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

6 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
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
Externally published	Yes

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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.}",

note = "Publisher Copyright: {\textcopyright} 2023, Springer Nature Limited.",

year = "2023",

month = dec,

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

language = "English",

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AU - McGee, John P.

AU - Su, Pei

AU - Durbin, Kenneth R.

AU - Hollas, Michael A.R.

AU - Bateman, Nicholas W.

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.

PY - 2023/12

Y1 - 2023/12

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.

AB - 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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VL - 14

JO - Nature Communications

JF - Nature Communications

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Automated imaging and identification of proteoforms directly from ovarian cancer tissue

Abstract

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