4.7 Article

An integrated adipose-tissue-on-chip nanoplasmonic biosensing platform for investigating obesity-associated inflammation

Journal

LAB ON A CHIP
Volume 18, Issue 23, Pages 3550-3560

Publisher

ROYAL SOC CHEMISTRY
DOI: 10.1039/c8lc00605a

Keywords

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Funding

  1. National Science Foundation [CBET 1701322, CBET 1701363]
  2. National Institute of Health (NIH/NIBIB) [1R21EB025406-01A1]
  3. New York University Global Seed Grant
  4. NYU Clinical and Translational Science Institute Collaborative Translational Pilot Award [NIH/NCATS 1UL1 TR001445]
  5. Sackler Foundation at Rockefeller University
  6. Helmsley Center for Disorders of the Digestive System at Rockefeller University

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Although many advanced biosensing techniques have been proposed for cytokine profiling, there are no clinically available methods that integrate high-resolution immune cell monitoring and in situ multiplexed cytokine detection together in a biomimetic tissue microenvironment. The primary challenge arises due to the lack of suitable label-free sensing techniques and difficulty for sensor integration. In this work, we demonstrated a novel integration of a localized-surface plasmon resonance (LSPR)-based biosensor with a biomimetic microfluidic 'adipose-tissue-on-chip' platform for an in situ label-free, high-throughput and multiplexed cytokine secretion analysis of obese adipose tissue. Using our established adipose-tissue-on-chip platform, we were able to monitor the adipose tissue initiation, differentiation, and maturation and simulate the hallmark formation of crown-like structures (CLSs) during pro-inflammatory stimulation. With integrated antibody-conjugated LSPR barcode sensor arrays, our platform enables simultaneous multiplexed measurements of pro-inflammatory (IL-6 and TNF-alpha) and anti-inflammatory (IL-10 and IL-4) cytokines secreted by the adipocytes and macrophages. As a result, our adipose-tissue-on-chip platform is capable of identifying stage-specific cytokine secretion profiles from a complex milieu during obesity progression, highlighting its potential as a high-throughput preclinical readout for personalized obesity treatment strategies.

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