Most lysosomal storage diseases (LSDs) involve progressive central nervous system (CNS) impairment, resulting from deficiency of a lysosomal enzyme. Treatment of neuronopathic LSDs remains a considerable challenge, as approved intravenously administered enzyme therapies are ineffective in modifying CNS disease because they do not effectively cross the blood-brain barrier (BBB). We describe a therapeutic platform for increasing the brain exposure of enzyme replacement therapies. The enzyme transport vehicle (ETV) is a lysosomal enzyme fused to an Fc domain that has been engineered to bind to the transferrin receptor, which facilitates receptor-mediated transcytosis across the BBB. We demonstrate that ETV fusions containing iduronate 2-sulfatase (ETV:IDS), the lysosomal enzyme deficient in mucopolysaccharidosis type II, exhibited high intrinsic activity and degraded accumulated substrates in both IDS-deficient cell and in vivo models. ETV substantially improved brain delivery of IDS in a preclinical model of disease, enabling enhanced cellular distribution to neurons, astrocytes, and microglia throughout the brain. Improved brain exposure for ETV:IDS translated to a reduction in accumulated substrates in these CNS cell types and peripheral tissues and resulted in a complete correction of downstream disease-relevant pathologies in the brain, including secondary accumulation of lysosomal lipids, perturbed gene expression, neuroinflammation, and neuroaxonal damage. These data highlight the therapeutic potential of the ETV platform for LSDs and provide preclinical proof of concept for TV-enabled therapeutics to treat CNS diseases more broadly.

The correlation of imaging mass spectrometry (IMS) with histopathology can help relate novel molecular findings obtained through IMS to the well-characterized and validated histopathology knowledge base. The quality of correlation between these two modalities is limited by the quality of the spatial mapping that is obtained by registration of the two image types. In this work, we develop novel workflows for MALDI IMS-tomicroscopy data registration and analysis using nondestructive IMScompatible wide field autofluorescence (AF) microscopy combined with computational image registration. First, a substantially automated procedure for high-accuracy registration between IMS and microscopy data of the same section is described that explicitly links the MALDI laser ablation pattern imaged by microscopy to its corresponding IMS pixel. Subsequent examination of the registered data allows for high-confidence colocalization of image features between the two modalities, down to single-cell scales within tissue. Building on this IMS-microscopy spatial mapping, we furthermore demonstrate the automated spatial correlation between IMS measurements from serial sections. This AF-registration-driven inter-section analysis, using a combination of nonlinear AF-to-AF and IMS-to-AF image registrations, can be applied to tissue sections that are prepared and imaged with different sample preparations (e.g., lipids vs proteins) and/or that are measured using different spatial resolutions. Importantly, all registrations, whether within a single section or across serial sections, are entirely independent of the IMS intensity signal content and thus unbiased by it.

Histology-directed imaging mass spectrometry (IMS) is a spatially-targeted IMS acquisition method informed by expert annotation that provides rapid molecular characterization of select tissue structures. The expert annotations are usually determined on digital whole slide images of histological stains where the staining preparation is incompatible with optimal IMS preparation, necessitating serial sections: one for annotation, one for IMS. Registration is then used to align staining annotations and IMS. Herein, we report a next-generation histology-directed platform implementing IMS-compatible autofluorescence (AF) microscopy taken prior to any staining or IMS. The platform enables two histology-directed workflows, one that improves the registration process between two separate tissue sections using automated, computational mono-modal AF-to-AF microscopy image registration, and a registration-free approach that utilizes AF directly to identify ROIs and acquire IMS on the same section. The registration approach is fully automated and delivers state of the art accuracy in histology-directed workflows for transfer of annotations (ca. 3-10 μm based on 4 organs from 2 species) while the direct AF approach is registration-free, allowing targeting of the finest structures visible by AF microscopy. We demonstrate the platform in biologically relevant case studies of liver stage malaria and human kidney disease with spatially targeted acquisition of sparsely distributed (composing less than one tenth of 1% of the tissue section area) malaria infected mouse hepatocytes, and glomeruli in the human kidney case study.

No abstract