Highly accurate and label-free discrimination of single cancer cell using a plasmonic oxide-based nanoprobe.

The detection of cancer cells at the single-cell level enables many novel functionalities such as next-generation cancer prognosis and accurate cellular analysis. While surface-enhanced Raman spectroscopy (SERS) has been widely considered as an effective tool in a low-cost and label-free manner, however, it is challenging to discriminate single cancer cells with an accuracy above 90% mainly due to the poor biocompatibility of the noble-metal-based SERS agents. Here, we report a dual-functional nanoprobe based on dopant-driven plasmonic oxides, demonstrating a maximum accuracy above 90% in distinguishing single THP-1 cell from peripheral blood mononuclear cell (PBMC) and human embryonic kidney (HEK) 293 from human macrophage cell line U937 based on their SERS patterns. Furthermore, this nanoprobe can be triggered by the bio-redox response from individual cells towards stimuli, empowering another complementary colorimetric cell detection, approximately achieving the unity discrimination accuracy at a single-cell level. Our strategy could potentially enable the future accurate and low-cost detection of cancer cells from mixed cell samples.

Facile PEG-based isolation and classification of cancer extracellular vesicles and particles with label-free surface-enhanced Raman scattering and pattern recognition algorithm.

Extracellular vesicles and particles (EVPs), which contain the same surface proteins as their mother cells, are promising biomarkers for cancer liquid biopsy. However, most of the isolation methods of EVPs are time-consuming and complicated, and hence, sensitive detection and classification methods are required for EVPs. Here, we report a facile polyethylene glycol (PEG)-based method for isolating and classifying EVPs with label-free surface-enhanced Raman scattering (SERS) and pattern recognition algorithm. There are only three steps in the PEG-based isolation method, and it does not require ultracentrifugation, which makes it a low-cost and easy-to-use method. Three types of common male cancer cell lines, namely leukemia (THP-1), prostate cancer (DU-145), and colorectal cancer (COLO-205), and one healthy male blood sample, were utilized to isolate EVPs. To collect the SERS spectra of EVPs, a novel planar nanomaterial, namely amino molybdenum oxide (AMO) nanoflakes, was applied, with the enhancement factor being obtained as 3.2 × 102. Based on the principal component analysis and support vector machine (PCA-SVM) algorithm, cancer and normal EVPs were classified with 97.4% accuracy. However, among the cancer EVPs, the accuracy, precision, and sensitivity were found to be 90.0%, 90.9%, and 83.3% for THP-1; 86.7%, 80.0%, and 92.3% for DU-145; 96.7%, 83.3%, and 100% for COLO-205, respectively. Thus, this work will improve the isolation, detection, and classification of EVPs and promote the development of cancer liquid biopsies.

Simulation and practice of particle inertial focusing in 3D-printed serpentine microfluidic chips via commercial 3D-printers.

Inertial focusing of particles in serpentine microfluidic chips has been studied over the past decade. Here, a study to investigate the particle inertial focusing in 3D-printed serpentine microfluidic chips was conducted by simulation and practice. A test model was designed and printed using four commercial 3D-printers. Commercial inkjet 3D-printers have shown the best printing channel resolution of up to 0.1 mm. The force analysis of particle inertial focusing in 3D-printed microfluidic chips with large cross-sectional channels was discussed. Important parameters such as the channel curvature and flow velocity were studied by simulation. The optimal channel curvature and flow velocity are 5.9 mm and 480 µL min-1 (Re: 29.8 and De: 4.49) in the 3D-printed microfluidic chips with 0.2 mm × 0.4 mm cross-sectional channels. Under these optimal conditions, particles were well focused in the middle of the channel. Furthermore, two kinds of cancer cells were focused in these 3D-printed serpentine microfluidic chips under the optimal conditions. We envision that this improved study would provide helpful insights into simulating particle inertial focusing in 3D-printed microfluidic chips and promoting 3D-printed microfluidic chips to commercial production.

Engineering of Removing Sacrificial Materials in 3D-Printed Microfluidics.

Three-dimensional (3D) printing will create a revolution in the field of microfluidics due to fabricating truly three-dimensional channels in a single step. During the 3D-printing process, sacrificial materials are usually needed to fulfill channels inside and support the printed chip outside. Removing sacrificial materials after printing is obviously crucial for applying these 3D printed chips to microfluidics. However, there are few standard methods to address this issue. In this paper, engineering techniques of removing outer and inner sacrificial materials were studied. Meanwhile, quantification methods of removal efficiency for outer and inner sacrificial materials were proposed, respectively. For outer sacrificial materials, a hot bath in vegetable oil can remove 89.9% ± 0.1% of sacrificial materials, which is better than mechanics removal, hot oven heating, and an ethanol bath. For inner sacrificial materials, injecting 70 °C vegetable oil for 720 min is an optimized approach because of the uniformly high transmittance (93.8% ± 6.8%) and no obvious deformation. For the industrialization of microfluidics, the cost-effective removing time is around 10 min, which considers the balance between time cost and chip transmittance. The optimized approach and quantification methods presented in this paper show general engineering sacrificial materials removal techniques, which promote removing sacrificial materials from 3D-printed microfluidics chips and take 3D printing a step further in microfluidic applications.