Deep Visual Proteomics defines single-cell identity and heterogeneity

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Deep Visual Proteomics defines single-cell identity and heterogeneity. / Mund, Andreas; Coscia, Fabian; Kriston, András; Hollandi, Réka; Kovács, Ferenc; Brunner, Andreas-David; Migh, Ede; Schweizer, Lisa; Santos, Alberto; Bzorek, Michael; Naimy, Soraya; Rahbek-Gjerdrum, Lise Mette; Dyring-Andersen, Beatrice; Bulkescher, Jutta; Lukas, Claudia; Eckert, Mark Adam; Lengyel, Ernst; Gnann, Christian; Lundberg, Emma; Horvath, Peter; Mann, Matthias.

In: Nature Biotechnology, Vol. 40, 2022, p. 1231-1240.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Mund, A, Coscia, F, Kriston, A, Hollandi, R, Kovács, F, Brunner, A-D, Migh, E, Schweizer, L, Santos, A, Bzorek, M, Naimy, S, Rahbek-Gjerdrum, LM, Dyring-Andersen, B, Bulkescher, J, Lukas, C, Eckert, MA, Lengyel, E, Gnann, C, Lundberg, E, Horvath, P & Mann, M 2022, 'Deep Visual Proteomics defines single-cell identity and heterogeneity', Nature Biotechnology, vol. 40, pp. 1231-1240. https://doi.org/10.1038/s41587-022-01302-5

APA

Mund, A., Coscia, F., Kriston, A., Hollandi, R., Kovács, F., Brunner, A-D., Migh, E., Schweizer, L., Santos, A., Bzorek, M., Naimy, S., Rahbek-Gjerdrum, L. M., Dyring-Andersen, B., Bulkescher, J., Lukas, C., Eckert, M. A., Lengyel, E., Gnann, C., Lundberg, E., ... Mann, M. (2022). Deep Visual Proteomics defines single-cell identity and heterogeneity. Nature Biotechnology, 40, 1231-1240. https://doi.org/10.1038/s41587-022-01302-5

Vancouver

Mund A, Coscia F, Kriston A, Hollandi R, Kovács F, Brunner A-D et al. Deep Visual Proteomics defines single-cell identity and heterogeneity. Nature Biotechnology. 2022;40:1231-1240. https://doi.org/10.1038/s41587-022-01302-5

Author

Mund, Andreas ; Coscia, Fabian ; Kriston, András ; Hollandi, Réka ; Kovács, Ferenc ; Brunner, Andreas-David ; Migh, Ede ; Schweizer, Lisa ; Santos, Alberto ; Bzorek, Michael ; Naimy, Soraya ; Rahbek-Gjerdrum, Lise Mette ; Dyring-Andersen, Beatrice ; Bulkescher, Jutta ; Lukas, Claudia ; Eckert, Mark Adam ; Lengyel, Ernst ; Gnann, Christian ; Lundberg, Emma ; Horvath, Peter ; Mann, Matthias. / Deep Visual Proteomics defines single-cell identity and heterogeneity. In: Nature Biotechnology. 2022 ; Vol. 40. pp. 1231-1240.

Bibtex

@article{72fa4506a5714270874b46207187448f,
title = "Deep Visual Proteomics defines single-cell identity and heterogeneity",
abstract = "Despite the availabilty of imaging-based and mass-spectrometry-based methods for spatial proteomics, a key challenge remains connecting images with single-cell-resolution protein abundance measurements. Here, we introduce Deep Visual Proteomics (DVP), which combines artificial-intelligence-driven image analysis of cellular phenotypes with automated single-cell or single-nucleus laser microdissection and ultra-high-sensitivity mass spectrometry. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. By individually excising nuclei from cell culture, we classified distinct cell states with proteomic profiles defined by known and uncharacterized proteins. In an archived primary melanoma tissue, DVP identified spatially resolved proteome changes as normal melanocytes transition to fully invasive melanoma, revealing pathways that change in a spatial manner as cancer progresses, such as mRNA splicing dysregulation in metastatic vertical growth that coincides with reduced interferon signaling and antigen presentation. The ability of DVP to retain precise spatial proteomic information in the tissue context has implications for the molecular profiling of clinical samples.",
author = "Andreas Mund and Fabian Coscia and Andr{\'a}s Kriston and R{\'e}ka Hollandi and Ferenc Kov{\'a}cs and Andreas-David Brunner and Ede Migh and Lisa Schweizer and Alberto Santos and Michael Bzorek and Soraya Naimy and Rahbek-Gjerdrum, {Lise Mette} and Beatrice Dyring-Andersen and Jutta Bulkescher and Claudia Lukas and Eckert, {Mark Adam} and Ernst Lengyel and Christian Gnann and Emma Lundberg and Peter Horvath and Matthias Mann",
note = "{\textcopyright} 2022. The Author(s).",
year = "2022",
doi = "10.1038/s41587-022-01302-5",
language = "English",
volume = "40",
pages = "1231--1240",
journal = "Nature Biotechnology",
issn = "1087-0156",
publisher = "nature publishing group",

}

RIS

TY - JOUR

T1 - Deep Visual Proteomics defines single-cell identity and heterogeneity

AU - Mund, Andreas

AU - Coscia, Fabian

AU - Kriston, András

AU - Hollandi, Réka

AU - Kovács, Ferenc

AU - Brunner, Andreas-David

AU - Migh, Ede

AU - Schweizer, Lisa

AU - Santos, Alberto

AU - Bzorek, Michael

AU - Naimy, Soraya

AU - Rahbek-Gjerdrum, Lise Mette

AU - Dyring-Andersen, Beatrice

AU - Bulkescher, Jutta

AU - Lukas, Claudia

AU - Eckert, Mark Adam

AU - Lengyel, Ernst

AU - Gnann, Christian

AU - Lundberg, Emma

AU - Horvath, Peter

AU - Mann, Matthias

N1 - © 2022. The Author(s).

PY - 2022

Y1 - 2022

N2 - Despite the availabilty of imaging-based and mass-spectrometry-based methods for spatial proteomics, a key challenge remains connecting images with single-cell-resolution protein abundance measurements. Here, we introduce Deep Visual Proteomics (DVP), which combines artificial-intelligence-driven image analysis of cellular phenotypes with automated single-cell or single-nucleus laser microdissection and ultra-high-sensitivity mass spectrometry. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. By individually excising nuclei from cell culture, we classified distinct cell states with proteomic profiles defined by known and uncharacterized proteins. In an archived primary melanoma tissue, DVP identified spatially resolved proteome changes as normal melanocytes transition to fully invasive melanoma, revealing pathways that change in a spatial manner as cancer progresses, such as mRNA splicing dysregulation in metastatic vertical growth that coincides with reduced interferon signaling and antigen presentation. The ability of DVP to retain precise spatial proteomic information in the tissue context has implications for the molecular profiling of clinical samples.

AB - Despite the availabilty of imaging-based and mass-spectrometry-based methods for spatial proteomics, a key challenge remains connecting images with single-cell-resolution protein abundance measurements. Here, we introduce Deep Visual Proteomics (DVP), which combines artificial-intelligence-driven image analysis of cellular phenotypes with automated single-cell or single-nucleus laser microdissection and ultra-high-sensitivity mass spectrometry. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. By individually excising nuclei from cell culture, we classified distinct cell states with proteomic profiles defined by known and uncharacterized proteins. In an archived primary melanoma tissue, DVP identified spatially resolved proteome changes as normal melanocytes transition to fully invasive melanoma, revealing pathways that change in a spatial manner as cancer progresses, such as mRNA splicing dysregulation in metastatic vertical growth that coincides with reduced interferon signaling and antigen presentation. The ability of DVP to retain precise spatial proteomic information in the tissue context has implications for the molecular profiling of clinical samples.

U2 - 10.1038/s41587-022-01302-5

DO - 10.1038/s41587-022-01302-5

M3 - Journal article

C2 - 35590073

VL - 40

SP - 1231

EP - 1240

JO - Nature Biotechnology

JF - Nature Biotechnology

SN - 1087-0156

ER -

ID: 308125035