周围神经损伤

    Opportunities for agent based modeling of retinal stem cell transplantation
  • Figure 1|Agent based modeling (ABM) provides underexplored opportunities to enrich cell replacement therapies in the adult retina. 

    Late stage blindness and visual impairment (BVI) affects over 400 million adults worldwide. These disabilities severely impact the ability of adults to function independently, reduce their quality of life, and worsen socio-economic burdens on health care systems. Importantly, the World Health Organization projects worldwide BVI from degenerated retina to more than double by the year 2050 (Bourne et al., 2021). To understand the clinical problem, consider Figure 1A depicting the retina’s seven neuronal cell types that interconnect across three nuclear layers. Retinal photoreceptors of the outer nuclear layer (ONL) are light sensitive neurons that absorb and convert photons into bioelectrical signals. Photoreceptors synapse with neurons in the inner nuclear layer, which in turn synapse with neurons of the retinal ganglion layer to transduce photonic signals through the optic nerve to the brain. Degeneration or dysfunction in any of these neuronal components can disrupt the visual circuity and result in BVI. 

    The ABM approach simulates both individual and collective behaviors in sufficient detail to predict both local behaviors and global scale tissue or organ phenomena. As shown schematically in Figure 1B, the biological structure of organisms can be deconstructed into hierarchical components: An organism is comprised of organs, the organ of tissues, the tissues of cells, and the cells are regulated by molecular mechanisms. The architecture of ABM mirrors this hierarchy, but from bottom up, rather than from top down. Agents, representing cells, operate according to rules, producing population, aggregate, and ultimately tissue- or organ- level behaviors (Glen et al., 2019). The resulting simulations can then be validated in a variety of ways including using in vitro microfluidic platforms or in vivo/ex vivo methods such cyrosectioning, imaging, or live cell tracking. 

    ABM advantages–spatial architecture: To illustrate how ABMs achieve the flexibility and verisimilitude needed to model cell replacement in the retina, consider Figure 1C, where we provide a visual representation of a retinal photoreceptor that is modeled as a column of overlapping spherical agents. By specifying agent placement, separation, compressive and bending stiffness, etc., we can model any shape and mechanical response desired. In the ONL of the retina, columnar photoreceptors have lengths 20–30 μm and diameters 1–2 μm and are configured in a tightly packed arrangement with center-to-center distances as little as 2 μm. This leaves nanometer spaces for replacement cells to migrate (Wells-Gray et al., 2016), yet transplanted cells are introduced into the subretinal space as 10 μm-diameter cells. These spatial restrictions require replacement cells to infiltrate a columnar network of cells with limited spacing, a process that involves migration in response to chemotactic gradients and adhesive interactions as well as shape changes to propel the replacement cell into the network. Figure 1D shows a preliminary simulation of a single replacement cell infiltrating between two tightly packed photoreceptor columns at three successive time points. Notice that an ABM is uniquely suited to this kind of analysis: the photoreceptor agents are linked together into columns, while the infiltrating cell can change shape, for example by extending the leading, smaller, red agent, by changing relative sizes of agents, or by spawning new agents as the cell shape evolves.  Moreover as shown in the inset to Figure 1D, force balances required for forward migration can be accurately modeled, with stronger adhesion ahead and weaker behind, as mediated by cadherin trafficking within the cell and stimulated by chemotactic gradients (Lauffenburger and Horwitz, 1996). Additionally, ABMs more naturally represent these force balances than more abstract numerical discretization of differential equations whose values change discontinuously across material interfaces. This facilitates analysis of changes in retinal tissue structure, such as the bending stiffness of the photoreceptor cells, and the cellular morphological changes that result from interactions between transplanted and native cells. All these advantages provide a clear cut framework for understanding the biomechanics involved in key processes and inform future research and clinical directions in a transparent way.
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  • 发布日期: 2022-03-10  浏览: 375
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