Automated imaging and identification of proteoforms directly from ovarian cancer tissue

John P. McGee; Pei Su; Kenneth R. Durbin; Michael A.R. Hollas; Nicholas W. Bateman; G. Larry Maxwell; Thomas P. Conrads; Ryan T. Fellers; Rafael D. Melani; Jeannie M. Camarillo; Jared O. Kafader; Neil L. Kelleher

doi:10.1038/s41467-023-42208-3

Automated imaging and identification of proteoforms directly from ovarian cancer tissue

John P. McGee, Pei Su, Kenneth R. Durbin, Michael A.R. Hollas, Nicholas W. Bateman, G. Larry Maxwell, Thomas P. Conrads, Ryan T. Fellers, Rafael D. Melani, Jeannie M. Camarillo, Jared O. Kafader, Neil L. Kelleher^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

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 (MS²) directly from tissue microenvironments in a semi-automated manner. AutoPiMS directly off human ovarian cancer sections allowed for MS² 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.

Original language	English (US)
Article number	6478
Journal	Nature communications
Volume	14
Issue number	1
DOIs	https://doi.org/10.1038/s41467-023-42208-3
State	Published - Dec 2023

Funding

This study was supported by NIH P41 GM108569 (N.L.K.), P30 DA018310 (N.L.K.), P30 CA060553 (awarded to the Robert H. Lurie Comprehensive Cancer Center), F31 AG069456 (JPM), U.S. Department of Defense—Uniformed Services University of the Health Sciences (HU0001-16-2-0006, HU0001-19-2-0031, HU0001-20-2-0033, and HU0001-21-2-0027 (N.W.B., G.L.M., T.P.C.)). The views expressed herein are those of the authors and do not reflect the official policy of the Uniformed Services University of the Health Sciences, the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., the Department of Army/Navy/Air Force, Department of Defense, or U.S. Government. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government. This study was supported by NIH P41 GM108569 (N.L.K.), P30 DA018310 (N.L.K.), P30 CA060553 (awarded to the Robert H. Lurie Comprehensive Cancer Center), F31 AG069456 (JPM), U.S. Department of Defense—Uniformed Services University of the Health Sciences (HU0001-16-2-0006, HU0001-19-2-0031, HU0001-20-2-0033, and HU0001-21-2-0027 (N.W.B., G.L.M., T.P.C.)). The views expressed herein are those of the authors and do not reflect the official policy of the Uniformed Services University of the Health Sciences, the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., the Department of Army/Navy/Air Force, Department of Defense, or U.S. Government. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government.

ASJC Scopus subject areas

General Physics and Astronomy
General Chemistry
General Biochemistry, Genetics and Molecular Biology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1038/s41467-023-42208-3

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title = "Automated imaging and identification of proteoforms directly from ovarian cancer tissue",

abstract = "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 (MS2) directly from tissue microenvironments in a semi-automated manner. AutoPiMS directly off human ovarian cancer sections allowed for MS2 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.",

author = "McGee, {John P.} and Pei Su and Durbin, {Kenneth R.} and Hollas, {Michael A.R.} and Bateman, {Nicholas W.} and Maxwell, {G. Larry} and Conrads, {Thomas P.} and Fellers, {Ryan T.} and Melani, {Rafael D.} and Camarillo, {Jeannie M.} and Kafader, {Jared O.} and Kelleher, {Neil L.}",

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doi = "10.1038/s41467-023-42208-3",

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AU - Maxwell, G. Larry

AU - Conrads, Thomas P.

AU - Fellers, Ryan T.

AU - Melani, Rafael D.

AU - Camarillo, Jeannie M.

AU - Kafader, Jared O.

AU - Kelleher, Neil L.

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N2 - 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 (MS2) directly from tissue microenvironments in a semi-automated manner. AutoPiMS directly off human ovarian cancer sections allowed for MS2 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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Automated imaging and identification of proteoforms directly from ovarian cancer tissue

Abstract

Funding

ASJC Scopus subject areas

UN SDGs

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