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Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform
Conventional proteomic approaches measure the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of signals arising from subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cel...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Biochemistry and Molecular Biology
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9944986/ https://www.ncbi.nlm.nih.gov/pubmed/36603806 http://dx.doi.org/10.1016/j.mcpro.2022.100491 |
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author | Liao, Yen-Chen Fulcher, James M. Degnan, David J. Williams, Sarah M. Bramer, Lisa M. Veličković, Dušan Zemaitis, Kevin J. Veličković, Marija Sontag, Ryan L. Moore, Ronald J. Paša-Tolić, Ljiljana Zhu, Ying Zhou, Mowei |
author_facet | Liao, Yen-Chen Fulcher, James M. Degnan, David J. Williams, Sarah M. Bramer, Lisa M. Veličković, Dušan Zemaitis, Kevin J. Veličković, Marija Sontag, Ryan L. Moore, Ronald J. Paša-Tolić, Ljiljana Zhu, Ying Zhou, Mowei |
author_sort | Liao, Yen-Chen |
collection | PubMed |
description | Conventional proteomic approaches measure the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of signals arising from subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot unambiguously define and quantify proteoforms, which are intact (i.e., functional) forms of proteins capturing genetic variations, alternatively spliced transcripts and posttranslational modifications. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantitation directly from tissue sections. The spatial TDP platform consisted of a nanodroplet processing in one pot for trace samples–based sample preparation system and an laser capture microdissection–based cell isolation system. We improved the nanodroplet processing in one pot for trace samples sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ∼200 cultured cells as test samples, this approach increased total proteoform identifications from 493 to 700; with newly identified proteoforms primarily corresponding to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed laser capture microdissection–isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 509 proteoforms within the union of top-down mass spectrometry–based proteoform identification and characterization and TDPortal identifications to match with features from protein mass extractor. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential posttranslational modification–specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections. |
format | Online Article Text |
id | pubmed-9944986 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Society for Biochemistry and Molecular Biology |
record_format | MEDLINE/PubMed |
spelling | pubmed-99449862023-02-23 Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform Liao, Yen-Chen Fulcher, James M. Degnan, David J. Williams, Sarah M. Bramer, Lisa M. Veličković, Dušan Zemaitis, Kevin J. Veličković, Marija Sontag, Ryan L. Moore, Ronald J. Paša-Tolić, Ljiljana Zhu, Ying Zhou, Mowei Mol Cell Proteomics Research Article Collection: Separation Technology Conventional proteomic approaches measure the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of signals arising from subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot unambiguously define and quantify proteoforms, which are intact (i.e., functional) forms of proteins capturing genetic variations, alternatively spliced transcripts and posttranslational modifications. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantitation directly from tissue sections. The spatial TDP platform consisted of a nanodroplet processing in one pot for trace samples–based sample preparation system and an laser capture microdissection–based cell isolation system. We improved the nanodroplet processing in one pot for trace samples sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ∼200 cultured cells as test samples, this approach increased total proteoform identifications from 493 to 700; with newly identified proteoforms primarily corresponding to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed laser capture microdissection–isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 509 proteoforms within the union of top-down mass spectrometry–based proteoform identification and characterization and TDPortal identifications to match with features from protein mass extractor. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential posttranslational modification–specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections. American Society for Biochemistry and Molecular Biology 2023-01-02 /pmc/articles/PMC9944986/ /pubmed/36603806 http://dx.doi.org/10.1016/j.mcpro.2022.100491 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Article Collection: Separation Technology Liao, Yen-Chen Fulcher, James M. Degnan, David J. Williams, Sarah M. Bramer, Lisa M. Veličković, Dušan Zemaitis, Kevin J. Veličković, Marija Sontag, Ryan L. Moore, Ronald J. Paša-Tolić, Ljiljana Zhu, Ying Zhou, Mowei Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform |
title | Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform |
title_full | Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform |
title_fullStr | Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform |
title_full_unstemmed | Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform |
title_short | Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform |
title_sort | spatially resolved top-down proteomics of tissue sections based on a microfluidic nanodroplet sample preparation platform |
topic | Research Article Collection: Separation Technology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9944986/ https://www.ncbi.nlm.nih.gov/pubmed/36603806 http://dx.doi.org/10.1016/j.mcpro.2022.100491 |
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