Hyungsoon Im


Research Projects

Nano-plasmonic sensors

In the past, we have developed nanoplasmonic sensing systems (nPLEX/NPS) for exosome analysis in cancer. Ongoing projects are in the fields of highly multiplexed exosome analysis as well as for single molecule analysis.

  • Yang KS*, Im H*, Hong S*, Pergolini I, Del Castillo AF, Wang R, Clardy S, Huang CH, Pille C, Ferrone S, Yang R, Castro CM, Lee H, Del Castillo CF, Weissleder R. Multiparametric plasma EV profiling facilitates diagnosis of pancreatic malignancy. Sci Transl Med. 2017;9(391):eaal3226 – PMID: 28539469.
  • Im H*, Shao H*, Park YI, Peterson VM, Castro CM, Weissleder R*, Lee H*. Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor. Nature Biotechnol. 2014;32(5):490-5 – PMID: 24752081

- The nPLEX sensor was highlighted as one of the greatest hits among 20 years of Nature Biotechnology biomedical research.

Digital diffraction diagnostics (D3)

Exploiting digital diffraction principles we have developed advanced photonics diagnostics implemented on smartphones or free standing optical systems. Inexpensive miniaturized systems incorporating cloud computing have been developed for global health applications.

  • Im H, Castro CM, Shao H, Liong M, Song J, Pathania D, Fexon L, Min C, Avila-Wallace M, Zurkiya O, Rho J, Magaoay B, Tambouret RH, Pivovarov M, Weissleder R, Lee H. Digital diffraction analysis enables low-cost molecular diagnostics on a smartphone. Proc Natl Acad Sci U S A. 2015;112(18):5613-8 – PMID: 25870273 – PMCID: PMC4426451.
  • Pathania D*, Im H*, Kilcoyne A, Sohani AR, Fexon L, Pivovarov M, Abramson JS, Randall TC, Chabner BA, Weissleder R, Lee H, Castro CM. Holographic Assessment of Lymphoma Tissue (HALT) for Global Oncology Field Applications. Theranostics. 2016;6(10):1603-10 – PMID: 27446494 – PMCID: PMC4955059.
  • Im H, Park YI, Pathania D, Castro CM, Weissleder R, Lee H. Digital diffraction detection of protein markers for avian influenza. Lab Chip. 2016;16(8):1340-5 – PMID: 26980325 – PMCID: PMC4829473.

Artificial intelligence

We are looking for a new opportunity to obtain diagnostic information accurately and effectively from acquired data. Using machine learning-based data analysis, we aim to interpret multiplexed data for spectroscopy, digital diffraction and imaging analysis. Combined with cloud computing, deep learning approaches could be the next revolutionary changes in analytical science and medicine.

Revised on 2017-06-01 16:04:34 UTC