Iron Oxide-based SERS Bioprobe for Detecting Tumor Cells in Serous Effusion
Description
We developed an iron oxide-based surface-enhanced Raman spectroscopy (SERS) bioprobe for detecting tumor cells in serous effusions. First, we characterized the bioprobe. Second, we validated its targeting ability and specificity. We first incubated the bioprobe with its target tumor cells for varying durations to determine the optimal incubation time. Then, we incubated it with different tumor cells to observe its specific targeting capability. Next, we evaluated its performance in detecting lung cancer cells in simulated pleural effusion and ovarian cancer cells in ascites. We incubated the IGR bioprobe with pleural effusion containing lung cancer cells and with pleural effusion without lung cancer cells, respectively; additionally, we incubated the IGR bioprobe (without antibodies) with pleural effusion containing lung cancer cells. We then compared the SERS signals among the three groups. We prepared simulated samples with tumor cells at varying concentrations and measured their average Raman signals. Subsequently, we established a classification model to differentiate tumor cells based on their concentrations. Finally, we validated the model using 105 clinical samples and employed machine learning to enhance the accuracy of the SERS bioprobe in classifying tumor cells according to their concentration.
Files
Steps to reproduce
The absorption characteristics were measured using a T10 CS UV−vis spectrophotometer manufactured by Beijing Purkinje General Instrument CO., Ltd. The samples' X-ray diffraction (XRD) analysis was conducted with a BRUKER D8 ADVANCE DAVINCI diffractometer utilizing Cu Kα radiation (λ = 1.54056 Å). Zeta potential measurements were completed by dynamic light scattering (Malvern, Zetasizer). The Intelligent Fourier Transform Infrared Spectrometer (FTIR) recorded the FT-IR spectra. For morphological characterization, the sample suspensions were applied onto copper meshes and examined under a high-resolution transmission electron microscope (HRTEM, HT7800, 120 kV, HITACHI). The fluorescence profiles were obtained via a confocal laser scanning microscope (TCS SP8, Leica), while the Raman spectra were captured using a confocal Raman microscope (Labram Odyssey, Horiba). HCC827 cells were co-incubated with IGRE for 15, 30, and 60 minutes. To fix the cells with 1 mL of 4% paraformaldehyde. After fixation, the cell nuclei were stained using Hoechst33342 dye (blue) and subsequently examined using a confocal laser scanning microscope (CLSM). Similarly, SKOV3 cells were co-incubated with IGRF, followed by CLSM detection. To verify the specificity of SERS bioprobes, two groups of WBCs were co-incubated with IGRE and IGRF, respectively. In addition, HCC827 and MCF-7 cells were co-incubated with IGRE, and SKOV3 and A549 cells were co-incubated with IGRF. Ten HCC827 cells were thoroughly mixed with 1 mL of non-tumorous pleural effusion. The mixture was incubated with IGRE. Confocal Raman spectroscopy was performed using a laser scanning microscope. The sample was scanned in a grid of 400 points using an excitation wavelength of 532 nm at a 10× microscope. The average SERS spectrum was then calculated. In addition, 1 mL of pleural effusion with approximately 10 HCC827 cells was incubated with 10 µg of IGR (IO-GO-R6G-rBSA). Additionally, 1 mL of non-tumorous pleural effusion was incubated with IGRE. Finally, the average SERS spectrum was measured by the researchers. Similarly, SKOV3 cells in asites were respectively co-incubated with IGRF and IGR. We categorized cell concentrations into three groups: low cellularity group (≥10 and <10,000 HCC827 cells/mL), moderate cellularity group (≥10,000 and <50,000 HCC827 cells/mL), and high cellularity group (≥50,000 HCC827 cells/mL). The SERS classification model for SKOV3 cells was established following the previously mentioned procedures. 105 cases of pleural effusion were assessed using a blinded approach. According to the cell block technique, tumor cells can be counted using a hemocytometer under an optical microscope for positive cases. Using the measured SERS signal intensity and the SERS classification model, we categorized these cases. Subsequently, we compared the results with those obtained from cell counting.
Institutions
- Zhejiang University School of Medicine First Affiliated Hospital
Categories
Funders
- Noncommunicable Chronic Diseases-National Science and Technology Major ProjectGrant ID: 2023ZD0500902
- the Key Scientific and Technological Special Project of Ningbo CityGrant ID: 2023Z209
- Ningbo Youth Science and Technology Innovation Leading Talents ProjectGrant ID: 2024QL029
- Development Project of National Major Scientific Research InstrumentGrant ID: 82027803
- the Key Research and Development Project of Zhejiang ProvinceGrant ID: 2024C03092