Large‐Range HS‐AFM Imaging of DNA Self‐Assembly through In Situ Data‐Driven Control

Abstract

Understanding hierarchical self‐assembly of biological structures requires real‐time measurement of the self‐assembly process over a broad range of length‐ and timescales. The success of high‐speed atomic force microscopy (HS‐AFM) in imaging small‐scale molecular interactions has fueled attempts to introduce this method as a routine technique for studying biological and artificial self‐assembly processes. Current state‐of‐the‐art HS‐AFM scanners achieve their high imaging speed by trading achievable field of view for bandwidth. This limits their suitability when studying larger biological structures. In ambient conditions, large‐range scanners with lower resonance frequencies offer a solution when combined with first principle model–based schemes. For imaging molecular self‐assembly processes in fluid, however, such traditional control techniques are less suited. In liquid, the time‐varying changes in the behavior of the complex system necessitate frequent update of the compensating controller. Recent developments in data‐driven control theory offer a model‐free, automatable approach to compensate the complex system behavior and its changes. Here, a data‐driven control design method is presented to extend the imaging speed of a conventional AFM tube scanner by one order of magnitude. This enables the recording of the self‐assembly process of DNA tripods into a hexagonal lattice at multiple length scales.

Time-resolved scanning ion conductance microscopy for three-dimensional tracking of nanoscale cell surface dynamics

“…A low-pass filter setting between 10-100 kHz was implemented after the pre-amplifier (Stanford SR560). A custom SICM controller (Supplementary Notes 1) consisting of a data-driven scan engine 7,42 and adaptive hopping mode was implemented in LabVIEW on a NI USB-7856R OEM R Series (National Instruments, Austin, TX, USA) in the framework of a home-built extensible high-performance scanning probe controller.…”

The role of convolutional neural networks in scanning probe microscopy: a review

“…In data-driven control strategies such as the ones developed in one of our labs [128] the traditional optimization-based solvers can be replaced by machine learning, thereby ensuring faster and more accurate tracking of the sample without exciting unwanted resonances.…”

“…Machine learning techniques, in general, can be used to increase the image acquisition speed, by reducing the number of data points that need to be acquired, as discussed earlier. In data-driven control strategies such as the ones developed in one of our labs [ 128 ] the traditional optimization-based solvers can be replaced by machine learning, thereby ensuring faster and more accurate tracking of the sample without exciting unwanted resonances.…”

Research progress of single molecule force spectroscopy technology based on atomic force microscopy in polymer materials: Structure, design strategy and probe modification

“…To a large extent, the structure of G‐quadruplex nucleic acid has high thermodynamic stability under close physiological conditions. [ 126–129 ] Biffi et al. reported that G‐quadruplex nucleic acid can be engineered to produce and apply structure‐specific antibodies, which can quantitatively deliver the structure of DNA G‐quadruplex in human cells.…”

“…To a large extent, the structure of G-quadruplex nucleic acid has high thermodynamic stability under close physiological conditions. [126][127][128][129] Biffi et al reported that G-quadruplex nucleic acid can be engineered to produce and apply structure-specific antibodies, which can quantitatively deliver the structure of DNA G-quadruplex in human cells. [130] They found that the small molecule ligand captures the cell structure and accurately locates the G-quadruplex structure map through the genome sequence.…”