Ligand Nanocluster Array Enables Artificial-Intelligence-Based Detection of Hidden Features in T-Cell Architecture

Area of Science:

• Cellular Biology
• Nanotechnology
• Biophysics

Background:

• Protein patterning is crucial for studying cell adhesion and response.
• Existing methods for creating sub-micrometer periodic stimuli and analyzing cellular responses require standardization.
• Understanding how cells respond to spatially structured stimuli is key to cell biology.

Purpose of the Study:

• To develop a novel technique for fabricating nanopatterns for cell interrogation.
• To analyze T cell organization and actin network dynamics in response to patterned ligand distribution.
• To establish a standardized toolbox for fabricating and analyzing cellular responses to nanoscale stimuli.

Main Methods:

• Fabrication of nanopatterns using electron beam lithography and surface functionalization.
• Utilizing advanced imaging techniques compatible with fabricated nanopatterns.
• Employing a deep-learning algorithm for analyzing cellular responses to patterned stimuli.

Main Results:

• The developed technique successfully created nanopatterns suitable for advanced imaging.
• A deep-learning algorithm detected distinct T cell membrane and actin network organization differences between clustered and homogeneous ligand distributions.
• These subtle cellular responses were not discernible by human observation, even with extensive image analysis.

Conclusions:

• The proposed fabrication and analysis toolbox offers a powerful means to study cell behavior at the nanoscale.
• The findings highlight the sensitivity of T cells to the spatial distribution of external stimuli.
• This approach provides a valuable tool for investigating the correlation between structured subcellular stimulation and cellular responses.