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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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Reconstruction, analysis, and segmentation of LA-ICP-MS imaging data using Python for the identification of sub-organ regions in tissues
Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) imaging has been extensively used to determine the distributions of metals in biological tissues for a wide variety of applications. To be useful for identifying metal biodistributions, the acquired raw data needs to be reconstructed into a two-dimensional image. Several approaches have been developed for LA-ICP-MS image reconstruction, but less focus has been placed on software for more in-depth statistical processing of the imaging data. Yet, improved image processing can allow the biological ramifications of metal distributions in tissues to be better understood. In this work, we describe software written in Python that automatically reconstructs, analyzes, and segments images from LA-ICP-MS imaging data. Image segmentation is achieved using LA-ICP-MS signals from the biological metals Fe and Zn together with k -means clustering to automatically identify sub-organ regions in different tissues. Spatial awareness also can be incorporated into the images through a neighboring pixel evaluation that allows regions of interest to be identified that are at the limit of the LA-ICP-MS imaging resolution. The value of the described algorithms is demonstrated for LA-ICP-MS images of nanomaterial biodistributions. The developed image reconstruction and processing approach reveals that nanomaterials distribute in different sub-organ regions based on their chemical and physical properties, opening new possibilities for understanding the impact of such nanomaterials in vivo .
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- Award ID(s):
- 1808199
- PAR ID:
- 10161834
- Date Published:
- Journal Name:
- The Analyst
- Volume:
- 145
- Issue:
- 10
- ISSN:
- 0003-2654
- Page Range / eLocation ID:
- 3705 to 3712
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/c9an02472g
