Deep learning-based proteomics enables accurate classification of bulk and single-cell samples Article Swipe
Karl Kristian Krull
,
Arlene Kühn
,
Julia Höhn
,
Titus J. Brinker
,
Jeroen Krijgsveld
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.02.03.578734
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.02.03.578734
Proteins are the main drivers of cell function and disease, making their analysis a powerful technique to characterize determinants of cell identity and to identify biomarkers. Current proteomic technology has the breadth to profile thousands of proteins and even the sensitivity to access single cells, however limitations in throughput restrict its application, e.g. not allowing classification of samples according to biological or clinical status in large sample cohorts. Therefore, we developed a deep learning-based approach for the analysis of mass spectrometric (MS) data, assigning proteomic profiles to sample identity. Specifically, we designed an architecture referred to as Proformer, and show that it is superior to convolutional neural network-driven architectures, is explainable, and demonstrates robustness towards batch-effects. Based on its tabular approach, we highlight the integration of all four dimensions of proteomic measurements (retention time, mass-to-charge, intensity and ion mobility), and demonstrate enhanced sample discrimination involving a treatment with IFN-γ, despite its subtle effect on the cell’s proteome. In addition, the Proformer is not restricted to proteomic depth, and can classify cells by cell type and their differentiation status even using single-cell proteomic data. Collectively, this work presents a novel deep learning-based model for rapid classification of proteomic data, with important future implications to enhance patient stratification, early detection and single-cell analysis.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.02.03.578734
- https://www.biorxiv.org/content/biorxiv/early/2024/02/09/2024.02.03.578734.full.pdf
- OA Status
- green
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391648678
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391648678Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.02.03.578734Digital Object Identifier
- Title
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Deep learning-based proteomics enables accurate classification of bulk and single-cell samplesWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-02-07Full publication date if available
- Authors
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Karl Kristian Krull, Arlene Kühn, Julia Höhn, Titus J. Brinker, Jeroen KrijgsveldList of authors in order
- Landing page
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https://doi.org/10.1101/2024.02.03.578734Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2024/02/09/2024.02.03.578734.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2024/02/09/2024.02.03.578734.full.pdfDirect OA link when available
- Concepts
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Proteomics, Computer science, Convolutional neural network, Proteome, Robustness (evolution), Artificial intelligence, Deep learning, Computational biology, Sample (material), Machine learning, Pattern recognition (psychology), Bioinformatics, Biology, Chemistry, Chromatography, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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32Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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