Protein nanobarcodes enable single-step multiplexed fluorescence imaging.

Area of Science:

• Biotechnology
• Molecular Biology
• Cellular Imaging

Background:

• Multiplexed cellular imaging often involves complex, sequential probe application.
• Existing methods using antibodies or DNA barcodes are time-consuming.

Purpose of the Study:

• To develop a streamlined method for multiplexed protein detection and identification.
• To enable precise analysis of large numbers of protein combinations in a single step.

Main Methods:

• Developed protein nanobarcodes using epitope combinations and specific nanobodies.
• Utilized nanobodies conjugated to distinct fluorophores for a single imaging step.
• Applied deep neural networks to fluorescence images for protein identification.

Main Results:

• Achieved precise protein identification using fluorescence images from nanobarcodes.
• Demonstrated an efficient and straightforward protein identification method.
• Successfully applied the method to a multicell competition assay with neurexin and neuroligin isoforms.

Conclusions:

• Protein nanobarcodes offer an efficient solution for complex multiplexed cellular imaging.
• The deep learning-based approach enables high-precision protein identification.
• This method is applicable to various biological assays, including isoform binding studies.