Multiplexed molecular imaging with surface enhanced resonance Raman scattering nanoprobes reveals immunotherapy response in mice via multichannel image segmentation. Issue 12 (26th October 2022)

• Record Type: Journal Article
• Authors: Andreou, Chrysafis; Plakas, Konstantinos; Berisha, Naxhije; Gigoux, Mathieu; Rosch, Lauren E.; Mirsafavi, Rustin; Oseledchyk, Anton; Pal, Suchetan; Zamarin, Dmitriy; Merghoub, Taha; Detty, Michael R.; Kircher, Moritz F.
• Is Part Of: Nanoscale horizons. Volume 7:Issue 12(2022)
• Page Start: 1540
• Page End: 1552
• Publication Date: 2022-10-26
• Subjects: Nanoscience -- Periodicals; Nanotechnology -- Periodicals; 620.505
• DOI: 10.1039/d2nh00331g
• Languages: English
• ISSNs: 2055-6756

Abstract

Abstract : Multiplexed imaging of 8 molecular targets with SERS nanoprobes in mice reveals spatially heterogeneous tumor responses to immunotherapy. Multichannel image segmentation enables classification of the tumor regions into responders and naïve. Abstract : Visualizing the presence and distribution of multiple specific molecular markers within a tumor can reveal the composition of its microenvironment, inform diagnosis, stratify patients, and guide treatment. Raman imaging with multiple molecularly-targeted surface enhanced Raman scattering (SERS) nanoprobes could help investigate emerging cancer treatments preclinically or enable personalized treatment assessment. Here, we report a comprehensive strategy for multiplexed imaging using SERS nanoprobes and machine learning (ML) to monitor the early effects of immune checkpoint blockade (ICB) in tumor-bearing mice. We used antibody-functionalized SERS nanoprobes to visualize 7 + 1 immunotherapy-related targets simultaneously. The multiplexed images were spectrally resolved and then spatially segmented into superpixels based on the unmixed signals. The superpixels were used to train ML models, leading to the successful classification of mice into treated and untreated groups, and identifying tumor regions with variable responses to treatment. This method may help predict treatment efficacy in tumors and identify areas of tumor variability and therapy resistance.