skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Deep Learning for Live Cell Shape Detection and Automated AFM Navigation
Atomic force microscopy (AFM) provides a platform for high-resolution topographical imaging and the mechanical characterization of a wide range of samples, including live cells, proteins, and other biomolecules. AFM is also instrumental for measuring interaction forces and binding kinetics for protein–protein or receptor–ligand interactions on live cells at a single-molecule level. However, performing force measurements and high-resolution imaging with AFM and data analytics are time-consuming and require special skill sets and continuous human supervision. Recently, researchers have explored the applications of artificial intelligence (AI) and deep learning (DL) in the bioimaging field. However, the applications of AI to AFM operations for live-cell characterization are little-known. In this work, we implemented a DL framework to perform automatic sample selection based on the cell shape for AFM probe navigation during AFM biomechanical mapping. We also established a closed-loop scanner trajectory control for measuring multiple cell samples at high speed for automated navigation. With this, we achieved a 60× speed-up in AFM navigation and reduced the time involved in searching for the particular cell shape in a large sample. Our innovation directly applies to many bio-AFM applications with AI-guided intelligent automation through image data analysis together with smart navigation.  more » « less
Award ID(s):
1751503
PAR ID:
10428261
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Bioengineering
Volume:
9
Issue:
10
ISSN:
2306-5354
Page Range / eLocation ID:
522
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; (, Frontiers in Physics)
    Atomic force microscopy (AFM) is a part of the scanning probe microscopy family. It provides a platform for high-resolution topographical imaging, surface analysis as well as nanomechanical property mapping for stiff and soft samples (live cells, proteins, and other biomolecules). AFM is also crucial for measuring single-molecule interaction forces and important parameters of binding dynamics for receptor-ligand interactions or protein-protein interactions on live cells. However, performing AFM measurements and the associated data analytics are tedious, laborious experimental procedures requiring specific skill sets and continuous user supervision. Significant progress has been made recently in artificial intelligence (AI) and deep learning (DL), extending into microscopy. In this review, we summarize how researchers have implemented machine learning approaches so far to improve the performance of atomic force microscopy (AFM), make AFM data analytics faster, and make data measurement procedures high-throughput. We also shed some light on the different application areas of AFM that have significantly benefited from applications of machine learning frameworks and discuss the scope and future possibilities of these crucial approaches. 
    more » « less
  2. ; (, Advanced Theory and Simulations)
    Abstract A model of the scanning‐caused soft sample deformation during atomic force microscope contact‐mode (CM) imaging is developed. CM imaging has been widely used for topography characterization of live biological samples such as live cells. However, due to the intrinsic softness of these samples, the sample surface deformation caused by the probe–sample interaction leads to significant error in the topography images obtained, and damage to the live biological sample. Although the deformation can be reduced by imaging at a rather slow scan rate (e.g., less than 0.2 Hz), such a low‐speed imaging not only is time consuming, but also inevitably induces large temporal error. In this work, the scanning‐caused surface deformation of soft samples is modeled and quantified, including live biological samples in CM imaging. Effects of both the scanning and the coupling between the vertical and the lateral deformation on the deformation are characterized. The proposed deformation model is validated by implementing it to quantify the topography image difference caused by the scanning‐caused surface deformation of live prostate cancer cells imaged at two different (high and low) speeds, respectively. 
    more » « less
  3. ; ; ; (, International Journal of Molecular Sciences)
    Atomic Force Microscopy (AFM) is widely used for topographic imaging of DNA and protein-DNA complexes in ambient conditions with nanometer resolution. In AFM studies of protein-DNA complexes, identifying the protein’s location on the DNA substrate is one of the major goals. Such studies require distinguishing between the DNA ends, which can be accomplished by end-specific labeling of the DNA substrate. We selected as labels three-way DNA junctions (3WJ) assembled from synthetic DNA oligonucleotides with two arms of 39–40 bp each. The third arm has a three-nucleotide overhang, GCT, which is paired with the sticky end of the DNA substrate generated by the SapI enzyme. Ligation of the 3WJ results in the formation of a Y-type structure at the end of the linear DNA mole cule, which is routinely identified in the AFM images. The yield of labeling is 69%. The relative orientation of arms in the Y-end varies, such dynamics were directly visualized with time-lapse AFM studies using high-speed AFM (HS-AFM). This labeling approach was applied to the characterization of the nucleosome arrays assembled on different DNA templates. HS-AFM experiments revealed a high dynamic of nucleosomes resulting in a spontaneous unraveling followed by disassembly of nucleosomes. 
    more » « less
  4. ; ; ; (, Proceedings of the National Academy of Sciences)
    na (Ed.)
    Fast, nondestructive three-dimensional (3D) imaging of live suspension cells remains challenging without substrate treatment or fixation, precluding scalable single-cell morphometry with minimal alterations. While optical sectioning techniques achieve 3D live cell imaging, lateral versus depth resolution differences further complicate analysis. We present a scalable microfluidic method capable of 3D fluorescent isotropic imaging of live, nonadherent cells suspended inside picoliter droplets with high-speed single-cell volumetric readout (800 to 1,200 slices in 5 to 8 s) and near-diffraction limit resolution (~216 nm). The platform features a droplet trap array that leverages flow-induced droplet interfacial shear to generate intradroplet microvortices, which rotate single cells on their axis to enable optical projection tomography (OPT)-based imaging. This allows gentle (~1 mPa shear stress) observation of cells encapsulated inside nontoxic isotonic buffer droplets, facilitating scalable OPT acquisition by simultaneous spinning of hundreds of cells. We demonstrate 3D imaging of live myeloid and lymphoid cells in suspension, including K562 cells, as well as naive and activated T cells—small cells prone to movement in their suspended phenotype. Our fully suspended, orientation-independent cell morphometry, driven by isotropic imaging and spherical harmonic analysis, enabled the study of primary T cells across various immunological activation states. This approach unveiled six distinct nuclear content distributions, contrasting with conventional 2D images that typically portray spheroid and bean-like nuclear shapes associated with lymphocytes. Our arrayed-droplet OPT technology is capable of isotropic, single live-cell 3D imaging, with the potential to perform large-scale morphometry of immune cell effector function states while providing compatibility with microfluidic droplet operations. 
    more » « less
  5. ; ; ; ; ; ; ; (, 50th IEEE Photovoltaic Specialists Conference)
    Microstructural properties of thin-film absorber layers play a vital role in developing high-performance solar cells. Scanning probe microscopy is frequently used for measuring spatially inhomogeneous properties of thin-film solar cells. While powerful, the nanoscale probe can be sensitive to the roughness of samples, introducing convoluted signals and unintended artifacts into the measurement. Here, we apply a glancing-angle focused ion beam (FIB) technique to reduce the surface roughness of CdTe while preserving the subsurface optoelectronic properties of the solar cells. We compare the nanoscale optoelectronic properties “before” and “after” the FIB polishing. Simultaneously collected Kelvin-probe force microscopy (KPFM) and atomic force microscopy (AFM) images show that the contact potential difference (CPD) of CdTe pristine (peak-to-valley roughness > 600 nm) follows the topography. In contrast, the CPD map of polished CdTe (< 20 nm) is independent of the surface roughness. We demonstrate the smooth CdTe surface also enables high-resolution photoluminescence (PL) imaging at a resolution much smaller than individual grains (< 1 μm). Our finite-difference time-domain (FDTD) simulations illustrate how the local light excitation interacts with CdTe surfaces. Our work supports low-angle FIB polishing can be beneficial in studying buried sub-microstructural properties of thin-film solar cells with care for possible ion-beam damage near the surface. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email