Xiaofan Lai
Papers
2
Total Citations
16
H-Index
2
About
Xiaofan Lai is a neuroscientist pioneering high-content physiological phenotyping at single-cell resolution. Her key research areas include neuronal classification, calcium imaging, and automated physiological assays. Lai’s major contribution is the development of APPOINT (Automated Physiological Phenotyping of Individual Neuronal Types), a groundbreaking platform that combines robotic fluid handling with calcium imaging to profile and classify individual neurons based on their physiological responses. This innovation overcomes the longstanding limitation of high-throughput assays that lose single-cell resolution, enabling unbiased, subtype-specific analyses of activation mechanisms and drug effects. Her most-cited work, "A high-content platform for physiological profiling and unbiased classification of individual neurons" (2021), has accumulated 16 citations, reflecting its growing influence in the field. By providing a scalable tool for dissecting neuronal diversity, Lai’s research holds significant promise for advancing drug discovery and understanding neurological disorders. Her work exemplifies the integration of automation and imaging to unlock new dimensions in cellular neuroscience.
Research Focus
Key Achievements
Top Papers
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