Abstract
Quantification of exosomal multi-miRNA can reveal the initiation, progression, and metastasis of tumors, which is conducive to the noninvasive early diagnosis of cancer. However, low-sensitivity and single-plex detection characteristics of traditional methods seriously hinder the accuracy and specificity of exosomal miRNAs in cancer diagnosis. Herein, we design an ultramultiplexing strategy that enables simultaneous and sensitive detection of multiple exosomal miRNAs by nanosatellites (magnetic beads (MBs) @ NaLnF4) and catalytic hairpin assembly (CHA) amplification in combination with inductively coupled plasma-mass spectrometry (ICP-MS) to diagnose cancer accurately. The competitive binding of target exosomal miRNAs with the recognition sequences on nanosatellites triggers the drop of NaLnF4 from MBs, followed by a CHA reaction that releases more NaLnF4 labels for ICP-MS detection. This method is used to detect ten types of miRNAs simultaneously with a detection limit of 0.01 fM, which is one order of magnitude lower than the quantitative reverse transcription polymerase chain reaction (qRT-PCR) method. Linear discriminant analysis as a machine learning algorithm is subsequently applied to analyze the signals of exosomal multi-miRNA, and the discrimination accuracy of ten cell exosomes reaches 98.6%. In a clinical cohort of 42 patients, including five cancer types and healthy controls, exosomal multi-miRNA analysis achieves accurate cancer diagnosis and classification with 100% accuracy. Our results show that the combination of nanosatellites, CHA, and ICP-MS provides a universal biosensing platform for simultaneous and ultrasensitive detection of multiple targets.
Published Version
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