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Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells
Nanoscale or single-cell technologies are critical for biomedical applications. However, current mass spectrometry (MS)-based proteomic approaches require samples comprising a minimum of thousands of cells to provide in-depth profiling. Here, we report the development of a nanoPOTS (nanodroplet proc...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5830451/ https://www.ncbi.nlm.nih.gov/pubmed/29491378 http://dx.doi.org/10.1038/s41467-018-03367-w |
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author | Zhu, Ying Piehowski, Paul D. Zhao, Rui Chen, Jing Shen, Yufeng Moore, Ronald J. Shukla, Anil K. Petyuk, Vladislav A. Campbell-Thompson, Martha Mathews, Clayton E. Smith, Richard D. Qian, Wei-Jun Kelly, Ryan T. |
author_facet | Zhu, Ying Piehowski, Paul D. Zhao, Rui Chen, Jing Shen, Yufeng Moore, Ronald J. Shukla, Anil K. Petyuk, Vladislav A. Campbell-Thompson, Martha Mathews, Clayton E. Smith, Richard D. Qian, Wei-Jun Kelly, Ryan T. |
author_sort | Zhu, Ying |
collection | PubMed |
description | Nanoscale or single-cell technologies are critical for biomedical applications. However, current mass spectrometry (MS)-based proteomic approaches require samples comprising a minimum of thousands of cells to provide in-depth profiling. Here, we report the development of a nanoPOTS (nanodroplet processing in one pot for trace samples) platform for small cell population proteomics analysis. NanoPOTS enhances the efficiency and recovery of sample processing by downscaling processing volumes to <200 nL to minimize surface losses. When combined with ultrasensitive liquid chromatography-MS, nanoPOTS allows identification of ~1500 to ~3000 proteins from ~10 to ~140 cells, respectively. By incorporating the Match Between Runs algorithm of MaxQuant, >3000 proteins are consistently identified from as few as 10 cells. Furthermore, we demonstrate quantification of ~2400 proteins from single human pancreatic islet thin sections from type 1 diabetic and control donors, illustrating the application of nanoPOTS for spatially resolved proteome measurements from clinical tissues. |
format | Online Article Text |
id | pubmed-5830451 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-58304512018-03-05 Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells Zhu, Ying Piehowski, Paul D. Zhao, Rui Chen, Jing Shen, Yufeng Moore, Ronald J. Shukla, Anil K. Petyuk, Vladislav A. Campbell-Thompson, Martha Mathews, Clayton E. Smith, Richard D. Qian, Wei-Jun Kelly, Ryan T. Nat Commun Article Nanoscale or single-cell technologies are critical for biomedical applications. However, current mass spectrometry (MS)-based proteomic approaches require samples comprising a minimum of thousands of cells to provide in-depth profiling. Here, we report the development of a nanoPOTS (nanodroplet processing in one pot for trace samples) platform for small cell population proteomics analysis. NanoPOTS enhances the efficiency and recovery of sample processing by downscaling processing volumes to <200 nL to minimize surface losses. When combined with ultrasensitive liquid chromatography-MS, nanoPOTS allows identification of ~1500 to ~3000 proteins from ~10 to ~140 cells, respectively. By incorporating the Match Between Runs algorithm of MaxQuant, >3000 proteins are consistently identified from as few as 10 cells. Furthermore, we demonstrate quantification of ~2400 proteins from single human pancreatic islet thin sections from type 1 diabetic and control donors, illustrating the application of nanoPOTS for spatially resolved proteome measurements from clinical tissues. Nature Publishing Group UK 2018-02-28 /pmc/articles/PMC5830451/ /pubmed/29491378 http://dx.doi.org/10.1038/s41467-018-03367-w Text en © The Author(s) 2018 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/. |
spellingShingle | Article Zhu, Ying Piehowski, Paul D. Zhao, Rui Chen, Jing Shen, Yufeng Moore, Ronald J. Shukla, Anil K. Petyuk, Vladislav A. Campbell-Thompson, Martha Mathews, Clayton E. Smith, Richard D. Qian, Wei-Jun Kelly, Ryan T. Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells |
title | Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells |
title_full | Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells |
title_fullStr | Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells |
title_full_unstemmed | Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells |
title_short | Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells |
title_sort | nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5830451/ https://www.ncbi.nlm.nih.gov/pubmed/29491378 http://dx.doi.org/10.1038/s41467-018-03367-w |
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