•In this study, we propose a learning framework to identify the interactive genes based on multi-omics data, which is important to explore multiple related genes within the same co-expression network ...influences the manifestation of a phenotype.•We integrate different omics based on their similarities and apply spectral clustering to identify cancer subtypes. We measure the similarity in different omics based on shared neighbors, which is more comparable between omics since different omics have different data distributions.•The interactive genes for each cancer subtype are detected by learning the dense subgraphs embedded in the gene co-expression networks. The results by systematic gene ontology enrichment analysis show that different cancer subtypes exhibit distinct gene co-expression networks and interactive gene groups with different functional enrichment.
Recent advances in multi-omics databases offer the opportunity to explore complex systems of cancers across hierarchical biological levels. Some methods have been proposed to identify the genes that play a vital role in disease development by integrating multi-omics. However, the existing methods identify the related genes separately, neglecting the gene interactions that are related to the multigenic disease. In this study, we develop a learning framework to identify the interactive genes based on multi-omics data including gene expression. Firstly, we integrate different omics based on their similarities and apply spectral clustering for cancer subtype identification. Then, a gene co-expression network is construct for each cancer subtype. Finally, we detect the interactive genes in the co-expression network by learning the dense subgraphs based on the L1 prosperities of eigenvectors in the modularity matrix. We apply the proposed learning framework on a multi-omics cancer dataset to identify the interactive genes for each cancer subtype. The detected genes are examined by DAVID and KEGG tools for systematic gene ontology enrichment analysis. The analysis results show that the detected genes have relationships to cancer development and the genes in different cancer subtypes are related to different biological processes and pathways, which are expected to yield important references for understanding tumor heterogeneity and improving patient survival.
The detection of differentially expressed genes (DEGs) is one of most important computational challenges in the analysis of single-cell RNA sequencing (scRNA-seq) data. However, due to the high ...heterogeneity and dropout noise inherent in scRNAseq data, challenges in detecting DEGs exist when using a single distribution of gene expression levels, leaving much room to improve the precision and robustness of current DEG detection methods. Here, we propose the use of a new method, DEGman, which utilizes several possible diverse distributions in combination with Bhattacharyya distance. DEGman can automatically select the best-fitting distributions of gene expression levels, and then detect DEGs by permutation testing of Bhattacharyya distances of the selected distributions from two cell groups. Compared with several popular DEG analysis tools on both large-scale simulation data and real scRNA-seq data, DEGman shows an overall improvement in the balance of sensitivity and precision. We applied DEGman to scRNA-seq data of TRAP; Ai14 mouse neurons to detect fear-memory-related genes that are significantly differentially expressed in neurons with and without fear memory. DEGman detected well-known fear-memory-related genes and many novel candidates. Interestingly, we found 25 DEGs in common in five neuron clusters that are functionally enriched for synaptic vesicles, indicating that the coupled dynamics of synaptic vesicles across in neurons plays a critical role in remote memory formation. The proposed method leverages the advantage of the use of diverse distributions in DEG analysis, exhibiting better performance in analyzing composite scRNA-seq datasets in real applications.
Cysticercosis is a neglected tropical disease caused by the larvae of
in pigs and humans. The current diagnosis of porcine cysticercosis is difficult, and traditional pathological tests cannot meet ...the needs of detection. This study established a UPT-LF assay for the detection of
. UCP particles were bound to two antigens, TSOL18 and GP50; samples were captured, and the signal from the UCP particles was converted into a detectable signal for analysis using a biosensor. Compared to ELISA, UPT-LF has higher sensitivity and specificity, with a sensitivity of 93.59% and 97.44%, respectively, in the case of TSOL18 and GP50 antigens and a specificity of 100% for both. Given its rapidness, small volume, high sensitivity and specificity, and good stability and reproducibility, this method could be used in the diagnosis of cysticercosis.
Short-term memory (STM) is crucial for animals to hold information for a small period of time. Persistent or recurrent neural activity, together with neural oscillations, is known to encode the STM ...at the cellular level. However, the coding mechanisms at the microcircuitry level remain a mystery. Here, we performed two-photon imaging on behaving mice to monitor the activity of neuronal microcircuitry. We discovered a neuronal subpopulation in the medial prefrontal cortex (mPFC) that exhibited emergent properties in a context-dependent manner underlying a STM-like behavior paradigm. These neuronal subpopulations exclusively comprise excitatory neurons and mainly represent a group of neurons with stronger functional connections. Microcircuitry plasticity was maintained for minutes and was absent in an animal model of Alzheimer’s disease (AD). Thus, these results point to a functional coding mechanism that relies on the emergent behavior of a functionally defined neuronal assembly to encode STM.
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•Two-photon imaging enables the monitoring of neural activities on behaving animals•A neuronal subpopulation in the mPFC that exhibited emergent properties encodes STM•These neuronal subpopulations exclusively comprise excitatory neurons•This STM coding mechanism is absent in an animal model of AD
Using two-photon imaging on behaving mice, Tian et al. discover a functional coding mechanism that relies on the emergent behavior of a functionally defined neuronal assembly to encode short-term memory. They further demonstrate that this coding mechanism is absent in an animal model of Alzheimer’s disease (AD).
Glutamate is the principal excitatory neurotransmitter in the brain. The alpha-amino-3-hydroxyl-5-methyl-4-isoxazolepropionic (AMPA) receptors, as one of several types of endogenous ionotropic ...glutamate receptors, mediate the fast excitatory synaptic transmission that is essential for information processing and integration in the mammalian brain. Modifications of AMPA receptors are assumed to be the molecular basis underlying learning and memory, and impairments of AMPA receptors cause certain neurological diseases, including epilepsy, autism spectrum disorders, and Alzheimer's disease. Thus, extensive studies have been conducted, and these have revealed a complex protein–protein network controlling the expression, trafficking, and function of AMPA receptors in neurons. Here, we summarize the interacting partners of AMPA-type glutamate receptors and the functional implications of these interactions.
Elucidation of cell subpopulations at high resolution is a key and challenging goal of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) data analysis. Although unsupervised clustering ...methods have been proposed for de novo identification of cell populations, their performance and robustness suffer from the high variability, low capture efficiency and high dropout rates which are characteristic of scRNA-seq experiments. Here, we present a novel unsupervised method for Single-cell Clustering by Enhancing Network Affinity (SCENA), which mainly employed three strategies: selecting multiple gene sets, enhancing local affinity among cells and clustering of consensus matrices. Large-scale validations on 13 real scRNA-seq datasets show that SCENA has high accuracy in detecting cell populations and is robust against dropout noise. When we applied SCENA to large-scale scRNA-seq data of mouse brain cells, known cell types were successfully detected, and novel cell types of interneurons were identified with differential expression of gamma-aminobutyric acid receptor subunits and transporters. SCENA is equipped with CPU + GPU (Central Processing Units + Graphics Processing Units) heterogeneous parallel computing to achieve high running speed. The high performance and running speed of SCENA combine into a new and efficient platform for biological discoveries in clustering analysis of large and diverse scRNA-seq datasets.
The placenta-derived extracellular matrix is an ideal material for making freeze-dried sponges as a wound dressing. In order to prepare a porous sponge, collapse temperature is one of the most ...important parameters in process of freeze-drying. This work used an image processing method to determine the collapse temperature of extracellular matrix slurry during freeze-drying. By using this technology, the variation of collapse temperature under different cooling rates, heating rates, annealing conditions are studied, and the influence of ice crystal size on collapse temperature is further analyzed. In addition, an annealing operation is used to control the size of ice crystals inside the sample. The actual results are consistent with the fact that annealing increases the ice crystal size and collapse temperature. The image processing algorithm can greatly improve the accuracy of obtaining the collapse temperature to shorten the period of exploration of the key freeze-drying parameters of solid-liquid mixtures.
With the continuous development of single-cell sequencing technology, through the gene expression data obtained by single-cell sequencing technology, we can have a deeper understanding of the ...heterogeneity between cells and the underlying mechanisms that exist between cells. However, due to the complexity of the data, single-cell identification and clustering have also brought us huge challenges. We found that many classic clustering algorithms performed poorly in single-cell clustering. Our research found that the key reason was that no mark was found. gene. First remove genes with low expression levels, and then calculate the variance value of genes, select the top 1000 genes with the largest variance, and then perform a T test to remove noise. Finally, the obtained genes are clustered using Cosine similarity algorithm and k-means. Found that it has a good clustering performance.
Parylene-C is a popular polymer material in biomedical applications, with excellent physicochemical properties and microfabrication capability. Like many aromatic polymers, parylene-C also has ...autofluorescence, which was usually taken as a negative background noise in biomedical detection studies. However, the fluorescence intensity of thin-film (<1 μm) parylene-C was relatively weak, which may be a big limitation in visualization. In this work, we reported a simple annealing method to significantly enhance the fluorescence and achieve sufficient intensity as a visual marker. We studied the behaviors and mechanisms of the enhanced parylene-C fluorescence, then verified the feasibility and reliability of parylene-C for preparing fluorescent pipettes in targeted neuronal electrophysiology, where fluorescent guidance was strongly needed. The powerful parylene-C fabrication technique enables a precisely-controlled conformal coating along with a mass production capability, which further resulted in high-quality electrophysiological recordings of both cultured hippocampal neurons and acute hippocampal brain slices. Moreover, the enhanced parylene-C fluorescence can also be applied in more general biological operations, such as designable fluorescent micro-patterns for visualization in broader biomedical fields.
Taenia solium cysts were collected from pig skeletal muscle and analyzed via a shotgun proteomic approach to identify known proteins in the cyst fluid and to explore host-parasite interactions. Cyst ...fluid was aseptically collected and analyzed with shotgun liquid chromatography-tandem mass spectrometry (LC-MS/MS). Gene alignment and annotation were performed using Blast2GO software followed by gene ontology analysis of the annotated proteins. The pathways were further analyzed with the Kyoto Encyclopedia of Genes and Genomes (KEGG), and a protein-protein interaction (PPI) network map was generated using STRING software. A total of 158 known proteins were identified, most of which were low-molecular-mass proteins. These proteins were mainly involved in cellular and metabolic processes, and their molecular functions were predominantly related to catalytic activity and binding functions. The pathway enrichment analysis revealed that the known proteins were mainly enriched in the PI3K-Akt and glycolysis/ gluconeogenesis signaling pathways. The nodes in the PPI network mainly consisted of enzymes involved in sugar metabolism. The cyst fluid proteins screened in this study may play important roles in the interaction between the cysticerci and the host. The shotgun LC-MS/MS, gene ontology, KEGG, and PPI network map data will be used to identify and analyze the cyst fluid proteome of cysticerci, which will provide a basis for further exploration of the invasion and activities of T. solium.
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