Ultrasensitive Detection of Circulating Plasma Cells Using Surface-Enhanced Raman Spectroscopy and Machine Learning for Multiple Myeloma Monitoring

Abstract

Multiple myeloma is a hematologic malignancy characterized by the proliferation of abnormal plasma cells in the bone marrow. Despite therapeutic advancements, there remains a critical need for reliable, noninvasive methods to monitor multiple myeloma. Circulating plasma cells (CPCs) in peripheral blood are robust and independent prognostic markers, but their detection is challenging due to their low abundance. Next-generation flow cytometry is commonly used for CPC detection but is not performed in routine clinical practice because it requires expensive instruments, is costly, and time-consuming. This study introduces a cost-effective, rapid surface-enhanced Raman spectroscopy (SERS) assay leveraging gold-deposited magnetic nanoparticles and plasmonic nanoparticles functionalized with anti-CD138 and anti-CD38 antibodies for detecting CPCs in peripheral blood samples. A portable optical device was used for signal recording, enhancing the potential for point-of-care applications. The developed assay is highly sensitive and specific, capable of detecting as few as one or two cells. The application of machine learning algorithms to SERS signal analysis yielded area under the curve values ranging from 0.90 to 0.95, demonstrating excellent performance in differentiating multiple myeloma patients from healthy donors. This SERS method provides a sensitive and accessible way for CPC detection, showing significant potential for multiple myeloma diagnosis, treatment monitoring, and prognosis prediction.