The human brain is highly connected over a number of different length and time scales spanning communication between neurons to communication across distant brain regions. The study of how the structure of the brain is connected to its many functions is an emerging approach to understanding neurological and psychiatric disorders. This includes tracking changes associated with normal development and aging, as well as exploring the specific brain networks that are active when somebody performs a specific activity. However, while advances in brain imaging methods are producing maps of structure or function with unprecedented accuracy and resolution, the ways in which structure shapes function is still not known. Dr. Elizabeth Nance and her research team, based in the UW Department of Chemical Engineering, have recently begun to develop models that can integrate multiple types of data to better understand and predict structural and functional relationships in the brain.
The Nance Lab uses nanoparticles – tiny (10,000x smaller than the diameter of a human hair), highly-adaptable particles, to survey the brain environment by tracking their movement. Nanoparticle movement in the brain is influenced by the spacing between cells; by the interaction between the nanoparticle, cells, and proteins in the brain; and by the local differences in fluid flow seen within the brain. Nance Lab graduate students Chad Curtis and Mike McKenna use nanoparticles that can move easily within the healthy brain. They add these nanoparticles to live and functioning slices of brain tissue taken from animals such as rats. One rat brain can produce up to 20 slices, each retaining structural and biological complexity of the working brain, but allowing for faster work using fewer animals. Curtis and McKenna then apply a technique called multiple particle tracking, which traces 100,000’s of individual nanoparticle movements over time in a single region of the brain. They analyze the resulting traces to see how each particle behaved in the brain tissue.
Next, the team overlays the traces with fluorescent images of the same slice. Curtis, in collaboration with eScience senior data scientist Ariel Rokem, has developed a way to process the images and identify key features of the cells that the particles are moving around. This provides the biological context in which the nanoparticle traces exist. Curtis can then generate relationships between the cell features and the nanoparticle movements. For example, a region with many cells often has more confined spacing between those cells, which limits the nanoparticles ability to move, and results in a more confined trace. Aligning these traces with biological data from the brain itself can then elucidate changes in brain structure at multiple scales. For instance, in the injured brain, the ways that the particles move will change, which reflects changes that are occurring in the brain because of the injury. These changes in nanoparticle movement can then be tied to specific changes in gene expression of the number and type of cells you might see in the injured brain.
Video: Nanoparticles (red) diffusing around and interacting with perineuronal nets (green). The team has developed a Python package capable of taking raw video files as inputs and outputting quantifiable diffusion parameters, including nanoparticle trajectories (displayed below), diffusion coefficients, and trajectory aspect ratios.
Nance and her team recognize the complexity of trying to use nanoparticle traces to relate to the function of that region of the brain. Therefore, their current efforts are focused on performing particle tracking in specific structures within the brain that are known to be tied to a certain function. One such structure is a perineuronal net, which wraps around specific neurons in the brain and is one factor that determines how many connections those neurons make with other neurons in a process known as plasticity. Changes in the structure of the perineuronal net can change whether a neuron is able to make connections with other cells, reducing normal communication and disrupting normal brain function. McKenna and undergraduate student Hugo Pontes are working to validate the sensitivity of the particle tracking method in detecting small changes in the structure of these nets by breaking down perineuronal nets in a controlled manner and tracking nanoparticles at each stage of the breakdown process. McKenna and Pontes will then measure nanoparticle movement in these nets in both healthy and diseased tissue, and measure the function and connectivity of neurons relative to their surrounding nets.
Nance and her team expect the utility of their particle tracking approach, combined with data science and machine learning tools, to extend beyond evaluating structure-function relationships in brain tissue. In addition to applying this technique in other tissues, like the kidney and liver, Curtis and new Nance Lab graduate students Hawley Helmbrecht and David Shackelford are using the analysis of nanoparticle movement to build a model that predicts how nanoparticles will behave when put into living organisms. For example, whether a nanoparticle will go into a cell or not is based on the nanoparticle’s size and surface characteristics. This can have important implications for the nanotoxicology field and the drug and gene delivery fields. For drug and gene delivery, nanoparticles are often used to increase the availability of a drug in the body, as well as improving the drug’s or gene’s ability to reach its target site, and only have an effect at that site and not elsewhere else. Having the ability to predict where these nanoparticles will go once administered into the body can direct the design and engineering of nanoparticles so that the fate of each nanoparticle can be known and controlled.