As an emerging source for liquid biopsy, exosomes hold significant promise for clinical diagnosis. However, commonly used exosome isolation methods (e.g., ultracentrifugation) suffer from low throughput for a large number of clinical samples. Herein, a dysprosium-metal organic framework was synthesized and doped with nanofibers by electrospinning for efficient capture of exosomes from body fluid. With the integration of multichannel of pipet or robot automatic workstation, high throughput exosome isolation can be achieved with clinical samples with high reproducibility. To evaluate the clinical value of the developed method, urinary exosomes were enriched from 34 liver disease samples of different stages for the profiling of metabolites by mass spectrometry. The results showed that HCC, cirrhosis, and healthy controls can be significantly differentiated by the Random Forest classification model. The dysprosium-metal organic framework has promising applications in exosome-based liquid biopsy for large-scale clinical disease diagnosis.
Keywords: Dy-MOF; Electrospinning; Exosome; Machine learning; Metabolite.