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Abstract Macrophages are key effectors of host defense and metabolism, making them promising targets for transient genetic therapy. Gene editing through the delivery of Cas9‐ribonucleoprotein (RNP) provides multiple advantages over gene delivery–based strategies for introducing CRISPR machinery to the cell. There are, however, significant physiological, cellular, and intracellular barriers to the effective delivery of the Cas9 protein and guide RNA (sgRNA) that have to date, restricted in vivo Cas9 protein–based approaches to local/topical delivery applications. Described herein is a new nanoassembled platform featuring coengineered nanoparticles and Cas9 protein that has been developed to provide efficient Cas9‐sgRNA delivery and concomitant CRISPR editing through systemic tail‐vein injection into mice, achieving >8% gene editing efficiency in macrophages of the liver and spleen. 

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. 

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 .