A systematic investigation of stem cell-derived neural interfaces can facilitate the discovery of the molecular mechanisms behind cell behavior in neurological disorders and accelerate the development of stem cell-bas...A systematic investigation of stem cell-derived neural interfaces can facilitate the discovery of the molecular mechanisms behind cell behavior in neurological disorders and accelerate the development of stem cell-based therapies.Nevertheless,high-throughput investigation of the cell-type-specific biophysical cues associated with stem cell-derived neural interfaces continues to be a significant obstacle to overcome.To this end,we developed a combinatorial nanoarray-based method for high-throughput investigation of neural interface micro-/nanostructures(physical cues comprising geometrical,topographical,and mechanical aspects)and the effects of these complex physical cues on stem cell fate decisions.Furthermore,by applying a machine learning(ML)-based analytical approach to a large number of stem cell-derived neural interfaces,we comprehensively mapped stem cell adhesion,differentiation,and proliferation,which allowed for the cell-type-specific design of biomaterials for neural interfacing,including both adult and human-induced pluripotent stem cells(hiPSCs)with varying genetic backgrounds.In short,we successfully demonstrated how an innovative combinatorial nanoarray and ML-based platform technology can aid with the rational design of stem cell-derived neural interfaces,potentially facilitating precision,and personalized tissue engineering applications.展开更多
基金support from the NSF(CBET-1803517),the New Jersey Commissionon Spinal Cord(CSCR17IRG010and CSCR22ERG023)SAS-Grossman Innovation Prize and NIH(R01 c1R01DC016612,3R01DC016612-01S1,and 5R01DC016612-02S1)Thanapat Pongkulapa would like to acknowledge the postdoc training fellowship from the NIH(5T32EB005583).
文摘A systematic investigation of stem cell-derived neural interfaces can facilitate the discovery of the molecular mechanisms behind cell behavior in neurological disorders and accelerate the development of stem cell-based therapies.Nevertheless,high-throughput investigation of the cell-type-specific biophysical cues associated with stem cell-derived neural interfaces continues to be a significant obstacle to overcome.To this end,we developed a combinatorial nanoarray-based method for high-throughput investigation of neural interface micro-/nanostructures(physical cues comprising geometrical,topographical,and mechanical aspects)and the effects of these complex physical cues on stem cell fate decisions.Furthermore,by applying a machine learning(ML)-based analytical approach to a large number of stem cell-derived neural interfaces,we comprehensively mapped stem cell adhesion,differentiation,and proliferation,which allowed for the cell-type-specific design of biomaterials for neural interfacing,including both adult and human-induced pluripotent stem cells(hiPSCs)with varying genetic backgrounds.In short,we successfully demonstrated how an innovative combinatorial nanoarray and ML-based platform technology can aid with the rational design of stem cell-derived neural interfaces,potentially facilitating precision,and personalized tissue engineering applications.
