Sensitive phenotyping of serum extracellular vesicles on a SERS-microfluidic platform for early-stage clinical diagnosis of ovarian carcinoma
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- Key Laboratory of Emergency and Trauma, Ministry of Education, Key Laboratory of Haikou Trauma, Key Laboratory of Hainan Trauma and Disaster Rescue, The First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, China
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- Engineering Research Center for Hainan Bio-Smart Materials and Bio-Medical Devices, Key Laboratory of Hainan Functional Materials and Molecular Imaging, College of Emergency and Trauma, Hainan Medical University, Haikou, 571199, China
Received 28 June 2024, Revised 19 August 2024, Accepted 28 August 2024, Available online 7 September 2024, Version of Record 10 September 2024.
Volume 267, 1 January 2025, 116724
https://doi.org/10.1016/j.bios.2024.116724
细胞外囊泡作为液体活检生物标志物，在癌症诊断中有巨大的潜在应用价值。卵巢癌（OvCa）由于其高死亡率和缺乏有效的早期诊断方法，对女性健康构成了严重的威胁。有证据表明，携带来自OvCa的细胞特异性成分的纳米级小细胞外囊泡（sEVs）可以作为潜在的诊断生物标志物。然而，开发一种能够快速、有效、灵敏地检测纳米级sEVs的液体活检技术，对于实现卵巢癌的早期和准确诊断具有重要意义。
近日，本课题组在Elsevier期刊Biosensors and Bioelectronics上发表题为“Sensitive phenotyping of serum extracellular vesicles on a SERS-microfluidic platform for early-stage clinical diagnosis of ovarian carcinoma”的研究成果。该研究通过设计一种表面增强拉曼散射（SERS）-多通道芯片检测sEVs（S-MMEV）的方法来研究sEVs的表面蛋白特征，成功通过对sEVs的表型分析区分健康人群和卵巢癌患者，实现了对早期卵巢癌患者进行精准诊断。为基于sEVs的癌症早期诊断提供了一种具有重要应用价值的新方法。
Abstract
Ovarian carcinoma (OvCa) poses a severe threat to women's health due to its high mortality rate and lack of efficient early diagnosis approach. There is evidence to suggest that nanosized small extracellular vesicles (sEVs) which carrying cell-specific components from OvCa can serve as potential diagnostic biomarkers. Herein, we reported a Surface-enhanced Raman Scattering (SERS)-multichannel microchip for sEVs (S-MMEV) assay to investigate the phenotype changes of sEVs. The microchip composed of seven microchannels, which enabled the parallel detection of multiple biomarkers to improve the detection accuracy. Using SERS probes conjugated with antibodies recognizing different biomarkers including ubiquitous EV biomarkers (i.e., tetraspanins; CD9, CD81) and putative OvCa tumor biomarkers (i.e. EpCAM, CD24, CA125, EGFR), we successfully analyzed the phenotypic changes of sEVs and accurately differentiated OvCa patients from healthy controls, even at early stage (I-II), with high sensitivity, high specificity and an area under the curve value of 0.9467. Additionally, the proposed approach exhibited higher sensitivity than conventional methods, demonstrating the efficiency of precise detection from cell culture and clinical samples. Collectively, the developed EV phenotyping approach S-MMEV could serve as a potential tool to achieve the early clinical diagnosis of OvCa for further precise diagnosis and personal treatment monitoring.
Keywords
Small extracellular vesicles
SERS
Microfluidics
Clinical diagnosis
Ovarian carcinoma
Fig. 1. Synthesis procedure and characterization of AuNPs, Au@Ag core-shell NPs and SERS probes. (A) Synthesis procedure of SERS probes. (B) TEM image of AuNPs, the scale bar is 50 nm. (C) UV–Vis spectra of Au@Ag core-shell nanoparticles. (D) Photography of solution after adding silver nitrate of 0–80 μL. (E) TEM image of Au@Ag nanospheres. The scale bar is 50 nm. Figure inset shows the thickness of the Ag layer is 5 nm. (F) & (G) are the high-resolution TEM-EDS mapping element analysis images and corresponding EDS analysis spectra of Au@Ag nanoparticles. The scale bar is 50 nm. (H) 3D waterfall spectra of SERS probe after adding different volumes of silver nitrate and ascorbic acid solution. (I) SERS spectra of various nanoparticles showing the changes in signal intensity at 1616 cm−1. (J) DLS distributions for SERS probe characterization. (K) SERS spectra of nanoparticles before (orange) and after (green) antibody conjugation. (L) Stability test of SERS signal intensity of SERS probes within 7 days (the typical peak of MGITC is 1616 cm−1, showing the highest signal).
Scheme 1. Schematic illustration of the working mechanism of investigation on phenotypic changes of sEVs based on S-MMEV assay for clinical diagnosis of ovarian cancer.
Fig. 2. (A) Schematic of the assembly of the multilayer chip. (B) Design of the microchip. (C) Functionalization of microchip to facilitate sEVs' measurement. (D) Modification of microchannels using different methods (i–v) and the corresponding fluorescence images (vi-x) to verify the functionalization protocol.
Fig. 3. Characterization of HO23, OVCAR3 and human serum derived sEVs. (A) TEM images. (B) NTA results. (C) representative images of the immunoblotting outcome of sEVs' biomarkers including anti-CD63, anti-EpCAM and anti-EGFR, GAPDH was used as loading control.
Fig. 4. (A) Quantitative analysis of the SERS spectra of OVCAR3-and HO23-derived sEVs. Green, blue, violet, yellow, orange, red and pink columns corresponding to the signals from CD81−, CD9−, EpCAM-, EGFR-, CD24−, CA125-, and IgG-SERS nanotags. (B) i) SERS spectra of EpCAM of purified OVCAR3-secreted EVs with concentrations in the range of 0 to 1010 particles/mL (0 represents blank group). ii) shows the enlarged spectra of MGITC peaks in figure i), and figure iii) shows the enlarged spectra of dotted area in figure ii). (C) and (E) are the data plots of relative Raman signal to increasing concentration of sEVs derived from HO23 and OVCAR3 cells Data shown as mean (n > 3). (D) and (F) The corresponding linear calibration results of SERS signal intensity at 1616 cm−1 shown in Figure (C) and (E), respectively Data shown as mean (n > 3). (G) Comparison of the detection sensitivity of S-MMEV assay and ELISA. The curve of S-MMEV assay was generated from Fig. C by normalization. The ELISA result was determined by using known quantities sEVs and testing the signal of EpCAM with chemiluminescence.
Fig. 5. Molecular profiling of OvCa protein biomarkers. (A) The heatmap of serum derived sEVs' profiling using six OvCa markers (n = 10, healthy individuals; n = 26, OvCa patients including 15 early-stage OvCa (stage I-II) cases, 7 advanced-stage OvCa (stage III-IV) cases and 4 OvCa participants). The expression of each biomarker was normalized (z-score) and displayed. (B) Violin analysis of the data displayed in Fig. 5 A. OvCa-sEVs scores were higher in stages I - stages IV OvCa patients than in healthy donors but showed similar outcome within OvCa cohorts (*P < 0.01, ***P < 0.001, ****P < 0.0001). (C) The ROC curve of different biomarkers between healthy controls and OvCa patients, each individual biomarker (i-vi), five biomarkers with EV biomarker (CD9 or CD81) and tumor biomarker (EpCAM, EGFR, CD24 and CA125) (vii and viii), and all six combined biomarkers (ix).
4. Conclusions
Sensitive and accurate characterization of protein profiles of OvCa sEVs using small volume of serum is meaningful due to the possibility in confirming early signs of OvCa based on liquid biopsy. In this research, we have developed a S-MMEV biosensor to selectively capture sEVs and utilize SERS sensing to facilitate multidimensional detection of six biomarkers simultaneously. The S-MMEV biosensor is demonstrated to be a sensitive (wide range of linearity of 7-log and low LOD of 10 particles/mL) method which can enable rapid detection (within 1h) and low sample consumption (30 μL). Based on the combination of tumor sEVs biomarkers (CD81, CD9, EpCAM, EGFR, CD24 and CA125) and the use of a total of 36 healthy controls and cancer patients, we demonstrated that the proposed S-MMEV biosensor can differentiate various ovarian cancer patients from healthy controls with a high AUC value of 0.9538, even at an early stage (0.9467), demonstrating its possibility in achieving early clinical diagnosis of OvCa. The S-MMEV assay not only exhibited high sensitivity, high specificity and reproducibility, but also outperformed conventional ELISA method in sensitivity. In the next step, a larger cohort of patients will be enrolled, together with blind screening trial, to validate the clinical utility of the S-MMEV assay. Furthermore, to improve the performance of the developed platform, microchip design with other mixing units or integration with portable device will be considered in the near future. Since the S-MMEV assay is a universal sensing technique, to test the wide applicability of the S-MMEV platform, it can be employed to other types of cancers merely by using different SERS probes conjugated with specific antibodies recognizing corresponding biomarkers, which has great potential in early diagnosis and treatment monitoring of various cancers.
