Abstract
Photoactivatable dyes with spectrally-resolvable single-molecule fluorescence must be developed to enable simultaneous imaging of structurally-distinct biomolecules with nanometer precision in densely-labeled samples. To fill this crucial gap, we synthesized a library of 16 borondipyrromethene (BODIPY) derivatives sharing a common functional group compatible with photoactivation but differing in a single substituent required for spectral tuning. We demonstrated that the nature of this particular substituent controls the spectral position of the emission maximum and the relative intensity of a vibronic shoulder in their single-molecule emission spectra. We implemented a convolutional neural network (CNN) to discriminate individual molecules with different substituents from these subtle spectral differences. This deep-learning algorithm can distinguish the two components of up to 41 pairs of dyes and the three components of up to 16 triads of dyes with a classification accuracy greater than 0.7 from the unique spectral signatures encoded in their single-molecule fluorescence.
Citation
Yeting Zheng, Wei-Hong Yeo, Andrea Tomassini, Colin E. Hayter, Hao F. Zhang, Yang Zhang, and Françisco M. Raymo. 2024. “Spectroscopic Single-Molecule Discrimination of BODIPY Fluorophores with Deep Learning.” Chem. https://dx.doi.org/10.2139/ssrn.4888508.
@article{Zheng2024,
author = {Yeting Zheng and Wei-Hong Yeo and Andrea Tomassini and Colin E. Hayter and Hao F. Zhang and Yang Zhang and Françisco M. Raymo},
doi = {10.2139/ssrn.4888508},
journal = {Chem},
title = {Spectroscopic Single-Molecule Discrimination of BODIPY Fluorophores with Deep Learning},
year = {2024}}