Top, hyperspectral visualization with data from a standard 9-hour experiment compared with hyperspectral visualization with data from a proposed 1-hour experiment. Image courtesy the Beckman Institute for Advanced Science and Technology.
December 15, 2020 — A new computational mass spectrometry imaging method enables researchers to achieve high mass resolution and high spatial resolution for biological samples while providing data sets exponentially faster.
Researchers at theBeckman Institute for Advanced Science and Technologydeveloped a subspace mass spectrometry imaging approach that accelerates the speed of data acquisition -- without sacrificing the quality -- by designing a model-based reconstruction strategy.
The technique, which was developed using animal models, could have important implications for many applications, including analytical chemistry and clinical studies, with results available at a fraction of the time. It also can detect a wide range of biomolecules — from small molecules such as neurotransmitters and amino acids to larger molecules such as lipids or peptides.
The paper "Accelerating Fourier Transform-Ion Cyclotron Resonance Mass Spectrometry Imaging Using a Subspace Approach" was published in theJournal of the American Society of Mass Spectrometry.
"Fourier transform-ion cyclotron resonance is a really powerful instrument, providing the highest mass resolution," said Yuxuan Richard Xie, a bioengineering graduate student at theUniversity of Illinois Urbana-Champaign他是这篇论文的第一作者。“但FT-ICR的一个缺点是速度非常慢。所以本质上，如果人们想要达到一定的分辨率，他们必须等待几天来获取数据集。我们的计算方法加快了这个采集过程，可能从一天增加到一到两个小时，基本上将数据采集速度提高了十倍。"
"Our method is changing the way that we acquire the data," Xie said. "Instead of acquiring mass spectra per pixel, the technique recognizes the redundancy in the high-dimensional imaging data and uses a low-dimensional subspace model to exploit this redundancy to reconstruct multispectral images from only a subset of the data."
Xie collaborated withFan Lam, an assistant professor of bioengineering, andJonathan V. Sweedler, the James R. Eiszner Family Endowed Chair in Chemistry and the director of the School of Chemical Sciences, who are co-principal investigators on the paper. Daniel Castro, a graduate student in molecular and integrative physiology, also contributed.
"We have been using subspace models in our MRI and MR spectroscopic imaging work for a long time," Lam said. "It is really nice to see that it also has great potentials for a different biochemical imaging modality."
Sweedler说:“获得增强的化学信息和化学物质在复杂样本(如大脑的一部分)中的位置的能力使我们的神经化学研究成为可能。”
The subspace imaging concept was pioneered by Zhi-Pei Liang, a professor of electrical and computer engineering and full-time Beckman faculty member, who is a world-leading expert in MRI and MRSI.
随着研究人员寻求将这项技术应用于三维成像，这项研究仍在继续。“(这种方法)可能会对科学界更大范围的三维成像产生更大的影响，比如大脑，”谢说。“因为如果我们在FT-ICR上拍摄50片，目前需要几周的时间，但(使用这项技术)我们可以在几天内实现可观的覆盖。
"I believe that computational imaging, especially the data driven approach, is like a new shining star. It's getting more and more powerful, and we should definitely utilize some of those techniques for chemical analysis of tissue through mass spectrometry imaging."
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August 10, 2022
