Engineered coiled-coil HIF1α protein domain mimic.

The development of targeted anti-cancer therapeutics offers the potential for increased efficacy of drugs and diagnostics. Utilizing modalities agnostic to tumor type, such as the hypoxic tumor microenvironment (TME), may assist in the development of universal tumor targeting agents. The hypoxia-inducible factor (HIF), in particular HIF1, plays a key role in tumor adaptation to hypoxia, and inhibiting its interaction with p300 has been shown to provide therapeutic potential. Using a multivalent assembled protein (MAP) approach based on the self-assembly of the cartilage oligomeric matrix protein coiled-coil (COMPcc) domain fused to the critical residues of the C-terminal transactivation domain (C-TAD) of the α subunit of HIF1 (HIF1α), we generate HIF1α-MAP (H-MAP). The resulting H-MAP demonstrates picomolar binding affinity to p300, the ability to downregulate hypoxia-inducible genes, and in vivo tumor targeting capability.

Coiled-Coil Protein Hydrogels Engineered with Minimized Fiber Diameters for Sustained Release of Doxorubicin in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) lacks expressed protein targets, making therapy development challenging. Hydrogels offer a promising new route in this regard by improving the chemotherapeutic efficacy through increased solubility and sustained release. Moreover, subcutaneous hydrogel administration reduces patient burden by requiring less therapy and shorter treatment times. We recently established the design principles for the supramolecular assembly of single-domain coiled-coils into hydrogels. Using a modified computational design algorithm, we designed Q8, a hydrogel with rapid assembly for faster therapeutic hydrogel preparation. Q8 encapsulates and releases doxorubicin (Dox), enabling localized sustained release via subcutaneous injection. Remarkably, a single subcutaneous injection of Dox-laden Q8 (Q8•Dox) significantly suppresses tumors within just 1 week. This work showcases the bottom-up engineering of a fully protein-based drug delivery vehicle for improved TBNC treatment via noninvasive localized therapy.

Protein-Engineered Fibers For Drug Encapsulation Traceable via 19F Magnetic Resonance.

Theranostic materials research is experiencing rapid growth driven by the interest in integrating both therapeutic and diagnostic modalities. These materials offer the unique capability to not only provide treatment but also track the progression of a disease. However, to create an ideal theranostic biomaterial without compromising drug encapsulation, diagnostic imaging must be optimized for improved sensitivity and spatial localization. Herein, we create a protein-engineered fluorinated coiled-coil fiber, Q2TFL, capable of improved sensitivity to 19F magnetic resonance spectroscopy (MRS) detection. Leveraging residue-specific noncanonical amino acid incorporation of trifluoroleucine (TFL) into the coiled-coil, Q2, which self-assembles into nanofibers, we generate Q2TFL. We demonstrate that fluorination results in a greater increase in thermostability and 19F magnetic resonance detection compared to the nonfluorinated parent, Q2. Q2TFL also exhibits linear ratiometric 19F MRS thermoresponsiveness, allowing it to act as a temperature probe. Furthermore, we explore the ability of Q2TFL to encapsulate the anti-inflammatory small molecule, curcumin (CCM), and its impact on the coiled-coil structure. Q2TFL also provides hyposignal contrast in 1H MRI, echogenic signal with high-frequency ultrasound and sensitive detection by 19F MRS in vivo illustrating fluorination of coiled-coils for supramolecular assembly and their use with 1H MRI, 19F MRS and high frequency ultrasound as multimodal theranostic agents.

Longitudinal in Utero Analysis of Engrailed-1 Knockout Mouse Embryonic Phenotypes Using High-Frequency Ultrasound.

Large-scale international efforts to generate and analyze loss-of-function mutations in each of the approximately 20,000 protein-encoding gene mutations are ongoing using the "knockout" mouse as a model organism. Because one-third of gene knockouts are expected to result in embryonic lethality, it is important to develop non-invasive in utero imaging methods to detect and monitor mutant phenotypes in mouse embryos. We describe the utility of 3-D high-frequency (40-MHz) ultrasound (HFU) for longitudinal in utero imaging of mouse embryos between embryonic days (E) 11.5 and E14.5, which represent critical stages of brain and organ development. Engrailed-1 knockout (En1-ko) mouse embryos and their normal control littermates were imaged with HFU in 3-D, enabling visualization of morphological phenotypes in the developing brains, limbs and heads of the En1-ko embryos. Recently developed deep learning approaches were used to automatically segment the embryonic brain ventricles and bodies from the 3-D HFU images, allowing quantitative volumetric analyses of the En1-ko brain phenotypes. Taken together, these results show great promise for the application of longitudinal 3-D HFU to analyze knockout mouse embryos in utero.

A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis.

Metastasis is a complex process, requiring cells to overcome barriers that are only incompletely modeled by in vitro assays. A systematic workflow was established using robust, reproducible in vivo models and standardized methods to identify novel players in melanoma metastasis. This approach allows for data inference at specific experimental stages to precisely characterize a gene's role in metastasis. Models are established by introducing genetically modified melanoma cells via intracardiac, intradermal, or subcutaneous injections into mice, followed by monitoring with serial in vivo imaging. Once preestablished endpoints are reached, primary tumors and/or metastases-bearing organs are harvested and processed for various analyses. Tumor cells can be sorted and subjected to any of several 'omics' platforms, including single-cell RNA sequencing. Organs undergo imaging and immunohistopathological analyses to quantify the overall burden of metastases and map their specific anatomic location. This optimized pipeline, including standardized protocols for engraftment, monitoring, tissue harvesting, processing, and analysis, can be adopted for patient-derived, short-term cultures and established human and murine cell lines of various solid cancer types.

A Deep Learning Approach for Segmentation, Classification, and Visualization of 3-D High-Frequency Ultrasound Images of Mouse Embryos.

Segmentation and mutant classification of high-frequency ultrasound (HFU) mouse embryo brain ventricle (BV) and body images can provide valuable information for developmental biologists. However, manual segmentation and identification of BV and body requires substantial time and expertise. This article proposes an accurate, efficient and explainable deep learning pipeline for automatic segmentation and classification of the BV and body. For segmentation, a two-stage framework is implemented. The first stage produces a low-resolution segmentation map, which is then used to crop a region of interest (ROI) around the target object and serve as the probability map of the autocontext input for the second-stage fine-resolution refinement network. The segmentation then becomes tractable on high-resolution 3-D images without time-consuming sliding windows. The proposed segmentation method significantly reduces inference time (102.36-0.09 s/volume ≈ 1000× faster) while maintaining high accuracy comparable to previous sliding-window approaches. Based on the BV and body segmentation map, a volumetric convolutional neural network (CNN) is trained to perform a mutant classification task. Through backpropagating the gradients of the predictions to the input BV and body segmentation map, the trained classifier is found to largely focus on the region where the Engrailed-1 (En1) mutation phenotype is known to manifest itself. This suggests that gradient backpropagation of deep learning classifiers may provide a powerful tool for automatically detecting unknown phenotypes associated with a known genetic mutation.

Characterization of Vortex Flow in a Mouse Model of Ventricular Dyssynchrony by Plane-Wave Ultrasound Using Hexplex Processing.

The rodent heart is frequently used to study human cardiovascular disease (CVD). Although advanced cardiovascular ultrasound imaging methods are available for human clinical practice, application of these techniques to small animals remains limited due to the temporal and spatial-resolution demands. Here, an ultrasound vector-flow workflow is demonstrated that enables visualization and quantification of the complex hemodynamics within the mouse heart. Wild type (WT) and fibroblast growth factor homologous factor 2 (FHF2)-deficient mice (Fhf2 KO/Y ), which present with hyperthermia-induced ECG abnormalities highly reminiscent of Brugada syndrome, were used as a mouse model of human CVD. An 18-MHz linear array was used to acquire high-speed (30 kHz), plane-wave data of the left ventricle (LV) while increasing core body temperature up to 41.5 °C. Hexplex (i.e., six output) processing of the raw data sets produced the output of vector-flow estimates (magnitude and phase); B-mode and color-Doppler images; Doppler spectrograms; and local time histories of vorticity and pericardium motion. Fhf2 WT/Y mice had repeatable beat-to-beat cardiac function, including vortex formation during diastole, at all temperatures. In contrast, Fhf2 KO/Y mice displayed dyssynchronous contractile motion that disrupted normal inflow vortex formation and impaired LV filling as temperature rose. The hexplex processing approach demonstrates the ability to visualize and quantify the interplay between hemodynamic and mechanical function in a mouse model of human CVD.

DEEP MOUSE: AN END-TO-END AUTO-CONTEXT REFINEMENT FRAMEWORK FOR BRAIN VENTRICLE & BODY SEGMENTATION IN EMBRYONIC MICE ULTRASOUND VOLUMES.

The segmentation of the brain ventricle (BV) and body in embryonic mice high-frequency ultrasound (HFU) volumes can provide useful information for biological researchers. However, manual segmentation of the BV and body requires substantial time and expertise. This work proposes a novel deep learning based end-to-end auto-context refinement framework, consisting of two stages. The first stage produces a low resolution segmentation of the BV and body simultaneously. The resulting probability map for each object (BV or body) is then used to crop a region of interest (ROI) around the target object in both the original image and the probability map to provide context to the refinement segmentation network. Joint training of the two stages provides significant improvement in Dice Similarity Coefficient (DSC) over using only the first stage (0.818 to 0.906 for the BV, and 0.919 to 0.934 for the body). The proposed method significantly reduces the inference time (102.36 to 0.09 s/volume ≈1000x faster) while slightly improves the segmentation accuracy over the previous methods using slide-window approaches.

AUTOMATIC BODY LOCALIZATION AND BRAIN VENTRICLE SEGMENTATION IN 3D HIGH FREQUENCY ULTRASOUND IMAGES OF MOUSE EMBRYOS.

This paper presents a fully automatic segmentation system for whole-body high-frequency ultrasound (HFU) images of mouse embryos that can simultaneously segment the body contour and the brain ventricles (BVs). Our system first locates a region of interest (ROI), which covers the interior of the uterus, by sub-surface analysis. Then, it segments the ROI into BVs, the body, the amniotic fluid, and the uterine wall, using nested graph cut. Simultaneously multilevel thresholding is applied to the whole-body image to propose candidate BV components. These candidates are further truncated by the embryo mask (body+BVs) to refine the BV candidates. Finally, subsets of all candidate BVs are compared with pre-trained spring models describing valid BV structures, to identify true BV components. The system can segment the body accurately in most cases based on visual inspection, and achieves average Dice similarity coefficient of 0.8924 ± 0.043 for the BVs on 36 HFU image volumes.

DEEP BV: A FULLY AUTOMATED SYSTEM FOR BRAIN VENTRICLE LOCALIZATION AND SEGMENTATION IN 3D ULTRASOUND IMAGES OF EMBRYONIC MICE.

Volumetric analysis of brain ventricle (BV) structure is a key tool in the study of central nervous system development in embryonic mice. High-frequency ultrasound (HFU) is the only non-invasive, real-time modality available for rapid volumetric imaging of embryos in utero. However, manual segmentation of the BV from HFU volumes is tedious, time-consuming, and requires specialized expertise. In this paper, we propose a novel deep learning based BV segmentation system for whole-body HFU images of mouse embryos. Our fully automated system consists of two modules: localization and segmentation. It first applies a volumetric convolutional neural network on a 3D sliding window over the entire volume to identify a 3D bounding box containing the entire BV. It then employs a fully convolutional network to segment the detected bounding box into BV and background. The system achieves a Dice Similarity Coefficient (DSC) of 0.8956 for BV segmentation on an unseen 111 HFU volume test set surpassing the previous state-of-the-art method (DSC of 0.7119) by a margin of 25%.

High-speed, high-frequency ultrasound, in utero vector-flow imaging of mouse embryos.

Real-time imaging of the embryonic murine cardiovascular system is challenging due to the small size of the mouse embryo and rapid heart rate. High-frequency, linear-array ultrasound systems designed for small-animal imaging provide high-frame-rate and Doppler modes but are limited in regards to the field of view that can be imaged at fine-temporal and -spatial resolution. Here, a plane-wave imaging method was used to obtain high-speed image data from in utero mouse embryos and multi-angle, vector-flow algorithms were applied to the data to provide information on blood flow patterns in major organs. An 18-MHz linear array was used to acquire plane-wave data at absolute frame rates ≥10 kHz using a set of fixed transmission angles. After beamforming, vector-flow processing and image compounding, effective frame rates were on the order of 2 kHz. Data were acquired from the embryonic liver, heart and umbilical cord. Vector-flow results clearly revealed the complex nature of blood-flow patterns in the embryo with fine-temporal and -spatial resolution.

Multimodal Genetic Approach for Molecular Imaging of Vasculature in a Mouse Model of Melanoma.

PURPOSE: In this study, we evaluated a genetic approach for in vivo multimodal molecular imaging of vasculature in a mouse model of melanoma. PROCEDURES: We used a novel transgenic mouse, Ts-Biotag, that genetically biotinylates vascular endothelial cells. After inoculating these mice with B16 melanoma cells, we selectively targeted endothelial cells with (strept)avidinated contrast agents to achieve multimodal contrast enhancement of Tie2-expressing blood vessels during tumor progression. RESULTS: This genetic targeting system provided selective labeling of tumor vasculature and showed in vivo binding of avidinated probes with high specificity and sensitivity using microscopy, near infrared, ultrasound, and magnetic resonance imaging. We further demonstrated the feasibility of conducting longitudinal three-dimensional (3D) targeted imaging studies to dynamically assess changes in vascular Tie2 from early to advanced tumor stages. CONCLUSIONS: Our results validated the Ts-Biotag mouse as a multimodal targeted imaging system with the potential to provide spatio-temporal information about dynamic changes in vasculature during tumor progression.

A Mathematical Model of Granule Cell Generation During Mouse Cerebellum Development.

Determining the cellular basis of brain growth is an important problem in developmental neurobiology. In the mammalian brain, the cerebellum is particularly amenable to studies of growth because it contains only a few cell types, including the granule cells, which are the most numerous neuronal subtype. Furthermore, in the mouse cerebellum granule cells are generated from granule cell precursors (gcps) in the external granule layer (EGL), from 1 day before birth until about 2 weeks of age. The complexity of the underlying cellular processes (multiple cell behaviors, three spatial dimensions, time-dependent changes) requires a quantitative framework to be fully understood. In this paper, a differential equation-based model is presented, which can be used to estimate temporal changes in granule cell numbers in the EGL. The model includes the proliferation of gcps and their differentiation into granule cells, as well as the process by which granule cells leave the EGL. Parameters describing these biological processes were derived from fitting the model to histological data. This mathematical model should be useful for understanding altered gcp and granule cell behaviors in mouse mutants with abnormal cerebellar development and cerebellar cancers.

Nested Graph Cut for Automatic Segmentation of High-Frequency Ultrasound Images of the Mouse Embryo.

We propose a fully automatic segmentation method called nested graph cut to segment images (2D or 3D) that contain multiple objects with a nested structure. Compared to other graph-cut-based methods developed for multiple regions, our method can work well for nested objects without requiring manual selection of initial seeds, even if different objects have similar intensity distributions and some object boundaries are missing. Promising results were obtained for separating the brain ventricles, the head, and the uterus region in the mouse-embryo head images obtained using high-frequency ultrasound imaging. The proposed method achieved mean Dice similarity coefficients of 0.87 ±0.04 and 0.89 ±0.06 for segmenting BVs and the head, respectively, compared to manual segmentation results by experts on 40 3D images over five gestation stages.

Coherence-Weighted Synthetic Focusing Applied to Photoacoustic Imaging Using a High-Frequency Annular-Array Transducer.

This paper presents an adaptive synthetic-focusing scheme that, when applied to photoacoustic (PA) data acquired using an annular array, improves focusing across a greater imaging depth and enhances spatial resolution. The imaging system was based on a 40-MHz, 5-element, annular-array transducer with a focal length of 12 mm and an 800-µm diameter hole through its central element to facilitate coaxial delivery of 532-nm laser. The transducer was raster-scanned to facilitate 3D acquisition of co-registered ultrasound and PA image data. Three synthetic-focusing schemes were compared for obtaining PA A-lines for each scan location: delay-and-sum (DAS), DAS weighted with a coherence factor (DAS + CF), and DAS weighted with a sign-coherence factor (DAS + SCF). Bench-top experiments that used an 80-µm hair were performed to assess the enhancement provided by the two coherence-based schemes. Both coherence-based schemes increased the signal-to-noise ratio by approximately 10 dB. When processed using the DAS-only scheme, the lateral dimension of the hair in a PA image with 20 dB dynamic range was between 300 µm and 1 mm for imaging depth ranging from 8 to 20 mm. In comparison, the DAS + CF scheme resulted in a lateral dimension of 200 to 450 µm over the same range. The DAS + SCF synthetic focusing further improved the smallest-resolvable dimension, which was between 150 and 400 µm over the same range of imaging depth. When used on PA data obtained from a 12-day-old mouse embryo, the DAS + SCF processing improved visualization of neurovasculature.

3D mapping of neuronal migration in the embryonic mouse brain with magnetic resonance microimaging.

A prominent feature of the developing mammalian brain is the widespread migration of neural progenitor (NP) cells during embryogenesis. A striking example is provided by NP cells born in the ventral forebrain of mid-gestation stage mice, which subsequently migrate long distances to their final positions in the cortex and olfactory bulb. Previous studies have used two-dimensional histological methods, making it difficult to analyze three-dimensional (3D) migration patterns. Unlike histology, magnetic resonance microimaging (micro-MRI) is a non-destructive, quantitative and inherently 3D imaging method for analyzing mouse embryos. To allow mapping of migrating NP cells with micro-MRI, cells were labeled in situ in the medial (MGE) and lateral (LGE) ganglionic eminences, using targeted in utero ultrasound-guided injection of micron-sized particles of iron-oxide (MPIO). Ex vivo micro-MRI and histology were then performed 5-6days after injection, demonstrating that the MPIO had magnetically labeled the migrating NP populations, which enabled 3D visualization and automated segmentation of the labeled cells. This approach was used to analyze the distinct patterns of migration from the MGE and LGE, and to construct rostral-caudal migration maps from each progenitor region. Furthermore, abnormal migratory phenotypes were observed in Nkx2.1(-/-) embryos, most notably a significant increase in cortical neurons derived from the Nkx2.1(-/-) LGE. Taken together, these results demonstrate that MPIO labeling and micro-MRI provide an efficient and powerful approach for analyzing 3D cell migration patterns in the normal and mutant mouse embryonic brain.

In vivo Mn-enhanced MRI for early tumor detection and growth rate analysis in a mouse medulloblastoma model.

Mouse models have increased our understanding of the pathogenesis of medulloblastoma (MB), the most common malignant pediatric brain tumor that often forms in the cerebellum. A major goal of ongoing research is to better understand the early stages of tumorigenesis and to establish the genetic and environmental changes that underlie MB initiation and growth. However, studies of MB progression in mouse models are difficult due to the heterogeneity of tumor onset times and growth patterns and the lack of clinical symptoms at early stages. Magnetic resonance imaging (MRI) is critical for noninvasive, longitudinal, three-dimensional (3D) brain tumor imaging in the clinic but is limited in resolution and sensitivity for imaging early MBs in mice. In this study, high-resolution (100 µm in 2 hours) and high-throughput (150 µm in 15 minutes) manganese-enhanced MRI (MEMRI) protocols were optimized for early detection and monitoring of MBs in a Patched-1 (Ptch1) conditional knockout (CKO) model. The high tissue contrast obtained with MEMRI revealed detailed cerebellar morphology and enabled detection of MBs over a wide range of stages including pretumoral lesions as early as 2 to 3 weeks postnatal with volumes close to 0.1 mm(3). Furthermore, longitudinal MEMRI allowed noninvasive monitoring of tumors and demonstrated that lesions within and between individuals have different tumorigenic potentials. 3D volumetric studies allowed quantitative analysis of MB tumor morphology and growth rates in individual Ptch1-CKO mice. These results show that MEMRI provides a powerful method for early in vivo detection and longitudinal imaging of MB progression in the mouse brain.

High-throughput, high-frequency 3-D ultrasound for in utero analysis of embryonic mouse brain development.

With the emergence of the mouse as the predominant model system for studying mammalian brain development, in utero imaging methods are urgently required to analyze the dynamics of brain growth and patterning in mouse embryos. To address this need, we combined synthetic focusing with a high-frequency (38-MHz) annular-array ultrasound imaging system for extended depth-of-field, coded excitation for improved penetration and respiratory-gated transmit/receive. This combination allowed non-invasive in utero acquisition of motion-free 3-D data from individual embryos in approximately 2 min, and data from four or more embryos in a pregnant mouse in less than 30 min. Data were acquired from 148 embryos spanning 5 d of early to mid-gestational stages of brain development. The results indicated that brain anatomy and cerebral vasculature can be imaged with this system and that quantitative analyses of segmented cerebral ventricles can be used to characterize volumetric changes associated with mouse brain development.

High-frequency annular array with coaxial illumination for dual-modality ultrasonic and photoacoustic imaging.

This paper presents a combined ultrasound and photoacoustic (PA) imaging (PAI) system used to obtain high-quality, co-registered images of mouse-embryo anatomy and vasculature. High-frequency ultrasound (HFU, >20 MHz) is utilized to obtain high-resolution anatomical images of small animals while PAI provides high-contrast images of the vascular network. The imaging system is based on a 40 MHz, 5-element, 6 mm aperture annular-array transducer with a 800 µm diameter hole through its central element. The transducer was integrated in a cage-plate assembly allowing for a collimated laser beam to pass through the hole so that the optical and acoustic beams were collinear. The assembly was mounted on a two-axis, motorized stage to enable the simultaneous acquisition of co-registered HFU and PA volumetric data. Data were collected from all five elements in receive and a synthetic-focusing algorithm was applied in post-processing to beamform the data and increase the spatial resolution and depth-of-field (DOF) of the HFU and PA images. Phantom measurements showed that the system could achieve high-resolution images (down to 90 µm for HFU and 150 µm for PAI) and a large DOF of >8 mm. Volume renderings of a mouse embryo showed that the scanner allowed for visualizing morphologically precise anatomy of the entire embryo along with corresponding co-registered vasculature. Major head vessels, such as the superior sagittal sinus or rostral vein, were clearly identified as well as limb bud vasculature.

Novel genetic approach for in vivo vascular imaging in mice.

RATIONALE: The formation and maintenance of a functional vasculature is essential for normal embryonic development, and genetic changes that affect the vasculature underlie pathogenesis in many human diseases. In vivo imaging in mouse models is required to understand the full complexity of mammalian vascular formation, which is a dynamic and 3-dimensional process. Optical microscopy of genetically expressed fluorescent reporter proteins offers high resolution but limited depth of penetration in vivo. Conversely, there are a plethora of molecular probes for alternative in vivo vascular imaging modalities, but few options for genetic control of contrast enhancement. OBJECTIVE: To develop a reporter system for multimodal imaging of genetic processes involved in mammalian vascular biology. METHODS AND RESULTS: To approach this problem, we developed an optimal tagging system based on Biotag-BirA technology. In the resulting Biotag reporter system, coexpression of 2 interacting proteins results in biotin labeling of cell membranes, thus enabling multimodal imaging with "avidinated" probes. To assess this approach for in vivo imaging, we generated transgenic mice that expressed the Biotag-BirA transgene from a minimal Tie2 promoter. A variety of imaging methods were used to show the utility of this approach for quantitative analysis in embryonic and adult models of vascular development, using intravascular injection of avidinated probes for near infrared, ultrasound, and magnetic resonance imaging. CONCLUSIONS: The present results demonstrate the versatility of the Biotag system for studies of vascular biology in genetically engineered mice, providing a robust approach for multimodal in vivo imaging of genetic processes in the vasculature.