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
- NSF-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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Abstract Detrital zircon (DZ) U–Pb geochronology has improved the way geologists approach questions of sediment provenance and stratigraphic age. However, there is debate about what constitutes an appropriate sample size (i.e., the number of dates in a DZ sample,
n ), which depends on project objectives, sample complexity, and, critically, analytical budget. Additionally, there is ongoing concern about bias introduced by zircon grain size. We tested a recently developed rapid (3 s/analysis) data acquisition method by multicollector laser ablation‐inductively coupled plasma‐mass spectrometry (LA‐ICP‐MS) that incorporates an automated selection routine and calculates two‐dimensional grain geometry from polished sample surfaces. Eleven samples were analysed from below and above the Late Cretaceous (Campanian) basal Castlegate unconformity of the Book Cliffs, Utah, in a down‐depositional‐dip transect including Price, Horse, Tusher, and Thompson canyons. 12,448 new concordant dates were generated during two measurement sessions. Results are consistent with recent studies suggesting there is no major provenance change and little time (1–2 Myr) represented across the unconformity. Grain size and sample size both exert a strong control on sample dissimilarity. Age distributions constructed from subsamples of large grains are systematically less similar to whole samples; age distributions composed of small grains are overall more similar to whole samples. As such, North American sediment sources that produce large grains such as the Grenville and Yavapi‐Mazatzal belts can bias age distributions if only large grains are analysed. A sample size ofn = 100 is inadequate for characterizing age distributions as complex as those of the Book Cliffs, whereas a sample size ofn = 300 provides good characterization. Sample size ofn ≈ 1000 or more is unnecessary unless project objectives include scanning for subordinate age groups, such as when identifying the youngest grains for calculating a maximum depositional age (MDA). Dates used in MDA calculations acquired with rapid acquisition are best re‐analysed with longer LA‐ICP‐MS acquisition methods or isotope dilution thermal ionization mass spectrometry for increased accuracy and precision. We include new MATLAB code and open‐source software programs,DZpick andDZmda , for automated spot picking and calculating MDAs. -
null (Ed.)Glucuronidation is a common phase II metabolic process for drugs and xenobiotics which increases their solubility for excretion. Acyl glucuronides (glucuronides of carboxylic acids) present concerns of toxicity as they have been implicated in gastrointestinal toxicity and hepatic failure. Despite the substantial success in the bulk analysis of these species, little is known about their localization in tissues. Herein, we used nanospray desorption electrospray ionization mass spectrometry imaging (nano-DESI-MSI) to examine the localization of diclofenac, a widely used nonsteroidal anti-inflammatory drug, and its metabolites in mouse kidney and liver tissues. Nano-DESI allows for label-free imaging with high spatial resolution and sensitivity without special sample pretreatment. Using nano-DESI-MSI, ion images for diclofenac and its major metabolites were produced. MSI data acquired over a broad m/z range showed fairly low signals of the drug and its metabolites. At least an order of magnitude improvement in the signals was obtained using selected ion monitoring (SIM), with m/z windows centered around the low-abundance ions of interest. Using nano-DESI MSI in SIM mode, we observed that diclofenac acyl glucuronide is localized to the inner medulla and hydroxydiclofenac to the cortex of the kidney. The distributions observed for both metabolites closely match the previously reported localization of enzymes that process diclofenac into its respective metabolites. The localization of diclofenac acyl glucuronide to medulla likely indicates that the toxic metabolite is being excreted from the tissue. In contrast, a uniform distribution of diclofenac, hydroxydiclofenac and the diclofenac acyl glucuronide metabolite was observed in the liver tissue. Semiquantitative analysis found the metabolite to diclofenac ratios calculated from nano-DESI in agreement to those calculated from liquid chromatography tandem mass spectrometry (LC-MS/MS) experiments. Collectively, our results demonstrate nano-DESI-MSI can be successfully used to image diclofenac and its primary metabolites in dosed liver and kidney tissues from mice and derive semi-quantitative data from localized tissue regions.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/c9an02472g
