Direct imaging of single-molecule electrochemical reactions in solution.
Jinrun DongYuxian LuYang XuFanfan ChenJinmei YangYuang ChenJiandong FengPublished in: Nature (2021)
Chemical reactions tend to be conceptualized in terms of individual molecules transforming into products, but are usually observed in experiments that probe the average behaviour of the ensemble. Single-molecule methods move beyond ensemble averages and reveal the statistical distribution of reaction positions, pathways and dynamics1-3. This has been shown with optical traps and scanning probe microscopy manipulating and observing individual reactions at defined locations with high spatial resolution4,5, and with modern optical methods using ultrasensitive photodetectors3,6,7 that enable high-throughput single-molecule measurements. However, effective probing of single-molecule solution chemistry remains challenging. Here we demonstrate optical imaging of single-molecule electrochemical reactions7 in aqueous solution and its use for super-resolution microscopy. The method utilizes a chemiluminescent reaction involving a ruthenium complex electrochemically generated at an electrode8, which ensures minimal background signal. This allows us to directly capture single photons of the electrochemiluminescence of individual reactions, and to develop super-resolved electrochemiluminescence microscopy for imaging the adhesion dynamics of live cells with high spatiotemporal resolution. We anticipate that our method will advance the fundamental understanding of electrochemical reactions and prove useful for bioassays and cell-imaging applications.
Keyphrases
- single molecule
- high resolution
- living cells
- atomic force microscopy
- gold nanoparticles
- molecularly imprinted
- single cell
- quantum dots
- high speed
- induced apoptosis
- ionic liquid
- label free
- aqueous solution
- genome wide
- gene expression
- oxidative stress
- dna methylation
- mesenchymal stem cells
- staphylococcus aureus
- biofilm formation
- machine learning
- cell therapy
- deep learning
- tandem mass spectrometry
- electron microscopy
- cell cycle arrest