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Automated imaging and identification of proteoforms directly from ovarian cancer tissue

The molecular identification of tissue proteoforms by top-down mass spectrometry (TDMS) is significantly limited by throughput and dynamic range. We introduce AutoPiMS, a single-ion MS based multiplexed workflow for top-down tandem MS (MS(2)) directly from tissue microenvironments in a semi-automate...

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Autores principales: McGee, John P., Su, Pei, Durbin, Kenneth R., Hollas, Michael A. R., Bateman, Nicholas W., Maxwell, G. Larry, Conrads, Thomas P., Fellers, Ryan T., Melani, Rafael D., Camarillo, Jeannie M., Kafader, Jared O., Kelleher, Neil L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10576781/
https://www.ncbi.nlm.nih.gov/pubmed/37838706
http://dx.doi.org/10.1038/s41467-023-42208-3
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author McGee, John P.
Su, Pei
Durbin, Kenneth R.
Hollas, Michael A. R.
Bateman, Nicholas W.
Maxwell, G. Larry
Conrads, Thomas P.
Fellers, Ryan T.
Melani, Rafael D.
Camarillo, Jeannie M.
Kafader, Jared O.
Kelleher, Neil L.
author_facet McGee, John P.
Su, Pei
Durbin, Kenneth R.
Hollas, Michael A. R.
Bateman, Nicholas W.
Maxwell, G. Larry
Conrads, Thomas P.
Fellers, Ryan T.
Melani, Rafael D.
Camarillo, Jeannie M.
Kafader, Jared O.
Kelleher, Neil L.
author_sort McGee, John P.
collection PubMed
description The molecular identification of tissue proteoforms by top-down mass spectrometry (TDMS) is significantly limited by throughput and dynamic range. We introduce AutoPiMS, a single-ion MS based multiplexed workflow for top-down tandem MS (MS(2)) directly from tissue microenvironments in a semi-automated manner. AutoPiMS directly off human ovarian cancer sections allowed for MS(2) identification of 73 proteoforms up to 54 kDa at a rate of <1 min per proteoform. AutoPiMS is directly interfaced with multifaceted proteoform imaging MS data modalities for the identification of proteoform signatures in tumor and stromal regions in ovarian cancer biopsies. From a total of ~1000 proteoforms detected by region-of-interest label-free quantitation, we discover 303 differential proteoforms in stroma versus tumor from the same patient. 14 of the top proteoform signatures are corroborated by MSI at 20 micron resolution including the differential localization of methylated forms of CRIP1, indicating the importance of proteoform-enabled spatial biology in ovarian cancer.
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spelling pubmed-105767812023-10-16 Automated imaging and identification of proteoforms directly from ovarian cancer tissue McGee, John P. Su, Pei Durbin, Kenneth R. Hollas, Michael A. R. Bateman, Nicholas W. Maxwell, G. Larry Conrads, Thomas P. Fellers, Ryan T. Melani, Rafael D. Camarillo, Jeannie M. Kafader, Jared O. Kelleher, Neil L. Nat Commun Article The molecular identification of tissue proteoforms by top-down mass spectrometry (TDMS) is significantly limited by throughput and dynamic range. We introduce AutoPiMS, a single-ion MS based multiplexed workflow for top-down tandem MS (MS(2)) directly from tissue microenvironments in a semi-automated manner. AutoPiMS directly off human ovarian cancer sections allowed for MS(2) identification of 73 proteoforms up to 54 kDa at a rate of <1 min per proteoform. AutoPiMS is directly interfaced with multifaceted proteoform imaging MS data modalities for the identification of proteoform signatures in tumor and stromal regions in ovarian cancer biopsies. From a total of ~1000 proteoforms detected by region-of-interest label-free quantitation, we discover 303 differential proteoforms in stroma versus tumor from the same patient. 14 of the top proteoform signatures are corroborated by MSI at 20 micron resolution including the differential localization of methylated forms of CRIP1, indicating the importance of proteoform-enabled spatial biology in ovarian cancer. Nature Publishing Group UK 2023-10-14 /pmc/articles/PMC10576781/ /pubmed/37838706 http://dx.doi.org/10.1038/s41467-023-42208-3 Text en © The Author(s) 2023, corrected publication 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
McGee, John P.
Su, Pei
Durbin, Kenneth R.
Hollas, Michael A. R.
Bateman, Nicholas W.
Maxwell, G. Larry
Conrads, Thomas P.
Fellers, Ryan T.
Melani, Rafael D.
Camarillo, Jeannie M.
Kafader, Jared O.
Kelleher, Neil L.
Automated imaging and identification of proteoforms directly from ovarian cancer tissue
title Automated imaging and identification of proteoforms directly from ovarian cancer tissue
title_full Automated imaging and identification of proteoforms directly from ovarian cancer tissue
title_fullStr Automated imaging and identification of proteoforms directly from ovarian cancer tissue
title_full_unstemmed Automated imaging and identification of proteoforms directly from ovarian cancer tissue
title_short Automated imaging and identification of proteoforms directly from ovarian cancer tissue
title_sort automated imaging and identification of proteoforms directly from ovarian cancer tissue
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10576781/
https://www.ncbi.nlm.nih.gov/pubmed/37838706
http://dx.doi.org/10.1038/s41467-023-42208-3
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