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Peer-reviewedCC BYAnalytical chemistry

Single-Tube Mass Spectrometry-Based Workflow for Multi-Omics Profiling of Diverse Biomolecules.

  • Maxim Itkin1
  • Bareket Dassa1
  • Yishai Levin1,2
  • Andrii Vainer3
  • Asaph Aharoni3
  • Sergey Malitsky1
  1. 1Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot7610001, Israel.
  2. 2de Botton Institute for Protein Profiling, Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot7610001, Israel.
  3. 3Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot7610001, Israel.
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Abstract

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.

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Cite

BibTeXRIS
BibTeX
@article{itkin2026singletube,
  title = {{Single-Tube Mass Spectrometry-Based Workflow for Multi-Omics Profiling of Diverse Biomolecules.}},
  author = {Maxim Itkin and Bareket Dassa and Yishai Levin and Andrii Vainer and Asaph Aharoni and Sergey Malitsky},
  journal = {Analytical chemistry},
  year = {2026},
  month = sep,
  doi = {10.1021/acs.analchem.6c03036},
  url = {https://europepmc.org/article/MED/42916352},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Single-Tube Mass Spectrometry-Based Workflow for Multi-Omics Profiling of Diverse Biomolecules.
AU  - Maxim Itkin
AU  - Bareket Dassa
AU  - Yishai Levin
AU  - Andrii Vainer
AU  - Asaph Aharoni
AU  - Sergey Malitsky
PY  - 2026
DA  - 2026/09/01
JO  - Analytical chemistry
DO  - 10.1021/acs.analchem.6c03036
UR  - https://europepmc.org/article/MED/42916352
AB  - 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.
SN  - 0003-2700
SN  - 1520-6882
ER  -