Dynamic contrast with reversibly photoswitchable fluorescent labels for imaging living cells

De novo creation of fluorescent molecules via adversarial generative modeling

The development of AI for fluorescent materials design is technologically demanding due to the issue of accurately forecasting fluorescent properties. Besides the huge efforts made in predicting the photoluminescent properties of organic dyes in terms of machine learning techniques, this article aims to introduce an adversarial generation paradigm for the rational design of fluorescent molecules. Molecular SMILES is employed as the input of a GRU based autoencoder, where the encoding and decoding of the string information are processed. A generative adversarial network is applied on the latent space with a generator to generate samples to mimic the latent space, and a discriminator to distinguish samples from the latent space. It is found that the excited state property distributions of generated molecules fully match those of the original samples, with the molecular synthesizability being accessible as well. Further screening of the generated samples delivers a remarkable luminescence efficiency of molecules epitomized by the significant oscillator strength and charge transfer characteristics, demonstrating the great potential of the adversarial model in enriching the fluorescent library.

Resonances at Fundamental and Harmonic Frequencies for Selective Imaging of Sine-Wave Illuminated Reversibly Photoactivatable Labels

We introduce HIGHLIGHT as a simple and general strategy to selectively image a reversibly photoactivatable fluorescent label associated with a given kinetics. The label is submitted to sine-wave illumination of large amplitude, which generates oscillations of its concentration and fluorescence at higher harmonic frequencies. For singularizing a label, HIGHLIGHT uses specific frequencies and mean light intensities associated with resonances of the amplitudes of concentration and fluorescence oscillations at harmonic frequencies. Several non-redundant resonant observables are simultaneously retrieved from a single experiment with phase-sensitive detection. HIGHLIGHT is used for selective imaging of four spectrally similar fluorescent proteins that had not been discriminated so far. Moreover, labels out of targeted locations can be discarded in an inhomogeneous spatial profile of illumination. HIGHLIGHT opens roads for simplified optical setups at reduced cost and easier maintenance.

De novo creation of a naked eye-detectable fluorescent molecule based on quantum chemical computation and machine learning

Designing fluorescent molecules requires considering multiple interrelated molecular properties, as opposed to properties that straightforwardly correlated with molecular structure, such as light absorption of molecules. In this study, we have used a de novo molecule generator (DNMG) coupled with quantum chemical computation (QC) to develop fluorescent molecules, which are garnering significant attention in various disciplines. Using massive parallel computation (1024 cores, 5 days), the DNMG has produced 3643 candidate molecules. We have selected an unreported molecule and seven reported molecules and synthesized them. Photoluminescence spectrum measurements demonstrated that the DNMG can successfully design fluorescent molecules with 75% accuracy (n = 6/8) and create an unreported molecule that emits fluorescence detectable by the naked eye.

Recent Development of Fluorescent Light-Up RNA Aptamers

Technologies for RNA imaging in live cells play an important role in understanding the function and regulatory process of RNAs. One approach for genetically encoded fluorescent RNA imaging involves fluorescent light-up aptamers (FLAPs), which are short RNA sequences that can bind cognate fluorogens and activate their fluorescence greatly. Over the past few years, FLAPs have emerged as genetically encoded RNA-based fluorescent biosensors for the cellular imaging and detection of various targets of interest. In this review, we first give a brief overview of the development of the current FLAPs based on various fluorogens. Then we further discuss on the photocycles of the reversibly photoswitching properties in FLAPs and their photostability. Finally, we focus on the applications of FLAPs as genetically encoded RNA-based fluorescent biosensors in biosensing and bioimaging, including RNA, non-nucleic acid molecules, metal ions imaging and quantitative imaging. Their design strategies and recent cellular applications are emphasized and summarized in detail.

Advances in optical and electrochemical techniques for biomedical imaging

Yi-Tao Long and Thomas J. Meade introduce the Chemical Science retrospective themed collection on advances in optical and electrochemical techniques for biomedical imaging.