Sensitive tear screening of diabetic retinopathy with dual biomarkers enabled using a rapid electrokinetic patterning platform

Jen Yi Wang, Jae Sung Kwon, Sheng Min Hsu, Han Sheng Chuang

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


Bead-based immunosensors have intrigued the scientific community over the past decades due to their rapid and multiplexed capabilities in the detection of various biological targets. Nevertheless, their use in the detection of low-abundance analytes remains a continuing challenge because of their limited number of active enrichment approaches. To this end, our research presents a delicate microbead enrichment technique using an optoelectrokinetic platform, followed by the detection of dual biomarkers for the sensitive screening of an eye disease termed diabetic retinopathy (DR). In this study, microbeads turned fluorescent as their surfaces formed sandwiched immunocomplexes in the presence of target antigens. The tiny fluorescent dots were then concentrated using the optoelectrokinetic platform for the enhancement of their signals. The signal rapidly escalated in 10 s, and the optimal limit of detection was nearly 100 pg mL-1. For practical DR screening, two biomarkers, lipocalin 1 (LCN1) and vascular endothelial growth factor (VEGF), were used. Approximately 20 μL of analytes were collected from the tear samples of the tested patients. The concentrations of both biomarkers showed escalating trends with the severity of DR. Two concentration thresholds of LCN1 and VEGF that indicate proliferative DR were determined out of 24 clinical samples based on the receiver operating characteristic curves. For verification, a single-blind test was conducted with additional clinical tear samples from five random subjects. The final outcome of this evaluation showed an accuracy of >80%. This non-invasive screening provides a potential means for the early diagnosis of DR and may increase the screening rate among the high-risk diabetic population in the future.

Original languageEnglish
Pages (from-to)356-362
Number of pages7
JournalLab on a Chip
Issue number2
Publication statusPublished - 2020 Jan 21

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Biochemistry
  • General Chemistry
  • Biomedical Engineering


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