MoS2 nanopore identifies single amino acids with sub-1 Dalton resolution.

The sequencing of single protein molecules using nanopores is faced with a huge challenge due to the lack of resolution needed to resolve single amino acids. Here we report the direct experimental identification of single amino acids in nanopores. With atomically engineered regions of sensitivity comparable to the size of single amino acids, MoS2 nanopores provide a sub-1 Dalton resolution for discriminating the chemical group difference of single amino acids, including recognizing the amino acid isomers. This ultra-confined nanopore system is further used to detect the phosphorylation of individual amino acids, demonstrating its capability for reading post-translational modifications. Our study suggests that a sub-nanometer engineered pore has the potential to be applied in future chemical recognition and de novo protein sequencing at the single-molecule level.

Imaging of Single Bacteria with Electrochemiluminescence Microscopy.

Rapid and accurate identification of pathogens is crucial for public healthcare and patient treatment. However, the commonly used analytic tools such as molecular diagnostics and mass spectrometry are either expensive or have long turnaround times for sample purification and amplification. Here, we introduce electrochemiluminescence (ECL) microscopy with a high spatiotemporal resolution and a unique chemical contrast to image and identify single bacteria. Direct bacterial counting and classification with an accuracy of up to 90.5% is demonstrated. We further report a novel tunable ECL imaging mode which can switch from the negative contrast ECL imaging without labeling to positive contrast ECL imaging with adsorption of tris(2,2'-bipyridyl) ruthenium(II) for bacterial imaging. With this contrast tuning effect, single-molecule ECL microscopy is employed for imaging the microscopic structures of single bacteria. This work shows that ECL microscopy can offer a powerful quantitative imaging methodology with chemical information for bacterial characterization.

Deep Learning Enhanced Electrochemiluminescence Microscopy.

Limited by the efficiency of electrochemiluminescence, tens of seconds of exposure time are typically required to get a high-quality image. Image enhancement of short exposure time images to obtain a well-defined electrochemiluminescence image can meet the needs of high-throughput or dynamic imaging. Here, we propose deep enhanced ECL microscopy (DEECL), a general strategy that utilizes artificial neural networks to reconstruct electrochemiluminescence images with millisecond exposure times to have similar quality as high-quality electrochemiluminescence images with second-long exposure time. Electrochemiluminescence imaging of fixed cells demonstrates that DEECL allows improvement of the imaging efficiency by 1 to 2 orders than usual. This approach is further used for a data-intensive analysis application, cell classification, achieving an accuracy of 85% with ECL data at an exposure time of 50 ms. We anticipate that the computationally enhanced electrochemiluminescence microscopy will enable fast and information-rich imaging and prove useful for understanding dynamic chemical and biological processes.