Brena Rodrigues Mota Ikehara¹, Natalia Reis de Almeida², Vinicius Hideyuki Ikehara¹, Frederico Garcia Pinto¹, Timothy J. Garrett³

Understanding disease mechanisms is paramount. Similar to genomes, metabolomes, and transcriptomes, studying lipidomes helps better understand critical cellular functions, such as energy storage, signaling, and interaction with other cells, affecting proliferation and programmed death processes. Furthermore, lipid metabolism has been studied as a diagnostic tool for different types of cancer and infectious diseases. Paper Spray Ionization mass spectrometry (PSI-MS) is a viable option for assessing lipid content in a sample offering a rapid, stable, and low-cost analysis. In PSI-MS, samples are deposited onto specialized paper, prepared beforehand (e.g., blood, stool, or cells) or unprepared (e.g., urine), then dried and stored in the dark at room temperature. During analysis, the paper is wetted with a solvent before an electric charge is applied. However, there is no consensus regarding the optimal solvent for enhanced analytical performance for lipids in human serum. The goal of this study is to optimize the PSI-MS analysis of human serum by testing different wetting solvents, focusing on increasing signal intensity and quality. All analyses were conducted using the Prosolia Velox 360^TM PSI source coupled to a Thermo Scientific Q Exactive high-resolution mass spectrometer set to a 40-second analytical method. The tested solvents included MeOH and MeOH (5 mM NH₄Ac), with the latter exhibiting 9 compounds with higher intensity, according to Volcano Plot analysis (p-value < 0.05, FC > 1.0). PLS-DA explained 83.5% of the observed variation. The findings are supported by recent literature, which confirms the enhanced detection of triacylglycerols after addition of NH₄Ac, known to increase the ionization efficiency of the mixture. Thus, MeOH (5 mM NH₄Ac) offers superior signal quality and enables the identification of the highest number of lipid compounds, which contributes to the optimization of lipidomic methodologies, enhancing their application in clinical research and biomarker discovery.

Agradecimentos: I would like to thank the Timothy J. Garrett Laboratory at the University of Florida for their support and hospitality during the development of this work. I also thank the Federal University of Viçosa – Rio Paranaíba Campus and the Research Group on Metabolomic Analyses (GPAM-UFV) for their technical and scientific support. Financial support from CNPq is also gratefully acknowledged.