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 journalArticlepeer-review

3 Scopus citations


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 languageEnglish
Article number6478
JournalNature Communications
Issue number1
StatePublished - Dec 2023
Externally publishedYes


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