Toward nanoscale molecular mass spectrometry imaging via physically constrained machine learning on co-registered multimodal data
作者机构：Center for Nanophase Materials SciencesOak Ridge National LaboratoryOak RidgeTN 37831USA University of TennesseeKnoxvilleTNUSA
出 版 物：《npj Computational Materials》 (计算材料学（英文）)
年 卷 期：2020年第6卷第1期
页 面：956-963页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：This research was conducted at the Center for Nanophase Materials Sciences,which is a DOE Office of Science User Facility,and using instrumentation within ORNL’s Materials Characterization Core provided by UT-Battelle,LLC under Contract No.DEAC05-00OR22725 with the US Department of Energy This research was supported in part by an appointment to the Oak Ridge National Laboratory HERE Program,sponsored by the US Department of Energy and administered by the Oak Ridge Institute for Science and Education
主 题：modal mass constrained
摘 要：Mass spectrometry imaging(MSI)plays a pivotal role in investigating the chemical nature of complex systems that underly our understanding in biology and *** fields of life science such as proteomics,lipidomics and metabolomics benefit from the ability to simultaneously identify molecules and pinpoint their distribution across a ***,achieving the necessary submicron spatial resolution to distinguish chemical differences between individual cells and generating intact molecular spectra is still a challenge with any single imaging ***,we developed an approach that combines two MSI techniques,matrix-assisted laser desorption/ionization(MALDI)and time-of-flight secondary ion mass spectrometry(ToF-SIMS),one with low spatial resolution but intact molecular spectra and the other with nanometer spatial resolution but fragmented molecular signatures,to predict molecular MSI spectra with submicron spatial resolution.
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