Linking genome structures to functions by simultaneous single-cell Hi-C and RNA-seq.

Much progress has been made recently in single-cell chromosome conformation capture technologies. However, a method that allows simultaneous profiling of chromatin architecture and gene expression has not been reported. Here, we developed an assay named "Hi-C and RNA-seq employed simultaneously" (HiRES) and performed it on thousands of single cells from developing mouse embryos. Single-cell three-dimensional genome structures, despite being heavily determined by the cell cycle and developmental stages, gradually diverged in a cell type-specific manner as development progressed. By comparing the pseudotemporal dynamics of chromatin interactions with gene expression, we found a widespread chromatin rewiring that occurred before transcription activation. Our results demonstrate that the establishment of specific chromatin interactions is tightly related to transcriptional control and cell functions during lineage specification.

Recognition and localization of asymmetric spectra in FBG sensing networks.

We propose a deep learning demodulation method based on a long short-term memory (LSTM) neural network for fiber Bragg grating (FBG) sensing networks. Interestingly, we find that both low demodulation error and distorted spectrum recognition are realized using the proposed LSTM-based method. Compared with conventional demodulation methods, including Gaussian-fitting, convolutional neural network, and the gated recurrent unit, the proposed method improves the demodulation accuracy being close to 1 pm and achieves a demodulation time of 0.1s for 128-FBG sensors. Furthermore, our approach can realize 100% accuracy of distorted spectra recognition and complete the location of spectra with spectrally encoded FBG sensors.

Study of the Sensing Kinetics of G Protein-Coupled Estrogen Receptor Sensors for Common Estrogens and Estrogen Analogs.

Endogenous and exogenous estrogens are widely present in food and food packaging, and high levels of natural estrogens and the misuse or illegal use of synthetic estrogens can lead to endocrine disorders and even cancer in humans. Therefore, it is consequently important to accurately evaluate the presence of food-functional ingredients or toxins with estrogen-like effects. In this study, an electrochemical sensor based on G protein-coupled estrogen receptors (GPERs) was fabricated by self-assembly, modified by double-layered gold nanoparticles, and used to measure the sensing kinetics for five GPER ligands. The interconnected allosteric constants (Ka) of the sensor for 17ß-estradiol, resveratrol, G-1, G-15, and bisphenol A were 8.90 × 10-17, 8.35 × 10-16, 8.00 × 10-15, 5.01 × 10-15, and 6.65 × 10-16 mol/L, respectively. The sensitivity of the sensor for the five ligands followed the order of 17ß-estradiol > bisphenol A > resveratrol > G-15 > G-1. The receptor sensor also demonstrated higher sensor sensitivity for natural estrogens than exogenous estrogens. The results of molecular simulation docking showed that the residues Arg, Glu, His, and Asn of GPER mainly formed hydrogen bonds with -OH, C-O-C, or -NH-. In this study, simulating the intracellular receptor signaling cascade with an electrochemical signal amplification system enabled us to directly measure GPER-ligand interactions and explore the kinetics after the self-assembly of GPERs on a biosensor. This study also provides a novel platform for the accurate functional evaluation of food-functional components and toxins.