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Imaging Flow Cytometer and Image-Activated Single Cell Dispensing Platform with Artificial Intelligence

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Abstract

The application of imaging flow cytometry (IFC) technology in high-throughput cellular image processing and analysis can provide valuable insights into the fields of biology and medicine. The ability to classify, map, and isolate cells based on high-content cellular images is a powerful tool for researchers and medical professionals to better understand the connection between the phototype and genotype among heterogeneous cell populations. This dissertation outlines the methodology for conducting high-throughput cellular image analysis and single cell isolation using the 3D-IFC based single cell dispensing platform. Our work demonstrated the workflow for conducting a high-throughput label-free cell study using IFC systems and artificial intelligence. Additionally, we extended the system capability by incorporating the unsupervised learning and improving the interpretability of the machine learning model performance. Our work forms an unprecedented bridge between single-cell molecular analysis and single-cell image analysis to connect phenotype and genotype analysis with single-cell resolution. This system provides a new avenue for conducting cellular analysis and sorting based on interpretable machine vision and artificial intelligence.

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This item is under embargo until July 3, 2025.