Development of quantitative imaging mass spectrometry (q -IMS) for drug visualization using animal tissues

Toward Omics-Scale Quantitative Mass Spectrometry Imaging of Lipids in Brain Tissue Using a Multiclass Internal Standard Mixture

Mass spectrometry imaging (MSI) has accelerated our understanding of lipid metabolism and spatial distribution in tissues and cells. However, few MSI studies have approached lipid imaging quantitatively and those that have focused on a single lipid class. We overcome this limitation by using a multiclass internal standard (IS) mixture sprayed homogeneously over the tissue surface with concentrations that reflect those of endogenous lipids. This enabled quantitative MSI (Q-MSI) of 13 lipid classes and subclasses representing almost 200 sum-composition lipid species using both MALDI (negative ion mode) and MALDI-2 (positive ion mode) and pixel-wise normalization of each lipid species in a manner analogous to that widely used in shotgun lipidomics. The Q-MSI approach covered 3 orders of magnitude in dynamic range (lipid concentrations reported in pmol/mm2) and revealed subtle changes in distribution compared to data without normalization. The robustness of the method was evaluated by repeating experiments in two laboratories using both timsTOF and Orbitrap mass spectrometers with an ∼4-fold difference in mass resolution power. There was a strong overall correlation in the Q-MSI results obtained by using the two approaches. Outliers were mostly rationalized by isobaric interferences or the higher sensitivity of one instrument for a particular lipid species. These data provide insight into how the mass resolving power can affect Q-MSI data. This approach opens up the possibility of performing large-scale Q-MSI studies across numerous lipid classes and subclasses and revealing how absolute lipid concentrations vary throughout and between biological tissues.

Boron Delivery to Brain Cells via Cerebrospinal Fluid (CSF) Circulation in BNCT of Brain-Tumor-Model Rats-Ex Vivo Imaging of BPA Using MALDI Mass Spectrometry Imaging

The blood-brain barrier (BBB) is likely to be intact during the early stages of brain metastatic melanoma development, and thereby inhibits sufficient drug delivery into the metastatic lesions. Our laboratory has been developing a system for boron drug delivery to brain cells via cerebrospinal fluid (CSF) as a viable pathway to circumvent the BBB in boron neutron capture therapy (BNCT). BNCT is a cell-selective cancer treatment based on the use of boron-containing drugs and neutron irradiation. Selective tumor targeting by boron with minimal normal tissue toxicity is required for effective BNCT. Boronophenylalanine (BPA) is widely used as a boron drug for BNCT. In our previous study, we demonstrated that application of the CSF administration method results in high BPA accumulation in the brain tumor even with a low dose of BPA. In this study, we evaluate BPA biodistribution in the brain following application of the CSF method in brain-tumor-model rats (melanoma) utilizing matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI). We observed increased BPA penetration to the tumor tissue, where the color contrast on mass images indicates the border of BPA accumulation between tumor and normal cells. Our approach could be useful as drug delivery to different types of brain tumor, including brain metastases of melanoma.

Matrix-Assisted Laser Desorption/Ionization (MALDI) Mass Spectrometry Imaging of L-4-Phenylalanineboronic Acid (BPA) in a Brain Tumor Model Rat for Boron Neutron Capture Therapy (BNCT)

Boron neutron capture therapy (BNCT) is a cell-selective particle therapy for cancer using boron containing drugs. Boron compounds are accumulated in high concentration of tens ppm level of boron in target tumors to cause lethal damage to tumor tissue. The examination of boron distribution in target tumor and normal tissue is important to evaluate the efficiency of therapy. The matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is a powerful tool to visualize the distribution of target analyte in biological samples. In this manuscript, we report a trial to visualize the distribution of a typical BNCT drug, L-4-phenylalanine boronic acid (BPA) in a brain tumor model rat using MALDI-MSI technique. We performed a MALDI-MSI with high mass resolution targeting to [BPA+H]⁺ at m/z 210 in a BPA-treated rat brain section using a spiral orbit-type time of flight (SpiralTOF) mass spectrometer. Several BPA ion species, [BPA+H]⁺, [BPA-H₂O+Na]⁺, [BPA+DHB-2H₂O+Na]⁺ and [BPA+DHB-2H₂O+K]⁺ were detected separate from peaks originated from biomolecules or matrix reagent by achieving the mass resolving power of approximately 20,000 (full width at half maximum; FWHM) at m/z 210. The mass images with 60 μm spatial resolution obtained from these BPA ion species in a mass window of 0.02 Da revealed their localization in the tumor region. Additionally, the mass image obtained from [BPA+H]⁺ also likely showed the distribution of BPA inside the tumor. MALDI-MSI with high mass resolution targeting to [BPA+H]⁺ has a great potential to visualize the distribution of BPA in brain tissue with tumor.

Batch Effects in MALDI Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) has become an indispensible tool for spatially resolved molecular investigation of tissues. One of the key application areas is biomedical research, where matrix-assisted laser desorption/ionization (MALDI) MSI is predominantly used due to its high-throughput capability, flexibility in the molecular class to investigate, and ability to achieve single cell spatial resolution. While many of the initial technical challenges have now been resolved, so-called batch effects, a phenomenon already known from other omics fields, appear to significantly impede reliable comparison of data from particular midsized studies typically performed in translational clinical research. This critical insight will discuss at what levels (pixel, section, slide, time, and location) batch effects can manifest themselves in MALDI-MSI data and what consequences this might have for biomarker discovery or multivariate classification. Finally, measures are presented that could be taken to recognize and/or minimize these potentially detrimental effects, and an outlook is provided on what is still needed to ultimately overcome these effects.

Quantification and assessment of detection capability in imaging mass spectrometry using a revised mimetic tissue model

Aim: A revised method of preparing the mimetic tissue model for quantitative imaging mass spectrometry (IMS) is evaluated. Concepts of assessing detection capability are adapted from other imaging or mass spectrometry (MS)-based technologies to improve upon the reliability of IMS quantification. Materials & methods: The mimetic tissue model is prepared by serially freezing spiked-tissue homogenates into a cylindrical mold to create a plug of tissue with a stepped concentration gradient of matrix-matched standards. Weighted least squares (WLS) linear regression is applied due to the heteroscedastisity (change in variance with intensity) of most MS data. Results & conclusions: Imaging poses several caveats for quantification which are unique compared with other MS-based methods. Aspects of the design, construction, application, and evaluation of the matrix-matched standard curve for the mimetic tissue model are discussed. In addition, the criticality of the ion distribution in the design of a purposeful liquid chromatography coupled to mass spectrometry (LC-MS) validation is reviewed.

Multicenter Validation Study of Quantitative Imaging Mass Spectrometry

The aim of this study was to assess potential sources of variability in quantitative imaging mass spectrometry (IMS) across multiple sites, analysts, and instruments. A sample from rat liver perfused with clozapine was distributed to three sites for analysis by three analysts using a predefined protocol to standardize the sample preparation, acquisition, and data analysis parameters. In addition, two commonly used approaches to IMS quantification, the mimetic tissue model and dilution series, were used to quantify clozapine and its major metabolite norclozapine in isolated perfused rat liver. The quantification was evaluated in terms of precision and accuracy with comparison to liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). The results of this study showed that, across three analysts with six replicates each, both quantitative IMS methods achieved relative standard deviations in the low teens and accuracies of around 80% compared to LC-MS/MS quantification of adjacent tissue sections. The utility of a homogeneously coated stable-isotopically labeled standard (SIL) for normalization was appraised in terms of its potential to improve precision and accuracy of quantification as well as qualitatively reduce variability in the sample tissue images. SIL normalization had a larger influence on the dilution series, where the use of the internal standard was necessary to achieve accuracy and precision comparable to the non-normalized mimetic tissue model data. Normalization to the internal standard appeared most effective when the intensity ratio of the analyte to internal standard was approximately one, and thus precludes this method as a universal normalization approach for all ions in the acquisition.