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Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition

Single-cell resolution analysis of complex biological tissues is fundamental to capture cell-state heterogeneity and distinct cellular signaling patterns that remain obscured with population-based techniques. The limited amount of material encapsulated in a single cell however, raises significant te...

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Autores principales: Petrosius, Valdemaras, Aragon-Fernandez, Pedro, Üresin, Nil, Kovacs, Gergo, Phlairaharn, Teeradon, Furtwängler, Benjamin, Op De Beeck, Jeff, Skovbakke, Sarah L., Goletz, Steffen, Thomsen, Simon Francis, Keller, Ulrich auf dem, Natarajan, Kedar N., Porse, Bo T., Schoof, Erwin M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517177/
https://www.ncbi.nlm.nih.gov/pubmed/37737208
http://dx.doi.org/10.1038/s41467-023-41602-1
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author Petrosius, Valdemaras
Aragon-Fernandez, Pedro
Üresin, Nil
Kovacs, Gergo
Phlairaharn, Teeradon
Furtwängler, Benjamin
Op De Beeck, Jeff
Skovbakke, Sarah L.
Goletz, Steffen
Thomsen, Simon Francis
Keller, Ulrich auf dem
Natarajan, Kedar N.
Porse, Bo T.
Schoof, Erwin M.
author_facet Petrosius, Valdemaras
Aragon-Fernandez, Pedro
Üresin, Nil
Kovacs, Gergo
Phlairaharn, Teeradon
Furtwängler, Benjamin
Op De Beeck, Jeff
Skovbakke, Sarah L.
Goletz, Steffen
Thomsen, Simon Francis
Keller, Ulrich auf dem
Natarajan, Kedar N.
Porse, Bo T.
Schoof, Erwin M.
author_sort Petrosius, Valdemaras
collection PubMed
description Single-cell resolution analysis of complex biological tissues is fundamental to capture cell-state heterogeneity and distinct cellular signaling patterns that remain obscured with population-based techniques. The limited amount of material encapsulated in a single cell however, raises significant technical challenges to molecular profiling. Due to extensive optimization efforts, single-cell proteomics by Mass Spectrometry (scp-MS) has emerged as a powerful tool to facilitate proteome profiling from ultra-low amounts of input, although further development is needed to realize its full potential. To this end, we carry out comprehensive analysis of orbitrap-based data-independent acquisition (DIA) for limited material proteomics. Notably, we find a fundamental difference between optimal DIA methods for high- and low-load samples. We further improve our low-input DIA method by relying on high-resolution MS1 quantification, thus enhancing sensitivity by more efficiently utilizing available mass analyzer time. With our ultra-low input tailored DIA method, we are able to accommodate long injection times and high resolution, while keeping the scan cycle time low enough to ensure robust quantification. Finally, we demonstrate the capability of our approach by profiling mouse embryonic stem cell culture conditions, showcasing heterogeneity in global proteomes and highlighting distinct differences in key metabolic enzyme expression in distinct cell subclusters.
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spelling pubmed-105171772023-09-24 Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition Petrosius, Valdemaras Aragon-Fernandez, Pedro Üresin, Nil Kovacs, Gergo Phlairaharn, Teeradon Furtwängler, Benjamin Op De Beeck, Jeff Skovbakke, Sarah L. Goletz, Steffen Thomsen, Simon Francis Keller, Ulrich auf dem Natarajan, Kedar N. Porse, Bo T. Schoof, Erwin M. Nat Commun Article Single-cell resolution analysis of complex biological tissues is fundamental to capture cell-state heterogeneity and distinct cellular signaling patterns that remain obscured with population-based techniques. The limited amount of material encapsulated in a single cell however, raises significant technical challenges to molecular profiling. Due to extensive optimization efforts, single-cell proteomics by Mass Spectrometry (scp-MS) has emerged as a powerful tool to facilitate proteome profiling from ultra-low amounts of input, although further development is needed to realize its full potential. To this end, we carry out comprehensive analysis of orbitrap-based data-independent acquisition (DIA) for limited material proteomics. Notably, we find a fundamental difference between optimal DIA methods for high- and low-load samples. We further improve our low-input DIA method by relying on high-resolution MS1 quantification, thus enhancing sensitivity by more efficiently utilizing available mass analyzer time. With our ultra-low input tailored DIA method, we are able to accommodate long injection times and high resolution, while keeping the scan cycle time low enough to ensure robust quantification. Finally, we demonstrate the capability of our approach by profiling mouse embryonic stem cell culture conditions, showcasing heterogeneity in global proteomes and highlighting distinct differences in key metabolic enzyme expression in distinct cell subclusters. Nature Publishing Group UK 2023-09-22 /pmc/articles/PMC10517177/ /pubmed/37737208 http://dx.doi.org/10.1038/s41467-023-41602-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Petrosius, Valdemaras
Aragon-Fernandez, Pedro
Üresin, Nil
Kovacs, Gergo
Phlairaharn, Teeradon
Furtwängler, Benjamin
Op De Beeck, Jeff
Skovbakke, Sarah L.
Goletz, Steffen
Thomsen, Simon Francis
Keller, Ulrich auf dem
Natarajan, Kedar N.
Porse, Bo T.
Schoof, Erwin M.
Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition
title Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition
title_full Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition
title_fullStr Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition
title_full_unstemmed Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition
title_short Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition
title_sort exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517177/
https://www.ncbi.nlm.nih.gov/pubmed/37737208
http://dx.doi.org/10.1038/s41467-023-41602-1
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