Barcode-Based SlipChip for High-Multiplexed and Trace Sample Digital Quantification with Femtomolar Sensitivity.

Journal: ACS sensors
Published Date: Dec 15, 2025

Abstract

The development of the digital enzyme-linked immunosorbent assay (ELISA) has improved the detection of low-abundance protein biomarkers in biological samples, achieving a 1000-fold increase in sensitivity compared to conventional protein detection methods. However, existing digital ELISA technologies encounter difficulties in simultaneously identifying numerous protein biomarkers at ultralow concentrations, particularly in trace samples. This limitation calls for improvements in both detection multiplicity and sensitivity. Herein, a simple digital ELISA platform, the Barcodes Integrated SlipChip (BIS-chip), was developed for the ultrasensitive identification of multiple proteins from trace samples. The BIS-chip employs a two-step loading method that eliminates substrate precatalysis, thereby ensuring the specificity of 10-plex target detection. Additionally, its high bead-loading efficiency improves the assay sensitivity for each target. By counting digital "On" and "Off" signals, the BIS-chip quantifies a panel of 10 cytokines with low limits of detection (LoD) in the femtogram per milliliter (fg/mL) range, achieving a sensitivity improvement of several orders of magnitude compared with standard highly multiplexed suspension array technology. To validate its clinical applicability, the BIS-chip was used to simultaneously measure 10 cytokines from only 20 μL of intralesional plasma of patients diagnosed with oral lichenoid reactions (OLRs), a condition where cytokines are typically undetectable via commercial multiplexed immunoassays. Supported by machine-learning algorithms, the OLR diagnostic models were successfully developed with 95.7% sensitivity and 100% specificity. With its exceptional detection multiplicity, ultrasensitivity, and straightforward workflow, the BIS-chip provides an innovative solution for the quantitative analysis of clinical low-abundance biomarkers in trace samples.

Authors

  • Yutong Zhang
    Department of Biochemistry and Molecular Biology, Harbin Medical University, Harbin 150081, China.
  • Xiye Li
    Department of Oral Medicine, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, 639 Zhizaoju Road, Huangpu District, Shanghai 200011, China.
  • Jingwei Yi
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Weiyuan Lyu
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Heni Wang
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Qingsheng Guo
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Tingting Tang
    Department of Internal Medicine, Jinping District People's Hospital of Shantou, Shantou, China.
  • Feiyang Ou
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Hongchen Gu
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Feng Shen
    Department of Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China.
  • Yufeng Wang
    People's Hospital of Gaoxin, 768 Fudong Road, Weifang 261205, China.
  • Hong Xu
    Department of Neurosurgery, Changshu Hospital Affiliated to Soochow University, Changshu, China.

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