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- Title
A magneto-activated nanoscale cytometry platform for molecular profiling of small extracellular vesicles.
- Authors
Chen, Kangfu; Duong, Bill T. V.; Ahmed, Sharif U.; Dhavarasa, Piriththiv; Wang, Zongjie; Labib, Mahmoud; Flynn, Connor; Xu, Jingya; Zhang, Yi Y.; Wang, Hansen; Yang, Xiaolong; Das, Jagotamoy; Zargartalebi, Hossein; Ma, Yuan; Kelley, Shana O.
- Abstract
Exosomal PD-L1 (exoPD-L1) has recently received significant attention as a biomarker predicting immunotherapeutic responses involving the PD1/PD-L1 pathway. However, current technologies for exosomal analysis rely primarily on bulk measurements that do not consider the heterogeneity found within exosomal subpopulations. Here, we present a nanoscale cytometry platform NanoEPIC, enabling phenotypic sorting and exoPD-L1 profiling from blood plasma. We highlight the efficacy of NanoEPIC in monitoring anti-PD-1 immunotherapy through the interrogation of exoPD-L1. NanoEPIC generates signature exoPD-L1 patterns in responders and non-responders. In mice treated with PD1-targeted immunotherapy, exoPD-L1 is correlated with tumor growth, PD-L1 burden in tumors, and the immune suppression of CD8+ tumor-infiltrating lymphocytes. Small extracellular vesicles (sEVs) with different PD-L1 expression levels display distinctive inhibitory effects on CD8 + T cells. NanoEPIC offers robust, high-throughput profiling of exosomal markers, enabling sEV subpopulation analysis. This platform holds the potential for enhanced cancer screening, personalized treatment, and therapeutic response monitoring. Exosomal PD-L1 (exoPD-L1) is a biomarker predicting immunotherapeutic responses. Here the authors report NanoEPIC, a nanoscale cytometry platform that enables phenotypic sorting and exoPD-L1 profiling from blood plasma by using magnetic-activated ranking to differentiate exosomal subpopulations.
- Subjects
EXTRACELLULAR vesicles; PROGRAMMED cell death 1 receptors; CYTOMETRY; BLOOD plasma; TUMOR-infiltrating immune cells; IMMUNOSUPPRESSION
- Publication
Nature Communications, 2023, Vol 14, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-023-41285-8