Fig 2: EPAC sensitivity.The EPAC sensitivity was studied by analyzing designated concentrations of SK-MEL-28 cell–derived EVs from (A) the conditioned culture medium using an anti-CD63 functionalized EPAC and (B) the simulated patient plasma using an anti-MCSP functionalized EPAC, followed by labeling with MCSP-MBA SERS nanotags. The left side shows the representative false-color SERS spectral images, and the right side is the concentration-dependent average SERS intensity at 1075 cm-1. Data are represented as means ± standard deviation, where error bars represent standard deviation of three separate experiments. Means not sharing a common letter are significantly different (P < 0.05). Scale bars, 10 µm. a.u., arbitrary units.

Fig 3: Schematic for EV phenotyping by EPAC.(A) A melanoma cell with a BRAF V600E mutation secretes EVs into circulation or cell culture medium. (B) The sample is directly injected into EPAC, where the applied nanomixing fluid flow increases EV collisions with the capture antibody and SERS nanotags and shears off nontarget molecules (e.g., protein aggregates and apoptotic bodies) and free SERS nanotags. (C) The characterization of EV phenotypes is performed by SERS mapping. The false-color SERS spectral images are established on the basis of the characteristic peak intensities of SERS nanotags (MCSP-MBA, red; MCAM-TFMBA, blue; ErbB3-DTNB, green; LNGFR-MPY, yellow). (D) EV phenotypes defined by the relative expression levels of four biomarkers are extracted from the average signal spectra of false-color SERS spectral images. EV phenotypes are unique to each EV subpopulation. By analyzing EV samples before, during, and after BRAF inhibitor treatment, the phenotypic evolution can be tracked to provide information on treatment responses and early signs of drug resistance.

Fig 4: The anti-CD63 functionalized EPAC for monitoring phenotypic changes of EVs from melanoma patient–derived cell lines in response to BRAF inhibitor treatment.EVs released from (A) LM-MEL-64 cells without treatment were used as a control and followed for 30 days. EVs derived from (B) LM-MEL-33, (C) SK-MEL-28, (D) LM-MEL-64, and (E) LM-MEL-35 cell lines were collected before (day 0), during (days 3 to 30), and after treatment (days 33 and 39). (A to E) Average biomarker signals are represented by red (MCSP), blue (MCAM), green (ErbB3), and yellow (LNGFR). LM-MEL-35 cell line is BRAF wild type but NRAS mutant, and the other three cell lines are BRAF mutant. Data in (A) to (E) are represented as means ± standard deviation, where error bars represent standard deviation of three separate experiments. (F to J) Clustering of EV populations before, during, and after treatment via LDA of SERS signals.

Fig 5: Anti-CD63 functionalized EPAC specificity.The specificity was studied using EV samples released from SK-MEL-28 and MCF7 cell lines, as well as control experiments including (++) EV-free cell culture medium, (-+) without the CD63 capture antibody, and (+-) with nontarget CD45 detection antibodies on SERS nanotags. (A) The expressions of MCSP, MCAM, ErbB3, and LNGFR in SK-MEL-28 and MCF7 cells were detected by flow cytometry. (B) Representative false-color SERS spectral images, (C) average SERS spectra obtained from corresponding SERS mapping datasets, and (D) average SERS intensities at 1075 cm-1 (red, MCSP), 1375 cm-1 (blue, MCAM), 1335 cm-1 (green, ErbB3), and 1000 cm-1 (yellow, LNGFR). Data in (D) are represented as means ± standard deviation, where error bars represent standard deviation of three separate experiments. Scale bars, 10 µm.