Issue 21, 2021

SERS and MALDI-TOF MS based plasma exosome profiling for rapid detection of osteosarcoma

Abstract

Osteosarcoma is the most frequent primary bone cancer, particularly among children and adolescents. The early diagnosis of osteosarcoma is significant for timely clinical treatment to reduce the mortality of patients. Exosomes play a significant role in intercellular communication and serve as promising biomarkers in liquid biopsy for the diagnosis and monitoring of tumors. Herein, we report the utility of surface-enhanced Raman scattering (SERS) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for rapid identification of osteosarcoma. We firstly profiled the intrinsic SERS signals and MALDI-TOF mass fingerprints of different subgroups of extracellular vesicles (EVs) and the corresponding cells, demonstrating that the SERS signals and MALDI-TOF mass spectra of exosomes from different types of cells were more discriminative compared to those of large and medium EVs and the cells themselves. Then, we characterized plasma-derived exosomes of 15 osteosarcoma patients and 15 healthy volunteers using SERS and MALDI-TOF MS, revealing distinctive biochemical differences in the spectra. We further utilized a data fusion approach to combine the two types of spectroscopic techniques, differentiating osteosarcoma patients from healthy controls with higher precision than either technique. The results reveal that the non-invasive liquid biopsy method using SERS and MALDI-TOF MS fingerprinting of exosomes has great potential for rapid diagnosis of osteosarcoma.

Graphical abstract: SERS and MALDI-TOF MS based plasma exosome profiling for rapid detection of osteosarcoma

Supplementary files

Article information

Article type
Paper
Submitted
30 Jūn. 2021
Accepted
23 Aug. 2021
First published
07 Sept. 2021

Analyst, 2021,146, 6496-6505

SERS and MALDI-TOF MS based plasma exosome profiling for rapid detection of osteosarcoma

Z. Han, J. Yi, Y. Yang, D. Li, C. Peng, S. Long, X. Peng, Y. Shen, B. Liu and L. Qiao, Analyst, 2021, 146, 6496 DOI: 10.1039/D1AN01163D

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