A microfluidic platform for systems pathology: Multiparameter single-cell signaling measurements of clinical brain tumor specimens

Jing Sun, Michael D. Masterman-Smith, Nicholas A. Graham, Jing Jiao, Jack Mottahedeh, Dan R. Laks, Minori Ohashi, Jason DeJesus, Ken Ichiro Kamei, Ki Bum Lee, Hao Wang, Zeta T.F. Yu, Yi Tsung Lu, Shuang Hou, Keyu Li, Max Liu, Nangang Zhang, Shutao Wang, Brigitte Angenieux, Eduard PanosyanEric R. Samuels, Jun Park, Dirk Williams, Vera Konkankit, David Nathanson, R. Michael Van Dam, Michael E. Phelps, Hong Wu, Linda M. Liau, Paul S. Mischel, Jorge A. Lazareff, Harley I. Kornblum, William H. Yong, Thomas G. Graeber, Hsian Rong Tseng

Research output: Contribution to journalArticlepeer-review


The clinical practice of oncology is being transformed by molecular diagnostics that will enable predictive and personalized medicine. Current technologies for quantitation of the cancer proteome are either qualitative (e.g., immunohistochemistry) or require large sample sizes (e.g., flow cytometry). Here, we report a microfluidic platform - microfluidic image cytometry (MIC) - capable of quantitative, single-cell proteomic analysis of multiple signaling molecules using only 1,000 to 2,800 cells. Using cultured cell lines, we show simultaneous measurement of four critical signaling proteins (EGFR, PTEN, phospho-Akt, and phospho-S6) within the oncogenic phosphoinositide 3-kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR) signaling pathway. To show the clinical application of the MIC platform to solid tumors, we analyzed a panel of 19 human brain tumor biopsies, including glioblastomas. Our MIC measurements were validated by clinical immunohistochemistry and confirmed the striking intertumoral and intratumoral heterogeneity characteristic of glioblastoma. To interpret the multiparameter, single-cell MIC measurements, we adapted bioinformatic methods including self-organizing maps that stratify patients into clusters that predict tumor progression and patient survival. Together with bioinformatic analysis, the MIC platform represents a robust, enabling in vitro molecular diagnostic technology for systems pathology analysis and personalized medicine.

Original languageEnglish (US)
Pages (from-to)6128-6138
Number of pages11
JournalCancer Research
Issue number15
StatePublished - Aug 1 2010

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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