Automated patterning and probing with multiple nanoscale tools for single-cell analysis. (October 2017)

• Record Type: Journal Article
• Authors: Li, Jiayao; Kim, Yeonuk; Liu, Boyin; Qin, Ruwen; Li, Jian; Fu, Jing
• Is Part Of: Micron. Volume 101(2017)
• Page Start: 132
• Page End: 137
• Publication Date: 2017-10
• Subjects: Focused ion beam -- Atomic force microscopy -- Automation; Microscopy -- Periodicals; Electron Probe Microanalysis -- Periodicals; Microscopy -- Periodicals; Microscopie -- Périodiques; Microscopy; Periodicals; 502.82
• DOI: 10.1016/j.micron.2017.06.002
• Languages: English
• ISSNs: 0968-4328

Abstract

Highlights: Formulate the automation of multi-target imaging and fabrication as a covering problem and provide mathematical solution. Demonstrate the automated application of combining FIB and analytical approaches including AFM to probe single cells. A potential technical solution for high-throughput imaging and analysis using electron and ion beam microscopy. Abstract: The nano-manipulation approach that combines Focused Ion Beam (FIB) milling and various imaging and probing techniques enables researchers to investigate the cellular structures in three dimensions. Such fusion approach, however, requires extensive effort on locating and examining randomly-distributed targets due to limited Field of View (FOV) when high magnification is desired. In the present study, we present the development that automates 'pattern and probe' particularly for single-cell analysis, achieved by computer aided tools including feature recognition and geometric planning algorithms. Scheduling of serial FOVs for imaging and probing of multiple cells was considered as a rectangle covering problem, and optimal or near-optimal solutions were obtained with the heuristics developed. FIB milling was then employed automatically followed by downstream analysis using Atomic Force Microscopy (AFM) to probe the cellular interior. Our strategy was applied to examine bacterial cells ( Klebsiella pneumoniae ) and achieved high efficiency with limited human interference. The developed algorithms can be easily adapted and integrated with different imaging platforms towards high-throughput imaging analysis of single cells.