput Nanoflow Proteomics Using a Dual-Column Electrospray Source.

With current trends in proteomics, especially regarding clinical and low input (to single cell) samples, it is increasingly important to both maximize the throughput of the analysis and maintain as much sensitivity as possible. The new generation of mass spectrometers (MS) are taking a huge leap in sensitivity, allowing analysis of samples with shorter liquid chromatography (LC) methods while digging as deep in the proteome. However, the throughput can be doubled by implementing a dual column nano-LC-MS configuration. For this purpose, we used a dual-column setup with a two-outlet electrospray source and compared it to a classic dual-column setup with a single-outlet source.