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Generation of self-organized sensory ganglion organoids and retinal ganglion cells from fibroblasts.

ABSTRACT: Neural organoids provide a powerful tool for investigating neural development, modeling neural diseases, screening drugs, and developing cell-based therapies. Somatic cells have previously been reprogrammed by transcription factors (TFs) into sensory ganglion (SG) neurons but not SG organoids. We identify a combination of triple TFs Ascl1, Brn3b/3a, and Isl1 (ABI) as an efficient means to reprogram mouse and human fibroblasts into self-organized and networked induced SG (iSG) organoids. The iSG neurons exhibit molecular features, subtype diversity, electrophysiological and calcium response properties, and innervation patterns characteristic of peripheral sensory neurons. Moreover, we have defined retinal ganglion cell (RGC)-specific identifiers to demonstrate the ability for ABI to reprogram induced RGCs (iRGCs) from fibroblasts. Unlike iSG neurons, iRGCs maintain a scattering distribution pattern characteristic of endogenous RGCs. iSG organoids may serve as a model to decipher the pathogenesis of sensorineural diseases and screen effective drugs and a source for cell replacement therapy.

SUBMITTER: Xiao D

PROVIDER: S-EPMC7259937 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:With the accelerated development of robot technologies, control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of specific objectives for the task at hand. While very successful in many applications, self-organized control schemes seem to be favored in large complex systems with unknown dynamics or which are difficult to model. Reasons are the expected scalability, robustness, and resilience of self-organizing systems. The paper presents a self-learning neurocontroller based on extrinsic differential plasticity introduced recently, applying it to an anthropomorphic musculoskeletal robot arm with attached objects of unknown physical dynamics. The central finding of the paper is the following effect: by the mere feedback through the internal dynamics of the object, the robot is learning to relate each of the objects with a very specific sensorimotor pattern. Specifically, an attached pendulum pilots the arm into a circular motion, a half-filled bottle produces axis oriented shaking behavior, a wheel is getting rotated, and wiping patterns emerge automatically in a table-plus-brush setting. By these object-specific dynamical patterns, the robot may be said to recognize the object's identity, or in other words, it discovers dynamical affordances of objects. Furthermore, when including hand coordinates obtained from a camera, a dedicated hand-eye coordination self-organizes spontaneously. These phenomena are discussed from a specific dynamical system perspective. Central is the dedicated working regime at the border to instability with its potentially infinite reservoir of (limit cycle) attractors "waiting" to be excited. Besides converging toward one of these attractors, variate behavior is also arising from a self-induced attractor morphing driven by the learning rule. We claim that experimental investigations with this anthropomorphic, self-learning robot not only generate interesting and potentially useful behaviors, but may also help to better understand what subjective human muscle feelings are, how they can be rooted in sensorimotor patterns, and how these concepts may feed back on robotics.

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Generation of self-organized sensory ganglion organoids and retinal ganglion cells from fibroblasts.

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