In large-scale studies, uncontrolled systematic variability introduced during sample preparation, processing, and storage can interfere with the detection of subtle biological signals. This study evaluates storage conditions, including two sample preparation methods and storage durations, to minimize systematic variability in the analysis of extracted lipids from latent fingerprints. In the traditional approach, samples are prepared immediately, stored as lipid extracts, and processed in multiple batches. In an alternative method, samples are stored directly on the deposition foil, and preparation is delayed until all can be processed in a single batch. Storage duration is evaluated to determine if shorter storage with analysis in multiple batches is more effective than longer storage with analysis in a single batch. Our findings demonstrate that storage of latent fingerprint samples on the deposition foil is a viable option, with minimal degradation of key features even after eight months of storage. While some differences in lipid profiles were observed across storage conditions, these differences were minor and would likely have little impact in larger studies where biological variability is greater. These insights offer practical guidance for implementing latent fingerprint sampling in large-scale studies by identifying optimal conditions that preserve sample quality and streamline workflows.

The advancements made in the mass spectrometry imaging (MSI) field have allowed for the generation of very large-scale data sets. These data are often interrogated by machine learning (ML), although storing and handling data sets of this size can be difficult. To aid impacted researchers, we seek to evaluate feature reduction strategies that will minimize the amount of data stored while still maintaining the ability to correctly classify the data. Two different feature selection strategies are tested on six different data sets, leveraging XGBoost as the machine learning algorithm. The study provides evidence that selecting features based on the greatest average abundance across all samples is best suited to scale down the feature set at a more modest trimming level, while selecting features based on statistical analysis via a Student’s t-test is better suited for a more aggressive trimming level. These trends were present regardless of training set size or cross-validation strategy. The results from this work provide insight into when these feature filtering steps can be used effectively and when another data reduction strategy, including not restricting the data set, should be considered.

Mass spectrometry-based metabolomics approaches generate high-dimensional datasets that enable comprehensive profiling of metabolites and other biomolecules. However, these datasets are influenced by multiple sources of variability, including both the biological variation of interest and unwanted technical or systematic variation introduced throughout experimental or analytical workflows. Distinguishing between these sources of variation is important for ensuring robust and reliable downstream interpretation. This review examines commonly used approaches for the detection and quantification of variability in MS-based omics datasets, including statistical and multivariate methods such as coefficient of variation analysis, principal component analysis, variance-based metrics and other quality assessment and visualization techniques used to evaluate data structure and reproducibility. The review further highlights how these approaches can be applied to identify systematic sources of variation to support the appropriate implementation of normalization strategies. Improved detection and characterization of variability enhance data quality, reproducibility, and interpretability, which thereby support more reliable biomarker discovery and broader omics applications.