Lung cancer exhibits strong heterogeneity, and its early diagnosis and precise subtyping are of great importance, as they can increase the ability to deliver personalized medicines by tailoring therapy regimens. Tissue biopsy, albeit the gold standard, is invasive, costly and provides limited information about the tumor and its molecular landscape. Exosomes, as promising biomarkers for lung cancer, are a heterogeneous collection of membranous vesicles containing tumor-specific information for liquid biopsy to identify lung cancer subtypes. However, the small size, complex structure, and heterogeneous molecular features of exosomes pose significant challenges for their effective isolation and analysis. Herein, we report a deep learning-driven microfluidic chip with surface-enhanced Raman scattering (SERS) readout to characterize the differences in exosomes for the early diagnosis and molecular subtyping of non-small cell lung cancer (NSCLC). This integration comprises a processing unit for exosome capture and enrichment using polystyrene microspheres (PS) binding gold nanocubes (AuNCs) and anti-CD-9 antibody (denoted as PACD), and an optical sensing unit to trap the PACD and detect SERS signals from these exosomes. This system achieved a maximum trapping efficiency of 85%, and could distinguish three different NSCLC cell lines from the normal cell line with an overall accuracy of 97.88% and an area under the curve (AUC) of over 0.95 for each category. This work highlights the combined power of deep learning, SERS, and microfluidics in realizing the capture, detection, and analysis of exosomes from biological matrices, which may pave the way for clinical exosome-based cancer diagnosis and prognostication in the future.

Kidney disease is highly prevalent and may result in severe clinical outcomes. Serum creatinine (Scr) and blood urea nitrogen (BUN) are the most widely used biomarkers for kidney function assessment, yet when measured alone, the result can be affected by a variety of parameters such as age, gender, protein consumption, etc. Measuring Scr and BUN simultaneously can eliminate most of the external influences and greatly improve the assessment of kidney function. In this study, a real-time kidney function assessment system based on dual biomarker detection was proposed. Scr and BUN were determined using surface-enhanced Raman scattering (SERS) within the concentration range of 10⁻¹ to 10⁻⁶ M and 0.28 to 100 mg dl⁻¹, respectively. A one-dimensional convolutional neural network (1D-CNN) model was employed to quantitatively analyze the concentration of biomarkers from the SERS spectral measurements. Moreover, we simulated a variety of kidney health conditions with 16 groups of mixed Scr and BUN in serum. The proposed CNN-assisted SERS method was used to quantify both biomarkers and provide diagnostic results. The Au core-Ag shell nanoprobes provided ultra-sensitive SERS detection and the CNN model achieved excellent regression results with an R² of 0.9871 in the testing dataset. The system demonstrated a rapid and robust evaluation for the assessment of kidney function, providing a promising idea for medical diagnosis with the help of spectroscopy and deep learning methods.

Discovering novel drugs for cancer immunotherapy requires a robust in vitro drug screening platform that allows for straightforward probing of cell–cell communications. Here, we combined surface-enhanced Raman scattering (SERS) nanoprobes with microfluidic networks to monitor in situ the cancer–immune system intercellular communications. The microfluidic platform links up immune cells with cancer cells, where the cancer-cell secretions act as signaling mediators. First, gold@silver core–shell nanorods were employed to fabricate SERS immunoprobes for analysis of the signaling molecules. Multiple cancer secretions in a tumor microenvironment were quantitatively analyzed by a SERS-assisted three-dimensional (3D) barcode immunoassay with high sensitivity (1 ng/mL). Second, in an on-chip cell proliferation assay, multiple immunosuppressive proteins secreted by cancer cells were found to inhibit activation of immune cells, indicating that the platform simulates the physiological process of cancer–immune system communications. Furthermore, potential drug candidates were tested on this platform. A quantitative SERS immunoassay was performed to evaluate drug efficacy at regulating the secretion behavior of cancer cells and the activity of immune cells. This assay showed the suitability of this platform for in vitro drug screening. It is expected that the fully integrated and highly automated SERS-microfluidic platform will become a powerful analytical tool for probing intercellular communications and should accelerate the discovery and clinical validation of novel drugs.