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Background: Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets. Objective: The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics. Methods: Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics. Results: In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed. Conclusion: In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Increasing studies have shown that specific mRNAs and miRNAs expressed in mature sperm may be related to sperm motility. However, the expression profiles and roles of lncRNAs in sperm remain unknown. In the present study, numerous lncRNAs were identified in human sperm, and some lncRNAs were expressed exclusively in sperm and testis. Compared with those in normal sperm, the lncRNA expression profiles in asthenozoospermia (AZS) sperm showed significant differences. Gene ontology and pathway analyses showed that function of differentially expressed lncRNA targets and mRNAs between AZS and normal sperm were closely linked with many processes involved in spermatogenesis and sperm function. Furthermore, among the upregulated lncRNAs in AZS sperm, lnc32058, lnc09522, and lnc98487, which exhibited specific/enriched sperm and testicular expression, increased simultaneously in the same AZS sperm samples, and their expression levels were correlated with sperm progressive motility. This is the first systematic study of lncRNA expression profiles in human mature sperm indicating an association between lncRNA expression and sperm motility. The study provides a preliminary database for identifying lncRNAs crucial for human spermatogenesis and sperm function, and new insights into our understanding of the regulation of sperm motility and causes of male infertility.

Background: Due to its ability to provide quantitative and dynamic information on tumor genesis and development by directly profiling protein expression, the proteomics has become intensely popular for characterizing the functional proteins driving the transformation of malignancy, tracing the large-scale protein alterations induced by anticancer drug, and discovering the innovative targets and first-in-class drugs for oncologic disorders. Objective: To quantify cancer proteomics data, the label-free quantification (LFQ) is frequently employed. However, low precision, poor reproducibility and inaccuracy of the LFQ of proteomics data have been recognized as the key “technical challenge” in the discovery of anticancer targets and drugs. In this paper, the recent advances and development in the computational perspective of LFQ in cancer proteomics were therefore systematically reviewed and analyzed. Methods: PubMed and Web of Science database were searched for label-free quantification approaches, cancer proteomics and computational advances. Results: First, a variety of popular acquisition techniques and state-of-the-art quantification tools are systematically discussed and critically assessed. Then, many processing approaches including transformation, normalization, filtering and imputation are subsequently discussed, and their impacts on improving LFQ performance of cancer proteomics are evaluated. Finally, the future direction for enhancing the computation-based quantification technique for cancer proteomics are also proposed. Conclusion: There is a dramatic increase in LFQ approaches in recent year, which significantly enhance the diversity of the possible quantification strategies for studying cancer proteomics.

Bacillus Calmette-Guérin (BCG) vaccination is a prototype model for the study of trained immunity (TI) in humans, and results in a more effective response of innate immune cells upon stimulation with heterologous stimuli. Here, we investigate the heterogeneity of TI induction by single-cell RNA sequencing of immune cells collected from 156 samples. We observe that both monocytes and CD8+T cells show heterologous transcriptional responses to lipopolysaccharide, with an active crosstalk between these two cell types. Furthermore, the interferon-γ pathway is crucial in BCG-induced TI, and it is upregulated in functional high responders. Data-driven analyses and functional experiments reveal STAT1to be one of the important transcription factors for TI shared in all identified monocyte subpopulations. Finally, we report the role of type I interferon-related and neutrophil-related TI transcriptional programs in patients with sepsis. These findings provide comprehensive insights into the importance of monocyte heterogeneity during TI in humans.