Bioorthogonal nanozymes for breast cancer imaging and therapy.

Bioorthogonal catalysis via transition metal catalysts (TMCs) enables the generation of therapeutics locally through chemical reactions not accessible by biological systems. This localization can enhance the efficacy of anticancer treatment while minimizing off-target effects. The encapsulation of TMCs into nanomaterials generates "nanozymes" to activate imaging and therapeutic agents. Here, we report the use of cationic bioorthogonal nanozymes to create localized "drug factories" for cancer therapy in vivo. These nanozymes remained present at the tumor site at least seven days after a single injection due to the interactions between cationic surface ligands and negatively charged cell membranes and tissue components. The prodrug was then administered systemically, and the nanozymes continuously converted the non-toxic molecules into active drugs locally. This strategy substantially reduced the tumor growth in an aggressive breast cancer model, with significantly reduced liver damage compared to traditional chemotherapy.

LA-ICP-MS and MALDI-MS image registration for correlating nanomaterial biodistributions and their biochemical effects.

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.

Nanodelivery vehicles induce remote biochemical changes in vivo.

Nanomaterial-based platforms are promising vehicles for the controlled delivery of therapeutics. For these systems to be both efficacious and safe, it is essential to understand where the carriers accumulate and to reveal the site-specific biochemical effects they produce in vivo. Here, a dual-mode mass spectrometry imaging (MSI) method is used to evaluate the distributions and biochemical effects of anti-TNF-α nanoparticle stabilized capsules (NPSCs) in mice. It is found that most of the anticipated biochemical changes occur in sub-organ regions that are separate from where the nanomaterials accumulate. In particular, TNF-α-specific lipid biomarker levels change in immune cell-rich regions of organs, while the NPSCs accumulate in spatially isolated filtration regions. Biochemical changes that are associated with the nanomaterials themselves are also observed, demonstrating the power of matrix-assisted laser desorption/ionization (MALDI) MSI to reveal markers indicating possible off-target effects of the delivery agent. This comprehensive assessment using MSI provides spatial context of nanomaterial distributions and efficacy that cannot be easily achieved with other imaging methods, demonstrating the power of MSI to evaluate both expected and unexpected outcomes associated with complex therapeutic delivery systems.

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.

Dual Mass Spectrometric Tissue Imaging of Nanocarrier Distributions and Their Biochemical Effects.

Nanomaterial-based drug delivery vehicles are able to deliver therapeutics in a controlled, targeted manner. Currently, however, there are limited analytical methods that can detect both nanomaterial distributions and their biochemical effects concurrently. In this study, we demonstrate that matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and laser ablation inductively coupled plasma mass spectrometry imaging (LA-ICP-MSI) can be used together to obtain nanomaterial distributions and biochemical consequences. These studies employ nanoparticle-stabilized capsules (NPSCs) loaded with siRNA as a testbed. MALDI-MSI experiments on spleen tissues from intravenously injected mice indicate that NPSCs loaded with anti-TNF-α siRNA cause changes to the lipid composition in white pulp regions of the spleen, as anticipated, based on pathways known to be affected by TNF-α, whereas NPSCs loaded with scrambled siRNA do not cause the predicted changes. Interestingly, LA-ICP-MSI experiments reveal that the NPSCs primarily localize in the red pulp, suggesting that the observed changes in lipid composition are due to diffusive rather than localized effects on TNF-α production. Such information is only accessible by combining data from the two modalities, which we accomplish by using the heme signals from MALDI-MSI and iron signals from LA-ICP-MSI to overlay the images. Several unexpected changes in lipid composition also occur in regions where the NPSCs are found, suggesting that the NPSCs themselves can influence tissue biochemistry as well.

Oligo p-Phenylenevinylene Derivatives as Electron Transfer Matrices for UV-MALDI.

Phenylenevinylene oligomers (PVs) have outstanding photophysical characteristics for applications in the growing field of organic electronics. Yet, PVs are also versatile molecules, the optical and physicochemical properties of which can be tuned by manipulation of their structure. We report the synthesis, photophysical, and MS characterization of eight PV derivatives with potential value as electron transfer (ET) matrices for UV-MALDI. UV-vis analysis show the presence of strong characteristic absorption bands in the UV region and molar absorptivities at 355 nm similar or higher than those of traditional proton (CHCA) and ET (DCTB) MALDI matrices. Most of the PVs exhibit non-radiative quantum yields (φ) above 0.5, indicating favorable thermal decay. Ionization potential values (IP) for PVs, calculated by the Electron Propagator Theory (EPT), range from 6.88 to 7.96 eV, making these oligomers good candidates as matrices for ET ionization. LDI analysis of PVs shows only the presence of radical cations (M+.) in positive ion mode and absence of clusters, adducts, or protonated species; in addition, M+. threshold energies for PVs are lower than for DCTB. We also tested the performance of four selected PVs as ET MALDI matrices for analytes ranging from porphyrins and phthalocyanines to polyaromatic compounds. Two of the four PVs show S/N enhancement of 1961% to 304% in comparison to LDI, and laser energy thresholds from 0.17 µJ to 0.47 µJ compared to 0.58 µJ for DCTB. The use of PV matrices also results in lower LODs (low fmol range) whereas LDI LODs range from pmol to nmol. Graphical Abstract ᅟ.