microMS: A Python Platform for Image-Guided Mass Spectrometry Profiling
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Image-guided mass spectrometry (MS) profiling provides a facile framework for analyzing samples ranging from single cells to tissue sections. The fundamental workflow utilizes a whole-slide microscopy image to select targets of interest, determine their spatial locations, and subsequently perform MS analysis at those locations. Improving upon prior reported methodology, a software package was developed for working with microscopy images. microMS, for microscopy-guided mass spectrometry, allows the user to select and profile diverse samples using a variety of target patterns and mass analyzers. Written in Python, the program provides an intuitive graphical user interface to simplify image-guided MS for novice users. The class hierarchy of instrument interactions permits integration of new MS systems while retaining the feature-rich image analysis framework. microMS is a versatile platform for performing targeted profiling experiments using a series of mass spectrometers. The flexibility in mass analyzers greatly simplifies serial analyses of the same targets by different instruments. The current capabilities of microMS are presented, and its application for off-line analysis of single cells on three distinct instruments is demonstrated. The software has been made freely available for research purposes.
KeywordsImage-guided mass spectrometry Single cell analysis Software MALDI SIMS
The authors thank Stanislav Rubakhin for assistance with sample preparation, and Tong Si for useful discussions. They also gratefully acknowledge support from the National Institutes of Health, Award Number P30 DA018310 from the National Institute on Drug Abuse, and from the National Institute of Mental Health, Award Number 1U01 MH109062. T.J.C. and E.K.N. acknowledge support from the National Science Foundation Graduate Research Fellowship Program and the Springborn Fellowship. T.J.C. received additional support through the Training Program at Chemistry-Interface with Biology (T32 GM070421).
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