Dorsal root ganglia (DRG) is an essential part of the peripheral nervous system and the hub of the peripheral sensory afferent. The dynamic changes of neuronal cells and their gene expression during the development of dorsal root ganglion have been studied through single-cell RNAseq analysis, while the dynamic changes of non-neuronal cells have not been systematically studied. Using single cell RNA sequencing technology, we conducted a research on the non-neuronal cells in the dorsal root ganglia of rats at different developmental stage. In this study, primary cell suspension was obtained from using the dorsal root ganglions (DRGs, L4-L5) of ten 7-day-old rats and three 3-month-old rats. The 10×Genomics platform was used for single cell dissociation and RNA sequencing. Twenty cell subsets were acquired through cluster dimension reduction analysis, and the marker genes of different types of cells in DRG were identified according to previous researches about DRG single cell transcriptome sequencing. In order to find out the non-neuronal cell subsets with significant differences at different development stage, the cells were classified into different cell types according to markers collected from previous researches. We performed pseudotime analysis of 4 types Schwann cells. It was found that subtype Ⅱ Schwann cells emerged firstly, and then were subtype Ⅲ Schwann cells and subtype Ⅳ Schwann cells, while subtype Ⅰ Schwann cells existed during the whole development procedure. Pseudotime analysis indicated the essential genes influencing cell fate of different subtypes of Schwann cell in DRG, such as Ntrk2 and Pmp2, which affected cell fate of Schwann cells during the development period. GO analysis of differential expressed genes showed that the up-regulated genes, such as Cst3 and Spp1, were closely related to biological process of tissue homeostasis and multi-multicellular organism process. The down regulated key genes, such as Col3a1 and Col4a1, had close relationship with the progress of extracellular structure organization and negative regulation of cell adhesion. This suggested that the expression of genes enhancing cell homestasis increased, while the expression of related genes regulating ECM-receptor interaction pathway decreased during the development. The discovery provided valuable information and brand-new perspectives for the study on the physical and developmental mechanism of Schwann cell as well as the non-neuronal cell changes in DRG at different developmental stage. The differential gene expression results provided crucial references for the mechanism of somatosensory maturation during development.
Rats ; Animals ; Ganglia, Spinal/metabolism* ; Rats, Sprague-Dawley ; Transcriptome ; Neurons/metabolism* ; Schwann Cells/physiology*

Antimicrobial peptides (AMPs) are small molecule peptides that are widely found in living organisms with broad-spectrum antibacterial activity and immunomodulatory effect. Due to slower emergence of resistance, excellent clinical potential and wide range of application, AMP is a strong alternative to conventional antibiotics. AMP recognition is a significant direction in the field of AMP research. The high cost, low efficiency and long period shortcomings of the wet experiment methods prevent it from meeting the need for the large-scale AMP recognition. Therefore, computer-aided identification methods are important supplements to AMP recognition approaches, and one of the key issues is how to improve the accuracy. Protein sequences could be approximated as a language composed of amino acids. Consequently, rich features may be extracted using natural language processing (NLP) techniques. In this paper, we combine the pre-trained model BERT and the fine-tuned structure Text-CNN in the field of NLP to model protein languages, develop an open-source available antimicrobial peptide recognition tool and conduct a comparison with other five published tools. The experimental results show that the optimization of the two-phase training approach brings an overall improvement in accuracy, sensitivity, specificity, and Matthew correlation coefficient, offering a novel approach for further research on AMP recognition.
Anti-Bacterial Agents/chemistry* ; Amino Acid Sequence ; Antimicrobial Cationic Peptides/chemistry* ; Antimicrobial Peptides ; Natural Language Processing

Objective: To explore method for isolating and culturing human epidermal stem cells ( EPSCs) in vitro and isolating and purifying epidermal stem cell exsomes ( EPSCs-Exo) by optimizing the technical process． Method: Firstly，the improved separating enzyme was used to isolate the EPSCs derived from human skin tissue．Then，an improved serum-free culture medium and 10 specific factors were combined to construct optimized 2D culture medium which could stimulate the growth of EPSCs，promote the secretion of EPSCs-Exo，maintain the stemness and proliferation of EPSCs，and delay the differentiation and maturation of EPSCs． Further，the conditions of differential centrifugation was optimized，and then the human EPSCs-Exo was successfully extracted with high efficiency and high purity． Results: The human skin tissue was confirmed with the expressions of markers for epidermal cells． EPSCs were verified with high expression levels of integrin-α6，integrin-β1，P63 and CK19 by immunofluorescence staining and Western blot． The nanoparticle tracking analysis results showed the particles separated for EPSCs supernatant was saucepan with the detected diameter between 30 － 150 nm． The Western blot results showed the positive expression of membrane markers Tsg101，CD9 and CD63 and the negative expression of intracellular markers Calnexin and GAPDH． Conclusion The results show that the human-derived EPSCs have been successfully isolated and cultured in vitro，and the EPSCs-Exo have been successfully isolated and identified．

Chromatography is a basic process in the current proteomics workflow, and the retention time alignment of the chromatogram is one of the important steps to effectively improve the identification and quantification accuracy. After years of development, a series of algorithms for retention time alignment have been developed. This review summarizes the advances of chromatographic retention time alignment algorithms and tools for proteomics analysis from the perspective of proteomics users, and discusses the development and future application directions.
Algorithms ; Proteomics/methods*

Cancer is a heterogeneous disease with complex mechanisms that requires targeted precision medicine strategies. The growth of precision medicine is indispensable from the rapid development of genomics. However, genomics has certain limitations in molecular phenotype analysis, proteogenomics thus arose at the right time. Proteogenomics is the merging of proteomics and genomics. This review describes the limitations of genomic analysis and highlights the importance of proteogenomics to re-understand precision oncology from a proteogenomic perspective. In addition, the application of proteogenomics in precision oncology is briefly introduced, the related public data projects are described, and finally, the challenges that need to be addressed at this stage are proposed.
Humans ; Proteogenomics ; Precision Medicine ; Neoplasms/genetics* ; Proteomics ; Genomics

Objective To understand the correlation between atmospheric particulate matter and confirmed cases of influenza in Pudong New Area, Shanghai, and to provide a basis for formulating relevant control measures. Methods The meteorological factors (average temperature, relative humidity, and atmospheric pressure), atmospheric pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃) and confirmed cases of influenza of different ages and genders from January 1, 2014 to December 31, 2018 were collected. Data was fitted to a generalized additive model of Poisson distribution to assess the correlation between atmospheric particulate matter (PM_2.5, PM₁₀) and the number of confirmed cases of influenza. Results There was a correlation between atmospheric particulate matter and the number of confirmed cases of influenza in Pudong New Area. For each increase of 10 μg/m³ in the concentration of the two types of particulate matter, the confirmed cases increased by 0.638% (95%CI: 0.413%~0.864%), and 0.520% (95%CI: 0.324%~0.715%), respectively, when the lag was 0-7d (lag07). People of different ages and genders were affected by atmospheric particulate matter differently. After incorporating the effects of SO₂, NO₂, CO, and O₃ in the multi-pollutant model, the effect of atmospheric particulate matter on the number of influenza cases had changed. Conclusion The increase of atmospheric particulate matter (PM_2.5, PM₁₀) concentration increased the number of confirmed cases of influenza in Pudong New Area.

Cancers have been widely recognized as highly heterogeneous diseases, and early diagnosis and prognosis of cancer types have become the focus of cancer research. In the era of big data, efficient mining of massive biomedical data has become a grand challenge for bioinformatics research. As a typical neural network model, the autoencoder is able to efficiently learn the features of input data by unsupervised training method and further help integrate and mine the biological data. In this article, the primary structure and workflow of the autoencoder model are introduced, followed by summarizing the advances of the autoencoder model in cancer informatics using various types of biomedical data. Finally, the challenges and perspectives of the autoencoder model are discussed.
Algorithms ; Humans ; Informatics ; Neoplasms/diagnosis* ; Neural Networks, Computer

In case/control gene expression data, differential expression (DE) represents changes in gene expression levels across various biological conditions, whereas differential co-expression (DC) represents an alteration of correlation coefficients between gene pairs. Both DC and DE genes have been studied extensively in human diseases. However, effective approaches for integrating DC-DE analyses are lacking. Here, we report a novel analytical framework named DC&DEmodule for integrating DC and DE analyses and combining information from multiple case/control expression datasets to identify disease-related gene co-expression modules. This includes activated modules (gaining co-expression and up-regulated in disease) and dysfunctional modules (losing co-expression and down-regulated in disease). By applying this framework to microarray data associated with liver, gastric and colon cancer, we identified two, five and two activated modules and five, five and one dysfunctional module(s), respectively. Compared with the other methods, pathway enrichment analysis demonstrated the superior sensitivity of our method in detecting both known cancer-related pathways and those not previously reported. Moreover, we identified 17, 69, and 11 module hub genes that were activated in three cancers, which included 53 known and three novel cancer prognostic markers. Random forest classifiers trained by the hub genes showed an average of 93% accuracy in differentiating tumor and adjacent normal samples in the TCGA and GEO database. Comparison of the three cancers provided new insights into common and tissue-specific cancer mechanisms. A series of evaluations demonstrated the framework is capable of integrating the rapidly accumulated expression data and facilitating the discovery of dysregulated processes.
Gene Expression Profiling ; Gene Regulatory Networks ; Humans ; Microarray Analysis ; Neoplasms/genetics*

With the development of mass spectrometry technologies and bioinformatics analysis algorithms, disease research-driven human proteome project (HPP) is advancing rapidly. Protein biomarkers play critical roles in clinical applications and the biomarker discovery strategies and methods have become one of research hotspots. Feature selection and machine learning methods have good effects on solving the "dimensionality" and "sparsity" problems of proteomics data, which have been widely used in the discovery of protein biomarkers. Here, we systematically review the strategy of protein biomarker discovery and the frequently-used machine learning methods. Also, the review illustrates the prospects and limitations of deep learning in this field. It is aimed at providing a valuable reference for corresponding researchers.
Algorithms ; Biomarkers ; Humans ; Machine Learning ; Mass Spectrometry ; Proteomics

Extracellular matrix (ECM) proteins play an important role in a series of biological processes in the cell, and their abnormal regulation can lead to many diseases. The theoretical ECM reference dataset is the basis for efficient identification of extracellular matrix proteins. Researchers have developed various ECM protein prediction tools based on machine learning methods. In this review, the main strategy of development of ECM protein prediction tools that based on machine learning methods has been introduced. Then, advances and specific characters of the existing ECM protein prediction tools have been summarized. Finally, the challenges and possible improvements of ECM protein prediction tools are discussed.
Extracellular Matrix ; Extracellular Matrix Proteins