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Laser ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) imaging and matrix assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) are complementary methods that measure distributions of elements and biomolecules in tissue sections. Quantitative correlations of the information provided by these two imaging modalities requires that the datasets be registered in the same coordinate system, allowing for pixel-by-pixel comparisons. We describe here a computational workflow written in Python that accomplishes this registration, even for adjacent tissue sections, with accuracies within ±50 μm. The value of this registration process is demonstrated by correlating images of tissue sections from mice injected with gold nanomaterial drug delivery systems. Quantitative correlations of the nanomaterial delivery vehicle, as detected by LA-ICP-MS imaging, with biochemical changes, as detected by MALDI-MSI, provide deeper insight into how nanomaterial delivery systems influence lipid biochemistry in tissues. Moreover, the registration process allows the more precise images associated with LA-ICP-MS imaging to be leveraged to achieve improved segmentation in MALDI-MS images, resulting in the identification of lipids that are most associated with different sub-organ regions in tissues.
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Interpretable dimensionality reduction and classification of mass spectrometry imaging data in a visceral pain model via non-negative matrix factorization
Mass spectrometry imaging (MSI) is a powerful scientific tool for understanding the spatial distribution of biochemical compounds in tissue structures. In this paper, we introduce three novel approaches in MSI data processing to perform the tasks of data augmentation, feature ranking, and image registration. We use these approaches in conjunction with non-negative matrix factorization (NMF) to resolve two of the biggest challenges in MSI data analysis, namely: 1) the large file sizes and associated computational resource requirements and 2) the complexity of interpreting the very high dimensional raw spectral data. There are many dimensionality reduction techniques that address the first challenge but do not necessarily result in readily interpretable features, leaving the second challenge unaddressed. We demonstrate that NMF is an effective dimensionality reduction algorithm that reduces the size of MSI datasets by three orders of magnitude with limited loss of information, yielding spatial and spectral components with meaningful correlation to tissue structure that may be used directly for subsequent data analysis without the need for additional clustering steps. This analysis is demonstrated on an MSI dataset from female Sprague-Dawley rats for an animal model of comorbid visceral pain hypersensitivity (CPH). We find that high-dimensional MSI data (∼ 100,000 ions per pixel) can be reduced to 20 spectral NMF components with < 20% loss in reconstruction accuracy. The resulting spatial NMF components are reproducible and correlate well with H&E-stained tissue images. These components may also be used to generate images with enhanced specificity for different tissue types. Small patches of NMF data (i.e., 20 spatial NMF components over 20 × 20 pixels) provide an accuracy of ∼ 87% in classifying CPH vs naïve control subjects. This paper presents the novel data processing methodologies that were used to produce these results, encompassing novel data processing pipelines for data augmentation to support training for classification, ranking of features according to their contribution to classification, and image registration to enhance tissue-specific imaging.
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- Award ID(s):
- 2020624
- PAR ID:
- 10565622
- Editor(s):
- Yousefi, Bardia
- Publisher / Repository:
- PLOS
- Date Published:
- Journal Name:
- PLOS ONE
- Volume:
- 19
- Issue:
- 10
- ISSN:
- 1932-6203
- Page Range / eLocation ID:
- e0300526
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1371/journal.pone.0300526
