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Organ-on-a-chip platforms have potential to offer more cost-effective, ethical, and human-resembling models than animal models for disease study and drug discovery. Particularly, the Blood-Brain-Barrier-on-a-chip (BBB-oC) has emerged as a promising tool to investigate several neurological disorders since it promises to provide a model of the multifunctional tissue working as an important node to control pathogen entry, drug delivery and neuroinflammation. A comprehensive understanding of the multiple physiological functions of the tissue model requires biosensors detecting several tissue-secreted substances in a BBB-oC system. However, current sensor-integrated BBB-oC platforms are only available for tissue membrane integrity characterization based on permeability measurement. Protein secretory pathways are closely associated with the tissue's various diseased conditions. At present, no biosensor-integrated BBB-oC platform exists that permits in situ tissue protein secretion analysis over time, which prohibits researchers from fully understanding the time-evolving pathology of a tissue barrier. Herein, the authors present a platform named "Digital Tissue-BArrier-CytoKine-counting-on-a-chip (DigiTACK)," which integrates digital immunosensors into a tissue chip system and demonstrates on-chip multiplexed, ultrasensitive, longitudinal cytokine secretion profiling of cultured brain endothelial barrier tissues. The integrated digital sensors utilize a novel beadless microwell format to perform an ultrafast "digital fingerprinting" of the analytes while achieving a low limit of detection (LoD) around 100-500 fg/mL for mouse MCP1 (CCL2), IL-6 and KC (CXCL1). The DigiTACK platform is extensively applicable to profile temporal cytokine secretion of other barrier-related organ-on-a-chip systems and can provide new insight into the secretory dynamics of the BBB by sequentially controlled experiments.

Serial measurement of a large panel of protein biomarkers near the bedside could provide a promising pathway to transform the critical care of acutely ill patients. However, attaining the combination of high sensitivity and multiplexity with a short assay turnaround poses a formidable technological challenge. Here, the authors develop a rapid, accurate, and highly multiplexed microfluidic digital immunoassay by incorporating machine learning-based autonomous image analysis. The assay has achieved 12-plexed biomarker detection in sample volume