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

Abstract : Protein patterning has emerged as a powerful means to interrogate adhering cells. However, the tools to apply a sub-micrometer periodic stimulus and the analysis of the response are still being standardized. We propose a technique combining electron beam lithography and surface functionalization to fabricate nanopatterns compatible with advanced imaging. The repetitive pattern enables a deep-learning algorithm to reveal that T cells organize their membrane and actin network differently depending upon whether the ligands are clustered or homogeneously distributed, an effect invisible to the unassisted human eye even after extensive image analysis. This fabrication and analysis toolbox should be useful, both together and separately, for exploring general correlation between a spatially structured subcellular stimulation and a subtle cellular response.
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Submitted on : Friday, November 26, 2021 - 3:44:59 PM
Last modification on : Tuesday, January 4, 2022 - 6:31:08 AM
Long-term archiving on: : Sunday, February 27, 2022 - 7:42:51 PM

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Aya Nassereddine, Ahmed Abdelrahman, Emmanuelle Benard, F. Bedu, Igor Ozerov, et al.. Ligand Nanocluster Array Enables Artificial-Intelligence-Based Detection of Hidden Features in T-Cell Architecture. Nano Letters, American Chemical Society, 2021, 21, pp.5606-5613. ⟨10.1021/acs.nanolett.1c01073⟩. ⟨hal-03271524⟩

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