Deep Learning for Live Cell Shape Detection and Automated AFM Navigation.
Jaydeep RadeJuntao ZhangSoumik SarkarAdarsh KrishnamurthyJuan RenAnwesha SarkarPublished in: Bioengineering (Basel, Switzerland) (2022)
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.
Keyphrases
- atomic force microscopy
- high speed
- deep learning
- high resolution
- artificial intelligence
- single molecule
- big data
- machine learning
- convolutional neural network
- data analysis
- living cells
- induced apoptosis
- single cell
- protein protein
- cell therapy
- small molecule
- high throughput
- mass spectrometry
- magnetic resonance
- endothelial cells
- cell cycle arrest
- computed tomography
- quantum dots
- endoplasmic reticulum stress
- binding protein
- fluorescent probe
- contrast enhanced