A challenge faced by investigators who use Adeno Associated Virus (AAV) vectors lies in verifying successful assembly of the genetic construct developed for a given experimental manipulation. Failure at this step can introduce mutations that produce non-functional proteins and failure to obtain the desired expression. For these reasons, quality control measures support generation of valuable data and prevention of resource loss. Using molecular techniques such as PCR and Sanger Sequencing, we developed a quality control pipeline to amplify and identify viral sequences. Viruses were selected from stock for an identity assay based on relevance to planned optogenetics procedures, and PCR amplicons were designed to contain functional regions of each viral construct. Amplification was carried out using universal primers targeting 5'ITR and WPRE regions, which were common to all assembled viruses. Sanger sequencing was used to confirm that the amplicon sequence matched the viral template. Using the outlined procedure, we can now amplify, purify, and sequence the vector genome to gain nucleotide level confirmation of coding gene sequences prior to use in experiments.

Brain stimulation has emerged as a novel treatment for stroke, a prevalent cause of death and disability worldwide. Studies in rodent models have shown that post-stroke electrical stimulation results in plasticity and neuroprotective benefits. However, techniques that were effective in rodents have rarely translated into clinically viable therapies in humans due to the significant differences in rodent and human neurophysiology and anatomy. Therefore, the goal of our study is to obtain clinically relevant outcomes that describe the mechanisms of stimulation induced plasticity in non-human primates. We combined electrophysiology and immunohistochemistry to investigate the degree of stimulation-induced plasticity and network dynamics after photothrombotic stroke in 4 macaques. We quantified the expression of two biomarkers, postsynaptic density-95 (PSD-95) and growth-associated protein-43 (GAP-43) in cells within ~10mm from the lesion penumbra. Since PSD-95 is important for the maturation of excitatory synapses and GAP-43 is involved in axonal branching and elongation, evaluating the expression of these two proteins around the lesion core allowed us to compare post-stroke synaptic and axonal plasticity in 2 control and 2 stimulated monkeys. Based on wide-field epifluorescence imaging, we identified the distance from the lesion penumbra at which there was a distinct difference in biomarker immunoreactivity in control and stimulated animals, and performed high-magnification confocal imaging to further investigate the structure of biomarker expression. Furthermore, analysis of the electrocorticography signal showed a largescale downregulation of neural activity following electrical stimulation, while Nissl staining revealed that stimulated monkeys had smaller lesion volumes than controls. These results indicate that stimulation elicits changes at both neurophysiological and cellular level, and may exert a neuroprotective effect on the post-stroke network by reducing metabolic energy consumption.. Therefore, this study investigates the effects of electrical stimulation on neuroplasticity and protection following injury, which may have a profound impact on future therapeutic interventions for stroke.

Despite recent advancements in cancer treatment, the overall 5-year survival rate for glioblastoma, a very aggressive brain cancer, is only 7.4%. The greatest challenge in brain cancer treatment is the blood-brain barrier (BBB), a physical barrier that protects the central nervous system (CNS) from circulating solutes in the blood but prevents therapeutics from entering the brain space. While surgery is the gold-standard treatment, this procedure is high-risk. Thus, research into nanoscale injectable therapies that can cross the BBB to treat brain tumors is critical as they are non-invasive and can be targeted to specific cells. The Pun Lab is developing nanoparticles to cross the BBB via receptor-mediated transcytosis (RMT) via the transferrin receptor. To further this research, I developed a cellular model of the BBB to assess the ability of different nanoparticle formulations to cross the BBB in vitro. Specifically, I developed a Transwell culture of brain endothelial cells, which are the main regulators of the BBB due to tight junction formation. I investigated additional targeting ligands through analysis of a polymer panel to improve transport through the BBB. Finally, I will validate endosomal escape through the barrier with confocal microscopy. Through this project, I (i) developed a representative in vitro model of the BBB, (ii) explored alternative receptor-binding ligands and (iii) validated the mechanism through which the nanoparticles travel to enhance nanoparticle transport through the BBB. Ultimately, these three aims enable better direction of nanoparticle behavior in vivo and across the BBB. Non-invasive nano-therapeutics are critical to the future treatment of glioblastoma as current treatment options are limited, extremely risky, and lack long term efficacy.

During development, cardiomyocytes (CMs) undergo a hypertrophic growth phase to generate load for the heart to pump sufficient blood. However, in the early stages of pathological hypertrophy, stress-induced signal transduction promotes the addition of new contractile units through poorly understood mechanisms to maintain tensional homeostasis. Microtubules provide mechanical resistance in CMs. Our previous data shows that inhibition of contraction by expression of D65A cTnC (a point mutation on the calcium binding site of troponin C) results in complete myofibrillar disarray, with muscle stress fibers emerging in the cellular periphery. Surprisingly, when given topological cues, these cells show aligned myofibrils in the absence of contraction. Thus, the mechanism behind the maintenance of myofibril and passive tension in non-contractile CMs is not explained. My goal was to determine the role of microtubules in maintaining tensional homeostasis in response to change in internal tension in CMs. Here, wild-type (WT) human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) were transduced to express cardiac troponin C (cTnC) with point mutations L48Q (hyper-contractile), I61Q (hypo-contractile), and D65A (non-contraction) to study the effect of varying levels of contractility or internal load on microtubules. The percent coverage of microtubules (staining within the CMs for cTnC L48Q, WT, I61Q, D65A) was 42.32%, 50.20%, 64.09%, 75.70% respectively (n=300). Complementary proteomics data have indicated that protein levels related to microtubule proliferation (MAP4) and assembly (Tubb3, Tubb4b, Tuba1a) were elevated in CMs expressing cTnC D65A. This new data gives us insight into how the microtubule remodeling in non-contractile and dysfunctional cardiomyocytes maintains tension in early stages and its possible role in myofibril formation.

Cancer is a disease that is characterized by uncontrolled cell growth and proliferation. As these cells spread throughout the body they can invade and damage major organs resulting in death. It has been shown that most cancers arise from genetic changes in the signaling pathways that control the cell growth and proliferation. One major challenge cancer researchers face is to understand the behavior of these pathways. As an example, the behavior of the Endothelial Growth Factor receptor (EGFR) pathway, a commonly mutated pathway in cancer, has a broad range of behaviors. Under different conditions, the pathway can exhibit bistability, amplification and even oscillations in protein levels. Predicting such behaviors can significantly aid our understanding of how cancer arises as well as provide us with insights to therapeutically treat patients. The goal of this project was to develop a high accuracy machine learning model that is capable of predicting the behaviors of signaling pathways. Over the first quarter, I generated over 3000 artificial signaling networks utilizing existing software written by the lab. For each of these networks I then extracted the network architecture, the connections and relationships between species in the pathways, into matrices such as the stoichiometry matrix and scaled Jacobian matrix. These matrices were then treated as pseudo-images and used to train a convolutional neural network to predict oscillatory behavior. We expect this model to have greater than 70 percent accuracy given preliminary models trained on similar smaller datasets. The development of this high accuracy model provides cancer researchers with valuable insights into the behavior of these signaling pathways and how they can best therapeutically treat patients.

As of 2021, Alzheimer’s Disease (AD) afflicts 6.2 million Americans. This number is expected to climb rapidly in the coming decades as the number of individuals at the age of increased risk rises. AD is characterized by the deposition of insoluble, β-sheet-rich amyloid plaques of the amyloid β (Aβ) peptide. However, the AD-causing toxicity of Aβ is mediated by soluble, oligomeric species of the peptide formed in the process of aggregating into plaques (amyloidogenesis). These oligomers have been shown to adopt a unique secondary structure known as “α-sheet.” The onset of AD is typically sporadic, but some cases, known as familial Alzheimer’s Disease (FAD), are inherited via mutations in Aβ. None of these mutations have been studied for their ability to adopt the pathogenic α-sheet conformation. Therefore, I am investigating the impact of the Arctic (E22G), Iowa (D23N), Flemish (A21G), and Osaka (E22Δ) mutations on the amyloidogenesis and toxicity of Aβ to explain their role in FAD. I accomplish this through a Soluble Oligomer Binding Assay (SOBA) designed in the Daggett Lab to capture and quantify α-sheet conformations. Additionally I use circular dichroism, MTT cell toxicity assays and Thioflavin-T curves to investigate the aggregation pathway of these mutants. I have found that these mutations confer unique aggregation patterns to Aβ. Many of these Aβ mutants appear to adopt a stabilized α-sheet consistent with their tie to FAD. This provides an explanation of how these mutants cause FAD and offers insight into the factors that contribute to the adoption of α-sheet structure and pathogenesis of AD.

Hypertrophic cardiomyopathy is a disease affecting millions of people worldwide, characterized by thickened heart tissue, which makes pumping difficult for the heart. Since the restructuring of the heart is influenced by remodeling at the cellular level, the overall goal of the project is to investigate the mechanics of how cells sense tension in their environment and its relationship to regulating cell remodeling. The direction in which sarcomeres, the basic contractile unit in the heart, are added influences the shape of the cells and consequently the shape of the overall heart. To study this relationship, it is necessary to visualize the sarcomeres for correlating acquired tension data from FRET sensors. By attaching a fluorophore to a sarcomeric protein such as alpha-actinin, it is possible to visualize sarcomeres using fluorescent microscopy. For this project, I developed a blue fluorescent protein (BFP)-tagged alpha-actinin plasmid through molecular cloning techniques. I used restriction enzymes and PCR to isolate and amplify the genes of interest from a different plasmid, then used Gibson Assembly to insert the genes into a plasmid containing ampicillin resistance to construct the final BPF-tagged alpha-actinin plasmid. Preliminary results showed successful expression of the transiently transfected BFP construct in cardiomyocytes. The next steps are to optimize the transfection for higher efficiency, adapt an existing data analysis pipeline for analyzing sarcomere dynamics, and develop a set of parameters for efficient image acquisition. Many existing therapies for hypertrophic cardiomyopathy only address symptoms but do not solve the underlying issue of systolic dysfunction. Rather than taking a genetic or biochemical approach, which can be difficult to develop, this research project focuses on the mechanical interactions in the heart and studying the contractile forces may yield more insight into this disease and build the informational foundation for developing future therapies to prevent or treat hypertrophic cardiomyopathy.

 Microglia, the resident immune cells in the brain, have multiple functions including synaptic pruning to preserve resources, phagocytosis of apoptotic cells, and isolation and removal of foreign material. Depending on local environmental stimuli, microglia can change their shape between multiple states including highly branched, branched, or ameboid. To better understand microglia responses to changes in the brain environment, I investigated morphological shape features that include changes in area, circularity, and aspect ratio among other important features. I specifically focused on the microglial response to oxygen-glucose deprivation (OGD). Oxygen-glucose deprivation is a condition where the brain fails to receive the necessary oxygen and nutrients for growth and maintenance, resulting in higher levels of stress and cytotoxicity. Investigating the effects of OGD on microglia is part of a larger effort - developing a fluorescent imaging pipeline called microFIBER. Our goal for microFIBER is to create an unbiased, detailed, and replicable analysis pipeline for the robust characterization of microglia morphology. Images are from a previous investigation into effects of OGD on neonatal rat brains in the Nance Lab. We used SciKit-Image along with other Python packages to segment, label, and quantify the geometry of fluorescent-labeled microglia cells in the images. SciKit-Image’s module RegionProps was used to quantify shape features by drawing certain properties over the objects and then measuring those drawings. I then analyzed the response of microglia in non-treated, 1.5-hour OGD exposure, and 3-hour OGD exposure via data analysis in Python and Excel. I further divided these treatment groups into regional comparisons of the cortex, hippocampus, and thalamus. Results from statistical analysis supported differences between treatment groups and brain region, including statistically relevant differences in microglial circularity, area, and axes lengths. Differences in shape features could be used in the future as markers for diseased or distressed conditions for medical diagnosis.

 The CD28 receptor provides co-stimulatory signaling as part of T-cell activation, thereby driving T-cell proliferation, differentiation, cytokine production, and survival required for effective immune responses. Given this important role, CD28 has broad therapeutic implications, serving as a target for cancer immunotherapy, treatment of autoimmune disorders, and production of adoptively transferred T cells. Current approaches for targeting CD28 rely on antibodies, which can be employed in vivo or ex vivo to promote or block CD28 signaling depending on the application. While effective, antibody-based targeting is costly and rigid in design, owing to their biological production and reduced control over binding. Aptamers are small, single-stranded oligonucleotides with sequence-defined architectures that can bind specific targets of interest at high specificity and affinity. Aptamers can be produced at low cost and the inherent properties of oligonucleotides permit flexibility in reversing binding and fine-tuning affinity strength for optimal receptor targeting. This project proposes to develop the first aptamer that targets human CD28 using a combinatorial selection strategy that incorporates protein- and cell-based selections. Aptamer candidates will be identified and characterized to evaluate their binding specificities and kinetics. The selected aptamer will then be used to design a T cell activation assay. A 12-round selection has been completed and binding specificities of individual aptamer candidates will be evaluated. A second selection using a modified approach is currently in progress. If successful, this project has the potential to improve the T-cell activation process in manufacturing adoptive T cell therapies and facilitate the development of novel therapeutics for treating cancer and autoimmune diseases.

Urine is an attractive biospecimen for in vitro diagnostics, and urine-based lateral flow assays are low-cost devices suitable for point-of-care testing, particularly in low-resource settings. However, some of the lateral flow assays exhibit limited diagnostic utility because the urinary biomarker concentration is significantly lower than the assay detection limit, which compromises the sensitivity. To address the challenge, we developed an osmotic processor that statically and spontaneously concentrated biomarkers. The specimen in the device interfaces with the aqueous polymer solution via a dialysis membrane. The polymer solution induces an osmotic pressure difference that extracts water from the specimen, while the membrane retains the biomarkers. The evaluation demonstrated that osmosis induced by various water-soluble polymers efficiently extracted water from the specimens, ca. 5 – 15 mL/hr. The osmotic processor concentrated the specimens to improve the lateral flow assays’ detection limits for the model analytes—human chorionic gonadotropin and SARS-CoV-2 nucleocapsid protein. After the treatment via the osmotic processor, the lateral flow assays detected the corresponding biomarkers in the concentrated specimens. The test band intensities of the assays with the concentrated specimens were very similar to the reference assays with 100-fold concentrations. The mass spectrometry analysis estimated the SARS-CoV-2 nucleocapsid protein concentration increased ca. 200-fold after the osmosis. With its simplicity and flexibility, this device demonstrates a great potential to be utilized in conjunction with the existing lateral flow assays for enabling highly sensitive detection of dilute target analytes in urine.

Life depends on a series of well-orchestrated biochemical reactions facilitated by proteins, which are differentially transcribed and activated in response to changing conditions. Hydrogels, water-swollen polymeric biomaterials, have proven useful as synthetic platforms to probe and direct biological activities by enabling researchers to recapitulate many aspects of the native cell environment. Though current hydrogel protein patterning techniques are capable of driving specific cell fates in individual cells in time and space (i.e. 4D), the timescales for patterning place dramatic limits on the types of biological functions that can be controlled. Furthermore, current techniques rely on slowly diffusing bioactive proteins into materials prior to immobilization within gels, so complete temporal control of protein activation within hydrogels remains out of reach. To address these limitations, my project focuses on directly photoactivating proteins within hydrogels using cytocompatible light. We predict that the extent of protein activation can be controlled dose-dependently by varying light exposure duration and intensity. We intend to use this platform to direct stem cell migration, differentiation, and proliferation in 4D on physiologically relevant timescales, which has tremendous utility in stem cell biology and regenerative medicine.

Cells in the body grow inside the extracellular matrix (ECM), which is composed of a combination of carbohydrates and proteins, presenting chemical and mechanical cues to the cells inside. Nearly all cell types are sensitive to the mechanics of the ECM and respond to cues such as stress, strain, and curvature, which influence organism development and disease progression. Hydrogel biomaterials are water-swollen polymer networks that mimic the properties of the ECM in vitro, allowing researchers to study cellular behavior in a controlled environment. In this project, we aim to develop a hydrogel platform where strain on the material, generated by contractile cells embedded within it, can be activated externally by a researcher in order to induce curvature in an engineered tissue, which we will use to investigate the effects of mechanical cues on cells encapsulated inside the hydrogel. We have synthesized a peptide crosslinker that acts as a two-input Boolean AND gate, with one half degradable by cell-secreted enzymes and the other half degradable by sortase, a researcher-added enzyme. We predict that when a cell-adhesive hydrogel is made with this crosslinker, contractile cells will be unable to expand until the addition of sortase; after sortase degrades one arm of the cyclic AND-type crosslinker, they will be able to locally degrade the hydrogel, spread within the gel, and then contract to generate stress and strain. We intend to encapsulate immature cardiac stem cells partway through differentiation, predicting that curvature alone will trigger further specification of these cells into their mature subtypes. Understanding the mechanism by which mechanical cues affect development will help identify new therapeutic targets for diseases where tissue curvature is important, and it will also inform new stimuli to improve the similarity of tissue grown in vitro to native tissue.

Women with dense breasts have increased amounts of fibroglandular tissue (FGT) and are at higher risk of developing breast cancer. Quantitative measurement of FGT from magnetic resonance imaging (MRI) could provide more robust measurement of density, supplanting conventional qualitative radiologist assessments. Current quantitative methods involve manual selection of a signal intensity threshold, which can be time consuming and subjective. Fuzzy c-means (FCM) clustering is an automated approach to tissue segmentation, offering a reproducible process for quantifying FGT volume. The aim of this study is to evaluate the efficacy of the FCM clustering in identifying FGT compared to manual thresholding. Women (N=10) who underwent screening breast MRIs at our institution were evaluated in this preliminary study. Fat-suppressed T₁-weighted pre-contrast images acquired as part of their clinical breast MRI exams were used for FGT segmentation. Prior to segmentation, I cropped the images to include only the breast. FGT was then segmented two ways, 1) manually, using a signal intensity threshold that I chose and adjusted and 2) automatically, using existing lab software for FCM clustering. The Sørensen-Dice similarity coefficient was calculated between the manual and automatic segmentations for each patient to determine the degree of overlap. The concordance correlation coefficient (CCC) was calculated between automatic and manual segmentation volumes across the whole data set. Across the 10 patients, an average (± standard deviation) Dice coefficient of 0.81±0.04 was observed, indicating good spatial agreement between the manual and automatic segmentations. The CCC between the FGT volume from manual and automated segmentation was 0.89, demonstrating high correlation in volume estimates between the two methods. Fuzzy c-means clustering was determined to be an effective and efficient method of FGT segmentation in breast MRI data. Future work will evaluate the application of this technique in assessment of background parenchymal enhancement, a clinical marker of cancer risk.

The misfolding and consequent aggregation of amyloid peptides to form insoluble fibril plaques has long been known to be implicated in amyloid diseases such as Alzheimer's Disease (AD), Type 2 Diabetes (T2D), and Transthyretin Amyloidosis (ATTR Amyloidosis). More recently, these peptides have been shown to form intermediates of unique secondary structure called alpha-sheet. Furthermore, it has been shown that these alpha-sheet intermediates, rather than the insoluble fibrils, are the toxic conformation of amyloid peptides, causing effects such as the death of neural cells and beta-cells, in AD and T2D respectively. Using this knowledge, in this project we have characterized the aggregation and structure of the amyloid peptides such as amyloid-beta and islet amyloid polypeptide under reproduceable conditions and through a variety of techniques such as thioflavin T fluorescence assays and circular dichroism. Additionally, we have shown the ability of synthetic alpha-sheet peptides to inhibit amyloid aggregation and reduce overall fibril content. Using this idea of selective alpha-sheet binding, we have developed a soluble oligomer binding assay (SOBA) that is able to detect toxic amyloid species. These SOBA experiments have shown promising results and have been used to confirm amyloid aggregation pathways as well as detect toxic amyloid at physiologically relevant concentrations, with applications in the early diagnosis of amyloid diseases.

The SARS-CoV-2 Delta variant, first detected in India, has contributed significantly to the 78 million global COVID-19 cases throughout the course of the pandemic. As such, effective diagnostic tools remain crucial for controlling widespread infection. DNA aptamers are single-stranded, self-folding oligonucleotides that can bind to specific targets with high specificity and affinity. DNA aptamers are especially useful for diagnostic applications because they are stable, inexpensive, consistent between batches, and allow for additional chemical modifications for diagnostic applications. On the other hand, commonly-used alternatives such as antibodies are difficult to store and are produced through a labor-intensive cellular process that makes them susceptible to batch-to-batch variation. This project selected for DNA aptamers that bound to the S1 domain of the SARS-CoV-2 Delta variant spike protein using the iterative method SELEX (Systematic Evolution of Ligands by Exponential Enrichment). In each round of SELEX, a starting aptamer pool was first exposed to undesirable proteins in a process known as negative selection. Aptamers that bound strongly to these unwanted proteins were removed from the aptamer pool using magnetic Dynabeads. In an analogous process of positive selection, aptamers with high affinity for the Delta S1 spike protein were retained in the aptamer pool, while nonspecific aptamers were discarded. With each passing round of SELEX, the stringency of aptamer binding was increased such that only the highest affinity aptamers remained in the final SELEX rounds. These final rounds were then sequenced through Next-Generation Sequencing (NGS) and the aptamers that displayed the highest enrichment were characterized using a combination of ELISA (enzyme-linked immunosorbent assay) and biolayer interferometry. In the near future, effective aptamers discovered through this process will be applied in antigen testing applications through the use of tools like lateral flow assays.

Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk-factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data, and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific autoantibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes exhibiting divergent acute severity and PASC. We found that immunological associations between PASC factors diminish over time leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.

Liver disease is a global health burden accounting for two million deaths a year worldwide. The demand for transplant has quickly risen all over the world in the last decade due to an increase in the prevalence of organ failure, rising surgical success and better posttranslational outcomes. In the U.S., seventeen people die each day waiting for an organ transplant. Bioengineering tissue could address these demands and help minimize death, surgical complications, and improve patient well-being overall. To bioengineer a transplantable liver in the lab, it must successfully model the structures and functions found in native tissue. The liver is a complex organ that carries out thousands of tasks, such as energy homeostasis, nutrient metabolism, and protein recycling. To perform these functions simultaneously, the liver separates its jobs into zones of high or low oxygen, also known as "zonation." For bioengineered tissue to reach the clinic in the future, it is essential that it demonstrates zonation and resembles what is observed in vivo. The present study aimed to test a bioengineered liver model that was designed by the Stevens lab group. Immunofluorescent staining was used to examine the expression of zonated proteins in engineered tissue and compare to the expression in native liver. We tested markers that are restricted to regions of low oxygen (glutamine synthetase), intermediate levels of oxygen (CYP2E1), or found at highly oxygenated areas (HAL). It was also hypothesized that fluctuations in oxygen availability would result in differing levels of mitochondria and generate reactive oxygen species (ROS), so these were assessed by immunohistochemistry. Results confirm zonation in bioengineered liver tissue, assuring it is a more-physiologically accurate model for tissue engineering. Future studies will explore the mechanism of liver zonation in vitro, using organoids to test known murine drivers of zonation such as Wnt proteins and oxygen gradients.

The discovery of fluorescent proteins led to the development of various protein-based biosensors that are vital in the goal to decipher the complexity of neural networks. Genetically encoded fluorescent indicators (GEFIs) are protein-based sensors with cell type specificity that increase in fluorescence upon ligand binding and allow for passive monitoring of neuronal signals. However, the development of such sensors is limited by the slow throughput of traditional protein engineering which has long engineering cycles of new plasmid variants. My project tackles this problem through the development of an optogenetic microwell array screening system (Opto-MASS) that effectively generates and screens unbiased genetic libraries of GEFIs in mammalian cells. The platform identifies high performing sensor variants on a custom microarray and effectively isolates and recovers their genetic material. This new platform was used to develop a sensor for the μ-opioid receptor (MOR), which is a G-protein coupled receptor and is responsible for the pain relieving effects of opioids and addiction. The platform has developed a MOR sensor that surpasses the standard in the literature in response to the synthetic opioid peptide agonist [d-Ala2, N-Me-Phe4, Gly-ol5]enkephalin (DAMGO). I used this platform to engineer a new class of MOR sensors that are ligand-specific to endogenous opioids versus exogenous opioids and optimized the sensors for maximum spatial and temporal precision. The development of a MOR sensor through this iterative process allows researchers to further investigate the molecular mechanisms underlying the pathology of addiction and provides a novel platform for protein engineers to more efficiently develop a wide variety of biosensors.