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• Web-Application for assessing lipid and metabolite pre-analytical stability from biomatrices. • Broad and flexible applicability by pro- and retrospective stability assessment approaches. • Experiment-based stability information with full annotation. • Intuitive user interface and comprehensive output target a diverse scientific audience. In lipidomic and metabolomic studies, pre-analytical pitfalls enhance the risk of misusing resources such as time and money, as samples that are analyzed may not yield accurate or reliable data due to poor sample handling. Guidance and pre-analytic know-how are necessary for translation of omics technologies into routine clinical testing. The present work aims to enable decision making regarding sample stability in every phase of lipidomics- and metabolomics-centered studies. Data of multiple pre-analytic studies were aggregated into a database. Flexible approaches for evaluating these data were implemented in an RShiny-based web-application, tailored towards broad applicability in clinical and bioanalytic research. Our "Application for lipid stability evaluation & research" - ALISTER facilitates decision making on blood sample stability during lipidomic and metabolomic studies, such as biomarker research, analysis of biobank samples and clinical testing. The interactive tool gives sampling recommendations when planning sample collection or aids in the assessment of sample quality of experiments retrospectively. ALISTER is available for use under https://itmp.shinyapps.io/alister/. The application enables and simplifies data-driven decision making concerning pre-analytic blood sample handling and fits the needs of clinical investigations from multiple perspectives. [ABSTRACT FROM AUTHOR]

• We comprehensively depicted the lipidomic alterations in the serum and urine of initiate SSNS patients. We found the change of serum lipidomic was more visible than urine when compared with healthy control. • untargeted metabolomics analyses revealed that several lipids that may precisely predict or strategy for the SSNS patients. Steroid-sensitive nephrotic syndrome (SSNS) accounts for approximately 80% of cases of nephrotic syndrome. The involvement of aberrant lipid metabolism in early SSNS is poorly understood, warranting further investigation. This study aimed to explore alterations in lipid metabolism associated with SSNS pathogenesis. A screening cohort containing serum (50 SSNS, 37 controls) and urine samples (27 SSNS, 26 controls) was analyzed by untargeted lipidomic profiling using UHPLC-QTOF-MS. Then, a validation cohort (20 SSNS, 56 controls) underwent further analysis to check the potential clinical application by ROC curve analysis. Lipidomic profiling of serum and urine samples revealed significant lipid alterations in SSNS patients, with the alterations in the serum samples being more significant. An elevated concentration of PE and PG and downregulated concentration of FA were observed in SSNS serum. A total of 38 dysregulated lipids and 5 lipid metabolic pathways were identified in the serum samples in SSNS patients. Validation in the second cohort confirmed differential regulation of nine kinds of lipids, including 5 up-regulated substances [SM d33:2 (m / z = 686.5361), SHexCer d34:1 (m / z = 779.521), PI 20:4_22:4 (m / z = 934.5558), Cer_NS d18:1_23:0 (m / z = 635.6216), and GM3 d36:1 (m / z = 1180.7431)], as well as 4 down-regulated substances: [CE 18:1 (m / z = 650.601), PE 38:6 (m / z = 763.5205), PC 17:0_20:4 (m / z = 795.5868) and EtherPC 16:2e_20:4 (m / z = 763.5498)]. Untargeted lipidomic analysis successfully identified specific lipid class changes in patients with SSNS, providing a deeper understanding of lipid alterations and underlying mechanisms associated with SSNS. [ABSTRACT FROM AUTHOR]