Formation of a Photoelectrochemical Z-Scheme Structure with Inorganic/Organic Hybrid Materials for Evaluation of Receptor Protein Expression on the Membrane of Cancer Cells.
Zizheng WangJing LiWenwen TuHuaisheng WangZhaoyin WangZhihui DaiPublished in: ACS applied materials & interfaces (2020)
Quantitative analysis of receptor protein expression is essential to give new insights into tumor-related research. Benefitting from their high sensitivity and low background, photoelectrochemical (PEC) platforms are considered as powerful tools for evaluating the expression of receptor proteins. Herein, to reduce the cytotoxicity and facilitate the subsequent assembly, l-cysteine-modified Ag-ZnIn2S4 quantum dots (l-Cys AZIS QDs) are prepared and PEC responses under the irradiation of long wavelength light are obtained. To further improve the PEC behavior, iron phthalocyanine (FePc) is employed to form a Z-scheme structure with l-Cys AZIS QDs. The Z-scheme structure based on l-Cys AZIS QDs/FePc hybrid materials exhibits high photo-to-electric conversion efficiency and can be excited with near-infrared range light. Because hyaluronic acid linked to photoactive materials can recognize CD44 expressed on the membrane of cancer cells, cancer cells are immobilized onto l-Cys AZIS QDs/FePc hybrid materials, inducing a decrease of the photocurrent intensity. Consequently, a PEC cytosensor is constructed to quantify cancer cells expressing CD44. The PEC analytical platform is able to determine A549 cells in the range of 2 × 102 to 4.5 × 106 cells/mL, and a detection limit of 15 cells/mL is realized in the case of S/N = 3. In addition, the expression of CD44 in A549 and other five cancer cells is measured with this PEC method. Depending on our data, the expression of CD44 in different cancer cells is distinct, indicating great potential of this method in receptor protein-related studies.
Keyphrases
- quantum dots
- induced apoptosis
- binding protein
- poor prognosis
- cell cycle arrest
- hyaluronic acid
- sensitive detection
- visible light
- endoplasmic reticulum stress
- cell proliferation
- risk assessment
- long non coding rna
- machine learning
- small molecule
- ionic liquid
- amino acid
- high intensity
- big data
- single cell
- liquid chromatography
- highly efficient
- radiation induced