The external fertilization and transparent embryos of zebrafish make them an informative model of vertebrate embryonic development from the 1-cell stage. In this study, we examine the impact of de novo GTP synthesis on the formation of the embryonic somites, which are embryonic cells which develop into segmented blocks of muscle that run the length of the body. We hypothesize the de novo GTP synthesis is required for the correct patterning of somite borders in zebrafish embryos, and that this process facilitates the formation of a vertebrate body plan. Inosine monophosphate dehydrogenase 2 (IMPDH2) is the enzyme which catalyzes the conversion of inosine monophosphate (IMP) towards the de novo synthesis of GTP instead of ATP. To test the impact of de novo GTP synthesis on somite formation, we inhibited IMPDH2 function with mycophenolic acid (MPA) both before and after somite formation began. MPA caused stronger defects in the somite morphology and embryonic body shape when added to embryos before somite formation began, earlier in development. We performed in situ hybridization against xirp2a to assess the effect of inhibiting IMPDH2 function on the formation and patterning of the somite borders. MPA treatment decreased the definition of somite borders we could observe in the posterior tail. Inhibiting IMPDH2 with MPA produced somites with smooth, round borders instead of the chevron-shape typical of zebrafish. We next conducted immunohistochemistry against IMPDH2 to examine the expression and localization of this enzyme in embryonic cells when GTP conditions are low. In MPA-treated embryos, we observed increased expression of IMPDH2 across the entire embryo. We will next explore how GTP abundance affects activity of the clock, a mechanism which synchronizes gene expression of embryonic cells.

The ability to manipulate and ligate proteins has been a driving force in advancing our understanding of the complex regulation of biological processes in space and time. Protein ligation, in which two or more polypeptides are covalently linked, is a powerful strategy in biomacromolecular engineering, enabling precise control over protein modifications, stability, and functionality. This is particularly useful in understanding protein function and interactions, as well as modulating protein activity, including immobilization of protein-based signals within materials triggered by cytocompatible light. One proven system known for its specificity and ease of use is SpyTag/SpyCatcher, a peptide-protein pair capable of irreversible ligation via isopeptide bond formation. Recent work has demonstrated the ability to control SpyTag/Catcher ligation using cytocompatible light due to its non-invasive nature and spatiotemporal (i.e., 4D control) manipulation of protein signals on a biologically relevant timescale. However, the application of this reported photoligation strategy is hindered by the use of genetic code expansion which limits protein yield, entails additional orthogonal protein machinery, and involves translational incorporation of a non-canonical amino acid. To address these challenges, we aim to develop a photocontrolled protein ligation strategy using native protein activity while maintaining spatial and temporal control. We predict this strategy will enable dose-dependent reconstitution of ligation by varying light exposure duration and intensity in native protein systems while sidestepping challenges associated with genetic code expansion. We intend to use this strategy to further assess our capability to control split protein reconstitution and for future applications in directing complex cell fate, which has significant utility in stem cell biology and regenerative medicine.

Cell shape opens a powerful window into the genetic and mechanical processes that drive cell behavior and, ultimately, tissue morphogenesis during development. By identifying cell shape, we can track specific cells and their responses to different gene expressions - creating a clearer mapping of which cells are affected by various manipulations. In this project we combine computational tools with quantitative microscopy to measure nucleus shape, and use these readouts to identify different cell types in the pectoral fins of zebrafish embryos. High resolution images of pectoral fin nuclei were taken using confocal microscopy - a technique commonly used when capturing tissue and cell data. Following nucleus identification and segmentation during data pre-processing, the FlowShape analysis package was utilized to extract quantitative "shape vectors" that encode the morphology of each nucleus. We plan to leverage the spherical harmonic weights produced within FlowShape to cluster and identify key shape-types that emerge from the collected nuclei. These shape readouts will serve as the basis for future analyses aimed at classifying different nucleus morphologies within the pectoral fin. Ultimately we hope to use nucleus morphology to predict the expression of key marker genes. This approach provides a powerful method for bridging the gap between the rich gene expression information provided by single-cell RNA-seq atlases, and the dynamical and morphological information produced by in vivo microscopy.

Architectural and spatiotemporal aspects of epigenetic regulation and cell behavior are critical for maintaining overall health. Unintentional genetic mutations can create dynamic dysregulation in the epigenome and transcriptomes at the cellular level which is implicated in diseases ranging from fibrosis to cancer. However, our tools to probe and understand these behaviors are limited by a lack of spatiotemporal control. To address this, we propose installing four-dimensional control over the potent CRISPR inhibition transcriptional effectors to establish epigenetic control at cellular scale resolutions.  CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) is a genetic modification system that relies on Cas9 proteins to splice and deactivate genes as controlled by a programmable guide RNA sequence. CRISPR inhibition relies on a deactivated Cas9 protein that does not directly alter the genetic material in order to sterically hinder transcription. Our work aims to formulate a CRISPR inhibitor system which can unbind from the target DNA with two photon activation via a photo-cageable noncanonical amino acid insertion. This would allow for four-dimensional spatiotemporal control over the system, thus increasing the level of control in epigenetic regulation. Currently, work is being done to test the CRISPR inhibition system in HEK 293 cells that have been lentivirally transduced with both a test sequence and the deactivated Cas9 protein. After testing is completed for this simpler system, we will move toward creating a system that incorporates the photocaged noncanonical lysine variant, giving us control over the CRISPR inhibition system with regards to both space and time.

Biological processes rely on the intricate functions of proteins, which drive essential biochemical reactions. Given their critical role, various methods have been developed to regulate protein functions in biomaterials and in vitro. Enhancing the precision of gene editing is crucial for advancing applications in gene therapy and minimizing off-target effects. My project focuses on integrating photoactivatable proteins with prime editors, a modified version of the widely known gene editor CRISPR/Cas9, to improve spatial and temporal control over gene modifications. By utilizing genetic code expansion, non-canonical amino acids are incorporated into human cells to express photocaged prime editor proteins and altering host genomes. This system enables optical stimulation to precisely regulate protein activity. Through the deployment of well-characterized photolabile groups, we expect to be able to render protein activity controllable in a dose dependent way. A key application of this approach is the development of a photoactivatable prime editor system to induce precise gene edits. Traditional CRISPR/Cas9 methods lack spatiotemporal control over activation. To address this, the system is adapted for use in hydrogels, where two-photon patterning allows visualization of prime editor protein activation in three dimensions. Our study aims to demonstrate the feasibility of optically controlling gene editing with high specificity, offering a novel strategy for advancing cell lineage tracing and gene therapy applications.

Tumor angiogenesis is characterized by unregulated blood vessel formation, impairing vascular networks and biological transport. It represents a critical stage in cancer progression, where malignant tumors metastasize and exploit the human body’s resources, which lie in vascular networks. However, the complex tumor microenvironment presents significant challenges in studying tumor angiogenesis and identifying its biomarkers. Towards addressing this concern, hydrogels—water-swollen, polymeric networks—can be used to recapitulate the tumor microenvironment, whose physiochemical properties can be precisely tuned to match that found in vivo. The DeForest Lab has developed methods and techniques in bioorthogonal chemistry and light-based subtractive manufacturing to tune such hydrogel materials with precise and unique 4D control, all at subcellular resolutions. In this project, I will exploit image-guided multiphoton lithography to engineer natively complex tumor vasculature patterns within photodegradable hydrogels. We will further embed tumor vascular spheroids within these hydrogels, providing a platform to model and assay tumor progression in vitro. This study has exciting implications for translational research and preclinical studies, particularly for disease modeling and therapeutic screening, as well as reducing ethical concerns regarding tissue and animal models in preclinical studies.

User-controlled cell behavior is useful for studying wound healing because the isolated therapeutic effects of individual signals can be observed at the wound site. Aptamers are single-stranded oligonucleotides that fold into three-dimensional structures that can capture and inhibit proteins. The biological capacity of cells to deploy traction forces as a release mechanism for extracellular proteins can be engineered through clever deployment of aptamer-bound proteins with peptide handles. Scientists at the Imperial College London recently synthesized TrAPs: Traction Force-Activated Payloads that enable precise control of cell behavior using such a strategy. We bound photocaged TrAPs lacking adhesion handles to functionalized collagen hydrogels. Peptide immobilization was then selectively patterned using 365 nm light to spatially confine cell access to captured vascular endothelial growth factor (VEGF). After surface seeding endothelial cells, observations were made regarding changes in cells’ physical characteristics as a result of  protein release. Through SELEX (Systematic Evolution of Ligands by Exponential Enrichment), TrAPs can be designed for any target protein in the extracellular matrix. The wide scope and biorthogonality of this project allows for many applications in medical technology and user-controlled cell fate. 

Hydrogel biomaterials have many applications in tissue engineering and drug delivery. Stimuli-responsive hydrogels allow for controlled drug release, dependent on a user-defined trigger. However, current stimuli-responsive hydrogels are case-specific and cannot be used for broader applications, such as targeted disease treatment. Most hydrogels can only respond to one input, making them difficult to use in treating diseases with multiple markers. We developed a fully recombinant protein-based material with protease degradable cross links that follow Boolean logic (YES/AND/OR) in response to multiple inputs to allow for user controlled material degradation and drug release. The protease degradable sequences can be easily switched out before expression depending on the application, making our hydrogel generalizable. The hydrogel will be crosslinked with Boolean logic constructs, each of which are flanked by a click-like chemistry protein system. This allows the crosslinks to be covalently ligated to a linker made from elastin-like polypeptides (ELP), which holds the hydrogel network together. The crosslinks and ELP were expressed recombinantly in E. coli and purified on an ӒKTA Pure (Cytiva). A degradation study was conducted by adding different combinations of proteases to prove that material degradation is dependent on the combination of proteases added. We then conducted rheometry to determine the mechanical properties of the hydrogels, and verified that material stiffness followed the expected logical operation, where correct inputs resulted in material degradation. Finally, we tested the hydrogel’s ability to release drugs by incorporating human epidermal growth factor (hEGF) into the gel and measuring activation of the ERK signaling pathway through a Western Blot. The Western Blot showed activation of the ERK pathway only when the correct combination of proteases was added, indicating release of a bioactive protein drug. If successful, this hydrogel could be used for therapeutic delivery of drugs and broader tissue engineering applications.

Mountain snow is an important source of freshwater for forest and meadow ecosystems. However, extreme events such as heat waves and low snowpack pose a significant threat to the availability of these essential resources. Current technology for detecting snow coverage from satellite imagery is limited due to insufficient image quality, especially in forests and meadows. To address this, we developed a Python package that improves access to a machine learning-based snow coverage detector at meter-scale resolution. Built upon research by Yang et al., 2023, this package integrates machine learning models to generate snow coverage masks from Planet satellite imagery. The package includes five core components to assist researchers in leveraging this technology: data searching and downloading from Planet's satellite imagery, custom training and fine-tuning of random forest models for snow coverage detection, prediction of snow-covered areas using the model, and geometry simplification for defining areas of interest. Other features include pre-trained models and sample datasets for quick and efficient implementation. By consolidating their research into a Python package, we aim to improve accessibility to software and research for those seeking to track mountain snow coverage and its environmental impact. Ultimately, our goal is to empower researchers to accelerate progress in understanding spatial patterns of snow in forests and mountain meadows. Additionally, we aim to support resource managers in more effectively tracking snow distribution across complex terrain.

Hydrogels with tunable stiffnesses are a versatile method to study the interactions of human cells in vitro. These systems recreate human extracellular matrix (ECM) and capture the stiffness changes associated with a variety of biological processes and diseases, like cancer and cirrhosis. Photoresponsive chemistries allow light to be used to modulate the stiffness in these materials with high resolution. However, when creating more complex patterned gels with photomasks, bulk property analysis cannot capture the variation. To circumvent this and measure the stiffness of these complex gels, I performed rheology and fluorescence recovery after photobleaching (FRAP) to establish a correlation between diffusivity and stiffness in flood-illuminated gels. By finding and using the correlation, I am able to calculate the stiffness of the more complex patterned gels based off of their FRAP-derived diffusivity measurements. This method allows for better fine tuning of gels for use as a platform to study human cell growth through a range of stiffening events in multiple different parts of the body.