Three-Dimensional Deep-Tissue Functional and Molecular Imaging by Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT).

Non-invasive small-animal imaging technologies, such as optical imaging, magnetic resonance imaging and x -ray computed tomography, have enabled researchers to study normal biological phenomena or disease progression in their native conditions. However, existing small-animal imaging technologies often lack either the penetration capability for interrogating deep tissues (e.g., optical microscopy), or the functional and molecular sensitivity for tracking specific activities (e.g., magnetic resonance imaging). To achieve functional and molecular imaging in deep tissues, we have developed an integrated photoacoustic, ultrasound and acoustic angiographic tomography (PAUSAT) system by seamlessly combining light and ultrasound. PAUSAT can perform three imaging modes simultaneously with complementary contrast: high-frequency B-mode ultrasound imaging of tissue morphology, microbubble-enabled acoustic angiography of tissue vasculature, and multi-spectral photoacoustic imaging of molecular probes. PAUSAT can provide three-dimensional (3D) multi-contrast images that are co-registered, with high spatial resolutions at large depths. Using PAUSAT, we performed proof-of-concept in vivo experiments on various small animal models: monitoring longitudinal development of placenta and embryo during mouse pregnancy, tracking biodistribution and metabolism of near-infrared organic dye on the whole-body scale, and detecting breast tumor expressing genetically-encoded photoswitchable phytochromes. These results have collectively demonstrated that PAUSAT has broad applicability in biomedical research, providing comprehensive structural, functional, and molecular imaging of small animal models.

Validation of a combined ultrasound and bioluminescence imaging system with magnetic resonance imaging in orthotopic pancreatic murine tumors.

Preclinical mouse solid tumor models are widely used to evaluate efficacy of novel cancer therapeutics. Recent reports have highlighted the need for utilizing orthotopic implantation to represent clinical disease more accurately, however the deep tissue location of these tumors makes longitudinal assessment challenging without the use of imaging techniques. The purpose of this study was to evaluate the performance of a new multi-modality high-throughput in vivo imaging system that combines bioluminescence imaging (BLI) with robotic, hands-free ultrasound (US) for evaluating orthotopic mouse models. Long utilized in cancer research as independent modalities, we hypothesized that the combination of BLI and US would offer complementary advantages of detection sensitivity and quantification accuracy, while mitigating individual technological weaknesses. Bioluminescent pancreatic tumor cells were injected into the pancreas tail of C57BL/6 mice and imaged weekly with the combination system and magnetic resonance imaging (MRI) to serve as a gold standard. BLI photon flux was quantified to assess tumor activity and distribution, and US and MRI datasets were manually segmented for gross tumor volume. Robotic US and MRI demonstrated a strong agreement (R2 = 0.94) for tumor volume measurement. BLI showed a weak overall agreement with MRI (R2 = 0.21), however, it offered the greatest sensitivity to detecting the presence of tumors. We conclude that combining BLI with robotic US offers an efficient screening tool for orthotopic tumor models.

Assessing Polycystic Kidney Disease in Rodents: Comparison of Robotic 3D Ultrasound and Magnetic Resonance Imaging.

Polycystic kidney disease (PKD) is an inherited disorder characterized by renal cyst formation and enlargement of the kidney. PKD severity can be staged noninvasively by measuring total kidney volume (TKV), a promising biomarker that has recently received regulatory qualification. In preclinical mouse models, where the disease is studied and potential therapeutics are evaluated, the most popular noninvasive method of measuring TKV is magnetic resonance imaging (MRI). Although MRI provides excellent 3D resolution and contrast, these systems are expensive to operate, have long acquisition times, and, consequently, are not heavily used in preclinical PKD research. In this study, a new imaging instrument, based on robotic ultrasound (US), was evaluated as a complementary approach for assessing PKD in rodent models. The objective was to determine the extent to which TKV measurements on the robotic US scanner correlated with both in vivo and ex vivo reference standards (MRI and Vernier calipers, respectively). A cross-sectional study design was implemented that included both PKD-affected mice and healthy wild types, spanning sex and age for a wide range of kidney volumes. It was found that US-derived TKV measurements and kidney lengths were strongly associated with both in vivo MRI and ex vivo Vernier caliper measurements (R 2=0.94 and 0.90, respectively). In addition to measuring TKV, renal vascular density was assessed using acoustic angiography (AA), a novel contrast-enhanced US methodology. AA image intensity, indicative of volumetric vascularity, was seen to have a strong negative correlation with TKV (R 2=0.82), suggesting impaired renal vascular function in mice with larger kidneys. These studies demonstrate that robotic US can provide a rapid and accurate approach for noninvasively evaluating PKD in rodent models.

Kinetic Characterization of 100 Glycoside Hydrolase Mutants Enables the Discovery of Structural Features Correlated with Kinetic Constants.

The use of computational modeling algorithms to guide the design of novel enzyme catalysts is a rapidly growing field. Force-field based methods have now been used to engineer both enzyme specificity and activity. However, the proportion of designed mutants with the intended function is often less than ten percent. One potential reason for this is that current force-field based approaches are trained on indirect measures of function rather than direct correlation to experimentally-determined functional effects of mutations. We hypothesize that this is partially due to the lack of data sets for which a large panel of enzyme variants has been produced, purified, and kinetically characterized. Here we report the kcat and KM values of 100 purified mutants of a glycoside hydrolase enzyme. We demonstrate the utility of this data set by using machine learning to train a new algorithm that enables prediction of each kinetic parameter based on readily-modeled structural features. The generated dataset and analyses carried out in this study not only provide insight into how this enzyme functions, they also provide a clear path forward for the improvement of computational enzyme redesign algorithms.