Plasma Exosome Analysis for Protein Mutation Identification Using a Combination of Raman Spectroscopy and Deep Learning

Seungmin Kim, Byeong Hyeon Choi, Hyunku Shin, Kihun Kwon, Sung Yong Lee, Hyun Bin Yoon, Hyun Koo Kim, Yeonho Choi

Research output: Contribution to journalArticlepeer-review

Abstract

Protein mutation detection using liquid biopsy can be simply performed periodically, making it easy to detect the occurrence of newly emerging mutations rapidly. However, it has low diagnostic accuracy since there are more normal proteins than mutated proteins in body fluids. To increase the diagnostic accuracy, we analyzed plasma exosomes using nanoplasmonic spectra and deep learning. Exosomes, a promising biomarker, are abundant in plasma and stably carry intact proteins originating from mother cells. However, the mutated exosomal proteins cannot be detected sensitively because of the subtle changes in their structure. Therefore, we obtained Raman spectra that provide molecular information about structural changes in mutated proteins. To extract the unique features of the protein from complex Raman spectra, we developed a deep-learning classification algorithm with two deep-learning models. Consequently, controls with wild-type proteins and patients with mutated proteins were classified with high accuracy. As a proof of concept, we discriminated the lung cancer patients with mutations in the epidermal growth factor receptor (EGFR), L858R, E19del, L858R + T790M, and E19del + T790M, from controls with an accuracy of 0.93. Moreover, the protein mutation status of the patients with primary (E19del, L858R) and secondary (+T790M) mutations was clearly monitored. Overall, our technique is expected to be applied as a novel method for companion diagnostic and treatment monitoring.

Original languageEnglish
Pages (from-to)2391-2400
Number of pages10
JournalACS Sensors
Volume8
Issue number6
DOIs
Publication statusPublished - 2023 Jun 23

Bibliographical note

Funding Information:
This research was supported by a grant from the Korea Health Technology R&D Project provided by the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HR14C0007, PI: Y.C.), and the Basic Science Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2021M3H4A4079630, PI: Y.C.). Figure 1 and Figures 3–5 include source images created by BioRender.com. ddPCR was performed by Gencurix Inc. (Seoul, Republic of Korea).

Funding Information:
This research was supported by a grant from the Korea Health Technology R&D Project provided by the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HR14C0007, PI: Y.C.), and the Basic Science Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2021M3H4A4079630, PI: Y.C.). Figure 1 and Figures 3-5 include source images created by BioRender.com. ddPCR was performed by Gencurix Inc. (Seoul, Republic of Korea).

Publisher Copyright:
© 2023 American Chemical Society

Keywords

  • Raman spectrum
  • deep learning
  • exosome
  • liquid biopsy
  • mutation
  • protein

ASJC Scopus subject areas

  • Bioengineering
  • Instrumentation
  • Process Chemistry and Technology
  • Fluid Flow and Transfer Processes

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