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Automated imaging and identification of proteoforms directly from ovarian cancer tissue

Authors :
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
Source :
Nature Communications, Vol 14, Iss 1, Pp 1-11 (2023)
Publication Year :
2023
Publisher :
Nature Portfolio, 2023.

Abstract

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

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
14
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.7587327dde1148caba435a60d10f292c
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-023-42208-3