Biophysics

Genetically encodable reporters that produce signals detectable in deep tissues offer a powerful tool for noninvasive monitoring of molecular events in vivo. Although magnetic resonance imaging (MRI) is a standard technique for noninvasive clinical imaging, its wider application in detecting molecular activities has been constrained by the lack of programmable sensors. This limitation is in stark contrast to the widespread use of fluorescent reporter–derived sensors in cultured cells and in transparent specimens. To overcome this limitation, we recently developed the modular aquaporin-based protease-activatable probe for enhanced reporting (MAPPER) platform. This sensor engineering framework integrates a metal-free MRI reporter derived from human aquaporin-1 (hAqp1) with synthetic protease-based circuits. This integration facilitates the modular and scalable creation of a wide range of sensors by regulating protease activity through precise molecular events, such as protein–protein interactions, pharmacological inhibition, and second messenger signaling. In this paper, we present a detailed protocol for constructing and deploying sensors using the MAPPER paradigm. The protocol encompasses genetic design, lentiviral production, stable cell line generation, biochemical and microscopic validation of sensor function, diffusion-weighted MRI, and MR image analysis to quantify sensor signals in terms of the apparent diffusion coefficient. We describe two distinct MAPPER architectures: DD-MAPPER, which leverages protease-controlled protein degradation, and ER-MAPPER, which utilizes protease-controlled, subcellular trafficking. The MAPPER framework allows adaptation to various molecular targets without the need to redesign the core MRI reporter mechanism, making MAPPER a versatile platform for noninvasive biosensing in living cells and tissues.

Magnetic resonance imaging (MRI) is an invaluable method of choice for anatomical and functional in vivo imaging of the brain. Still, accurate delineation of the brain structures remains a crucial task of MR image evaluation. This study presents a novel analytical algorithm developed in MATLAB for the automatic segmentation of cerebrospinal fluid (CSF) spaces in preclinical non-contrast MR images of the mouse brain. The algorithm employs adaptive thresholding and region growing to accurately and repeatably delineate CSF space regions in 3D constructive interference steady-state (3D-CISS) images acquired using a 9.4 Tesla MR system and a cryogenically cooled transmit/receive resonator. Key steps include computing a bounding box enclosing the brain parenchyma in three dimensions, applying an adaptive intensity threshold, and refining CSF regions independently in sagittal, axial, and coronal planes. In its original application, the algorithm provided objective and repeatable delineation of CSF regions in 3D-CISS images of sub-optimal signal-to-noise ratio, acquired with (33 μm)³ isometric voxel dimensions. It allowed revealing subtle differences in CSF volumes between aquaporin-4-null and wild-type littermate mice, showing robustness and reliability. Despite the increasing use of artificial neural networks in image analysis, this analytical approach provides robustness, especially when the dataset is insufficiently small and limited for training the network. By adjusting parameters, the algorithm is flexible for application in segmenting other types of anatomical structures or other types of 3D images. This automated method significantly reduces the time and effort compared to manual segmentation and offers higher repeatability, making it a valuable tool for preclinical and potentially clinical MRI applications.

Tracking macrophages by non-invasive molecular imaging can provide useful insights into the immunobiology of inflammatory disorders in preclinical disease models. Perfluorocarbon nanoemulsions (PFC-NEs) have been well documented in their ability to be taken up by macrophages through phagocytosis and serve as ¹⁹F magnetic resonance imaging (MRI) tracers of inflammation in vivo and ex vivo. Incorporation of near-infrared fluorescent (NIRF) dyes in PFC-NEs can help monitor the spatiotemporal distribution of macrophages in vivo during inflammatory processes, using NIRF imaging as a complementary methodology to MRI. Here, we discuss in depth how both colloidal and fluorescence stabilities of the PFC-NEs are essential for successful and reliable macrophage tracking in vivo and for their detection in excised tissues ex vivo by NIRF imaging. Furthermore, PFC-NE quality assures NIRF imaging reproducibility and reliability across preclinical studies, providing insights into inflammation progression and therapeutic response. Previous studies focused on assessments of colloidal property changes in response to stress and during storage as a means of quality control. We recently focused on the joint evaluation of both colloidal and fluorescence properties and their relationship to NIRF imaging outcomes. In this protocol, we summarize the key assessments of the fluorescent dye–labeled nanoemulsions, which include long-term particle size distribution monitoring as the measure of colloidal stability and monitoring of the fluorescence signal. Due to its simplicity and reproducibility, our protocols are easy to adopt for researchers to assess the quality of PFC-NEs for in vivo NIRF imaging applications.

Translational work in rodents elucidates basic mechanisms that drive complex behaviors relevant to psychiatric and neurological conditions. Nonetheless, numerous promising studies in rodents later fail in clinical trials, highlighting the need for improving the translational utility of preclinical studies in rodents. Imaging of small rodents provides an important strategy to address this challenge, as it enables a whole-brain unbiased search for structural and dynamic changes that can be directly compared to human imaging. The functional significance of structural changes identified using imaging can then be further investigated using molecular and genetic tools available for the mouse. Here, we describe a pipeline for unbiased search and characterization of structural changes and network properties, based on diffusion MRI data covering the entire mouse brain at an isotropic resolution of 100 µm. We first used unbiased whole-brain voxel-based analyses to identify volumetric and microstructural alterations in the brain of adult mice exposed to unpredictable postnatal stress (UPS), which is a mouse model of complex early life stress (ELS). Brain regions showing structural abnormalities were used as nodes to generate a grid for assessing structural connectivity and network properties based on graph theory. The technique described here can be broadly applied to understand brain connectivity in other mouse models of human disorders, as well as in genetically modified mouse strains.

Graphic abstract:

Pipeline for characterizing structural connectome in the mouse brain using diffusion magnetic resonance imaging. Scale bar = 1 mm.