A novel absorption spectrometric method, based on graphene nanomaterials, for detection of hepatocellular carcinoma-specific T lymphocyte cells

Abstract

Introduction

Detection of antigen-specific cytotoxic T lymphocytes (CTLs) is the foundation for understanding hepatocellular carcinoma immune pathology and hepatocellular carcinoma immunotherapy. However, the classical method for labeling CTLs, major histocompatibility complex (MHC)–peptide tetramer, has drawbacks and needs further improvement.

Materials and methods

Here, as a new detection probe, a graphene-based MHC–peptide multimer was developed for sensitively and selectively identifying hepatocellular carcinoma-specific T-cells. To assess its detection efficiency, reduced graphene oxide (RGO) was functionalized with hemin and streptavidin to prepare a functionalized HRGO–streptavidin complex. Biotinylated MHC–peptide monomer was subsequently constructed onto HRGO to generate a detection probe for CTL labeling. The number of T-cells was detected through the reaction between HRGO and tetramethylbenzidine.

Results

Using HRGO/MHC–peptide multimers, the number of T-cells was efficiently detected in both the induction system in vitro and in peripheral blood of patients.

Conclusion

HRGO/MHC-peptide multimers methodology has application prospects in the detection of antigen peptide-specific T cells.

Keywords:

• tetramer
• graphene
• hemin
• major histocompatibility complex multimer
• cytotoxic T lymphocytes
• hepatocellular carcinoma

Supplementary material

Figure S1 Optimized data for experimental conditions of TMB calorimetric reaction.

Notes: Optimized data of (A) TMB, (B) H₂O₂, and (C) colorimetric reaction time. For all the experiments, the same volume of HRGO (10 µg/mL) was added. As shown in the figure, the optimal catalytic reaction needed 100 µL TMB substrate, 1 µL H₂O₂ (500 mM), and incubation for 30 minutes. The value of the absorbance is 0.942 OD.

Abbreviations: HRGO, hemin reduced graphene oxide; TMB, tetramethylbenzidine.

Acknowledgments

This work is supported by the Medicine and Health Research Foundation of Zhejiang Province (2018KY147, 2016DTB001), National Natural Science Foundation of China (81772664, 81501824), Fund for Integration of Cloud Computing and Big data, Innovation of Science and Education (2017A11036), Traditional Chinese Medicine Scientific Research Foundation of Zhejiang Province (2018ZY001, 2015ZZ001, 2014ZQ005), Natural Science Foundation of Zhejiang Province (LY15C090004, LQ17H160017), and the Health Science and Technology Project of Hangzhou (2018B072), Hangzhou Bureau of Science and Technology (20140633B67).

Disclosure

The authors report no conflicts of interest in this work.