Methanotrophs are prokaryotes that naturally consume the potent greenhouse gas methane for energy. Through metabolic engineering at an industrial scale, these microorganisms hold potential to mitigate the contribution of methane emissions to global warming. In particular, Methylotuvimicrobium buryatense can sustain robust growth both in nature and experimental settings; it is a promising engineering candidate. To develop a robust metabolic engineering platform using M. buryatense, biologists require a deeper understanding of the genetic mechanisms by which it functions. Here, I present an open-source software tool designed to interactively explore the transcriptome of M. buryatense. By integrating bulk RNA-seq datasets collected from experiments over the past decade and applying an array of unsupervised machine learning clustering algorithms, we cluster genes by their expression profiles in differing growth conditions. These gene clusters are annotated with gene ontology (GO) terms using statistical enrichment analysis to assist in functional interpretation of the clusters and the genes that comprise them. To enhance domain-expert researchers’ ability to explore and drill-down into specific queries, I unify these cluster-specific analyses in a web-hosted tool using interactive data visualization techniques centered on a ReactJS frontend and Azure Cloud backend. With both exploratory and query-focused use cases, this software tool can support M. buryatense biologist workflows for predicting functions of hypothetical proteins, showcase new or confirming putative regulatory processes, and generate new experimental hypotheses from the presented transcriptomic trends.

In the body, cells grow in the extracellular matrix (ECM), which presents biochemical and mechanical signals to the cells inside. Hydrogel biomaterials are water-laden polymer networks that can mimic the properties of the ECM, allowing controlled study of cellular behavior in vitro. Many cells are mechanosensitive, but mechanical cues other than stiffness have not been fully investigated. This project aims to develop a platform in which degradability and strain can be activated by a researcher bio-orthogonally. We have synthesized a cyclic peptide crosslinker for a synthetic poly(ethylene glycol) hydrogel that acts as a Boolean AND-gate: one half is degradable by cell-secreted enzymes, and the other half is degradable by sortase, a bacterial enzyme, added by a researcher. We quantified the degradation of hydrogels made with this crosslink via fluorescence release and demonstrated that degradation only occurs after exposure to both enzymatic inputs. We further demonstrated that cells encapsulated in this material retain strong viability. We predict that cells will be unable to spread in this material until after a researcher adds sortase. After sortase addition, we expect that contractile cells will be able to locally degrade the material, spread, and generate strain. We intend to quantify spreading and strain with encapsulated fibroblasts. We also plan to use this platform to study development, by encapsulating immature cardiac stem cells and investigating the effect of fibroblast driven strain as a model; we predict that strain will trigger further specification of these immature cells. In addition to understanding the pathways for development, this research may help identify new therapeutic targets for disease, and it will also inform new strategies to grow tissue in vitro that more closely mimic the native environment.

Across a variety of signaling pathways, soluble factors in the extracellular matrix bind to protein receptors that span the cell wall, thereby triggering an information cascade that affects cell activity or function. It follows that by controlling the binding of signaling factors to these receptors, cell behavior and activity can be guided with substantial precision. In this project, we aim to design a system that allows de novo-developed protein agonists and antagonists, referred to as binders, to be activated with a high degree of temporal and spatial control within cell-encapsulating hydrogels. Towards this end, we employ methods derived from protein semisynthesis and click chemistry to tether binders to the hydrogel polymer network and then subsequently photo-release them from the network. We expect a difference in the functionality of binders when they are bound to the network compared to when they are released through light exposure and solubilized, thus achieving light-dependent control of the binder-receptor interaction and cell activity. This system will be the first to employ de novo developed agonist and antagonist biomolecules for the interrogation and control of cellular behavior. In so doing, it will expand the tool box of biomaterial engineering to include finer control over cells grown in 3D matrices, with direct implications in fields as diverse as therapeutic development, regenerative medicine, and organ-on-a-chip engineering.

With rising greenhouse gas emissions, both emissions reductions and greenhouse gas capture and conversion are necessary to mitigate the impacts of climate change. Though carbon dioxide comprises the largest proportion of global greenhouse gas emissions, methane causes over 80 times more global warming per unit than carbon dioxide. While methane can be converted catalytically at high temperatures and pressures, bacteria called methanotrophs transform methane into biomass at ambient temperatures and pressures. Engineering these organisms to consume methane globally can help slow climate change. At the Lidstrom Lab, I study the relationship between the methanotroph genome and metabolic regulation. Through this work, I have focused on finding genes that facilitate responses to environmental conditions and am now looking at genes that impact overall growth rate. I design and construct mutant strains, eliminating genes that appear to use the cell’s energy unnecessarily based on transcriptomics data. We expect that eliminating such genes will allow the cells to devote more energy to methane consumption and growth, improving growth rates under low methane conditions. This work is being extended to experiments in a bioreactor to observe how selected mutations and growth conditions impact growth rate at low methane. For this work, I am setting up the bioreactor to maintain the intended reaction conditions and utilizing a gas chromatogram to monitor methane consumption over time. These experiments reveal how the methanotrophs grow under specific conditions. I am also analyzing the results of the experiments using Python, MATLAB, and Excel. Long-term, this research will help prepare methanotrophs for deployment in the field to consume methane in areas of atmospheric methane release including landfills and agricultural sites.

Semiconductors are integral to many industries. As electronics trend towards more powerful devices, research efforts now focus on developing tools with the ability to achieve higher feature densities, such as the use of extreme ultraviolet (EUV) photolithography. Current photoresist technologies center around the use of 193 nm wavelength light with photo-sensitive organic thin films. However, these tools cannot produce feature sizes smaller than 20 nm. To overcome this limitation, semiconductor manufacturing is exploring 13.5 nm (EUV) wavelength light. Current commercial polymer photoresists lose sensitivity at this wavelength due to their low atomic absorption and photoemission of EUV light. Transition metal atoms have higher EUV absorption and can be incorporated to improve polymer photoresist sensitivity. To that end, molecular layer deposition (MLD) is a promising, scalable tool capable of creating thin films with angstrom-level precision. Unlike other deposition techniques, MLD does not require the use of solvents for deposition, boasts high compositional control, and is area selective deposition capable. In this work, the synthesis, characterization, and stability of novel thin films deposited using MLD are tested to determine their suitability as EUV photoresists, with the goal of improving semiconductor feature density, reducing the use of hazardous solvents, and decreasing energy and material costs in semiconductor production. Six films were studied using diethyl zinc (DEZ) as an inorganic EUV absorbent with six organic reactants: ethylene glycol; cis-2-butene-1,4-diol; 2-Methylene-1,3-propanediol; 1,5-hexadiene-3,4-diol; 1,4-butyne diol; and 3,4-dihydroxy-1-butene. Film thickness was measured using ellipsometry. Photosensitivity was measured upon exposure to 254 nm wavelength UV-C light. Degradation upon solvent exposure in an inert environment was examined using acetone, chloroform, ethanol, toluene, and deionized water, which proved most effective at removing the films, with thickness reductions up to ~90%. Results of solvent stability and light sensitivity will be used to propose new EUV photoresist processes for production-scale semiconductor manufacturing.

Chronic kidney disease is the ninth leading cause of death in the United States. The current process for pathological diagnosis involves pathologists manually reviewing histochemically stained tissue slides. This analysis is also used to inform further treatment and is therefore critical to patient outcomes. In this project, we aim to improve the robustness of the diagnostic process by utilizing machine learning models to identify and classify features indicative of kidney disease on whole slide images. We manually annotate Masson’s Trichrome stained kidney tissue images from our collaborators at the University of Illinois for three functional structures (tubular cytoplasm, tubular basement membrane, glomerulus) and three indicators of damage to the kidney (fibrosis, edema, and inflammation). These annotations are used to train our VGG16 convolutional neural network model to classify patches of unmarked whole slide images into the four categories: tubular cytoplasm, fibrosis, inflammation, and glomerulus. We also address data variability that often comes from differences in the histochemical staining procedure across labs resulting in inconsistency across stains/imaging that can typically affect the generalizability of deep learning models. To address this, we are training a CycleGAN for image-to-image translation as a method of stain normalization and investigating the effect on the accuracy of our VGG16 model. Additionally, I will be training a model to identify the cortex versus medulla regions of the kidney to add to the pipeline for area-specific evaluations. Our research with integrating machine learning models within renal pathology aims to decrease the time and manual labor needed in the process and increase the accuracy of diagnoses.

Lignocellulosic biomass, composed of cellulose, hemicellulose, and lignin, has become an attractive renewable carbon source for microbial bioproduction of value-added chemicals. Lignin is relatively difficult to depolymerize, and its enzymatic hydrolysate contains mostly aromatic compounds known to inhibit microbial growth when used as a carbon source. Pseudomonas putida (P. putida), a soil bacteria known for its tolerance to aromatics, has been engineered to catabolize lignin hydrolysate. Engineered microbes have also been encapsulated in hydrogels for on-demand bioproduction and exhibited enhanced tolerance to harsh processing conditions, i.e. freeze-drying and exposure to organic solvents. This research aims to create microbe-laden hydrogels from encapsulating engineered P. putida KT2440 cells in F127-bisurethane methacrylate (F127-BUM) hydrogels for robust on-demand bioproduction when using lignin hydrolysate as a substrate. To mimic growth rate inhibition that often happens in hydrolysate environments, we first examine if P. putida-laden hydrogels remain active when grown in a less-ideal medium, such as M9 minimal media (MM9) known to yield a slower microbial growth rate. We find that hydrogel-encapsulated P. putida containing a plasmid for heterologous expression of a green fluorescent protein (sfGFP) maintained its activity in MM9 continuous culture over two days as measured via fluorescence of the expressed sfGFP. This preliminary result on encapsulated P. putida growth and activity in a less desirable culture environment highlights the potential for utilizing aromatics-rich lignin hydrolysate in bioproduction as a more economical and renewable feedstock alternative.

Engineered genetic circuits provide an environmentally friendly path to chemical industries, including fine chemicals and therapeutics. To effectively modulate genetic circuits, a programmable tool to control multiple genes is necessary. CRISPR-mediated gene activation (CRISPRa) is an emerging tool suitable for this purpose. In CRISPRa, a nuclease-deficient dCas9 protein is used to deliver a transcriptional activator domain (MCP-SoxS) upstream of genes of interest. A complementary guide RNA (gRNA) enables dCas9 recruitment to any DNA target. Despite the programmability of CRISPRa, the number of genes that can be simultaneously regulated remain unexplored. In this work, we aim to experimentally investigate the number of gRNAs limitation in the chemical bioproduction context. First, we designed CRISPRa circuits with an increasing number of guide RNAs encoded on plasmids constructed with a scalable and high-throughput technique via Golden Gate Assembly. CRISPRa circuit performance was then evaluated by simultaneously regulating multiple fluorescent proteins as a proxy for multi-enzyme cascade in biosynthetic pathways. Increasing the number of gRNAs was found to decrease CRISPRa activity, suggesting competition of CRISPRa components. Furthermore, we applied the constructed circuits for metabolically engineered pathways in P. putida regulating production of p-aminocinnamic acid (pACA), a precursor for polymer synthesis vital in photovoltaic and biomedical applications. Bioproduction of pACA in P. putida was enabled by simultaneously regulating 9 heterologous genes. The outcome of CRISPRa circuits will be analyzed via High-Performance Liquid Chromatography (HPLC).The implication of this work will allow us to construct large scale CRISPR genetic circuits and optimize multi-gRNA CRISPR circuit integrations into other systems such as non-model organisms and cell-free systems, which will expand metabolic engineering capabilities and chemical productions beneficial in a wide range of biosynthetic applications.

 Immune cells make up the body's defense against cancer and observing their spatial distribution in a tumor can provide information about patient prognosis. However, it is difficult and time consuming to identify each immune cell in images from cancer biopsies in order to perform spatial analysis. Additionally, stained immune cells are hard to distinguish by appearance in unprocessed multispectral images due to the overlapping or "mixing" of signals coming from different channels. A computational tool could efficiently identify the immune cells in a tumor. The goal of this project is to build a digital pipeline to identify each immune cell in a multispectral image of a tumor and make it generalizable to multispectral images from any source. First, we use an unsupervised method to break up mixed multispectral images into clusters. The user selects a subset of clusters that do a good job of isolating each type of immune cell. A sample of these user-selected results are used to train a supervised machine learning model. The trained model assigns a label to each cluster to classify the entire image. Preliminary results have shown that clusters can usually be assigned to the correct label with over 50% certainty. We anticipate that the clusters will show good agreement with clinician classifications. This pipeline will allow for immune cell identification with less human involvement than pathologist annotation and without requiring spectral unmixing, a preprocessing step that typically takes hours. In the future, we will test this pipeline with varying amounts of training data coming from different sources and integrate it with spatial analysis to capture immune signatures of disease.

Lead perovskites have attracted the interest of the industry for optoelectronic devices applications due to their strong and tunable absorptions. However, their stability and environmental toxicity impose a challenge in its use for commercial application. Bismuth perovskites are a promising alternative due to their similar ionic radius to lead with long-term stability and lower toxicity. To replace lead-based perovskites with bismuth-based ones, it is necessary to increase the photoluminescent quantum yields and extend the emission wavelength range. The crystalline structure of bismuth perovskites can be altered with a dopant to redshift the usually blue light emission. We doped bismuth-based perovskites with rare-earth metals via sonication. The produced materials were analyzed by ultraviolet-visible spectroscopy and photoluminescence spectroscopy. The collected measurements determine if a redshift was produced on the emission spectra. It is expected to see a redshift from the current 400 nm wavelength on the doped perovskites. However, the current results do not show a redshift, instead they show a change in intensity. The comprehension of how to efficiently produce redshifted bismuth perovskites can propel the industrial level use of this less toxic alternative.

Regenerative medicine compromises to repair and replace cells, tissues, and organs damaged by disease or aging. To control cell fate in regenerative medicine, methods enabling irreversible and spatiotemporally controlled protein activation would be beneficial, particularly to those that could be applied for both inter- and extracellular activation. Furthermore, an ideal strategy could be applied to virtually any protein and afford rapid activation. In my work, I have sought to develop and exploit such a method through protein photochemistry; in response to mild and cytocompatibile light exposure, user-specified proteins are irreversibly assembled into their bioactive form. I have optimized the processes for hydrogel formation and modifications to increase cell viability. Results further inform that I can biochemically customize the landscape both intra- and extra-cellularly with a photoactivatable mCherry construct. Moving forward, I intend to apply this technique to activate epidermal growth factors and other proteins in multiple physiological systems. Successful protein photoactivation provides a potential, less invasive mechanism for controlling cells in the extracellular matrix for tissue engineering and regenerative medicine.

Neonatal hypoxic-ischemic encephalopathy (HIE), caused by a lack of blood flow and oxygen to the brain, is a major cause of infant mortality. Primary and secondary energy failure caused by HIE activates microglia, resulting in morphological changes and inflammatory cascades that mediate ongoing pathology. Proinflammatory microglia release cytokines and reactive oxygen species that damage oligodendrocytes, the myelinating cells in the brain that supports neuronal function, thereby causing demyelination of neurons. Previous studies in term-equivalent in vivo ferret models showed that microglia respond to injury and treatments with region-dependent cell morphology changes. However, the effect of combinatorial therapy on microglia and oligodendrocyte in a preterm model is unknown. This project aims to quantify image-based morphological features of microglia and oligodendrocyte in response to neuroinflammation and separate and combinatorial treatments in different brain regions of an in vivo preterm ferret model. Using machine learning supported image processing, I quantified microglia and oligodendrocyte morphology in the healthy control group, injury group of two hours of oxygen-glucose deprivation, and treatment groups of azithromycin (AZ), erythropoietin (Epo), and combined AZ+Epo treatment followed by injury. The machine learning algorithm clusters microglia and oligodendrocytes into distinct shape modes with different morphological parameters, such as perimeter, circularity, and aspect ratio. Perimeter and circularity of both microglia and oligodendrocytes show regional heterogeneity within each shape mode while aspect ratio is homogeneous. Microglia perimeter decreases upon injury in crescent and rod-like shape modes. Epo treatment reverses the decrease to the level of nontreated control, but AZ+Epo treatment only partially reversed the decrease. By quantifying microglia and oligodendrocyte morphological response to neuroinflammation and treatments across regions, I non-destructively assessed therapeutic performance of separate and combinatorial treatments in the preterm ferret model. The assessed performance informs therapeutic choices for preterm populations and have the potential for translating to larger animal models.

The extra cellular matrix (ECM) is a complex, heterogenous environment that plays an important role in cellular functions such as proliferation, signaling, movement, and differentiation. The mechanical properties of the ECM vary spatially and temporally, across and within tissues, i.e., during development and disease progression. 3D biomaterial platforms, such as hydrogels – water-swollen polymeric networks—provide a greater understanding of matrix-cell interactions and can be used to study drug delivery and basic disease mechanisms. My research works to create a double network (DN) hydrogel system that allows for spatial control of ECM mechanics in 3D. Our system contains two different polymer networks, one of which uses light polymerization. I have optimized concentrations of multiple gel components and gel light exposure conditions to allow for accurately patterned stiffnesses within the gels. Currently, I am encapsulating live cells to study the amount of cell spreading and movement in the stiff and soft regions of the gels over the course of a week. I then fix, stain, and image each gel to quantify relative cellular spreading. Additionally, I have synthesized multiple components necessary for gel formation, cultured enzyme producing bacteria to degrade formed gels, and performed western blotting to analyze cellular protein concentrations. Imaging results have shown the DNs and the patterning process are cytocompatible. Current experiments have shown differences in fibroblast spreading between stiff and soft regions; future results are expected to show differences in protein expression within mechanosensitive pathways between patterning conditions. Using multiple, intertwined hydrogel networks, I have engineered a dynamic, heterogenous model of the ECM, enabling me to study cellular responses to mechanical stimuli. Accurate modeling of the ECM will allow for a better understanding of how diseases such as breast cancer progress based on differences in environmental stiffness and provide an in vitro platform for future cellular response research.

Hypoxia ischemia encephalopathy (HIE) is characterized as a lack of oxygen and blood flow to the brain, and is a leading cause of neonatal mortality and morbidity within the United States. HIE causes immediate cell death and oxidative stress resulting in inflammation, energy failure, and ongoing injury. Though there is a lack of effective therapies for HIE, extracellular vesicles (EVs) have shown incredible potential in attenuating oxidative stress and inflammation. EVs are biological nanoparticles with a lipid membrane containing essential biomolecules. EVs participate in cell-to-cell communication as they travel between membranes of cells within the central nervous system (CNS). Previous studies on adult brain injury models show the potential for EVs to drive neuroprotective and anti-inflammatory processes in the brain. The aim of my project is to evaluate neonatal injury responses to brain-derived EVs (BEVs) following HI injury on ex vivo brain tissues. To mimic an ischemic brain environment, I used an oxygen glucose deprivation (OGD) model to induce hypoxia in neonatal rat brain tissues. I quantified time-dependent changes in the gene expression profiles of brain tissues after BEV treatment by performing RNA extractions and reverse transcription-quantitative polymerase chain reactions (RT-qPCR). This allowed me to compare the expression levels of pro-inflammatory and anti-inflammatory markers to determine the therapeutic efficacy of BEVs on an ischemic model. My results suggested that BEV exposure in OGD-injured models decreased cytotoxicity by encouraging microglia (the brain’s immune cells) to transition from inflammatory to anti-inflammatory phenotypes. Results from the RT-qPCR analysis further suggested that BEVs reduced inflammation through the upregulation of anti-inflammatory cytokines observed in the study. This demonstrates that BEVs play a role in reducing cell death and activating anti-inflammatory pathways in the neonatal brain, providing insight into their potential as a therapeutic tool for future interventions aimed at treating HIE.

Immunofluorescent images are a common way to analyze cell response in the presence of brain disease. Microglia - the brain's immune cells - have a range of functional states dependent on their local environment to keep the brain environment healthy. Microglia are typically stained and viewed with immunofluorescent imaging to study the brain's immune response. Microglial functionality and microglia morphology (shape) are highly correlated [5]. By taking and quantifying images of microglia in healthy and diseased brains, we can gain insights into their functional state and their local environment. In addition, most fundamental research about microglia involves the use of animal models, where many species are used to model brain disease. However, limited research directly compares microglia response in one species to another. Previously, research within the Nance Lab has focused on quantifying rat microglial features such as area, perimeter, or circularity [3]. Here, we developed a method to quantify features of microglia, with a focus on microglial branching – the arm-like protrusions from the cell body expanding upon previous work by adding additional branching features to the quantification pipeline to look at the number and length of branches around each cell, which gives us information on the functional state of the cell. We investigated the species-dependent effect on the microglial shape by analyzing images of cells obtained from the neonatal human-term equivalent rat (postnatal day 10, P10), ferret (P21), and mouse (P12). We see qualitative differences in morphology, such as more extensive branching in the rat compared to the ferret. Our ongoing work aims to quantify feature differences in microglia between the rat and ferret and expand to other species.

Hypoxic Ischemic Encephalopathy (HIE) is an injury to a newborn that can occur during pregnancy or the birthing process. HIE is caused by a restriction of blood flow to the brain which leads to inflammation and neuronal death. A myriad of symptoms and disorders including developmental delays and cerebral palsy can result from HIE. The widespread treatment for HIE among clinicians is therapeutic hypothermia; however, this treatment method can only reduce the severity of the injury, is not a curative procedure, and is only effective in a small percentage of babies with HIE. One possible solution to the lack of effective HIE therapies is a nanoparticle-based therapeutic used to decrease inflammatory responses in the brain following HIE. Prior work in the Nance Lab has shown a polymer nanoparticle can specifically target microglial cells in a pro-inflammatory state in an injured brain. In this study, I have successfully loaded the antioxidant N-Acetyl-Cysteine (NAC) into the polymer nanoparticle platform. Using an ex vivo model of neuroinflammation, I displayed that polymer nanoparticles loaded with NAC can be harnessed to change microglia in a proinflammatory state to a more anti-inflammatory phenotype. This nanoparticle treatment was also combined with a priming dose of azithromycin (AZ) prior to NAC-encapsulated nanoparticle administration to further reduce the rate of microglial inflammation in the injured brain. The reduction of microglial inflammation and increase in anti-inflammatory microglial phenotype presence caused by these two therapeutics provides a platform that can be tested in preclinical models of HIE, with the long-term goal to improve the quality of life for children and families affected by HIE.

Hypoxic-ischemic encephalopathy (HIE), resulting from a lack of blood and oxygen flow to the brain, is the leading cause of morbidity and mortality in newborns, and currently has no cure. Our lab is investigating curcumin for use as a neuroprotectant agent, as it has anti-inflammatory, antioxidant, and antiapoptotic effects. Our current studies have been focused on further improving the drug encapsulation of curcumin in a polymeric nanoparticle platform, as well as methods to increase long-term shelf-life stability of the nanoparticle therapeutics. Previous research from our lab has successfully loaded curcumin into poly(ethylene glycol)-poly(lactic-co-glycolic acid) (PEG-PLGA) nanoparticles as a delivery vehicle. PEG-PLGA is an FDA approved, biodegradable polymer platform that allows for improved drug delivery efficiency, controlled and sustained drug release, and improved penetration and diffusion in the brain. We have shown that curcumin loaded PEG-PLGA nanoparticles have resulted in significant neuroprotection when used as a treatment for hypoxic-ischemic neonatal rats (term equivalent to human). In order to progress towards scale-up and clinical translation of the therapeutic, I have tested variations to the formulation method at every step of the formulation process, including changes in PEG-PLGA molecular weight ratios, surfactants, and organic solvents used. I have assessed the impacts of each formulation parameter on colloidal stability and drug loading, with the aim to create a scalable, stable platform that can retain drug delivery and drug activity properties during distribution and shelf-life storage. I have identified that nanoparticle drop size, surfactant type and concentration, and freezing protectant (cryoprotectant) have the biggest impact on drug loading and stability. Improving the stability is the first step in making the therapeutic more accessible, cheaper, and easier to transport for a larger impact.

Treatment of individuals with HIV using antiretroviral therapy (ART) is highly effective, but effective clinical management depends on maintaining therapeutic drug concentrations. Antiretroviral (ARV) drug concentrations in patients with HIV can vary due to differences in drug metabolism, medication adherence, or interactions between multiple drugs. These individuals may have subtherapeutic or supratherapeutic drug concentrations, putting them at risk of treatment failure, acquisition of drug resistance, and risk of hospitalization or death. Current measurement of ARV concentration is done through liquid chromatography tandem mass spectrometry, which requires expensive equipment and requires a labor-intensive protocol. This restricts accessibility to specialized laboratories, making it difficult for persons with HIV to have routine measurements of ARV drug concentrations. The goal of the project is to develop an assay that is simple to perform and uses standard equipment to increase access to routine clinic-based drug level monitoring to improve HIV care. We designed an assay using a 2-step process of DNA strand transfer and quantitative polymerase chain reaction (qPCR) to quantify integrase strand transfer inhibitors (INSTIs). We tested for dolutegravir (DTG) and cabotegravir (CAB) in both buffer and plasma -- the latter to simulate patient blood samples. We were able to demonstrate that the assay could quantify clinically relevant drug concentrations of DTG and CAB. By developing an assay that can be readily integrated into most clinical laboratories, we will contribute to increasing access to routine HIV drug level monitoring to improve clinical HIV care and maintaining viral suppression in persons with HIV.