Direct kinetic fingerprinting and digital counting of single protein molecules

单个蛋白质分子的直接动力学指纹识别和数字计数

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作者:Tanmay Chatterjee, Achim Knappik, Erin Sandford, Muneesh Tewari, Sung Won Choi, William B Strong, Evan P Thrush, Kenneth J Oh, Ning Liu, Nils G Walter, Alexander Johnson-Buck

Abstract

The sensitive and accurate quantification of protein biomarkers plays important roles in clinical diagnostics and biomedical research. Sandwich ELISA and its variants accomplish the capture and detection of a target protein via two antibodies that tightly bind at least two distinct epitopes of the same antigen and have been the gold standard for sensitive protein quantitation for decades. However, existing antibody-based assays cannot distinguish between signal arising from specific binding to the protein of interest and nonspecific binding to assay surfaces or matrix components, resulting in significant background signal even in the absence of the analyte. As a result, they generally do not achieve single-molecule sensitivity, and they require two high-affinity antibodies as well as stringent washing to maximize sensitivity and reproducibility. Here, we show that surface capture with a high-affinity antibody combined with kinetic fingerprinting using a dynamically binding, low-affinity fluorescent antibody fragment differentiates between specific and nonspecific binding at the single-molecule level, permitting the direct, digital counting of single protein molecules with femtomolar-to-attomolar limits of detection (LODs). We apply this approach to four exemplary antigens spiked into serum, demonstrating LODs 55- to 383-fold lower than commercially available ELISA. As a real-world application, we establish that endogenous interleukin-6 (IL-6) can be quantified in 2-µL serum samples from chimeric antigen receptor T cell (CAR-T cell) therapy patients without washing away excess serum or detection probes, as is required in ELISA-based approaches. This kinetic fingerprinting thus exhibits great potential for the ultrasensitive, rapid, and streamlined detection of many clinically relevant proteins.

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