Gathering information about a disease in order to direct treatment is difficult because the disease microenvironment is dynamic, heterogeneous, and variable from person to person. Delivering drugs to this environment is also challenging, because the body and the disease are very effective at keeping drugs out or adapting to minimize the effect of the drug. Many drugs are directed at one aspect of the disease but the interactions of the drug within the body are not always well-understood. The use of nanotechnology to improve drug delivery, efficacy, and specificity has been successful preclinically, but has failed to translate to clinical use at a rate that reflects the preclinical potential. Of the current clinically approved nanotechnologies in 2019, none are indicated for non-cancerous neurological disease, which represents 13% of the global disease burden. Therefore, our overarching goal is to develop tools that inform how we can more effectively treat the diseased brain, using nanotechnology as both a probe and as a therapeutic delivery vehicle. 

Importantly, our work is highly interdisciplinary, requiring equal understanding of chemical engineering, drug delivery, transport, data science, and neurobiology principles. To do innovative research, we need individuals who are committed to the team and lab family, who are creative, motivated, passionate, and compassionate. We believe being inclusive and holistic in our support for lab members allows us to effectively tackle complex problems in health and medicine.

Check out some of our active project areas below!

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What We Do

Quantify and predict nanoparticle transport, compartmentalization and fate

Engineer effective nanotherapeutics for neurological disease

Probe developmental and disease processes to elucidate insights into structure-function

Develop robust, unbiased, and quantitative fluorescent image analysis pipelines

Quantify and predict nanoparticle fate

When treating disease, the uptake, penetration, and cellular interaction of a therapeutic is critical to its success. Understanding the in vivo interactions of nano-based drug delivery platforms with blood, tissue, and cellular compartments in healthy and diseased states is an ongoing challenge. Additionally, the type of model can significantly affect our understanding of a nanotherapeutic’s interactions and subsequent fate. Therefore, our group seeks to (1) coalesce contradictory findings in the field by using multiple models to study nanoparticle behavior and transport, and (2) predict nanoparticle compartmentalization and fate in vivo. We approach this using a transport-oriented view of nanotechnology’s ability to navigate barriers in the body, and specifically in the brain. Check out some of our key recent findings below, and click on "read more" for the paper.

Particles that are often considered stable in saline, serum, or whole blood are not stable in the presence of divalent cations that are abundant in free form in the brain.

Tracking short-term deviation from Brownian motion of colloidally stable nanoparticles can produce insights into nanoparticle compartmentalization and fate.

The model used to study nanoparticle behavior has a significant effect on interpretation of results.

Current research directions: This area of research is generally agnostic to nanoparticle type, giving us flexibility and broader scope in evaluating optimal nanoparticle design and implementation in vivo. We are pursuing several aspects of this work: (1) evaluating cellular fate of organic nanoparticles, including biodegradable polymer nanoparticles, dendrimers, and peptide-based nanoparticles, (2) exploring the utility of alternative machine learning algorithms to improve our predictive ability for nanoparticle fate, (3) pursuing nanoparticle transport studies over longer time scales in cultured slices from brain, spinal cord, liver, and lung, and (4) integrating pharmacokinetic (PK) modeling  in rodents through collaboration with pharmacologists.  

Engineer nanotherapeutics for neurological disease

When delivery limitations of a nanoparticle platform are better understood, an optimal formulation can be engineered and evaluated for therapeutic efficacy in clinically relevant animal models of brain disease. We focus on developing nanoparticle-based therapeutic approaches for improved neurological outcomes in perinatal brain injury, specifically those where neuroinflammation, oxidative stress, and excitotoxicity play key roles. Our area of application in pediatric and neonatal brain disease/injury models is motivated by several key factors: (1) technology development for these patient populations is vastly underserved, (2) there is currently no effective cure for any neurological disease that affects a newborn, and these diseases can persist over a lifetime, which results in opportunity to have significant impact, and (3) animal models in this field are well established and reproducible across multiple labs, enabling higher likelihood of success for clinical translation. Check out some of our key recent findings below, and click on "read more" for the paper.

PLGA-PEG nanoparticles made using standard formulation methods can result in tailored enzymatic protection but can also be toxic to brain cells in oxidatively stressed tissue.

Curcumin can safely be delivered in PLGA-PEG to neonatal rats, and reduce brain injury and improve neuropathology in neonatal rats with hypoxic-ischemic (HI) brain injury

Brain slice models can capture pathological processes and be used to screen therapeutics, such as SOD, which is effective against HI injury in cultured brain slices

Current research directions: We are furthering our targeting capabilities by evaluating how the surfactant used to stabilize PLGA-PEG formulations can direct site-specific cellular uptake in the neonatal brain. We are currently exploring SOD and catalase-loaded PLGA-PEG particle applications in vivo in the neonatal HI model (, and the use of NR-loaded particles in HI injured rat brain slices. We have partnered with Dr. Rajiv Saigal (UW Neurosurgery) to study nanoparticle delivery via transdural microneedle patches for spinal cord injury. We are continuing to develop therapeutics to test in both rat and ferret models of neonatal/perinatal brain injury.

Probe developmental and disease processes

The human body is connected on a variety of different length and time scales. The connections between structure and function are emerging as prospective markers for many diseases, especially those impacting the brain. For example, in the presence of ongoing injury, morphological changes in glial cell populations are associated with dysregulation of surrounding extracellular matrix proteins and structure, and correlated to loss of neurological function. However, in the context of neurological and psychiatric disease, precise interplay between cell phenotype, brain microenvironment, and function is still not well-understood. The means to gather real-time molecular information from a diseased organ is limited, and high-throughput platforms that can assay sub-micron changes in tissue in the presence of injury are still lacking. Additionally, methods to quantify common assessment tools, such as immunofluorescent or immunohistochemical imaging, are not robust, reproducible, or unbiased. Importantly, we have accumulated a large amount of detailed information at the cellular and subcellular level, but assembling this information in a way that explains the correspondingly complex biological functions these structures perform is currently not possible. Therefore, we have sought to address the need for methods that can integrate diverse biological data sets to both understand and predict the relationship between phenotype and function. Check out some of our key recent findings below, and click on "read more" for the paper.

Nanoparticle diffusion data from the brain extracellular space can be used to predict biological features, like chronological age. 

Injury severity and treatment, in addition to nanoparticle type, drive nanoparticle-glial interactions

Brain slice models can capture regionally dependent pathological processes and response to treatment

Current research directions: We are currently working to understand the mechanism of diffusive changes in the brain in response to development and injury. This approach is bringing in molecular biology and analytical tools like LC-MS. One goal of this work will be to identify what specific changes in the brain microenvironment result in changes in diffusion. In achieving this goal, we will also establish the sensitivity of the MPT technique for serving as a metric of biological changes in in vivo-like environments. This incorporates efforts in the group to quantify nanoparticle-cell interactions in response to injury and treatment, and to quantify cell-cell interactions, both of which require development of higher throughout slice culturing and imaging platforms, and live-cell imaging with high spatial resolution. Our long-term goal in this research area is to understand structure-function in the context of disease in the brain to: (1) provide insights into pathological processes, such as pathological aging, and (2) direct engineering of therapeutic interventions that attenuate, protect, or mitigate neuropathological processes.  

Develop quantitative fluorescent image analysis pipelines

We aim to develop robust, unbiased quantitative imaging analysis pipelines to increase the reproducibility and reliability of image analysis, particularly for fluorescent imaging. Immunohistochemical and immunofluorescent images acquisition is ubiquitous in the neuroscience and nanotechnology fields, albeit for different reasons. However, without using stereological methods, quantifying features in these images is currently not standardized, scalable, or reproducible researcher to research. We have created several software packages (diff_register, diff_classifier), adapted existing analysis tools (VAMPIRE from the Wirtz lab at Johns Hopkins), and initiated a data workflow pipeline to meet the needs of this area. The Framework for neuroImage Based Experimental Routines (FIBER) in particular integrates experimental data sets (e.g. images) and data science data sets into a tailorable workflow to develop a data conscious for each experiment planned. We can then implement FIBER with scikit specific packages and our own Python code to threshold (via IfThreshold), separate, segment, object identify, and analyze features of cells or cell components in immunofluorescence or immunohistochemical images from the brain, as an example. This pipeline is currently being applied to images from the ferret (FIBERf) and piglet (FIBERp) in collaboration with pediatric and neonatal brain injury research groups. Check out some of our recent efforts and findings below!

Computer-aided image analysis captures microglial morphological heterogeneity in the ischemic brain.

ifThresholds uses segmenting and neuromorphology features to automatically score thresholds and provide a ranked list

To access our codes and repositories check out the Nance Lab git


The Nance Lab

Department of Chemical Engineering
University of Washington
Box 351750
3781 Okanogan Lane NE 
Seattle, WA, 98195-1750


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