2026-10-10T22:20:40Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33756163 2026-09-14T20:07:47Z category_4 category_14 category_39 category_734 category_931 category_61 category_64 category_106 portal_63 item_type_3 month_year_09_2026

Single-Tube Mass Spectrometry-Based Workflow for Multi-Omics Profiling of Diverse Biomolecules Maxim Itkin (6988097) Bareket Dassa (242860) Yishai Levin (702481) Andrii Vainer (24899188) Asaph Aharoni (157344) Sergey Malitsky (219758) Biochemistry Molecular Biology Ecology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Developmental Biology Cancer Science Policy providing temporal signatures multiple biomolecule types machine learning framework exhibit dynamic changes diverse biomolecules advances diverse omics technologies across omics layers standardizing data acquisition polar ), lipids diverse biological tissues omics platform enables tube mass spectrometry mass spectrometry omics profiling omics platform flesh tissues biological processes throughput data integrate data unsupervised integration throughput manner single tube simultaneous detection robust detection native peptides fruit development comprehensive exploration cleavage preferences chromatographic systems bioinformatic pipeline based workflow Advances in mass spectrometry-based omics technologies enable detailed exploration of biological processes. However, robust detection of multiple biomolecule types from the same biological sample remains challenging. We developed a multi-omics platform for the simultaneous detection of metabolites (polar and semi-polar), lipids, proteins, and native peptides, all detected in a high-throughput manner from a single tube. As a proof-of-concept for the platform, we profiled the profound and dynamic molecular changes occurring in tomato during fruit development. This included optimizing sample collection, standardizing data acquisition from the chromatographic systems, and developing a bioinformatic pipeline to integrate data from the diverse omics technologies. Across omics layers, we detected thousands of biomolecules whose abundances shifted significantly during fruit development in peel and flesh tissues, providing temporal signatures that differentiate fruit developmental stages. This includes the characterization of numerous naturally produced endogenous peptides that exhibit dynamic changes in composition and cleavage preferences. Finally, we demonstrate the use of a machine learning framework for unsupervised integration of this type of high-throughput data. Collectively, our multi-omics platform enables a comprehensive exploration of diverse biological tissues and biofluids with broad applicability across research fields. 2026-09-14T00:00:00Z Dataset Dataset 10.1021/acs.analchem.6c03036.s001 https://figshare.com/articles/dataset/Single-Tube_Mass_Spectrometry-Based_Workflow_for_Multi-Omics_Profiling_of_Diverse_Biomolecules/33756163 CC BY-NC 4.0