Research Highlights

High-Content Screening and Analysis of Stem Cell-Derived Neural Interfaces Using a Combinatorial Nanotechnology and Machine Learning Approach

Furthermore, 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, allowing 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 were able to show how innovative combinatorial nanoarray and machine learning (ML)-driven platform technology can help with the rational design of stem cell-derived neural interfaces, which could help make tissue engineering applications more precise and individualized.

PUBLICATION: This work was recently published in Research (

CORRESPONDENCE: Prof. Ki-Bum Lee (Rutgers University),

KBLEE Group Team: Dr. Letao Yang, Dr. Brian Conley, Dr. Christopher Rathnam, Dr. Thanapat Pongkulapa, Brandon Conklin, Yannan Hou,

Group Members

Ki Bum Lee Ph 

Letao Yang Web    

Brian Conley Web

Brandon Conklin Web

Yannan Hou Web