Research

Long-term Research Vision: Artificial Intelligence Applied to Quantitative Live Cell Imaging 

The rapid growth of cell biology data from new developments of microscopy (3D, super-resolution) as well as single-cell technology reveals much more heterogeneity than we have imagined before, presenting the next “big data” challenge  for biomedical research. AI (Artificial Intelligence) is making tremendous progress and has shown that machines can outperform humans in the analysis of heterogeneous and high-dimensional big datasets. Therefore, we need AI to make progress in understanding basic cell biology and  diseases mechanisms. Current AI applications in cell biology, however, focused on static datasets such as single-cell RNA-seq and immunofluorescence images. AI has not been extensively used for dynamic information from high-resolution live cell images. To fill these scientific and technological voids, we are focusing on developing an AI platform that identifies subtle or unknown dynamic phenotypes in live cell movies that cannot be detected by the human eye. Using this platform, we will unravel the phenotypic heterogeneity of cellular morphodynamics/motility in angiogenesis and cancer, and develop precision cancer diagnosis/therapeutics.

 

Research Projects

  • AI Platform for Phenotyping Cellular Morphodynamics/Motility
    • Deep learning to deconvolve phenotypic heterogeneity of cell protrusion and retraction at the subcellular and single-cell levels
    • High-througput live cell image acquistion and analysis by deep learning
    • Deconvolution of heterogeneous effects of genetic/pharmacological perturbations
    • Drug discovery based on cellular morphodynamics for angiogenic therapy

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  • AI-based Breast Cancer Diagnosis
    • Deep learning analysis of lensless inline holography images
    • Dissecting the heterogeniety of breast cancer cells based on molecular markers
    • Develop live-cell markers for breast cancer diagnosis

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  • Systems Understanding of Celluar Morphodynamics Using AI
    • Integrated analyses of the dynamics of cellular morphology, cytoskeleton (actin & microtubule), adhesions, mechanical force, and matrix degradation in endothelial or cancer cells
    • Investigate how mechanical force affects the coordination of multiple cytoskeletal systems and morphodynamic phenotypes during endothelial/cancer cell migration

Collaboration Projects

  • Machine Learning Analysis of Spheroid Morphology (Prof. Yongho Bae, Department of Pathology and Anatomical Sciences, University at Buffalo) 
  • Mordphodynamic Analysis of Megakaryocytes (Prof. Joe Italiano, Vascular Biology Program, Boston Children's Hospital/Harvard Medical School)
  • Computational Imaging and Deep Learning (Prof. Hakho Lee, Center for Systems Biology, Massachusetts General Hospital/Harvard Medical School)
  • Morphodynamics Analysis of Cancer Stem Cells (Prof. Randy Watnick, Vascular Biology Program, Boston Children's Hospital/Harvard Medical School)
  • Spatiotemporal forecasting of COVID-19 spread (Prof. Mauricio Santillana Guzman, Computational Health Informatics Program, Boston Children’s Hospital)

Funding
NIH/NIGMS R35 (PI), DoD Breast Cancer Research Program (PI), BCH Start-up (PI)

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