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Vascular Biology Program Research

Research Overview

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 Background

Dr. Kwonmoo Lee was trained in Marc Kirschner’s lab at Harvard Medical School as a graduate student in MIT Physics Ph.D. program. He did his post-doctoral training as an NIH post-doctoral fellow in Gaudenz Danuser’s lab at Harvard Medical School. He held an assistant professor position in the Department of Biomedical Engineering at Worcetster Polytechnic Institute. He joined Vascular Biology Program at Boston Children's Hostpital and Department of Surgery at Harvard Medical School in 2020. He is focusing on developing machine learning methods for cell biology and mechanobiology. Particularly, he is interested in how subcellular morphodynamic heterogeneity governs cell motility in healthy and pathological conditions.

 

Publications

  1. Breast Cancer-Derived Extracellular Vesicles Modulate the Cytoplasmic and Cytoskeletal Dynamics of Blood-Brain Barrier Endothelial Cells. J Extracell Vesicles. 2025 Jan; 14(1):e70038. View Abstract
  2. Survivin modulates stiffness-induced vascular smooth muscle cell motility. bioRxiv. 2024 Dec 12. View Abstract
  3. Interpretable Fine-Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning. Adv Sci (Weinh). 2024 Nov; 11(41):e2403547. View Abstract
  4. Deep learning-based automated analysis of NK cell cytotoxicity in single cancer cell arrays. BioChip Journal. 2024. View Abstract
  5. Fine-Grained Forecasting of COVID-19 Trends at the County Level in the United States. medRxiv. 2024 Mar 25. View Abstract
  6. Heterogeneity-Preserving Discriminative Feature Selection for Subtype Discovery. bioRxiv. 2023 Dec 20. View Abstract
  7. Survivin as a mediator of stiffness-induced cell cycle progression and proliferation of vascular smooth muscle cells. APL Bioeng. 2023 Dec; 7(4):046108. View Abstract
  8. Survivin regulates intracellular stiffness and extracellular matrix production in vascular smooth muscle cells. APL Bioeng. 2023 Dec; 7(4):046104. View Abstract
  9. Unsupervised Contour Tracking of Live Cells by Mechanical and Cycle Consistency Losses. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2023 Jun; 2023:227-236. View Abstract
  10. Screening adequacy of unstained thyroid fine needle aspiration samples using a deep learning-based classifier. Sci Rep. 2023 08 19; 13(1):13525. View Abstract
  11. Deep cross-modal feature learning applied to predict acutely decompensated heart failure using in-home collected electrocardiography and transthoracic bioimpedance. Artif Intell Med. 2023 06; 140:102548. View Abstract
  12. Protocol for live cell image segmentation to profile cellular morphodynamics using MARS-Net. STAR Protoc. 2022 09 16; 3(3):101469. View Abstract
  13. A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation. Sci Rep. 2021 12 02; 11(1):23285. View Abstract
  14. Increased prevalence of indoor Aspergillus and Penicillium species is associated with indoor flooding and coastal proximity: a case study of 28 moldy buildings. Environ Sci Process Impacts. 2021 Nov 17; 23(11):1681-1687. View Abstract
  15. A deep learning-based segmentation pipeline for profiling cellular morphodynamics using multiple types of live cell microscopy. Cell Rep Methods. 2021 11 22; 1(7). View Abstract
  16. Emerging machine learning approaches to phenotyping cellular motility and morphodynamics. Phys Biol. 2021 06 17; 18(4). View Abstract
  17. Mechanosensitive expression of lamellipodin promotes intracellular stiffness, cyclin expression and cell proliferation. J Cell Sci. 2021 06 15; 134(12). View Abstract
  18. Mechanosensitive expression of lamellipodin promotes intracellular stiffness, cyclin expression, and cell proliferation. J Cell Sci. 2021 May 25. View Abstract
  19. Deep transfer learning-based hologram classification for molecular diagnostics. Sci Rep. 2018 11 19; 8(1):17003. View Abstract
  20. Deconvolution of subcellular protrusion heterogeneity and the underlying actin regulator dynamics from live cell imaging. Nat Commun. 2018 04 27; 9(1):1688. View Abstract
  21. Functional hierarchy of redundant actin assembly factors revealed by fine-grained registration of intrinsic image fluctuations. Cell Syst. 2015 Jul 29; 1(1):37-50. View Abstract
  22. Self-assembly of filopodia-like structures on supported lipid bilayers. Science. 2010 Sep 10; 329(5997):1341-5. View Abstract
  23. A Stochastic Model of Conductance Transitions in Voltage-Gated IonChannels. J Biol Phys. 2002 Jun; 28(2):279-88. View Abstract
  24. Effects of nonequilibrium fluctuations on ionic transport through biomembranes. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 1999 Oct; 60(4 Pt B):4681-6. View Abstract

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