Identifying TAD-like domains on single-cell Hi-C data by graph embedding and changepoint detection.

MOTIVATION: Topologically associating domains (TADs) are fundamental building blocks of 3D genome. TAD-like domains in single cells are regarded as the underlying genesis of TADs discovered in bulk cells. Understanding the organization of TAD-like domains helps to get deeper insights into their regulatory functions. Unfortunately, it remains a challenge to identify TAD-like domains on single-cell Hi-C data due to its ultra-sparsity. RESULTS: We propose scKTLD, an in silico tool for the identification of TAD-like domains on single-cell Hi-C data. It takes Hi-C contact matrix as the adjacency matrix for a graph, embeds the graph structures into a low-dimensional space with the help of sparse matrix factorization followed by spectral propagation, and the TAD-like domains can be identified using a kernel-based changepoint detection in the embedding space. The results tell that our scKTLD is superior to the other methods on the sparse contact matrices, including downsampled bulk Hi-C data as well as simulated and experimental single-cell Hi-C data. Besides, we demonstrated the conservation of TAD-like domain boundaries at single-cell level apart from heterogeneity within and across cell types, and found that the boundaries with higher frequency across single cells are more enriched for architectural proteins and chromatin marks, and they preferentially occur at TAD boundaries in bulk cells, especially at those with higher hierarchical levels. AVAILABILITY AND IMPLEMENTATION: scKTLD is freely available at https://github.com/lhqxinghun/scKTLD.

scHiCPTR: unsupervised pseudotime inference through dual graph refinement for single-cell Hi-C data.

MOTIVATION: The emerging single-cell Hi-C technology provides opportunities to study dynamics of chromosomal organization. How to construct a pseudotime path using single-cell Hi-C contact matrices to order cells along developmental trajectory is a challenging topic, since these matrices produced by the technology are inherently high dimensional and sparse, they suffer from noises and biases, and the topology of trajectory underlying them may be diverse. RESULTS: We present scHiCPTR, an unsupervised graph-based pipeline to infer pseudotime from single-cell Hi-C contact matrices. It provides a workflow consisting of imputation and embedding, graph construction, dual graph refinement, pseudotime calculation and result visualization. Beyond the few existing methods, scHiCPTR ties to optimize graph structure by two parallel procedures of graph pruning, which help reduce the spurious cell links resulted from noises and determine a global developmental directionality. Besides, it has an ability to handle developmental trajectories with multiple topologies, including linear, bifurcated and circular ones, and is competitive with methods developed for single-cell RNA-seq data. The comparative results tell that our scHiCPTR can achieve higher performance in pseudotime inference, and the inferred developmental trajectory exhibit a reasonable biological significance. AVAILABILITY AND IMPLEMENTATION: scHiCPTR is freely available at https://github.com/lhqxinghun/scHiCPTR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quantitative measurement of dynamic nanostrain based on a phase-sensitive optical time domain reflectometer.

A sensing system is proposed for quantitative measurement of large-range dynamic nanostrain based on a phase-sensitive optical time domain reflectometer, where the coherent detection and I/Q demodulation methods are employed to demodulate both the phase and the amplitude of the Rayleigh scattering light in real time. A nanopositioning translation stage is utilized to apply precise nanostrain to fiber. By measuring phase differences between two adjacent sections, the quantitative nanostrain with a large measurement range is demonstrated; this is also a method to measure the strain parameter of refractive index. For the Panda polarization-maintaining fiber under test in the experiment, the strain parameter of phase difference is measured to be 8.714 mrad/(nε·m), while the strain parameter of refractive index is measured to be -0.3751ε-1. As a proof of the concept, the dynamic strain sensing with a range of 10-1000 nε is experimentally demonstrated, and the strain resolution is 1 or 2 nε, corresponding to 5 or 2.5 m spatial resolution, respectively. The experimental measurement also shows a triangular wave with a 12-Hz vibrating frequency and a 100-nε strain amplitude as well as a 188-Hz resonant signal of the tensile section.

TADfit is a multivariate linear regression model for profiling hierarchical chromatin domains on replicate Hi-C data.

Topologically associating domains (TADs) are fundamental building blocks of three dimensional genome, and organized into complex hierarchies. Identifying hierarchical TADs on Hi-C data helps to understand the relationship between genome architectures and gene regulation. Herein we propose TADfit, a multivariate linear regression model for profiling hierarchical chromatin domains, which tries to fit the interaction frequencies in Hi-C contact matrix with and without replicates using all-possible hierarchical TADs, and the significant ones can be determined by the regression coefficients obtained with the help of an online learning solver called Follow-The-Regularized-Leader (FTRL). Beyond the existing methods, TADfit has an ability to handle multiple contact matrix replicates and find partially overlapping TADs on them, which helps to find the comprehensive underlying TADs across replicates from different experiments. The comparative results tell that TADfit has better accuracy and reproducibility, and the hierarchical TADs called by it exhibit a reasonable biological relevance.

Comparison of normalization methods for Hi-C data.

Hi-C has been predominately used to study the genome-wide interactions of genomes. In Hi-C experiments, it is believed that biases originating from different systematic deviations lead to extraneous variability among raw samples, and affect the reliability of downstream interpretations. As an important pipeline in Hi-C analysis, normalization seeks to remove the unwanted systematic biases; thus, a comparison between Hi-C normalization methods benefits their choice and the downstream analysis. In this article, a comprehensive comparison is proposed to investigate six Hi-C normalization methods in terms of multiple considerations. In light of comparison results, it has been shown that a cross-sample approach significantly outperforms individual sample methods in most considerations. The differences between these methods are analyzed, some practical recommendations are given, and the results are summarized in a table to facilitate the choice of the six normalization methods. The source code for the implementation of these methods is available at https://github.com/lhqxinghun/bioinformatics/tree/master/Hi-C/NormCompare.

Controlled synthesis of Au-loaded Fe3O4@C composite microspheres with superior SERS detection and catalytic degradation abilities for organic dyes.

Bifunctional Au-loaded Fe3O4@C composite microspheres were controllably synthesized by coating of Au nanoparticles (NPs) on the surface of the poly(diallyldimethylammonium chloride) (PDDA) functionalized Fe3O4@C microspheres. The amount of Au loading can be effectively tuned by altering the feeding amounts of solution Au NPs or further growth. The obtained Au-loaded Fe3O4@C composite microspheres exhibit both superior surface-enhanced Raman scattering (SERS) sensitivity and catalytic degradation activity for organic dyes. The SERS signal intensity of methylene blue (MB) distinctly enhances with the increase of Au loading, which endows increased Raman 'hot spots' and provides a significant enhancement of the Raman signal through electromagnetic (EM) field enhancements. Furthermore, the catalytic experiments of the Fe3O4@C@Au composite microspheres with the highest Au loading demonstrate that the model organic dye of MB molecules could be degraded within 10 min and the catalytic activity could be recovered without sharp activity loss in six runs, which indicates their superior catalytic degradation activity. The reason could be mainly ascribed to the synergistic effects of small size of Au NPs, the good adsorption behavior of carbon layers and the excellent dispersivity of the composite microspheres induced by the sandwiched carbon layers. The results indicate that the bifunctional Au-loaded Fe3O4@C composite microspheres could be served as promising materials in wastewater treatment.