Automated imaging and identification of proteoforms directly from ovarian cancer tissue

John P. McGee & Neil L. Kelleher et al. · 2023-10-14

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 MS2identification 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.

Funding

NCI NIH HHS

UH3 CA246635

NIDA NIH HHS

P30 DA018310

NIGMS NIH HHS

P41 GM108569

NIAID NIH HHS

K99 AI183290

NCI NIH HHS

P30 CA060553

NIA NIH HHS

F31 AG069456

U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences

P41 GM108569

U.S. Department of Health & Human Services | NIH | National Institute on Drug Abuse

P30 DA018310

U.S. Department of Health & Human Services | NIH | National Cancer Institute

P30 CA060553

U.S. Department of Health & Human Services | NIH | National Institute on Aging

F31 AG069456

U.S. Department of Defense

HU0001-16-2-0006

U.S. Department of Defense

HU0001-19-2-0031

U.S. Department of Defense

HU0001-20-2-0033

U.S. Department of Defense

HU0001-21-2-0027