Multiplexed Integrin Detection and Cancer Cell Classification Using Multicolor Gap-Enhanced Gold Nanorods and Machine Learning Algorithm

Integrins, cell-surface adhesion receptors involved in tumor progression, invasion, and metastasis, serve as crucial biomarkers for cancer diagnosis and therapeutic targeting. Multiplexed detection of integrins and cancer cell classification at the single-cell level allows for comprehensive profiling, facilitating precise identification and categorization of tumor cells that are heterogeneous in integrin expression and cell subtype. In this study, we developed a five-plex detection platform and demonstrated integrin profile for cancer cell classification leveraging surface-enhanced Raman scattering (SERS) with gap-enhanced gold nanorods (GENRs) in conjunction with advanced computational analysis. Specifically, we synthesized GENRs bearing five distinct Raman nanotags, each producing a unique spectral fingerprint upon targeting a specific integrin subtype expressed on cancer cell surfaces. SERS signals from single cancer cells-after labeling simultaneously with the five-color SERS nanotags-were collected on single cells and subsequently analyzed with classical least squares regression to reliably deconvolute and quantify expression level of five different integrin monomers. Utilizing a random forest classifier trained on integrin profiles from individual cancer cell lines, we achieved simultaneous detections of three different breast cancer cell lines, with exceptional classification accuracy of 99.9%. The feasibility of this method for multiplexed detection of circulating tumor cells was tested using peripheral blood mononuclear cells (PBMCs) spiked with mixed breast cancer cells from three cell lines. By integrating GENRs, multiplexed SERS nanotag technology, and machine learning, our platform significantly advances cancer diagnostics through accurate integrin-based cell profiling and classification. These findings highlight the potential of multiplexed integrin detection using SERS technology as a powerful diagnostic approach, ultimately supporting improved cancer subtype characterization, personalized diagnostics, and more targeted therapeutic strategies.