Reading amino acids in a nanopore

In a step toward nanopore sequencing of proteins, an aerolysin pore discriminates many of the proteinogenic amino acids

Principles of Small-Molecule Transport through Synthetic Nanopores

Synthetic nanopores made from DNA replicate the key biological processes of transporting molecular cargo across lipid bilayers. Understanding transport across the confined lumen of the nanopores is of fundamental interest and of relevance to their rational design for biotechnological applications. Here we reveal the transport principles of organic molecules through DNA nanopores by synergistically combining experiments and computer simulations. Using a highly parallel nanostructured platform, we synchronously measure the kinetic flux across hundreds of individual pores to obtain rate constants. The single-channel transport kinetics are close to the theoretical maximum, while selectivity is determined by the interplay of cargo charge and size, the pores' sterics and electrostatics, and the composition of the surrounding lipid bilayer. The narrow distribution of transport rates implies a high structural homogeneity of DNA nanopores. The molecular passageway through the nanopore is elucidated via coarse-grained constant-velocity steered molecular dynamics simulations. The ensemble simulations pinpoint with high resolution and statistical validity the selectivity filter within the channel lumen and determine the energetic factors governing transport. Our findings on these synthetic pores' structure-function relationship will serve to guide their rational engineering to tailor transport selectivity for cell biological research, sensing, and drug delivery

Deep learning assisted mechanotyping of individual cells through repeated deformations and relaxations in undulating channels

Mechanical properties of cells are important features that are tightly regulated and are dictated by various pathologies. Deformability cytometry allows for the characterization of the mechanical properties at a rate of hundreds of cells per second, opening the way to differentiating cells via mechanotyping. A remaining challenge for detecting and classifying rare sub-populations is the creation of a combined experimental and analysis protocol that approaches the maximum potential classification accuracy for single cells. In order to find this maximum accuracy, we designed a microfluidic channel that subjects each cell to repeated deformations and relaxations and provides a comprehensive set of mechanotyping parameters. We track the shape dynamics of individual cells with high time resolution and apply sequence-based deep learning models for feature extraction. In order to create a dataset based solely on differing mechanical properties, a model system was created with treated and untreated HL60 cells. Treated cells were exposed to chemical agents that perturb either the actin or microtubule networks. Multiple recurrent and convolutional neural network architectures were trained using time sequences of cell shapes and were found to achieve high classification accuracy based on cytoskeletal properties alone. The best model classified two of the sub-populations of HL60 cells with an accuracy over 90%, significantly higher than the 75% we achieved with traditional methods. This increase in accuracy corresponds to a fivefold increase in potential enrichment of a sample for a target population. This work establishes the application of sequence-based deep learning models to dynamic deformability cytometry