The duplex formation mediated by Hg(2+) in a properly designed ssDNA generates a stable hairpin structure, which greatly alters the translocation profile of the ssDNA through α-hemolysin nanopore. From the 2D-events contour plot, the presence of Hg(2+) can be confirmed in as little as 30 min at ∼7 nM or higher. The sensor is highly selective to Hg(2+), without interference from other metal ions. It can be fabricated from readily available materials, without the processes of synthesis, purification, probe-making, and so forth. This sensing strategy opens new possibilities for detecting many types of analytes which have specific interactions with DNA molecules.

Single-cell RNA sequencing (scRNA-seq) is able to give an insight into the gene–gene associations or transcriptional networks among cell populations based on the sequencing of a large number of cells. However, traditional network methods are limited to the grouped cells instead of each single cell, and thus the heterogeneity of single cells will be erased. We present a new method to construct a cell-specific network (CSN) for each single cell from scRNA-seq data (i.e. one network for one cell), which transforms the data from ‘unstable’ gene expression form to ‘stable’ gene association form on a single-cell basis. In particular, it is for the first time that we can identify the gene associations/network at a single-cell resolution level. By CSN method, scRNA-seq data can be analyzed for clustering and pseudo-trajectory from network perspective by any existing method, which opens a new way to scRNA-seq data analyses. In addition, CSN is able to find differential gene associations for each single cell, and even ‘dark’ genes that play important roles at the network level but are generally ignored by traditional differential gene expression analyses. In addition, CSN can be applied to construct individual network of each sample bulk RNA-seq data. Experiments on various scRNA-seq datasets validated the effectiveness of CSN in terms of accuracy and robustness.

Identifying early warning signals of critical transitions during disease progression is a key to achieving early diagnosis of complex diseases. By exploiting rich information of high-throughput data, a novel model-free method has been developed to detect early warning signals of diseases. Its theoretical foundation is based on dynamical network biomarker (DNB), which is also called as the driver (or leading) network of the disease because components or molecules in DNB actually drive the whole system from one state (e.g. normal state) to another (e.g. disease state). In this article, we first reviewed the concept and main results of DNB theory, and then applied the new method to the analysis of type 2 diabetes mellitus (T2DM). Specifically, based on the temporal-spatial gene expression data of T2DM, we identified tissue-specific DNBs corresponding to the critical transitions occurring in liver, adipose and muscle during T2DM development and progression. Actually, we found that there are two different critical states during T2DM development characterized as responses to insulin resistance and serious inflammation, respectively. Interestingly, a new T2DM-associated function, i.e. steroid hormone biosynthesis, was discovered, and those related genes were significantly dysregulated in liver and adipose at the first critical transition during T2DM deterioration. Moreover, the dysfunction of genes related to responding hormone was also detected in muscle at the similar period. Based on the functional and network analysis on pathogenic molecular mechanism of T2DM, we showed that most of DNB genes, in particular the core ones, tended to be located at the upstream of biological pathways, which implied that DNB genes act as the causal factors rather than the consequence to drive the downstream molecules to change their transcriptional activities. This also validated our theoretical prediction of DNB as the driver network. As shown in this study, DNB can not only signal the emergence of the critical transitions for early diagnosis of diseases, but can also provide the causal network of the transitions for revealing molecular mechanisms of disease initiation and progression at a network level.

No abstract

Although existing computational models have identified many common driver genes, it remains challenging to identify the personalized driver genes by using samples of an individual patient. Recently, the methods of exploiting the structure-based control principles of complex networks provide new clues for identifying minimum number of driver nodes to drive the state transition of large-scale complex networks from an initial state to the desired state. However, the structure-based network control methods cannot be directly applied to identify the personalized driver genes due to the unknown network dynamics of the personalized system. Here we proposed the personalized network control model (PNC) to identify the personalized driver genes by employing the structure-based network control principle on genetic data of individual patients. In PNC model, we firstly presented a paired single sample network construction method to construct the personalized state transition network for capturing the phenotype transitions between healthy and disease states. Then, we designed a novel structure-based network control method from the Feedback Vertex Sets-based control perspective to identify the personalized driver genes. The wide experimental results on 13 cancer datasets from The Cancer Genome Atlas firstly showed that PNC model outperforms current state-of-the-art methods, in terms of F-measures for identifying cancer driver genes enriched in the gold-standard cancer driver gene lists. Furthermore, these results showed that personalized driver genes can be explored by their network characteristics even when they are hidden factors in transcription and mutation profiles. Our PNC gives novel insights and useful tools into understanding the tumor heterogeneity in cancer. The PNC package and data resources used in this work can be freely downloaded from https://github.com/NWPU-903PR/PNC.

Viviparous (live-bearing) vertebrates have evolved repeatedly within otherwise oviparous (egg-laying) clades. Over two-thirds of these changes in vertebrate reproductive parity mode happened in squamate reptiles, where the transition has happened between 98 and 129 times. The transition from oviparity to viviparity requires numerous physiological, morphological, and immunological changes to the female reproductive tract, including eggshell reduction, delayed oviposition, placental development for supply of water and nutrition to the embryo by the mother, enhanced gas exchange, and suppression of maternal immune rejection of the embryo. We performed genomic and transcriptomic analyses of a closely related oviparous–viviparous pair of lizards (Phrynocephalus przewalskii and Phrynocephalus vlangalii) to examine these transitions. Expression patterns of maternal oviduct through reproductive development of the egg and embryo differ markedly between the two species. We found changes in expression patterns of appropriate genes that account for each of the major aspects of the oviparity to viviparity transition. In addition, we compared the gene sequences in transcriptomes of four oviparous–viviparous pairs of lizards in different genera (Phrynocephalus, Eremias, Scincella, and Sphenomorphus) to look for possible gene convergence at the sequence level. We discovered low levels of convergence in both amino acid replacement and evolutionary rate shift. This suggests that most of the changes that produce the oviparity–viviparity transition are changes in gene expression, so occasional reversals to oviparity from viviparity may not be as difficult to achieve as has been previously suggested.

Supplementary data are available at Bioinformatics online.

The fluorescence method has made great progress in the construction of sensitive sensors but the background fluorescence of the matrix and photobleaching limit its broad application in clinical diagnosis. Here, we propose a digital single virus immunoassay for multiplex virus detection by using fluorescent magnetic multifunctional nanospheres as both capture carriers and signal labels. The superparamagnetism and strong magnetic response ability of nanospheres can realize efficient capture and separation of targets without sample pretreatment. Due to their distinguishable fluorescence imaging and photostability, the nanospheres enable single-particle counting for ultrasensitive multiplexed detection. Furthermore, the integration of digital analysis provided a reliable quantitative strategy for the detection of rare targets. Based on multifunctional nanospheres and digital analysis, a digital single virus immunoassay was proposed for simultaneous detection of H9N2, H1N1, and H7N9 avian influenza virus without complex signal amplification, whose detection limits were 0.02 pg/mL. Owing to its good specificity and anti-interference ability, the method showed great potential in single biomolecules, multiplexed detection, and early diagnosis of diseases.