Effectively Clustering Single Cell RNA Sequencing Data by Sparse Representation.

Clustering analysis has been widely used in analyzing single-cell RNA-sequencing (scRNA-seq) data to study various biological problems at cellular level. Although a number of scRNA-seq data clustering methods have been developed, most of them evaluate the similarity of pairwise cells while ignoring the global relationships among cells, which sometimes cannot effectively capture the latent structure of cells. In this paper, we propose a new clustering method SPARC for scRNA-seq data. The most important feature of SPARC is a novel similarity metric that uses the sparse representation coefficients of each cell in terms of the other cells to measure the relationships among cells. In addition, we develop an outlier detection method to help parameter selection in SPARC. We compare SPARC with nine existing scRNA-seq data clustering methods on twelve real datasets. Experimental results show that SPARC achieves the state of the art performance. By further analyzing the cell similarity data derived from sparse representations, we find that SPARC is much more effective in mining high quality clusters of scRNA-seq data than two traditional similarity metrics. In conclusion, this study provides a new way to effectively cluster scRNA-seq data and achieves more accurate clustering results than the state of art methods.

Boosting scRNA-seq data clustering by cluster-aware feature weighting.

BACKGROUND: The rapid development of single-cell RNA sequencing (scRNA-seq) enables the exploration of cell heterogeneity, which is usually done by scRNA-seq data clustering. The essence of scRNA-seq data clustering is to group cells by measuring the similarities among genes/transcripts of cells. And the selection of features for cell similarity evaluation is of great importance, which will significantly impact clustering effectiveness and efficiency. RESULTS: In this paper, we propose a novel method called CaFew to select genes based on cluster-aware feature weighting. By optimizing the clustering objective function, CaFew obtains a feature weight matrix, which is further used for feature selection. The genes have large weights in at least one cluster or the genes whose weights vary greatly in different clusters are selected. Experiments on 8 real scRNA-seq datasets show that CaFew can obviously improve the clustering performance of existing scRNA-seq data clustering methods. Particularly, the combination of CaFew with SC3 achieves the state-of-art performance. Furthermore, CaFew also benefits the visualization of scRNA-seq data. CONCLUSION: CaFew is an effective scRNA-seq data clustering method due to its gene selection mechanism based on cluster-aware feature weighting, and it is a useful tool for scRNA-seq data analysis.

Enzymatic Extraction of Bioactive and Self-Assembling Wool Keratin for Biomedical Applications.

Keratin is widely recognized as a high-quality renewable protein resource for biomedical applications. Despite their extensive existence, keratin resources such as feathers, wool, and hair exhibit high stability and mechanical properties because of their high disulfide bond content. Consequently, keratin extraction is challenging and its application is greatly hindered. In this work, a biological extraction strategy is proposed for the preparation of bioactive keratin and the fabrication of self-assembled keratin hydrogels (KHs). Based on moderate and controlled hydrolysis by keratinase, keratin with a high molecular weight of approximately 45 and 28 kDa that retain its intrinsic bioactivities is obtained. The keratin products show excellent ability to promote cell growth and migration and are conferred with significant antioxidant ability because of their intrinsically high cysteine content. In addition, without the presence of any cross-linking agent, the extracted keratin can self-assemble into injectable hydrogels. The KHs exhibit a porous network structure and 3D culture ability, showing potential in promoting wound healing. This enzyme-driven keratin extraction strategy opens up a new approach for the preparation of keratin that can self-assemble into injectable hydrogels for biomedical engineering.

Fabrication and characterization of high molecular keratin based nanofibrous membranes for wound healing.

Keratin is widely used in the biomaterial application, but the keratin prepared by the physical or chemical approach has relatively low molecular weight and mechanical properties. Here we report the preparation of high molecular keratin (HMK) with molecular weight of 120 kDa via multi-enzyme cascade pathway and its application in wound healing. Briefly, we prepared the soluble keratin from wool by keratinase and improved the molecular weight of keratin by transglutaminase (TGase). The HMK was coelectrospun with poly(3-hydroxybutyric acid-co-3-hydroxyvaleric acid) (PHBV) and the prepared nanofibrous mats demonstrated improved mechanical properties. Ag nanoparticles (AgNPs) were synthesized on the nanofibers via in situ bioreduction, using the above-mentioned keratinase as the reducing agent. It is demonstrated that the PHBV/HMK/AgNPs nanofibrous mats possess favorable antibacterial properties and good biocompatibility. Moreover, in vivo wound healing assessment, the PHBV/HMK/AgNPs membrane displayed better wound healing ability than the control group. These results indicate that PHBV/HMK/AgNPs mats exhibit significant potential in tissue engineering.

PhosD: inferring kinase-substrate interactions based on protein domains.

MOTIVATION: Identifying the kinase-substrate relationships is vital to understanding the phosphorylation events and various biological processes, especially signal transductions. Although large amount of phosphorylation sites have been detected, unfortunately, it is rarely known which kinases activate those sites. Despite distinct computational approaches have been proposed to predict the kinase-substrate interactions, the prediction accuracy still needs to be improved. RESULTS: In this paper, we propose a novel probabilistic model named as PhosD to predict kinase-substrate relationships based on protein domains with the assumption that kinase-substrate interactions are accomplished with kinase-domain interactions. By further taking into account protein-protein interactions, our PhosD outperforms other popular approaches on several benchmark datasets with higher precision. In addition, some of our predicted kinase-substrate relationships are validated by signaling pathways, indicating the predictive power of our approach. Furthermore, we notice that given a kinase, the more substrates are known for the kinase the more accurate its predicted substrates will be, and the domains involved in kinase-substrate interactions are found to be more conserved across proteins phosphorylated by multiple kinases. These findings can help develop more efficient computational approaches in the future. AVAILABILITY AND IMPLEMENTATION: The data and results are available at http://comp-sysbio.org/phosd. CONTACT: xm_zhao@tongji.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying Disease Associated miRNAs Based on Protein Domains.

MicroRNAs (miRNAs) are a class of small endogenous non-coding genes, acting as regulators in the post-transcriptional processes. Recently, the miRNAs are found to be widely involved in different types of diseases. Therefore, the identification of disease associated miRNAs can help understand the mechanisms that underlie the disease and identify new biomarkers. However, it is not easy to identify the miRNAs related to diseases due to its extensive involvements in various biological processes. In this work, we present a new approach to identify disease associated miRNAs based on domains, the functional and structural blocks of proteins. The results on real datasets demonstrate that our method can effectively identify disease related miRNAs with high precision.

[Factors affecting the DAPI fluorescence direct count in the tidal river sediment].

The factors affecting the DAPI (4', 6-diamidino-2-phenylidole) fluorescence direct count in the tidal river sediment were examined. Sediment samples were collected from the Guangzhou section of the Pearl River. Besides sediment texture and organic matter, an improved staining procedure and the involved parameters were analyzed. Results showed that the procedure with the sediment with 2000 fold dilution and ultrasonic water bath for 10 min, and with a final DAPI concentration of 10 microg x mL(-1) and staining time for more than 30 min produced the optimum results of DAPI direct count in the sediment. The total bacterial number was correlated to the proportion of the non-nucleoid-containing cells to the total bacterial number (r = 0.587, p = 0.004). The organic matter content also correlated to the ration. The clay content had a strong correlation with the organic matter, through which the clay content also affected the ratio. A multiple regression analysis between the ration versus the organic matter, the total bacterial number, and the clay content showed that the regression equation fit the measure values satisfactorily (r = 0.694). These results indicated that the above factors needed to be considered in the applications of the DAPI fluorescence direct counting method to the tidal river sediment.